diff --git a/CHRONOLOGY.csv b/CHRONOLOGY.csv new file mode 100644 index 0000000000000000000000000000000000000000..b925839b1a6905b737ce0e6ff31b9f169adf02aa --- /dev/null +++ b/CHRONOLOGY.csv @@ -0,0 +1,86 @@ +artifact,sha256,recorded_dates,recorded_status,external_pre_execution_registration +jlens_materials/experiments/answer-code-binding-2026-07-17/PROTOCOL.md,30645be4b0c4818d8da326cc3c741dd2963b23d57364edf9b325f61e5e89c3eb,"{""literal_date_mentions"": [""2026-07-17T12:06:00.884636+00:00""]}",See original narrative record; dates are not independently verified.,No qualifying external timestamp identified in inspected records; no claim of external preregistration. +jlens_materials/experiments/answer-code-binding-2026-07-17/execution-attempt-1-pre-forward.json,2b564eb6f37238ad7ad7b6fa363883393b2f91158d2508ac5f2e00029bf074ae,{},,No qualifying external timestamp identified in inspected records; no claim of external preregistration. +jlens_materials/experiments/answer-code-binding-2026-07-17/protocol-amendment-v1.json,56e464f6e7ef9252ad837f0dde3c8d170c493f97516208b501f9afffbc53ed97,"{""frozen_at"": ""2026-07-17T12:06:00.884636+00:00""}",prospectively frozen scientific design; protocol amendment v1 was made after execution attempt 1 aborted before any model forward pass,No qualifying external timestamp identified in inspected records; no claim of external preregistration. +jlens_materials/experiments/answer-code-binding-2026-07-17/protocol.json,41b19f84e31cc123cc90e98ca6058cd269addf83f433c0c7a519cf1724df08c3,"{""frozen_at"": ""2026-07-17T12:06:00.884636+00:00""}",prospectively frozen before any model forward pass for this answer-code cohort; motivated by the completed late-transition replication,No qualifying external timestamp identified in inspected records; no claim of external preregistration. +jlens_materials/experiments/candidate-activation-patching-2026-07-16/FALSIFICATION_PROTOCOL.md,671807e0972c7421072ec649ca797202733c786fa453ae54550a5731ad1c6afd,"{""literal_date_mentions"": [""2026-07-16T22:47:13-04:00""]}",See original narrative record; dates are not independently verified.,No qualifying external timestamp identified in inspected records; no claim of external preregistration. +jlens_materials/experiments/candidate-activation-patching-2026-07-16/PROTOCOL.md,9498f4417019c7080dd4d0e5cfa925fc39579c9eae5fccd50eb78f6b0cfceed4,"{""literal_date_mentions"": [""2026-07-16T22:26:08-04:00""]}",See original narrative record; dates are not independently verified.,No qualifying external timestamp identified in inspected records; no claim of external preregistration. +jlens_materials/experiments/candidate-activation-patching-2026-07-16/falsification_protocol.json,d07821596c37f05a43d621af1491fce32cd602d56a1f95a605ae97564ce410b7,"{""frozen_at"": ""2026-07-16T22:47:13-04:00""}",post-hoc falsification protocol frozen before distance-matched control output,No qualifying external timestamp identified in inspected records; no claim of external preregistration. +jlens_materials/experiments/candidate-activation-patching-2026-07-16/protocol.json,447327e8f277ca5cfc60944a5514e526aca89d168bf5435575390f512fe54258,"{""frozen_at"": ""2026-07-16T22:26:08-04:00""}",frozen before any activation-patching output; prospective analysis on an inspected cohort,No qualifying external timestamp identified in inspected records; no claim of external preregistration. +jlens_materials/experiments/candidate-followups-2026-07-16/EXECUTION.md,11c8ea55a7a14f61e3c81cb2207d3e62e6aab57988aa59d23c12491ecb471a2b,"{""literal_date_mentions"": [""2026-07-16"", ""2026-07-16T21:24:18-04:00"", ""2026-07-17T01:36:54Z""]}",See original narrative record; dates are not independently verified.,No qualifying external timestamp identified in inspected records; no claim of external preregistration. +jlens_materials/experiments/candidate-followups-2026-07-16/PROTOCOL.md,3128665d0c8f2faf447a85fe61b5be70549ecc23bf6de1eed51a92ff7ca8db09,"{""literal_date_mentions"": [""2026-07-16T21:24:18-04:00""]}",See original narrative record; dates are not independently verified.,No qualifying external timestamp identified in inspected records; no claim of external preregistration. +jlens_materials/experiments/candidate-followups-2026-07-16/protocol.json,e36c5feb67fd55638cab69b3baa3b290b0ff4387d5c788ba3ae6ab29f738fcd2,"{""frozen_at"": ""2026-07-16T21:24:18-04:00""}",frozen before new candidate intervention output and before follow-up summary computation,No qualifying external timestamp identified in inspected records; no claim of external preregistration. +jlens_materials/experiments/candidate-nonsteering-2026-07-16/PROTOCOL.md,b0fae5c1c8c04cd4990c0aa3d1cd6ba65518d037369b070cbb00383b5b1d8821,"{""literal_date_mentions"": [""2026-07-16T22:10:18-04:00""]}",See original narrative record; dates are not independently verified.,No qualifying external timestamp identified in inspected records; no claim of external preregistration. +jlens_materials/experiments/candidate-nonsteering-2026-07-16/protocol.json,4df9d7fb2356148db8b54f0a2881a122aaefd464970b4036305230c5dec4455c,"{""frozen_at"": ""2026-07-16T22:10:18-04:00""}",retrospective candidate protocol frozen before corresponding calculations,No qualifying external timestamp identified in inspected records; no claim of external preregistration. +jlens_materials/experiments/causal-relational-knockout-2026-09-02/12b/run_record.json,9487b4ffa2b2004b7441d61ae55272345c2704f956b9712f3bf74cb61678b6f4,{},,No qualifying external timestamp identified in inspected records; no claim of external preregistration. +jlens_materials/experiments/causal-relational-knockout-2026-09-02/31b/run_record.json,0d633d482a9bf5543be9d0aa90c4456f0a52c28dcd4ef56c53063d71c5be0ace,{},,No qualifying external timestamp identified in inspected records; no claim of external preregistration. +jlens_materials/experiments/causal-relational-knockout-2026-09-02/4b/run_record.json,2e0be3b0bd81977606ae93cee4d9fd230c0e22f6639d17ab6f497442ca2b6aff,{},,No qualifying external timestamp identified in inspected records; no claim of external preregistration. +jlens_materials/experiments/causal-relational-knockout-2026-09-02/CONTROL_AMENDMENT.md,92ddf8dd7cb1b9f717f6d09d231e6682e6d034a7277da0ab59630b9900ec10a8,"{""literal_date_mentions"": [""2026-09-02""]}",See original narrative record; dates are not independently verified.,No qualifying external timestamp identified in inspected records; no claim of external preregistration. +jlens_materials/experiments/causal-relational-knockout-2026-09-02/PROTOCOL.md,a42086162ccd47eecbdbfaaf392e5475c3841ae21e465b6335c11ce6ba893757,"{""literal_date_mentions"": []}",See original narrative record; dates are not independently verified.,No qualifying external timestamp identified in inspected records; no claim of external preregistration. +jlens_materials/experiments/causal-relational-knockout-2026-09-02/control_amendment.json,bfa5d834c739c6795d1284a18e0bf33f04e10baf4ce7dfa661f943fd22724372,"{""frozen_at"": ""2026-09-02T11:49:56Z""}",frozen-before-any-amended-control-output,No qualifying external timestamp identified in inspected records; no claim of external preregistration. +jlens_materials/experiments/causal-relational-knockout-2026-09-02/debug-label-signed-random/12b-run_record.json,3332618913c3fd3e095ca472b747050f2667889c350e9f40a5cbcda04945f7d1,{},,No qualifying external timestamp identified in inspected records; no claim of external preregistration. +jlens_materials/experiments/causal-relational-knockout-2026-09-02/debug-label-signed-random/4b-run_record.json,8f0ac4d583701721c8db6575057b704e4ba58936d1dbf0ba3bea977b6ae20307,{},,No qualifying external timestamp identified in inspected records; no claim of external preregistration. +jlens_materials/experiments/causal-relational-knockout-2026-09-02/debug-raw-coordinate/4b/run_record.json,8f0ac4d583701721c8db6575057b704e4ba58936d1dbf0ba3bea977b6ae20307,{},,No qualifying external timestamp identified in inspected records; no claim of external preregistration. +jlens_materials/experiments/causal-relational-knockout-2026-09-02/protocol.json,29360c5b6a27808a38b76020476314920c6ee46faea961d39b930916d1c6b943,"{""frozen_at"": ""2026-09-02T11:03:03.653464+00:00""}",frozen-before-any-study-model-output,No qualifying external timestamp identified in inspected records; no claim of external preregistration. +jlens_materials/experiments/cross-mechanism-activation-patching-2026-07-18/AMENDMENT_V1.md,18c5cabd71637848c35e21c8bf4840dceccebb89723e738c24f9052a2cd32375,"{""literal_date_mentions"": [""2026-07-17T18:16:50-04:00""]}",See original narrative record; dates are not independently verified.,No qualifying external timestamp identified in inspected records; no claim of external preregistration. +jlens_materials/experiments/cross-mechanism-activation-patching-2026-07-18/PROTOCOL.md,5fd3659f5470bdc5c69db819b84301223f851d5db1e7ea1d46f6a10228d3d822,"{""literal_date_mentions"": [""2026-07-17"", ""2026-07-17T17:50:28-04:00""]}",See original narrative record; dates are not independently verified.,No qualifying external timestamp identified in inspected records; no claim of external preregistration. +jlens_materials/experiments/cross-mechanism-activation-patching-2026-07-18/execution-attempts.json,a12b95e29f6344a662299b7b039aae1f4579f6739b88fe9b20b51dba2ef865dc,{},,No qualifying external timestamp identified in inspected records; no claim of external preregistration. +jlens_materials/experiments/cross-mechanism-activation-patching-2026-07-18/protocol-amendment-v1.json,b607d74a733b432ef91a79aac9fb4255c59fb99be5fad4815f3c350e05639270,"{""frozen_at"": ""2026-07-17T18:16:50-04:00""}",,No qualifying external timestamp identified in inspected records; no claim of external preregistration. +jlens_materials/experiments/cross-mechanism-activation-patching-2026-07-18/protocol.json,a3d41ff944b9c602ce87fa55d6efcfb238353fa7cd4a2837c56d1344091c0f57,"{""frozen_at"": ""2026-07-17T17:50:28-04:00""}",frozen before any cross-mechanism patching output,No qualifying external timestamp identified in inspected records; no claim of external preregistration. +jlens_materials/experiments/cross-mechanism-outcome-2026-07-18/PROTOCOL.md,295fb84812ae4db47f04cfec655264a02f5c4da4fe9a3654e57d0511b78640a7,"{""literal_date_mentions"": [""2026-07-18"", ""2026-07-18T01:20:00-04:00""]}",See original narrative record; dates are not independently verified.,No qualifying external timestamp identified in inspected records; no claim of external preregistration. +jlens_materials/experiments/cross-mechanism-outcome-2026-07-18/protocol.json,575c0f9ec664f1708ee740689727c6cb60973284f25d98166ca9f4c99c13614b,"{""frozen_at"": ""2026-07-18T01:20:00-04:00""}",newly frozen post-hoc analysis of the inspected natural question-end cohort; no cross-mechanism outcome statistic computed before freeze,No qualifying external timestamp identified in inspected records; no claim of external preregistration. +jlens_materials/experiments/elicited-physics-abstraction-2026-07-18/PROTOCOL.md,5ae78d8e64030fa28c452610dda3d7398d52a6d95f8a016ae423b449402d8b1d,"{""literal_date_mentions"": []}",See original narrative record; dates are not independently verified.,No qualifying external timestamp identified in inspected records; no claim of external preregistration. +jlens_materials/experiments/elicited-physics-abstraction-2026-07-18/protocol.json,3d3365423e6d4fd04a56336ae1413d732d72b6c44da6ffdc88b888a8d5709c1a,"{""frozen_at"": ""2026-07-18T19:38:32.011822+00:00""}",frozen-before-scaffolded-output,No qualifying external timestamp identified in inspected records; no claim of external preregistration. +jlens_materials/experiments/ensemble-steering-v2-preregistration.json,dda786697e3b8b1858611350c7ef6ff75533b310e8ece03b1f1cb59c771101ed,"{""frozen_at"": ""2026-07-16T15:05:00-04:00""}",frozen before calibration intervention outputs; implementation-only amendment recorded below,No qualifying external timestamp identified in inspected records; no claim of external preregistration. +jlens_materials/experiments/equation-free-relational-physics-2026-08-21/PROTOCOL.md,b16dafc90cfdba268c080c88b496bda8af57d7c8a29fe557be044cb181c9bbf5,"{""literal_date_mentions"": []}",See original narrative record; dates are not independently verified.,No qualifying external timestamp identified in inspected records; no claim of external preregistration. +jlens_materials/experiments/equation-free-relational-physics-2026-08-21/protocol.json,d17ec2548f717d579e08ce03878129312f594212234d676bcf997a975cddf606,"{""frozen_at"": ""2026-08-21T11:10:08.058282+00:00""}",full-prompt-format-transfer-frozen-before-model-output,No qualifying external timestamp identified in inspected records; no claim of external preregistration. +jlens_materials/experiments/fresh-law-abstraction-confirmation-2026-07-18/PROTOCOL.md,4f894a4a6fcd882f0cff21264b229196de7be8e0918a4d97f08afd2c2b73e931,"{""literal_date_mentions"": []}",See original narrative record; dates are not independently verified.,No qualifying external timestamp identified in inspected records; no claim of external preregistration. +jlens_materials/experiments/fresh-law-abstraction-confirmation-2026-07-18/protocol.json,a3788b55f3c0196a31baa735abcd46c7a235573683dd5b32c343225275d965fe,"{""frozen_at"": ""2026-07-18T19:51:31.649626+00:00""}",frozen-before-any-fresh-model-output,No qualifying external timestamp identified in inspected records; no claim of external preregistration. +jlens_materials/experiments/gemma-equation-free-multiscale-2026-08-25/PROTOCOL.md,d77ef67b009ade9c1f672d60354e2b777526eec2014e5801a8cbe5c44a8f7f18,"{""literal_date_mentions"": [""2026-07-18"", ""2026-08-21"", ""2026-08-25""]}",See original narrative record; dates are not independently verified.,No qualifying external timestamp identified in inspected records; no claim of external preregistration. +jlens_materials/experiments/gemma-multiscale-heldout-v1-2026-08-25/PROTOCOL.md,c7d1890d7869e708db0a95f5e6a0fc196abb888cafe41789f5ec1003c4d78397,"{""literal_date_mentions"": []}",See original narrative record; dates are not independently verified.,No qualifying external timestamp identified in inspected records; no claim of external preregistration. +jlens_materials/experiments/gemma-scale-heldout-v1-2026-08-23/PROTOCOL.md,1bde9c6fc49de3a8744ea6a97441a40862efa4122f3ea2ef510b76924430db59,"{""literal_date_mentions"": []}",See original narrative record; dates are not independently verified.,No qualifying external timestamp identified in inspected records; no claim of external preregistration. +jlens_materials/experiments/gemma4-12b-equation-free-relational-v1/protocol.json,e508e7ed0446c75cf26057fb0981d783f4189da4463e5d57eb1d507396df812d,"{""frozen_at"": ""2026-08-25T13:23:19.123055+00:00""}",frozen-before-model-output,No qualifying external timestamp identified in inspected records; no claim of external preregistration. +jlens_materials/experiments/gemma4-31b-equation-free-relational-layer40-v1/protocol.json,d2cc9e5d83d8a6d7ac784c7883fe395e441381c6a914f3991b4654ddfd0bdeca,"{""frozen_at"": ""2026-08-23T13:05:05.582856+00:00""}",frozen-before-model-output,No qualifying external timestamp identified in inspected records; no claim of external preregistration. +jlens_materials/experiments/gemma4-31b-equation-free-relational-v1/protocol.json,c300edab49fbd0943af9e3bb6ce1e54f3872656b552c8d11eba95505ec5b542d,"{""frozen_at"": ""2026-08-23T09:58:00.035909+00:00""}",frozen-before-model-output,No qualifying external timestamp identified in inspected records; no claim of external preregistration. +jlens_materials/experiments/gemma4-31b-semantic-streams-2026-08-26/PROTOCOL.md,990a7c75d2166c8b9eb999f7386aff21ba389a3d2a7f261b7c647f93099463ec,"{""literal_date_mentions"": []}",See original narrative record; dates are not independently verified.,No qualifying external timestamp identified in inspected records; no claim of external preregistration. +jlens_materials/experiments/graph-isomorphism-generalization-2026-07-18/GAUGE_PROTOCOL.md,29dcebfb3ef53ebfeaa8e890075c0942435f1a0ef7bc3f1d6a9748b3bdbf9a8a,"{""literal_date_mentions"": [""2026-07-17T21:20:50-04:00""]}",See original narrative record; dates are not independently verified.,No qualifying external timestamp identified in inspected records; no claim of external preregistration. +jlens_materials/experiments/graph-isomorphism-generalization-2026-07-18/PARTITION_PROTOCOL.md,4b0e7819311f6232a98cd470365c832ae7c369f13654b5f24a0a07088d708dea,"{""literal_date_mentions"": [""2026-07-17T21:55:00-04:00""]}",See original narrative record; dates are not independently verified.,No qualifying external timestamp identified in inspected records; no claim of external preregistration. +jlens_materials/experiments/graph-isomorphism-generalization-2026-07-18/PROTOCOL.md,21714ad8b8d2c5da5801f31edd69dba5358422804b1cb9e5f4a5f77c8d2dfc69,"{""literal_date_mentions"": [""2026-07-17T20:48:17-04:00"", ""2026-07-18""]}",See original narrative record; dates are not independently verified.,No qualifying external timestamp identified in inspected records; no claim of external preregistration. +jlens_materials/experiments/graph-isomorphism-generalization-2026-07-18/SPECTRAL_PROTOCOL.md,4e4f29f87d00b98b0cf1e75773dbcff5a29cea757810608d72233706532f7d9f,"{""literal_date_mentions"": [""2026-07-17T21:46:00-04:00""]}",See original narrative record; dates are not independently verified.,No qualifying external timestamp identified in inspected records; no claim of external preregistration. +jlens_materials/experiments/graph-isomorphism-generalization-2026-07-18/gauge_protocol.json,e8d3fbf640113fa45d45f8755245722e03d51b2fff21ed45b4538f9794bf0c74,"{""frozen_at"": ""2026-07-17T21:20:50-04:00""}",post_failure_exploratory_with_frozen_endpoints,No qualifying external timestamp identified in inspected records; no claim of external preregistration. +jlens_materials/experiments/graph-isomorphism-generalization-2026-07-18/partition_protocol.json,61cb95fb7a09eb4b34cf977c92ea351d58ccc77bdf974e80826b7ee41f5f4437,"{""frozen_at"": ""2026-07-17T21:55:00-04:00""}",post_spectral_exploratory_with_frozen_endpoints,No qualifying external timestamp identified in inspected records; no claim of external preregistration. +jlens_materials/experiments/graph-isomorphism-generalization-2026-07-18/protocol.json,e9ad6cfeff23a65dbbd173c9143c0901691808f24be143522701a76235c6f3fc,"{""frozen_at"": ""2026-07-17T20:48:17-04:00""}",post_hoc_analysis_with_frozen_endpoints,No qualifying external timestamp identified in inspected records; no claim of external preregistration. +jlens_materials/experiments/graph-isomorphism-generalization-2026-07-18/spectral_protocol.json,e1686bcd3307c1093eaa30db6d81ad28e0f995ee661cae30f54690993bc3e622,"{""frozen_at"": ""2026-07-17T21:46:00-04:00""}",post_gin_exploratory_with_frozen_endpoints,No qualifying external timestamp identified in inspected records; no claim of external preregistration. +jlens_materials/experiments/graph-topology-rigorous-2026-07-17/PROTOCOL.md,4a55505f25818f18f3d2221b42bc3ef5aaefe5cc833de00b3ebe9ee87699e3ef,"{""literal_date_mentions"": [""2026-07-17T18:44:02Z""]}",See original narrative record; dates are not independently verified.,No qualifying external timestamp identified in inspected records; no claim of external preregistration. +jlens_materials/experiments/graph-topology-rigorous-2026-07-17/execution_record.json,867888689f91f976eb56348ccdc85e330f81145980fda0244f2b2aa0b990b73e,{},,No qualifying external timestamp identified in inspected records; no claim of external preregistration. +jlens_materials/experiments/graph-topology-rigorous-2026-07-17/protocol-amendment-v1.json,d9188528fc9a5bea450cd146d8f68e0cf7b07a3b3020e8328a4b3b5554c645af,"{""frozen_utc"": ""2026-07-17T19:12:00Z""}",Post-hoc falsification amendment frozen after inspecting the first rigorous graph outputs and before computing the diagnostics below,No qualifying external timestamp identified in inspected records; no claim of external preregistration. +jlens_materials/experiments/graph-topology-rigorous-2026-07-17/protocol-amendment-v2-correction.json,42a97f8dd1c963cab21e45c4eb249b6548ea275410556e774fff59c7030cdb72,"{""frozen_utc"": ""2026-07-17T19:29:19Z""}",Correction frozen after the first v2 execution halted on a design invariant and before any v2 endpoint was computed,No qualifying external timestamp identified in inspected records; no claim of external preregistration. +jlens_materials/experiments/graph-topology-rigorous-2026-07-17/protocol-amendment-v2.json,0ab5647292ecfd41ba7691ea270b828cd4e1aab4fac412d63bc6b7b2ea1df01a,"{""frozen_utc"": ""2026-07-17T19:24:33Z""}",Post-hoc robustness amendment frozen after the v1 surface-form diagnostics and before computing the endpoints below,No qualifying external timestamp identified in inspected records; no claim of external preregistration. +jlens_materials/experiments/graph-topology-rigorous-2026-07-17/protocol.json,ee5a358bbcfc74aecade979856dd0802b3338ac3b477af360e2b7d21fdcaf561,"{""frozen_utc"": ""2026-07-17T18:44:02Z""}",Frozen before computing outputs in this directory; prospectively specified analysis of archived and previously inspected representations,No qualifying external timestamp identified in inspected records; no claim of external preregistration. +jlens_materials/experiments/jacobian-steering-pilot-v1-preregistration.json,c892f3c35e2b1828ce5a07549ce54422d30551a01aeb3686e5003fd91f2bc258,"{""frozen_at"": ""2026-07-16T00:00:00-04:00""}",frozen after clean-answer preflight and before any intervention output,No qualifying external timestamp identified in inspected records; no claim of external preregistration. +jlens_materials/experiments/late-physics-representation-replication-2026-07-17/PROTOCOL.md,7024847f3c4ab18b3b80175c00433c8da891ae6908dea8268b4851b141afe860,"{""literal_date_mentions"": [""2026-07-17T11:55:14.186577+00:00""]}",See original narrative record; dates are not independently verified.,No qualifying external timestamp identified in inspected records; no claim of external preregistration. +jlens_materials/experiments/late-physics-representation-replication-2026-07-17/protocol.json,115ed08045abdb2a515026739dd35b701a11274ae83fc84481f58fd3da3b40c7,"{""frozen_at"": ""2026-07-17T11:55:14.186577+00:00""}",prospectively frozen before any forward pass on this disjoint replication cohort; late window was selected from the completed discovery cohort and is not an independent choice,No qualifying external timestamp identified in inspected records; no claim of external preregistration. +jlens_materials/experiments/lexical-adversarial-representation-2026-07-17/PROTOCOL.md,668fe6e64f4f36f04e65730fb2ae5f44ee71a90f1b4fdeed90490261fd39308a,"{""literal_date_mentions"": [""2026-07-17T11:46:24.759321+00:00""]}",See original narrative record; dates are not independently verified.,No qualifying external timestamp identified in inspected records; no claim of external preregistration. +jlens_materials/experiments/lexical-adversarial-representation-2026-07-17/protocol.json,4a0484b37ce552996fcfba0c9980b4def7d2636cda446cfa26db6de3cb4cae69,"{""frozen_at"": ""2026-07-17T11:46:24.759321+00:00""}","prospectively frozen before any model forward pass, hidden-state inspection, lens readout, or target-free vocabulary output",No qualifying external timestamp identified in inspected records; no claim of external preregistration. +jlens_materials/experiments/materials-heldout-v1-execution.md,1e94d1361509793b2521f2dfc1df0b3d3e9ece406f967362d51221796272a624,"{""literal_date_mentions"": [""2026-07-14""]}",See original narrative record; dates are not independently verified.,No qualifying external timestamp identified in inspected records; no claim of external preregistration. +jlens_materials/experiments/materials-heldout-v1-latent-geometry-protocol.md,82079295dc337155093413a8220848c410b483a5556ad80c1a86c82742374325,"{""literal_date_mentions"": []}",See original narrative record; 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dates are not independently verified.,No qualifying external timestamp identified in inspected records; no claim of external preregistration. +jlens_materials/experiments/semantic-steering-v3-preregistration.json,dcf5e5b3784d54d184543b2973fe007318780736809d854d4d7f60b8d03aa6ea,"{""frozen_at"": ""2026-07-16T16:50:39-04:00""}",frozen before any v3 model forward pass or intervention output; implementation-only amendment recorded below,No qualifying external timestamp identified in inspected records; no claim of external preregistration. +jlens_materials/experiments/semantic-steering-v3_execution.md,e665469f2dfcd25b7e9d2c1038a27c26b788052b4345f95605523907fef9a9fc,"{""literal_date_mentions"": [""2026-07-16""]}",See original narrative record; dates are not independently verified.,No qualifying external timestamp identified in inspected records; no claim of external preregistration. diff --git a/CHRONOLOGY.json b/CHRONOLOGY.json new file mode 100644 index 0000000000000000000000000000000000000000..78c89045191a5ede5cae5474abae2ab3bda1d364 --- /dev/null +++ b/CHRONOLOGY.json @@ -0,0 +1,813 @@ +{ + "interpretation": "Dates below are values transcribed from authors' internal records. 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Historical preregistration terminology denotes internal records unless external evidence is separately identified. Corrections and retrospective audits are documented separately.", + "models_and_lenses": "Base-model weights and fitted Jacobian lens weights remain in their cited Hugging Face model repositories; they are not needed to recompute the archived statistical analyses." +} diff --git a/NOTICE b/NOTICE new file mode 100644 index 0000000000000000000000000000000000000000..1b0dba00af18d700d3bafb3c2d58ffdd6679a690 --- /dev/null +++ b/NOTICE @@ -0,0 +1,11 @@ +Substrates +Copyright 2026 Markus J. Buehler + +This product includes portions of the Jacobian Lens software, Copyright 2026 +Anthropic PBC, licensed under the Apache License, Version 2.0. The vendored +runtime is derived from: + + https://github.com/lamm-mit/jacobian-lens + +The upstream copyright and SPDX notices are retained in the vendored source +files under jlens_materials/_vendor_jlens/. diff --git a/README.md b/README.md new file mode 100644 index 0000000000000000000000000000000000000000..8203bb7b9c0d223c84c967dd9b8fd6804d310681 --- /dev/null +++ b/README.md @@ -0,0 +1,45 @@ +--- +language: +- en +tags: +- materials-science +- mechanistic-interpretability +- reproducibility +- gemma +pretty_name: Gemma Materials-Science Interpretability Research Archive +--- + +# Gemma materials-science interpretability research archive + +Research records supporting *Reading and Steering Representations of Materials-Science Mechanisms in an Open-Weight Language Model*, Markus J. Buehler. Release identifier: `paper-revision-2026-09-06`. + +This archive supplies the original observations, supporting state arrays, exact prompts, protocols, intervention records, statistics, analysis source, and generated research figures. It includes the original 4B readout and geometry studies; steering and patching; supplied-equation and equation-free relational experiments; 12B/31B scale extensions; and the three-scale coordinate intervention and retrospective Comment 3 overlap audit. Development and diagnostic records needed to understand those experiments are retained, including negative results and superseded controls. Not every archived development record is a confirmatory endpoint in the paper. + +The archive preserves the original `jlens_materials/` layout. `MANIFEST.json` and `FILE_INVENTORY.csv` enumerate the research-record files with their sizes, SHA-256 hashes, and inclusion basis. Release documentation and verification reports are separately identified; they were prepared after the experiments. `RESULT_MAP.csv` maps manuscript figures and tables to supporting records and reproduction commands. Companion manuscript files are under `paper/` when present. + +## Download and verify + +Use the full dataset commit hash cited by the revised paper/release index: + +```python +from huggingface_hub import snapshot_download +snapshot_download(repo_id="lamm-mit/gemma4-interpretability", repo_type="dataset", + revision="main", local_dir="gemma4-interpretability") +``` + +`main` retrieves the current snapshot; replace it with a full commit hash for a fixed version. From the downloaded directory run `python verify_release.py`. The checker verifies every original research-record file against the manifest. `REPRODUCIBILITY.md` describes analysis-only reproduction and its validation scope. Work in a copy when rerunning historical analysis scripts because they write derived outputs in place. + +## Protocol chronology and corrections + +“Frozen,” “prespecified,” and historical filenames containing “preregistration” describe the authors' internal research records. They do not, by themselves, establish independently timestamped preregistration. The public GitHub repository was created on 22 July 2026, after the July experiments. This data release is later still. `CHRONOLOGY.json` and `CHRONOLOGY.csv` preserve recorded dates and status statements without treating editable timestamps or hashes as external timing evidence. Analyses developed after inspection of data remain post hoc even when a protocol preceded the subsequent calculation. + +Original files are retained byte for byte, including historical terminology and original validator labels. For the causal-coordinate experiment, the original validator checked distinct law identifiers; a later audit found three repeated physical relations in the development and target cohorts. The corrected interpretation and sensitivity results are in `jlens_materials/experiments/causal-relational-comment3-audit-2026-09-06/` and revised SI S7A/Table S45. Historical classifications in original files should be read alongside that correction. Superseded Gaussian control diagnostics are retained for provenance and excluded from the amended primary inference. + +## Related resources + +- [Public source repository](https://github.com/lamm-mit/Substrates) +- [Fitted Jacobian lenses](https://huggingface.co/lamm-mit/gemma4-jacobian-lenses): three 4B, three 12B, and one 31B paper fits. +- [Original exploratory latent vectors](https://huggingface.co/datasets/lamm-mit/gemma4-materials-latent-vectors) +- [Reusable prompt dataset](https://huggingface.co/datasets/lamm-mit/gemma4-materials-mechanism-prompts) + +The Gemma base checkpoints and fitted lens weights are not duplicated here. They are unnecessary for recomputing analyses from the saved observations and states; rerunning model inference requires the exact checkpoint/lens revisions and applicable access conditions. The included Apache-2.0 LICENSE and NOTICE originate from the public source repository and cover its source code; upstream model and third-party resource terms remain applicable. diff --git a/REPRODUCIBILITY.md b/REPRODUCIBILITY.md new file mode 100644 index 0000000000000000000000000000000000000000..90d7c5ed6f4799597fca4a748aeb1c706df82c80 --- /dev/null +++ b/REPRODUCIBILITY.md @@ -0,0 +1,54 @@ +# Reproducing the archived analyses + +Download a pinned dataset snapshot, then run `python verify_release.py` from its root. `MANIFEST.json` identifies the byte-for-byte original research records. The accompanying manuscript's current source is in `paper/`; the historical graph analyzers also use the exact graph prompt bundle at `jlens_materials/paper/graph_data/`. + +## Analysis-only checks + +The validation below ran in a clean working copy and compared original numerical statistics with regenerated statistics at relative tolerance 1e-8 and absolute tolerance 1e-10. Every comparison passed. `REPRODUCTION_VALIDATION.json` records commands, counts, and scope. These tolerances accommodate harmless floating-point differences; substantive endpoints and verdicts are unchanged. + +Install `requirements-analysis.txt` in a suitable Python environment. The scale-analysis helper imports the original runner's modules, so it also requires Torch, Transformers, and the source dependencies recorded in `analysis_environment.json` and `environment.yml`; it does not load model weights or run inference. On restricted systems, use writable Matplotlib and Numba cache directories. + +From the downloaded dataset root: + +```bash +python reproduction/run_reproduction.py --source . --scratch ./analysis-working-copy --report ./analysis-checks.json +``` + +The script copies records into the working directory and restores original inputs between independent checks. Historical analyzers write derived files in place, so do not point a manual analyzer at the sole archival copy. Run individual scripts from `jlens_materials/` in a working copy; `RESULT_MAP.csv` lists the relevant commands and artifact groups for each figure/table block. + +| Check | Result | Numerical values compared | +|---|---|---:| +| controlled_readout | passed | 58,740 | +| target_free_classification | passed | 331 | +| broad_steering | passed | 260 | +| grain_steering | passed | 47 | +| full_state_patching | passed | 121 | +| cross_mechanism_patching | passed | 144 | +| supplied_equation_60_laws | passed | 35 | +| equation_free_4b | passed | 162 | +| equation_free_scale_comparison | passed | 92 | +| readability_scale_comparison | passed | 265 | +| coordinate_interventions | passed | 488 | +| coordinate_integrity | passed | 0 | +| lexical_adversarial | passed | 132 | +| late_replication | passed | 203 | +| answer_scaffold | passed | 32 | +| answer_code | passed | 240 | +| graph_integrity | passed | 0 | +| graph_generalization_integrity | passed | 0 | +| graph_figures | passed | 0 | +| latent_geometry | passed | 304 | +| equation_free_12b | passed | 47 | +| equation_free_31b | passed | 47 | + +The integrity-only checks validate graph row/seed counts, finite arrays, fingerprints, and causal cohort completeness. The graph publication figures were regenerated from the archived graph outputs. Graph-network training was not repeated. Gemma inference, Jacobian-lens fitting, and the external automated-rater calls were not repeated; the latter's analysis was recomputed from its saved responses. Thus these checks establish reproducibility of the archived analyses, not an independent replication with new model outputs. + +The Comment 3 sensitivity analysis can also be reproduced independently with: + +```bash +python paper/comment3_causal_audit_data/reproduce_sensitivity.py --output-dir ./comment3-reproduced +``` + +This compares all 12 model/cohort combinations with the released values at absolute tolerance 1e-10. The original full audit additionally records development-axis reconstruction and overlap taxonomy checks. It contains historical references to the internal revision workspace; the portable runner is the self-contained route for reproducing the reported sensitivity statistics. + +The file inventory includes original scientific figures as well as the arrays and records from which they were derived. The manuscript package supplies the flat publication figure copies and LaTeX sources. Original run/protocol files are preserved even when the corrected manuscript uses different terminology or identifies a limitation in an original validator. diff --git a/REPRODUCTION_VALIDATION.json b/REPRODUCTION_VALIDATION.json new file mode 100644 index 0000000000000000000000000000000000000000..33a135f405e776d0660c1ed002f80376961c2c62 --- /dev/null +++ b/REPRODUCTION_VALIDATION.json @@ -0,0 +1,232 @@ +{ + "status": "passed", + "validation_scope": "Analysis-only reproduction from a clean local copy of the staged archive. Original numerical statistics were compared at rtol=1e-8, atol=1e-10. Model outputs and original research records were not regenerated or modified. Public-download verification is a separate check.", + "checks": [ + { + "name": "controlled_readout", + "command": "python reproduction/reproduce_controlled.py", + "exit_code": 0, + "seconds": 31.84, + "numeric_values_compared": 58740, + "differences": [], + "status": "passed", + "log": "controlled_readout.log" + }, + { + "name": "target_free_classification", + "command": "python scripts/analyze_candidate_nonsteering.py", + "exit_code": 0, + "seconds": 20.72, + "numeric_values_compared": 331, + "differences": [], + "status": "passed", + "log": "target_free_classification.log" + }, + { + "name": "broad_steering", + "command": "python scripts/analyze_semantic_steering_v3.py", + "exit_code": 0, + "seconds": 3.89, + "numeric_values_compared": 260, + "differences": [], + "status": "passed", + "log": "broad_steering.log" + }, + { + "name": "grain_steering", + "command": "python scripts/analyze_relational_grain_steering_v4.py", + "exit_code": 0, + "seconds": 1.84, + "numeric_values_compared": 47, + "differences": [], + "status": "passed", + "log": "grain_steering.log" + }, + { + "name": "full_state_patching", + "command": "python scripts/analyze_counterfactual_activation_patching.py", + "exit_code": 0, + "seconds": 1.8, + "numeric_values_compared": 121, + "differences": [], + "status": "passed", + "log": "full_state_patching.log" + }, + { + "name": "cross_mechanism_patching", + "command": "python scripts/analyze_cross_mechanism_activation_patching.py", + "exit_code": 0, + "seconds": 1.28, + "numeric_values_compared": 144, + "differences": [], + "status": "passed", + "log": "cross_mechanism_patching.log" + }, + { + "name": "supplied_equation_60_laws", + "command": "python scripts/analyze_neutral_anchored_relational_benchmark.py", + "exit_code": 0, + "seconds": 33.97, + "numeric_values_compared": 35, + "differences": [], + "status": "passed", + "log": "supplied_equation_60_laws.log" + }, + { + "name": "equation_free_4b", + "command": "python scripts/analyze_equation_free_relational_benchmark_amended.py", + "exit_code": 0, + "seconds": 33.69, + "numeric_values_compared": 162, + "differences": [], + "status": "passed", + "log": "equation_free_4b.log" + }, + { + "name": "equation_free_scale_comparison", + "command": "python scripts/analyze_gemma_equation_free_multiscale.py", + "exit_code": 0, + "seconds": 3.49, + "numeric_values_compared": 92, + "differences": [], + "status": "passed", + "log": "equation_free_scale_comparison.log" + }, + { + "name": "readability_scale_comparison", + "command": "python scripts/analyze_gemma_multiscale_heldout_v1.py", + "exit_code": 0, + "seconds": 44.08, + "numeric_values_compared": 265, + "differences": [], + "status": "passed", + "log": "readability_scale_comparison.log" + }, + { + "name": "coordinate_interventions", + "command": "python scripts/analyze_causal_relational_knockout.py", + "exit_code": 0, + "seconds": 2.68, + "numeric_values_compared": 488, + "differences": [], + "status": "passed", + "log": "coordinate_interventions.log" + }, + { + "name": "coordinate_integrity", + "command": "python scripts/validate_causal_relational_knockout.py", + "exit_code": 0, + "seconds": 0.18, + "numeric_values_compared": 0, + "differences": [], + "status": "passed", + "log": "coordinate_integrity.log" + }, + { + "name": "lexical_adversarial", + "command": "python scripts/analyze_lexical_adversarial_representation.py", + "exit_code": 0, + "seconds": 17.15, + "numeric_values_compared": 132, + "differences": [], + "status": "passed", + "log": "lexical_adversarial.log" + }, + { + "name": "late_replication", + "command": "python scripts/analyze_late_physics_replication.py", + "exit_code": 0, + "seconds": 29.52, + "numeric_values_compared": 203, + "differences": [], + "status": "passed", + "log": "late_replication.log" + }, + { + "name": "answer_scaffold", + "command": "python scripts/analyze_answer_scaffold_audit.py", + "exit_code": 0, + "seconds": 5.41, + "numeric_values_compared": 32, + "differences": [], + "status": "passed", + "log": "answer_scaffold.log" + }, + { + "name": "answer_code", + "command": "python scripts/analyze_answer_code_binding.py", + "exit_code": 0, + "seconds": 35.38, + "numeric_values_compared": 240, + "differences": [], + "status": "passed", + "log": "answer_code.log" + }, + { + "name": "graph_integrity", + "command": "python scripts/audit_graph_topology_rigorous.py", + "exit_code": 0, + "seconds": 0.35, + "numeric_values_compared": 0, + "differences": [], + "status": "passed", + "log": "graph_integrity.log" + }, + { + "name": "graph_generalization_integrity", + "command": "python scripts/validate_graph_generalization_audit.py", + "exit_code": 0, + "seconds": 0.61, + "numeric_values_compared": 0, + "differences": [], + "status": "passed", + "log": "graph_generalization_integrity.log" + }, + { + "name": "graph_figures", + "command": "python scripts/plot_graph_generalization_audit.py", + "exit_code": 0, + "seconds": 4.64, + "numeric_values_compared": 0, + "differences": [], + "status": "passed", + "log": "graph_figures.log" + }, + { + "name": "latent_geometry", + "command": "python scripts/analyze_materials_latent_geometry.py", + "exit_code": 0, + "seconds": 119.02, + "numeric_values_compared": 304, + "differences": [], + "status": "passed", + "log": "latent_geometry.log" + }, + { + "name": "equation_free_12b", + "command": "python reproduction/reproduce_scale.py --root --scale 12b", + "exit_code": 0, + "seconds": 34.33, + "numeric_values_compared": 47, + "differences": [], + "status": "passed", + "log": "equation_free_12b.log" + }, + { + "name": "equation_free_31b", + "command": "python reproduction/reproduce_scale.py --root --scale 31b", + "exit_code": 0, + "seconds": 34.27, + "numeric_values_compared": 47, + "differences": [], + "status": "passed", + "log": "equation_free_31b.log" + } + ], + "numeric_values_compared": 61690, + "limitations": [ + "The saved graph-network training outputs were integrity-checked and used to rebuild publication figures; graph-network training was not repeated.", + "The original Gemma inference, lens fitting, and external automated-rater calls were not repeated. The automated-rater analysis was recomputed from cached responses.", + "Original protocol terminology and validator labels are historical records. The SI and CHRONOLOGY documentation provide the corrected chronology and overlap interpretation." + ] +} diff --git a/RESULT_MAP.csv b/RESULT_MAP.csv new file mode 100644 index 0000000000000000000000000000000000000000..ae40fcf22b8413d1095010b64a35c23bc7b4dabb --- /dev/null +++ b/RESULT_MAP.csv @@ -0,0 +1,99 @@ +manuscript_source,source_line,kind,label,caption,scope,groups,artifact_prefixes,supporting_file_count,analysis_commands,publication_assets,manifest +paper/graph_generalization_audit.tex,18,table,,Mechanism orientation in the 72-prompt audit. The physical label is generated exactly by $y=sx$.,matched result group(s),"[""graphs""]","[""jlens_materials/experiments/cross-mechanism-outcome-2026-07-18/"", ""jlens_materials/experiments/graph-isomorphism-generalization-2026-07-18/"", ""jlens_materials/experiments/graph-topology-rigorous-2026-07-17/"", ""jlens_materials/experiments/late-physics-representation-replication-2026-07-17/"", ""jlens_materials/experiments/option-free-question-end-2026-07-18/"", ""jlens_materials/experiments/relation-graph-visualization-2026-07-18/""]",144,"[""python scripts/analyze_graph_topology_rigorous.py"", ""python scripts/plot_graph_generalization_audit.py"", ""python scripts/plot_relation_graph_robustness.py"", ""python scripts/validate_graph_generalization_audit.py""]",[],FILE_INVENTORY.csv +paper/graph_generalization_audit.tex,44,table,,"Whole-mechanism-held-out graph learning. The numerical-direction column is an input-readback positive control because that feature was supplied to the absolute-label network. Even with direction supplied, absolute physical polarity does not generalize; the separate polarity-free relation network also fails. ROC--AUC 0.5 denotes no ordering information.",matched result group(s),"[""graphs""]","[""jlens_materials/experiments/cross-mechanism-outcome-2026-07-18/"", ""jlens_materials/experiments/graph-isomorphism-generalization-2026-07-18/"", ""jlens_materials/experiments/graph-topology-rigorous-2026-07-17/"", ""jlens_materials/experiments/late-physics-representation-replication-2026-07-17/"", ""jlens_materials/experiments/option-free-question-end-2026-07-18/"", ""jlens_materials/experiments/relation-graph-visualization-2026-07-18/""]",144,"[""python scripts/analyze_graph_topology_rigorous.py"", ""python scripts/plot_graph_generalization_audit.py"", ""python scripts/plot_relation_graph_robustness.py"", ""python scripts/validate_graph_generalization_audit.py""]",[],FILE_INVENTORY.csv +paper/graph_generalization_audit.tex,70,table,,Community agreement as progressively more graded similarity information is retained. The smallest corrected $q$-value over the 12 secondary representation-by-density tests is 0.092.,matched result group(s),"[""graphs""]","[""jlens_materials/experiments/cross-mechanism-outcome-2026-07-18/"", ""jlens_materials/experiments/graph-isomorphism-generalization-2026-07-18/"", ""jlens_materials/experiments/graph-topology-rigorous-2026-07-17/"", ""jlens_materials/experiments/late-physics-representation-replication-2026-07-17/"", ""jlens_materials/experiments/option-free-question-end-2026-07-18/"", ""jlens_materials/experiments/relation-graph-visualization-2026-07-18/""]",144,"[""python scripts/analyze_graph_topology_rigorous.py"", ""python scripts/plot_graph_generalization_audit.py"", ""python scripts/plot_relation_graph_robustness.py"", ""python scripts/validate_graph_generalization_audit.py""]",[],FILE_INVENTORY.csv +paper/graph_generalization_audit.tex,92,table,,Exhaustive partition results. Values are six-family mean ARI; $p$ is the exact blockwise-null probability.,matched result group(s),"[""graphs""]","[""jlens_materials/experiments/cross-mechanism-outcome-2026-07-18/"", ""jlens_materials/experiments/graph-isomorphism-generalization-2026-07-18/"", ""jlens_materials/experiments/graph-topology-rigorous-2026-07-17/"", ""jlens_materials/experiments/late-physics-representation-replication-2026-07-17/"", ""jlens_materials/experiments/option-free-question-end-2026-07-18/"", ""jlens_materials/experiments/relation-graph-visualization-2026-07-18/""]",144,"[""python scripts/analyze_graph_topology_rigorous.py"", ""python scripts/plot_graph_generalization_audit.py"", ""python scripts/plot_relation_graph_robustness.py"", ""python scripts/validate_graph_generalization_audit.py""]",[],FILE_INVENTORY.csv +paper/graph_primary_edge_inventory.tex,6,longtable,,,matched result group(s),"[""graphs""]","[""jlens_materials/experiments/cross-mechanism-outcome-2026-07-18/"", ""jlens_materials/experiments/graph-isomorphism-generalization-2026-07-18/"", ""jlens_materials/experiments/graph-topology-rigorous-2026-07-17/"", ""jlens_materials/experiments/late-physics-representation-replication-2026-07-17/"", ""jlens_materials/experiments/option-free-question-end-2026-07-18/"", ""jlens_materials/experiments/relation-graph-visualization-2026-07-18/""]",144,"[""python scripts/analyze_graph_topology_rigorous.py"", ""python scripts/plot_graph_generalization_audit.py"", ""python scripts/plot_relation_graph_robustness.py"", ""python scripts/validate_graph_generalization_audit.py""]",[],FILE_INVENTORY.csv +paper/graph_primary_edge_inventory.tex,41,longtable,,,matched result group(s),"[""graphs""]","[""jlens_materials/experiments/cross-mechanism-outcome-2026-07-18/"", ""jlens_materials/experiments/graph-isomorphism-generalization-2026-07-18/"", ""jlens_materials/experiments/graph-topology-rigorous-2026-07-17/"", ""jlens_materials/experiments/late-physics-representation-replication-2026-07-17/"", ""jlens_materials/experiments/option-free-question-end-2026-07-18/"", ""jlens_materials/experiments/relation-graph-visualization-2026-07-18/""]",144,"[""python scripts/analyze_graph_topology_rigorous.py"", ""python scripts/plot_graph_generalization_audit.py"", ""python scripts/plot_relation_graph_robustness.py"", ""python scripts/validate_graph_generalization_audit.py""]",[],FILE_INVENTORY.csv +paper/graph_primary_edge_inventory.tex,76,longtable,,,matched result group(s),"[""graphs""]","[""jlens_materials/experiments/cross-mechanism-outcome-2026-07-18/"", ""jlens_materials/experiments/graph-isomorphism-generalization-2026-07-18/"", ""jlens_materials/experiments/graph-topology-rigorous-2026-07-17/"", ""jlens_materials/experiments/late-physics-representation-replication-2026-07-17/"", ""jlens_materials/experiments/option-free-question-end-2026-07-18/"", ""jlens_materials/experiments/relation-graph-visualization-2026-07-18/""]",144,"[""python scripts/analyze_graph_topology_rigorous.py"", ""python scripts/plot_graph_generalization_audit.py"", ""python scripts/plot_relation_graph_robustness.py"", ""python scripts/validate_graph_generalization_audit.py""]",[],FILE_INVENTORY.csv +paper/graph_primary_edge_inventory.tex,111,longtable,,,matched result group(s),"[""graphs""]","[""jlens_materials/experiments/cross-mechanism-outcome-2026-07-18/"", ""jlens_materials/experiments/graph-isomorphism-generalization-2026-07-18/"", ""jlens_materials/experiments/graph-topology-rigorous-2026-07-17/"", ""jlens_materials/experiments/late-physics-representation-replication-2026-07-17/"", ""jlens_materials/experiments/option-free-question-end-2026-07-18/"", ""jlens_materials/experiments/relation-graph-visualization-2026-07-18/""]",144,"[""python scripts/analyze_graph_topology_rigorous.py"", ""python scripts/plot_graph_generalization_audit.py"", ""python scripts/plot_relation_graph_robustness.py"", ""python scripts/validate_graph_generalization_audit.py""]",[],FILE_INVENTORY.csv +paper/graph_primary_edge_inventory.tex,146,longtable,,,matched result group(s),"[""graphs""]","[""jlens_materials/experiments/cross-mechanism-outcome-2026-07-18/"", ""jlens_materials/experiments/graph-isomorphism-generalization-2026-07-18/"", ""jlens_materials/experiments/graph-topology-rigorous-2026-07-17/"", ""jlens_materials/experiments/late-physics-representation-replication-2026-07-17/"", ""jlens_materials/experiments/option-free-question-end-2026-07-18/"", ""jlens_materials/experiments/relation-graph-visualization-2026-07-18/""]",144,"[""python scripts/analyze_graph_topology_rigorous.py"", ""python scripts/plot_graph_generalization_audit.py"", ""python scripts/plot_relation_graph_robustness.py"", ""python scripts/validate_graph_generalization_audit.py""]",[],FILE_INVENTORY.csv +paper/graph_primary_edge_inventory.tex,181,longtable,,,matched result group(s),"[""graphs""]","[""jlens_materials/experiments/cross-mechanism-outcome-2026-07-18/"", ""jlens_materials/experiments/graph-isomorphism-generalization-2026-07-18/"", ""jlens_materials/experiments/graph-topology-rigorous-2026-07-17/"", ""jlens_materials/experiments/late-physics-representation-replication-2026-07-17/"", ""jlens_materials/experiments/option-free-question-end-2026-07-18/"", ""jlens_materials/experiments/relation-graph-visualization-2026-07-18/""]",144,"[""python scripts/analyze_graph_topology_rigorous.py"", ""python scripts/plot_graph_generalization_audit.py"", ""python scripts/plot_relation_graph_robustness.py"", ""python scripts/validate_graph_generalization_audit.py""]",[],FILE_INVENTORY.csv +paper/materials_jacobian_lens.tex,134,figure,fig:design,"\textbf{Overview of the study.} A materials-science prompt is evaluated with a frozen open-weight language model while its intermediate states are recorded. We study those states in three complementary ways: vocabulary readout tests which scientific concepts are readable; matched counterfactual comparison tests whether state changes preserve constitutive orientation; and causal intervention tests whether changing an internal representation changes the model's engineering decision. The right-hand panels illustrate the corresponding evidence types (decoded terms, relational trends, and intervention-induced answer shifts). In the causal card, arrows show the raw change in ``higher''-minus-``lower'' answer preference: refinement shifts toward ``higher'' and coarsening toward ``lower'', so the physically correct effects have opposite signs. Full experiments and statistical tests are reported in the following sections. \hlyellow{The three branches represent complementary evidence types; the diagram does not imply that one shared feature is followed across all experiments.}",matched result group(s),"[""readout"", ""relational"", ""causal_coordinate""]","[""jlens_materials/experiments/causal-relational-comment3-audit-2026-09-06/"", ""jlens_materials/experiments/causal-relational-knockout-2026-09-02/"", ""jlens_materials/experiments/elicited-physics-abstraction-2026-07-18/"", ""jlens_materials/experiments/equation-free-relational-physics-2026-08-21/"", ""jlens_materials/experiments/gemma-equation-free-multiscale-2026-08-25/"", ""jlens_materials/experiments/gemma-multiscale-heldout-v1-2026-08-25/"", ""jlens_materials/experiments/gemma4-12b-equation-free-relational-v1/"", ""jlens_materials/experiments/gemma4-31b-equation-free-layer-comparison-2026-08-23/"", ""jlens_materials/experiments/gemma4-31b-equation-free-relational-layer40-v1/"", ""jlens_materials/experiments/gemma4-31b-equation-free-relational-v1/"", ""jlens_materials/experiments/materials-heldout-v1"", ""jlens_materials/experiments/neutral-anchored-relational-physics-2026-07-18/"", ""jlens_materials/experiments/relational-contrast-confirmation-2026-07-18/"", ""jlens_materials/runs/""]",300,"[""python scripts/analyze_causal_relational_knockout.py"", ""python scripts/analyze_equation_free_relational_benchmark_amended.py"", ""python scripts/analyze_gemma31b_equation_free_layers.py"", ""python scripts/analyze_gemma_equation_free_multiscale.py"", ""python scripts/analyze_gemma_multiscale_heldout_v1.py"", ""python scripts/analyze_materials_heldout_v1.py"", ""python scripts/analyze_neutral_anchored_relational_benchmark.py"", ""python scripts/validate_causal_relational_knockout.py""]","[{""publication_path"": ""paper/figures/figure1-study-summary-tikz.tex"", ""sha256"": ""d67e7620934fb1b0d02903d50edbe440ca0e332bbebf37497327a4bd4560b2b8"", ""matching_archived_files"": []}]",FILE_INVENTORY.csv +paper/materials_jacobian_lens.tex,141,table,tab:study-map,"\textbf{Study map and evidential status.} \hlyellow{Here ``frozen'' means that the listed endpoint and success rule were recorded as fixed before the corresponding output or calculation in our internal chronology; it does not establish externally timestamped preregistration or imply that every underlying state array or motivating cohort was unseen.} ``Jacobian-specific'' requires a matched advantage over direct unembedding or a direct-direction control, rather than merely a positive Jacobian result.",study-wide summary/provenance; consult named rows,"[""readout"", ""target_free"", ""geometry"", ""physical_equivalence"", ""graphs"", ""relational"", ""steering"", ""patching"", ""causal_coordinate"", ""multitoken"", ""31b_streams""]","[""jlens_materials/experiments/answer-code-binding-2026-07-17/"", ""jlens_materials/experiments/answer-scaffold-audit-2026-07-17/"", ""jlens_materials/experiments/candidate-activation-patching-2026-07-16/"", ""jlens_materials/experiments/candidate-followups-2026-07-16/"", ""jlens_materials/experiments/candidate-nonsteering-2026-07-16/"", ""jlens_materials/experiments/causal-relational-comment3-audit-2026-09-06/"", ""jlens_materials/experiments/causal-relational-knockout-2026-09-02/"", ""jlens_materials/experiments/cross-mechanism-activation-patching-2026-07-18/"", ""jlens_materials/experiments/cross-mechanism-outcome-2026-07-18/"", ""jlens_materials/experiments/elicited-physics-abstraction-2026-07-18/"", ""jlens_materials/experiments/equation-free-relational-physics-2026-08-21/"", ""jlens_materials/experiments/gemma-equation-free-multiscale-2026-08-25/"", ""jlens_materials/experiments/gemma-multiscale-heldout-v1-2026-08-25/"", ""jlens_materials/experiments/gemma4-12b-equation-free-relational-v1/"", ""jlens_materials/experiments/gemma4-31b-equation-free-layer-comparison-2026-08-23/"", ""jlens_materials/experiments/gemma4-31b-equation-free-relational-layer40-v1/"", ""jlens_materials/experiments/gemma4-31b-equation-free-relational-v1/"", ""jlens_materials/experiments/gemma4-31b-semantic-streams-2026-08-26/"", ""jlens_materials/experiments/graph-isomorphism-generalization-2026-07-18/"", ""jlens_materials/experiments/graph-topology-rigorous-2026-07-17/"", ""jlens_materials/experiments/late-physics-representation-replication-2026-07-17/"", ""jlens_materials/experiments/lexical-adversarial-representation-2026-07-17/"", ""jlens_materials/experiments/materials-heldout-v1"", ""jlens_materials/experiments/materials-heldout-v1_latent"", ""jlens_materials/experiments/multitoken-sequence-robustness-2026-07-18/"", ""jlens_materials/experiments/neutral-anchored-relational-physics-2026-07-18/"", ""jlens_materials/experiments/option-free-question-end-2026-07-18/"", ""jlens_materials/experiments/relation-graph-visualization-2026-07-18/"", ""jlens_materials/experiments/relational-contrast-confirmation-2026-07-18/"", ""jlens_materials/experiments/relational-grain-steering-v4"", ""jlens_materials/experiments/semantic-steering-v3"", ""jlens_materials/runs/""]",641,"[""python scripts/analyze_answer_code_binding.py"", ""python scripts/analyze_answer_scaffold_audit.py"", ""python scripts/analyze_candidate_followups.py"", ""python scripts/analyze_candidate_nonsteering.py"", ""python scripts/analyze_causal_relational_knockout.py"", ""python scripts/analyze_counterfactual_activation_patching.py"", ""python scripts/analyze_cross_mechanism_activation_patching.py"", ""python scripts/analyze_equation_free_relational_benchmark_amended.py"", ""python scripts/analyze_gemma31b_equation_free_layers.py"", ""python scripts/analyze_gemma_equation_free_multiscale.py"", ""python scripts/analyze_gemma_multiscale_heldout_v1.py"", ""python scripts/analyze_graph_topology_rigorous.py"", ""python scripts/analyze_late_physics_replication.py"", ""python scripts/analyze_lexical_adversarial_representation.py"", ""python scripts/analyze_materials_heldout_v1.py"", ""python scripts/analyze_materials_latent_geometry.py"", ""python scripts/analyze_neutral_anchored_relational_benchmark.py"", ""python scripts/analyze_relational_grain_steering_v4.py"", ""python scripts/analyze_semantic_steering_v3.py"", ""python scripts/build_final_paper_figures.py"", ""python scripts/build_gemma31b_semantic_streams.py"", ""python scripts/plot_graph_generalization_audit.py"", ""python scripts/plot_multitoken_sequence_robustness.py"", ""python scripts/plot_relation_graph_robustness.py"", ""python scripts/validate_causal_relational_knockout.py"", ""python scripts/validate_graph_generalization_audit.py""]",[],FILE_INVENTORY.csv +paper/materials_jacobian_lens.tex,166,table,tab:families,\textbf{Complete structure of the held-out dataset.} Every row contains five independent descriptions of the listed physical evidence. The right column gives all terms declared before execution and omitted from every corresponding input. Daggers mark documented multi-token terms excluded from the one-token rank endpoint. The Supplementary Information prints all 50 exact prompts and their terms.,matched result group(s),"[""readout"", ""graphs"", ""multitoken""]","[""jlens_materials/experiments/cross-mechanism-outcome-2026-07-18/"", ""jlens_materials/experiments/gemma-multiscale-heldout-v1-2026-08-25/"", ""jlens_materials/experiments/graph-isomorphism-generalization-2026-07-18/"", ""jlens_materials/experiments/graph-topology-rigorous-2026-07-17/"", ""jlens_materials/experiments/late-physics-representation-replication-2026-07-17/"", ""jlens_materials/experiments/materials-heldout-v1"", ""jlens_materials/experiments/multitoken-sequence-robustness-2026-07-18/"", ""jlens_materials/experiments/option-free-question-end-2026-07-18/"", ""jlens_materials/experiments/relation-graph-visualization-2026-07-18/"", ""jlens_materials/runs/""]",240,"[""python scripts/analyze_gemma_multiscale_heldout_v1.py"", ""python scripts/analyze_graph_topology_rigorous.py"", ""python scripts/analyze_materials_heldout_v1.py"", ""python scripts/plot_graph_generalization_audit.py"", ""python scripts/plot_multitoken_sequence_robustness.py"", ""python scripts/plot_relation_graph_robustness.py"", ""python scripts/validate_graph_generalization_audit.py""]",[],FILE_INVENTORY.csv +paper/materials_jacobian_lens.tex,213,figure,fig:controlled,"\textbf{Can either readout recover an engineering term that was deliberately omitted from the description?} Before running the 50 prompts, we declared 150 scientifically appropriate one-token terms and then asked where each term ranked among Gemma's 262,144 vocabulary entries. (A) The cutoff $k$ is relaxed from rank 1 to rank 100; a higher curve means more declared terms have become strongly readable. Thin teal curves are the three independent Jacobian fits, the thick teal curve is their mean, and gray is direct unembedding of the same states. At $k=100$, the fractions are 7.2\% and 3.3\%, respectively. (B) Each point is one prompt's recovery area under the curve (AUC); a point above the diagonal favors Jacobian transport. Eleven prompts improve, 36 tie, and three decline. (C) The same differences are grouped at the scientifically appropriate level: gray circles are the five phrasings and diamonds are the ten family means. Positive values favor the Jacobian lens. Boundary attack and notch resistance produce the largest gains, whereas cyclic damage and particle strengthening favor direct unembedding. Across families, the mean difference is $+0.0129$, but the hierarchical 95\% interval spans $-0.0187$ to $+0.0486$ and the one-sided family sign-flip test gives $p=0.234$. Thus the experiment finds selective, mechanism-dependent gains rather than a universal Jacobian advantage.",matched result group(s),"[""readout"", ""graphs""]","[""jlens_materials/experiments/cross-mechanism-outcome-2026-07-18/"", ""jlens_materials/experiments/gemma-multiscale-heldout-v1-2026-08-25/"", ""jlens_materials/experiments/graph-isomorphism-generalization-2026-07-18/"", ""jlens_materials/experiments/graph-topology-rigorous-2026-07-17/"", ""jlens_materials/experiments/late-physics-representation-replication-2026-07-17/"", ""jlens_materials/experiments/materials-heldout-v1"", ""jlens_materials/experiments/option-free-question-end-2026-07-18/"", ""jlens_materials/experiments/relation-graph-visualization-2026-07-18/"", ""jlens_materials/runs/""]",225,"[""python scripts/analyze_gemma_multiscale_heldout_v1.py"", ""python scripts/analyze_graph_topology_rigorous.py"", ""python scripts/analyze_materials_heldout_v1.py"", ""python scripts/plot_graph_generalization_audit.py"", ""python scripts/plot_relation_graph_robustness.py"", ""python scripts/validate_graph_generalization_audit.py""]","[{""publication_path"": ""paper/figures/heldout-controlled-recovery.pdf"", ""sha256"": ""22627c15c08d9a34414403b533ec6cfa5bbfb9ab675b6641ac89096832233729"", ""matching_archived_files"": [""jlens_materials/figures/materials-heldout-v1/heldout-controlled-recovery.pdf""]}]",FILE_INVENTORY.csv +paper/materials_jacobian_lens.tex,236,figure,fig:openvocab,"\textbf{Target-free family vocabularies.} Each row is one physical family and each cell is a word selected without the declared concept list. Support is the number of independent phrasings, out of five, in which the word appeared under the three-fit consensus rule; darker blue-green cells indicate stronger background-corrected support. Asterisks mark exact declared-word overlap added only after ranking. Read across a row to ask whether the words form a recognizable materials neighborhood. Boundary attack, surface oxidation, and rapid transformation show coherent mechanism or microstructure terms; high-temperature deformation and cleavage show why stable decoding can still be generic. The figure therefore measures what assembles naturally, not whether the algorithm can find a supplied answer.",matched result group(s),"[""readout"", ""target_free"", ""graphs"", ""causal_coordinate""]","[""jlens_materials/experiments/candidate-nonsteering-2026-07-16/"", ""jlens_materials/experiments/causal-relational-comment3-audit-2026-09-06/"", ""jlens_materials/experiments/causal-relational-knockout-2026-09-02/"", ""jlens_materials/experiments/cross-mechanism-outcome-2026-07-18/"", ""jlens_materials/experiments/gemma-multiscale-heldout-v1-2026-08-25/"", ""jlens_materials/experiments/graph-isomorphism-generalization-2026-07-18/"", ""jlens_materials/experiments/graph-topology-rigorous-2026-07-17/"", ""jlens_materials/experiments/late-physics-representation-replication-2026-07-17/"", ""jlens_materials/experiments/materials-heldout-v1"", ""jlens_materials/experiments/option-free-question-end-2026-07-18/"", ""jlens_materials/experiments/relation-graph-visualization-2026-07-18/"", ""jlens_materials/runs/""]",340,"[""python scripts/analyze_candidate_nonsteering.py"", ""python scripts/analyze_causal_relational_knockout.py"", ""python scripts/analyze_gemma_multiscale_heldout_v1.py"", ""python scripts/analyze_graph_topology_rigorous.py"", ""python scripts/analyze_materials_heldout_v1.py"", ""python scripts/plot_graph_generalization_audit.py"", ""python scripts/plot_relation_graph_robustness.py"", ""python scripts/validate_causal_relational_knockout.py"", ""python scripts/validate_graph_generalization_audit.py""]","[{""publication_path"": ""paper/figures/heldout-open-vocabulary.pdf"", ""sha256"": ""2c81592b3b5d6e032186859a0531570a58636d31628440af6331de77570dc5a5"", ""matching_archived_files"": []}]",FILE_INVENTORY.csv +paper/materials_jacobian_lens.tex,243,figure,fig:targetfree-classification,"\textbf{Do the freely discovered words identify the materials mechanism without a supplied answer list?} Each bar is leave-one-phrasing-out accuracy across the 50 prompts and ten balanced mechanism families. A classifier learned four phrasings per family and predicted the held-out fifth using only target-free candidate words from the corresponding readout. ``Exact prompt words removed'' deletes literal input copies; ``morphology-aware removal'' additionally deletes conservative prefix-related variants. The dotted line is 10\% chance. The dashed line is a TF-IDF classifier that instead sees the prompt words themselves. The target-free Jacobian and direct lists remain well above chance after the strictest removal, at 58\% and 64\%, but the prompt baseline reaches 56\% and direct does not differ significantly from Jacobian. Thus the discovered vocabulary contains cross-phrasing mechanism information, while the comparison rules out a lens-specific superiority claim. The complete confusion matrix and per-prompt predictions are in the Supplementary Information.",matched result group(s),"[""readout"", ""target_free"", ""physical_equivalence""]","[""jlens_materials/experiments/answer-code-binding-2026-07-17/"", ""jlens_materials/experiments/answer-scaffold-audit-2026-07-17/"", ""jlens_materials/experiments/candidate-nonsteering-2026-07-16/"", ""jlens_materials/experiments/gemma-multiscale-heldout-v1-2026-08-25/"", ""jlens_materials/experiments/late-physics-representation-replication-2026-07-17/"", ""jlens_materials/experiments/lexical-adversarial-representation-2026-07-17/"", ""jlens_materials/experiments/materials-heldout-v1"", ""jlens_materials/runs/""]",167,"[""python scripts/analyze_answer_code_binding.py"", ""python scripts/analyze_answer_scaffold_audit.py"", ""python scripts/analyze_candidate_nonsteering.py"", ""python scripts/analyze_gemma_multiscale_heldout_v1.py"", ""python scripts/analyze_late_physics_replication.py"", ""python scripts/analyze_lexical_adversarial_representation.py"", ""python scripts/analyze_materials_heldout_v1.py""]","[{""publication_path"": ""paper/figures/semantic-classification-summary.pdf"", ""sha256"": ""a81e1352f536565e0c031991c023f822b7e164ba13e79e623f33d8f179c4b10c"", ""matching_archived_files"": [""jlens_materials/experiments/candidate-nonsteering-2026-07-16/figures/semantic-classification-summary.pdf""]}]",FILE_INVENTORY.csv +paper/materials_jacobian_lens.tex,273,figure,fig:streams,"\textbf{What does the stricter target-free filter remove, and what materials vocabulary remains?} Rows A-E correspond to the five exact prompts printed in the text. Each left panel shows the original-style unrestricted leading words from lens fit 0. Each right panel removes input and output words, the frozen 214-word function list, and the globally common readout scaffold, then requires the word to occur in all three independent fits at that layer. No declared concept was supplied to either selection. The gray region marks the fixed 38-92\% analysis band. The comparison shows that much of the visually continuous unfiltered stream is generic discourse vocabulary, while the stricter view retains localized physical neighborhoods: \code{corrosion}/\code{damage} (A), \code{resilience}/\code{protection} (B), \code{irreversible}/\code{mechanism} (C), and \code{collapse}/\code{disintegration} (D). The generic cleavage stream (E) is a useful negative case. Gaps mean that none of the displayed words passed the strict rule at that layer; they are not evidence of absent computation. Ribbon thickness is a rank-derived visualization score, not token probability, causal influence, or a literal chain of thought.",matched result group(s),"[""readout"", ""target_free"", ""physical_equivalence"", ""graphs"", ""causal_coordinate""]","[""jlens_materials/experiments/answer-code-binding-2026-07-17/"", ""jlens_materials/experiments/answer-scaffold-audit-2026-07-17/"", ""jlens_materials/experiments/candidate-nonsteering-2026-07-16/"", ""jlens_materials/experiments/causal-relational-comment3-audit-2026-09-06/"", ""jlens_materials/experiments/causal-relational-knockout-2026-09-02/"", ""jlens_materials/experiments/cross-mechanism-outcome-2026-07-18/"", ""jlens_materials/experiments/gemma-multiscale-heldout-v1-2026-08-25/"", ""jlens_materials/experiments/graph-isomorphism-generalization-2026-07-18/"", ""jlens_materials/experiments/graph-topology-rigorous-2026-07-17/"", ""jlens_materials/experiments/late-physics-representation-replication-2026-07-17/"", ""jlens_materials/experiments/lexical-adversarial-representation-2026-07-17/"", ""jlens_materials/experiments/materials-heldout-v1"", ""jlens_materials/experiments/option-free-question-end-2026-07-18/"", ""jlens_materials/experiments/relation-graph-visualization-2026-07-18/"", ""jlens_materials/runs/""]",387,"[""python scripts/analyze_answer_code_binding.py"", ""python scripts/analyze_answer_scaffold_audit.py"", ""python scripts/analyze_candidate_nonsteering.py"", ""python scripts/analyze_causal_relational_knockout.py"", ""python scripts/analyze_gemma_multiscale_heldout_v1.py"", ""python scripts/analyze_graph_topology_rigorous.py"", ""python scripts/analyze_late_physics_replication.py"", ""python scripts/analyze_lexical_adversarial_representation.py"", ""python scripts/analyze_materials_heldout_v1.py"", ""python scripts/plot_graph_generalization_audit.py"", ""python scripts/plot_relation_graph_robustness.py"", ""python scripts/validate_causal_relational_knockout.py"", ""python scripts/validate_graph_generalization_audit.py""]","[{""publication_path"": ""paper/figures/figure4-semantic-streams.pdf"", ""sha256"": ""8fb79e9f897187beef29d19fc9ade92549ca6ec93a6384443fd755c80c244ea5"", ""matching_archived_files"": []}]",FILE_INVENTORY.csv +paper/materials_jacobian_lens.tex,294,figure,fig:geometry,"\textbf{Do differently worded descriptions of the same mechanism remain close in the model's full state space?} (A) For each of five folds, four phrasings per family define ten centroids and the fifth phrasing is classified; the procedure is repeated at every registered layer and for every baseline representation. (B) Full-dimensional Jacobian classification peaks at 62\% at 43.9\% depth: 31 of 50 descriptions are correct, above the 13-of-50 threshold obtained after shuffling family labels and retaining the best of 25 layers ($p=0.0002$). (C) UMAP is only a display of those 50 best-layer states. Its axes have no physical units and no test uses them; color and two-letter code denote family, marker shape denotes phrasing, and stars are family centroids. (D) The full-dimensional confusion matrix reveals the physical specificity: boundary attack (BA), line-defect motion (LD), and notch resistance (NR) are separated across all five phrasings, while ductile failure (DU), high-temperature deformation (HT), and surface oxidation (OX) overlap. CL, CY, PS, and RT denote cleavage, cyclic damage, particle strengthening, and rapid transformation. (E) The same classifier reaches 54\% on raw hidden states and 76\% on a simple average of the prompt's own word embeddings. The result therefore supports statistically reliable family structure in the transported states, but it does not show that Gemma created that structure independently of the engineering vocabulary in the prompt.",matched result group(s),"[""geometry"", ""physical_equivalence"", ""steering"", ""causal_coordinate""]","[""jlens_materials/experiments/answer-code-binding-2026-07-17/"", ""jlens_materials/experiments/answer-scaffold-audit-2026-07-17/"", ""jlens_materials/experiments/candidate-followups-2026-07-16/"", ""jlens_materials/experiments/causal-relational-comment3-audit-2026-09-06/"", ""jlens_materials/experiments/causal-relational-knockout-2026-09-02/"", ""jlens_materials/experiments/late-physics-representation-replication-2026-07-17/"", ""jlens_materials/experiments/lexical-adversarial-representation-2026-07-17/"", ""jlens_materials/experiments/materials-heldout-v1_latent"", ""jlens_materials/experiments/relational-grain-steering-v4"", ""jlens_materials/experiments/semantic-steering-v3"", ""jlens_materials/runs/""]",280,"[""python scripts/analyze_answer_code_binding.py"", ""python scripts/analyze_answer_scaffold_audit.py"", ""python scripts/analyze_candidate_followups.py"", ""python scripts/analyze_causal_relational_knockout.py"", ""python scripts/analyze_late_physics_replication.py"", ""python scripts/analyze_lexical_adversarial_representation.py"", ""python scripts/analyze_materials_latent_geometry.py"", ""python scripts/analyze_relational_grain_steering_v4.py"", ""python scripts/analyze_semantic_steering_v3.py"", ""python scripts/build_final_paper_figures.py"", ""python scripts/validate_causal_relational_knockout.py""]","[{""publication_path"": ""paper/figures/figure5-latent-geometry.pdf"", ""sha256"": ""d2c4a08c5c59797cb6ac174455da7c40072dbd0e160f81fb35e5704cf6d0e3cd"", ""matching_archived_files"": []}]",FILE_INVENTORY.csv +paper/materials_jacobian_lens.tex,319,figure,fig:physical-equivalence,"\textbf{Does physical equivalence overcome deliberately stronger wording similarity?} Every triplet compares an anchor with a differently worded prompt that has the same physical answer and a near-verbatim prompt whose physical relation is reversed. Positive margin means that the same-physics pair is closer; negative margin means that wording dominates. (A) In the disjoint replication cohort, both Jacobian and direct decoder-basis states begin strongly wording-aligned and rise sharply after roughly 60\% depth. The shaded region is the prospectively frozen 80-96\% late window. The frozen late-minus-middle Jacobian change is $+1.402$ ($0.951$-$1.793$), positive in 24/24 triplets and 6/6 families. (B) The absolute late-window means are positive in both the discovery and disjoint cohorts, but their family-hierarchical intervals cross zero; the replication's registered primary endpoint therefore failed. (C) Four replication families end positive, obstacle spacing is approximately neutral, and porosity remains wording-aligned, demonstrating real mechanism heterogeneity. (D) A target-free comparison of decoded word sets also trends positive late, but its intervals cross zero. Teal and purple nearly coincide throughout, showing that the transition is not uniquely produced by Jacobian transport. This is a representational similarity test, not a causal intervention or a literal chain of thought.",matched result group(s),"[""readout"", ""target_free"", ""physical_equivalence"", ""causal_coordinate""]","[""jlens_materials/experiments/answer-code-binding-2026-07-17/"", ""jlens_materials/experiments/answer-scaffold-audit-2026-07-17/"", ""jlens_materials/experiments/candidate-nonsteering-2026-07-16/"", ""jlens_materials/experiments/causal-relational-comment3-audit-2026-09-06/"", ""jlens_materials/experiments/causal-relational-knockout-2026-09-02/"", ""jlens_materials/experiments/gemma-multiscale-heldout-v1-2026-08-25/"", ""jlens_materials/experiments/late-physics-representation-replication-2026-07-17/"", ""jlens_materials/experiments/lexical-adversarial-representation-2026-07-17/"", ""jlens_materials/experiments/materials-heldout-v1"", ""jlens_materials/runs/""]",261,"[""python scripts/analyze_answer_code_binding.py"", ""python scripts/analyze_answer_scaffold_audit.py"", ""python scripts/analyze_candidate_nonsteering.py"", ""python scripts/analyze_causal_relational_knockout.py"", ""python scripts/analyze_gemma_multiscale_heldout_v1.py"", ""python scripts/analyze_late_physics_replication.py"", ""python scripts/analyze_lexical_adversarial_representation.py"", ""python scripts/analyze_materials_heldout_v1.py"", ""python scripts/validate_causal_relational_knockout.py""]","[{""publication_path"": ""paper/figures/late-physics-representation-replication.pdf"", ""sha256"": ""f39e8a384b2575b2ce0972b8d3e6485d85bbd57b95941fda284f6661c68d084e"", ""matching_archived_files"": [""jlens_materials/experiments/late-physics-representation-replication-2026-07-17/figures/late-physics-representation-replication.pdf""]}]",FILE_INVENTORY.csv +paper/materials_jacobian_lens.tex,338,figure,fig:relation-graph,"\textbf{Where does option-free comparative graph structure appear, and where does it fail?} Every node is one complete materials question and every selected edge points to the most similar eligible different material case. AUC uses all eligible candidates rather than only the selected edge; 0.5 denotes no ordering information. (A) The same 72 scientific stems are read at three prompt boundaries. At the natural question end, no answer word or response scaffold is present and Jacobian AUC is 0.642. A checkpoint-suffixed variant shows no detectable signal, while the explicit answer-mapping condition reaches 0.816. Because suffix and token position differ across conditions, this comparison localizes prompt-boundary sensitivity rather than a unique causal effect of the added words. Direct unembedding and raw states follow the same pattern. (B) At the natural question end, solid bars show the fraction of 144 selected edges that preserve the registered binary relation and hatched bars show all-candidate AUC. The dashed line is the exact case-preserving null mean. Jacobian selected-edge precision is 0.674 ($p=0.02195$) and AUC is 0.642 ($p=0.01235$); word and character TF-IDF are at or below the null. (C) The positive result depends on supplying the governing mechanism. When candidates come from other mechanisms, AUC falls to 0.513; when raw numerical direction deliberately conflicts with physical outcome, it falls to 0.471. (D) The actual 72-node natural-question graph contains six supplied mechanism islands and 144 selected edges. Circles, squares, and triangles are anchor, paraphrase, and near-verbatim counterfactual prompts. Filled and open nodes are the two registered orientations; teal and coral edges preserve or reverse the registered binary relation. Ninety-seven edges are teal. This figure demonstrates an option-free comparative regularity; Figure~\ref{fig:graph-identifiability} determines what that regularity can identify.",matched result group(s),"[""physical_equivalence"", ""graphs"", ""causal_coordinate""]","[""jlens_materials/experiments/answer-code-binding-2026-07-17/"", ""jlens_materials/experiments/answer-scaffold-audit-2026-07-17/"", ""jlens_materials/experiments/causal-relational-comment3-audit-2026-09-06/"", ""jlens_materials/experiments/causal-relational-knockout-2026-09-02/"", ""jlens_materials/experiments/cross-mechanism-outcome-2026-07-18/"", ""jlens_materials/experiments/graph-isomorphism-generalization-2026-07-18/"", ""jlens_materials/experiments/graph-topology-rigorous-2026-07-17/"", ""jlens_materials/experiments/late-physics-representation-replication-2026-07-17/"", ""jlens_materials/experiments/lexical-adversarial-representation-2026-07-17/"", ""jlens_materials/experiments/option-free-question-end-2026-07-18/"", ""jlens_materials/experiments/relation-graph-visualization-2026-07-18/""]",285,"[""python scripts/analyze_answer_code_binding.py"", ""python scripts/analyze_answer_scaffold_audit.py"", ""python scripts/analyze_causal_relational_knockout.py"", ""python scripts/analyze_graph_topology_rigorous.py"", ""python scripts/analyze_late_physics_replication.py"", ""python scripts/analyze_lexical_adversarial_representation.py"", ""python scripts/plot_graph_generalization_audit.py"", ""python scripts/plot_relation_graph_robustness.py"", ""python scripts/validate_causal_relational_knockout.py"", ""python scripts/validate_graph_generalization_audit.py""]","[{""publication_path"": ""paper/figures/relation-graph-robustness.pdf"", ""sha256"": ""1f3042ebdbd328088543cc4cf0808350acd812d2986350577aaddb38ce16ee5f"", ""matching_archived_files"": [""jlens_materials/experiments/relation-graph-visualization-2026-07-18/figures/relation-graph-robustness.pdf""]}]",FILE_INVENTORY.csv +paper/materials_jacobian_lens.tex,361,figure,fig:graph-identifiability,"\textbf{What can the option-free graph identify?} (A) A numerical increase raises the property for a direct law but lowers it for an inverse law. In this prompt set, physical polarity is exactly $y=sx$; within one mechanism, the same/different physical relation collapses algebraically to the same/different numerical relation. (B) A graph isomorphism network is trained on four mechanisms, selected on a fifth, and tested on an unseen sixth. The purple AUC of 1.0 is a positive-control input readback: numerical direction $x$ was deliberately supplied to the absolute-label network, so this curve does not show graph discovery. The actual test asks whether graph structure can combine that supplied $x$ with an unseen law to infer physical polarity $y$; it fails, as does a separate same/different relation network that receives neither $x$ nor surface-variant identity. Points are held-out mechanisms and connecting lines guide the eye. (C) Community agreement rises when graded similarities are retained instead of only the strongest edge, showing that sparsification discards structure. Direct and especially raw states are at least as strong as the Jacobian states, so this organization is not lens-specific. (D) Exhaustively evaluating every balanced partition shows that the registered physical split is not the uniquely preferred division, including when counterfactual phrasing is held out. Bars are means, dots are mechanisms, and intervals show the corresponding blockwise null distribution. Together, the panels support transferable comparative geometry while rejecting a universal, Jacobian-specific physical-sign graph.",matched result group(s),"[""geometry"", ""graphs""]","[""jlens_materials/experiments/cross-mechanism-outcome-2026-07-18/"", ""jlens_materials/experiments/graph-isomorphism-generalization-2026-07-18/"", ""jlens_materials/experiments/graph-topology-rigorous-2026-07-17/"", ""jlens_materials/experiments/late-physics-representation-replication-2026-07-17/"", ""jlens_materials/experiments/materials-heldout-v1_latent"", ""jlens_materials/experiments/option-free-question-end-2026-07-18/"", ""jlens_materials/experiments/relation-graph-visualization-2026-07-18/"", ""jlens_materials/runs/""]",201,"[""python scripts/analyze_graph_topology_rigorous.py"", ""python scripts/analyze_materials_latent_geometry.py"", ""python scripts/build_final_paper_figures.py"", ""python scripts/plot_graph_generalization_audit.py"", ""python scripts/plot_relation_graph_robustness.py"", ""python scripts/validate_graph_generalization_audit.py""]","[{""publication_path"": ""paper/figures/graph-identifiability-summary.pdf"", ""sha256"": ""2c67031f8d824a6371c2fcd78fe844f2d3783f1c11ee985ea525784675c72338"", ""matching_archived_files"": []}]",FILE_INVENTORY.csv +paper/materials_jacobian_lens.tex,398,figure,fig:relational-physics,"\hlyellow{\textbf{Constitutive orientation persists without a supplied equation across Gemma scale.} Every law contributes eight matched up-minus-down state contrasts along a model-specific direction fitted only on the same earlier development laws. (A) The matched 4B prompt-format comparison. Open markers supplied the equation and prescribed sign composition; filled markers used the corresponding equation-free questions. Both use the original 4B neutral scale, and black segments are medians. (B) The complete 60-law distributions for every available condition. Each waterfall independently orders all laws from most inverse to most direct; horizontal bars begin at that condition's calibrated neutral zero. The 4B supplied and equation-free facets are the matched format pair. The 12B and 31B facets contain equation-free tests only. Each facet has its own horizontal robust-$z$ scale, so signs and category ordering are comparable across facets but absolute bar lengths are not common hidden-state units. Purple, gray, and teal denote inverse, neutral, and direct relations. (C) Direct-inverse AUC for the hidden-state direction, output-logit change, and lexical controls across the same four available conditions. Hidden and output separation survives scaffold removal and reaches 1.000 at 12B and 31B, whereas word and character controls remain near chance. (D) The three registered pairwise AUCs, neutral-cut directional accuracy, and exact generated-answer accuracy for the equation-free cohort. Both larger checkpoints orient 40 of 40 directional laws and reach ceiling on all three relational AUCs; only 31B converts that representation into nearly perfect generated answers. The comparison supports a scale-robust ordinal constitutive orientation, not an invariant law-by-law magnitude, unfamiliar-law generalization, or a literal reasoning trace.}",matched result group(s),"[""geometry"", ""physical_equivalence"", ""relational""]","[""jlens_materials/experiments/answer-code-binding-2026-07-17/"", ""jlens_materials/experiments/answer-scaffold-audit-2026-07-17/"", ""jlens_materials/experiments/elicited-physics-abstraction-2026-07-18/"", ""jlens_materials/experiments/equation-free-relational-physics-2026-08-21/"", ""jlens_materials/experiments/gemma-equation-free-multiscale-2026-08-25/"", ""jlens_materials/experiments/gemma4-12b-equation-free-relational-v1/"", ""jlens_materials/experiments/gemma4-31b-equation-free-layer-comparison-2026-08-23/"", ""jlens_materials/experiments/gemma4-31b-equation-free-relational-layer40-v1/"", ""jlens_materials/experiments/gemma4-31b-equation-free-relational-v1/"", ""jlens_materials/experiments/late-physics-representation-replication-2026-07-17/"", ""jlens_materials/experiments/lexical-adversarial-representation-2026-07-17/"", ""jlens_materials/experiments/materials-heldout-v1_latent"", ""jlens_materials/experiments/neutral-anchored-relational-physics-2026-07-18/"", ""jlens_materials/experiments/relational-contrast-confirmation-2026-07-18/"", ""jlens_materials/runs/""]",247,"[""python scripts/analyze_answer_code_binding.py"", ""python scripts/analyze_answer_scaffold_audit.py"", ""python scripts/analyze_equation_free_relational_benchmark_amended.py"", ""python scripts/analyze_gemma31b_equation_free_layers.py"", ""python scripts/analyze_gemma_equation_free_multiscale.py"", ""python scripts/analyze_late_physics_replication.py"", ""python scripts/analyze_lexical_adversarial_representation.py"", ""python scripts/analyze_materials_latent_geometry.py"", ""python scripts/analyze_neutral_anchored_relational_benchmark.py"", ""python scripts/build_final_paper_figures.py""]","[{""publication_path"": ""paper/figures/equation-free-relational-multiscale.pdf"", ""sha256"": ""4189fbe4097f242a5b7f717c097cbc4d53f70fd355dae92fbaa6544383aaa131"", ""matching_archived_files"": [""jlens_materials/experiments/gemma-equation-free-multiscale-2026-08-25/figures/equation-free-relational-multiscale.pdf""]}]",FILE_INVENTORY.csv +paper/materials_jacobian_lens.tex,417,figure,fig:blinded,"\textbf{Can an automated interpreter recognize a materials mechanism from discovered words alone?} (A) Twenty candidate sets (ten mechanism families from each of two readouts) were shuffled and stripped of the original prompt, method identity, and answer key. The automated interpreter selected one of ten family labels for each set in five order-randomized passes; it did not solve the original engineering question. (B) Accuracy stays far above the 10\% chance level on every pass. (C) Majority-vote correctness for every family and readout: both columns are correct for nine families and both miss cleavage. Thus both naturally assembled vocabularies carry recognizable materials meaning, with no Jacobian-specific advantage in this secondary test (paired difference 0, $p=1.0$). (D) Each point is one Jacobian family. The horizontal coordinate is predeclared exact-term recovery; the vertical coordinate is the fraction of automated passes assigning the target-free words correctly. Cleavage is weak on both measures, but most families are semantically identified even when exact targets are rarely top-ranked, producing only a weak relationship ($\rho=0.254$). Controlled recovery and target-free recognition therefore measure complementary properties. The five passes are repeated judgments from one automated system, not independent scientific expert (hence this figure is limited to reproducible machine-based semantic validation).",matched result group(s),"[""readout"", ""target_free"", ""graphs""]","[""jlens_materials/experiments/candidate-nonsteering-2026-07-16/"", ""jlens_materials/experiments/cross-mechanism-outcome-2026-07-18/"", ""jlens_materials/experiments/gemma-multiscale-heldout-v1-2026-08-25/"", ""jlens_materials/experiments/graph-isomorphism-generalization-2026-07-18/"", ""jlens_materials/experiments/graph-topology-rigorous-2026-07-17/"", ""jlens_materials/experiments/late-physics-representation-replication-2026-07-17/"", ""jlens_materials/experiments/materials-heldout-v1"", ""jlens_materials/experiments/option-free-question-end-2026-07-18/"", ""jlens_materials/experiments/relation-graph-visualization-2026-07-18/"", ""jlens_materials/runs/""]",246,"[""python scripts/analyze_candidate_nonsteering.py"", ""python scripts/analyze_gemma_multiscale_heldout_v1.py"", ""python scripts/analyze_graph_topology_rigorous.py"", ""python scripts/analyze_materials_heldout_v1.py"", ""python scripts/plot_graph_generalization_audit.py"", ""python scripts/plot_relation_graph_robustness.py"", ""python scripts/validate_graph_generalization_audit.py""]","[{""publication_path"": ""paper/figures/automated-family-identification.pdf"", ""sha256"": ""418fcc81622d200d702f4dcb3d08ee69c835afbeea0489d111f2f4232aa0d6ee"", ""matching_archived_files"": []}]",FILE_INVENTORY.csv +paper/materials_jacobian_lens.tex,434,figure,fig:cases,"\textbf{Two magnified examples of the controlled-rank measurement.} The panels print the exact prompts, predeclared absent words, and one-token continuations. (A-C) The notch-resistance example is an earlier development item excluded from all held-out inference. Panel B plots \code{toughness}'s full-vocabulary rank at every sampled layer for three independent Jacobian fits and direct unembedding; upward is stronger because rank 1 is best. The registered 38-92\% band is shaded. Panel C reduces each word to its best rank within that band, showing that the localized rank-2 event is specific to \code{toughness}. (D-F) The boundary-attack example is one of the 50 held-out prompts and was already counted in Figure~\ref{fig:controlled}; it is not a new test. All three lenses sustain \code{corrosion} at rank 1 over two sampled layers, while direct unembedding reaches rank 111. Panel F shows that \code{sensitization} and \code{boundary} do not receive the same advantage. Thus Figure~\ref{fig:controlled} asks how often declared concepts are recovered across the frozen population, whereas this figure shows the layer location, fit-to-fit agreement, duration, and word specificity that its aggregate statistic necessarily discards. Neither trajectory is a literal chain of thought.",matched result group(s),"[""readout"", ""geometry"", ""graphs"", ""steering""]","[""jlens_materials/experiments/candidate-followups-2026-07-16/"", ""jlens_materials/experiments/cross-mechanism-outcome-2026-07-18/"", ""jlens_materials/experiments/gemma-multiscale-heldout-v1-2026-08-25/"", ""jlens_materials/experiments/graph-isomorphism-generalization-2026-07-18/"", ""jlens_materials/experiments/graph-topology-rigorous-2026-07-17/"", ""jlens_materials/experiments/late-physics-representation-replication-2026-07-17/"", ""jlens_materials/experiments/materials-heldout-v1"", ""jlens_materials/experiments/materials-heldout-v1_latent"", ""jlens_materials/experiments/option-free-question-end-2026-07-18/"", ""jlens_materials/experiments/relation-graph-visualization-2026-07-18/"", ""jlens_materials/experiments/relational-grain-steering-v4"", ""jlens_materials/experiments/semantic-steering-v3"", ""jlens_materials/runs/""]",289,"[""python scripts/analyze_candidate_followups.py"", ""python scripts/analyze_gemma_multiscale_heldout_v1.py"", ""python scripts/analyze_graph_topology_rigorous.py"", ""python scripts/analyze_materials_heldout_v1.py"", ""python scripts/analyze_materials_latent_geometry.py"", ""python scripts/analyze_relational_grain_steering_v4.py"", ""python scripts/analyze_semantic_steering_v3.py"", ""python scripts/build_final_paper_figures.py"", ""python scripts/plot_graph_generalization_audit.py"", ""python scripts/plot_relation_graph_robustness.py"", ""python scripts/validate_graph_generalization_audit.py""]","[{""publication_path"": ""paper/figures/figure7-layer-resolved-cases.pdf"", ""sha256"": ""d535c6ca713c1ef188db35dd4082d2d9308846564c27c6c5691cd9b968b4889c"", ""matching_archived_files"": [""jlens_materials/figures/materials-heldout-v1/figure7-layer-resolved-cases.pdf""]}]",FILE_INVENTORY.csv +paper/materials_jacobian_lens.tex,455,figure,fig:steering-screen,"\textbf{Broad steering is mechanism dependent.} Each panel averages ten new physical conditions and plots how an intermediate-state intervention changes the log odds of the first scientific answer relative to the second: \code{grooves} versus \code{clean} for corrosion (A), \code{hard} versus \code{soft} for transformation (B), and \code{higher} versus \code{lower} for grain size (C). Zero is the unperturbed model; the horizontal coordinate is the added direction's size relative to the final-prompt residual norm. Points are condition-weighted means and shaded bands are one standard error across physical conditions. The teal curve applies the scientifically matched Jacobian direction. Purple applies the two unrelated materials directions at the identical layer, green constructs the matched direction without Jacobian transport, and gray averages ten random directions. Corrosion is the only family that passes every \hlyellow{internally prespecified gate}: its matched curve is strong, monotonic, and distinct from all controls. Transformation trends in the intended direction but lacks \hlyellow{the prespecified specificity criterion}. The grain curve averages refinement, for which \code{higher} is correct, with coarsening, for which \code{lower} is correct; its cancellation motivated the relation-aware experiment in Figure~\ref{fig:steering}. The figure therefore shows both the causal promise and the non-universality of mechanism steering.",matched result group(s),"[""physical_equivalence"", ""steering"", ""causal_coordinate""]","[""jlens_materials/experiments/answer-code-binding-2026-07-17/"", ""jlens_materials/experiments/answer-scaffold-audit-2026-07-17/"", ""jlens_materials/experiments/candidate-followups-2026-07-16/"", ""jlens_materials/experiments/causal-relational-comment3-audit-2026-09-06/"", ""jlens_materials/experiments/causal-relational-knockout-2026-09-02/"", ""jlens_materials/experiments/late-physics-representation-replication-2026-07-17/"", ""jlens_materials/experiments/lexical-adversarial-representation-2026-07-17/"", ""jlens_materials/experiments/relational-grain-steering-v4"", ""jlens_materials/experiments/semantic-steering-v3""]",223,"[""python scripts/analyze_answer_code_binding.py"", ""python scripts/analyze_answer_scaffold_audit.py"", ""python scripts/analyze_candidate_followups.py"", ""python scripts/analyze_causal_relational_knockout.py"", ""python scripts/analyze_late_physics_replication.py"", ""python scripts/analyze_lexical_adversarial_representation.py"", ""python scripts/analyze_relational_grain_steering_v4.py"", ""python scripts/analyze_semantic_steering_v3.py"", ""python scripts/validate_causal_relational_knockout.py""]","[{""publication_path"": ""paper/figures/family_dose_response.pdf"", ""sha256"": ""bd3ce94f4bfa2be41679874eedced9ffa5d1ff7a690dcb5fccba5c15cee811a8"", ""matching_archived_files"": [""jlens_materials/figures/semantic-steering-v3/family_dose_response.pdf""]}]",FILE_INVENTORY.csv +paper/materials_jacobian_lens.tex,472,figure,fig:steering,"\textbf{Can one frozen grain-size mechanism direction change a scientific answer in the relation-appropriate direction?} Six new materials were each written as a matched pair: grain refinement and grain coarsening with alloy identity, the two grain sizes, controlled covariates, and answer words held fixed. At the final prompt token in layer 16, the same frozen Jacobian direction was swept from $-4\%$ to $+4\%$ of the state's norm; the full strings \code{higher} and \code{lower} were scored at each dose. (A) For every material, the raw change in \code{higher}-minus-\code{lower} log odds is positive for refinement and negative for coarsening. Each connecting line therefore crosses zero: the direction responds to the relation rather than always favoring one word. (B) Signs are aligned so that positive always means movement toward the physically correct answer, then refinement and coarsening are averaged within material. Every circle is one independent matched pair; diamonds and bars are the mean and pair-clustered 95\% interval. The matched Jacobian effect is $+0.852$ and exceeds random, unrelated-mechanism, and direct controls in all six pairs. (C) The mean dose curves show the reversal continuously rather than only at endpoints. The experiment prospectively confirms a localized context-dependent causal pathway; it does not reveal a prose chain of thought or establish general materials reasoning. Exact prompts, both answer orders, clean outputs, all 2,400 intervention rows, and the full protocol are printed or linked in the Supplementary Information.",matched result group(s),"[""readout"", ""physical_equivalence"", ""steering"", ""causal_coordinate""]","[""jlens_materials/experiments/answer-code-binding-2026-07-17/"", ""jlens_materials/experiments/answer-scaffold-audit-2026-07-17/"", ""jlens_materials/experiments/candidate-followups-2026-07-16/"", ""jlens_materials/experiments/causal-relational-comment3-audit-2026-09-06/"", ""jlens_materials/experiments/causal-relational-knockout-2026-09-02/"", ""jlens_materials/experiments/gemma-multiscale-heldout-v1-2026-08-25/"", ""jlens_materials/experiments/late-physics-representation-replication-2026-07-17/"", ""jlens_materials/experiments/lexical-adversarial-representation-2026-07-17/"", ""jlens_materials/experiments/materials-heldout-v1"", ""jlens_materials/experiments/relational-grain-steering-v4"", ""jlens_materials/experiments/semantic-steering-v3"", ""jlens_materials/runs/""]",304,"[""python scripts/analyze_answer_code_binding.py"", ""python scripts/analyze_answer_scaffold_audit.py"", ""python scripts/analyze_candidate_followups.py"", ""python scripts/analyze_causal_relational_knockout.py"", ""python scripts/analyze_gemma_multiscale_heldout_v1.py"", ""python scripts/analyze_late_physics_replication.py"", ""python scripts/analyze_lexical_adversarial_representation.py"", ""python scripts/analyze_materials_heldout_v1.py"", ""python scripts/analyze_relational_grain_steering_v4.py"", ""python scripts/analyze_semantic_steering_v3.py"", ""python scripts/validate_causal_relational_knockout.py""]","[{""publication_path"": ""paper/figures/relational_confirmation.pdf"", ""sha256"": ""37895d87ab6e4b8be70fa5a1d11856cf5ba81d599dc43cece3843994e2ddbd9c"", ""matching_archived_files"": [""jlens_materials/figures/relational-grain-steering-v4/relational_confirmation.pdf""]}]",FILE_INVENTORY.csv +paper/materials_jacobian_lens.tex,497,figure,fig:patching,"\textbf{Can a hidden state from the opposite grain-size relation causally transfer its answer?} A receiver prompt and donor prompt use the same answer words but describe opposite refinement/coarsening relations. At one layer, the receiver's final-prompt-token state is replaced by the donor state; positive values mean movement toward the answer appropriate to the donor's reversed relation. If the state contains only wording or alloy identity, the reversed transplant should not consistently redirect the answer. (A) Read from left to right, same-material and cross-material reverse patches have little effect early but produce a sharp late-layer shift of roughly 11 log-odds units at the peak. Relation-preserving and answer-order-only directions are rescaled to the same receiver-donor distance but remain much smaller. The shaded vertical region is the fixed 38-92\% analysis band; the band-mean same-material reverse effect is $+6.249$ ($5.663$-$6.795$). (B) The same-material reverse effect appears in all six matched alloy pairs, excluding a result driven by one material. (C) Layers where refinement and coarsening are more separable by a vocabulary readout tend also to show larger patching effects. The Jacobian level correlation is $\rho=0.818$ and the direct correlation is $\rho=0.571$, but onset and first-difference checks support only a broad late-stage correspondence, not exact layer-by-layer localization. The experiment establishes causal sufficiency in this constrained decision: a naturally occurring reversed-relation state can redirect the answer and can transfer between alloys. It does not identify a unique one-dimensional mechanism, prove necessity, or reveal a prose chain of thought. It is called exploratory because it reused a grain-size cohort whose strong effect was already known, not because the intervention is noncausal.",matched result group(s),"[""physical_equivalence"", ""steering"", ""patching"", ""causal_coordinate""]","[""jlens_materials/experiments/answer-code-binding-2026-07-17/"", ""jlens_materials/experiments/answer-scaffold-audit-2026-07-17/"", ""jlens_materials/experiments/candidate-activation-patching-2026-07-16/"", ""jlens_materials/experiments/candidate-followups-2026-07-16/"", ""jlens_materials/experiments/causal-relational-comment3-audit-2026-09-06/"", ""jlens_materials/experiments/causal-relational-knockout-2026-09-02/"", ""jlens_materials/experiments/cross-mechanism-activation-patching-2026-07-18/"", ""jlens_materials/experiments/late-physics-representation-replication-2026-07-17/"", ""jlens_materials/experiments/lexical-adversarial-representation-2026-07-17/"", ""jlens_materials/experiments/relational-grain-steering-v4"", ""jlens_materials/experiments/semantic-steering-v3""]",265,"[""python scripts/analyze_answer_code_binding.py"", ""python scripts/analyze_answer_scaffold_audit.py"", ""python scripts/analyze_candidate_followups.py"", ""python scripts/analyze_causal_relational_knockout.py"", ""python scripts/analyze_counterfactual_activation_patching.py"", ""python scripts/analyze_cross_mechanism_activation_patching.py"", ""python scripts/analyze_late_physics_replication.py"", ""python scripts/analyze_lexical_adversarial_representation.py"", ""python scripts/analyze_relational_grain_steering_v4.py"", ""python scripts/analyze_semantic_steering_v3.py"", ""python scripts/validate_causal_relational_knockout.py""]","[{""publication_path"": ""paper/figures/counterfactual-activation-patching.pdf"", ""sha256"": ""04b3fed9c3fd6f8e9cb28a62494e10c0adbe855c8130395007fd17a98dd10060"", ""matching_archived_files"": []}]",FILE_INVENTORY.csv +paper/materials_jacobian_lens.tex,520,figure,fig:causal-knockout,"\textbf{A development-fitted relational coordinate makes a selective, context-dependent causal contribution to equation-free physical decisions.} (A) Setting only the coordinate to its development midpoint reduces the correct-answer margin at all three scales; equal-length activation-matched controls remain near zero. Pale points are 24 law identities; large markers and bars are law means and 95\% law-bootstrap intervals. (B) Replacing only the coordinate with its value from the matched reversed-input prompt moves the decision toward the counterfactual answer. Full-state replacement shows the total transferable effect; matched controls remain near zero. (C) The coordinate-only share of full-state transfer decreases with scale, but restoring the original coordinate within the counterfactual state removes approximately one-half of the total effect at every scale. (D) Neutralization changes discrete accuracy chiefly at 4B. Full-state counterfactuals reverse almost all original decisions, while coordinate restoration recovers many of them. The experiment establishes selective causal involvement, not a unique or universally necessary representation.",matched result group(s),"[""geometry"", ""relational"", ""causal_coordinate""]","[""jlens_materials/experiments/causal-relational-comment3-audit-2026-09-06/"", ""jlens_materials/experiments/causal-relational-knockout-2026-09-02/"", ""jlens_materials/experiments/elicited-physics-abstraction-2026-07-18/"", ""jlens_materials/experiments/equation-free-relational-physics-2026-08-21/"", ""jlens_materials/experiments/gemma-equation-free-multiscale-2026-08-25/"", ""jlens_materials/experiments/gemma4-12b-equation-free-relational-v1/"", ""jlens_materials/experiments/gemma4-31b-equation-free-layer-comparison-2026-08-23/"", ""jlens_materials/experiments/gemma4-31b-equation-free-relational-layer40-v1/"", ""jlens_materials/experiments/gemma4-31b-equation-free-relational-v1/"", ""jlens_materials/experiments/materials-heldout-v1_latent"", ""jlens_materials/experiments/neutral-anchored-relational-physics-2026-07-18/"", ""jlens_materials/experiments/relational-contrast-confirmation-2026-07-18/"", ""jlens_materials/runs/""]",276,"[""python scripts/analyze_causal_relational_knockout.py"", ""python scripts/analyze_equation_free_relational_benchmark_amended.py"", ""python scripts/analyze_gemma31b_equation_free_layers.py"", ""python scripts/analyze_gemma_equation_free_multiscale.py"", ""python scripts/analyze_materials_latent_geometry.py"", ""python scripts/analyze_neutral_anchored_relational_benchmark.py"", ""python scripts/build_final_paper_figures.py"", ""python scripts/validate_causal_relational_knockout.py""]","[{""publication_path"": ""paper/figures/causal-relational-knockout.pdf"", ""sha256"": ""584f0d774905411e25fa227be967d6a78eae30709d93c831fd45e9a9d32e3dca"", ""matching_archived_files"": []}]",FILE_INVENTORY.csv +paper/materials_jacobian_lens_supplement.tex,78,longtable,,,study-wide summary/provenance; consult named rows,"[""readout"", ""target_free"", ""geometry"", ""physical_equivalence"", ""graphs"", ""relational"", ""steering"", ""patching"", ""causal_coordinate"", ""multitoken"", ""31b_streams""]","[""jlens_materials/experiments/answer-code-binding-2026-07-17/"", ""jlens_materials/experiments/answer-scaffold-audit-2026-07-17/"", ""jlens_materials/experiments/candidate-activation-patching-2026-07-16/"", ""jlens_materials/experiments/candidate-followups-2026-07-16/"", ""jlens_materials/experiments/candidate-nonsteering-2026-07-16/"", ""jlens_materials/experiments/causal-relational-comment3-audit-2026-09-06/"", ""jlens_materials/experiments/causal-relational-knockout-2026-09-02/"", ""jlens_materials/experiments/cross-mechanism-activation-patching-2026-07-18/"", ""jlens_materials/experiments/cross-mechanism-outcome-2026-07-18/"", ""jlens_materials/experiments/elicited-physics-abstraction-2026-07-18/"", ""jlens_materials/experiments/equation-free-relational-physics-2026-08-21/"", ""jlens_materials/experiments/gemma-equation-free-multiscale-2026-08-25/"", ""jlens_materials/experiments/gemma-multiscale-heldout-v1-2026-08-25/"", ""jlens_materials/experiments/gemma4-12b-equation-free-relational-v1/"", ""jlens_materials/experiments/gemma4-31b-equation-free-layer-comparison-2026-08-23/"", ""jlens_materials/experiments/gemma4-31b-equation-free-relational-layer40-v1/"", ""jlens_materials/experiments/gemma4-31b-equation-free-relational-v1/"", ""jlens_materials/experiments/gemma4-31b-semantic-streams-2026-08-26/"", ""jlens_materials/experiments/graph-isomorphism-generalization-2026-07-18/"", ""jlens_materials/experiments/graph-topology-rigorous-2026-07-17/"", ""jlens_materials/experiments/late-physics-representation-replication-2026-07-17/"", ""jlens_materials/experiments/lexical-adversarial-representation-2026-07-17/"", ""jlens_materials/experiments/materials-heldout-v1"", ""jlens_materials/experiments/materials-heldout-v1_latent"", ""jlens_materials/experiments/multitoken-sequence-robustness-2026-07-18/"", ""jlens_materials/experiments/neutral-anchored-relational-physics-2026-07-18/"", ""jlens_materials/experiments/option-free-question-end-2026-07-18/"", ""jlens_materials/experiments/relation-graph-visualization-2026-07-18/"", ""jlens_materials/experiments/relational-contrast-confirmation-2026-07-18/"", ""jlens_materials/experiments/relational-grain-steering-v4"", ""jlens_materials/experiments/semantic-steering-v3"", ""jlens_materials/runs/""]",641,"[""python scripts/analyze_answer_code_binding.py"", ""python scripts/analyze_answer_scaffold_audit.py"", ""python scripts/analyze_candidate_followups.py"", ""python scripts/analyze_candidate_nonsteering.py"", ""python scripts/analyze_causal_relational_knockout.py"", ""python scripts/analyze_counterfactual_activation_patching.py"", ""python scripts/analyze_cross_mechanism_activation_patching.py"", ""python scripts/analyze_equation_free_relational_benchmark_amended.py"", ""python scripts/analyze_gemma31b_equation_free_layers.py"", ""python scripts/analyze_gemma_equation_free_multiscale.py"", ""python scripts/analyze_gemma_multiscale_heldout_v1.py"", ""python scripts/analyze_graph_topology_rigorous.py"", ""python scripts/analyze_late_physics_replication.py"", ""python scripts/analyze_lexical_adversarial_representation.py"", ""python scripts/analyze_materials_heldout_v1.py"", ""python scripts/analyze_materials_latent_geometry.py"", ""python scripts/analyze_neutral_anchored_relational_benchmark.py"", ""python scripts/analyze_relational_grain_steering_v4.py"", ""python scripts/analyze_semantic_steering_v3.py"", ""python scripts/build_final_paper_figures.py"", ""python scripts/build_gemma31b_semantic_streams.py"", ""python scripts/plot_graph_generalization_audit.py"", ""python scripts/plot_multitoken_sequence_robustness.py"", ""python scripts/plot_relation_graph_robustness.py"", ""python scripts/validate_causal_relational_knockout.py"", ""python scripts/validate_graph_generalization_audit.py""]",[],FILE_INVENTORY.csv +paper/materials_jacobian_lens_supplement.tex,138,table,,Primary held-out controlled result. Lens-fit seeds are averaged within prompt; population uncertainty uses ten mechanism families.,matched result group(s),"[""readout""]","[""jlens_materials/experiments/gemma-multiscale-heldout-v1-2026-08-25/"", ""jlens_materials/experiments/materials-heldout-v1"", ""jlens_materials/runs/""]",81,"[""python scripts/analyze_gemma_multiscale_heldout_v1.py"", ""python scripts/analyze_materials_heldout_v1.py""]",[],FILE_INVENTORY.csv +paper/materials_jacobian_lens_supplement.tex,162,table,tab:s-multiscale-equation-free,"\hlyellow{Equation-free relational physics across Gemma scale. The same 128 development prompts and 960 test prompts are used at every checkpoint. A separate development-law direction is fitted in each model's raw-state coordinates at approximately 83\% normalized depth; no test state contributes to the fit. ``D/I,'' ``D/N,'' and ``N/I'' are direct--inverse, direct--validation-neutral, and validation-neutral--inverse AUC.}",matched result group(s),"[""geometry"", ""relational""]","[""jlens_materials/experiments/elicited-physics-abstraction-2026-07-18/"", ""jlens_materials/experiments/equation-free-relational-physics-2026-08-21/"", ""jlens_materials/experiments/gemma-equation-free-multiscale-2026-08-25/"", ""jlens_materials/experiments/gemma4-12b-equation-free-relational-v1/"", ""jlens_materials/experiments/gemma4-31b-equation-free-layer-comparison-2026-08-23/"", ""jlens_materials/experiments/gemma4-31b-equation-free-relational-layer40-v1/"", ""jlens_materials/experiments/gemma4-31b-equation-free-relational-v1/"", ""jlens_materials/experiments/materials-heldout-v1_latent"", ""jlens_materials/experiments/neutral-anchored-relational-physics-2026-07-18/"", ""jlens_materials/experiments/relational-contrast-confirmation-2026-07-18/"", ""jlens_materials/runs/""]",182,"[""python scripts/analyze_equation_free_relational_benchmark_amended.py"", ""python scripts/analyze_gemma31b_equation_free_layers.py"", ""python scripts/analyze_gemma_equation_free_multiscale.py"", ""python scripts/analyze_materials_latent_geometry.py"", ""python scripts/analyze_neutral_anchored_relational_benchmark.py"", ""python scripts/build_final_paper_figures.py""]",[],FILE_INVENTORY.csv +paper/materials_jacobian_lens_supplement.tex,193,table,tab:s-causal-knockout,\hlyellow{Original 24-relation equation-free intervention across Gemma scale. The retrospective overlap-exclusion analysis is reported separately in Section S7A. Values in brackets are 95\% law-bootstrap intervals. Margin loss is clean correct-answer margin minus neutralized margin; counterfactual movement is toward the answer appropriate to the reversed numerical input. Controls average both signs of three activation-derived axes at the identical intervention length.},matched result group(s),"[""relational"", ""causal_coordinate""]","[""jlens_materials/experiments/causal-relational-comment3-audit-2026-09-06/"", ""jlens_materials/experiments/causal-relational-knockout-2026-09-02/"", ""jlens_materials/experiments/elicited-physics-abstraction-2026-07-18/"", ""jlens_materials/experiments/equation-free-relational-physics-2026-08-21/"", ""jlens_materials/experiments/gemma-equation-free-multiscale-2026-08-25/"", ""jlens_materials/experiments/gemma4-12b-equation-free-relational-v1/"", ""jlens_materials/experiments/gemma4-31b-equation-free-layer-comparison-2026-08-23/"", ""jlens_materials/experiments/gemma4-31b-equation-free-relational-layer40-v1/"", ""jlens_materials/experiments/gemma4-31b-equation-free-relational-v1/"", ""jlens_materials/experiments/neutral-anchored-relational-physics-2026-07-18/"", ""jlens_materials/experiments/relational-contrast-confirmation-2026-07-18/""]",219,"[""python scripts/analyze_causal_relational_knockout.py"", ""python scripts/analyze_equation_free_relational_benchmark_amended.py"", ""python scripts/analyze_gemma31b_equation_free_layers.py"", ""python scripts/analyze_gemma_equation_free_multiscale.py"", ""python scripts/analyze_neutral_anchored_relational_benchmark.py"", ""python scripts/validate_causal_relational_knockout.py""]",[],FILE_INVENTORY.csv +paper/materials_jacobian_lens_supplement.tex,216,table,,Post hoc cross-law continuous-state audit. The contrast is same-physical/opposite-numerical similarity minus same-numerical/opposite-physical similarity across nine direct-law--inverse-law mechanism pairs. Positive values would favor physical outcome.,study-wide summary/provenance; consult named rows,"[""readout"", ""target_free"", ""geometry"", ""physical_equivalence"", ""graphs"", ""relational"", ""steering"", ""patching"", ""causal_coordinate"", ""multitoken"", ""31b_streams""]","[""jlens_materials/experiments/answer-code-binding-2026-07-17/"", ""jlens_materials/experiments/answer-scaffold-audit-2026-07-17/"", ""jlens_materials/experiments/candidate-activation-patching-2026-07-16/"", ""jlens_materials/experiments/candidate-followups-2026-07-16/"", ""jlens_materials/experiments/candidate-nonsteering-2026-07-16/"", ""jlens_materials/experiments/causal-relational-comment3-audit-2026-09-06/"", ""jlens_materials/experiments/causal-relational-knockout-2026-09-02/"", ""jlens_materials/experiments/cross-mechanism-activation-patching-2026-07-18/"", ""jlens_materials/experiments/cross-mechanism-outcome-2026-07-18/"", ""jlens_materials/experiments/elicited-physics-abstraction-2026-07-18/"", ""jlens_materials/experiments/equation-free-relational-physics-2026-08-21/"", ""jlens_materials/experiments/gemma-equation-free-multiscale-2026-08-25/"", ""jlens_materials/experiments/gemma-multiscale-heldout-v1-2026-08-25/"", ""jlens_materials/experiments/gemma4-12b-equation-free-relational-v1/"", ""jlens_materials/experiments/gemma4-31b-equation-free-layer-comparison-2026-08-23/"", ""jlens_materials/experiments/gemma4-31b-equation-free-relational-layer40-v1/"", ""jlens_materials/experiments/gemma4-31b-equation-free-relational-v1/"", ""jlens_materials/experiments/gemma4-31b-semantic-streams-2026-08-26/"", ""jlens_materials/experiments/graph-isomorphism-generalization-2026-07-18/"", ""jlens_materials/experiments/graph-topology-rigorous-2026-07-17/"", ""jlens_materials/experiments/late-physics-representation-replication-2026-07-17/"", ""jlens_materials/experiments/lexical-adversarial-representation-2026-07-17/"", ""jlens_materials/experiments/materials-heldout-v1"", ""jlens_materials/experiments/materials-heldout-v1_latent"", ""jlens_materials/experiments/multitoken-sequence-robustness-2026-07-18/"", ""jlens_materials/experiments/neutral-anchored-relational-physics-2026-07-18/"", ""jlens_materials/experiments/option-free-question-end-2026-07-18/"", ""jlens_materials/experiments/relation-graph-visualization-2026-07-18/"", ""jlens_materials/experiments/relational-contrast-confirmation-2026-07-18/"", ""jlens_materials/experiments/relational-grain-steering-v4"", ""jlens_materials/experiments/semantic-steering-v3"", ""jlens_materials/runs/""]",641,"[""python scripts/analyze_answer_code_binding.py"", ""python scripts/analyze_answer_scaffold_audit.py"", ""python scripts/analyze_candidate_followups.py"", ""python scripts/analyze_candidate_nonsteering.py"", ""python scripts/analyze_causal_relational_knockout.py"", ""python scripts/analyze_counterfactual_activation_patching.py"", ""python scripts/analyze_cross_mechanism_activation_patching.py"", ""python scripts/analyze_equation_free_relational_benchmark_amended.py"", ""python scripts/analyze_gemma31b_equation_free_layers.py"", ""python scripts/analyze_gemma_equation_free_multiscale.py"", ""python scripts/analyze_gemma_multiscale_heldout_v1.py"", ""python scripts/analyze_graph_topology_rigorous.py"", ""python scripts/analyze_late_physics_replication.py"", ""python scripts/analyze_lexical_adversarial_representation.py"", ""python scripts/analyze_materials_heldout_v1.py"", ""python scripts/analyze_materials_latent_geometry.py"", ""python scripts/analyze_neutral_anchored_relational_benchmark.py"", ""python scripts/analyze_relational_grain_steering_v4.py"", ""python scripts/analyze_semantic_steering_v3.py"", ""python scripts/build_final_paper_figures.py"", ""python scripts/build_gemma31b_semantic_streams.py"", ""python scripts/plot_graph_generalization_audit.py"", ""python scripts/plot_multitoken_sequence_robustness.py"", ""python scripts/plot_relation_graph_robustness.py"", ""python scripts/validate_causal_relational_knockout.py"", ""python scripts/validate_graph_generalization_audit.py""]",[],FILE_INVENTORY.csv +paper/materials_jacobian_lens_supplement.tex,237,table,,Frozen 60-law neutral-anchored relational result. The hidden score is fitted on earlier laws and then read only through matched numerical reversals. Calibration-neutral laws define the center and scale; validation-neutral laws remain test cases.,matched result group(s),"[""geometry"", ""relational""]","[""jlens_materials/experiments/elicited-physics-abstraction-2026-07-18/"", ""jlens_materials/experiments/equation-free-relational-physics-2026-08-21/"", ""jlens_materials/experiments/gemma-equation-free-multiscale-2026-08-25/"", ""jlens_materials/experiments/gemma4-12b-equation-free-relational-v1/"", ""jlens_materials/experiments/gemma4-31b-equation-free-layer-comparison-2026-08-23/"", ""jlens_materials/experiments/gemma4-31b-equation-free-relational-layer40-v1/"", ""jlens_materials/experiments/gemma4-31b-equation-free-relational-v1/"", ""jlens_materials/experiments/materials-heldout-v1_latent"", ""jlens_materials/experiments/neutral-anchored-relational-physics-2026-07-18/"", ""jlens_materials/experiments/relational-contrast-confirmation-2026-07-18/"", ""jlens_materials/runs/""]",182,"[""python scripts/analyze_equation_free_relational_benchmark_amended.py"", ""python scripts/analyze_gemma31b_equation_free_layers.py"", ""python scripts/analyze_gemma_equation_free_multiscale.py"", ""python scripts/analyze_materials_latent_geometry.py"", ""python scripts/analyze_neutral_anchored_relational_benchmark.py"", ""python scripts/build_final_paper_figures.py""]",[],FILE_INVENTORY.csv +paper/materials_jacobian_lens_supplement.tex,259,table,,Retrospective target-free cross-phrasing classification. Each entry is accuracy across 50 prompts and ten balanced families. The prompt TF--IDF baseline is 56\%; balanced chance is 10\%.,matched result group(s),"[""readout"", ""target_free""]","[""jlens_materials/experiments/candidate-nonsteering-2026-07-16/"", ""jlens_materials/experiments/gemma-multiscale-heldout-v1-2026-08-25/"", ""jlens_materials/experiments/materials-heldout-v1"", ""jlens_materials/runs/""]",102,"[""python scripts/analyze_candidate_nonsteering.py"", ""python scripts/analyze_gemma_multiscale_heldout_v1.py"", ""python scripts/analyze_materials_heldout_v1.py""]",[],FILE_INVENTORY.csv +paper/materials_jacobian_lens_supplement.tex,278,table,,"Broad mechanism-steering results. Endpoints are positive-answer minus negative-answer log odds at $+4\%$ dose minus the same contrast at $-4\%$. The family verdict required every frozen gate, not merely a positive mean.",matched result group(s),"[""steering""]","[""jlens_materials/experiments/candidate-followups-2026-07-16/"", ""jlens_materials/experiments/relational-grain-steering-v4"", ""jlens_materials/experiments/semantic-steering-v3""]",64,"[""python scripts/analyze_candidate_followups.py"", ""python scripts/analyze_relational_grain_steering_v4.py"", ""python scripts/analyze_semantic_steering_v3.py""]",[],FILE_INVENTORY.csv +paper/materials_jacobian_lens_supplement.tex,299,table,,Prospective relational grain-size results. Positive values are aligned toward the frozen correct answer before refinement and coarsening are averaged within material.,matched result group(s),"[""steering""]","[""jlens_materials/experiments/candidate-followups-2026-07-16/"", ""jlens_materials/experiments/relational-grain-steering-v4"", ""jlens_materials/experiments/semantic-steering-v3""]",64,"[""python scripts/analyze_candidate_followups.py"", ""python scripts/analyze_relational_grain_steering_v4.py"", ""python scripts/analyze_semantic_steering_v3.py""]",[],FILE_INVENTORY.csv +paper/materials_jacobian_lens_supplement.tex,325,table,,Exploratory counterfactual activation patching. Positive values mean that a receiver moved toward the answer appropriate to the donor's reversed grain-size relation. Means and 95\% intervals average the fixed 38--92\% layer band with the six material pairs as independent units.,matched result group(s),"[""steering"", ""patching""]","[""jlens_materials/experiments/candidate-activation-patching-2026-07-16/"", ""jlens_materials/experiments/candidate-followups-2026-07-16/"", ""jlens_materials/experiments/cross-mechanism-activation-patching-2026-07-18/"", ""jlens_materials/experiments/relational-grain-steering-v4"", ""jlens_materials/experiments/semantic-steering-v3""]",106,"[""python scripts/analyze_candidate_followups.py"", ""python scripts/analyze_counterfactual_activation_patching.py"", ""python scripts/analyze_cross_mechanism_activation_patching.py"", ""python scripts/analyze_relational_grain_steering_v4.py"", ""python scripts/analyze_semantic_steering_v3.py""]",[],FILE_INVENTORY.csv +paper/materials_jacobian_lens_supplement.tex,347,table,,Exploratory geometry and secondary blinded results.,matched result group(s),"[""readout"", ""geometry""]","[""jlens_materials/experiments/gemma-multiscale-heldout-v1-2026-08-25/"", ""jlens_materials/experiments/materials-heldout-v1"", ""jlens_materials/experiments/materials-heldout-v1_latent"", ""jlens_materials/runs/""]",81,"[""python scripts/analyze_gemma_multiscale_heldout_v1.py"", ""python scripts/analyze_materials_heldout_v1.py"", ""python scripts/analyze_materials_latent_geometry.py"", ""python scripts/build_final_paper_figures.py""]",[],FILE_INVENTORY.csv +paper/materials_jacobian_lens_supplement.tex,367,table,,Retained negative secondary analyses. These tests constrain interpretation and are not evidence for the proposed structures.,study-wide summary/provenance; consult named rows,"[""readout"", ""target_free"", ""geometry"", ""physical_equivalence"", ""graphs"", ""relational"", ""steering"", ""patching"", ""causal_coordinate"", ""multitoken"", ""31b_streams""]","[""jlens_materials/experiments/answer-code-binding-2026-07-17/"", ""jlens_materials/experiments/answer-scaffold-audit-2026-07-17/"", ""jlens_materials/experiments/candidate-activation-patching-2026-07-16/"", ""jlens_materials/experiments/candidate-followups-2026-07-16/"", ""jlens_materials/experiments/candidate-nonsteering-2026-07-16/"", ""jlens_materials/experiments/causal-relational-comment3-audit-2026-09-06/"", ""jlens_materials/experiments/causal-relational-knockout-2026-09-02/"", ""jlens_materials/experiments/cross-mechanism-activation-patching-2026-07-18/"", ""jlens_materials/experiments/cross-mechanism-outcome-2026-07-18/"", ""jlens_materials/experiments/elicited-physics-abstraction-2026-07-18/"", ""jlens_materials/experiments/equation-free-relational-physics-2026-08-21/"", ""jlens_materials/experiments/gemma-equation-free-multiscale-2026-08-25/"", ""jlens_materials/experiments/gemma-multiscale-heldout-v1-2026-08-25/"", ""jlens_materials/experiments/gemma4-12b-equation-free-relational-v1/"", ""jlens_materials/experiments/gemma4-31b-equation-free-layer-comparison-2026-08-23/"", ""jlens_materials/experiments/gemma4-31b-equation-free-relational-layer40-v1/"", ""jlens_materials/experiments/gemma4-31b-equation-free-relational-v1/"", ""jlens_materials/experiments/gemma4-31b-semantic-streams-2026-08-26/"", ""jlens_materials/experiments/graph-isomorphism-generalization-2026-07-18/"", ""jlens_materials/experiments/graph-topology-rigorous-2026-07-17/"", ""jlens_materials/experiments/late-physics-representation-replication-2026-07-17/"", ""jlens_materials/experiments/lexical-adversarial-representation-2026-07-17/"", ""jlens_materials/experiments/materials-heldout-v1"", ""jlens_materials/experiments/materials-heldout-v1_latent"", ""jlens_materials/experiments/multitoken-sequence-robustness-2026-07-18/"", ""jlens_materials/experiments/neutral-anchored-relational-physics-2026-07-18/"", ""jlens_materials/experiments/option-free-question-end-2026-07-18/"", ""jlens_materials/experiments/relation-graph-visualization-2026-07-18/"", ""jlens_materials/experiments/relational-contrast-confirmation-2026-07-18/"", ""jlens_materials/experiments/relational-grain-steering-v4"", ""jlens_materials/experiments/semantic-steering-v3"", ""jlens_materials/runs/""]",641,"[""python scripts/analyze_answer_code_binding.py"", ""python scripts/analyze_answer_scaffold_audit.py"", ""python scripts/analyze_candidate_followups.py"", ""python scripts/analyze_candidate_nonsteering.py"", ""python scripts/analyze_causal_relational_knockout.py"", ""python scripts/analyze_counterfactual_activation_patching.py"", ""python scripts/analyze_cross_mechanism_activation_patching.py"", ""python scripts/analyze_equation_free_relational_benchmark_amended.py"", ""python scripts/analyze_gemma31b_equation_free_layers.py"", ""python scripts/analyze_gemma_equation_free_multiscale.py"", ""python scripts/analyze_gemma_multiscale_heldout_v1.py"", ""python scripts/analyze_graph_topology_rigorous.py"", ""python scripts/analyze_late_physics_replication.py"", ""python scripts/analyze_lexical_adversarial_representation.py"", ""python scripts/analyze_materials_heldout_v1.py"", ""python scripts/analyze_materials_latent_geometry.py"", ""python scripts/analyze_neutral_anchored_relational_benchmark.py"", ""python scripts/analyze_relational_grain_steering_v4.py"", ""python scripts/analyze_semantic_steering_v3.py"", ""python scripts/build_final_paper_figures.py"", ""python scripts/build_gemma31b_semantic_streams.py"", ""python scripts/plot_graph_generalization_audit.py"", ""python scripts/plot_multitoken_sequence_robustness.py"", ""python scripts/plot_relation_graph_robustness.py"", ""python scripts/validate_causal_relational_knockout.py"", ""python scripts/validate_graph_generalization_audit.py""]",[],FILE_INVENTORY.csv +paper/materials_jacobian_lens_supplement.tex,381,table,,"Lexical-adversarial physical-equivalence results. Margin is centered cosine(anchor, physical paraphrase) minus cosine(anchor, near-verbatim physical counterfactual). Positive means that physics outranks wording. Intervals use the frozen two-stage family/triplet bootstrap.",matched result group(s),"[""physical_equivalence"", ""graphs""]","[""jlens_materials/experiments/answer-code-binding-2026-07-17/"", ""jlens_materials/experiments/answer-scaffold-audit-2026-07-17/"", ""jlens_materials/experiments/cross-mechanism-outcome-2026-07-18/"", ""jlens_materials/experiments/graph-isomorphism-generalization-2026-07-18/"", ""jlens_materials/experiments/graph-topology-rigorous-2026-07-17/"", ""jlens_materials/experiments/late-physics-representation-replication-2026-07-17/"", ""jlens_materials/experiments/lexical-adversarial-representation-2026-07-17/"", ""jlens_materials/experiments/option-free-question-end-2026-07-18/"", ""jlens_materials/experiments/relation-graph-visualization-2026-07-18/""]",191,"[""python scripts/analyze_answer_code_binding.py"", ""python scripts/analyze_answer_scaffold_audit.py"", ""python scripts/analyze_graph_topology_rigorous.py"", ""python scripts/analyze_late_physics_replication.py"", ""python scripts/analyze_lexical_adversarial_representation.py"", ""python scripts/plot_graph_generalization_audit.py"", ""python scripts/plot_relation_graph_robustness.py"", ""python scripts/validate_graph_generalization_audit.py""]",[],FILE_INVENTORY.csv +paper/materials_jacobian_lens_supplement.tex,404,table,,"Post hoc answer-scaffold audit and prospectively frozen arbitrary-code negative control. The scaffold comparison reuses stored states and was defined after both source experiments were inspected. Its first four rows are positive-minus-negative decoder-logit differences, equivalently pairwise log-odds units; they are not the centered-cosine margins reported in the preceding table.",matched result group(s),"[""physical_equivalence""]","[""jlens_materials/experiments/answer-code-binding-2026-07-17/"", ""jlens_materials/experiments/answer-scaffold-audit-2026-07-17/"", ""jlens_materials/experiments/late-physics-representation-replication-2026-07-17/"", ""jlens_materials/experiments/lexical-adversarial-representation-2026-07-17/""]",65,"[""python scripts/analyze_answer_code_binding.py"", ""python scripts/analyze_answer_scaffold_audit.py"", ""python scripts/analyze_late_physics_replication.py"", ""python scripts/analyze_lexical_adversarial_representation.py""]",[],FILE_INVENTORY.csv +paper/materials_jacobian_lens_supplement.tex,423,table,,"Complete mechanism inventory for the two lexical-adversarial cohorts. Each row contains four material systems, each rendered as anchor, physical paraphrase, and lexical counterfactual. ``Positive/negative'' are the exact one-token scientific outcomes used only for clean-answer and answer-scaffold audits; the full-state similarity endpoint does not search for these words.",matched result group(s),"[""physical_equivalence""]","[""jlens_materials/experiments/answer-code-binding-2026-07-17/"", ""jlens_materials/experiments/answer-scaffold-audit-2026-07-17/"", ""jlens_materials/experiments/late-physics-representation-replication-2026-07-17/"", ""jlens_materials/experiments/lexical-adversarial-representation-2026-07-17/""]",65,"[""python scripts/analyze_answer_code_binding.py"", ""python scripts/analyze_answer_scaffold_audit.py"", ""python scripts/analyze_late_physics_replication.py"", ""python scripts/analyze_lexical_adversarial_representation.py""]",[],FILE_INVENTORY.csv +paper/materials_jacobian_lens_supplement.tex,448,table,,"\textbf{Post hoc signed-relation graph across different material cases.} A source prompt selects one nearest eligible target in each other surface-variant group. Same material cases are prohibited. Precision is the fraction of 144 directed edges whose source and target share the registered binary relation. The mechanism family is supplied when candidates are defined. The later exact audit shows that this within-family physical relation is identical to same/different numerical direction, so these values measure comparative structure rather than independent identification of a constitutive-law sign.",matched result group(s),"[""geometry"", ""physical_equivalence"", ""graphs"", ""relational""]","[""jlens_materials/experiments/answer-code-binding-2026-07-17/"", ""jlens_materials/experiments/answer-scaffold-audit-2026-07-17/"", ""jlens_materials/experiments/cross-mechanism-outcome-2026-07-18/"", ""jlens_materials/experiments/elicited-physics-abstraction-2026-07-18/"", ""jlens_materials/experiments/equation-free-relational-physics-2026-08-21/"", ""jlens_materials/experiments/gemma-equation-free-multiscale-2026-08-25/"", ""jlens_materials/experiments/gemma4-12b-equation-free-relational-v1/"", ""jlens_materials/experiments/gemma4-31b-equation-free-layer-comparison-2026-08-23/"", ""jlens_materials/experiments/gemma4-31b-equation-free-relational-layer40-v1/"", ""jlens_materials/experiments/gemma4-31b-equation-free-relational-v1/"", ""jlens_materials/experiments/graph-isomorphism-generalization-2026-07-18/"", ""jlens_materials/experiments/graph-topology-rigorous-2026-07-17/"", ""jlens_materials/experiments/late-physics-representation-replication-2026-07-17/"", ""jlens_materials/experiments/lexical-adversarial-representation-2026-07-17/"", ""jlens_materials/experiments/materials-heldout-v1_latent"", ""jlens_materials/experiments/neutral-anchored-relational-physics-2026-07-18/"", ""jlens_materials/experiments/option-free-question-end-2026-07-18/"", ""jlens_materials/experiments/relation-graph-visualization-2026-07-18/"", ""jlens_materials/experiments/relational-contrast-confirmation-2026-07-18/"", ""jlens_materials/runs/""]",373,"[""python scripts/analyze_answer_code_binding.py"", ""python scripts/analyze_answer_scaffold_audit.py"", ""python scripts/analyze_equation_free_relational_benchmark_amended.py"", ""python scripts/analyze_gemma31b_equation_free_layers.py"", ""python scripts/analyze_gemma_equation_free_multiscale.py"", ""python scripts/analyze_graph_topology_rigorous.py"", ""python scripts/analyze_late_physics_replication.py"", ""python scripts/analyze_lexical_adversarial_representation.py"", ""python scripts/analyze_materials_latent_geometry.py"", ""python scripts/analyze_neutral_anchored_relational_benchmark.py"", ""python scripts/build_final_paper_figures.py"", ""python scripts/plot_graph_generalization_audit.py"", ""python scripts/plot_relation_graph_robustness.py"", ""python scripts/validate_graph_generalization_audit.py""]",[],FILE_INVENTORY.csv +paper/materials_jacobian_lens_supplement.tex,472,table,,\textbf{Graph-identifiability audit summary.} All endpoints reuse the same 72 natural questions and archived states; no new prompt or Gemma forward pass was added.,matched result group(s),"[""graphs""]","[""jlens_materials/experiments/cross-mechanism-outcome-2026-07-18/"", ""jlens_materials/experiments/graph-isomorphism-generalization-2026-07-18/"", ""jlens_materials/experiments/graph-topology-rigorous-2026-07-17/"", ""jlens_materials/experiments/late-physics-representation-replication-2026-07-17/"", ""jlens_materials/experiments/option-free-question-end-2026-07-18/"", ""jlens_materials/experiments/relation-graph-visualization-2026-07-18/""]",144,"[""python scripts/analyze_graph_topology_rigorous.py"", ""python scripts/plot_graph_generalization_audit.py"", ""python scripts/plot_relation_graph_robustness.py"", ""python scripts/validate_graph_generalization_audit.py""]",[],FILE_INVENTORY.csv +paper/materials_jacobian_lens_supplement.tex,491,table,,"\textbf{Post hoc cross-phrasing mechanism graph from the original 50-prompt suite.} Every prompt selects one nearest neighbor in each other phrasing fold; 10\% is balanced mechanism chance. This graph asks whether differently worded descriptions from the same family remain neighbors. It is complementary to, and less lexically controlled than, the signed-relation graph.",matched result group(s),"[""readout"", ""physical_equivalence"", ""graphs""]","[""jlens_materials/experiments/answer-code-binding-2026-07-17/"", ""jlens_materials/experiments/answer-scaffold-audit-2026-07-17/"", ""jlens_materials/experiments/cross-mechanism-outcome-2026-07-18/"", ""jlens_materials/experiments/gemma-multiscale-heldout-v1-2026-08-25/"", ""jlens_materials/experiments/graph-isomorphism-generalization-2026-07-18/"", ""jlens_materials/experiments/graph-topology-rigorous-2026-07-17/"", ""jlens_materials/experiments/late-physics-representation-replication-2026-07-17/"", ""jlens_materials/experiments/lexical-adversarial-representation-2026-07-17/"", ""jlens_materials/experiments/materials-heldout-v1"", ""jlens_materials/experiments/option-free-question-end-2026-07-18/"", ""jlens_materials/experiments/relation-graph-visualization-2026-07-18/"", ""jlens_materials/runs/""]",272,"[""python scripts/analyze_answer_code_binding.py"", ""python scripts/analyze_answer_scaffold_audit.py"", ""python scripts/analyze_gemma_multiscale_heldout_v1.py"", ""python scripts/analyze_graph_topology_rigorous.py"", ""python scripts/analyze_late_physics_replication.py"", ""python scripts/analyze_lexical_adversarial_representation.py"", ""python scripts/analyze_materials_heldout_v1.py"", ""python scripts/plot_graph_generalization_audit.py"", ""python scripts/plot_relation_graph_robustness.py"", ""python scripts/validate_graph_generalization_audit.py""]",[],FILE_INVENTORY.csv +paper/materials_jacobian_lens_supplement.tex,527,figure,fig:s1-reproducibility,"\textbf{Independent lens fits give nearly identical controlled ranks.} Each cell is the Spearman correlation across all 150 held-out prompt--concept pairs. The three Jacobian fits use independent 1,000-record WikiText samples yet agree at $\rho=0.9978$--$0.9987$. Direct unembedding is much less correlated with their ordering ($\rho\approx0.21$). This figure establishes repeatability of the fitted measurement, not that its decoded terms are causal or universally more accurate.",matched result group(s),"[""readout"", ""physical_equivalence"", ""graphs"", ""causal_coordinate""]","[""jlens_materials/experiments/answer-code-binding-2026-07-17/"", ""jlens_materials/experiments/answer-scaffold-audit-2026-07-17/"", ""jlens_materials/experiments/causal-relational-comment3-audit-2026-09-06/"", ""jlens_materials/experiments/causal-relational-knockout-2026-09-02/"", ""jlens_materials/experiments/cross-mechanism-outcome-2026-07-18/"", ""jlens_materials/experiments/gemma-multiscale-heldout-v1-2026-08-25/"", ""jlens_materials/experiments/graph-isomorphism-generalization-2026-07-18/"", ""jlens_materials/experiments/graph-topology-rigorous-2026-07-17/"", ""jlens_materials/experiments/late-physics-representation-replication-2026-07-17/"", ""jlens_materials/experiments/lexical-adversarial-representation-2026-07-17/"", ""jlens_materials/experiments/materials-heldout-v1"", ""jlens_materials/experiments/option-free-question-end-2026-07-18/"", ""jlens_materials/experiments/relation-graph-visualization-2026-07-18/"", ""jlens_materials/runs/""]",366,"[""python scripts/analyze_answer_code_binding.py"", ""python scripts/analyze_answer_scaffold_audit.py"", ""python scripts/analyze_causal_relational_knockout.py"", ""python scripts/analyze_gemma_multiscale_heldout_v1.py"", ""python scripts/analyze_graph_topology_rigorous.py"", ""python scripts/analyze_late_physics_replication.py"", ""python scripts/analyze_lexical_adversarial_representation.py"", ""python scripts/analyze_materials_heldout_v1.py"", ""python scripts/plot_graph_generalization_audit.py"", ""python scripts/plot_relation_graph_robustness.py"", ""python scripts/validate_causal_relational_knockout.py"", ""python scripts/validate_graph_generalization_audit.py""]","[{""publication_path"": ""paper/figures/figure-s1-fit-reproducibility.pdf"", ""sha256"": ""81c47c89e887f68597ae7812858faca864cf327aa78b1e467f40ebdc60932f76"", ""matching_archived_files"": [""jlens_materials/figures/materials-heldout-v1/figure-s1-fit-reproducibility.pdf""]}]",FILE_INVENTORY.csv +paper/materials_jacobian_lens_supplement.tex,534,figure,fig:s2-development-stream,"\textbf{Sample semantic stream.} The exact prompt was: ``After substantial plastic elongation, a metal bar separated along a surface filled with microscopic pits around second-phase particles. Neighboring pits had joined into larger depressions.'' The one-token continuation was ``The.'' The ribbons were selected from the unrestricted leading decoded tokens at each layer, so the plotting code was not given a word list. The scientifically meaningful term \texttt{coalescence} appears naturally among those tokens, alongside generic discourse words that reveal the readout's noise. However, \texttt{coalescence} had been declared separately as a tracked concept when this earlier development prompt was designed. This is therefore an illustrative one-lens development display, not an unanticipated held-out discovery and not a literal chain of thought.",matched result group(s),"[""readout"", ""geometry""]","[""jlens_materials/experiments/gemma-multiscale-heldout-v1-2026-08-25/"", ""jlens_materials/experiments/materials-heldout-v1"", ""jlens_materials/experiments/materials-heldout-v1_latent"", ""jlens_materials/runs/""]",81,"[""python scripts/analyze_gemma_multiscale_heldout_v1.py"", ""python scripts/analyze_materials_heldout_v1.py"", ""python scripts/analyze_materials_latent_geometry.py"", ""python scripts/build_final_paper_figures.py""]","[{""publication_path"": ""paper/figures/figure-s2-development-semantic-stream.pdf"", ""sha256"": ""9f9c20c11b3eb6265efa5ca54f61d25c2f6f3c9615da64b3b808746dabaee821"", ""matching_archived_files"": [""jlens_materials/figures/materials-heldout-v1/figure-s2-development-semantic-stream.pdf""]}]",FILE_INVENTORY.csv +paper/materials_jacobian_lens_supplement.tex,541,figure,fig:s-targetfree-classification,"\textbf{Complete target-free cross-phrasing classification.} (A) Accuracy for Jacobian and direct candidate lists before and after removing exact or morphologically related prompt words. The dotted line is balanced chance; the dashed line is a classifier that sees TF--IDF prompt words. The strict-filter result remains above chance for both readouts but does not favor the Jacobian lens. (B) Prompt-level confusion matrix for the strict Jacobian configuration. Each row contains five independently written prompts. Boundary attack and surface oxidation are perfectly identified, whereas cleavage, ductile failure, high-temperature deformation, and notch resistance share vocabulary with neighboring mechanisms. The complete 50-row predictions, family accuracies, macro-F1 scores, paired correctness, and permutation outputs are stored in \texttt{experiments/candidate-nonsteering-2026-07-16/}.",matched result group(s),"[""target_free"", ""physical_equivalence"", ""steering""]","[""jlens_materials/experiments/answer-code-binding-2026-07-17/"", ""jlens_materials/experiments/answer-scaffold-audit-2026-07-17/"", ""jlens_materials/experiments/candidate-followups-2026-07-16/"", ""jlens_materials/experiments/candidate-nonsteering-2026-07-16/"", ""jlens_materials/experiments/late-physics-representation-replication-2026-07-17/"", ""jlens_materials/experiments/lexical-adversarial-representation-2026-07-17/"", ""jlens_materials/experiments/relational-grain-steering-v4"", ""jlens_materials/experiments/semantic-steering-v3""]",150,"[""python scripts/analyze_answer_code_binding.py"", ""python scripts/analyze_answer_scaffold_audit.py"", ""python scripts/analyze_candidate_followups.py"", ""python scripts/analyze_candidate_nonsteering.py"", ""python scripts/analyze_late_physics_replication.py"", ""python scripts/analyze_lexical_adversarial_representation.py"", ""python scripts/analyze_relational_grain_steering_v4.py"", ""python scripts/analyze_semantic_steering_v3.py""]","[{""publication_path"": ""paper/figures/semantic-classification.pdf"", ""sha256"": ""ef77c61162bf146cace69711ae5407556f392027cad6be51c5493a0745c3ef59"", ""matching_archived_files"": [""jlens_materials/experiments/candidate-nonsteering-2026-07-16/figures/semantic-classification.pdf""]}]",FILE_INVENTORY.csv +paper/materials_jacobian_lens_supplement.tex,552,figure,fig:s-ontology-rsa,"\textbf{The frozen expert ontologies do not align significantly with Jacobian family geometry.} At every layer, pairwise distances among the ten family centroids are compared with (A) four broad response classes and (B) a multi-attribute materials ontology. Curves show Jacobian-transported and raw states; horizontal lines show target-layer and prompt-embedding baselines. The best multi-attribute Jacobian correlation is $\rho=0.148$ at 58.5\% depth, but the maximum-over-layers corrected value is $p=0.9695$. Prompt embeddings align more strongly at $\rho=0.453$. The result does not support a claim that the lens recovered this expert ontology.",matched result group(s),"[""target_free"", ""geometry""]","[""jlens_materials/experiments/candidate-nonsteering-2026-07-16/"", ""jlens_materials/experiments/materials-heldout-v1_latent"", ""jlens_materials/runs/""]",78,"[""python scripts/analyze_candidate_nonsteering.py"", ""python scripts/analyze_materials_latent_geometry.py"", ""python scripts/build_final_paper_figures.py""]","[{""publication_path"": ""paper/figures/materials-ontology-rsa.pdf"", ""sha256"": ""673f763e195d6581d6401bd61ea49e6b9544f14a9930cf7a5e9a67dd636337b5"", ""matching_archived_files"": [""jlens_materials/experiments/candidate-nonsteering-2026-07-16/figures/materials-ontology-rsa.pdf""]}]",FILE_INVENTORY.csv +paper/materials_jacobian_lens_supplement.tex,559,figure,fig:s-concept-roles,"\textbf{No universal entity--process--property depth ordering is detected.} Predeclared single-token concepts were assigned before calculation to physical entity/state, process/mechanism, or response/property roles. The panels show the distribution of each concept's strongest registered depth under Jacobian and direct readouts. The largest Jacobian separation among role medians is 8.2 percentage points and does not exceed a family-blocked role-label null ($p=0.1988$). Missing sustained events are retained separately rather than assigned an artificial late depth.",matched result group(s),"[""readout"", ""target_free"", ""physical_equivalence""]","[""jlens_materials/experiments/answer-code-binding-2026-07-17/"", ""jlens_materials/experiments/answer-scaffold-audit-2026-07-17/"", ""jlens_materials/experiments/candidate-nonsteering-2026-07-16/"", ""jlens_materials/experiments/gemma-multiscale-heldout-v1-2026-08-25/"", ""jlens_materials/experiments/late-physics-representation-replication-2026-07-17/"", ""jlens_materials/experiments/lexical-adversarial-representation-2026-07-17/"", ""jlens_materials/experiments/materials-heldout-v1"", ""jlens_materials/runs/""]",167,"[""python scripts/analyze_answer_code_binding.py"", ""python scripts/analyze_answer_scaffold_audit.py"", ""python scripts/analyze_candidate_nonsteering.py"", ""python scripts/analyze_gemma_multiscale_heldout_v1.py"", ""python scripts/analyze_late_physics_replication.py"", ""python scripts/analyze_lexical_adversarial_representation.py"", ""python scripts/analyze_materials_heldout_v1.py""]","[{""publication_path"": ""paper/figures/concept-role-depths.pdf"", ""sha256"": ""a4168ba62f5feba46bf4c488ce2b1099a23769336a1bd5200feea427a042350f"", ""matching_archived_files"": [""jlens_materials/experiments/candidate-nonsteering-2026-07-16/figures/concept-role-depths.pdf""]}]",FILE_INVENTORY.csv +paper/materials_jacobian_lens_supplement.tex,567,figure,fig:s-lexical-adversarial,"\textbf{The broad physical-equivalence hypothesis fails in the first frozen cohort.} Each triplet contains an anchor, a physically equivalent paraphrase whose wording and units change, and a near-verbatim counterfactual whose physical answer reverses. Positive margin means the anchor is closer to the physical paraphrase than to the lexical counterfactual. The registered 38--92\% band mean is negative under both readouts (panel A), and no family has a positive mean (panel B): wording dominates over the broad window. Panel C shows the unregistered late rise that motivated, but does not itself confirm, a new disjoint cohort with its late window fixed before execution. This retained negative result is why the manuscript claims a temporal shift rather than general physics-first geometry.",matched result group(s),"[""geometry"", ""physical_equivalence""]","[""jlens_materials/experiments/answer-code-binding-2026-07-17/"", ""jlens_materials/experiments/answer-scaffold-audit-2026-07-17/"", ""jlens_materials/experiments/late-physics-representation-replication-2026-07-17/"", ""jlens_materials/experiments/lexical-adversarial-representation-2026-07-17/"", ""jlens_materials/experiments/materials-heldout-v1_latent"", ""jlens_materials/runs/""]",122,"[""python scripts/analyze_answer_code_binding.py"", ""python scripts/analyze_answer_scaffold_audit.py"", ""python scripts/analyze_late_physics_replication.py"", ""python scripts/analyze_lexical_adversarial_representation.py"", ""python scripts/analyze_materials_latent_geometry.py"", ""python scripts/build_final_paper_figures.py""]","[{""publication_path"": ""paper/figures/lexical-adversarial-representation.pdf"", ""sha256"": ""251abac14605833c435f2448cbb9b5017ec9ebef8cded2ddfc88a7868184ecd6"", ""matching_archived_files"": [""jlens_materials/experiments/lexical-adversarial-representation-2026-07-17/figures/lexical-adversarial-representation.pdf""]}]",FILE_INVENTORY.csv +paper/materials_jacobian_lens_supplement.tex,575,figure,fig:s-answer-scaffold,"\textbf{Explicit semantic answer scaffolding strongly amplifies the relation contrast.} The audit reuses stored states from the disjoint cohort and was specified only after both lexical-adversarial studies were inspected. (A) Before the answer choices, the late pre-choice state weakly separates the physically equivalent pair from the counterfactual under both readouts; after the prompt presents ordinary semantic choices such as \texttt{higher}/\texttt{lower}, the final-state contrast is much larger. Lines connect the same triplets and open symbols are cohort means. (B) The after-minus-before gain is positive in every mechanism family. (C) Ordinary semantic answer pairs are selected more reliably than the arbitrary A/B codes used in the separate falsification, both overall (87.5\% versus 62.5\%) and in most families; the dashed line is 50\% forced-pair chance. Because position, suffix, and answer-word exposure change together, this post hoc comparison does not identify which factor causes the gain. It does show why the late physical-equivalence transition cannot be interpreted as an option-free constitutive variable.",matched result group(s),"[""readout"", ""physical_equivalence"", ""relational""]","[""jlens_materials/experiments/answer-code-binding-2026-07-17/"", ""jlens_materials/experiments/answer-scaffold-audit-2026-07-17/"", ""jlens_materials/experiments/elicited-physics-abstraction-2026-07-18/"", ""jlens_materials/experiments/equation-free-relational-physics-2026-08-21/"", ""jlens_materials/experiments/gemma-equation-free-multiscale-2026-08-25/"", ""jlens_materials/experiments/gemma-multiscale-heldout-v1-2026-08-25/"", ""jlens_materials/experiments/gemma4-12b-equation-free-relational-v1/"", ""jlens_materials/experiments/gemma4-31b-equation-free-layer-comparison-2026-08-23/"", ""jlens_materials/experiments/gemma4-31b-equation-free-relational-layer40-v1/"", ""jlens_materials/experiments/gemma4-31b-equation-free-relational-v1/"", ""jlens_materials/experiments/late-physics-representation-replication-2026-07-17/"", ""jlens_materials/experiments/lexical-adversarial-representation-2026-07-17/"", ""jlens_materials/experiments/materials-heldout-v1"", ""jlens_materials/experiments/neutral-anchored-relational-physics-2026-07-18/"", ""jlens_materials/experiments/relational-contrast-confirmation-2026-07-18/"", ""jlens_materials/runs/""]",271,"[""python scripts/analyze_answer_code_binding.py"", ""python scripts/analyze_answer_scaffold_audit.py"", ""python scripts/analyze_equation_free_relational_benchmark_amended.py"", ""python scripts/analyze_gemma31b_equation_free_layers.py"", ""python scripts/analyze_gemma_equation_free_multiscale.py"", ""python scripts/analyze_gemma_multiscale_heldout_v1.py"", ""python scripts/analyze_late_physics_replication.py"", ""python scripts/analyze_lexical_adversarial_representation.py"", ""python scripts/analyze_materials_heldout_v1.py"", ""python scripts/analyze_neutral_anchored_relational_benchmark.py""]","[{""publication_path"": ""paper/figures/answer-scaffold-audit.pdf"", ""sha256"": ""6e606c73c72ab695df6c9d7db1d7383cf5c63c8af1b7538fbead739adf83f046"", ""matching_archived_files"": [""jlens_materials/experiments/answer-scaffold-audit-2026-07-17/figures/answer-scaffold-audit.pdf""]}]",FILE_INVENTORY.csv +paper/materials_jacobian_lens_supplement.tex,582,figure,fig:s-answer-code,"\textbf{The arbitrary A/B answer-code falsification is inconclusive because its manipulation check fails.} The prospective protocol replaced meaningful answer words with arbitrary \texttt{A}/\texttt{B} labels and reversed their mapping across matched prompt variants. (A) Before the mapping appears, the physical-equivalence separation remains small and heterogeneous. (B) After the mapping, the required A/B contrast is also unstable across layers; the orange line is after mapping and gray is the pre-mapping reference. Shading in A and B is the frozen late window. (C) Family means do not show a consistent transition from pre-code physics separation (circles) to post-mapping A/B separation (squares). (D) Clean forced-pair code accuracy is only 62.5\% overall, with 0\% for obstacle spacing; the dashed line is 50\% chance. Only 5 of 24 triplets formed a complete anchor--counterfactual--remapped pattern, and neither code token was the global top continuation in any of 72 prompts. The frozen geometric endpoint is therefore not interpretable as successful answer-independent binding. Retaining this failure prevents the semantic-scaffold result from being presented as if arbitrary output codes had already reproduced it.",matched result group(s),"[""physical_equivalence""]","[""jlens_materials/experiments/answer-code-binding-2026-07-17/"", ""jlens_materials/experiments/answer-scaffold-audit-2026-07-17/"", ""jlens_materials/experiments/late-physics-representation-replication-2026-07-17/"", ""jlens_materials/experiments/lexical-adversarial-representation-2026-07-17/""]",65,"[""python scripts/analyze_answer_code_binding.py"", ""python scripts/analyze_answer_scaffold_audit.py"", ""python scripts/analyze_late_physics_replication.py"", ""python scripts/analyze_lexical_adversarial_representation.py""]","[{""publication_path"": ""paper/figures/answer-code-binding.pdf"", ""sha256"": ""df36ec04638be489e87d95e7295c7926a471fa3c6c6288c519174588f213a848"", ""matching_archived_files"": [""jlens_materials/experiments/answer-code-binding-2026-07-17/figures/answer-code-binding.pdf""]}]",FILE_INVENTORY.csv +paper/materials_jacobian_lens_supplement.tex,590,figure,fig:s-mechanism-graph,"\textbf{Complementary graph evidence from the 50 held-out mechanism descriptions.} Every prompt selects one neighbor in each other phrasing fold. A correct edge stays inside the same mechanism family. (A) Layerwise same-family edge fraction for Jacobian and raw states, with word and character TF--IDF shown as horizontal lexical references. The gray band is the frozen 38--92\% summary window and the dashed gray line is 10\% chance. (B) After pairwise Jacobian similarity is residualized against word TF--IDF, character TF--IDF, token overlap, prompt length difference, and phrasing fold, the frozen-band graph still reaches 19.0\% ($p=0.00026$), although raw states are similar. (C) For each family, filled symbols compare the most similar true-family target with the most lexically similar wrong-family target; positive values favor the physical family. Open symbols restrict the comparison to 126 lexical traps in which TF--IDF itself prefers the wrong family. RT, PS, NR, LD, OX, HT, DU, CY, CL, and BA denote the ten families defined in the main paper. (D) One frozen-band Jacobian adjacency matrix. Teal marks selected same-family edges and light gray marks cross-family edges; rows and columns are grouped only for display. This analysis shows statistically reliable cross-phrasing organization and resistance to selected lexical traps, but input-token and TF--IDF graphs remain competitive. It therefore does not establish a fully non-lexical materials ontology or a Jacobian-specific advantage.",matched result group(s),"[""readout"", ""target_free"", ""physical_equivalence"", ""graphs"", ""causal_coordinate""]","[""jlens_materials/experiments/answer-code-binding-2026-07-17/"", ""jlens_materials/experiments/answer-scaffold-audit-2026-07-17/"", ""jlens_materials/experiments/candidate-nonsteering-2026-07-16/"", ""jlens_materials/experiments/causal-relational-comment3-audit-2026-09-06/"", ""jlens_materials/experiments/causal-relational-knockout-2026-09-02/"", ""jlens_materials/experiments/cross-mechanism-outcome-2026-07-18/"", ""jlens_materials/experiments/gemma-multiscale-heldout-v1-2026-08-25/"", ""jlens_materials/experiments/graph-isomorphism-generalization-2026-07-18/"", ""jlens_materials/experiments/graph-topology-rigorous-2026-07-17/"", ""jlens_materials/experiments/late-physics-representation-replication-2026-07-17/"", ""jlens_materials/experiments/lexical-adversarial-representation-2026-07-17/"", ""jlens_materials/experiments/materials-heldout-v1"", ""jlens_materials/experiments/option-free-question-end-2026-07-18/"", ""jlens_materials/experiments/relation-graph-visualization-2026-07-18/"", ""jlens_materials/runs/""]",387,"[""python scripts/analyze_answer_code_binding.py"", ""python scripts/analyze_answer_scaffold_audit.py"", ""python scripts/analyze_candidate_nonsteering.py"", ""python scripts/analyze_causal_relational_knockout.py"", ""python scripts/analyze_gemma_multiscale_heldout_v1.py"", ""python scripts/analyze_graph_topology_rigorous.py"", ""python scripts/analyze_late_physics_replication.py"", ""python scripts/analyze_lexical_adversarial_representation.py"", ""python scripts/analyze_materials_heldout_v1.py"", ""python scripts/plot_graph_generalization_audit.py"", ""python scripts/plot_relation_graph_robustness.py"", ""python scripts/validate_causal_relational_knockout.py"", ""python scripts/validate_graph_generalization_audit.py""]","[{""publication_path"": ""paper/figures/mechanism-graph-evidence.pdf"", ""sha256"": ""f8dc7c4a233828d60a9404992d1fefacbf543963c8602063ef16e4a0e22ad31e"", ""matching_archived_files"": [""jlens_materials/experiments/graph-topology-rigorous-2026-07-17/figures/mechanism-graph-evidence.pdf""]}]",FILE_INVENTORY.csv +paper/materials_jacobian_lens_supplement.tex,598,figure,fig:s-relation-ranking,"\textbf{Exact-null and full-candidate checks for the 72-prompt signed-relation graph.} (A) The gray histogram contains all 46,656 balanced base-case assignments under the exact null. Each assignment preserves all three surface variants of every material case, including the counterfactual sign transform. Its mean same-direction edge fraction is 0.56; the observed Jacobian graph is 0.82 at the far right ($p=0.009623$). (B) Rather than evaluating only the selected edge, pairwise ROC--AUC uses every eligible same-direction and opposite-direction candidate. Jacobian, direct decoder-basis, and raw states reach AUC 0.82--0.83, while word TF--IDF, character TF--IDF, and answer order remain at or below 0.48. Error bars show the leave-one-mechanism-out range. The hatched numerical-direction oracle is a ceiling derived directly from the experimental numbers, not from Gemma. The later identifiability proof shows that, inside each supplied family, the registered physical pair label and this numerical-direction oracle are exactly the same target. The figure therefore establishes distributed comparative ranking, not an independent constitutive-law representation.",matched result group(s),"[""physical_equivalence"", ""graphs"", ""relational""]","[""jlens_materials/experiments/answer-code-binding-2026-07-17/"", ""jlens_materials/experiments/answer-scaffold-audit-2026-07-17/"", ""jlens_materials/experiments/cross-mechanism-outcome-2026-07-18/"", ""jlens_materials/experiments/elicited-physics-abstraction-2026-07-18/"", ""jlens_materials/experiments/equation-free-relational-physics-2026-08-21/"", ""jlens_materials/experiments/gemma-equation-free-multiscale-2026-08-25/"", ""jlens_materials/experiments/gemma4-12b-equation-free-relational-v1/"", ""jlens_materials/experiments/gemma4-31b-equation-free-layer-comparison-2026-08-23/"", ""jlens_materials/experiments/gemma4-31b-equation-free-relational-layer40-v1/"", ""jlens_materials/experiments/gemma4-31b-equation-free-relational-v1/"", ""jlens_materials/experiments/graph-isomorphism-generalization-2026-07-18/"", ""jlens_materials/experiments/graph-topology-rigorous-2026-07-17/"", ""jlens_materials/experiments/late-physics-representation-replication-2026-07-17/"", ""jlens_materials/experiments/lexical-adversarial-representation-2026-07-17/"", ""jlens_materials/experiments/neutral-anchored-relational-physics-2026-07-18/"", ""jlens_materials/experiments/option-free-question-end-2026-07-18/"", ""jlens_materials/experiments/relation-graph-visualization-2026-07-18/"", ""jlens_materials/experiments/relational-contrast-confirmation-2026-07-18/""]",316,"[""python scripts/analyze_answer_code_binding.py"", ""python scripts/analyze_answer_scaffold_audit.py"", ""python scripts/analyze_equation_free_relational_benchmark_amended.py"", ""python scripts/analyze_gemma31b_equation_free_layers.py"", ""python scripts/analyze_gemma_equation_free_multiscale.py"", ""python scripts/analyze_graph_topology_rigorous.py"", ""python scripts/analyze_late_physics_replication.py"", ""python scripts/analyze_lexical_adversarial_representation.py"", ""python scripts/analyze_neutral_anchored_relational_benchmark.py"", ""python scripts/plot_graph_generalization_audit.py"", ""python scripts/plot_relation_graph_robustness.py"", ""python scripts/validate_graph_generalization_audit.py""]","[{""publication_path"": ""paper/figures/relation-ranking-robustness.pdf"", ""sha256"": ""c9104aafa3600fcd6caafdb2199ce06d62032568097b32320a2743b77dd4b6ab"", ""matching_archived_files"": [""jlens_materials/experiments/graph-topology-rigorous-2026-07-17/figures/relation-ranking-robustness.pdf""]}]",FILE_INVENTORY.csv +paper/materials_jacobian_lens_supplement.tex,606,figure,fig:s-option-free-question,"\textbf{Frozen natural question-end robustness result.} The runner stops after the final token of the complete scientific question and adds no answer choice, answer word, A/B code, response instruction, or checkpoint marker. (A) Solid bars are selected-edge precision and hatched bars are full-candidate AUC in the 38--92\% band. The dashed line is the exact case-preserving null mean. (B) Layerwise precision rises late for Jacobian, direct, and raw states. (C) Five of six mechanism-family AUCs exceed 0.5, with substantial heterogeneity. (D) The same 72 stems at three positions show why prompt boundary must be controlled: the natural question end is significant, the checkpoint-suffixed condition is null, and the answer mapping is strongly amplified. Suffix and token position change together, so the comparison does not isolate a unique cause. This is a frozen positional robustness result on an inspected cohort, not an independent replication.",matched result group(s),"[""readout"", ""physical_equivalence"", ""graphs""]","[""jlens_materials/experiments/answer-code-binding-2026-07-17/"", ""jlens_materials/experiments/answer-scaffold-audit-2026-07-17/"", ""jlens_materials/experiments/cross-mechanism-outcome-2026-07-18/"", ""jlens_materials/experiments/gemma-multiscale-heldout-v1-2026-08-25/"", ""jlens_materials/experiments/graph-isomorphism-generalization-2026-07-18/"", ""jlens_materials/experiments/graph-topology-rigorous-2026-07-17/"", ""jlens_materials/experiments/late-physics-representation-replication-2026-07-17/"", ""jlens_materials/experiments/lexical-adversarial-representation-2026-07-17/"", ""jlens_materials/experiments/materials-heldout-v1"", ""jlens_materials/experiments/option-free-question-end-2026-07-18/"", ""jlens_materials/experiments/relation-graph-visualization-2026-07-18/"", ""jlens_materials/runs/""]",272,"[""python scripts/analyze_answer_code_binding.py"", ""python scripts/analyze_answer_scaffold_audit.py"", ""python scripts/analyze_gemma_multiscale_heldout_v1.py"", ""python scripts/analyze_graph_topology_rigorous.py"", ""python scripts/analyze_late_physics_replication.py"", ""python scripts/analyze_lexical_adversarial_representation.py"", ""python scripts/analyze_materials_heldout_v1.py"", ""python scripts/plot_graph_generalization_audit.py"", ""python scripts/plot_relation_graph_robustness.py"", ""python scripts/validate_graph_generalization_audit.py""]","[{""publication_path"": ""paper/figures/option-free-question-end.pdf"", ""sha256"": ""d3d86f598e5900e6ccdae3bf71e48fa5bef3dc5da7170e554c3cbf9583e17aa1"", ""matching_archived_files"": []}]",FILE_INVENTORY.csv +paper/materials_jacobian_lens_supplement.tex,613,figure,fig:s-cross-mechanism-graph,\textbf{The natural-question relation graph does not generalize across governing mechanisms.} Each source is ranked against cases from the other five mechanisms. The counter-numeric subset keeps only mechanism pairs for which numerical increase/decrease maps to physical outcome with opposite orientation. Jacobian overall AUC is 0.513 and counter-numeric AUC is 0.471; neither passes the exact 20-assignment orientation null. Direct and raw states behave similarly. Pair and layer panels retain all mechanism combinations and show that a few positive pairs do not form one transferable physical-outcome coordinate.,matched result group(s),"[""geometry"", ""graphs""]","[""jlens_materials/experiments/cross-mechanism-outcome-2026-07-18/"", ""jlens_materials/experiments/graph-isomorphism-generalization-2026-07-18/"", ""jlens_materials/experiments/graph-topology-rigorous-2026-07-17/"", ""jlens_materials/experiments/late-physics-representation-replication-2026-07-17/"", ""jlens_materials/experiments/materials-heldout-v1_latent"", ""jlens_materials/experiments/option-free-question-end-2026-07-18/"", ""jlens_materials/experiments/relation-graph-visualization-2026-07-18/"", ""jlens_materials/runs/""]",201,"[""python scripts/analyze_graph_topology_rigorous.py"", ""python scripts/analyze_materials_latent_geometry.py"", ""python scripts/build_final_paper_figures.py"", ""python scripts/plot_graph_generalization_audit.py"", ""python scripts/plot_relation_graph_robustness.py"", ""python scripts/validate_graph_generalization_audit.py""]","[{""publication_path"": ""paper/figures/cross-mechanism-outcome.pdf"", ""sha256"": ""db8670f52854f81597bb2fc95d82d5fa50c0c14b5c04a57b909f6d46c6c1e242"", ""matching_archived_files"": []}]",FILE_INVENTORY.csv +paper/materials_jacobian_lens_supplement.tex,620,figure,fig:s-graph-model-tests,"\textbf{Held-out graph learning separates numerical direction from physical meaning.} The graph audit uses the same 72 natural questions and archived states; it creates no additional scientific prompts. Every panel holds out each entire mechanism in turn. (A) A graph isomorphism network (GIN) trained to identify absolute physical polarity fails (blue), as do the graphless and edge-shuffled controls. Numerical increase/decrease was explicitly included among the absolute-label node features, so its perfect purple readback is a positive-control check rather than graph discovery. The meaningful contrast is that graph structure cannot convert this supplied direction into physical polarity for an unseen law. Below-chance physical AUC can occur when a coherent two-way split receives the wrong unnamed orientation. (B) A separate polarity-free same/different relation GIN evaluates 72 ordered different-case, cross-variant pairs within each held-out 12-node mechanism graph. It excludes numerical direction and surface variant and does not consistently exceed its pair-MLP or shuffled-edge controls. (C) Without fitting a graph network, graded cosine similarity ranks same-direction pairs above opposite-direction pairs in all six mechanisms; Jacobian, direct, and raw states nearly coincide. Because same/different physical and numerical relation are the identical target inside each family, panel C demonstrates comparative structure rather than independent physics.",matched result group(s),"[""graphs""]","[""jlens_materials/experiments/cross-mechanism-outcome-2026-07-18/"", ""jlens_materials/experiments/graph-isomorphism-generalization-2026-07-18/"", ""jlens_materials/experiments/graph-topology-rigorous-2026-07-17/"", ""jlens_materials/experiments/late-physics-representation-replication-2026-07-17/"", ""jlens_materials/experiments/option-free-question-end-2026-07-18/"", ""jlens_materials/experiments/relation-graph-visualization-2026-07-18/""]",144,"[""python scripts/analyze_graph_topology_rigorous.py"", ""python scripts/plot_graph_generalization_audit.py"", ""python scripts/plot_relation_graph_robustness.py"", ""python scripts/validate_graph_generalization_audit.py""]","[{""publication_path"": ""paper/figures/graph-generalization-model-tests.pdf"", ""sha256"": ""27454398429d9d97a511b67d5370302b2fa3547db50e01712bf4adbcdcf75e0b"", ""matching_archived_files"": [""jlens_materials/experiments/graph-isomorphism-generalization-2026-07-18/figures/graph-generalization-model-tests.pdf""]}]",FILE_INVENTORY.csv +paper/materials_jacobian_lens_supplement.tex,627,figure,fig:s-graph-partition-tests,"\textbf{Community results depend on retained graph density and do not yield a unique transferable physical partition.} Spectral clustering is label-blind and scored with adjusted Rand index (ARI), which is unchanged when the two community names are swapped. (A) The blue line is the six-mechanism Jacobian mean; gray lines are individual mechanisms. Retaining all graded candidate similarities raises mean ARI from 0.267 to 0.674, showing that the top-edge visualization discards information and that mechanisms are heterogeneous. (B) In the weighted top-one graph, direct states match Jacobian while raw-state behavior varies by family; positive ARI is therefore not lens-specific. (C) Bars show six-mechanism mean ARI and open circles show mechanisms after exhaustively scoring all 462 balanced divisions. The registered split is not the uniquely preferred partition, either on the complete graph or when counterfactuals are held out. Black marks summarize the surface-block-preserving null. The panels distinguish a favorable pairwise or spectral projection from a stable global partition.",matched result group(s),"[""graphs""]","[""jlens_materials/experiments/cross-mechanism-outcome-2026-07-18/"", ""jlens_materials/experiments/graph-isomorphism-generalization-2026-07-18/"", ""jlens_materials/experiments/graph-topology-rigorous-2026-07-17/"", ""jlens_materials/experiments/late-physics-representation-replication-2026-07-17/"", ""jlens_materials/experiments/option-free-question-end-2026-07-18/"", ""jlens_materials/experiments/relation-graph-visualization-2026-07-18/""]",144,"[""python scripts/analyze_graph_topology_rigorous.py"", ""python scripts/plot_graph_generalization_audit.py"", ""python scripts/plot_relation_graph_robustness.py"", ""python scripts/validate_graph_generalization_audit.py""]","[{""publication_path"": ""paper/figures/graph-generalization-partition-tests.pdf"", ""sha256"": ""58c87377948c6936c4dce368886ed2210094fa65450b1d3e2ebe28b1e6d3862c"", ""matching_archived_files"": [""jlens_materials/experiments/graph-isomorphism-generalization-2026-07-18/figures/graph-generalization-partition-tests.pdf""]}]",FILE_INVENTORY.csv +paper/materials_jacobian_lens_supplement.tex,634,figure,fig:s-graph-network-examples,"\textbf{Representative weighted mechanism graphs show what the statistical audit evaluates.} Nodes are complete option-free materials questions, not words. Each mechanism contains four material cases rendered as anchor (circle), physical paraphrase (square), and lexical counterfactual (diamond); edge width encodes graded Jacobian-state similarity. The node numbers identify the twelve prompts and the two colors identify the registered binary directions. (A) Particle fraction has a clearly recovered split, (B) crosslink density is only partly separated, and (C) Orowan spacing is not recovered by the exhaustive partition endpoint. Direct-response and inverse-response mechanisms can nevertheless display similar two-sided form while assigning opposite physical meanings to the sides. That symmetry is why an unlabeled graph may transfer comparative structure without identifying which side means that the material property rises. These examples visualize archived measurements; they are not reasoning chains or additional statistical observations.",matched result group(s),"[""geometry"", ""physical_equivalence"", ""graphs""]","[""jlens_materials/experiments/answer-code-binding-2026-07-17/"", ""jlens_materials/experiments/answer-scaffold-audit-2026-07-17/"", ""jlens_materials/experiments/cross-mechanism-outcome-2026-07-18/"", ""jlens_materials/experiments/graph-isomorphism-generalization-2026-07-18/"", ""jlens_materials/experiments/graph-topology-rigorous-2026-07-17/"", ""jlens_materials/experiments/late-physics-representation-replication-2026-07-17/"", ""jlens_materials/experiments/lexical-adversarial-representation-2026-07-17/"", ""jlens_materials/experiments/materials-heldout-v1_latent"", ""jlens_materials/experiments/option-free-question-end-2026-07-18/"", ""jlens_materials/experiments/relation-graph-visualization-2026-07-18/"", ""jlens_materials/runs/""]",248,"[""python scripts/analyze_answer_code_binding.py"", ""python scripts/analyze_answer_scaffold_audit.py"", ""python scripts/analyze_graph_topology_rigorous.py"", ""python scripts/analyze_late_physics_replication.py"", ""python scripts/analyze_lexical_adversarial_representation.py"", ""python scripts/analyze_materials_latent_geometry.py"", ""python scripts/build_final_paper_figures.py"", ""python scripts/plot_graph_generalization_audit.py"", ""python scripts/plot_relation_graph_robustness.py"", ""python scripts/validate_graph_generalization_audit.py""]","[{""publication_path"": ""paper/figures/graph-generalization-network-examples.pdf"", ""sha256"": ""6e23bece2bc73398b225861380b9fb6acf626eb3aec85c51d5bd12d7ffe7f7b8"", ""matching_archived_files"": [""jlens_materials/experiments/graph-isomorphism-generalization-2026-07-18/figures/graph-generalization-network-examples.pdf""]}]",FILE_INVENTORY.csv +paper/materials_jacobian_lens_supplement.tex,641,figure,fig:s-relation-atlas,"\textbf{All 25 actual option-free relation graphs.} Each panel contains the same 72 natural-question nodes in a fixed layout and all 144 selected Jacobian edges at one registered layer. The six compact islands, ordered Orowan, porosity, pearlite, dislocation, particles, and crosslinks, are supplied mechanism strata rather than discovered communities. Teal edges preserve the registered binary relation and coral edges do not. Circles, squares, and triangles denote anchor, physical paraphrase, and near-verbatim counterfactual; filled and open nodes denote the two registered orientations. Within each family this physical relation is exactly aliased with numerical direction, so the late transition is comparative rather than independently constitutive. Each panel prints the transformer layer, normalized depth, and relation-preserving edge percentage. No edge is interpreted as a reasoning step.",matched result group(s),"[""physical_equivalence"", ""graphs"", ""relational""]","[""jlens_materials/experiments/answer-code-binding-2026-07-17/"", ""jlens_materials/experiments/answer-scaffold-audit-2026-07-17/"", ""jlens_materials/experiments/cross-mechanism-outcome-2026-07-18/"", ""jlens_materials/experiments/elicited-physics-abstraction-2026-07-18/"", ""jlens_materials/experiments/equation-free-relational-physics-2026-08-21/"", ""jlens_materials/experiments/gemma-equation-free-multiscale-2026-08-25/"", ""jlens_materials/experiments/gemma4-12b-equation-free-relational-v1/"", ""jlens_materials/experiments/gemma4-31b-equation-free-layer-comparison-2026-08-23/"", ""jlens_materials/experiments/gemma4-31b-equation-free-relational-layer40-v1/"", ""jlens_materials/experiments/gemma4-31b-equation-free-relational-v1/"", ""jlens_materials/experiments/graph-isomorphism-generalization-2026-07-18/"", ""jlens_materials/experiments/graph-topology-rigorous-2026-07-17/"", ""jlens_materials/experiments/late-physics-representation-replication-2026-07-17/"", ""jlens_materials/experiments/lexical-adversarial-representation-2026-07-17/"", ""jlens_materials/experiments/neutral-anchored-relational-physics-2026-07-18/"", ""jlens_materials/experiments/option-free-question-end-2026-07-18/"", ""jlens_materials/experiments/relation-graph-visualization-2026-07-18/"", ""jlens_materials/experiments/relational-contrast-confirmation-2026-07-18/""]",316,"[""python scripts/analyze_answer_code_binding.py"", ""python scripts/analyze_answer_scaffold_audit.py"", ""python scripts/analyze_equation_free_relational_benchmark_amended.py"", ""python scripts/analyze_gemma31b_equation_free_layers.py"", ""python scripts/analyze_gemma_equation_free_multiscale.py"", ""python scripts/analyze_graph_topology_rigorous.py"", ""python scripts/analyze_late_physics_replication.py"", ""python scripts/analyze_lexical_adversarial_representation.py"", ""python scripts/analyze_neutral_anchored_relational_benchmark.py"", ""python scripts/plot_graph_generalization_audit.py"", ""python scripts/plot_relation_graph_robustness.py"", ""python scripts/validate_graph_generalization_audit.py""]","[{""publication_path"": ""paper/figures/relation-graph-layer-atlas.pdf"", ""sha256"": ""357d93ddbe210ca372a0caae8730635c15d7cc335b634d678fc586fc5dbcecc8"", ""matching_archived_files"": [""jlens_materials/experiments/relation-graph-visualization-2026-07-18/figures/relation-graph-layer-atlas.pdf""]}]",FILE_INVENTORY.csv +paper/materials_jacobian_lens_supplement.tex,648,figure,fig:s-relation-persistence,"\textbf{The 25-layer persistence union contains 391 unique directed edges.} Every line is an edge selected at least once across the 25 registered layers; width and opacity encode the fraction of layers in which it recurs. Teal preserves the registered within-family relation and coral does not. The display reveals recurring mechanism-conditioned comparative subgraphs, but the six islands are imposed by the candidate rule and physical relation is exactly aliased with numerical direction in this cohort. It is therefore a large, data-supported visualization of measured topology rather than evidence for a discovered universal materials network. The complete 3,600 layer-edge rows and 391-row persistence table are machine readable.",matched result group(s),"[""graphs""]","[""jlens_materials/experiments/cross-mechanism-outcome-2026-07-18/"", ""jlens_materials/experiments/graph-isomorphism-generalization-2026-07-18/"", ""jlens_materials/experiments/graph-topology-rigorous-2026-07-17/"", ""jlens_materials/experiments/late-physics-representation-replication-2026-07-17/"", ""jlens_materials/experiments/option-free-question-end-2026-07-18/"", ""jlens_materials/experiments/relation-graph-visualization-2026-07-18/""]",144,"[""python scripts/analyze_graph_topology_rigorous.py"", ""python scripts/plot_graph_generalization_audit.py"", ""python scripts/plot_relation_graph_robustness.py"", ""python scripts/validate_graph_generalization_audit.py""]","[{""publication_path"": ""paper/figures/relation-graph-persistence.pdf"", ""sha256"": ""3f8d278a856207707d7b0b612de9c9146059d28b44576033b35e75a5a4c8a4c1"", ""matching_archived_files"": [""jlens_materials/experiments/relation-graph-visualization-2026-07-18/figures/relation-graph-persistence.pdf""]}]",FILE_INVENTORY.csv +paper/materials_jacobian_lens_supplement.tex,655,figure,fig:s-multitoken,"\textbf{Complete-word scoring gives mixed multi-token evidence.} The exact two-piece contrast is \texttt{transgranular} versus \texttt{intergranular}; the exact three-piece contrast is \texttt{martensite} versus \texttt{bainite}. The first piece is scored from the intermediate readout and remaining pieces by teacher forcing through unchanged Gemma. Family means favor the target under all three Jacobian fits and exceed direct unembedding, but only 7 of 10 prompts are positive, failing the frozen 8-of-10 breadth gate. The fragment-level and full-sequence comparison shows why the rank of a first token piece is not a valid substitute for a technical word probability.",matched result group(s),"[""graphs"", ""multitoken""]","[""jlens_materials/experiments/cross-mechanism-outcome-2026-07-18/"", ""jlens_materials/experiments/graph-isomorphism-generalization-2026-07-18/"", ""jlens_materials/experiments/graph-topology-rigorous-2026-07-17/"", ""jlens_materials/experiments/late-physics-representation-replication-2026-07-17/"", ""jlens_materials/experiments/multitoken-sequence-robustness-2026-07-18/"", ""jlens_materials/experiments/option-free-question-end-2026-07-18/"", ""jlens_materials/experiments/relation-graph-visualization-2026-07-18/""]",159,"[""python scripts/analyze_graph_topology_rigorous.py"", ""python scripts/plot_graph_generalization_audit.py"", ""python scripts/plot_multitoken_sequence_robustness.py"", ""python scripts/plot_relation_graph_robustness.py"", ""python scripts/validate_graph_generalization_audit.py""]","[{""publication_path"": ""paper/figures/multitoken-sequence-robustness.pdf"", ""sha256"": ""e222b5b975dd054177dc40e3d8047e54e43d6960a7557ffa6227ebd0fa06fdea"", ""matching_archived_files"": []}]",FILE_INVENTORY.csv +paper/materials_jacobian_lens_supplement.tex,706,figure,fig:s-relational-layer-sweep,"\textbf{Descriptive depth profile for the disjoint 12-law confirmation.} At each layer, a new direction is fitted only on the 16 development laws and evaluated on the same 12 held-out direct and inverse laws. The purple point marks the development-selected layer 34 used for the frozen confirmation; the remaining depths are descriptive. The occasional high early values and small 12-law sample preclude a claim of first emergence, whereas the uninterrupted late plateau supports the narrower conclusion of late-stage stabilization. This sweep does not use the final 60-law cohort.",matched result group(s),"[""geometry"", ""physical_equivalence"", ""relational"", ""steering""]","[""jlens_materials/experiments/answer-code-binding-2026-07-17/"", ""jlens_materials/experiments/answer-scaffold-audit-2026-07-17/"", ""jlens_materials/experiments/candidate-followups-2026-07-16/"", ""jlens_materials/experiments/elicited-physics-abstraction-2026-07-18/"", ""jlens_materials/experiments/equation-free-relational-physics-2026-08-21/"", ""jlens_materials/experiments/gemma-equation-free-multiscale-2026-08-25/"", ""jlens_materials/experiments/gemma4-12b-equation-free-relational-v1/"", ""jlens_materials/experiments/gemma4-31b-equation-free-layer-comparison-2026-08-23/"", ""jlens_materials/experiments/gemma4-31b-equation-free-relational-layer40-v1/"", ""jlens_materials/experiments/gemma4-31b-equation-free-relational-v1/"", ""jlens_materials/experiments/late-physics-representation-replication-2026-07-17/"", ""jlens_materials/experiments/lexical-adversarial-representation-2026-07-17/"", ""jlens_materials/experiments/materials-heldout-v1_latent"", ""jlens_materials/experiments/neutral-anchored-relational-physics-2026-07-18/"", ""jlens_materials/experiments/relational-contrast-confirmation-2026-07-18/"", ""jlens_materials/experiments/relational-grain-steering-v4"", ""jlens_materials/experiments/semantic-steering-v3"", ""jlens_materials/runs/""]",311,"[""python scripts/analyze_answer_code_binding.py"", ""python scripts/analyze_answer_scaffold_audit.py"", ""python scripts/analyze_candidate_followups.py"", ""python scripts/analyze_equation_free_relational_benchmark_amended.py"", ""python scripts/analyze_gemma31b_equation_free_layers.py"", ""python scripts/analyze_gemma_equation_free_multiscale.py"", ""python scripts/analyze_late_physics_replication.py"", ""python scripts/analyze_lexical_adversarial_representation.py"", ""python scripts/analyze_materials_latent_geometry.py"", ""python scripts/analyze_neutral_anchored_relational_benchmark.py"", ""python scripts/analyze_relational_grain_steering_v4.py"", ""python scripts/analyze_semantic_steering_v3.py"", ""python scripts/build_final_paper_figures.py""]","[{""publication_path"": ""paper/figures/relational-contrast-layer-sweep.pdf"", ""sha256"": ""276df770f2a77441119fe550306bbfcc3f136624ec8cacb1d20d813b88ca197e"", ""matching_archived_files"": []}]",FILE_INVENTORY.csv +paper/materials_jacobian_lens_supplement.tex,713,figure,fig:s-relational-physics,"\textbf{Complete neutral-anchored relational summary.} (A) All inverse, neutral, and direct laws relative to the calibration-neutral median; black segments are category medians. (B) Every law ordered by its neutral-scaled matched contrast. (C) Hidden-state and output-logit relational AUCs compared with word and character TF--IDF controls. (D) Equation-surface transfer, threshold-free separation, and calibrated-cut accuracy. The figure summarizes all 60 laws; the generated inventories below print every law-level value and every one of the 480 matched comparisons.",matched result group(s),"[""relational""]","[""jlens_materials/experiments/elicited-physics-abstraction-2026-07-18/"", ""jlens_materials/experiments/equation-free-relational-physics-2026-08-21/"", ""jlens_materials/experiments/gemma-equation-free-multiscale-2026-08-25/"", ""jlens_materials/experiments/gemma4-12b-equation-free-relational-v1/"", ""jlens_materials/experiments/gemma4-31b-equation-free-layer-comparison-2026-08-23/"", ""jlens_materials/experiments/gemma4-31b-equation-free-relational-layer40-v1/"", ""jlens_materials/experiments/gemma4-31b-equation-free-relational-v1/"", ""jlens_materials/experiments/neutral-anchored-relational-physics-2026-07-18/"", ""jlens_materials/experiments/relational-contrast-confirmation-2026-07-18/""]",125,"[""python scripts/analyze_equation_free_relational_benchmark_amended.py"", ""python scripts/analyze_gemma31b_equation_free_layers.py"", ""python scripts/analyze_gemma_equation_free_multiscale.py"", ""python scripts/analyze_neutral_anchored_relational_benchmark.py""]","[{""publication_path"": ""paper/figures/neutral-anchored-relational-physics.pdf"", ""sha256"": ""bd7f3039b31997f625f0195a386c4d0454dc0c9d840c3319d7cc6d9fa11d17a6"", ""matching_archived_files"": [""jlens_materials/experiments/neutral-anchored-relational-physics-2026-07-18/figures/neutral-anchored-relational-physics.pdf""]}]",FILE_INVENTORY.csv +paper/materials_jacobian_lens_supplement.tex,725,longtable,tab:s-equation-free-examples,"\hlyellow{\textbf{Representative supplied-equation and equation-free prompt transformations.} The supplied-equation column gives the law-specific core added to the common instruction to infer monotonicity, determine numerical direction, and compose the two signs. The equation-free column prints the exact canonical surface-A prompt. Results summarize all 16 controlled variants of each law, not only the displayed string. The complete manifests retain all 960 exact prompts in each format.}",matched result group(s),"[""readout"", ""relational""]","[""jlens_materials/experiments/elicited-physics-abstraction-2026-07-18/"", ""jlens_materials/experiments/equation-free-relational-physics-2026-08-21/"", ""jlens_materials/experiments/gemma-equation-free-multiscale-2026-08-25/"", ""jlens_materials/experiments/gemma-multiscale-heldout-v1-2026-08-25/"", ""jlens_materials/experiments/gemma4-12b-equation-free-relational-v1/"", ""jlens_materials/experiments/gemma4-31b-equation-free-layer-comparison-2026-08-23/"", ""jlens_materials/experiments/gemma4-31b-equation-free-relational-layer40-v1/"", ""jlens_materials/experiments/gemma4-31b-equation-free-relational-v1/"", ""jlens_materials/experiments/materials-heldout-v1"", ""jlens_materials/experiments/neutral-anchored-relational-physics-2026-07-18/"", ""jlens_materials/experiments/relational-contrast-confirmation-2026-07-18/"", ""jlens_materials/runs/""]",206,"[""python scripts/analyze_equation_free_relational_benchmark_amended.py"", ""python scripts/analyze_gemma31b_equation_free_layers.py"", ""python scripts/analyze_gemma_equation_free_multiscale.py"", ""python scripts/analyze_gemma_multiscale_heldout_v1.py"", ""python scripts/analyze_materials_heldout_v1.py"", ""python scripts/analyze_neutral_anchored_relational_benchmark.py""]",[],FILE_INVENTORY.csv +paper/materials_jacobian_lens_supplement.tex,774,table,tab:s-v3-family,Complete \hlyellow{internally prespecified} broad-screen family verdicts. Parentheses are 95\% condition-bootstrap intervals. ``Valid'' is the fraction of matched rows whose global top beginning is one of the two registered answers.,matched result group(s),"[""steering""]","[""jlens_materials/experiments/candidate-followups-2026-07-16/"", ""jlens_materials/experiments/relational-grain-steering-v4"", ""jlens_materials/experiments/semantic-steering-v3""]",64,"[""python scripts/analyze_candidate_followups.py"", ""python scripts/analyze_relational_grain_steering_v4.py"", ""python scripts/analyze_semantic_steering_v3.py""]",[],FILE_INVENTORY.csv +paper/materials_jacobian_lens_supplement.tex,792,figure,fig:s3-v3-specificity,"\textbf{Layer-matched mechanism specificity.} Rows identify the scientific answer being scored; columns identify the mechanism direction applied at that row's frozen layer. The diagonal is the scientifically matched direction. Corrosion is strongly selective ($+0.904$ versus $-0.721$ transformation and $+0.254$ grain directions). Transformation is $+0.282$ on its diagonal versus $-0.076$ and $-0.235$. Grain size is $+0.166$ versus $+0.024$ and $-0.153$. This comparison asks whether any technical direction changes any answer, or whether the sign is mechanism dependent.",matched result group(s),"[""steering""]","[""jlens_materials/experiments/candidate-followups-2026-07-16/"", ""jlens_materials/experiments/relational-grain-steering-v4"", ""jlens_materials/experiments/semantic-steering-v3""]",64,"[""python scripts/analyze_candidate_followups.py"", ""python scripts/analyze_relational_grain_steering_v4.py"", ""python scripts/analyze_semantic_steering_v3.py""]","[{""publication_path"": ""paper/figures/cross_mechanism_specificity.pdf"", ""sha256"": ""c806f4c8b36b4efcecc297af626ddc666647d0fdff58bd7ef9b23aece822b357"", ""matching_archived_files"": [""jlens_materials/figures/semantic-steering-v3/cross_mechanism_specificity.pdf""]}]",FILE_INVENTORY.csv +paper/materials_jacobian_lens_supplement.tex,803,figure,fig:s4-v3-aligned,"\textbf{Hypothesis-generating correct-answer alignment.} Signs are reoriented after execution so that positive denotes movement toward the frozen correct answer. Circles are determinate physical conditions; diamonds and bars are mean and 95\% intervals. Grain size shows the most consistent context dependence, motivating a new \hlyellow{internally prespecified} refinement/coarsening cohort. Because this analysis was chosen after the sign pattern was observed, it is not confirmatory evidence by itself.",matched result group(s),"[""steering""]","[""jlens_materials/experiments/candidate-followups-2026-07-16/"", ""jlens_materials/experiments/relational-grain-steering-v4"", ""jlens_materials/experiments/semantic-steering-v3""]",64,"[""python scripts/analyze_candidate_followups.py"", ""python scripts/analyze_relational_grain_steering_v4.py"", ""python scripts/analyze_semantic_steering_v3.py""]","[{""publication_path"": ""paper/figures/expected_answer_alignment_secondary.pdf"", ""sha256"": ""8a6ffce0e2067ddd8a6632befd4cad863b73063e3296b410f3ab6193484840e9"", ""matching_archived_files"": [""jlens_materials/figures/semantic-steering-v3/expected_answer_alignment_secondary.pdf""]}]",FILE_INVENTORY.csv +paper/materials_jacobian_lens_supplement.tex,828,figure,fig:s-steering-transfer,"\textbf{The frozen grain direction does not generalize to new answer words or the inverse Hall--Petch regime.} The left cohort uses six new conventional polycrystals and replaces \texttt{higher}/\texttt{lower} by \texttt{increase}/\texttt{decrease}; only 7 of 12 condition signs and 1 of 6 complete refinement/coarsening pairs are correct. The right cohort explicitly states an inverse Hall--Petch nanocrystalline regime; only 6 of 12 condition signs and 0 of 6 pairs are correct. Across both cohorts, 23 of 24 conditions move toward the designated positive answer word. The unchanged vector therefore behaves primarily as an answer-vocabulary direction outside its original confirmation format.",matched result group(s),"[""steering""]","[""jlens_materials/experiments/candidate-followups-2026-07-16/"", ""jlens_materials/experiments/relational-grain-steering-v4"", ""jlens_materials/experiments/semantic-steering-v3""]",64,"[""python scripts/analyze_candidate_followups.py"", ""python scripts/analyze_relational_grain_steering_v4.py"", ""python scripts/analyze_semantic_steering_v3.py""]","[{""publication_path"": ""paper/figures/candidate-new-cohorts.pdf"", ""sha256"": ""5c435c12e8d8f386237690dc2fc105de692a6e3275d4722f8d43a404fabc3502"", ""matching_archived_files"": [""jlens_materials/experiments/candidate-followups-2026-07-16/figures/candidate-new-cohorts.pdf""]}]",FILE_INVENTORY.csv +paper/materials_jacobian_lens_supplement.tex,862,figure,fig:s-activation-patching,"\textbf{Complete exploratory activation-patching result.} (A) Layerwise movement toward the reversed-relation answer. The matched same-material and transferable cross-material reverse states become strongly causal late in the network. Relation-preserving and answer-order-only directions are exactly distance matched and remain much smaller. The legend is placed below the panels so it does not obscure the late-layer curves. (B) Same-material reverse shifts for each alloy pair; all six show the same late-layer transition. (C) Comparison between causal patching and vocabulary-readable relation separation. The correlation supports broad stage agreement but, together with onset and first-difference diagnostics, does not identify the exact causal layer.",matched result group(s),"[""physical_equivalence"", ""patching"", ""causal_coordinate""]","[""jlens_materials/experiments/answer-code-binding-2026-07-17/"", ""jlens_materials/experiments/answer-scaffold-audit-2026-07-17/"", ""jlens_materials/experiments/candidate-activation-patching-2026-07-16/"", ""jlens_materials/experiments/causal-relational-comment3-audit-2026-09-06/"", ""jlens_materials/experiments/causal-relational-knockout-2026-09-02/"", ""jlens_materials/experiments/cross-mechanism-activation-patching-2026-07-18/"", ""jlens_materials/experiments/late-physics-representation-replication-2026-07-17/"", ""jlens_materials/experiments/lexical-adversarial-representation-2026-07-17/""]",201,"[""python scripts/analyze_answer_code_binding.py"", ""python scripts/analyze_answer_scaffold_audit.py"", ""python scripts/analyze_causal_relational_knockout.py"", ""python scripts/analyze_counterfactual_activation_patching.py"", ""python scripts/analyze_cross_mechanism_activation_patching.py"", ""python scripts/analyze_late_physics_replication.py"", ""python scripts/analyze_lexical_adversarial_representation.py"", ""python scripts/validate_causal_relational_knockout.py""]","[{""publication_path"": ""paper/figures/counterfactual-activation-patching.pdf"", ""sha256"": ""04b3fed9c3fd6f8e9cb28a62494e10c0adbe855c8130395007fd17a98dd10060"", ""matching_archived_files"": []}]",FILE_INVENTORY.csv +paper/materials_jacobian_lens_supplement.tex,878,figure,fig:s-cross-mechanism-patching,"\textbf{Complete option-free cross-mechanism activation-patching result.} (A) Physical-outcome and numerical-direction contrasts for all 15 unordered mechanism pairs. (B) Pair-bootstrap intervals for all pairs and the three prespecified transfer subsets. (C) Both features emerge late, but numerical direction becomes stronger. (D) Donor-family effects reveal the failed breadth gate: four physical-outcome means are positive, while Orowan and porosity have the wrong physical sign. Because transfer survives different answer words, it is not simple token copying. Because numerical direction is stronger and family signs are heterogeneous, it is not a universal materials-relation state.",matched result group(s),"[""physical_equivalence"", ""patching""]","[""jlens_materials/experiments/answer-code-binding-2026-07-17/"", ""jlens_materials/experiments/answer-scaffold-audit-2026-07-17/"", ""jlens_materials/experiments/candidate-activation-patching-2026-07-16/"", ""jlens_materials/experiments/cross-mechanism-activation-patching-2026-07-18/"", ""jlens_materials/experiments/late-physics-representation-replication-2026-07-17/"", ""jlens_materials/experiments/lexical-adversarial-representation-2026-07-17/""]",107,"[""python scripts/analyze_answer_code_binding.py"", ""python scripts/analyze_answer_scaffold_audit.py"", ""python scripts/analyze_counterfactual_activation_patching.py"", ""python scripts/analyze_cross_mechanism_activation_patching.py"", ""python scripts/analyze_late_physics_replication.py"", ""python scripts/analyze_lexical_adversarial_representation.py""]","[{""publication_path"": ""paper/figures/cross-mechanism-activation-patching.pdf"", ""sha256"": ""d6f8ca0f2a576248a7ac3d325f024c69b9782a2fe6dbe045dfde7e5d7b98d093"", ""matching_archived_files"": []}]",FILE_INVENTORY.csv +paper/materials_jacobian_lens_supplement.tex,902,longtable,tab:s-causal-law-inventory,"Complete 24-law target inventory. ``Direct'' means that increasing the control increases the response; ``inverse'' means that it decreases the response. Exact systems, endpoint values, and all eight prompt variants per law are in the accompanying prompt files.",matched result group(s),"[""causal_coordinate""]","[""jlens_materials/experiments/causal-relational-comment3-audit-2026-09-06/"", ""jlens_materials/experiments/causal-relational-knockout-2026-09-02/""]",94,"[""python scripts/analyze_causal_relational_knockout.py"", ""python scripts/validate_causal_relational_knockout.py""]",[],FILE_INVENTORY.csv +paper/materials_jacobian_lens_supplement.tex,961,table,tab:s-causal-overlap-sensitivity,\hlyellow{Retrospective causal sensitivity after excluding three repeated development relations. Each scale retains 21 relations and 168 prompts. Margin effects subtract the mean of the six equal-length activation-derived controls; brackets are 95\% relation-bootstrap intervals. The accuracy p-values use paired relation-level accuracy differences.},matched result group(s),"[""geometry"", ""causal_coordinate""]","[""jlens_materials/experiments/causal-relational-comment3-audit-2026-09-06/"", ""jlens_materials/experiments/causal-relational-knockout-2026-09-02/"", ""jlens_materials/experiments/materials-heldout-v1_latent"", ""jlens_materials/runs/""]",151,"[""python scripts/analyze_causal_relational_knockout.py"", ""python scripts/analyze_materials_latent_geometry.py"", ""python scripts/build_final_paper_figures.py"", ""python scripts/validate_causal_relational_knockout.py""]",[],FILE_INVENTORY.csv +paper/materials_jacobian_lens_supplement.tex,989,figure,fig:s-causal-law-audit,"\hlyellow{\textbf{The neutralization effect is broad across target relations.} Each line is one law and connects the mean effect of six equal-length activation-matched controls to the effect of neutralizing the frozen relational coordinate. The expected physical-minus-control sign occurs in 23/24 laws at 4B and 24/24 at 12B and 31B. The only 4B exception, creep-rupture life versus temperature, has seven negative margins and one tied margin in the unmodified 4B checkpoint.}",matched result group(s),"[""causal_coordinate""]","[""jlens_materials/experiments/causal-relational-comment3-audit-2026-09-06/"", ""jlens_materials/experiments/causal-relational-knockout-2026-09-02/""]",94,"[""python scripts/analyze_causal_relational_knockout.py"", ""python scripts/validate_causal_relational_knockout.py""]","[{""publication_path"": ""paper/figures/causal-relational-knockout-law-audit.pdf"", ""sha256"": ""61aae3a85f6f148e45e3c414cd17caf1e26385d967fc0d61df20278e26c280bc"", ""matching_archived_files"": [""jlens_materials/experiments/causal-relational-knockout-2026-09-02/figures/causal-relational-knockout-law-audit.pdf""]}]",FILE_INVENTORY.csv +paper/materials_jacobian_lens_supplement.tex,996,figure,fig:s-causal-error-repair,\hlyellow{\textbf{Moving the coordinate toward the correct development centroid repairs a subset of natural errors.} Every line is a prompt with a strictly negative clean correct-answer margin; no case was selected by its post-intervention behavior. Teal lines cross the decision boundary: 4/8 errors at 4B and 2/8 at 12B are repaired. Gray lines remain on the wrong side. The 31B checkpoint made no baseline error in this cohort and is therefore omitted.},matched result group(s),"[""geometry"", ""causal_coordinate""]","[""jlens_materials/experiments/causal-relational-comment3-audit-2026-09-06/"", ""jlens_materials/experiments/causal-relational-knockout-2026-09-02/"", ""jlens_materials/experiments/materials-heldout-v1_latent"", ""jlens_materials/runs/""]",151,"[""python scripts/analyze_causal_relational_knockout.py"", ""python scripts/analyze_materials_latent_geometry.py"", ""python scripts/build_final_paper_figures.py"", ""python scripts/validate_causal_relational_knockout.py""]","[{""publication_path"": ""paper/figures/causal-relational-knockout-error-repair.pdf"", ""sha256"": ""0191a1323aaf88f3a8aa8f9b33af99b6d4baa3e5ec35912a835bd5c27c0e32c9"", ""matching_archived_files"": [""jlens_materials/experiments/causal-relational-knockout-2026-09-02/figures/causal-relational-knockout-error-repair.pdf""]}]",FILE_INVENTORY.csv +paper/materials_jacobian_lens_supplement.tex,1013,figure,fig:s-equation-free-cross-layer,"\hlyellow{\textbf{Equation-free physical ordering is preserved while the fitted 31B readout axis changes across depth.} (A) The independently fitted layer-40 and layer-49 physical-outcome directions are shown exactly in their two-vector spanning plane; their cosine is 0.352 (69.4 degrees). This is a two-depth comparison, not an observed continuous rotation trajectory. (B) Despite the changed axis, raw matched contrasts for the same 60 held-out laws are nearly identical across layers (Pearson $r=0.996$; Spearman $\rho=0.949$). (C) Each line follows one law between the two measured depths. Direct, neutral, and inverse relations retain their ordering. A separate direction was fitted at each layer using the unchanged supplied-equation development cohort and then evaluated on the identical equation-free prompts. The analysis was defined after both depth runs existed and is descriptive.}",matched result group(s),"[""geometry"", ""relational""]","[""jlens_materials/experiments/elicited-physics-abstraction-2026-07-18/"", ""jlens_materials/experiments/equation-free-relational-physics-2026-08-21/"", ""jlens_materials/experiments/gemma-equation-free-multiscale-2026-08-25/"", ""jlens_materials/experiments/gemma4-12b-equation-free-relational-v1/"", ""jlens_materials/experiments/gemma4-31b-equation-free-layer-comparison-2026-08-23/"", ""jlens_materials/experiments/gemma4-31b-equation-free-relational-layer40-v1/"", ""jlens_materials/experiments/gemma4-31b-equation-free-relational-v1/"", ""jlens_materials/experiments/materials-heldout-v1_latent"", ""jlens_materials/experiments/neutral-anchored-relational-physics-2026-07-18/"", ""jlens_materials/experiments/relational-contrast-confirmation-2026-07-18/"", ""jlens_materials/runs/""]",182,"[""python scripts/analyze_equation_free_relational_benchmark_amended.py"", ""python scripts/analyze_gemma31b_equation_free_layers.py"", ""python scripts/analyze_gemma_equation_free_multiscale.py"", ""python scripts/analyze_materials_latent_geometry.py"", ""python scripts/analyze_neutral_anchored_relational_benchmark.py"", ""python scripts/build_final_paper_figures.py""]","[{""publication_path"": ""paper/figures/equation-free-cross-layer-geometry.pdf"", ""sha256"": ""00c70381bbffe7f53e4a5252de8c5a806d69722546e3a221ab59d12d9f91aeb8"", ""matching_archived_files"": [""jlens_materials/experiments/gemma4-31b-equation-free-layer-comparison-2026-08-23/figures/equation-free-cross-layer-geometry.pdf""]}]",FILE_INVENTORY.csv +paper/materials_jacobian_lens_supplement.tex,1025,figure,fig:s-vocabulary-scale,"\hlyellow{\textbf{Controlled materials-term readability across Gemma scale.} The same 50 held-out prompts and 150 predeclared one-token concepts are evaluated at every checkpoint. (A) Jacobian recovery as the vocabulary cutoff is relaxed from rank 1 to rank 100. (B) Matched direct-unembedding recovery. (C) Mean controlled-recovery AUC with 95\% family-hierarchical intervals; filled circles are Jacobian and open squares are direct. (D) Within-checkpoint Jacobian-minus-direct AUC with the same population intervals. The 4B and 12B Jacobian curves average three independent lens fits, whereas 31B has one fit. Larger checkpoints show higher absolute readability, but the Jacobian-minus-direct contrast changes sign and every interval crosses zero. This secondary result therefore does not establish a general scale-dependent advantage of Jacobian transport.}",matched result group(s),"[""readout"", ""graphs""]","[""jlens_materials/experiments/cross-mechanism-outcome-2026-07-18/"", ""jlens_materials/experiments/gemma-multiscale-heldout-v1-2026-08-25/"", ""jlens_materials/experiments/graph-isomorphism-generalization-2026-07-18/"", ""jlens_materials/experiments/graph-topology-rigorous-2026-07-17/"", ""jlens_materials/experiments/late-physics-representation-replication-2026-07-17/"", ""jlens_materials/experiments/materials-heldout-v1"", ""jlens_materials/experiments/option-free-question-end-2026-07-18/"", ""jlens_materials/experiments/relation-graph-visualization-2026-07-18/"", ""jlens_materials/runs/""]",225,"[""python scripts/analyze_gemma_multiscale_heldout_v1.py"", ""python scripts/analyze_graph_topology_rigorous.py"", ""python scripts/analyze_materials_heldout_v1.py"", ""python scripts/plot_graph_generalization_audit.py"", ""python scripts/plot_relation_graph_robustness.py"", ""python scripts/validate_graph_generalization_audit.py""]","[{""publication_path"": ""paper/figures/heldout-readability-across-gemma-scales.pdf"", ""sha256"": ""7f671c9f04ddcd767946dff8ef4cb6e095a1e2dbdc9942f49dcd2a1607b96758"", ""matching_archived_files"": [""jlens_materials/experiments/gemma-multiscale-heldout-v1-2026-08-25/figures/heldout-readability-across-gemma-scales.pdf""]}]",FILE_INVENTORY.csv +paper/materials_jacobian_lens_supplement.tex,1050,figure,fig:s-31b-streams,"\hlyellow{\textbf{What vocabulary is readable across depth from the single 31B lens for the same five materials prompts used at 4B?} Rows A--E preserve the case order and paired format of the main-paper semantic-stream figure. Each left panel applies the identical unrestricted single-fit renderer. Each right panel removes exact input and continuation words, the frozen function list, low-frequency English strings, and lower-ranked prefix duplicates, then favors words that are specific across the complete 50-prompt 31B archive. The color keys in the empty early-depth region name the six displayed ribbons; moving the labels there is a layout operation and does not alter the data. The gray region marks the fixed 38--92\% analysis band. Chemical degradation (A), failure thresholds (B), permanent deformation (C), and joining (D) produce recognizable local neighborhoods, whereas the cleavage case (E) remains mixed. Gaps mean only that a displayed word was absent from the stored top-32 list at that depth. Ribbon thickness is a rank-derived visualization score, not probability, causal influence, or a literal chain of thought. Only one 31B lens was available, so this case series is descriptive and is not a fit-stability or model-scale comparison.}",matched result group(s),"[""readout"", ""graphs"", ""causal_coordinate"", ""31b_streams""]","[""jlens_materials/experiments/causal-relational-comment3-audit-2026-09-06/"", ""jlens_materials/experiments/causal-relational-knockout-2026-09-02/"", ""jlens_materials/experiments/cross-mechanism-outcome-2026-07-18/"", ""jlens_materials/experiments/gemma-multiscale-heldout-v1-2026-08-25/"", ""jlens_materials/experiments/gemma4-31b-semantic-streams-2026-08-26/"", ""jlens_materials/experiments/graph-isomorphism-generalization-2026-07-18/"", ""jlens_materials/experiments/graph-topology-rigorous-2026-07-17/"", ""jlens_materials/experiments/late-physics-representation-replication-2026-07-17/"", ""jlens_materials/experiments/materials-heldout-v1"", ""jlens_materials/experiments/option-free-question-end-2026-07-18/"", ""jlens_materials/experiments/relation-graph-visualization-2026-07-18/"", ""jlens_materials/runs/""]",327,"[""python scripts/analyze_causal_relational_knockout.py"", ""python scripts/analyze_gemma_multiscale_heldout_v1.py"", ""python scripts/analyze_graph_topology_rigorous.py"", ""python scripts/analyze_materials_heldout_v1.py"", ""python scripts/build_gemma31b_semantic_streams.py"", ""python scripts/plot_graph_generalization_audit.py"", ""python scripts/plot_relation_graph_robustness.py"", ""python scripts/validate_causal_relational_knockout.py"", ""python scripts/validate_graph_generalization_audit.py""]","[{""publication_path"": ""paper/figures/figure-s28-gemma31b-semantic-streams.pdf"", ""sha256"": ""2309efba07366b7e6213a9f68b60485ea17cc984cf1ecb1fb35f59dddf19a3cf"", ""matching_archived_files"": [""jlens_materials/experiments/gemma4-31b-semantic-streams-2026-08-26/figures/figure-s28-gemma31b-semantic-streams.pdf""]}]",FILE_INVENTORY.csv +paper/materials_jacobian_lens_supplement.tex,1067,longtable,,,study-wide summary/provenance; consult named rows,"[""readout"", ""target_free"", ""geometry"", ""physical_equivalence"", ""graphs"", ""relational"", ""steering"", ""patching"", ""causal_coordinate"", ""multitoken"", ""31b_streams""]","[""jlens_materials/experiments/answer-code-binding-2026-07-17/"", ""jlens_materials/experiments/answer-scaffold-audit-2026-07-17/"", ""jlens_materials/experiments/candidate-activation-patching-2026-07-16/"", ""jlens_materials/experiments/candidate-followups-2026-07-16/"", ""jlens_materials/experiments/candidate-nonsteering-2026-07-16/"", ""jlens_materials/experiments/causal-relational-comment3-audit-2026-09-06/"", ""jlens_materials/experiments/causal-relational-knockout-2026-09-02/"", ""jlens_materials/experiments/cross-mechanism-activation-patching-2026-07-18/"", ""jlens_materials/experiments/cross-mechanism-outcome-2026-07-18/"", ""jlens_materials/experiments/elicited-physics-abstraction-2026-07-18/"", ""jlens_materials/experiments/equation-free-relational-physics-2026-08-21/"", ""jlens_materials/experiments/gemma-equation-free-multiscale-2026-08-25/"", ""jlens_materials/experiments/gemma-multiscale-heldout-v1-2026-08-25/"", ""jlens_materials/experiments/gemma4-12b-equation-free-relational-v1/"", ""jlens_materials/experiments/gemma4-31b-equation-free-layer-comparison-2026-08-23/"", ""jlens_materials/experiments/gemma4-31b-equation-free-relational-layer40-v1/"", ""jlens_materials/experiments/gemma4-31b-equation-free-relational-v1/"", ""jlens_materials/experiments/gemma4-31b-semantic-streams-2026-08-26/"", ""jlens_materials/experiments/graph-isomorphism-generalization-2026-07-18/"", ""jlens_materials/experiments/graph-topology-rigorous-2026-07-17/"", ""jlens_materials/experiments/late-physics-representation-replication-2026-07-17/"", ""jlens_materials/experiments/lexical-adversarial-representation-2026-07-17/"", ""jlens_materials/experiments/materials-heldout-v1"", ""jlens_materials/experiments/materials-heldout-v1_latent"", ""jlens_materials/experiments/multitoken-sequence-robustness-2026-07-18/"", ""jlens_materials/experiments/neutral-anchored-relational-physics-2026-07-18/"", ""jlens_materials/experiments/option-free-question-end-2026-07-18/"", ""jlens_materials/experiments/relation-graph-visualization-2026-07-18/"", ""jlens_materials/experiments/relational-contrast-confirmation-2026-07-18/"", ""jlens_materials/experiments/relational-grain-steering-v4"", ""jlens_materials/experiments/semantic-steering-v3"", ""jlens_materials/runs/""]",641,"[""python scripts/analyze_answer_code_binding.py"", ""python scripts/analyze_answer_scaffold_audit.py"", ""python scripts/analyze_candidate_followups.py"", ""python scripts/analyze_candidate_nonsteering.py"", ""python scripts/analyze_causal_relational_knockout.py"", ""python scripts/analyze_counterfactual_activation_patching.py"", ""python scripts/analyze_cross_mechanism_activation_patching.py"", ""python scripts/analyze_equation_free_relational_benchmark_amended.py"", ""python scripts/analyze_gemma31b_equation_free_layers.py"", ""python scripts/analyze_gemma_equation_free_multiscale.py"", ""python scripts/analyze_gemma_multiscale_heldout_v1.py"", ""python scripts/analyze_graph_topology_rigorous.py"", ""python scripts/analyze_late_physics_replication.py"", ""python scripts/analyze_lexical_adversarial_representation.py"", ""python scripts/analyze_materials_heldout_v1.py"", ""python scripts/analyze_materials_latent_geometry.py"", ""python scripts/analyze_neutral_anchored_relational_benchmark.py"", ""python scripts/analyze_relational_grain_steering_v4.py"", ""python scripts/analyze_semantic_steering_v3.py"", ""python scripts/build_final_paper_figures.py"", ""python scripts/build_gemma31b_semantic_streams.py"", ""python scripts/plot_graph_generalization_audit.py"", ""python scripts/plot_multitoken_sequence_robustness.py"", ""python scripts/plot_relation_graph_robustness.py"", ""python scripts/validate_causal_relational_knockout.py"", ""python scripts/validate_graph_generalization_audit.py""]",[],FILE_INVENTORY.csv +paper/primary_prompt_inventory.tex,4,longtable,tab:all-primary-prompts,Complete primary association dataset: all 50 exact prompts and their predeclared candidate concepts. Each family contains five independent phrasings of the same physical mechanism. Asterisks mark declared multi-token labels retained in the manifest but dropped from direct single-token ranking.,matched result group(s),"[""readout"", ""graphs"", ""multitoken""]","[""jlens_materials/experiments/cross-mechanism-outcome-2026-07-18/"", ""jlens_materials/experiments/gemma-multiscale-heldout-v1-2026-08-25/"", ""jlens_materials/experiments/graph-isomorphism-generalization-2026-07-18/"", ""jlens_materials/experiments/graph-topology-rigorous-2026-07-17/"", ""jlens_materials/experiments/late-physics-representation-replication-2026-07-17/"", ""jlens_materials/experiments/materials-heldout-v1"", ""jlens_materials/experiments/multitoken-sequence-robustness-2026-07-18/"", ""jlens_materials/experiments/option-free-question-end-2026-07-18/"", ""jlens_materials/experiments/relation-graph-visualization-2026-07-18/"", ""jlens_materials/runs/""]",240,"[""python scripts/analyze_gemma_multiscale_heldout_v1.py"", ""python scripts/analyze_graph_topology_rigorous.py"", ""python scripts/analyze_materials_heldout_v1.py"", ""python scripts/plot_graph_generalization_audit.py"", ""python scripts/plot_multitoken_sequence_robustness.py"", ""python scripts/plot_relation_graph_robustness.py"", ""python scripts/validate_graph_generalization_audit.py""]",[],FILE_INVENTORY.csv +paper/protocol_chronology.tex,5,longtable,,,study-wide summary/provenance; consult named rows,"[""readout"", ""target_free"", ""geometry"", ""physical_equivalence"", ""graphs"", ""relational"", ""steering"", ""patching"", ""causal_coordinate"", ""multitoken"", ""31b_streams""]","[""jlens_materials/experiments/answer-code-binding-2026-07-17/"", ""jlens_materials/experiments/answer-scaffold-audit-2026-07-17/"", ""jlens_materials/experiments/candidate-activation-patching-2026-07-16/"", ""jlens_materials/experiments/candidate-followups-2026-07-16/"", ""jlens_materials/experiments/candidate-nonsteering-2026-07-16/"", ""jlens_materials/experiments/causal-relational-comment3-audit-2026-09-06/"", ""jlens_materials/experiments/causal-relational-knockout-2026-09-02/"", ""jlens_materials/experiments/cross-mechanism-activation-patching-2026-07-18/"", ""jlens_materials/experiments/cross-mechanism-outcome-2026-07-18/"", ""jlens_materials/experiments/elicited-physics-abstraction-2026-07-18/"", ""jlens_materials/experiments/equation-free-relational-physics-2026-08-21/"", ""jlens_materials/experiments/gemma-equation-free-multiscale-2026-08-25/"", ""jlens_materials/experiments/gemma-multiscale-heldout-v1-2026-08-25/"", ""jlens_materials/experiments/gemma4-12b-equation-free-relational-v1/"", ""jlens_materials/experiments/gemma4-31b-equation-free-layer-comparison-2026-08-23/"", ""jlens_materials/experiments/gemma4-31b-equation-free-relational-layer40-v1/"", ""jlens_materials/experiments/gemma4-31b-equation-free-relational-v1/"", ""jlens_materials/experiments/gemma4-31b-semantic-streams-2026-08-26/"", ""jlens_materials/experiments/graph-isomorphism-generalization-2026-07-18/"", 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""jlens_materials/experiments/gemma4-31b-equation-free-relational-layer40-v1/"", ""jlens_materials/experiments/gemma4-31b-equation-free-relational-v1/"", ""jlens_materials/experiments/neutral-anchored-relational-physics-2026-07-18/"", ""jlens_materials/experiments/relational-contrast-confirmation-2026-07-18/""]",125,"[""python scripts/analyze_equation_free_relational_benchmark_amended.py"", ""python scripts/analyze_gemma31b_equation_free_layers.py"", ""python scripts/analyze_gemma_equation_free_multiscale.py"", ""python scripts/analyze_neutral_anchored_relational_benchmark.py""]",[],FILE_INVENTORY.csv +paper/relational_physics_inventory.tex,720,table,,\textbf{Portable relational-physics SI files.} Hashes refer to the publication copies bundled with this Supplementary Information.,matched result group(s),"[""relational""]","[""jlens_materials/experiments/elicited-physics-abstraction-2026-07-18/"", ""jlens_materials/experiments/equation-free-relational-physics-2026-08-21/"", 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summary/provenance; consult named rows,"[""readout"", ""target_free"", ""geometry"", ""physical_equivalence"", ""graphs"", ""relational"", ""steering"", ""patching"", ""causal_coordinate"", ""multitoken"", ""31b_streams""]","[""jlens_materials/experiments/answer-code-binding-2026-07-17/"", ""jlens_materials/experiments/answer-scaffold-audit-2026-07-17/"", ""jlens_materials/experiments/candidate-activation-patching-2026-07-16/"", ""jlens_materials/experiments/candidate-followups-2026-07-16/"", ""jlens_materials/experiments/candidate-nonsteering-2026-07-16/"", ""jlens_materials/experiments/causal-relational-comment3-audit-2026-09-06/"", ""jlens_materials/experiments/causal-relational-knockout-2026-09-02/"", ""jlens_materials/experiments/cross-mechanism-activation-patching-2026-07-18/"", ""jlens_materials/experiments/cross-mechanism-outcome-2026-07-18/"", ""jlens_materials/experiments/elicited-physics-abstraction-2026-07-18/"", 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""jlens_materials/experiments/materials-heldout-v1_latent"", ""jlens_materials/experiments/multitoken-sequence-robustness-2026-07-18/"", ""jlens_materials/experiments/neutral-anchored-relational-physics-2026-07-18/"", ""jlens_materials/experiments/option-free-question-end-2026-07-18/"", ""jlens_materials/experiments/relation-graph-visualization-2026-07-18/"", ""jlens_materials/experiments/relational-contrast-confirmation-2026-07-18/"", ""jlens_materials/experiments/relational-grain-steering-v4"", ""jlens_materials/experiments/semantic-steering-v3"", ""jlens_materials/runs/""]",641,"[""python scripts/analyze_answer_code_binding.py"", ""python scripts/analyze_answer_scaffold_audit.py"", ""python scripts/analyze_candidate_followups.py"", ""python scripts/analyze_candidate_nonsteering.py"", ""python scripts/analyze_causal_relational_knockout.py"", ""python scripts/analyze_counterfactual_activation_patching.py"", ""python scripts/analyze_cross_mechanism_activation_patching.py"", ""python 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scripts/validate_causal_relational_knockout.py"", ""python scripts/validate_graph_generalization_audit.py""]",[],FILE_INVENTORY.csv +paper/review_robustness_prompt_inventory.tex,107,longtable,tab:s-multitoken-prompts,Complete multi-token prompt and sequence-score inventory.,matched result group(s),"[""multitoken""]","[""jlens_materials/experiments/multitoken-sequence-robustness-2026-07-18/""]",15,"[""python scripts/plot_multitoken_sequence_robustness.py""]",[],FILE_INVENTORY.csv +paper/review_robustness_prompt_inventory.tex,135,table,tab:s-cross-mechanism-pairs,All Jacobian counter-numeric mechanism-pair results.,study-wide summary/provenance; consult named rows,"[""readout"", ""target_free"", ""geometry"", ""physical_equivalence"", ""graphs"", ""relational"", ""steering"", ""patching"", ""causal_coordinate"", ""multitoken"", ""31b_streams""]","[""jlens_materials/experiments/answer-code-binding-2026-07-17/"", ""jlens_materials/experiments/answer-scaffold-audit-2026-07-17/"", 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""jlens_materials/experiments/gemma4-31b-equation-free-relational-layer40-v1/"", ""jlens_materials/experiments/gemma4-31b-equation-free-relational-v1/"", ""jlens_materials/experiments/gemma4-31b-semantic-streams-2026-08-26/"", ""jlens_materials/experiments/graph-isomorphism-generalization-2026-07-18/"", ""jlens_materials/experiments/graph-topology-rigorous-2026-07-17/"", ""jlens_materials/experiments/late-physics-representation-replication-2026-07-17/"", ""jlens_materials/experiments/lexical-adversarial-representation-2026-07-17/"", ""jlens_materials/experiments/materials-heldout-v1"", ""jlens_materials/experiments/materials-heldout-v1_latent"", ""jlens_materials/experiments/multitoken-sequence-robustness-2026-07-18/"", ""jlens_materials/experiments/neutral-anchored-relational-physics-2026-07-18/"", ""jlens_materials/experiments/option-free-question-end-2026-07-18/"", ""jlens_materials/experiments/relation-graph-visualization-2026-07-18/"", ""jlens_materials/experiments/relational-contrast-confirmation-2026-07-18/"", ""jlens_materials/experiments/relational-grain-steering-v4"", ""jlens_materials/experiments/semantic-steering-v3"", ""jlens_materials/runs/""]",641,"[""python scripts/analyze_answer_code_binding.py"", ""python scripts/analyze_answer_scaffold_audit.py"", ""python scripts/analyze_candidate_followups.py"", ""python scripts/analyze_candidate_nonsteering.py"", ""python scripts/analyze_causal_relational_knockout.py"", ""python scripts/analyze_counterfactual_activation_patching.py"", ""python scripts/analyze_cross_mechanism_activation_patching.py"", ""python scripts/analyze_equation_free_relational_benchmark_amended.py"", ""python scripts/analyze_gemma31b_equation_free_layers.py"", ""python scripts/analyze_gemma_equation_free_multiscale.py"", ""python scripts/analyze_gemma_multiscale_heldout_v1.py"", ""python scripts/analyze_graph_topology_rigorous.py"", ""python scripts/analyze_late_physics_replication.py"", ""python scripts/analyze_lexical_adversarial_representation.py"", ""python scripts/analyze_materials_heldout_v1.py"", ""python scripts/analyze_materials_latent_geometry.py"", ""python scripts/analyze_neutral_anchored_relational_benchmark.py"", ""python scripts/analyze_relational_grain_steering_v4.py"", ""python scripts/analyze_semantic_steering_v3.py"", ""python scripts/build_final_paper_figures.py"", ""python scripts/build_gemma31b_semantic_streams.py"", ""python scripts/plot_graph_generalization_audit.py"", ""python scripts/plot_multitoken_sequence_robustness.py"", ""python scripts/plot_relation_graph_robustness.py"", ""python scripts/validate_causal_relational_knockout.py"", ""python scripts/validate_graph_generalization_audit.py""]",[],FILE_INVENTORY.csv +paper/review_robustness_prompt_inventory.tex,160,table,tab:s-option-free-patching-subsets,Frozen option-free activation-patching endpoints. Intervals resample unordered mechanism pairs. The pair-sign and structured columns are two-sided exact probabilities.,matched result group(s),"[""patching""]","[""jlens_materials/experiments/candidate-activation-patching-2026-07-16/"", ""jlens_materials/experiments/cross-mechanism-activation-patching-2026-07-18/""]",42,"[""python scripts/analyze_counterfactual_activation_patching.py"", ""python scripts/analyze_cross_mechanism_activation_patching.py""]",[],FILE_INVENTORY.csv +paper/review_robustness_prompt_inventory.tex,177,table,tab:s-option-free-patching-families,Activation-patching results by donor mechanism. Positive physical transfer means that positive-outcome donors increased the receiver's positive-minus-negative answer margin relative to negative-outcome donors.,matched result group(s),"[""readout"", ""patching""]","[""jlens_materials/experiments/candidate-activation-patching-2026-07-16/"", ""jlens_materials/experiments/cross-mechanism-activation-patching-2026-07-18/"", ""jlens_materials/experiments/gemma-multiscale-heldout-v1-2026-08-25/"", ""jlens_materials/experiments/materials-heldout-v1"", ""jlens_materials/runs/""]",123,"[""python scripts/analyze_counterfactual_activation_patching.py"", ""python scripts/analyze_cross_mechanism_activation_patching.py"", ""python scripts/analyze_gemma_multiscale_heldout_v1.py"", ""python scripts/analyze_materials_heldout_v1.py""]",[],FILE_INVENTORY.csv +paper/steering_prompt_inventory.tex,31,longtable,,,matched result group(s),"[""steering""]","[""jlens_materials/experiments/candidate-followups-2026-07-16/"", ""jlens_materials/experiments/relational-grain-steering-v4"", ""jlens_materials/experiments/semantic-steering-v3""]",64,"[""python scripts/analyze_candidate_followups.py"", ""python scripts/analyze_relational_grain_steering_v4.py"", ""python scripts/analyze_semantic_steering_v3.py""]",[],FILE_INVENTORY.csv +paper/steering_prompt_inventory.tex,107,longtable,,,matched result group(s),"[""steering""]","[""jlens_materials/experiments/candidate-followups-2026-07-16/"", ""jlens_materials/experiments/relational-grain-steering-v4"", ""jlens_materials/experiments/semantic-steering-v3""]",64,"[""python scripts/analyze_candidate_followups.py"", ""python scripts/analyze_relational_grain_steering_v4.py"", ""python scripts/analyze_semantic_steering_v3.py""]",[],FILE_INVENTORY.csv +paper/steering_v3_result_table.tex,6,longtable,,,matched result group(s),"[""steering""]","[""jlens_materials/experiments/candidate-followups-2026-07-16/"", ""jlens_materials/experiments/relational-grain-steering-v4"", ""jlens_materials/experiments/semantic-steering-v3""]",64,"[""python scripts/analyze_candidate_followups.py"", ""python scripts/analyze_relational_grain_steering_v4.py"", ""python scripts/analyze_semantic_steering_v3.py""]",[],FILE_INVENTORY.csv +paper/steering_v4_result_table.tex,6,longtable,,,matched result group(s),"[""steering""]","[""jlens_materials/experiments/candidate-followups-2026-07-16/"", ""jlens_materials/experiments/relational-grain-steering-v4"", ""jlens_materials/experiments/semantic-steering-v3""]",64,"[""python scripts/analyze_candidate_followups.py"", ""python scripts/analyze_relational_grain_steering_v4.py"", ""python scripts/analyze_semantic_steering_v3.py""]",[],FILE_INVENTORY.csv diff --git a/RESULT_MAP.json b/RESULT_MAP.json new file mode 100644 index 0000000000000000000000000000000000000000..c068b328bf25612909e406ea391d3cc84dfbfa8d --- /dev/null +++ b/RESULT_MAP.json @@ -0,0 +1,4018 @@ +{ + "note": "Paths are relative to the downloaded dataset. Commands run from jlens_materials/. Historical analyzers can write outputs: use a working copy. Figure/table source locations and captions identify blocks without assuming stable pagination. Every supporting file has a checksum in FILE_INVENTORY.csv. Groups may cover multiple related endpoints.", + "records": [ + { + "manuscript_source": "paper/graph_generalization_audit.tex", + "source_line": 18, + "kind": "table", + "label": "", + "caption": "Mechanism orientation in the 72-prompt audit. 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The right column gives all terms declared before execution and omitted from every corresponding input. Daggers mark documented multi-token terms excluded from the one-token rank endpoint. 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(A) The cutoff $k$ is relaxed from rank 1 to rank 100; a higher curve means more declared terms have become strongly readable. Thin teal curves are the three independent Jacobian fits, the thick teal curve is their mean, and gray is direct unembedding of the same states. At $k=100$, the fractions are 7.2\\% and 3.3\\%, respectively. (B) Each point is one prompt's recovery area under the curve (AUC); a point above the diagonal favors Jacobian transport. Eleven prompts improve, 36 tie, and three decline. (C) The same differences are grouped at the scientifically appropriate level: gray circles are the five phrasings and diamonds are the ten family means. Positive values favor the Jacobian lens. Boundary attack and notch resistance produce the largest gains, whereas cyclic damage and particle strengthening favor direct unembedding. Across families, the mean difference is $+0.0129$, but the hierarchical 95\\% interval spans $-0.0187$ to $+0.0486$ and the one-sided family sign-flip test gives $p=0.234$. 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Support is the number of independent phrasings, out of five, in which the word appeared under the three-fit consensus rule; darker blue-green cells indicate stronger background-corrected support. Asterisks mark exact declared-word overlap added only after ranking. Read across a row to ask whether the words form a recognizable materials neighborhood. Boundary attack, surface oxidation, and rapid transformation show coherent mechanism or microstructure terms; high-temperature deformation and cleavage show why stable decoding can still be generic. The figure therefore measures what assembles naturally, not whether the algorithm can find a supplied answer.", + "scope": "matched result group(s)", + "groups": [ + "readout", + "target_free", + "graphs", + "causal_coordinate" + ], + "artifact_prefixes": [ + "jlens_materials/experiments/candidate-nonsteering-2026-07-16/", + "jlens_materials/experiments/causal-relational-comment3-audit-2026-09-06/", + "jlens_materials/experiments/causal-relational-knockout-2026-09-02/", + "jlens_materials/experiments/cross-mechanism-outcome-2026-07-18/", + "jlens_materials/experiments/gemma-multiscale-heldout-v1-2026-08-25/", + "jlens_materials/experiments/graph-isomorphism-generalization-2026-07-18/", + "jlens_materials/experiments/graph-topology-rigorous-2026-07-17/", + "jlens_materials/experiments/late-physics-representation-replication-2026-07-17/", + "jlens_materials/experiments/materials-heldout-v1", + "jlens_materials/experiments/option-free-question-end-2026-07-18/", + "jlens_materials/experiments/relation-graph-visualization-2026-07-18/", + "jlens_materials/runs/" + ], + "supporting_file_count": 340, + "analysis_commands": [ + "python scripts/analyze_candidate_nonsteering.py", + "python scripts/analyze_causal_relational_knockout.py", + "python scripts/analyze_gemma_multiscale_heldout_v1.py", + "python scripts/analyze_graph_topology_rigorous.py", + "python scripts/analyze_materials_heldout_v1.py", + "python scripts/plot_graph_generalization_audit.py", + "python scripts/plot_relation_graph_robustness.py", + "python scripts/validate_causal_relational_knockout.py", + "python scripts/validate_graph_generalization_audit.py" + ], + "publication_assets": [ + { + "publication_path": "paper/figures/heldout-open-vocabulary.pdf", + "sha256": "2c81592b3b5d6e032186859a0531570a58636d31628440af6331de77570dc5a5", + "matching_archived_files": [] + } + ], + "manifest": "FILE_INVENTORY.csv" + }, + { + "manuscript_source": "paper/materials_jacobian_lens.tex", + "source_line": 243, + "kind": "figure", + "label": "fig:targetfree-classification", + "caption": "\\textbf{Do the freely discovered words identify the materials mechanism without a supplied answer list?} Each bar is leave-one-phrasing-out accuracy across the 50 prompts and ten balanced mechanism families. A classifier learned four phrasings per family and predicted the held-out fifth using only target-free candidate words from the corresponding readout. ``Exact prompt words removed'' deletes literal input copies; ``morphology-aware removal'' additionally deletes conservative prefix-related variants. The dotted line is 10\\% chance. The dashed line is a TF-IDF classifier that instead sees the prompt words themselves. The target-free Jacobian and direct lists remain well above chance after the strictest removal, at 58\\% and 64\\%, but the prompt baseline reaches 56\\% and direct does not differ significantly from Jacobian. Thus the discovered vocabulary contains cross-phrasing mechanism information, while the comparison rules out a lens-specific superiority claim. The complete confusion matrix and per-prompt predictions are in the Supplementary Information.", + "scope": "matched result group(s)", + "groups": [ + "readout", + "target_free", + "physical_equivalence" + ], + "artifact_prefixes": [ + "jlens_materials/experiments/answer-code-binding-2026-07-17/", + "jlens_materials/experiments/answer-scaffold-audit-2026-07-17/", + "jlens_materials/experiments/candidate-nonsteering-2026-07-16/", + "jlens_materials/experiments/gemma-multiscale-heldout-v1-2026-08-25/", + "jlens_materials/experiments/late-physics-representation-replication-2026-07-17/", + "jlens_materials/experiments/lexical-adversarial-representation-2026-07-17/", + "jlens_materials/experiments/materials-heldout-v1", + "jlens_materials/runs/" + ], + "supporting_file_count": 167, + "analysis_commands": [ + "python scripts/analyze_answer_code_binding.py", + "python scripts/analyze_answer_scaffold_audit.py", + "python scripts/analyze_candidate_nonsteering.py", + "python scripts/analyze_gemma_multiscale_heldout_v1.py", + "python scripts/analyze_late_physics_replication.py", + "python scripts/analyze_lexical_adversarial_representation.py", + "python scripts/analyze_materials_heldout_v1.py" + ], + "publication_assets": [ + { + "publication_path": "paper/figures/semantic-classification-summary.pdf", + "sha256": "a81e1352f536565e0c031991c023f822b7e164ba13e79e623f33d8f179c4b10c", + "matching_archived_files": [ + "jlens_materials/experiments/candidate-nonsteering-2026-07-16/figures/semantic-classification-summary.pdf" + ] + } + ], + "manifest": "FILE_INVENTORY.csv" + }, + { + "manuscript_source": "paper/materials_jacobian_lens.tex", + "source_line": 273, + "kind": "figure", + "label": "fig:streams", + "caption": "\\textbf{What does the stricter target-free filter remove, and what materials vocabulary remains?} Rows A-E correspond to the five exact prompts printed in the text. Each left panel shows the original-style unrestricted leading words from lens fit 0. Each right panel removes input and output words, the frozen 214-word function list, and the globally common readout scaffold, then requires the word to occur in all three independent fits at that layer. No declared concept was supplied to either selection. The gray region marks the fixed 38-92\\% analysis band. The comparison shows that much of the visually continuous unfiltered stream is generic discourse vocabulary, while the stricter view retains localized physical neighborhoods: \\code{corrosion}/\\code{damage} (A), \\code{resilience}/\\code{protection} (B), \\code{irreversible}/\\code{mechanism} (C), and \\code{collapse}/\\code{disintegration} (D). The generic cleavage stream (E) is a useful negative case. Gaps mean that none of the displayed words passed the strict rule at that layer; they are not evidence of absent computation. Ribbon thickness is a rank-derived visualization score, not token probability, causal influence, or a literal chain of thought.", + "scope": "matched result group(s)", + "groups": [ + "readout", + "target_free", + "physical_equivalence", + "graphs", + "causal_coordinate" + ], + "artifact_prefixes": [ + "jlens_materials/experiments/answer-code-binding-2026-07-17/", + "jlens_materials/experiments/answer-scaffold-audit-2026-07-17/", + "jlens_materials/experiments/candidate-nonsteering-2026-07-16/", + "jlens_materials/experiments/causal-relational-comment3-audit-2026-09-06/", + "jlens_materials/experiments/causal-relational-knockout-2026-09-02/", + "jlens_materials/experiments/cross-mechanism-outcome-2026-07-18/", + "jlens_materials/experiments/gemma-multiscale-heldout-v1-2026-08-25/", + "jlens_materials/experiments/graph-isomorphism-generalization-2026-07-18/", + "jlens_materials/experiments/graph-topology-rigorous-2026-07-17/", + "jlens_materials/experiments/late-physics-representation-replication-2026-07-17/", + "jlens_materials/experiments/lexical-adversarial-representation-2026-07-17/", + "jlens_materials/experiments/materials-heldout-v1", + "jlens_materials/experiments/option-free-question-end-2026-07-18/", + "jlens_materials/experiments/relation-graph-visualization-2026-07-18/", + "jlens_materials/runs/" + ], + "supporting_file_count": 387, + "analysis_commands": [ + "python scripts/analyze_answer_code_binding.py", + "python scripts/analyze_answer_scaffold_audit.py", + "python scripts/analyze_candidate_nonsteering.py", + "python scripts/analyze_causal_relational_knockout.py", + "python scripts/analyze_gemma_multiscale_heldout_v1.py", + "python scripts/analyze_graph_topology_rigorous.py", + "python scripts/analyze_late_physics_replication.py", + "python scripts/analyze_lexical_adversarial_representation.py", + "python scripts/analyze_materials_heldout_v1.py", + "python scripts/plot_graph_generalization_audit.py", + "python scripts/plot_relation_graph_robustness.py", + "python scripts/validate_causal_relational_knockout.py", + "python scripts/validate_graph_generalization_audit.py" + ], + "publication_assets": [ + { + "publication_path": "paper/figures/figure4-semantic-streams.pdf", + "sha256": "8fb79e9f897187beef29d19fc9ade92549ca6ec93a6384443fd755c80c244ea5", + "matching_archived_files": [] + } + ], + "manifest": "FILE_INVENTORY.csv" + }, + { + "manuscript_source": "paper/materials_jacobian_lens.tex", + "source_line": 294, + "kind": "figure", + "label": "fig:geometry", + "caption": "\\textbf{Do differently worded descriptions of the same mechanism remain close in the model's full state space?} (A) For each of five folds, four phrasings per family define ten centroids and the fifth phrasing is classified; the procedure is repeated at every registered layer and for every baseline representation. (B) Full-dimensional Jacobian classification peaks at 62\\% at 43.9\\% depth: 31 of 50 descriptions are correct, above the 13-of-50 threshold obtained after shuffling family labels and retaining the best of 25 layers ($p=0.0002$). (C) UMAP is only a display of those 50 best-layer states. Its axes have no physical units and no test uses them; color and two-letter code denote family, marker shape denotes phrasing, and stars are family centroids. (D) The full-dimensional confusion matrix reveals the physical specificity: boundary attack (BA), line-defect motion (LD), and notch resistance (NR) are separated across all five phrasings, while ductile failure (DU), high-temperature deformation (HT), and surface oxidation (OX) overlap. CL, CY, PS, and RT denote cleavage, cyclic damage, particle strengthening, and rapid transformation. (E) The same classifier reaches 54\\% on raw hidden states and 76\\% on a simple average of the prompt's own word embeddings. The result therefore supports statistically reliable family structure in the transported states, but it does not show that Gemma created that structure independently of the engineering vocabulary in the prompt.", + "scope": "matched result group(s)", + "groups": [ + "geometry", + "physical_equivalence", + "steering", + "causal_coordinate" + ], + "artifact_prefixes": [ + "jlens_materials/experiments/answer-code-binding-2026-07-17/", + "jlens_materials/experiments/answer-scaffold-audit-2026-07-17/", + "jlens_materials/experiments/candidate-followups-2026-07-16/", + "jlens_materials/experiments/causal-relational-comment3-audit-2026-09-06/", + "jlens_materials/experiments/causal-relational-knockout-2026-09-02/", + "jlens_materials/experiments/late-physics-representation-replication-2026-07-17/", + "jlens_materials/experiments/lexical-adversarial-representation-2026-07-17/", + "jlens_materials/experiments/materials-heldout-v1_latent", + "jlens_materials/experiments/relational-grain-steering-v4", + "jlens_materials/experiments/semantic-steering-v3", + "jlens_materials/runs/" + ], + "supporting_file_count": 280, + "analysis_commands": [ + "python scripts/analyze_answer_code_binding.py", + "python scripts/analyze_answer_scaffold_audit.py", + "python scripts/analyze_candidate_followups.py", + "python scripts/analyze_causal_relational_knockout.py", + "python scripts/analyze_late_physics_replication.py", + "python scripts/analyze_lexical_adversarial_representation.py", + "python scripts/analyze_materials_latent_geometry.py", + "python scripts/analyze_relational_grain_steering_v4.py", + "python scripts/analyze_semantic_steering_v3.py", + "python scripts/build_final_paper_figures.py", + "python scripts/validate_causal_relational_knockout.py" + ], + "publication_assets": [ + { + "publication_path": "paper/figures/figure5-latent-geometry.pdf", + "sha256": "d2c4a08c5c59797cb6ac174455da7c40072dbd0e160f81fb35e5704cf6d0e3cd", + "matching_archived_files": [] + } + ], + "manifest": "FILE_INVENTORY.csv" + }, + { + "manuscript_source": "paper/materials_jacobian_lens.tex", + "source_line": 319, + "kind": "figure", + "label": "fig:physical-equivalence", + "caption": "\\textbf{Does physical equivalence overcome deliberately stronger wording similarity?} Every triplet compares an anchor with a differently worded prompt that has the same physical answer and a near-verbatim prompt whose physical relation is reversed. Positive margin means that the same-physics pair is closer; negative margin means that wording dominates. (A) In the disjoint replication cohort, both Jacobian and direct decoder-basis states begin strongly wording-aligned and rise sharply after roughly 60\\% depth. The shaded region is the prospectively frozen 80-96\\% late window. The frozen late-minus-middle Jacobian change is $+1.402$ ($0.951$-$1.793$), positive in 24/24 triplets and 6/6 families. (B) The absolute late-window means are positive in both the discovery and disjoint cohorts, but their family-hierarchical intervals cross zero; the replication's registered primary endpoint therefore failed. (C) Four replication families end positive, obstacle spacing is approximately neutral, and porosity remains wording-aligned, demonstrating real mechanism heterogeneity. (D) A target-free comparison of decoded word sets also trends positive late, but its intervals cross zero. Teal and purple nearly coincide throughout, showing that the transition is not uniquely produced by Jacobian transport. This is a representational similarity test, not a causal intervention or a literal chain of thought.", + "scope": "matched result group(s)", + "groups": [ + "readout", + "target_free", + "physical_equivalence", + "causal_coordinate" + ], + "artifact_prefixes": [ + "jlens_materials/experiments/answer-code-binding-2026-07-17/", + "jlens_materials/experiments/answer-scaffold-audit-2026-07-17/", + "jlens_materials/experiments/candidate-nonsteering-2026-07-16/", + "jlens_materials/experiments/causal-relational-comment3-audit-2026-09-06/", + "jlens_materials/experiments/causal-relational-knockout-2026-09-02/", + "jlens_materials/experiments/gemma-multiscale-heldout-v1-2026-08-25/", + "jlens_materials/experiments/late-physics-representation-replication-2026-07-17/", + "jlens_materials/experiments/lexical-adversarial-representation-2026-07-17/", + "jlens_materials/experiments/materials-heldout-v1", + "jlens_materials/runs/" + ], + "supporting_file_count": 261, + "analysis_commands": [ + "python scripts/analyze_answer_code_binding.py", + "python scripts/analyze_answer_scaffold_audit.py", + "python scripts/analyze_candidate_nonsteering.py", + "python scripts/analyze_causal_relational_knockout.py", + "python scripts/analyze_gemma_multiscale_heldout_v1.py", + "python scripts/analyze_late_physics_replication.py", + "python scripts/analyze_lexical_adversarial_representation.py", + "python scripts/analyze_materials_heldout_v1.py", + "python scripts/validate_causal_relational_knockout.py" + ], + "publication_assets": [ + { + "publication_path": "paper/figures/late-physics-representation-replication.pdf", + "sha256": "f39e8a384b2575b2ce0972b8d3e6485d85bbd57b95941fda284f6661c68d084e", + "matching_archived_files": [ + "jlens_materials/experiments/late-physics-representation-replication-2026-07-17/figures/late-physics-representation-replication.pdf" + ] + } + ], + "manifest": "FILE_INVENTORY.csv" + }, + { + "manuscript_source": "paper/materials_jacobian_lens.tex", + "source_line": 338, + "kind": "figure", + "label": "fig:relation-graph", + "caption": "\\textbf{Where does option-free comparative graph structure appear, and where does it fail?} Every node is one complete materials question and every selected edge points to the most similar eligible different material case. AUC uses all eligible candidates rather than only the selected edge; 0.5 denotes no ordering information. (A) The same 72 scientific stems are read at three prompt boundaries. At the natural question end, no answer word or response scaffold is present and Jacobian AUC is 0.642. A checkpoint-suffixed variant shows no detectable signal, while the explicit answer-mapping condition reaches 0.816. Because suffix and token position differ across conditions, this comparison localizes prompt-boundary sensitivity rather than a unique causal effect of the added words. Direct unembedding and raw states follow the same pattern. (B) At the natural question end, solid bars show the fraction of 144 selected edges that preserve the registered binary relation and hatched bars show all-candidate AUC. The dashed line is the exact case-preserving null mean. Jacobian selected-edge precision is 0.674 ($p=0.02195$) and AUC is 0.642 ($p=0.01235$); word and character TF-IDF are at or below the null. (C) The positive result depends on supplying the governing mechanism. When candidates come from other mechanisms, AUC falls to 0.513; when raw numerical direction deliberately conflicts with physical outcome, it falls to 0.471. (D) The actual 72-node natural-question graph contains six supplied mechanism islands and 144 selected edges. Circles, squares, and triangles are anchor, paraphrase, and near-verbatim counterfactual prompts. Filled and open nodes are the two registered orientations; teal and coral edges preserve or reverse the registered binary relation. Ninety-seven edges are teal. This figure demonstrates an option-free comparative regularity; Figure~\\ref{fig:graph-identifiability} determines what that regularity can identify.", + "scope": "matched result group(s)", + "groups": [ + "physical_equivalence", + "graphs", + "causal_coordinate" + ], + "artifact_prefixes": [ + "jlens_materials/experiments/answer-code-binding-2026-07-17/", + "jlens_materials/experiments/answer-scaffold-audit-2026-07-17/", + "jlens_materials/experiments/causal-relational-comment3-audit-2026-09-06/", + "jlens_materials/experiments/causal-relational-knockout-2026-09-02/", + "jlens_materials/experiments/cross-mechanism-outcome-2026-07-18/", + "jlens_materials/experiments/graph-isomorphism-generalization-2026-07-18/", + "jlens_materials/experiments/graph-topology-rigorous-2026-07-17/", + "jlens_materials/experiments/late-physics-representation-replication-2026-07-17/", + "jlens_materials/experiments/lexical-adversarial-representation-2026-07-17/", + "jlens_materials/experiments/option-free-question-end-2026-07-18/", + "jlens_materials/experiments/relation-graph-visualization-2026-07-18/" + ], + "supporting_file_count": 285, + "analysis_commands": [ + "python scripts/analyze_answer_code_binding.py", + "python scripts/analyze_answer_scaffold_audit.py", + "python scripts/analyze_causal_relational_knockout.py", + "python scripts/analyze_graph_topology_rigorous.py", + "python scripts/analyze_late_physics_replication.py", + "python scripts/analyze_lexical_adversarial_representation.py", + "python scripts/plot_graph_generalization_audit.py", + "python scripts/plot_relation_graph_robustness.py", + "python scripts/validate_causal_relational_knockout.py", + "python scripts/validate_graph_generalization_audit.py" + ], + "publication_assets": [ + { + "publication_path": "paper/figures/relation-graph-robustness.pdf", + "sha256": "1f3042ebdbd328088543cc4cf0808350acd812d2986350577aaddb38ce16ee5f", + "matching_archived_files": [ + "jlens_materials/experiments/relation-graph-visualization-2026-07-18/figures/relation-graph-robustness.pdf" + ] + } + ], + "manifest": "FILE_INVENTORY.csv" + }, + { + "manuscript_source": "paper/materials_jacobian_lens.tex", + "source_line": 361, + "kind": "figure", + "label": "fig:graph-identifiability", + "caption": "\\textbf{What can the option-free graph identify?} (A) A numerical increase raises the property for a direct law but lowers it for an inverse law. In this prompt set, physical polarity is exactly $y=sx$; within one mechanism, the same/different physical relation collapses algebraically to the same/different numerical relation. (B) A graph isomorphism network is trained on four mechanisms, selected on a fifth, and tested on an unseen sixth. The purple AUC of 1.0 is a positive-control input readback: numerical direction $x$ was deliberately supplied to the absolute-label network, so this curve does not show graph discovery. The actual test asks whether graph structure can combine that supplied $x$ with an unseen law to infer physical polarity $y$; it fails, as does a separate same/different relation network that receives neither $x$ nor surface-variant identity. Points are held-out mechanisms and connecting lines guide the eye. (C) Community agreement rises when graded similarities are retained instead of only the strongest edge, showing that sparsification discards structure. Direct and especially raw states are at least as strong as the Jacobian states, so this organization is not lens-specific. (D) Exhaustively evaluating every balanced partition shows that the registered physical split is not the uniquely preferred division, including when counterfactual phrasing is held out. Bars are means, dots are mechanisms, and intervals show the corresponding blockwise null distribution. Together, the panels support transferable comparative geometry while rejecting a universal, Jacobian-specific physical-sign graph.", + "scope": "matched result group(s)", + "groups": [ + "geometry", + "graphs" + ], + "artifact_prefixes": [ + "jlens_materials/experiments/cross-mechanism-outcome-2026-07-18/", + "jlens_materials/experiments/graph-isomorphism-generalization-2026-07-18/", + "jlens_materials/experiments/graph-topology-rigorous-2026-07-17/", + "jlens_materials/experiments/late-physics-representation-replication-2026-07-17/", + "jlens_materials/experiments/materials-heldout-v1_latent", + "jlens_materials/experiments/option-free-question-end-2026-07-18/", + "jlens_materials/experiments/relation-graph-visualization-2026-07-18/", + "jlens_materials/runs/" + ], + "supporting_file_count": 201, + "analysis_commands": [ + "python scripts/analyze_graph_topology_rigorous.py", + "python scripts/analyze_materials_latent_geometry.py", + "python scripts/build_final_paper_figures.py", + "python scripts/plot_graph_generalization_audit.py", + "python scripts/plot_relation_graph_robustness.py", + "python scripts/validate_graph_generalization_audit.py" + ], + "publication_assets": [ + { + "publication_path": "paper/figures/graph-identifiability-summary.pdf", + "sha256": "2c67031f8d824a6371c2fcd78fe844f2d3783f1c11ee985ea525784675c72338", + "matching_archived_files": [] + } + ], + "manifest": "FILE_INVENTORY.csv" + }, + { + "manuscript_source": "paper/materials_jacobian_lens.tex", + "source_line": 398, + "kind": "figure", + "label": "fig:relational-physics", + "caption": "\\hlyellow{\\textbf{Constitutive orientation persists without a supplied equation across Gemma scale.} Every law contributes eight matched up-minus-down state contrasts along a model-specific direction fitted only on the same earlier development laws. (A) The matched 4B prompt-format comparison. Open markers supplied the equation and prescribed sign composition; filled markers used the corresponding equation-free questions. Both use the original 4B neutral scale, and black segments are medians. (B) The complete 60-law distributions for every available condition. Each waterfall independently orders all laws from most inverse to most direct; horizontal bars begin at that condition's calibrated neutral zero. The 4B supplied and equation-free facets are the matched format pair. The 12B and 31B facets contain equation-free tests only. Each facet has its own horizontal robust-$z$ scale, so signs and category ordering are comparable across facets but absolute bar lengths are not common hidden-state units. Purple, gray, and teal denote inverse, neutral, and direct relations. (C) Direct-inverse AUC for the hidden-state direction, output-logit change, and lexical controls across the same four available conditions. Hidden and output separation survives scaffold removal and reaches 1.000 at 12B and 31B, whereas word and character controls remain near chance. (D) The three registered pairwise AUCs, neutral-cut directional accuracy, and exact generated-answer accuracy for the equation-free cohort. Both larger checkpoints orient 40 of 40 directional laws and reach ceiling on all three relational AUCs; only 31B converts that representation into nearly perfect generated answers. The comparison supports a scale-robust ordinal constitutive orientation, not an invariant law-by-law magnitude, unfamiliar-law generalization, or a literal reasoning trace.}", + "scope": "matched result group(s)", + "groups": [ + "geometry", + "physical_equivalence", + "relational" + ], + "artifact_prefixes": [ + "jlens_materials/experiments/answer-code-binding-2026-07-17/", + "jlens_materials/experiments/answer-scaffold-audit-2026-07-17/", + "jlens_materials/experiments/elicited-physics-abstraction-2026-07-18/", + "jlens_materials/experiments/equation-free-relational-physics-2026-08-21/", + "jlens_materials/experiments/gemma-equation-free-multiscale-2026-08-25/", + "jlens_materials/experiments/gemma4-12b-equation-free-relational-v1/", + "jlens_materials/experiments/gemma4-31b-equation-free-layer-comparison-2026-08-23/", + "jlens_materials/experiments/gemma4-31b-equation-free-relational-layer40-v1/", + "jlens_materials/experiments/gemma4-31b-equation-free-relational-v1/", + "jlens_materials/experiments/late-physics-representation-replication-2026-07-17/", + "jlens_materials/experiments/lexical-adversarial-representation-2026-07-17/", + "jlens_materials/experiments/materials-heldout-v1_latent", + "jlens_materials/experiments/neutral-anchored-relational-physics-2026-07-18/", + "jlens_materials/experiments/relational-contrast-confirmation-2026-07-18/", + "jlens_materials/runs/" + ], + "supporting_file_count": 247, + "analysis_commands": [ + "python scripts/analyze_answer_code_binding.py", + "python scripts/analyze_answer_scaffold_audit.py", + "python scripts/analyze_equation_free_relational_benchmark_amended.py", + "python scripts/analyze_gemma31b_equation_free_layers.py", + "python scripts/analyze_gemma_equation_free_multiscale.py", + "python scripts/analyze_late_physics_replication.py", + "python scripts/analyze_lexical_adversarial_representation.py", + "python scripts/analyze_materials_latent_geometry.py", + "python scripts/analyze_neutral_anchored_relational_benchmark.py", + "python scripts/build_final_paper_figures.py" + ], + "publication_assets": [ + { + "publication_path": "paper/figures/equation-free-relational-multiscale.pdf", + "sha256": "4189fbe4097f242a5b7f717c097cbc4d53f70fd355dae92fbaa6544383aaa131", + "matching_archived_files": [ + "jlens_materials/experiments/gemma-equation-free-multiscale-2026-08-25/figures/equation-free-relational-multiscale.pdf" + ] + } + ], + "manifest": "FILE_INVENTORY.csv" + }, + { + "manuscript_source": "paper/materials_jacobian_lens.tex", + "source_line": 417, + "kind": "figure", + "label": "fig:blinded", + "caption": "\\textbf{Can an automated interpreter recognize a materials mechanism from discovered words alone?} (A) Twenty candidate sets (ten mechanism families from each of two readouts) were shuffled and stripped of the original prompt, method identity, and answer key. The automated interpreter selected one of ten family labels for each set in five order-randomized passes; it did not solve the original engineering question. (B) Accuracy stays far above the 10\\% chance level on every pass. (C) Majority-vote correctness for every family and readout: both columns are correct for nine families and both miss cleavage. Thus both naturally assembled vocabularies carry recognizable materials meaning, with no Jacobian-specific advantage in this secondary test (paired difference 0, $p=1.0$). (D) Each point is one Jacobian family. The horizontal coordinate is predeclared exact-term recovery; the vertical coordinate is the fraction of automated passes assigning the target-free words correctly. Cleavage is weak on both measures, but most families are semantically identified even when exact targets are rarely top-ranked, producing only a weak relationship ($\\rho=0.254$). Controlled recovery and target-free recognition therefore measure complementary properties. The five passes are repeated judgments from one automated system, not independent scientific expert (hence this figure is limited to reproducible machine-based semantic validation).", + "scope": "matched result group(s)", + "groups": [ + "readout", + "target_free", + "graphs" + ], + "artifact_prefixes": [ + "jlens_materials/experiments/candidate-nonsteering-2026-07-16/", + "jlens_materials/experiments/cross-mechanism-outcome-2026-07-18/", + "jlens_materials/experiments/gemma-multiscale-heldout-v1-2026-08-25/", + "jlens_materials/experiments/graph-isomorphism-generalization-2026-07-18/", + "jlens_materials/experiments/graph-topology-rigorous-2026-07-17/", + "jlens_materials/experiments/late-physics-representation-replication-2026-07-17/", + "jlens_materials/experiments/materials-heldout-v1", + "jlens_materials/experiments/option-free-question-end-2026-07-18/", + "jlens_materials/experiments/relation-graph-visualization-2026-07-18/", + "jlens_materials/runs/" + ], + "supporting_file_count": 246, + "analysis_commands": [ + "python scripts/analyze_candidate_nonsteering.py", + "python scripts/analyze_gemma_multiscale_heldout_v1.py", + "python scripts/analyze_graph_topology_rigorous.py", + "python scripts/analyze_materials_heldout_v1.py", + "python scripts/plot_graph_generalization_audit.py", + "python scripts/plot_relation_graph_robustness.py", + "python scripts/validate_graph_generalization_audit.py" + ], + "publication_assets": [ + { + "publication_path": "paper/figures/automated-family-identification.pdf", + "sha256": "418fcc81622d200d702f4dcb3d08ee69c835afbeea0489d111f2f4232aa0d6ee", + "matching_archived_files": [] + } + ], + "manifest": "FILE_INVENTORY.csv" + }, + { + "manuscript_source": "paper/materials_jacobian_lens.tex", + "source_line": 434, + "kind": "figure", + "label": "fig:cases", + "caption": "\\textbf{Two magnified examples of the controlled-rank measurement.} The panels print the exact prompts, predeclared absent words, and one-token continuations. (A-C) The notch-resistance example is an earlier development item excluded from all held-out inference. Panel B plots \\code{toughness}'s full-vocabulary rank at every sampled layer for three independent Jacobian fits and direct unembedding; upward is stronger because rank 1 is best. The registered 38-92\\% band is shaded. Panel C reduces each word to its best rank within that band, showing that the localized rank-2 event is specific to \\code{toughness}. (D-F) The boundary-attack example is one of the 50 held-out prompts and was already counted in Figure~\\ref{fig:controlled}; it is not a new test. All three lenses sustain \\code{corrosion} at rank 1 over two sampled layers, while direct unembedding reaches rank 111. Panel F shows that \\code{sensitization} and \\code{boundary} do not receive the same advantage. Thus Figure~\\ref{fig:controlled} asks how often declared concepts are recovered across the frozen population, whereas this figure shows the layer location, fit-to-fit agreement, duration, and word specificity that its aggregate statistic necessarily discards. Neither trajectory is a literal chain of thought.", + "scope": "matched result group(s)", + "groups": [ + "readout", + "geometry", + "graphs", + "steering" + ], + "artifact_prefixes": [ + "jlens_materials/experiments/candidate-followups-2026-07-16/", + "jlens_materials/experiments/cross-mechanism-outcome-2026-07-18/", + "jlens_materials/experiments/gemma-multiscale-heldout-v1-2026-08-25/", + "jlens_materials/experiments/graph-isomorphism-generalization-2026-07-18/", + "jlens_materials/experiments/graph-topology-rigorous-2026-07-17/", + "jlens_materials/experiments/late-physics-representation-replication-2026-07-17/", + "jlens_materials/experiments/materials-heldout-v1", + "jlens_materials/experiments/materials-heldout-v1_latent", + "jlens_materials/experiments/option-free-question-end-2026-07-18/", + "jlens_materials/experiments/relation-graph-visualization-2026-07-18/", + "jlens_materials/experiments/relational-grain-steering-v4", + "jlens_materials/experiments/semantic-steering-v3", + "jlens_materials/runs/" + ], + "supporting_file_count": 289, + "analysis_commands": [ + "python scripts/analyze_candidate_followups.py", + "python scripts/analyze_gemma_multiscale_heldout_v1.py", + "python scripts/analyze_graph_topology_rigorous.py", + "python scripts/analyze_materials_heldout_v1.py", + "python scripts/analyze_materials_latent_geometry.py", + "python scripts/analyze_relational_grain_steering_v4.py", + "python scripts/analyze_semantic_steering_v3.py", + "python scripts/build_final_paper_figures.py", + "python scripts/plot_graph_generalization_audit.py", + "python scripts/plot_relation_graph_robustness.py", + "python scripts/validate_graph_generalization_audit.py" + ], + "publication_assets": [ + { + "publication_path": "paper/figures/figure7-layer-resolved-cases.pdf", + "sha256": "d535c6ca713c1ef188db35dd4082d2d9308846564c27c6c5691cd9b968b4889c", + "matching_archived_files": [ + "jlens_materials/figures/materials-heldout-v1/figure7-layer-resolved-cases.pdf" + ] + } + ], + "manifest": "FILE_INVENTORY.csv" + }, + { + "manuscript_source": "paper/materials_jacobian_lens.tex", + "source_line": 455, + "kind": "figure", + "label": "fig:steering-screen", + "caption": "\\textbf{Broad steering is mechanism dependent.} Each panel averages ten new physical conditions and plots how an intermediate-state intervention changes the log odds of the first scientific answer relative to the second: \\code{grooves} versus \\code{clean} for corrosion (A), \\code{hard} versus \\code{soft} for transformation (B), and \\code{higher} versus \\code{lower} for grain size (C). Zero is the unperturbed model; the horizontal coordinate is the added direction's size relative to the final-prompt residual norm. Points are condition-weighted means and shaded bands are one standard error across physical conditions. The teal curve applies the scientifically matched Jacobian direction. Purple applies the two unrelated materials directions at the identical layer, green constructs the matched direction without Jacobian transport, and gray averages ten random directions. Corrosion is the only family that passes every \\hlyellow{internally prespecified gate}: its matched curve is strong, monotonic, and distinct from all controls. Transformation trends in the intended direction but lacks \\hlyellow{the prespecified specificity criterion}. The grain curve averages refinement, for which \\code{higher} is correct, with coarsening, for which \\code{lower} is correct; its cancellation motivated the relation-aware experiment in Figure~\\ref{fig:steering}. The figure therefore shows both the causal promise and the non-universality of mechanism steering.", + "scope": "matched result group(s)", + "groups": [ + "physical_equivalence", + "steering", + "causal_coordinate" + ], + "artifact_prefixes": [ + "jlens_materials/experiments/answer-code-binding-2026-07-17/", + "jlens_materials/experiments/answer-scaffold-audit-2026-07-17/", + "jlens_materials/experiments/candidate-followups-2026-07-16/", + "jlens_materials/experiments/causal-relational-comment3-audit-2026-09-06/", + "jlens_materials/experiments/causal-relational-knockout-2026-09-02/", + "jlens_materials/experiments/late-physics-representation-replication-2026-07-17/", + "jlens_materials/experiments/lexical-adversarial-representation-2026-07-17/", + "jlens_materials/experiments/relational-grain-steering-v4", + "jlens_materials/experiments/semantic-steering-v3" + ], + "supporting_file_count": 223, + "analysis_commands": [ + "python scripts/analyze_answer_code_binding.py", + "python scripts/analyze_answer_scaffold_audit.py", + "python scripts/analyze_candidate_followups.py", + "python scripts/analyze_causal_relational_knockout.py", + "python scripts/analyze_late_physics_replication.py", + "python scripts/analyze_lexical_adversarial_representation.py", + "python scripts/analyze_relational_grain_steering_v4.py", + "python scripts/analyze_semantic_steering_v3.py", + "python scripts/validate_causal_relational_knockout.py" + ], + "publication_assets": [ + { + "publication_path": "paper/figures/family_dose_response.pdf", + "sha256": "bd3ce94f4bfa2be41679874eedced9ffa5d1ff7a690dcb5fccba5c15cee811a8", + "matching_archived_files": [ + "jlens_materials/figures/semantic-steering-v3/family_dose_response.pdf" + ] + } + ], + "manifest": "FILE_INVENTORY.csv" + }, + { + "manuscript_source": "paper/materials_jacobian_lens.tex", + "source_line": 472, + "kind": "figure", + "label": "fig:steering", + "caption": "\\textbf{Can one frozen grain-size mechanism direction change a scientific answer in the relation-appropriate direction?} Six new materials were each written as a matched pair: grain refinement and grain coarsening with alloy identity, the two grain sizes, controlled covariates, and answer words held fixed. At the final prompt token in layer 16, the same frozen Jacobian direction was swept from $-4\\%$ to $+4\\%$ of the state's norm; the full strings \\code{higher} and \\code{lower} were scored at each dose. (A) For every material, the raw change in \\code{higher}-minus-\\code{lower} log odds is positive for refinement and negative for coarsening. Each connecting line therefore crosses zero: the direction responds to the relation rather than always favoring one word. (B) Signs are aligned so that positive always means movement toward the physically correct answer, then refinement and coarsening are averaged within material. Every circle is one independent matched pair; diamonds and bars are the mean and pair-clustered 95\\% interval. The matched Jacobian effect is $+0.852$ and exceeds random, unrelated-mechanism, and direct controls in all six pairs. (C) The mean dose curves show the reversal continuously rather than only at endpoints. The experiment prospectively confirms a localized context-dependent causal pathway; it does not reveal a prose chain of thought or establish general materials reasoning. Exact prompts, both answer orders, clean outputs, all 2,400 intervention rows, and the full protocol are printed or linked in the Supplementary Information.", + "scope": "matched result group(s)", + "groups": [ + "readout", + "physical_equivalence", + "steering", + "causal_coordinate" + ], + "artifact_prefixes": [ + "jlens_materials/experiments/answer-code-binding-2026-07-17/", + "jlens_materials/experiments/answer-scaffold-audit-2026-07-17/", + "jlens_materials/experiments/candidate-followups-2026-07-16/", + "jlens_materials/experiments/causal-relational-comment3-audit-2026-09-06/", + "jlens_materials/experiments/causal-relational-knockout-2026-09-02/", + "jlens_materials/experiments/gemma-multiscale-heldout-v1-2026-08-25/", + "jlens_materials/experiments/late-physics-representation-replication-2026-07-17/", + "jlens_materials/experiments/lexical-adversarial-representation-2026-07-17/", + "jlens_materials/experiments/materials-heldout-v1", + "jlens_materials/experiments/relational-grain-steering-v4", + "jlens_materials/experiments/semantic-steering-v3", + "jlens_materials/runs/" + ], + "supporting_file_count": 304, + "analysis_commands": [ + "python scripts/analyze_answer_code_binding.py", + "python scripts/analyze_answer_scaffold_audit.py", + "python scripts/analyze_candidate_followups.py", + "python scripts/analyze_causal_relational_knockout.py", + "python scripts/analyze_gemma_multiscale_heldout_v1.py", + "python scripts/analyze_late_physics_replication.py", + "python scripts/analyze_lexical_adversarial_representation.py", + "python scripts/analyze_materials_heldout_v1.py", + "python scripts/analyze_relational_grain_steering_v4.py", + "python scripts/analyze_semantic_steering_v3.py", + "python scripts/validate_causal_relational_knockout.py" + ], + "publication_assets": [ + { + "publication_path": "paper/figures/relational_confirmation.pdf", + "sha256": "37895d87ab6e4b8be70fa5a1d11856cf5ba81d599dc43cece3843994e2ddbd9c", + "matching_archived_files": [ + "jlens_materials/figures/relational-grain-steering-v4/relational_confirmation.pdf" + ] + } + ], + "manifest": "FILE_INVENTORY.csv" + }, + { + "manuscript_source": "paper/materials_jacobian_lens.tex", + "source_line": 497, + "kind": "figure", + "label": "fig:patching", + "caption": "\\textbf{Can a hidden state from the opposite grain-size relation causally transfer its answer?} A receiver prompt and donor prompt use the same answer words but describe opposite refinement/coarsening relations. At one layer, the receiver's final-prompt-token state is replaced by the donor state; positive values mean movement toward the answer appropriate to the donor's reversed relation. If the state contains only wording or alloy identity, the reversed transplant should not consistently redirect the answer. (A) Read from left to right, same-material and cross-material reverse patches have little effect early but produce a sharp late-layer shift of roughly 11 log-odds units at the peak. Relation-preserving and answer-order-only directions are rescaled to the same receiver-donor distance but remain much smaller. The shaded vertical region is the fixed 38-92\\% analysis band; the band-mean same-material reverse effect is $+6.249$ ($5.663$-$6.795$). (B) The same-material reverse effect appears in all six matched alloy pairs, excluding a result driven by one material. (C) Layers where refinement and coarsening are more separable by a vocabulary readout tend also to show larger patching effects. The Jacobian level correlation is $\\rho=0.818$ and the direct correlation is $\\rho=0.571$, but onset and first-difference checks support only a broad late-stage correspondence, not exact layer-by-layer localization. The experiment establishes causal sufficiency in this constrained decision: a naturally occurring reversed-relation state can redirect the answer and can transfer between alloys. It does not identify a unique one-dimensional mechanism, prove necessity, or reveal a prose chain of thought. It is called exploratory because it reused a grain-size cohort whose strong effect was already known, not because the intervention is noncausal.", + "scope": "matched result group(s)", + "groups": [ + "physical_equivalence", + "steering", + "patching", + "causal_coordinate" + ], + "artifact_prefixes": [ + "jlens_materials/experiments/answer-code-binding-2026-07-17/", + "jlens_materials/experiments/answer-scaffold-audit-2026-07-17/", + "jlens_materials/experiments/candidate-activation-patching-2026-07-16/", + "jlens_materials/experiments/candidate-followups-2026-07-16/", + "jlens_materials/experiments/causal-relational-comment3-audit-2026-09-06/", + "jlens_materials/experiments/causal-relational-knockout-2026-09-02/", + "jlens_materials/experiments/cross-mechanism-activation-patching-2026-07-18/", + "jlens_materials/experiments/late-physics-representation-replication-2026-07-17/", + "jlens_materials/experiments/lexical-adversarial-representation-2026-07-17/", + "jlens_materials/experiments/relational-grain-steering-v4", + "jlens_materials/experiments/semantic-steering-v3" + ], + "supporting_file_count": 265, + "analysis_commands": [ + "python scripts/analyze_answer_code_binding.py", + "python scripts/analyze_answer_scaffold_audit.py", + "python scripts/analyze_candidate_followups.py", + "python scripts/analyze_causal_relational_knockout.py", + "python scripts/analyze_counterfactual_activation_patching.py", + "python scripts/analyze_cross_mechanism_activation_patching.py", + "python scripts/analyze_late_physics_replication.py", + "python scripts/analyze_lexical_adversarial_representation.py", + "python scripts/analyze_relational_grain_steering_v4.py", + "python scripts/analyze_semantic_steering_v3.py", + "python scripts/validate_causal_relational_knockout.py" + ], + "publication_assets": [ + { + "publication_path": "paper/figures/counterfactual-activation-patching.pdf", + "sha256": "04b3fed9c3fd6f8e9cb28a62494e10c0adbe855c8130395007fd17a98dd10060", + "matching_archived_files": [] + } + ], + "manifest": "FILE_INVENTORY.csv" + }, + { + "manuscript_source": "paper/materials_jacobian_lens.tex", + "source_line": 520, + "kind": "figure", + "label": "fig:causal-knockout", + "caption": "\\textbf{A development-fitted relational coordinate makes a selective, context-dependent causal contribution to equation-free physical decisions.} (A) Setting only the coordinate to its development midpoint reduces the correct-answer margin at all three scales; equal-length activation-matched controls remain near zero. Pale points are 24 law identities; large markers and bars are law means and 95\\% law-bootstrap intervals. (B) Replacing only the coordinate with its value from the matched reversed-input prompt moves the decision toward the counterfactual answer. Full-state replacement shows the total transferable effect; matched controls remain near zero. (C) The coordinate-only share of full-state transfer decreases with scale, but restoring the original coordinate within the counterfactual state removes approximately one-half of the total effect at every scale. (D) Neutralization changes discrete accuracy chiefly at 4B. Full-state counterfactuals reverse almost all original decisions, while coordinate restoration recovers many of them. 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Margin is centered cosine(anchor, physical paraphrase) minus cosine(anchor, near-verbatim physical counterfactual). Positive means that physics outranks wording. 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The scaffold comparison reuses stored states and was defined after both source experiments were inspected. Its first four rows are positive-minus-negative decoder-logit differences, equivalently pairwise log-odds units; they are not the centered-cosine margins reported in the preceding table.", + "scope": "matched result group(s)", + "groups": [ + "physical_equivalence" + ], + "artifact_prefixes": [ + "jlens_materials/experiments/answer-code-binding-2026-07-17/", + "jlens_materials/experiments/answer-scaffold-audit-2026-07-17/", + "jlens_materials/experiments/late-physics-representation-replication-2026-07-17/", + "jlens_materials/experiments/lexical-adversarial-representation-2026-07-17/" + ], + "supporting_file_count": 65, + "analysis_commands": [ + "python scripts/analyze_answer_code_binding.py", + "python scripts/analyze_answer_scaffold_audit.py", + "python scripts/analyze_late_physics_replication.py", + "python scripts/analyze_lexical_adversarial_representation.py" + ], + "publication_assets": [], + "manifest": "FILE_INVENTORY.csv" + }, + { + "manuscript_source": "paper/materials_jacobian_lens_supplement.tex", + "source_line": 423, + "kind": "table", + "label": "", + "caption": "Complete mechanism inventory for the two lexical-adversarial cohorts. Each row contains four material systems, each rendered as anchor, physical paraphrase, and lexical counterfactual. ``Positive/negative'' are the exact one-token scientific outcomes used only for clean-answer and answer-scaffold audits; the full-state similarity endpoint does not search for these words.", + "scope": "matched result group(s)", + "groups": [ + "physical_equivalence" + ], + "artifact_prefixes": [ + "jlens_materials/experiments/answer-code-binding-2026-07-17/", + "jlens_materials/experiments/answer-scaffold-audit-2026-07-17/", + "jlens_materials/experiments/late-physics-representation-replication-2026-07-17/", + "jlens_materials/experiments/lexical-adversarial-representation-2026-07-17/" + ], + "supporting_file_count": 65, + "analysis_commands": [ + "python scripts/analyze_answer_code_binding.py", + "python scripts/analyze_answer_scaffold_audit.py", + "python scripts/analyze_late_physics_replication.py", + "python scripts/analyze_lexical_adversarial_representation.py" + ], + "publication_assets": [], + "manifest": "FILE_INVENTORY.csv" + }, + { + "manuscript_source": "paper/materials_jacobian_lens_supplement.tex", + "source_line": 448, + "kind": "table", + "label": "", + "caption": "\\textbf{Post hoc signed-relation graph across different material cases.} A source prompt selects one nearest eligible target in each other surface-variant group. Same material cases are prohibited. Precision is the fraction of 144 directed edges whose source and target share the registered binary relation. The mechanism family is supplied when candidates are defined. The later exact audit shows that this within-family physical relation is identical to same/different numerical direction, so these values measure comparative structure rather than independent identification of a constitutive-law sign.", + "scope": "matched result group(s)", + "groups": [ + "geometry", + "physical_equivalence", + "graphs", + "relational" + ], + "artifact_prefixes": [ + "jlens_materials/experiments/answer-code-binding-2026-07-17/", + "jlens_materials/experiments/answer-scaffold-audit-2026-07-17/", + "jlens_materials/experiments/cross-mechanism-outcome-2026-07-18/", + "jlens_materials/experiments/elicited-physics-abstraction-2026-07-18/", + "jlens_materials/experiments/equation-free-relational-physics-2026-08-21/", + "jlens_materials/experiments/gemma-equation-free-multiscale-2026-08-25/", + "jlens_materials/experiments/gemma4-12b-equation-free-relational-v1/", + "jlens_materials/experiments/gemma4-31b-equation-free-layer-comparison-2026-08-23/", + "jlens_materials/experiments/gemma4-31b-equation-free-relational-layer40-v1/", + "jlens_materials/experiments/gemma4-31b-equation-free-relational-v1/", + "jlens_materials/experiments/graph-isomorphism-generalization-2026-07-18/", + "jlens_materials/experiments/graph-topology-rigorous-2026-07-17/", + "jlens_materials/experiments/late-physics-representation-replication-2026-07-17/", + "jlens_materials/experiments/lexical-adversarial-representation-2026-07-17/", + "jlens_materials/experiments/materials-heldout-v1_latent", + "jlens_materials/experiments/neutral-anchored-relational-physics-2026-07-18/", + "jlens_materials/experiments/option-free-question-end-2026-07-18/", + "jlens_materials/experiments/relation-graph-visualization-2026-07-18/", + "jlens_materials/experiments/relational-contrast-confirmation-2026-07-18/", + "jlens_materials/runs/" + ], + "supporting_file_count": 373, + "analysis_commands": [ + "python scripts/analyze_answer_code_binding.py", + "python scripts/analyze_answer_scaffold_audit.py", + "python scripts/analyze_equation_free_relational_benchmark_amended.py", + "python scripts/analyze_gemma31b_equation_free_layers.py", + "python scripts/analyze_gemma_equation_free_multiscale.py", + "python scripts/analyze_graph_topology_rigorous.py", + "python scripts/analyze_late_physics_replication.py", + "python scripts/analyze_lexical_adversarial_representation.py", + "python scripts/analyze_materials_latent_geometry.py", + "python scripts/analyze_neutral_anchored_relational_benchmark.py", + "python scripts/build_final_paper_figures.py", + "python scripts/plot_graph_generalization_audit.py", + "python scripts/plot_relation_graph_robustness.py", + "python scripts/validate_graph_generalization_audit.py" + ], + "publication_assets": [], + "manifest": "FILE_INVENTORY.csv" + }, + { + "manuscript_source": "paper/materials_jacobian_lens_supplement.tex", + "source_line": 472, + "kind": "table", + "label": "", + "caption": "\\textbf{Graph-identifiability audit summary.} All endpoints reuse the same 72 natural questions and archived states; no new prompt or Gemma forward pass was added.", + "scope": "matched result group(s)", + "groups": [ + "graphs" + ], + "artifact_prefixes": [ + "jlens_materials/experiments/cross-mechanism-outcome-2026-07-18/", + "jlens_materials/experiments/graph-isomorphism-generalization-2026-07-18/", + "jlens_materials/experiments/graph-topology-rigorous-2026-07-17/", + "jlens_materials/experiments/late-physics-representation-replication-2026-07-17/", + "jlens_materials/experiments/option-free-question-end-2026-07-18/", + "jlens_materials/experiments/relation-graph-visualization-2026-07-18/" + ], + "supporting_file_count": 144, + "analysis_commands": [ + "python scripts/analyze_graph_topology_rigorous.py", + "python scripts/plot_graph_generalization_audit.py", + "python scripts/plot_relation_graph_robustness.py", + "python scripts/validate_graph_generalization_audit.py" + ], + "publication_assets": [], + "manifest": "FILE_INVENTORY.csv" + }, + { + "manuscript_source": "paper/materials_jacobian_lens_supplement.tex", + "source_line": 491, + "kind": "table", + "label": "", + "caption": "\\textbf{Post hoc cross-phrasing mechanism graph from the original 50-prompt suite.} Every prompt selects one nearest neighbor in each other phrasing fold; 10\\% is balanced mechanism chance. This graph asks whether differently worded descriptions from the same family remain neighbors. It is complementary to, and less lexically controlled than, the signed-relation graph.", + "scope": "matched result group(s)", + "groups": [ + "readout", + "physical_equivalence", + "graphs" + ], + "artifact_prefixes": [ + "jlens_materials/experiments/answer-code-binding-2026-07-17/", + "jlens_materials/experiments/answer-scaffold-audit-2026-07-17/", + "jlens_materials/experiments/cross-mechanism-outcome-2026-07-18/", + "jlens_materials/experiments/gemma-multiscale-heldout-v1-2026-08-25/", + "jlens_materials/experiments/graph-isomorphism-generalization-2026-07-18/", + "jlens_materials/experiments/graph-topology-rigorous-2026-07-17/", + "jlens_materials/experiments/late-physics-representation-replication-2026-07-17/", + "jlens_materials/experiments/lexical-adversarial-representation-2026-07-17/", + "jlens_materials/experiments/materials-heldout-v1", + "jlens_materials/experiments/option-free-question-end-2026-07-18/", + "jlens_materials/experiments/relation-graph-visualization-2026-07-18/", + "jlens_materials/runs/" + ], + "supporting_file_count": 272, + "analysis_commands": [ + "python scripts/analyze_answer_code_binding.py", + "python scripts/analyze_answer_scaffold_audit.py", + "python scripts/analyze_gemma_multiscale_heldout_v1.py", + "python scripts/analyze_graph_topology_rigorous.py", + "python scripts/analyze_late_physics_replication.py", + "python scripts/analyze_lexical_adversarial_representation.py", + "python scripts/analyze_materials_heldout_v1.py", + "python scripts/plot_graph_generalization_audit.py", + "python scripts/plot_relation_graph_robustness.py", + "python scripts/validate_graph_generalization_audit.py" + ], + "publication_assets": [], + "manifest": "FILE_INVENTORY.csv" + }, + { + "manuscript_source": "paper/materials_jacobian_lens_supplement.tex", + "source_line": 527, + "kind": "figure", + "label": "fig:s1-reproducibility", + "caption": "\\textbf{Independent lens fits give nearly identical controlled ranks.} Each cell is the Spearman correlation across all 150 held-out prompt--concept pairs. The three Jacobian fits use independent 1,000-record WikiText samples yet agree at $\\rho=0.9978$--$0.9987$. Direct unembedding is much less correlated with their ordering ($\\rho\\approx0.21$). This figure establishes repeatability of the fitted measurement, not that its decoded terms are causal or universally more accurate.", + "scope": "matched result group(s)", + "groups": [ + "readout", + "physical_equivalence", + "graphs", + "causal_coordinate" + ], + "artifact_prefixes": [ + "jlens_materials/experiments/answer-code-binding-2026-07-17/", + "jlens_materials/experiments/answer-scaffold-audit-2026-07-17/", + "jlens_materials/experiments/causal-relational-comment3-audit-2026-09-06/", + "jlens_materials/experiments/causal-relational-knockout-2026-09-02/", + "jlens_materials/experiments/cross-mechanism-outcome-2026-07-18/", + "jlens_materials/experiments/gemma-multiscale-heldout-v1-2026-08-25/", + "jlens_materials/experiments/graph-isomorphism-generalization-2026-07-18/", + "jlens_materials/experiments/graph-topology-rigorous-2026-07-17/", + "jlens_materials/experiments/late-physics-representation-replication-2026-07-17/", + "jlens_materials/experiments/lexical-adversarial-representation-2026-07-17/", + "jlens_materials/experiments/materials-heldout-v1", + "jlens_materials/experiments/option-free-question-end-2026-07-18/", + "jlens_materials/experiments/relation-graph-visualization-2026-07-18/", + "jlens_materials/runs/" + ], + "supporting_file_count": 366, + "analysis_commands": [ + "python scripts/analyze_answer_code_binding.py", + "python scripts/analyze_answer_scaffold_audit.py", + "python scripts/analyze_causal_relational_knockout.py", + "python scripts/analyze_gemma_multiscale_heldout_v1.py", + "python scripts/analyze_graph_topology_rigorous.py", + "python scripts/analyze_late_physics_replication.py", + "python scripts/analyze_lexical_adversarial_representation.py", + "python scripts/analyze_materials_heldout_v1.py", + "python scripts/plot_graph_generalization_audit.py", + "python scripts/plot_relation_graph_robustness.py", + "python scripts/validate_causal_relational_knockout.py", + "python scripts/validate_graph_generalization_audit.py" + ], + "publication_assets": [ + { + "publication_path": "paper/figures/figure-s1-fit-reproducibility.pdf", + "sha256": "81c47c89e887f68597ae7812858faca864cf327aa78b1e467f40ebdc60932f76", + "matching_archived_files": [ + "jlens_materials/figures/materials-heldout-v1/figure-s1-fit-reproducibility.pdf" + ] + } + ], + "manifest": "FILE_INVENTORY.csv" + }, + { + "manuscript_source": "paper/materials_jacobian_lens_supplement.tex", + "source_line": 534, + "kind": "figure", + "label": "fig:s2-development-stream", + "caption": "\\textbf{Sample semantic stream.} The exact prompt was: ``After substantial plastic elongation, a metal bar separated along a surface filled with microscopic pits around second-phase particles. Neighboring pits had joined into larger depressions.'' The one-token continuation was ``The.'' The ribbons were selected from the unrestricted leading decoded tokens at each layer, so the plotting code was not given a word list. The scientifically meaningful term \\texttt{coalescence} appears naturally among those tokens, alongside generic discourse words that reveal the readout's noise. However, \\texttt{coalescence} had been declared separately as a tracked concept when this earlier development prompt was designed. This is therefore an illustrative one-lens development display, not an unanticipated held-out discovery and not a literal chain of thought.", + "scope": "matched result group(s)", + "groups": [ + "readout", + "geometry" + ], + "artifact_prefixes": [ + "jlens_materials/experiments/gemma-multiscale-heldout-v1-2026-08-25/", + "jlens_materials/experiments/materials-heldout-v1", + "jlens_materials/experiments/materials-heldout-v1_latent", + "jlens_materials/runs/" + ], + "supporting_file_count": 81, + "analysis_commands": [ + "python scripts/analyze_gemma_multiscale_heldout_v1.py", + "python scripts/analyze_materials_heldout_v1.py", + "python scripts/analyze_materials_latent_geometry.py", + "python scripts/build_final_paper_figures.py" + ], + "publication_assets": [ + { + "publication_path": "paper/figures/figure-s2-development-semantic-stream.pdf", + "sha256": "9f9c20c11b3eb6265efa5ca54f61d25c2f6f3c9615da64b3b808746dabaee821", + "matching_archived_files": [ + "jlens_materials/figures/materials-heldout-v1/figure-s2-development-semantic-stream.pdf" + ] + } + ], + "manifest": "FILE_INVENTORY.csv" + }, + { + "manuscript_source": "paper/materials_jacobian_lens_supplement.tex", + "source_line": 541, + "kind": "figure", + "label": "fig:s-targetfree-classification", + "caption": "\\textbf{Complete target-free cross-phrasing classification.} (A) Accuracy for Jacobian and direct candidate lists before and after removing exact or morphologically related prompt words. The dotted line is balanced chance; the dashed line is a classifier that sees TF--IDF prompt words. The strict-filter result remains above chance for both readouts but does not favor the Jacobian lens. (B) Prompt-level confusion matrix for the strict Jacobian configuration. Each row contains five independently written prompts. Boundary attack and surface oxidation are perfectly identified, whereas cleavage, ductile failure, high-temperature deformation, and notch resistance share vocabulary with neighboring mechanisms. The complete 50-row predictions, family accuracies, macro-F1 scores, paired correctness, and permutation outputs are stored in \\texttt{experiments/candidate-nonsteering-2026-07-16/}.", + "scope": "matched result group(s)", + "groups": [ + "target_free", + "physical_equivalence", + "steering" + ], + "artifact_prefixes": [ + "jlens_materials/experiments/answer-code-binding-2026-07-17/", + "jlens_materials/experiments/answer-scaffold-audit-2026-07-17/", + "jlens_materials/experiments/candidate-followups-2026-07-16/", + "jlens_materials/experiments/candidate-nonsteering-2026-07-16/", + "jlens_materials/experiments/late-physics-representation-replication-2026-07-17/", + "jlens_materials/experiments/lexical-adversarial-representation-2026-07-17/", + "jlens_materials/experiments/relational-grain-steering-v4", + "jlens_materials/experiments/semantic-steering-v3" + ], + "supporting_file_count": 150, + "analysis_commands": [ + "python scripts/analyze_answer_code_binding.py", + "python scripts/analyze_answer_scaffold_audit.py", + "python scripts/analyze_candidate_followups.py", + "python scripts/analyze_candidate_nonsteering.py", + "python scripts/analyze_late_physics_replication.py", + "python scripts/analyze_lexical_adversarial_representation.py", + "python scripts/analyze_relational_grain_steering_v4.py", + "python scripts/analyze_semantic_steering_v3.py" + ], + "publication_assets": [ + { + "publication_path": "paper/figures/semantic-classification.pdf", + "sha256": "ef77c61162bf146cace69711ae5407556f392027cad6be51c5493a0745c3ef59", + "matching_archived_files": [ + "jlens_materials/experiments/candidate-nonsteering-2026-07-16/figures/semantic-classification.pdf" + ] + } + ], + "manifest": "FILE_INVENTORY.csv" + }, + { + "manuscript_source": "paper/materials_jacobian_lens_supplement.tex", + "source_line": 552, + "kind": "figure", + "label": "fig:s-ontology-rsa", + "caption": "\\textbf{The frozen expert ontologies do not align significantly with Jacobian family geometry.} At every layer, pairwise distances among the ten family centroids are compared with (A) four broad response classes and (B) a multi-attribute materials ontology. Curves show Jacobian-transported and raw states; horizontal lines show target-layer and prompt-embedding baselines. The best multi-attribute Jacobian correlation is $\\rho=0.148$ at 58.5\\% depth, but the maximum-over-layers corrected value is $p=0.9695$. Prompt embeddings align more strongly at $\\rho=0.453$. The result does not support a claim that the lens recovered this expert ontology.", + "scope": "matched result group(s)", + "groups": [ + "target_free", + "geometry" + ], + "artifact_prefixes": [ + "jlens_materials/experiments/candidate-nonsteering-2026-07-16/", + "jlens_materials/experiments/materials-heldout-v1_latent", + "jlens_materials/runs/" + ], + "supporting_file_count": 78, + "analysis_commands": [ + "python scripts/analyze_candidate_nonsteering.py", + "python scripts/analyze_materials_latent_geometry.py", + "python scripts/build_final_paper_figures.py" + ], + "publication_assets": [ + { + "publication_path": "paper/figures/materials-ontology-rsa.pdf", + "sha256": "673f763e195d6581d6401bd61ea49e6b9544f14a9930cf7a5e9a67dd636337b5", + "matching_archived_files": [ + "jlens_materials/experiments/candidate-nonsteering-2026-07-16/figures/materials-ontology-rsa.pdf" + ] + } + ], + "manifest": "FILE_INVENTORY.csv" + }, + { + "manuscript_source": "paper/materials_jacobian_lens_supplement.tex", + "source_line": 559, + "kind": "figure", + "label": "fig:s-concept-roles", + "caption": "\\textbf{No universal entity--process--property depth ordering is detected.} Predeclared single-token concepts were assigned before calculation to physical entity/state, process/mechanism, or response/property roles. The panels show the distribution of each concept's strongest registered depth under Jacobian and direct readouts. The largest Jacobian separation among role medians is 8.2 percentage points and does not exceed a family-blocked role-label null ($p=0.1988$). Missing sustained events are retained separately rather than assigned an artificial late depth.", + "scope": "matched result group(s)", + "groups": [ + "readout", + "target_free", + "physical_equivalence" + ], + "artifact_prefixes": [ + "jlens_materials/experiments/answer-code-binding-2026-07-17/", + "jlens_materials/experiments/answer-scaffold-audit-2026-07-17/", + "jlens_materials/experiments/candidate-nonsteering-2026-07-16/", + "jlens_materials/experiments/gemma-multiscale-heldout-v1-2026-08-25/", + "jlens_materials/experiments/late-physics-representation-replication-2026-07-17/", + "jlens_materials/experiments/lexical-adversarial-representation-2026-07-17/", + "jlens_materials/experiments/materials-heldout-v1", + "jlens_materials/runs/" + ], + "supporting_file_count": 167, + "analysis_commands": [ + "python scripts/analyze_answer_code_binding.py", + "python scripts/analyze_answer_scaffold_audit.py", + "python scripts/analyze_candidate_nonsteering.py", + "python scripts/analyze_gemma_multiscale_heldout_v1.py", + "python scripts/analyze_late_physics_replication.py", + "python scripts/analyze_lexical_adversarial_representation.py", + "python scripts/analyze_materials_heldout_v1.py" + ], + "publication_assets": [ + { + "publication_path": "paper/figures/concept-role-depths.pdf", + "sha256": "a4168ba62f5feba46bf4c488ce2b1099a23769336a1bd5200feea427a042350f", + "matching_archived_files": [ + "jlens_materials/experiments/candidate-nonsteering-2026-07-16/figures/concept-role-depths.pdf" + ] + } + ], + "manifest": "FILE_INVENTORY.csv" + }, + { + "manuscript_source": "paper/materials_jacobian_lens_supplement.tex", + "source_line": 567, + "kind": "figure", + "label": "fig:s-lexical-adversarial", + "caption": "\\textbf{The broad physical-equivalence hypothesis fails in the first frozen cohort.} Each triplet contains an anchor, a physically equivalent paraphrase whose wording and units change, and a near-verbatim counterfactual whose physical answer reverses. Positive margin means the anchor is closer to the physical paraphrase than to the lexical counterfactual. The registered 38--92\\% band mean is negative under both readouts (panel A), and no family has a positive mean (panel B): wording dominates over the broad window. Panel C shows the unregistered late rise that motivated, but does not itself confirm, a new disjoint cohort with its late window fixed before execution. This retained negative result is why the manuscript claims a temporal shift rather than general physics-first geometry.", + "scope": "matched result group(s)", + "groups": [ + "geometry", + "physical_equivalence" + ], + "artifact_prefixes": [ + "jlens_materials/experiments/answer-code-binding-2026-07-17/", + "jlens_materials/experiments/answer-scaffold-audit-2026-07-17/", + "jlens_materials/experiments/late-physics-representation-replication-2026-07-17/", + "jlens_materials/experiments/lexical-adversarial-representation-2026-07-17/", + "jlens_materials/experiments/materials-heldout-v1_latent", + "jlens_materials/runs/" + ], + "supporting_file_count": 122, + "analysis_commands": [ + "python scripts/analyze_answer_code_binding.py", + "python scripts/analyze_answer_scaffold_audit.py", + "python scripts/analyze_late_physics_replication.py", + "python scripts/analyze_lexical_adversarial_representation.py", + "python scripts/analyze_materials_latent_geometry.py", + "python scripts/build_final_paper_figures.py" + ], + "publication_assets": [ + { + "publication_path": "paper/figures/lexical-adversarial-representation.pdf", + "sha256": "251abac14605833c435f2448cbb9b5017ec9ebef8cded2ddfc88a7868184ecd6", + "matching_archived_files": [ + "jlens_materials/experiments/lexical-adversarial-representation-2026-07-17/figures/lexical-adversarial-representation.pdf" + ] + } + ], + "manifest": "FILE_INVENTORY.csv" + }, + { + "manuscript_source": "paper/materials_jacobian_lens_supplement.tex", + "source_line": 575, + "kind": "figure", + "label": "fig:s-answer-scaffold", + "caption": "\\textbf{Explicit semantic answer scaffolding strongly amplifies the relation contrast.} The audit reuses stored states from the disjoint cohort and was specified only after both lexical-adversarial studies were inspected. (A) Before the answer choices, the late pre-choice state weakly separates the physically equivalent pair from the counterfactual under both readouts; after the prompt presents ordinary semantic choices such as \\texttt{higher}/\\texttt{lower}, the final-state contrast is much larger. Lines connect the same triplets and open symbols are cohort means. (B) The after-minus-before gain is positive in every mechanism family. (C) Ordinary semantic answer pairs are selected more reliably than the arbitrary A/B codes used in the separate falsification, both overall (87.5\\% versus 62.5\\%) and in most families; the dashed line is 50\\% forced-pair chance. Because position, suffix, and answer-word exposure change together, this post hoc comparison does not identify which factor causes the gain. It does show why the late physical-equivalence transition cannot be interpreted as an option-free constitutive variable.", + "scope": "matched result group(s)", + "groups": [ + "readout", + "physical_equivalence", + "relational" + ], + "artifact_prefixes": [ + "jlens_materials/experiments/answer-code-binding-2026-07-17/", + "jlens_materials/experiments/answer-scaffold-audit-2026-07-17/", + "jlens_materials/experiments/elicited-physics-abstraction-2026-07-18/", + "jlens_materials/experiments/equation-free-relational-physics-2026-08-21/", + "jlens_materials/experiments/gemma-equation-free-multiscale-2026-08-25/", + "jlens_materials/experiments/gemma-multiscale-heldout-v1-2026-08-25/", + "jlens_materials/experiments/gemma4-12b-equation-free-relational-v1/", + "jlens_materials/experiments/gemma4-31b-equation-free-layer-comparison-2026-08-23/", + "jlens_materials/experiments/gemma4-31b-equation-free-relational-layer40-v1/", + "jlens_materials/experiments/gemma4-31b-equation-free-relational-v1/", + "jlens_materials/experiments/late-physics-representation-replication-2026-07-17/", + "jlens_materials/experiments/lexical-adversarial-representation-2026-07-17/", + "jlens_materials/experiments/materials-heldout-v1", + "jlens_materials/experiments/neutral-anchored-relational-physics-2026-07-18/", + "jlens_materials/experiments/relational-contrast-confirmation-2026-07-18/", + "jlens_materials/runs/" + ], + "supporting_file_count": 271, + "analysis_commands": [ + "python scripts/analyze_answer_code_binding.py", + "python scripts/analyze_answer_scaffold_audit.py", + "python scripts/analyze_equation_free_relational_benchmark_amended.py", + "python scripts/analyze_gemma31b_equation_free_layers.py", + "python scripts/analyze_gemma_equation_free_multiscale.py", + "python scripts/analyze_gemma_multiscale_heldout_v1.py", + "python scripts/analyze_late_physics_replication.py", + "python scripts/analyze_lexical_adversarial_representation.py", + "python scripts/analyze_materials_heldout_v1.py", + "python scripts/analyze_neutral_anchored_relational_benchmark.py" + ], + "publication_assets": [ + { + "publication_path": "paper/figures/answer-scaffold-audit.pdf", + "sha256": "6e606c73c72ab695df6c9d7db1d7383cf5c63c8af1b7538fbead739adf83f046", + "matching_archived_files": [ + "jlens_materials/experiments/answer-scaffold-audit-2026-07-17/figures/answer-scaffold-audit.pdf" + ] + } + ], + "manifest": "FILE_INVENTORY.csv" + }, + { + "manuscript_source": "paper/materials_jacobian_lens_supplement.tex", + "source_line": 582, + "kind": "figure", + "label": "fig:s-answer-code", + "caption": "\\textbf{The arbitrary A/B answer-code falsification is inconclusive because its manipulation check fails.} The prospective protocol replaced meaningful answer words with arbitrary \\texttt{A}/\\texttt{B} labels and reversed their mapping across matched prompt variants. (A) Before the mapping appears, the physical-equivalence separation remains small and heterogeneous. (B) After the mapping, the required A/B contrast is also unstable across layers; the orange line is after mapping and gray is the pre-mapping reference. Shading in A and B is the frozen late window. (C) Family means do not show a consistent transition from pre-code physics separation (circles) to post-mapping A/B separation (squares). (D) Clean forced-pair code accuracy is only 62.5\\% overall, with 0\\% for obstacle spacing; the dashed line is 50\\% chance. Only 5 of 24 triplets formed a complete anchor--counterfactual--remapped pattern, and neither code token was the global top continuation in any of 72 prompts. The frozen geometric endpoint is therefore not interpretable as successful answer-independent binding. Retaining this failure prevents the semantic-scaffold result from being presented as if arbitrary output codes had already reproduced it.", + "scope": "matched result group(s)", + "groups": [ + "physical_equivalence" + ], + "artifact_prefixes": [ + "jlens_materials/experiments/answer-code-binding-2026-07-17/", + "jlens_materials/experiments/answer-scaffold-audit-2026-07-17/", + "jlens_materials/experiments/late-physics-representation-replication-2026-07-17/", + "jlens_materials/experiments/lexical-adversarial-representation-2026-07-17/" + ], + "supporting_file_count": 65, + "analysis_commands": [ + "python scripts/analyze_answer_code_binding.py", + "python scripts/analyze_answer_scaffold_audit.py", + "python scripts/analyze_late_physics_replication.py", + "python scripts/analyze_lexical_adversarial_representation.py" + ], + "publication_assets": [ + { + "publication_path": "paper/figures/answer-code-binding.pdf", + "sha256": "df36ec04638be489e87d95e7295c7926a471fa3c6c6288c519174588f213a848", + "matching_archived_files": [ + "jlens_materials/experiments/answer-code-binding-2026-07-17/figures/answer-code-binding.pdf" + ] + } + ], + "manifest": "FILE_INVENTORY.csv" + }, + { + "manuscript_source": "paper/materials_jacobian_lens_supplement.tex", + "source_line": 590, + "kind": "figure", + "label": "fig:s-mechanism-graph", + "caption": "\\textbf{Complementary graph evidence from the 50 held-out mechanism descriptions.} Every prompt selects one neighbor in each other phrasing fold. A correct edge stays inside the same mechanism family. (A) Layerwise same-family edge fraction for Jacobian and raw states, with word and character TF--IDF shown as horizontal lexical references. The gray band is the frozen 38--92\\% summary window and the dashed gray line is 10\\% chance. (B) After pairwise Jacobian similarity is residualized against word TF--IDF, character TF--IDF, token overlap, prompt length difference, and phrasing fold, the frozen-band graph still reaches 19.0\\% ($p=0.00026$), although raw states are similar. (C) For each family, filled symbols compare the most similar true-family target with the most lexically similar wrong-family target; positive values favor the physical family. Open symbols restrict the comparison to 126 lexical traps in which TF--IDF itself prefers the wrong family. RT, PS, NR, LD, OX, HT, DU, CY, CL, and BA denote the ten families defined in the main paper. (D) One frozen-band Jacobian adjacency matrix. Teal marks selected same-family edges and light gray marks cross-family edges; rows and columns are grouped only for display. This analysis shows statistically reliable cross-phrasing organization and resistance to selected lexical traps, but input-token and TF--IDF graphs remain competitive. It therefore does not establish a fully non-lexical materials ontology or a Jacobian-specific advantage.", + "scope": "matched result group(s)", + "groups": [ + "readout", + "target_free", + "physical_equivalence", + "graphs", + "causal_coordinate" + ], + "artifact_prefixes": [ + "jlens_materials/experiments/answer-code-binding-2026-07-17/", + "jlens_materials/experiments/answer-scaffold-audit-2026-07-17/", + "jlens_materials/experiments/candidate-nonsteering-2026-07-16/", + "jlens_materials/experiments/causal-relational-comment3-audit-2026-09-06/", + "jlens_materials/experiments/causal-relational-knockout-2026-09-02/", + "jlens_materials/experiments/cross-mechanism-outcome-2026-07-18/", + "jlens_materials/experiments/gemma-multiscale-heldout-v1-2026-08-25/", + "jlens_materials/experiments/graph-isomorphism-generalization-2026-07-18/", + "jlens_materials/experiments/graph-topology-rigorous-2026-07-17/", + "jlens_materials/experiments/late-physics-representation-replication-2026-07-17/", + "jlens_materials/experiments/lexical-adversarial-representation-2026-07-17/", + "jlens_materials/experiments/materials-heldout-v1", + "jlens_materials/experiments/option-free-question-end-2026-07-18/", + "jlens_materials/experiments/relation-graph-visualization-2026-07-18/", + "jlens_materials/runs/" + ], + "supporting_file_count": 387, + "analysis_commands": [ + "python scripts/analyze_answer_code_binding.py", + "python scripts/analyze_answer_scaffold_audit.py", + "python scripts/analyze_candidate_nonsteering.py", + "python scripts/analyze_causal_relational_knockout.py", + "python scripts/analyze_gemma_multiscale_heldout_v1.py", + "python scripts/analyze_graph_topology_rigorous.py", + "python scripts/analyze_late_physics_replication.py", + "python scripts/analyze_lexical_adversarial_representation.py", + "python scripts/analyze_materials_heldout_v1.py", + "python scripts/plot_graph_generalization_audit.py", + "python scripts/plot_relation_graph_robustness.py", + "python scripts/validate_causal_relational_knockout.py", + "python scripts/validate_graph_generalization_audit.py" + ], + "publication_assets": [ + { + "publication_path": "paper/figures/mechanism-graph-evidence.pdf", + "sha256": "f8dc7c4a233828d60a9404992d1fefacbf543963c8602063ef16e4a0e22ad31e", + "matching_archived_files": [ + "jlens_materials/experiments/graph-topology-rigorous-2026-07-17/figures/mechanism-graph-evidence.pdf" + ] + } + ], + "manifest": "FILE_INVENTORY.csv" + }, + { + "manuscript_source": "paper/materials_jacobian_lens_supplement.tex", + "source_line": 598, + "kind": "figure", + "label": "fig:s-relation-ranking", + "caption": "\\textbf{Exact-null and full-candidate checks for the 72-prompt signed-relation graph.} (A) The gray histogram contains all 46,656 balanced base-case assignments under the exact null. Each assignment preserves all three surface variants of every material case, including the counterfactual sign transform. Its mean same-direction edge fraction is 0.56; the observed Jacobian graph is 0.82 at the far right ($p=0.009623$). (B) Rather than evaluating only the selected edge, pairwise ROC--AUC uses every eligible same-direction and opposite-direction candidate. Jacobian, direct decoder-basis, and raw states reach AUC 0.82--0.83, while word TF--IDF, character TF--IDF, and answer order remain at or below 0.48. Error bars show the leave-one-mechanism-out range. The hatched numerical-direction oracle is a ceiling derived directly from the experimental numbers, not from Gemma. The later identifiability proof shows that, inside each supplied family, the registered physical pair label and this numerical-direction oracle are exactly the same target. The figure therefore establishes distributed comparative ranking, not an independent constitutive-law representation.", + "scope": "matched result group(s)", + "groups": [ + "physical_equivalence", + "graphs", + "relational" + ], + "artifact_prefixes": [ + "jlens_materials/experiments/answer-code-binding-2026-07-17/", + "jlens_materials/experiments/answer-scaffold-audit-2026-07-17/", + "jlens_materials/experiments/cross-mechanism-outcome-2026-07-18/", + "jlens_materials/experiments/elicited-physics-abstraction-2026-07-18/", + "jlens_materials/experiments/equation-free-relational-physics-2026-08-21/", + "jlens_materials/experiments/gemma-equation-free-multiscale-2026-08-25/", + "jlens_materials/experiments/gemma4-12b-equation-free-relational-v1/", + "jlens_materials/experiments/gemma4-31b-equation-free-layer-comparison-2026-08-23/", + "jlens_materials/experiments/gemma4-31b-equation-free-relational-layer40-v1/", + "jlens_materials/experiments/gemma4-31b-equation-free-relational-v1/", + "jlens_materials/experiments/graph-isomorphism-generalization-2026-07-18/", + "jlens_materials/experiments/graph-topology-rigorous-2026-07-17/", + "jlens_materials/experiments/late-physics-representation-replication-2026-07-17/", + "jlens_materials/experiments/lexical-adversarial-representation-2026-07-17/", + "jlens_materials/experiments/neutral-anchored-relational-physics-2026-07-18/", + "jlens_materials/experiments/option-free-question-end-2026-07-18/", + "jlens_materials/experiments/relation-graph-visualization-2026-07-18/", + "jlens_materials/experiments/relational-contrast-confirmation-2026-07-18/" + ], + "supporting_file_count": 316, + "analysis_commands": [ + "python scripts/analyze_answer_code_binding.py", + "python scripts/analyze_answer_scaffold_audit.py", + "python scripts/analyze_equation_free_relational_benchmark_amended.py", + "python scripts/analyze_gemma31b_equation_free_layers.py", + "python scripts/analyze_gemma_equation_free_multiscale.py", + "python scripts/analyze_graph_topology_rigorous.py", + "python scripts/analyze_late_physics_replication.py", + "python scripts/analyze_lexical_adversarial_representation.py", + "python scripts/analyze_neutral_anchored_relational_benchmark.py", + "python scripts/plot_graph_generalization_audit.py", + "python scripts/plot_relation_graph_robustness.py", + "python scripts/validate_graph_generalization_audit.py" + ], + "publication_assets": [ + { + "publication_path": "paper/figures/relation-ranking-robustness.pdf", + "sha256": "c9104aafa3600fcd6caafdb2199ce06d62032568097b32320a2743b77dd4b6ab", + "matching_archived_files": [ + "jlens_materials/experiments/graph-topology-rigorous-2026-07-17/figures/relation-ranking-robustness.pdf" + ] + } + ], + "manifest": "FILE_INVENTORY.csv" + }, + { + "manuscript_source": "paper/materials_jacobian_lens_supplement.tex", + "source_line": 606, + "kind": "figure", + "label": "fig:s-option-free-question", + "caption": "\\textbf{Frozen natural question-end robustness result.} The runner stops after the final token of the complete scientific question and adds no answer choice, answer word, A/B code, response instruction, or checkpoint marker. (A) Solid bars are selected-edge precision and hatched bars are full-candidate AUC in the 38--92\\% band. The dashed line is the exact case-preserving null mean. (B) Layerwise precision rises late for Jacobian, direct, and raw states. (C) Five of six mechanism-family AUCs exceed 0.5, with substantial heterogeneity. (D) The same 72 stems at three positions show why prompt boundary must be controlled: the natural question end is significant, the checkpoint-suffixed condition is null, and the answer mapping is strongly amplified. Suffix and token position change together, so the comparison does not isolate a unique cause. This is a frozen positional robustness result on an inspected cohort, not an independent replication.", + "scope": "matched result group(s)", + "groups": [ + "readout", + "physical_equivalence", + "graphs" + ], + "artifact_prefixes": [ + "jlens_materials/experiments/answer-code-binding-2026-07-17/", + "jlens_materials/experiments/answer-scaffold-audit-2026-07-17/", + "jlens_materials/experiments/cross-mechanism-outcome-2026-07-18/", + "jlens_materials/experiments/gemma-multiscale-heldout-v1-2026-08-25/", + "jlens_materials/experiments/graph-isomorphism-generalization-2026-07-18/", + "jlens_materials/experiments/graph-topology-rigorous-2026-07-17/", + "jlens_materials/experiments/late-physics-representation-replication-2026-07-17/", + "jlens_materials/experiments/lexical-adversarial-representation-2026-07-17/", + "jlens_materials/experiments/materials-heldout-v1", + "jlens_materials/experiments/option-free-question-end-2026-07-18/", + "jlens_materials/experiments/relation-graph-visualization-2026-07-18/", + "jlens_materials/runs/" + ], + "supporting_file_count": 272, + "analysis_commands": [ + "python scripts/analyze_answer_code_binding.py", + "python scripts/analyze_answer_scaffold_audit.py", + "python scripts/analyze_gemma_multiscale_heldout_v1.py", + "python scripts/analyze_graph_topology_rigorous.py", + "python scripts/analyze_late_physics_replication.py", + "python scripts/analyze_lexical_adversarial_representation.py", + "python scripts/analyze_materials_heldout_v1.py", + "python scripts/plot_graph_generalization_audit.py", + "python scripts/plot_relation_graph_robustness.py", + "python scripts/validate_graph_generalization_audit.py" + ], + "publication_assets": [ + { + "publication_path": "paper/figures/option-free-question-end.pdf", + "sha256": "d3d86f598e5900e6ccdae3bf71e48fa5bef3dc5da7170e554c3cbf9583e17aa1", + "matching_archived_files": [] + } + ], + "manifest": "FILE_INVENTORY.csv" + }, + { + "manuscript_source": "paper/materials_jacobian_lens_supplement.tex", + "source_line": 613, + "kind": "figure", + "label": "fig:s-cross-mechanism-graph", + "caption": "\\textbf{The natural-question relation graph does not generalize across governing mechanisms.} Each source is ranked against cases from the other five mechanisms. The counter-numeric subset keeps only mechanism pairs for which numerical increase/decrease maps to physical outcome with opposite orientation. Jacobian overall AUC is 0.513 and counter-numeric AUC is 0.471; neither passes the exact 20-assignment orientation null. Direct and raw states behave similarly. Pair and layer panels retain all mechanism combinations and show that a few positive pairs do not form one transferable physical-outcome coordinate.", + "scope": "matched result group(s)", + "groups": [ + "geometry", + "graphs" + ], + "artifact_prefixes": [ + "jlens_materials/experiments/cross-mechanism-outcome-2026-07-18/", + "jlens_materials/experiments/graph-isomorphism-generalization-2026-07-18/", + "jlens_materials/experiments/graph-topology-rigorous-2026-07-17/", + "jlens_materials/experiments/late-physics-representation-replication-2026-07-17/", + "jlens_materials/experiments/materials-heldout-v1_latent", + "jlens_materials/experiments/option-free-question-end-2026-07-18/", + "jlens_materials/experiments/relation-graph-visualization-2026-07-18/", + "jlens_materials/runs/" + ], + "supporting_file_count": 201, + "analysis_commands": [ + "python scripts/analyze_graph_topology_rigorous.py", + "python scripts/analyze_materials_latent_geometry.py", + "python scripts/build_final_paper_figures.py", + "python scripts/plot_graph_generalization_audit.py", + "python scripts/plot_relation_graph_robustness.py", + "python scripts/validate_graph_generalization_audit.py" + ], + "publication_assets": [ + { + "publication_path": "paper/figures/cross-mechanism-outcome.pdf", + "sha256": "db8670f52854f81597bb2fc95d82d5fa50c0c14b5c04a57b909f6d46c6c1e242", + "matching_archived_files": [] + } + ], + "manifest": "FILE_INVENTORY.csv" + }, + { + "manuscript_source": "paper/materials_jacobian_lens_supplement.tex", + "source_line": 620, + "kind": "figure", + "label": "fig:s-graph-model-tests", + "caption": "\\textbf{Held-out graph learning separates numerical direction from physical meaning.} The graph audit uses the same 72 natural questions and archived states; it creates no additional scientific prompts. Every panel holds out each entire mechanism in turn. (A) A graph isomorphism network (GIN) trained to identify absolute physical polarity fails (blue), as do the graphless and edge-shuffled controls. Numerical increase/decrease was explicitly included among the absolute-label node features, so its perfect purple readback is a positive-control check rather than graph discovery. The meaningful contrast is that graph structure cannot convert this supplied direction into physical polarity for an unseen law. Below-chance physical AUC can occur when a coherent two-way split receives the wrong unnamed orientation. (B) A separate polarity-free same/different relation GIN evaluates 72 ordered different-case, cross-variant pairs within each held-out 12-node mechanism graph. It excludes numerical direction and surface variant and does not consistently exceed its pair-MLP or shuffled-edge controls. (C) Without fitting a graph network, graded cosine similarity ranks same-direction pairs above opposite-direction pairs in all six mechanisms; Jacobian, direct, and raw states nearly coincide. Because same/different physical and numerical relation are the identical target inside each family, panel C demonstrates comparative structure rather than independent physics.", + "scope": "matched result group(s)", + "groups": [ + "graphs" + ], + "artifact_prefixes": [ + "jlens_materials/experiments/cross-mechanism-outcome-2026-07-18/", + "jlens_materials/experiments/graph-isomorphism-generalization-2026-07-18/", + "jlens_materials/experiments/graph-topology-rigorous-2026-07-17/", + "jlens_materials/experiments/late-physics-representation-replication-2026-07-17/", + "jlens_materials/experiments/option-free-question-end-2026-07-18/", + "jlens_materials/experiments/relation-graph-visualization-2026-07-18/" + ], + "supporting_file_count": 144, + "analysis_commands": [ + "python scripts/analyze_graph_topology_rigorous.py", + "python scripts/plot_graph_generalization_audit.py", + "python scripts/plot_relation_graph_robustness.py", + "python scripts/validate_graph_generalization_audit.py" + ], + "publication_assets": [ + { + "publication_path": "paper/figures/graph-generalization-model-tests.pdf", + "sha256": "27454398429d9d97a511b67d5370302b2fa3547db50e01712bf4adbcdcf75e0b", + "matching_archived_files": [ + "jlens_materials/experiments/graph-isomorphism-generalization-2026-07-18/figures/graph-generalization-model-tests.pdf" + ] + } + ], + "manifest": "FILE_INVENTORY.csv" + }, + { + "manuscript_source": "paper/materials_jacobian_lens_supplement.tex", + "source_line": 627, + "kind": "figure", + "label": "fig:s-graph-partition-tests", + "caption": "\\textbf{Community results depend on retained graph density and do not yield a unique transferable physical partition.} Spectral clustering is label-blind and scored with adjusted Rand index (ARI), which is unchanged when the two community names are swapped. (A) The blue line is the six-mechanism Jacobian mean; gray lines are individual mechanisms. Retaining all graded candidate similarities raises mean ARI from 0.267 to 0.674, showing that the top-edge visualization discards information and that mechanisms are heterogeneous. (B) In the weighted top-one graph, direct states match Jacobian while raw-state behavior varies by family; positive ARI is therefore not lens-specific. (C) Bars show six-mechanism mean ARI and open circles show mechanisms after exhaustively scoring all 462 balanced divisions. The registered split is not the uniquely preferred partition, either on the complete graph or when counterfactuals are held out. Black marks summarize the surface-block-preserving null. The panels distinguish a favorable pairwise or spectral projection from a stable global partition.", + "scope": "matched result group(s)", + "groups": [ + "graphs" + ], + "artifact_prefixes": [ + "jlens_materials/experiments/cross-mechanism-outcome-2026-07-18/", + "jlens_materials/experiments/graph-isomorphism-generalization-2026-07-18/", + "jlens_materials/experiments/graph-topology-rigorous-2026-07-17/", + "jlens_materials/experiments/late-physics-representation-replication-2026-07-17/", + "jlens_materials/experiments/option-free-question-end-2026-07-18/", + "jlens_materials/experiments/relation-graph-visualization-2026-07-18/" + ], + "supporting_file_count": 144, + "analysis_commands": [ + "python scripts/analyze_graph_topology_rigorous.py", + "python scripts/plot_graph_generalization_audit.py", + "python scripts/plot_relation_graph_robustness.py", + "python scripts/validate_graph_generalization_audit.py" + ], + "publication_assets": [ + { + "publication_path": "paper/figures/graph-generalization-partition-tests.pdf", + "sha256": "58c87377948c6936c4dce368886ed2210094fa65450b1d3e2ebe28b1e6d3862c", + "matching_archived_files": [ + "jlens_materials/experiments/graph-isomorphism-generalization-2026-07-18/figures/graph-generalization-partition-tests.pdf" + ] + } + ], + "manifest": "FILE_INVENTORY.csv" + }, + { + "manuscript_source": "paper/materials_jacobian_lens_supplement.tex", + "source_line": 634, + "kind": "figure", + "label": "fig:s-graph-network-examples", + "caption": "\\textbf{Representative weighted mechanism graphs show what the statistical audit evaluates.} Nodes are complete option-free materials questions, not words. Each mechanism contains four material cases rendered as anchor (circle), physical paraphrase (square), and lexical counterfactual (diamond); edge width encodes graded Jacobian-state similarity. The node numbers identify the twelve prompts and the two colors identify the registered binary directions. (A) Particle fraction has a clearly recovered split, (B) crosslink density is only partly separated, and (C) Orowan spacing is not recovered by the exhaustive partition endpoint. Direct-response and inverse-response mechanisms can nevertheless display similar two-sided form while assigning opposite physical meanings to the sides. That symmetry is why an unlabeled graph may transfer comparative structure without identifying which side means that the material property rises. These examples visualize archived measurements; they are not reasoning chains or additional statistical observations.", + "scope": "matched result group(s)", + "groups": [ + "geometry", + "physical_equivalence", + "graphs" + ], + "artifact_prefixes": [ + "jlens_materials/experiments/answer-code-binding-2026-07-17/", + "jlens_materials/experiments/answer-scaffold-audit-2026-07-17/", + "jlens_materials/experiments/cross-mechanism-outcome-2026-07-18/", + "jlens_materials/experiments/graph-isomorphism-generalization-2026-07-18/", + "jlens_materials/experiments/graph-topology-rigorous-2026-07-17/", + "jlens_materials/experiments/late-physics-representation-replication-2026-07-17/", + "jlens_materials/experiments/lexical-adversarial-representation-2026-07-17/", + "jlens_materials/experiments/materials-heldout-v1_latent", + "jlens_materials/experiments/option-free-question-end-2026-07-18/", + "jlens_materials/experiments/relation-graph-visualization-2026-07-18/", + "jlens_materials/runs/" + ], + "supporting_file_count": 248, + "analysis_commands": [ + "python scripts/analyze_answer_code_binding.py", + "python scripts/analyze_answer_scaffold_audit.py", + "python scripts/analyze_graph_topology_rigorous.py", + "python scripts/analyze_late_physics_replication.py", + "python scripts/analyze_lexical_adversarial_representation.py", + "python scripts/analyze_materials_latent_geometry.py", + "python scripts/build_final_paper_figures.py", + "python scripts/plot_graph_generalization_audit.py", + "python scripts/plot_relation_graph_robustness.py", + "python scripts/validate_graph_generalization_audit.py" + ], + "publication_assets": [ + { + "publication_path": "paper/figures/graph-generalization-network-examples.pdf", + "sha256": "6e23bece2bc73398b225861380b9fb6acf626eb3aec85c51d5bd12d7ffe7f7b8", + "matching_archived_files": [ + "jlens_materials/experiments/graph-isomorphism-generalization-2026-07-18/figures/graph-generalization-network-examples.pdf" + ] + } + ], + "manifest": "FILE_INVENTORY.csv" + }, + { + "manuscript_source": "paper/materials_jacobian_lens_supplement.tex", + "source_line": 641, + "kind": "figure", + "label": "fig:s-relation-atlas", + "caption": "\\textbf{All 25 actual option-free relation graphs.} Each panel contains the same 72 natural-question nodes in a fixed layout and all 144 selected Jacobian edges at one registered layer. The six compact islands, ordered Orowan, porosity, pearlite, dislocation, particles, and crosslinks, are supplied mechanism strata rather than discovered communities. Teal edges preserve the registered binary relation and coral edges do not. Circles, squares, and triangles denote anchor, physical paraphrase, and near-verbatim counterfactual; filled and open nodes denote the two registered orientations. Within each family this physical relation is exactly aliased with numerical direction, so the late transition is comparative rather than independently constitutive. Each panel prints the transformer layer, normalized depth, and relation-preserving edge percentage. 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Teal preserves the registered within-family relation and coral does not. The display reveals recurring mechanism-conditioned comparative subgraphs, but the six islands are imposed by the candidate rule and physical relation is exactly aliased with numerical direction in this cohort. It is therefore a large, data-supported visualization of measured topology rather than evidence for a discovered universal materials network. The complete 3,600 layer-edge rows and 391-row persistence table are machine readable.", + "scope": "matched result group(s)", + "groups": [ + "graphs" + ], + "artifact_prefixes": [ + "jlens_materials/experiments/cross-mechanism-outcome-2026-07-18/", + "jlens_materials/experiments/graph-isomorphism-generalization-2026-07-18/", + "jlens_materials/experiments/graph-topology-rigorous-2026-07-17/", + "jlens_materials/experiments/late-physics-representation-replication-2026-07-17/", + "jlens_materials/experiments/option-free-question-end-2026-07-18/", + "jlens_materials/experiments/relation-graph-visualization-2026-07-18/" + ], + "supporting_file_count": 144, + "analysis_commands": [ + "python scripts/analyze_graph_topology_rigorous.py", + "python scripts/plot_graph_generalization_audit.py", + "python scripts/plot_relation_graph_robustness.py", + "python scripts/validate_graph_generalization_audit.py" + ], + "publication_assets": [ + { + "publication_path": "paper/figures/relation-graph-persistence.pdf", + "sha256": "3f8d278a856207707d7b0b612de9c9146059d28b44576033b35e75a5a4c8a4c1", + "matching_archived_files": [ + "jlens_materials/experiments/relation-graph-visualization-2026-07-18/figures/relation-graph-persistence.pdf" + ] + } + ], + "manifest": "FILE_INVENTORY.csv" + }, + { + "manuscript_source": "paper/materials_jacobian_lens_supplement.tex", + "source_line": 655, + "kind": "figure", + "label": "fig:s-multitoken", + "caption": "\\textbf{Complete-word scoring gives mixed multi-token evidence.} The exact two-piece contrast is \\texttt{transgranular} versus \\texttt{intergranular}; the exact three-piece contrast is \\texttt{martensite} versus \\texttt{bainite}. The first piece is scored from the intermediate readout and remaining pieces by teacher forcing through unchanged Gemma. Family means favor the target under all three Jacobian fits and exceed direct unembedding, but only 7 of 10 prompts are positive, failing the frozen 8-of-10 breadth gate. 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The purple point marks the development-selected layer 34 used for the frozen confirmation; the remaining depths are descriptive. The occasional high early values and small 12-law sample preclude a claim of first emergence, whereas the uninterrupted late plateau supports the narrower conclusion of late-stage stabilization. This sweep does not use the final 60-law cohort.", + "scope": "matched result group(s)", + "groups": [ + "geometry", + "physical_equivalence", + "relational", + "steering" + ], + "artifact_prefixes": [ + "jlens_materials/experiments/answer-code-binding-2026-07-17/", + "jlens_materials/experiments/answer-scaffold-audit-2026-07-17/", + "jlens_materials/experiments/candidate-followups-2026-07-16/", + "jlens_materials/experiments/elicited-physics-abstraction-2026-07-18/", + "jlens_materials/experiments/equation-free-relational-physics-2026-08-21/", + "jlens_materials/experiments/gemma-equation-free-multiscale-2026-08-25/", + "jlens_materials/experiments/gemma4-12b-equation-free-relational-v1/", + "jlens_materials/experiments/gemma4-31b-equation-free-layer-comparison-2026-08-23/", + "jlens_materials/experiments/gemma4-31b-equation-free-relational-layer40-v1/", + "jlens_materials/experiments/gemma4-31b-equation-free-relational-v1/", + "jlens_materials/experiments/late-physics-representation-replication-2026-07-17/", + "jlens_materials/experiments/lexical-adversarial-representation-2026-07-17/", + "jlens_materials/experiments/materials-heldout-v1_latent", + "jlens_materials/experiments/neutral-anchored-relational-physics-2026-07-18/", + "jlens_materials/experiments/relational-contrast-confirmation-2026-07-18/", + "jlens_materials/experiments/relational-grain-steering-v4", + "jlens_materials/experiments/semantic-steering-v3", + "jlens_materials/runs/" + ], + "supporting_file_count": 311, + "analysis_commands": [ + "python scripts/analyze_answer_code_binding.py", + "python scripts/analyze_answer_scaffold_audit.py", + "python scripts/analyze_candidate_followups.py", + "python scripts/analyze_equation_free_relational_benchmark_amended.py", + "python scripts/analyze_gemma31b_equation_free_layers.py", + "python scripts/analyze_gemma_equation_free_multiscale.py", + "python scripts/analyze_late_physics_replication.py", + "python scripts/analyze_lexical_adversarial_representation.py", + "python scripts/analyze_materials_latent_geometry.py", + "python scripts/analyze_neutral_anchored_relational_benchmark.py", + "python scripts/analyze_relational_grain_steering_v4.py", + "python scripts/analyze_semantic_steering_v3.py", + "python scripts/build_final_paper_figures.py" + ], + "publication_assets": [ + { + "publication_path": "paper/figures/relational-contrast-layer-sweep.pdf", + "sha256": "276df770f2a77441119fe550306bbfcc3f136624ec8cacb1d20d813b88ca197e", + "matching_archived_files": [] + } + ], + "manifest": "FILE_INVENTORY.csv" + }, + { + "manuscript_source": "paper/materials_jacobian_lens_supplement.tex", + "source_line": 713, + "kind": "figure", + "label": "fig:s-relational-physics", + "caption": "\\textbf{Complete neutral-anchored relational summary.} (A) All inverse, neutral, and direct laws relative to the calibration-neutral median; black segments are category medians. (B) Every law ordered by its neutral-scaled matched contrast. (C) Hidden-state and output-logit relational AUCs compared with word and character TF--IDF controls. (D) Equation-surface transfer, threshold-free separation, and calibrated-cut accuracy. The figure summarizes all 60 laws; the generated inventories below print every law-level value and every one of the 480 matched comparisons.", + "scope": "matched result group(s)", + "groups": [ + "relational" + ], + "artifact_prefixes": [ + "jlens_materials/experiments/elicited-physics-abstraction-2026-07-18/", + "jlens_materials/experiments/equation-free-relational-physics-2026-08-21/", + "jlens_materials/experiments/gemma-equation-free-multiscale-2026-08-25/", + "jlens_materials/experiments/gemma4-12b-equation-free-relational-v1/", + "jlens_materials/experiments/gemma4-31b-equation-free-layer-comparison-2026-08-23/", + "jlens_materials/experiments/gemma4-31b-equation-free-relational-layer40-v1/", + "jlens_materials/experiments/gemma4-31b-equation-free-relational-v1/", + "jlens_materials/experiments/neutral-anchored-relational-physics-2026-07-18/", + "jlens_materials/experiments/relational-contrast-confirmation-2026-07-18/" + ], + "supporting_file_count": 125, + "analysis_commands": [ + "python scripts/analyze_equation_free_relational_benchmark_amended.py", + "python scripts/analyze_gemma31b_equation_free_layers.py", + "python scripts/analyze_gemma_equation_free_multiscale.py", + "python scripts/analyze_neutral_anchored_relational_benchmark.py" + ], + "publication_assets": [ + { + "publication_path": "paper/figures/neutral-anchored-relational-physics.pdf", + "sha256": "bd7f3039b31997f625f0195a386c4d0454dc0c9d840c3319d7cc6d9fa11d17a6", + "matching_archived_files": [ + "jlens_materials/experiments/neutral-anchored-relational-physics-2026-07-18/figures/neutral-anchored-relational-physics.pdf" + ] + } + ], + "manifest": "FILE_INVENTORY.csv" + }, + { + "manuscript_source": "paper/materials_jacobian_lens_supplement.tex", + "source_line": 725, + "kind": "longtable", + "label": "tab:s-equation-free-examples", + "caption": "\\hlyellow{\\textbf{Representative supplied-equation and equation-free prompt transformations.} The supplied-equation column gives the law-specific core added to the common instruction to infer monotonicity, determine numerical direction, and compose the two signs. The equation-free column prints the exact canonical surface-A prompt. Results summarize all 16 controlled variants of each law, not only the displayed string. The complete manifests retain all 960 exact prompts in each format.}", + "scope": "matched result group(s)", + "groups": [ + "readout", + "relational" + ], + "artifact_prefixes": [ + "jlens_materials/experiments/elicited-physics-abstraction-2026-07-18/", + "jlens_materials/experiments/equation-free-relational-physics-2026-08-21/", + "jlens_materials/experiments/gemma-equation-free-multiscale-2026-08-25/", + "jlens_materials/experiments/gemma-multiscale-heldout-v1-2026-08-25/", + "jlens_materials/experiments/gemma4-12b-equation-free-relational-v1/", + "jlens_materials/experiments/gemma4-31b-equation-free-layer-comparison-2026-08-23/", + "jlens_materials/experiments/gemma4-31b-equation-free-relational-layer40-v1/", + "jlens_materials/experiments/gemma4-31b-equation-free-relational-v1/", + "jlens_materials/experiments/materials-heldout-v1", + "jlens_materials/experiments/neutral-anchored-relational-physics-2026-07-18/", + "jlens_materials/experiments/relational-contrast-confirmation-2026-07-18/", + "jlens_materials/runs/" + ], + "supporting_file_count": 206, + "analysis_commands": [ + "python scripts/analyze_equation_free_relational_benchmark_amended.py", + "python scripts/analyze_gemma31b_equation_free_layers.py", + "python scripts/analyze_gemma_equation_free_multiscale.py", + "python scripts/analyze_gemma_multiscale_heldout_v1.py", + "python scripts/analyze_materials_heldout_v1.py", + "python scripts/analyze_neutral_anchored_relational_benchmark.py" + ], + "publication_assets": [], + "manifest": "FILE_INVENTORY.csv" + }, + { + "manuscript_source": "paper/materials_jacobian_lens_supplement.tex", + "source_line": 774, + "kind": "table", + "label": "tab:s-v3-family", + "caption": "Complete \\hlyellow{internally prespecified} broad-screen family verdicts. Parentheses are 95\\% condition-bootstrap intervals. ``Valid'' is the fraction of matched rows whose global top beginning is one of the two registered answers.", + "scope": "matched result group(s)", + "groups": [ + "steering" + ], + "artifact_prefixes": [ + "jlens_materials/experiments/candidate-followups-2026-07-16/", + "jlens_materials/experiments/relational-grain-steering-v4", + "jlens_materials/experiments/semantic-steering-v3" + ], + "supporting_file_count": 64, + "analysis_commands": [ + "python scripts/analyze_candidate_followups.py", + "python scripts/analyze_relational_grain_steering_v4.py", + "python scripts/analyze_semantic_steering_v3.py" + ], + "publication_assets": [], + "manifest": "FILE_INVENTORY.csv" + }, + { + "manuscript_source": "paper/materials_jacobian_lens_supplement.tex", + "source_line": 792, + "kind": "figure", + "label": "fig:s3-v3-specificity", + "caption": "\\textbf{Layer-matched mechanism specificity.} Rows identify the scientific answer being scored; columns identify the mechanism direction applied at that row's frozen layer. The diagonal is the scientifically matched direction. Corrosion is strongly selective ($+0.904$ versus $-0.721$ transformation and $+0.254$ grain directions). Transformation is $+0.282$ on its diagonal versus $-0.076$ and $-0.235$. Grain size is $+0.166$ versus $+0.024$ and $-0.153$. This comparison asks whether any technical direction changes any answer, or whether the sign is mechanism dependent.", + "scope": "matched result group(s)", + "groups": [ + "steering" + ], + "artifact_prefixes": [ + "jlens_materials/experiments/candidate-followups-2026-07-16/", + "jlens_materials/experiments/relational-grain-steering-v4", + "jlens_materials/experiments/semantic-steering-v3" + ], + "supporting_file_count": 64, + "analysis_commands": [ + "python scripts/analyze_candidate_followups.py", + "python scripts/analyze_relational_grain_steering_v4.py", + "python scripts/analyze_semantic_steering_v3.py" + ], + "publication_assets": [ + { + "publication_path": "paper/figures/cross_mechanism_specificity.pdf", + "sha256": "c806f4c8b36b4efcecc297af626ddc666647d0fdff58bd7ef9b23aece822b357", + "matching_archived_files": [ + "jlens_materials/figures/semantic-steering-v3/cross_mechanism_specificity.pdf" + ] + } + ], + "manifest": "FILE_INVENTORY.csv" + }, + { + "manuscript_source": "paper/materials_jacobian_lens_supplement.tex", + "source_line": 803, + "kind": "figure", + "label": "fig:s4-v3-aligned", + "caption": "\\textbf{Hypothesis-generating correct-answer alignment.} Signs are reoriented after execution so that positive denotes movement toward the frozen correct answer. Circles are determinate physical conditions; diamonds and bars are mean and 95\\% intervals. Grain size shows the most consistent context dependence, motivating a new \\hlyellow{internally prespecified} refinement/coarsening cohort. Because this analysis was chosen after the sign pattern was observed, it is not confirmatory evidence by itself.", + "scope": "matched result group(s)", + "groups": [ + "steering" + ], + "artifact_prefixes": [ + "jlens_materials/experiments/candidate-followups-2026-07-16/", + "jlens_materials/experiments/relational-grain-steering-v4", + "jlens_materials/experiments/semantic-steering-v3" + ], + "supporting_file_count": 64, + "analysis_commands": [ + "python scripts/analyze_candidate_followups.py", + "python scripts/analyze_relational_grain_steering_v4.py", + "python scripts/analyze_semantic_steering_v3.py" + ], + "publication_assets": [ + { + "publication_path": "paper/figures/expected_answer_alignment_secondary.pdf", + "sha256": "8a6ffce0e2067ddd8a6632befd4cad863b73063e3296b410f3ab6193484840e9", + "matching_archived_files": [ + "jlens_materials/figures/semantic-steering-v3/expected_answer_alignment_secondary.pdf" + ] + } + ], + "manifest": "FILE_INVENTORY.csv" + }, + { + "manuscript_source": "paper/materials_jacobian_lens_supplement.tex", + "source_line": 828, + "kind": "figure", + "label": "fig:s-steering-transfer", + "caption": "\\textbf{The frozen grain direction does not generalize to new answer words or the inverse Hall--Petch regime.} The left cohort uses six new conventional polycrystals and replaces \\texttt{higher}/\\texttt{lower} by \\texttt{increase}/\\texttt{decrease}; only 7 of 12 condition signs and 1 of 6 complete refinement/coarsening pairs are correct. The right cohort explicitly states an inverse Hall--Petch nanocrystalline regime; only 6 of 12 condition signs and 0 of 6 pairs are correct. Across both cohorts, 23 of 24 conditions move toward the designated positive answer word. The unchanged vector therefore behaves primarily as an answer-vocabulary direction outside its original confirmation format.", + "scope": "matched result group(s)", + "groups": [ + "steering" + ], + "artifact_prefixes": [ + "jlens_materials/experiments/candidate-followups-2026-07-16/", + "jlens_materials/experiments/relational-grain-steering-v4", + "jlens_materials/experiments/semantic-steering-v3" + ], + "supporting_file_count": 64, + "analysis_commands": [ + "python scripts/analyze_candidate_followups.py", + "python scripts/analyze_relational_grain_steering_v4.py", + "python scripts/analyze_semantic_steering_v3.py" + ], + "publication_assets": [ + { + "publication_path": "paper/figures/candidate-new-cohorts.pdf", + "sha256": "5c435c12e8d8f386237690dc2fc105de692a6e3275d4722f8d43a404fabc3502", + "matching_archived_files": [ + "jlens_materials/experiments/candidate-followups-2026-07-16/figures/candidate-new-cohorts.pdf" + ] + } + ], + "manifest": "FILE_INVENTORY.csv" + }, + { + "manuscript_source": "paper/materials_jacobian_lens_supplement.tex", + "source_line": 862, + "kind": "figure", + "label": "fig:s-activation-patching", + "caption": "\\textbf{Complete exploratory activation-patching result.} (A) Layerwise movement toward the reversed-relation answer. The matched same-material and transferable cross-material reverse states become strongly causal late in the network. Relation-preserving and answer-order-only directions are exactly distance matched and remain much smaller. The legend is placed below the panels so it does not obscure the late-layer curves. (B) Same-material reverse shifts for each alloy pair; all six show the same late-layer transition. (C) Comparison between causal patching and vocabulary-readable relation separation. The correlation supports broad stage agreement but, together with onset and first-difference diagnostics, does not identify the exact causal layer.", + "scope": "matched result group(s)", + "groups": [ + "physical_equivalence", + "patching", + "causal_coordinate" + ], + "artifact_prefixes": [ + "jlens_materials/experiments/answer-code-binding-2026-07-17/", + "jlens_materials/experiments/answer-scaffold-audit-2026-07-17/", + "jlens_materials/experiments/candidate-activation-patching-2026-07-16/", + "jlens_materials/experiments/causal-relational-comment3-audit-2026-09-06/", + "jlens_materials/experiments/causal-relational-knockout-2026-09-02/", + "jlens_materials/experiments/cross-mechanism-activation-patching-2026-07-18/", + "jlens_materials/experiments/late-physics-representation-replication-2026-07-17/", + "jlens_materials/experiments/lexical-adversarial-representation-2026-07-17/" + ], + "supporting_file_count": 201, + "analysis_commands": [ + "python scripts/analyze_answer_code_binding.py", + "python scripts/analyze_answer_scaffold_audit.py", + "python scripts/analyze_causal_relational_knockout.py", + "python scripts/analyze_counterfactual_activation_patching.py", + "python scripts/analyze_cross_mechanism_activation_patching.py", + "python scripts/analyze_late_physics_replication.py", + "python scripts/analyze_lexical_adversarial_representation.py", + "python scripts/validate_causal_relational_knockout.py" + ], + "publication_assets": [ + { + "publication_path": "paper/figures/counterfactual-activation-patching.pdf", + "sha256": "04b3fed9c3fd6f8e9cb28a62494e10c0adbe855c8130395007fd17a98dd10060", + "matching_archived_files": [] + } + ], + "manifest": "FILE_INVENTORY.csv" + }, + { + "manuscript_source": "paper/materials_jacobian_lens_supplement.tex", + "source_line": 878, + "kind": "figure", + "label": "fig:s-cross-mechanism-patching", + "caption": "\\textbf{Complete option-free cross-mechanism activation-patching result.} (A) Physical-outcome and numerical-direction contrasts for all 15 unordered mechanism pairs. (B) Pair-bootstrap intervals for all pairs and the three prespecified transfer subsets. (C) Both features emerge late, but numerical direction becomes stronger. (D) Donor-family effects reveal the failed breadth gate: four physical-outcome means are positive, while Orowan and porosity have the wrong physical sign. Because transfer survives different answer words, it is not simple token copying. Because numerical direction is stronger and family signs are heterogeneous, it is not a universal materials-relation state.", + "scope": "matched result group(s)", + "groups": [ + "physical_equivalence", + "patching" + ], + "artifact_prefixes": [ + "jlens_materials/experiments/answer-code-binding-2026-07-17/", + "jlens_materials/experiments/answer-scaffold-audit-2026-07-17/", + "jlens_materials/experiments/candidate-activation-patching-2026-07-16/", + "jlens_materials/experiments/cross-mechanism-activation-patching-2026-07-18/", + "jlens_materials/experiments/late-physics-representation-replication-2026-07-17/", + "jlens_materials/experiments/lexical-adversarial-representation-2026-07-17/" + ], + "supporting_file_count": 107, + "analysis_commands": [ + "python scripts/analyze_answer_code_binding.py", + "python scripts/analyze_answer_scaffold_audit.py", + "python scripts/analyze_counterfactual_activation_patching.py", + "python scripts/analyze_cross_mechanism_activation_patching.py", + "python scripts/analyze_late_physics_replication.py", + "python scripts/analyze_lexical_adversarial_representation.py" + ], + "publication_assets": [ + { + "publication_path": "paper/figures/cross-mechanism-activation-patching.pdf", + "sha256": "d6f8ca0f2a576248a7ac3d325f024c69b9782a2fe6dbe045dfde7e5d7b98d093", + "matching_archived_files": [] + } + ], + "manifest": "FILE_INVENTORY.csv" + }, + { + "manuscript_source": "paper/materials_jacobian_lens_supplement.tex", + "source_line": 902, + "kind": "longtable", + "label": "tab:s-causal-law-inventory", + "caption": "Complete 24-law target inventory. ``Direct'' means that increasing the control increases the response; ``inverse'' means that it decreases the response. Exact systems, endpoint values, and all eight prompt variants per law are in the accompanying prompt files.", + "scope": "matched result group(s)", + "groups": [ + "causal_coordinate" + ], + "artifact_prefixes": [ + "jlens_materials/experiments/causal-relational-comment3-audit-2026-09-06/", + "jlens_materials/experiments/causal-relational-knockout-2026-09-02/" + ], + "supporting_file_count": 94, + "analysis_commands": [ + "python scripts/analyze_causal_relational_knockout.py", + "python scripts/validate_causal_relational_knockout.py" + ], + "publication_assets": [], + "manifest": "FILE_INVENTORY.csv" + }, + { + "manuscript_source": "paper/materials_jacobian_lens_supplement.tex", + "source_line": 961, + "kind": "table", + "label": "tab:s-causal-overlap-sensitivity", + "caption": "\\hlyellow{Retrospective causal sensitivity after excluding three repeated development relations. Each scale retains 21 relations and 168 prompts. Margin effects subtract the mean of the six equal-length activation-derived controls; brackets are 95\\% relation-bootstrap intervals. The accuracy p-values use paired relation-level accuracy differences.}", + "scope": "matched result group(s)", + "groups": [ + "geometry", + "causal_coordinate" + ], + "artifact_prefixes": [ + "jlens_materials/experiments/causal-relational-comment3-audit-2026-09-06/", + "jlens_materials/experiments/causal-relational-knockout-2026-09-02/", + "jlens_materials/experiments/materials-heldout-v1_latent", + "jlens_materials/runs/" + ], + "supporting_file_count": 151, + "analysis_commands": [ + "python scripts/analyze_causal_relational_knockout.py", + "python scripts/analyze_materials_latent_geometry.py", + "python scripts/build_final_paper_figures.py", + "python scripts/validate_causal_relational_knockout.py" + ], + "publication_assets": [], + "manifest": "FILE_INVENTORY.csv" + }, + { + "manuscript_source": "paper/materials_jacobian_lens_supplement.tex", + "source_line": 989, + "kind": "figure", + "label": "fig:s-causal-law-audit", + "caption": "\\hlyellow{\\textbf{The neutralization effect is broad across target relations.} Each line is one law and connects the mean effect of six equal-length activation-matched controls to the effect of neutralizing the frozen relational coordinate. The expected physical-minus-control sign occurs in 23/24 laws at 4B and 24/24 at 12B and 31B. The only 4B exception, creep-rupture life versus temperature, has seven negative margins and one tied margin in the unmodified 4B checkpoint.}", + "scope": "matched result group(s)", + "groups": [ + "causal_coordinate" + ], + "artifact_prefixes": [ + "jlens_materials/experiments/causal-relational-comment3-audit-2026-09-06/", + "jlens_materials/experiments/causal-relational-knockout-2026-09-02/" + ], + "supporting_file_count": 94, + "analysis_commands": [ + "python scripts/analyze_causal_relational_knockout.py", + "python scripts/validate_causal_relational_knockout.py" + ], + "publication_assets": [ + { + "publication_path": "paper/figures/causal-relational-knockout-law-audit.pdf", + "sha256": "61aae3a85f6f148e45e3c414cd17caf1e26385d967fc0d61df20278e26c280bc", + "matching_archived_files": [ + "jlens_materials/experiments/causal-relational-knockout-2026-09-02/figures/causal-relational-knockout-law-audit.pdf" + ] + } + ], + "manifest": "FILE_INVENTORY.csv" + }, + { + "manuscript_source": "paper/materials_jacobian_lens_supplement.tex", + "source_line": 996, + "kind": "figure", + "label": "fig:s-causal-error-repair", + "caption": "\\hlyellow{\\textbf{Moving the coordinate toward the correct development centroid repairs a subset of natural errors.} Every line is a prompt with a strictly negative clean correct-answer margin; no case was selected by its post-intervention behavior. Teal lines cross the decision boundary: 4/8 errors at 4B and 2/8 at 12B are repaired. Gray lines remain on the wrong side. The 31B checkpoint made no baseline error in this cohort and is therefore omitted.}", + "scope": "matched result group(s)", + "groups": [ + "geometry", + "causal_coordinate" + ], + "artifact_prefixes": [ + "jlens_materials/experiments/causal-relational-comment3-audit-2026-09-06/", + "jlens_materials/experiments/causal-relational-knockout-2026-09-02/", + "jlens_materials/experiments/materials-heldout-v1_latent", + "jlens_materials/runs/" + ], + "supporting_file_count": 151, + "analysis_commands": [ + "python scripts/analyze_causal_relational_knockout.py", + "python scripts/analyze_materials_latent_geometry.py", + "python scripts/build_final_paper_figures.py", + "python scripts/validate_causal_relational_knockout.py" + ], + "publication_assets": [ + { + "publication_path": "paper/figures/causal-relational-knockout-error-repair.pdf", + "sha256": "0191a1323aaf88f3a8aa8f9b33af99b6d4baa3e5ec35912a835bd5c27c0e32c9", + "matching_archived_files": [ + "jlens_materials/experiments/causal-relational-knockout-2026-09-02/figures/causal-relational-knockout-error-repair.pdf" + ] + } + ], + "manifest": "FILE_INVENTORY.csv" + }, + { + "manuscript_source": "paper/materials_jacobian_lens_supplement.tex", + "source_line": 1013, + "kind": "figure", + "label": "fig:s-equation-free-cross-layer", + "caption": "\\hlyellow{\\textbf{Equation-free physical ordering is preserved while the fitted 31B readout axis changes across depth.} (A) The independently fitted layer-40 and layer-49 physical-outcome directions are shown exactly in their two-vector spanning plane; their cosine is 0.352 (69.4 degrees). This is a two-depth comparison, not an observed continuous rotation trajectory. (B) Despite the changed axis, raw matched contrasts for the same 60 held-out laws are nearly identical across layers (Pearson $r=0.996$; Spearman $\\rho=0.949$). (C) Each line follows one law between the two measured depths. Direct, neutral, and inverse relations retain their ordering. A separate direction was fitted at each layer using the unchanged supplied-equation development cohort and then evaluated on the identical equation-free prompts. The analysis was defined after both depth runs existed and is descriptive.}", + "scope": "matched result group(s)", + "groups": [ + "geometry", + "relational" + ], + "artifact_prefixes": [ + "jlens_materials/experiments/elicited-physics-abstraction-2026-07-18/", + "jlens_materials/experiments/equation-free-relational-physics-2026-08-21/", + "jlens_materials/experiments/gemma-equation-free-multiscale-2026-08-25/", + "jlens_materials/experiments/gemma4-12b-equation-free-relational-v1/", + "jlens_materials/experiments/gemma4-31b-equation-free-layer-comparison-2026-08-23/", + "jlens_materials/experiments/gemma4-31b-equation-free-relational-layer40-v1/", + "jlens_materials/experiments/gemma4-31b-equation-free-relational-v1/", + "jlens_materials/experiments/materials-heldout-v1_latent", + "jlens_materials/experiments/neutral-anchored-relational-physics-2026-07-18/", + "jlens_materials/experiments/relational-contrast-confirmation-2026-07-18/", + "jlens_materials/runs/" + ], + "supporting_file_count": 182, + "analysis_commands": [ + "python scripts/analyze_equation_free_relational_benchmark_amended.py", + "python scripts/analyze_gemma31b_equation_free_layers.py", + "python scripts/analyze_gemma_equation_free_multiscale.py", + "python scripts/analyze_materials_latent_geometry.py", + "python scripts/analyze_neutral_anchored_relational_benchmark.py", + "python scripts/build_final_paper_figures.py" + ], + "publication_assets": [ + { + "publication_path": "paper/figures/equation-free-cross-layer-geometry.pdf", + "sha256": "00c70381bbffe7f53e4a5252de8c5a806d69722546e3a221ab59d12d9f91aeb8", + "matching_archived_files": [ + "jlens_materials/experiments/gemma4-31b-equation-free-layer-comparison-2026-08-23/figures/equation-free-cross-layer-geometry.pdf" + ] + } + ], + "manifest": "FILE_INVENTORY.csv" + }, + { + "manuscript_source": "paper/materials_jacobian_lens_supplement.tex", + "source_line": 1025, + "kind": "figure", + "label": "fig:s-vocabulary-scale", + "caption": "\\hlyellow{\\textbf{Controlled materials-term readability across Gemma scale.} The same 50 held-out prompts and 150 predeclared one-token concepts are evaluated at every checkpoint. (A) Jacobian recovery as the vocabulary cutoff is relaxed from rank 1 to rank 100. (B) Matched direct-unembedding recovery. (C) Mean controlled-recovery AUC with 95\\% family-hierarchical intervals; filled circles are Jacobian and open squares are direct. (D) Within-checkpoint Jacobian-minus-direct AUC with the same population intervals. The 4B and 12B Jacobian curves average three independent lens fits, whereas 31B has one fit. Larger checkpoints show higher absolute readability, but the Jacobian-minus-direct contrast changes sign and every interval crosses zero. 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Apache-2.0. +"""LLM-powered analysis of a Jacobian-lens run. + +Reads a run record written by ``run_lens.py`` (``runs/.json``) and, for +each prompt, asks a frontier model to interpret *what the figures show* — +grounded in both the numeric per-layer readouts AND the rendered figures +themselves (sent as images via the vision API). It then writes a run-level +synthesis. Output is ``runs/_analysis.json`` (structured) which +``report.py`` / ``report_latex.py`` turn into a report. + +This automates the "what we see" write-up: instead of a human squinting at each +trajectory, a capable model reads the lens output through the lens of the +global-workspace paper and says what surfaced, in what order, and whether it is +signal or a small-model artifact. + +Provider choice (``--provider``): ``anthropic`` (Claude, default Opus 4.8) or +``openai`` (GPT-5.5). Auth uses each provider's standard env var +(``ANTHROPIC_API_KEY`` / an ``ant auth login`` profile, or ``OPENAI_API_KEY``). +With no credentials it falls back to a deterministic template written straight +from the numbers, so the report pipeline still runs — clearly marked ``offline``. + + python analyze.py --run runs/qwen2forcausallm.json # Claude Opus 4.8 + python analyze.py --run runs/qwen2forcausallm.json --provider openai # GPT-5.5 + python analyze.py --run runs/qwen2forcausallm.json --provider anthropic --model claude-fable-5 + python analyze.py --run runs/qwen2forcausallm.json --offline # skip the API +""" + +from __future__ import annotations + +import argparse +import base64 +import json +from pathlib import Path + +import llm + +SYSTEM = """\ +You are an interpretability researcher analysing Jacobian-lens ("J-lens") \ +readouts, in the framework of the paper "Verbalizable Representations Form a \ +Global Workspace in Language Models" (Anthropic, 2026). + +Background you can rely on: +- The J-lens transports an intermediate residual-stream activation into the \ +final-layer basis and decodes it with the model's own unembedding, giving the \ +ranked vocabulary tokens the activation is *disposed to make the model say* — \ +including concepts the model never actually outputs. +- Layers are reindexed to depth 0-100. The paper finds a mid-to-late \ +"workspace band" where interpretable, verbalizable content lives; the last few \ +layers switch to representing the literal next output token; early layers are \ +usually uninterpretable. +- Key claims to test against the data: (1) unspoken *intermediate* concepts \ +surface in the lens before the answer; (2) in multi-step reasoning the \ +intermediates appear *in the order they are computed*, at successively later \ +layers; (3) the lens can only track concepts that are single vocabulary tokens \ +(a real limitation); (4) a concept only surfaces if the model actually has it \ +— on a weak model the lens faithfully reports its absence. + +Protocol discipline: +- Treat the recorded fixed score span and workspace band as the only scored \ +region. Do not promote an all-position visual pattern into a metric. +- Compare every J-lens rank with the supplied vanilla logit-lens rank. A J-lens \ +signal is most interesting when it is substantially stronger than the matched \ +logit baseline; say plainly when the logit lens is equal or better. +- Respect the clean-answer and input/output-absence controls. If an item is \ +excluded, it may still contain a qualitative pattern, but never call it a clean \ +or registered protocol success. State the exclusion reason. +- This run may be exploratory or sample-insufficient. Do not infer population \ +rates from a handful of prompts. + +Write a precise, honest analysis. Distinguish genuine signal from small-model \ +or single-token artifacts. When the figure shows a real ordered emergence or a \ +clean concept, say so and cite the depths/ranks. When it shows nothing (flat \ +lines, only subword fragments), say that plainly and explain why. Do not \ +overclaim. Use complete sentences and name the concepts explicitly. +""" + + +def _img_block(path: Path) -> dict | None: + """Neutral image block (provider-agnostic); llm.py translates per provider.""" + if not path.is_file(): + return None + return {"type": "image_png", + "data": base64.standard_b64encode(path.read_bytes()).decode()} + + +def _prompt_user_content(rec: dict, base_dir: Path) -> list[dict]: + """Text summary of the numeric readouts + the rendered figures as images.""" + full_prompt = (rec.get("prompt_text") or rec.get("resolved_prompt") + or rec.get("prompt_used")) + if full_prompt: + prompt_label = "Exact prompt used" + prompt_text = full_prompt + else: + prompt_label = "Prompt tail" + prompt_text = ("..." + rec["prompt_tail"]) if rec.get("prompt_tail") else "" + lines = [ + f"# Prompt: {rec['title']} ({rec['shape']} / {rec['domain']})", + f"Description: {rec['description']}", + f"Scientific rationale: {rec['note']}", + f"{prompt_label}:\n{prompt_text}", + f"Recorded fixed workspace band: {rec['band'][0]}-{rec['band'][1]}% depth", + f"Protocol: {rec.get('protocol', 'legacy/unknown')}", + f"Registered metric status: " + f"{'included' if rec.get('valid_for_metrics') else 'excluded'}", + "", + ] + baseline = rec.get("baseline", {}) + if baseline.get("required"): + lines += [ + "## Clean-answer control:", + f" expected first-token answer(s): {baseline.get('expected', [])}", + f" model greedy token: {baseline.get('greedy_token')!r}", + f" registered correct: {baseline.get('correct')}", + ] + else: + lines += ["## Clean-answer control: not required for this item."] + if rec.get("generated_completion") is not None: + lines.append( + f" generated completion used for output-absence checking: " + f"{rec.get('generated_completion')!r}" + ) + if rec.get("excluded_reasons"): + lines.append( + " exclusion reason(s): " + "; ".join(rec["excluded_reasons"]) + ) + lines += [ + "", + ("## Auto-discovered rank comparison (exploratory):" + if rec.get("discovered") else + "## Tracked-rank comparison over the fixed score span and band:"), + ] + for e in rec["emergence"]: + depth = "n/a" if e["best_depth"] is None else f"{e['best_depth']}%" + star = " <- reached rank 1" if e["reached_top1"] else "" + logit_rank = e.get("logit_lens_best_rank") + logit_text = "n/a" if logit_rank is None else str(logit_rank + 1) + lines.append( + f" - {e['label']}: J-lens rank {e['best_rank'] + 1} at depth " + f"{depth}; logit-lens rank {logit_text}; sustained onset " + f"{e.get('onset_depth')}{star}" + ) + if rec["tracked_dropped"]: + lines.append(f" (dropped — multi-token, not trackable: " + f"{rec['tracked_dropped']})") + lines += ["", "## Per-layer top lens tokens at the readout position " + "(depth: tokens):"] + for r in rec["layer_readouts"]: + toks = ", ".join(r["top_tokens"][:6]) + lines.append(f" L{r['layer']:>2} (depth {r['depth']:>5}%): {toks}") + surp = rec.get("surprising") + if surp: + lines += ["", "## Exploratory surfaced candidates — heuristic top-1 " + "tokens absent from the prompt and, when output_checked=true, " + "from an actual generated/teacher-forced completion:"] + for s in surp[:10]: + lines.append(f" - {s['concept']} (tops {s['score']:g} band cells; " + f"peak rank {s['best_rank']} at depth {s['best_depth']}%, " + f"near '{s['near_token']}')") + + sw = rec.get("swap") + if sw: + lines += ["", "## Causal swap (intervention, not read-out):"] + if sw.get("protocol") == "verbal_report" and sw.get("trials"): + lines += [ + f" verbal-report clean source: {sw.get('clean_source')!r}", + f" candidate trials: {len(sw['trials'])}; protocol success " + f"rate: {sw.get('protocol_success_rate')}", + ] + for trial in sw["trials"]: + same_surface = ( + trial["target"].strip().casefold() + == sw.get("clean_source", "").casefold() + ) + top_after = trial.get("swapped_top", [["", 0]])[0][0] + lines.append( + f" - target {trial['target']!r}" + + (" [case-only self-target; not a sibling concept]" if same_surface else "") + + f": source rank {trial['source_rank_clean']+1} -> " + f"{trial['source_rank_swapped']+1}; target rank " + f"{trial['target_rank_clean']+1} -> " + f"{trial['target_rank_swapped']+1}; swapped top-1 " + f"{top_after!r}; protocol success {trial.get('protocol_success')}" + ) + else: + lines += [ + f" J-lens coordinate {sw['source']} -> {sw['target']} " + f"(alpha={sw['alpha']}) at every band layer.", + f" clean next-token top: " + f"{[w for w, _ in sw['clean_top'][:5]]}", + f" after-swap next-token top: " + f"{[w for w, _ in sw['swapped_top'][:5]]}", + f" source '{sw['source']}' full-vocab rank " + f"{sw['source_rank_clean']+1} -> {sw['source_rank_swapped']+1} " + f"(higher = more suppressed); target '{sw['target']}' rank " + f"{sw['target_rank_clean']+1} -> {sw['target_rank_swapped']+1} " + f"(lower = more installed).", + ] + + lines += ["", "The attached images are the rank-trajectory chart and the " + "layer x position slice grid for this prompt. Analyse what the " + "lens reveals about the model's unspoken reasoning here" + + (", and whether the causal swap confirms it (did suppressing the " + "source concept and adding the target actually change the " + "output, or only suppress?)" if sw else "") + + (". Explicitly review the exploratory candidate list as a " + "heuristic: which entries may be meaningful and which are " + "tokenizer/noise artifacts? Do not treat it as a paper metric." + if surp else "") + + " Keep it to 1-3 tight paragraphs."] + + content: list[dict] = [{"type": "text", "text": "\n".join(lines)}] + figs = rec.get("figures", {}) + for key in ("trajectory", "grid"): + if key in figs: + blk = _img_block(base_dir / figs[key]) + if blk: + content.append(blk) + return content + + +def _offline_analysis(rec: dict) -> str: + """Deterministic template from the numbers, when no API is available.""" + hits = [e for e in rec["emergence"] if 0 <= e["best_rank"] < 5] + top1 = [e for e in rec["emergence"] if e["reached_top1"]] + parts = [] + if top1: + names = ", ".join(f"'{e['label']}' (depth {e['best_depth']}%)" + for e in top1) + parts.append(f"Concepts reaching lens rank 1: {names}.") + if hits: + names = ", ".join(f"'{e['label']}' (rank {e['best_rank']+1}, " + f"depth {e['best_depth']}%)" for e in hits) + parts.append(f"Concepts reaching the lens top-5: {names}.") + if not hits: + parts.append("No tracked concept reached the lens top-5 at any depth " + "— either the model lacks these concepts or they are " + "multi-token and untrackable.") + if rec["tracked_dropped"]: + parts.append(f"Not trackable (multi-token, paper limitation section 9.1): " + f"{', '.join(rec['tracked_dropped'])}.") + surp = rec.get("surprising") + if surp: + parts.append("Exploratory surfaced candidates (heuristic, prompt/output " + "filtered where an actual completion was available): " + + ", ".join(s["concept"] for s in surp[:6]) + ".") + return "[offline template] " + " ".join(parts) + + +def analyze(run_path: str, *, provider: str = "anthropic", + model: str | None = None, offline: bool = False, + base_url: str | None = None) -> dict: + model = model or llm.default_model(provider) + run = json.loads(Path(run_path).read_text()) + base_dir = Path(run_path).resolve().parent.parent # jlens_materials/ + + analyses = [] + for rec in run["prompts"]: + if offline: + text, used = _offline_analysis(rec), "offline" + else: + try: + blocks = _prompt_user_content(rec, base_dir) + text = llm.complete(provider, model, SYSTEM, blocks, + base_url=base_url) + used = f"{provider}:{model}" + except Exception as exc: # noqa: BLE001 + print(f" [{rec['slug']}] {provider} error: " + f"{type(exc).__name__}: {exc}; using offline template") + text, used = _offline_analysis(rec), "offline" + offline = True # stop retrying a dead provider + print(f" [{rec['slug']}] analysed ({used})") + analyses.append({"slug": rec["slug"], "title": rec["title"], + "shape": rec["shape"], "domain": rec["domain"], + "model_used": used, "analysis": text}) + + synthesis = _synthesize(provider, model, run, analyses, offline, base_dir, + base_url=base_url) + + out = {"model": run["model"], "provider": provider, "analysis_model": model, + "offline": offline, "per_prompt": analyses, "synthesis": synthesis} + out_path = Path(run_path).with_name(Path(run_path).stem + "_analysis.json") + out_path.write_text(json.dumps(out, indent=2, ensure_ascii=False)) + print(f"wrote {out_path}") + return out + + +def top_hidden_thoughts(run, top=15): + """Aggregate exploratory surfaced candidates across the run, + summed by concept, strongest first: (concept, total_score, n_prompts).""" + agg: dict[str, list] = {} + for rec in run["prompts"]: + for s in rec.get("surprising", []): + e = agg.setdefault(s["concept"], [0.0, 0]) + e[0] += s["score"]; e[1] += 1 + ranked = sorted(agg.items(), key=lambda kv: -kv[1][0]) + return [(c, round(sc, 1), n) for c, (sc, n) in ranked[:top]] + + +def _synthesize(provider, model, run, analyses, offline, base_dir=None, + base_url=None) -> str: + hidden = top_hidden_thoughts(run) + hidden_str = ", ".join(f"{c} (×{n})" for c, _, n in hidden[:12]) + if offline: + by_shape: dict[str, list[str]] = {} + for a, rec in zip(analyses, run["prompts"]): + hit = any(0 <= e["best_rank"] < 5 for e in rec["emergence"]) + by_shape.setdefault(a["shape"], []).append("hit" if hit else "miss") + summary = "; ".join( + f"{s}: {v.count('hit')}/{len(v)} prompts with a top-5 concept" + for s, v in by_shape.items()) + extra = (f" Most frequent exploratory surfaced candidates across prompts: " + f"{hidden_str}." if hidden else "") + return ("[offline template] Run over " + f"{run['model']} ({run['n_layers']} layers). By experiment " + f"type — {summary}.{extra}") + joined = "\n\n".join(f"### {a['title']} ({a['shape']})\n{a['analysis']}" + for a in analyses) + methodology = run.get("methodology", {}) + sufficiency = run.get("insufficient_sample_shapes", []) + blocks = [{"type": "text", "text": + f"Below are per-prompt J-lens analyses for model {run['model']} " + f"({run['n_layers']} layers), across domains {run.get('domains', [])}. " + f"Run claims level: {methodology.get('claims_level', 'legacy/unknown')}; " + f"insufficient-sample groups: {sufficiency}. Do not make quantitative " + f"paper-level claims for exploratory or insufficient groups. " + f"Write a run-level synthesis (3-5 paragraphs): which experiment types " + f"(MULTIHOP / ASSOCIATION / RECOGNITION / MODULATION / REPORT_SWAP) " + f"showed the workspace phenomena most clearly, how the results compare " + f"to the paper's claims, what the single-token and model-capability " + f"limitations imply, and what a reader should conclude about using this " + f"target model versus a larger or more capable one for scientific " + f"interpretability. Respect every per-item metric exclusion in the " + f"analyses below and distinguish qualitative signals from registered " + f"protocol successes. " + f"Also comment on the exploratory surfaced candidates (most frequent: " + f"{hidden_str}), explicitly treating them as a heuristic rather than a " + f"paper metric and separating plausible signals from artifacts.\n\n{joined}"}] + _tag = run.get("tag") + _figs = (base_dir or Path(".")) / "figures" + hitbars = (_figs / _tag / "summary__hit_rates.png") if _tag else (_figs / "summary__hit_rates.png") + blk = (_img_block(hitbars) if run.get("format_version", 1) < 2 and + hitbars.is_file() else None) + if blk: + blocks.append(blk) + try: + return llm.complete(provider, model, SYSTEM, blocks, max_tokens=6000, + base_url=base_url) + except Exception as exc: # noqa: BLE001 + return f"[synthesis unavailable: {type(exc).__name__}: {exc}]" + + +def main() -> None: + ap = argparse.ArgumentParser() + ap.add_argument("--run", required=True, help="runs/.json from run_lens.py") + ap.add_argument("--provider", choices=["anthropic", "openai"], + default="anthropic", + help="anthropic (Claude, default Opus 4.8) or openai (GPT-5.5)") + ap.add_argument("--model", default=None, + help="override the model id (default per provider: " + "claude-opus-4-8 / gpt-5.5)") + ap.add_argument("--offline", action="store_true", + help="skip the API; deterministic template analysis") + ap.add_argument("--base-url", default=None, + help="OpenAI-compatible endpoint for a local model server " + "(mistral.rs, LM Studio, vLLM, Ollama), e.g. " + "http://localhost:1234/v1. Only used with " + "--provider openai; ignored for anthropic.") + args = ap.parse_args() + analyze(args.run, provider=args.provider, model=args.model, + offline=args.offline, base_url=args.base_url) + + +if __name__ == "__main__": + main() diff --git a/jlens_materials/animate.py b/jlens_materials/animate.py new file mode 100644 index 0000000000000000000000000000000000000000..4fb89f5a071be7bf4c135d8d0c1bf6868b9d7c48 --- /dev/null +++ b/jlens_materials/animate.py @@ -0,0 +1,121 @@ +# Copyright 2026. Apache-2.0. +"""Animate the dynamics of "thinking": a bar-race of the concepts the Jacobian +lens surfaces as the readout sweeps through model depth. + +Depth is time. At each depth the lens is disposed to make the model say some +ranked set of tokens; as depth increases those concepts rise, fall, and give way +to others. This renders that as a movie (GIF always; MP4 if ffmpeg is present) — +the animated companion to the static "thought stream" figure in the report. + + python animate.py --run runs/qwen2forcausallm.json # all prompts -> GIFs + python animate.py --run runs/qwen2forcausallm.json --slug fracture-fatigue --mp4 + python animate.py --run runs/qwen2forcausallm.json --fps 20 --top-n 12 + +Uses only ``layer_readouts`` from the run JSON (no model needed). Output goes to +``figures/animation/__thoughts.{gif,mp4}``. +""" + +from __future__ import annotations + +import argparse +import json +import shutil +from pathlib import Path + +import matplotlib +matplotlib.use("Agg") # headless; render frames to buffer +import numpy as np # noqa: E402 +import matplotlib.pyplot as plt # noqa: E402 +from matplotlib import animation # noqa: E402 + +import matviz # noqa: E402 (installs house style; SERIES/INK/ACCENT) + +HERE = Path(__file__).resolve().parent + + +def make_movie(rec: dict, outdir: Path, *, fps: int = 18, top_n: int = 10, + substeps: int = 7, mp4: bool = False, + max_concepts: int = 16) -> list[str]: + depths, labels, S = matviz._stream_series(rec["layer_readouts"], + max_concepts=max_concepts) + if not len(labels): + print(f" [{rec['slug']}] no surfaced concepts to animate") + return [] + # interpolate onto a fine depth axis for smooth motion + fine = np.linspace(float(depths.min()), float(depths.max()), + max(2, (len(depths) - 1) * substeps)) + Sf = np.vstack([np.interp(fine, depths, S[k]) for k in range(len(labels))]) + colors = {lab: matviz.SERIES[i % len(matviz.SERIES)] + for i, lab in enumerate(labels)} + K = float(S.max()) if S.size else 8.0 + + fig, ax = plt.subplots(figsize=(6.6, 3.9), dpi=90) + + def draw(fi: int): + ax.clear() + v = Sf[:, fi] + order = [k for k in np.argsort(v)[::-1][:top_n] if v[k] > 0] + y = (top_n - 1) - np.arange(len(order)) # strongest pinned to the top + ax.barh(y, [v[k] for k in order], height=0.72, + color=[colors[labels[k]] for k in order], alpha=0.9) + for yi, k in zip(y, order): + ax.text(v[k] + K * 0.02, yi, labels[k], va="center", fontsize=9.5, + color=matviz.INK) + ax.set_xlim(0, K * 1.18) + ax.set_ylim(-0.6, max(top_n, 1) - 0.4) + ax.set_yticks([]) + ax.set_xlabel("lens salience (disposition to say, top of vocabulary)", + fontsize=8) + ax.set_title(rec["title"], loc="left", fontsize=10, fontweight="bold") + ax.text(0.995, 1.04, f"depth {fine[fi]:.0f}%", transform=ax.transAxes, + ha="right", fontsize=11, color=matviz.ACCENT, fontweight="bold") + ax.spines[["top", "right", "left"]].set_visible(False) + ax.tick_params(labelsize=8) + + anim = animation.FuncAnimation(fig, draw, frames=len(fine), + interval=1000 / fps) + outdir.mkdir(parents=True, exist_ok=True) + base = outdir / f"{rec['slug']}__thoughts" + written = [] + gif = f"{base}.gif" + anim.save(gif, writer=animation.PillowWriter(fps=fps)) + written.append(gif) + if mp4: + if shutil.which("ffmpeg"): + mp4p = f"{base}.mp4" + anim.save(mp4p, writer=animation.FFMpegWriter(fps=fps, bitrate=1800)) + written.append(mp4p) + else: + print(" (ffmpeg not found; wrote GIF only)") + plt.close(fig) + print(f" [{rec['slug']}] wrote {', '.join(Path(w).name for w in written)}") + return written + + +def main() -> None: + ap = argparse.ArgumentParser() + ap.add_argument("--run", required=True, help="runs/.json") + ap.add_argument("--slug", default=None, help="animate one prompt (default: all)") + ap.add_argument("--fps", type=int, default=18) + ap.add_argument("--top-n", type=int, default=10, help="bars shown per frame") + ap.add_argument("--substeps", type=int, default=7, help="interpolation smoothness") + ap.add_argument("--mp4", action="store_true", help="also write MP4 (needs ffmpeg)") + args = ap.parse_args() + + run = json.loads(Path(args.run).read_text()) + recs = [p for p in run["prompts"] + if args.slug is None or p["slug"] == args.slug] + if not recs: + raise SystemExit(f"no prompt matching --slug {args.slug!r}") + # namespace movies per model, matching figures//... + tag = run.get("tag") or Path(args.run).stem + outdir = HERE / "figures" / tag / "animation" + print(f"animating {len(recs)} prompt(s) from {run['model']} (-> {outdir})") + for rec in recs: + make_movie(rec, outdir, fps=args.fps, top_n=args.top_n, + substeps=args.substeps, mp4=args.mp4) + print(f"done -> {outdir}") + + +if __name__ == "__main__": + main() diff --git a/jlens_materials/artifacts.py b/jlens_materials/artifacts.py new file mode 100644 index 0000000000000000000000000000000000000000..5983a413610eb20bd638ea8f061d8b4a29a5c9aa --- /dev/null +++ b/jlens_materials/artifacts.py @@ -0,0 +1,180 @@ +# Copyright 2026. Apache-2.0. +"""Sync a run's artifacts to / from a (private) HuggingFace **dataset** repo, so +a run produced on one machine can be continued on another. + +Everything a run produces is namespaced by its ````: + + lens_.pt the fitted Jacobian lens (the expensive artifact) + lens_.ckpt.pt the fit checkpoint (optional) + runs/.json provenance / fixed-span ranks / metrics / exploratory candidates / swaps + runs/_analysis.json the LLM "what we see" analysis + runs/_swaps.json auto_swap results (if any) + figures//** every PNG/SVG + animations + reports/_report.* the compiled report (.tex/.pdf/.md) + +``push`` uploads that set; ``pull`` restores it to the *same relative paths* on +another machine; ``list`` shows which tags a repo holds. Because the paths are +identical, after a pull you just point the normal CLIs at ``runs/.json`` or +reuse ``lens_.pt`` — no other bookkeeping. + + # after fitting the 1000-prompt lens on the Linux box: + python artifacts.py push --tag gemma4-e4b-it-1k --repo /substrates-artifacts + + # on the Mac, to continue the analysis: + python artifacts.py pull --tag gemma4-e4b-it-1k --repo /substrates-artifacts + python analyze.py --run runs/gemma4-e4b-it-1k.json --provider openai # continue here + + python artifacts.py list --repo /substrates-artifacts # what's stored + +Auth: a HuggingFace token via ``huggingface-cli login`` (cached) or the +``HF_TOKEN`` env var. The dataset repo is created private by default. +Set ``SUBSTRATES_HF_REPO`` to avoid passing ``--repo`` every time. +""" + +from __future__ import annotations + +import argparse +import os +from pathlib import Path + +TOOLKIT = Path(__file__).resolve().parent + +# Latex intermediates (.aux/.log/.fls/.fdb_latexmk/.out) are regenerable — the +# report deliverables we keep are just these three. +_REPORT_EXTS = ("tex", "pdf", "md") + + +def _root_files(tag: str, *, include_lens: bool = True) -> list[str]: + """Toolkit-relative paths for a tag's non-figure artifacts.""" + names = [ + f"runs/{tag}.json", + f"runs/{tag}_analysis.json", + f"runs/{tag}_swaps.json", + *[f"reports/{tag}_report.{ext}" for ext in _REPORT_EXTS], + ] + if include_lens: + names = [f"lens_{tag}.pt", f"lens_{tag}.ckpt.pt", *names] + return names + + +def local_files(tag: str, *, include_lens: bool = True) -> list[str]: + """Toolkit-relative paths that actually exist locally for ``tag``.""" + files = [n for n in _root_files(tag, include_lens=include_lens) + if (TOOLKIT / n).is_file()] + figdir = TOOLKIT / "figures" / tag + if figdir.is_dir(): + files += [str(p.relative_to(TOOLKIT)) + for p in sorted(figdir.rglob("*")) + if p.is_file() and not p.name.startswith(".")] # skip .DS_Store etc. + return files + + +def pull_patterns(tag: str, *, include_lens: bool = True) -> list[str]: + """Glob patterns for downloading a tag (figures unknown ahead of time).""" + pats = _root_files(tag, include_lens=include_lens) + pats += [f"figures/{tag}/*", f"figures/{tag}/**"] # fnmatch: both catch nested + return pats + + +def _human(nbytes: float) -> str: + for unit in ("B", "KB", "MB", "GB"): + if nbytes < 1024 or unit == "GB": + return f"{nbytes:.0f}{unit}" if unit == "B" else f"{nbytes:.1f}{unit}" + nbytes /= 1024.0 + return f"{nbytes:.1f}GB" + + +def _resolve_repo(repo: str | None) -> str: + repo = repo or os.environ.get("SUBSTRATES_HF_REPO") + if not repo: + raise SystemExit("no --repo given and SUBSTRATES_HF_REPO is unset " + "(expected e.g. /substrates-artifacts)") + return repo + + +def push(tag: str, repo: str, *, private: bool = True, include_lens: bool = True, + dry_run: bool = False) -> None: + files = local_files(tag, include_lens=include_lens) + if not files: + raise SystemExit(f"no local artifacts for tag {tag!r} under {TOOLKIT} " + f"(nothing to push)") + total = sum((TOOLKIT / f).stat().st_size for f in files) + print(f"tag {tag}: {len(files)} files, {_human(total)} -> " + f"dataset {repo} ({'private' if private else 'public'})") + for f in files: + print(f" + {f} ({_human((TOOLKIT / f).stat().st_size)})") + if dry_run: + print("[dry-run] nothing uploaded") + return + from huggingface_hub import HfApi, CommitOperationAdd + api = HfApi() + api.create_repo(repo, repo_type="dataset", private=private, exist_ok=True) + ops = [CommitOperationAdd(path_in_repo=f, path_or_fileobj=str(TOOLKIT / f)) + for f in files] + api.create_commit(repo_id=repo, repo_type="dataset", operations=ops, + commit_message=f"push artifacts for {tag}") + print(f"pushed {len(files)} files for {tag} -> https://huggingface.co/datasets/{repo}") + + +def pull(tag: str, repo: str, *, include_lens: bool = True) -> None: + from huggingface_hub import snapshot_download + print(f"pulling tag {tag} from dataset {repo} -> {TOOLKIT}") + snapshot_download(repo_id=repo, repo_type="dataset", + allow_patterns=pull_patterns(tag, include_lens=include_lens), + local_dir=str(TOOLKIT)) + got = local_files(tag, include_lens=include_lens) + print(f"restored {len(got)} files for {tag}:") + for f in got[:8]: + print(f" {f}") + if len(got) > 8: + print(f" … and {len(got) - 8} more (figures)") + print(f"\ncontinue with: python analyze.py --run runs/{tag}.json --provider openai\n" + f"or reuse the lens: python run_lens.py --model --lens lens_{tag}.pt --tag {tag} ...") + + +def list_tags(repo: str) -> None: + from huggingface_hub import HfApi + files = HfApi().list_repo_files(repo, repo_type="dataset") + tags = sorted({f[len("runs/"):-len(".json")] for f in files + if f.startswith("runs/") and f.endswith(".json") + and not f.endswith(("_analysis.json", "_swaps.json"))}) + print(f"dataset {repo}: {len(tags)} run tag(s)") + for t in tags: + has_lens = f"lens_{t}.pt" in files + n_fig = sum(1 for f in files if f.startswith(f"figures/{t}/")) + has_report = any(f.startswith(f"reports/{t}_report.") for f in files) + print(f" {t} [{'lens ' if has_lens else ''}" + f"{n_fig} figs {'report' if has_report else ''}]") + + +def main() -> None: + ap = argparse.ArgumentParser(description="Sync run artifacts to/from a HF dataset") + sub = ap.add_subparsers(dest="cmd", required=True) + + p = sub.add_parser("push", help="upload a tag's artifacts") + p.add_argument("--tag", required=True) + p.add_argument("--repo", default=None, help="/ (or $SUBSTRATES_HF_REPO)") + p.add_argument("--public", action="store_true", help="create a public repo (default: private)") + p.add_argument("--no-lens", action="store_true", help="skip the large lens_.pt") + p.add_argument("--dry-run", action="store_true", help="list what would upload, do nothing") + + q = sub.add_parser("pull", help="download a tag's artifacts") + q.add_argument("--tag", required=True) + q.add_argument("--repo", default=None, help="/ (or $SUBSTRATES_HF_REPO)") + q.add_argument("--no-lens", action="store_true", help="skip the large lens_.pt") + + r = sub.add_parser("list", help="list tags stored in a repo") + r.add_argument("--repo", default=None, help="/ (or $SUBSTRATES_HF_REPO)") + + args = ap.parse_args() + if args.cmd == "push": + push(args.tag, _resolve_repo(args.repo), private=not args.public, + include_lens=not args.no_lens, dry_run=args.dry_run) + elif args.cmd == "pull": + pull(args.tag, _resolve_repo(args.repo), include_lens=not args.no_lens) + elif args.cmd == "list": + list_tags(_resolve_repo(args.repo)) + + +if __name__ == "__main__": + main() diff --git a/jlens_materials/auto_swap.py b/jlens_materials/auto_swap.py new file mode 100644 index 0000000000000000000000000000000000000000..60d5c94cc608349dcc92b0985ac64332a5ff3c83 --- /dev/null +++ b/jlens_materials/auto_swap.py @@ -0,0 +1,773 @@ +# Copyright 2026. Apache-2.0. +"""Suggest and run J-lens coordinate swaps from an existing run. + +The normal run pipeline is discovery-first: fit/apply a lens, inspect what +concepts surfaced, then decide what causal swap to test. This CLI automates the +middle of that loop: + + runs/.json -> proposals -> approval -> alpha sweep -> plots/json + +The default provider is offline, using deterministic same-category domain +lexicons. Anthropic/OpenAI can be used to refine proposals, but the final list is +still filtered against concepts that actually surfaced in the run. + +These automatically proposed same-domain swaps are manipulation sanity checks. +They become graded causal experiments only when the item declares a clean and +counterfactual answer in advance. + +Examples: + python auto_swap.py --run runs/qwen2forcausallm.json --dry-run + python auto_swap.py --run runs/qwen2forcausallm.json --model Qwen/Qwen2.5-0.5B-Instruct \ + --lens lens_qwen2forcausallm.pt --provider offline --auto-approve --slugs fracture-report-swap +""" + +from __future__ import annotations + +import argparse +import json +import math +import os +import re +import sys +import tempfile +from dataclasses import asdict, dataclass +from datetime import datetime +from pathlib import Path +from typing import Any + +_CACHE_ROOT = Path(tempfile.gettempdir()) / "jlens-cache" +_CACHE_ROOT.mkdir(parents=True, exist_ok=True) +os.environ.setdefault("TOKENIZERS_PARALLELISM", "false") +os.environ.setdefault("MPLCONFIGDIR", str(_CACHE_ROOT / "matplotlib")) + +import llm +import swap as swapmod +import domain_prompts as dp + + +HERE = Path(__file__).resolve().parent + + +# --------------------------------------------------------------------------- # +# Domain lexicons +# --------------------------------------------------------------------------- # + +DOMAIN_GROUPS: dict[str, dict[str, list[str]]] = { + "fracture": { + "failure_modes": [ + "fracture", "fatigue", "creep", "corrosion", "buckling", + "cleavage", "wear", + ], + "mechanisms": [ + "crack", "dislocation", "plastic", "yield", "deformation", + "brittle", "ductile", "necking", + ], + "fracture_parameters": [ + "stress", "strain", "intensity", "toughness", "energy", + "surface", "length", "propagate", "opening", + ], + }, + "protein": { + "secondary_structure": [ + "helix", "sheet", "strand", "turn", "coil", "backbone", + "hydrogen", "bond", + ], + "folding": [ + "protein", "sequence", "amino", "residue", "fold", "structure", + "native", "core", "hydrophobic", + ], + "residues": [ + "glycine", "alanine", "leucine", "serine", "proline", + "cysteine", + ], + "membrane": [ + "membrane", "hydrophobic", "transmembrane", "lipid", "helix", + ], + "aggregation": [ + "amyloid", "fibril", "aggregate", "misfold", "beta", "plaque", + ], + "chemistry": [ + "disulfide", "cysteine", "bridge", "bond", "sulfur", "cross", + ], + }, +} + + +@dataclass +class Proposal: + slug: str + title: str + domain: str + shape: str + source: str + target: str + source_rank: int + source_depth: float | None + rationale: str + expected: str + confidence: float + provider: str = "offline" + target_token_id: int | None = None + source_token_id: int | None = None + + +# --------------------------------------------------------------------------- # +# Helpers +# --------------------------------------------------------------------------- # + +def _load_run(run_path: str | Path) -> dict: + return json.loads(Path(run_path).read_text()) + + +def _base_dir(run_path: str | Path) -> Path: + p = Path(run_path).resolve() + return p.parent.parent if p.parent.name == "runs" else HERE + + +def _csv_set(value: str | None) -> set[str] | None: + if not value: + return None + return {x.strip() for x in value.split(",") if x.strip()} + + +def _clean_word(s: str) -> str: + return re.sub(r"^\s+", "", str(s or "")).strip() + + +def _norm_word(s: str) -> str: + return _clean_word(s).lower() + + +def _rank1(e: dict) -> int: + return int(e.get("best_rank", 10**9)) + 1 + + +def _source_rank0(e: dict) -> int: + return int(e.get("best_rank", 10**9)) + + +def _depth(e: dict) -> float | None: + d = e.get("best_depth") + return None if d is None else float(d) + + +def _single_token_id(tokenizer, word: str) -> int | None: + forms = [" " + word, word, " " + word.capitalize(), word.capitalize(), + " " + word.lower(), word.lower()] + for form in forms: + ids = tokenizer.encode(form, add_special_tokens=False) + if len(ids) == 1: + return int(ids[0]) + return None + + +def _target_candidates(domain: str, source: str, prompt_labels: list[str]) -> list[str]: + """Same-category replacements from the domain lexicon, then prompt siblings.""" + src = _norm_word(source) + out: list[str] = [] + for group in DOMAIN_GROUPS.get(domain, {}).values(): + if src in {_norm_word(x) for x in group}: + for item in group: + if _norm_word(item) != src and item not in out: + out.append(item) + for label in prompt_labels: + if _norm_word(label) != src and label not in out: + out.append(label) + return out + + +def _confidence_from_rank(rank0: int) -> float: + # Rank 1 concepts get ~0.9; rank 20 concepts get ~0.62. + return round(max(0.35, min(0.95, 0.92 - 0.015 * rank0)), 2) + + +def _proposal_key(p: Proposal) -> tuple[str, str, str]: + return (_norm_word(p.slug), _norm_word(p.source), _norm_word(p.target)) + + +def _filtered_prompts(run: dict, *, slugs: set[str] | None, + domains: set[str] | None) -> list[dict]: + prompts = run.get("prompts", []) + if slugs: + prompts = [p for p in prompts if p.get("slug") in slugs] + if domains: + prompts = [p for p in prompts if p.get("domain") in domains] + return prompts + + +def deterministic_proposals( + run: dict, + *, + slugs: set[str] | None = None, + domains: set[str] | None = None, + max_source_rank: int = 20, + allow_weak: bool = False, + per_prompt: int = 2, + max_proposals: int = 20, +) -> list[Proposal]: + """Choose loaded source concepts and same-category targets without an LLM.""" + proposals: list[Proposal] = [] + for rec in _filtered_prompts(run, slugs=slugs, domains=domains): + ems = rec.get("emergence", []) + labels = [_clean_word(e.get("label", "")) for e in ems] + band = rec.get("band") or [0.0, 100.0] + band_lo, band_hi = float(band[0]), float(band[1]) + candidates = [] + for e in ems: + label = _clean_word(e.get("label", "")) + if not label or _depth(e) is None: + continue + depth = _depth(e) + if depth is None or depth < band_lo or depth > band_hi: + continue + rank0 = _source_rank0(e) + if rank0 < 0: + continue + if not allow_weak and rank0 >= max_source_rank: + continue + candidates.append(e) + candidates.sort(key=lambda e: (_source_rank0(e), _depth(e) or 999.0)) + + made_for_prompt = 0 + for e in candidates: + source = _clean_word(e["label"]) + targets = _target_candidates(rec.get("domain", ""), source, labels) + if not targets: + continue + target = targets[0] + rank0 = _source_rank0(e) + depth = _depth(e) + proposals.append(Proposal( + slug=rec.get("slug", ""), + title=rec.get("title", rec.get("slug", "")), + domain=rec.get("domain", ""), + shape=rec.get("shape", ""), + source=source, + target=target, + source_rank=rank0 + 1, + source_depth=depth, + rationale=( + f"'{source}' surfaced strongly in the run " + f"(rank {rank0 + 1} at depth {depth}%). " + f"'{target}' is a same-domain counterfactual target." + ), + expected=( + f"The swap should suppress '{source}', install '{target}', " + "and move the next-token distribution toward the target." + ), + confidence=_confidence_from_rank(rank0), + )) + made_for_prompt += 1 + if made_for_prompt >= per_prompt: + break + if len(proposals) >= max_proposals: + break + return proposals[:max_proposals] + + +# --------------------------------------------------------------------------- # +# LLM proposal refinement +# --------------------------------------------------------------------------- # + +SYSTEM = """\ +You propose causal Jacobian-lens coordinate swaps for scientific interpretability. +Use only source concepts that actually surfaced in the supplied J-lens run. +Prefer same-category targets, and avoid weak sources unless there is a clear +reason. Return ONLY strict JSON with this shape: +{"proposals":[{"slug":"...","source":"...","target":"...","rationale":"...", +"expected":"...","confidence":0.0}]} +""" + + +def _json_from_text(text: str) -> Any: + text = text.strip() + if text.startswith("```"): + text = re.sub(r"^```(?:json)?\s*", "", text) + text = re.sub(r"\s*```$", "", text) + try: + return json.loads(text) + except json.JSONDecodeError: + m = re.search(r"\{.*\}", text, re.S) + if not m: + raise + return json.loads(m.group(0)) + + +def _run_summary_for_llm(run: dict, seeds: list[Proposal]) -> str: + lines = [ + f"Model: {run.get('model')}", + "Candidate loaded concepts by prompt. Ranks are 1-based; lower is stronger.", + "", + ] + for rec in run.get("prompts", []): + rows = [] + for e in rec.get("emergence", []): + if e.get("best_depth") is None or int(e.get("best_rank", -1)) < 0: + continue + rows.append(f"{e['label']}=rank {_rank1(e)}@{e['best_depth']}%") + if rows: + lines.append(f"- {rec['slug']} ({rec.get('domain')}/{rec.get('shape')}): " + + ", ".join(rows[:10])) + lines += ["", "Deterministic seed proposals:"] + for p in seeds: + lines.append( + f"- {p.slug}: {p.source}->{p.target}; source rank {p.source_rank}; " + f"rationale: {p.rationale}" + ) + lines += ["", "Return at most 12 proposals. Keep source strings exactly as shown."] + return "\n".join(lines) + + +def llm_refine_proposals( + run: dict, + seeds: list[Proposal], + *, + provider: str, + model: str | None, + allow_weak: bool, + max_source_rank: int, +) -> list[Proposal]: + """Ask a provider to refine proposals, then filter against surfaced concepts.""" + model = model or llm.default_model(provider) + text = llm.complete( + provider, + model, + SYSTEM, + [{"type": "text", "text": _run_summary_for_llm(run, seeds)}], + max_tokens=2500, + effort="medium", + ) + parsed = _json_from_text(text) + raw = parsed.get("proposals", parsed if isinstance(parsed, list) else []) + + rec_by_slug = {p.get("slug"): p for p in run.get("prompts", [])} + out: list[Proposal] = [] + for item in raw: + slug = str(item.get("slug", "")) + rec = rec_by_slug.get(slug) + if rec is None: + continue + source = _clean_word(item.get("source", "")) + target = _clean_word(item.get("target", "")) + if not source or not target or _norm_word(source) == _norm_word(target): + continue + + match = None + for e in rec.get("emergence", []): + if _norm_word(e.get("label", "")) == _norm_word(source): + match = e + break + if match is None: + continue + band = rec.get("band") or [0.0, 100.0] + depth = _depth(match) + if depth is None or depth < float(band[0]) or depth > float(band[1]): + continue + rank0 = _source_rank0(match) + if not allow_weak and rank0 >= max_source_rank: + continue + conf = item.get("confidence", 0.5) + try: + conf = float(conf) + except (TypeError, ValueError): + conf = 0.5 + out.append(Proposal( + slug=slug, + title=rec.get("title", slug), + domain=rec.get("domain", ""), + shape=rec.get("shape", ""), + source=source, + target=target, + source_rank=rank0 + 1, + source_depth=_depth(match), + rationale=str(item.get("rationale", "")).strip() + or "LLM-proposed same-run counterfactual.", + expected=str(item.get("expected", "")).strip() + or f"Move the answer toward '{target}'.", + confidence=round(max(0.0, min(1.0, conf)), 2), + provider=f"{provider}:{model}", + )) + + # De-duplicate while preserving provider order. Fall back to seeds if the + # model returned nothing usable. + seen = set() + deduped = [] + for p in out: + if _proposal_key(p) not in seen: + seen.add(_proposal_key(p)) + deduped.append(p) + return deduped or seeds + + +# --------------------------------------------------------------------------- # +# Token validation, approval, and summaries +# --------------------------------------------------------------------------- # + +def validate_tokens(proposals: list[Proposal], model_name: str) -> list[Proposal]: + from transformers import AutoTokenizer + + tokenizer = AutoTokenizer.from_pretrained(model_name) + valid = [] + for p in proposals: + sid = _single_token_id(tokenizer, p.source) + tid = _single_token_id(tokenizer, p.target) + if sid is None or tid is None: + print( + f"skip {p.slug}: {p.source}->{p.target} is not single-token " + f"(source={sid}, target={tid})", + file=sys.stderr, + ) + continue + p.source_token_id = sid + p.target_token_id = tid + valid.append(p) + return valid + + +def _fmt_depth(depth: float | None) -> str: + return "n/a" if depth is None or math.isnan(depth) else f"{depth:.1f}%" + + +def _short(s: str, n: int) -> str: + s = str(s) + return s if len(s) <= n else s[: n - 1] + "." + + +def print_proposals(proposals: list[Proposal]) -> None: + if not proposals: + print("No swap proposals survived filtering.") + return + print("\nSwap proposals") + print("-" * 118) + print(f"{'#':>2} {'prompt':<28} {'source -> target':<27} {'rank@depth':<14} " + f"{'tok':<9} {'conf':<5} rationale") + print("-" * 118) + for i, p in enumerate(proposals, 1): + tok = "?" + if p.target_token_id is not None: + tok = str(p.target_token_id) + rank_depth = f"{p.source_rank}@{_fmt_depth(p.source_depth)}" + pair = f"{p.source} -> {p.target}" + print(f"{i:>2} {_short(p.slug, 28):<28} {_short(pair, 27):<27} " + f"{rank_depth:<14} {tok:<9} {p.confidence:<5.2f} " + f"{_short(p.rationale, 45)}") + print("-" * 118) + + +def approve_proposals(proposals: list[Proposal], *, auto_approve: bool) -> list[Proposal]: + if auto_approve: + return proposals + if not sys.stdin.isatty(): + raise SystemExit("Refusing to run swaps without an interactive terminal. " + "Use --auto-approve or --dry-run.") + approved: list[Proposal] = [] + approve_all = False + for i, p in enumerate(proposals, 1): + if approve_all: + approved.append(p) + continue + while True: + ans = input(f"Approve proposal {i} ({p.source}->{p.target})? " + "[y/n/all/skip] ").strip().lower() + if ans in {"y", "yes"}: + approved.append(p) + break + if ans in {"n", "no"}: + break + if ans in {"all", "a"}: + approved.append(p) + approve_all = True + break + if ans in {"skip", "s", "q", "quit"}: + return approved + print("Please answer y, n, all, or skip.") + return approved + + +# --------------------------------------------------------------------------- # +# Execution and plots +# --------------------------------------------------------------------------- # + +def _prompt_text_by_slug(run: dict) -> dict[str, str]: + return {p["slug"]: (p.get("prompt_text") or p.get("full_prompt") or + p.get("prompt_tail_full") or p.get("prompt") or + p.get("prompt_tail", "")) + for p in run.get("prompts", [])} + + +def _resolve_prompt_text(run: dict, proposal: Proposal, tokenizer=None) -> str: + for rec in run.get("prompts", []): + if rec.get("slug") == proposal.slug: + # run_lens stores the full resolved prompt only inside swap results + # for REPORT_SWAP prompts. For normal prompts, prompt_tail is all we + # have in the run JSON; those are still useful for manual/dry-run + # proposals, but execution needs the original prompt. + if rec.get("swap", {}).get("prompt"): + return rec["swap"]["prompt"] + if rec.get("prompt_text"): + return rec["prompt_text"] + if "full_prompt" in rec: + return rec["full_prompt"] + if tokenizer is not None: + try: + return dp.resolve_text(dp.by_slug(proposal.slug), tokenizer) + except KeyError: + pass + return rec.get("prompt_tail", "") + raise KeyError(proposal.slug) + + +def _metric_from_swap(result: dict) -> dict: + clean_top = result.get("clean_top", []) + swapped_top = result.get("swapped_top", []) + target = _norm_word(result.get("target", "")) + + def clean_tok(x): + return _norm_word(x[0]) if x else "" + + swapped_words = [clean_tok(x) for x in swapped_top[:5]] + return { + "metric_kind": "manipulation_sanity_check", + "source_suppressed": result["source_rank_swapped"] > result["source_rank_clean"], + "target_installed": result["target_rank_swapped"] < result["target_rank_clean"], + "target_top1": bool(swapped_words and swapped_words[0] == target), + "target_top5": target in swapped_words, + "top_changed": bool(clean_top and swapped_top and clean_tok(clean_top[0]) != clean_tok(swapped_top[0])), + "source_rank_delta": result["source_rank_swapped"] - result["source_rank_clean"], + "target_rank_delta": result["target_rank_clean"] - result["target_rank_swapped"], + "causal_success": result.get("causal_success"), + } + + +def execute_swaps(run: dict, proposals: list[Proposal], *, model_name: str, + lens_path: str, alphas: list[float], method: str) -> list[dict]: + print(f"\nLoading model and lens for {len(proposals)} approved proposal(s)...") + model, lens = swapmod._load(model_name, lens_path) + band = tuple(run.get("methodology", {}).get("workspace_band", (38.0, 92.0))) + records = [] + for p in proposals: + prompt = _resolve_prompt_text(run, p, model.tokenizer) + if not prompt: + print(f"skip {p.slug}: no executable prompt text in run JSON", file=sys.stderr) + continue + alpha_records = [] + print(f"\n{p.slug}: {p.source} -> {p.target}") + for alpha in alphas: + try: + out = swapmod.run_swap(model, lens, prompt, p.source, p.target, + alpha=alpha, method=method, band=band) + except Exception as exc: # noqa: BLE001 + alpha_records.append({"alpha": alpha, "error": f"{type(exc).__name__}: {exc}"}) + print(f" alpha={alpha:g}: ERROR {type(exc).__name__}: {exc}") + continue + metrics = _metric_from_swap(out) + alpha_records.append({"alpha": alpha, "swap": out, "metrics": metrics}) + print( + f" alpha={alpha:g}: source rank " + f"{out['source_rank_clean']+1}->{out['source_rank_swapped']+1}; " + f"target rank {out['target_rank_clean']+1}->{out['target_rank_swapped']+1}; " + f"top {out['clean_top'][0][0]!r}->{out['swapped_top'][0][0]!r}" + ) + records.append({"proposal": asdict(p), "alphas": alpha_records}) + return records + + +def plot_swap_effects(records: list[dict], out_dir: Path) -> list[str]: + import matplotlib.pyplot as plt + import numpy as np + + out_dir.mkdir(parents=True, exist_ok=True) + written = [] + by_slug: dict[str, list[dict]] = {} + for rec in records: + by_slug.setdefault(rec["proposal"]["slug"], []).append(rec) + + for slug, rows in by_slug.items(): + labels, target_delta, source_delta, colors = [], [], [], [] + for rec in rows: + prop = rec["proposal"] + pair = f"{prop['source']}->{prop['target']}" + for ar in rec["alphas"]: + if "metrics" not in ar: + continue + labels.append(f"{pair}\na={ar['alpha']:g}") + target_delta.append(ar["metrics"]["target_rank_delta"]) + source_delta.append(ar["metrics"]["source_rank_delta"]) + colors.append("#2b7a8c" if ar["metrics"]["target_top5"] else "#c1553b") + if not labels: + continue + x = np.arange(len(labels)) + fig, axes = plt.subplots(1, 2, figsize=(max(8.0, 1.1 * len(labels)), 4.0)) + axes[0].bar(x, target_delta, color=colors, alpha=0.9) + axes[0].axhline(0, color="#8a8a8a", lw=0.8) + axes[0].set_title("Target-direction rank movement (diagnostic)") + axes[0].set_ylabel("clean rank - swapped rank\n(positive = target moved up)") + axes[1].bar(x, source_delta, color="#6a51a3", alpha=0.9) + axes[1].axhline(0, color="#8a8a8a", lw=0.8) + axes[1].set_title("Source suppression") + axes[1].set_ylabel("swapped rank - clean rank\n(positive = source moved down)") + for ax in axes: + ax.set_xticks(x) + ax.set_xticklabels(labels, rotation=35, ha="right", fontsize=8) + ax.grid(True, axis="y", color="#e7e7e7", lw=0.6) + ax.spines[["top", "right"]].set_visible(False) + fig.suptitle(f"Swap effects: {slug}", x=0.01, ha="left", fontweight="bold") + fig.tight_layout() + path = out_dir / f"{slug}__swap_effect.png" + fig.savefig(path, dpi=200, bbox_inches="tight") + plt.close(fig) + written.append(str(path)) + return written + + +def write_results(base_dir: Path, tag: str, run_path: str, model_name: str, + lens_path: str, proposals: list[Proposal], approved: list[Proposal], + results: list[dict], provider: str, analysis_model: str | None, + plot_paths: list[str], method: str) -> Path: + out = { + "created_at": datetime.now().isoformat(timespec="seconds"), + "run": str(run_path), + "model": model_name, + "lens": str(lens_path), + "method": method, + "tag": tag, + "provider": provider, + "analysis_model": analysis_model, + "proposals": [asdict(p) for p in proposals], + "approved": [asdict(p) for p in approved], + "results": results, + "plots": [str(Path(p).relative_to(base_dir)) for p in plot_paths], + } + runs_dir = base_dir / "runs" + runs_dir.mkdir(exist_ok=True) + path = runs_dir / f"{tag}_swaps.json" + path.write_text(json.dumps(out, indent=2, ensure_ascii=False)) + return path + + +# --------------------------------------------------------------------------- # +# CLI +# --------------------------------------------------------------------------- # + +def _parse_alphas(s: str) -> list[float]: + vals = [float(x.strip()) for x in s.split(",") if x.strip()] + if not vals: + raise argparse.ArgumentTypeError("need at least one alpha") + return vals + + +def main() -> None: + ap = argparse.ArgumentParser(description="Suggest, approve, and run J-lens swaps.") + ap.add_argument("--run", required=True, help="runs/.json from run_lens.py") + ap.add_argument("--model", default=None, help="HF model id (default: run['model'])") + ap.add_argument("--lens", default=None, help="lens_.pt (default: sibling lens file)") + ap.add_argument("--provider", choices=["offline", "anthropic", "openai"], + default="offline") + ap.add_argument("--analysis-model", default=None, + help="provider model override for proposal generation") + ap.add_argument("--slugs", default=None, help="comma-separated prompt slugs") + ap.add_argument("--domains", default=None, help="comma-separated domains") + ap.add_argument("--alpha", type=_parse_alphas, default=[1.0], + help="comma-separated alpha sweep, e.g. 0.5,1,2,4") + ap.add_argument("--method", choices=["pinv", "projection"], default="pinv", + help="swap method: paper-style pseudoinverse coordinates " + "(pinv) or old projection approximation") + ap.add_argument("--auto-approve", action="store_true", + help="run all validated proposals without prompting") + ap.add_argument("--dry-run", action="store_true", + help="print proposals only; do not load model weights or run swaps") + ap.add_argument("--allow-weak", action="store_true", + help="allow source concepts worse than --max-source-rank") + ap.add_argument("--max-source-rank", type=int, default=20, + help="1-based maximum source rank to propose by default") + ap.add_argument("--per-prompt", type=int, default=2) + ap.add_argument("--max-proposals", type=int, default=20) + args = ap.parse_args() + + run_path = Path(args.run) + run = _load_run(run_path) + base_dir = _base_dir(run_path) + tag = run.get("tag") or run_path.stem + model_name = args.model or run.get("model") + lens_path = args.lens or str(base_dir / f"lens_{tag}.pt") + slugs = _csv_set(args.slugs) + domains = _csv_set(args.domains) + + seeds = deterministic_proposals( + run, + slugs=slugs, + domains=domains, + max_source_rank=args.max_source_rank, + allow_weak=args.allow_weak, + per_prompt=args.per_prompt, + max_proposals=args.max_proposals, + ) + proposals = seeds + provider_used = args.provider + if args.provider != "offline": + try: + proposals = llm_refine_proposals( + run, + seeds, + provider=args.provider, + model=args.analysis_model, + allow_weak=args.allow_weak, + max_source_rank=args.max_source_rank, + ) + except Exception as exc: # noqa: BLE001 + print(f"{args.provider} proposal call failed: {type(exc).__name__}: {exc}", + file=sys.stderr) + print("Falling back to offline deterministic proposals.", file=sys.stderr) + provider_used = "offline" + proposals = seeds + + if not args.dry_run: + if not model_name: + raise SystemExit("--model is required when the run JSON has no model field") + if not Path(lens_path).is_file(): + raise SystemExit(f"lens file not found: {lens_path}") + proposals = validate_tokens(proposals, model_name) + + print_proposals(proposals) + if args.dry_run: + print("\nDry run: no model weights loaded and no swaps executed.") + return + + approved = approve_proposals(proposals, auto_approve=args.auto_approve) + if not approved: + print("No swaps approved.") + return + + results = execute_swaps( + run, + approved, + model_name=model_name, + lens_path=lens_path, + alphas=args.alpha, + method=args.method, + ) + plot_dir = base_dir / "figures" / tag / "swaps" + plots = plot_swap_effects(results, plot_dir) + out_json = write_results( + base_dir, + tag, + str(run_path), + model_name, + lens_path, + proposals, + approved, + results, + provider_used, + args.analysis_model, + plots, + args.method, + ) + print(f"\nWrote swap results: {out_json}") + if plots: + print("Wrote plots:") + for p in plots: + print(f" {p}") + + +if __name__ == "__main__": + main() diff --git a/jlens_materials/compare.py b/jlens_materials/compare.py new file mode 100644 index 0000000000000000000000000000000000000000..f0ddef8d7a60d312d5e24e7124c8b973f1920993 --- /dev/null +++ b/jlens_materials/compare.py @@ -0,0 +1,490 @@ +# Copyright 2026. Apache-2.0. +"""Compare Jacobian-lens runs across models (e.g. Qwen vs Gemma). + +Loads two or more ``runs/.json`` records and produces: + + figures/compare/__hitrate.png grouped bars: item-level pass@5 + by evaluation shape and model + figures/compare/__concept_rank.png heatmap: each shared concept's best + lens rank, concepts x models + figures/compare/__swap.png (if swaps present) target install + rank after swap, per model + runs/comparison_.json the aligned numbers + runs/comparison__analysis.json cross-model LLM synthesis + reports/comparison__report.{tex,pdf} the comparison report + + python compare.py --runs runs/qwen2forcausallm.json runs/gpt2lmheadmodel.json + python compare.py --runs runs/*.json --provider openai --tag qwen_vs_gemma + +Provider/auth and the offline fallback work exactly as in analyze.py. +""" + +from __future__ import annotations + +import argparse +import base64 +import json +from pathlib import Path + +import matviz +import report_latex as rl +import llm + +HERE = Path(__file__).resolve().parent +FIGDIR = HERE / "figures" + + +def _short(model_id: str) -> str: + return model_id.split("/")[-1] + + +# --------------------------------------------------------------------------- # +# data alignment +# --------------------------------------------------------------------------- # + +def hit_rate_table(runs, top_k=5): + shapes = sorted({shape for r in runs for shape in ( + r.get("metrics", {}).get("by_shape", {}).keys() or + {p["shape"] for p in r["prompts"]})}, + key=lambda s: ["MULTIHOP", "ASSOCIATION", "RECOGNITION", + "MODULATION", "REPORT_SWAP"].index(s) + if s in ("MULTIHOP", "ASSOCIATION", "RECOGNITION", + "MODULATION", "REPORT_SWAP") else 99) + labels = [_short(r["model"]) for r in runs] + values, counts = [], {} + for r in runs: + paper_metrics = r.get("metrics", {}).get("by_shape", {}) + if paper_metrics: + row = [] + for shape in shapes: + metric = paper_metrics.get(shape) + if not metric or top_k not in metric["ks"]: + row.append(float("nan")) + continue + value = metric["jacobian_lens"]["pass_at_k"][metric["ks"].index(top_k)] + row.append(value) + counts[(_short(r["model"]), shape)] = (None, metric["n_items"]) + values.append(row) + continue + byshape: dict[str, list[bool]] = {} + for p in r["prompts"]: + hits = [0 <= e["best_rank"] < top_k for e in p["emergence"]] + byshape.setdefault(p["shape"], []).extend(hits) + row = [] + for s in shapes: + v = byshape.get(s) + row.append(sum(v) / len(v) if v else float("nan")) + if v: + counts[(_short(r["model"]), s)] = (int(sum(v)), len(v)) + values.append(row) + return shapes, labels, values, counts + + +def concept_rank_matrix(runs, max_rows=22): + """Per (prompt, concept) best lens rank across models, for concepts present + in >= 2 runs. Rows sorted by the best rank any model achieved.""" + labels = [_short(r["model"]) for r in runs] + table: dict[tuple[str, str], dict[int, int]] = {} + for mi, r in enumerate(runs): + for p in r["prompts"]: + for e in p["emergence"]: + table.setdefault((p["slug"], e["label"]), {})[mi] = e["best_rank"] + 1 + rows = [k for k, v in table.items() if len(v) >= 2] + rows.sort(key=lambda k: min(table[k].values())) + rows = rows[:max_rows] + row_labels = [f"{slug.split('-', 1)[-1]}: {c}" for (slug, c) in rows] + ranks = [[table[k].get(mi, float("nan")) for mi in range(len(runs))] + for k in rows] + return row_labels, labels, ranks + + +def swap_rows(runs): + out = [] + for r in runs: + for p in r["prompts"]: + sw = p.get("swap") + if sw: + out.append({"model": _short(r["model"]), "slug": p["slug"], + "source": sw["source"], "target": sw["target"], + "alpha": sw["alpha"], + "source_rank_clean": sw["source_rank_clean"] + 1, + "source_rank_swapped": sw["source_rank_swapped"] + 1, + "target_rank_clean": sw["target_rank_clean"] + 1, + "target_rank_swapped": sw["target_rank_swapped"] + 1}) + return out + + +# --------------------------------------------------------------------------- # +# figures +# --------------------------------------------------------------------------- # + +def make_figures(runs, tag): + out = (FIGDIR / "compare") + out.mkdir(parents=True, exist_ok=True) + figs = {} + + shapes, labels, values, _ = hit_rate_table(runs) + figs["hitrate"] = matviz.plot_grouped_bars( + shapes, labels, values, out / f"{tag}__hitrate", + title="Concept surfacing rate by experiment type — model comparison", + subtitle="item-level pass@5 over registered score spans and fixed bands", + ylabel="pass@5")[0] + + row_labels, cols, ranks = concept_rank_matrix(runs) + if row_labels: + figs["concept_rank"] = matviz.plot_rank_matrix( + row_labels, cols, ranks, out / f"{tag}__concept_rank", + title="Best lens rank per shared concept, across models", + subtitle="bright = concept strongly surfaces; grey = not present for " + "that model")[0] + + sw = swap_rows(runs) + if sw: + # target install rank after swap (lower = better), grouped by prompt + by_slug: dict[str, dict[str, float]] = {} + for s in sw: + by_slug.setdefault(s["slug"], {})[s["model"]] = s["target_rank_swapped"] + cats = list(by_slug) + models = sorted({s["model"] for s in sw}) + vals = [[by_slug[c].get(m, float("nan")) for c in cats] for m in models] + figs["swap"] = matviz.plot_grouped_bars( + [c.split("-", 1)[-1] for c in cats], models, vals, + out / f"{tag}__swap", + title="Causal swap: target-concept rank after the swap (lower = installed)", + subtitle="post-swap full-vocab rank of the swapped-in target concept", + ylabel="target rank after swap", pct=False)[0] + return figs + + +# --------------------------------------------------------------------------- # +# LLM comparative analysis +# --------------------------------------------------------------------------- # + +SYSTEM_CMP = """\ +You are an interpretability researcher comparing Jacobian-lens ("J-lens") runs \ +across different language models, in the framework of "Verbalizable \ +Representations Form a Global Workspace in Language Models" (Anthropic, 2026). + +You are given: (a) per-model top-5 concept-surfacing rates by experiment type \ +(MULTIHOP / ASSOCIATION / RECOGNITION / MODULATION / REPORT_SWAP); (b) a matrix \ +of each shared concept's best lens rank per model; (c) optionally causal-swap \ +outcomes per model. Write a comparative analysis: which model surfaces \ +verbalizable scientific concepts more strongly and where (which shapes, which \ +concepts), how vocabulary size / model capability explains differences \ +(single-token limitation, §9.1), how the causal swaps compare, and a practical \ +recommendation for which model to use for scientific interpretability. Cite the \ +numbers. Be precise and honest; note where a difference is small or where a \ +model simply lacks a concept. +""" + + +SYSTEM_PP = """\ +You are an interpretability researcher comparing how several language models \ +represent ONE prompt, read out with the Jacobian lens (global-workspace \ +framework, Anthropic 2026). You are given, for each model: the best lens rank \ +(and depth) of each tracked/discovered concept for this prompt, and that model's \ +own per-prompt read-out analysis. Write a tight 1-2 paragraph comparison for \ +THIS prompt: which model surfaces the unspoken intermediates more strongly or \ +earlier (cite ranks/depths), where the models agree or diverge, and what \ +explains the difference (capability, vocabulary, single-token limitation). Be \ +concrete and honest; if a model simply lacks the concept, say so. +""" + + +def _img(path): + p = Path(path) + if not p.is_file(): + return None + return {"type": "image_png", + "data": base64.standard_b64encode(p.read_bytes()).decode()} + + +# --------------------------------------------------------------------------- # +# per-prompt cross-model layer +# --------------------------------------------------------------------------- # + +def load_analyses(run_paths): + """For each run path, load its sibling _analysis.json (if present) as + slug -> 'what we see' text. Returns a list aligned with the runs.""" + out = [] + for p in run_paths: + ana = Path(p).with_name(Path(p).stem + "_analysis.json") + if ana.is_file(): + data = json.loads(ana.read_text()) + out.append({a["slug"]: a["analysis"] for a in data.get("per_prompt", [])}) + else: + out.append({}) + return out + + +def shared_prompts(runs): + """Prompts (by slug) present in >=2 runs, in first-appearance order.""" + order, meta = [], {} + for mi, r in enumerate(runs): + for p in r["prompts"]: + if p["slug"] not in meta: + meta[p["slug"]] = {"title": p["title"], "shape": p["shape"], + "domain": p["domain"], "models": []} + order.append(p["slug"]) + meta[p["slug"]]["models"].append(mi) + return [(s, meta[s]) for s in order if len(meta[s]["models"]) >= 2] + + +def _emergence_of(run, slug): + for p in run["prompts"]: + if p["slug"] == slug: + return {e["label"]: (e["best_rank"] + 1, e["best_depth"]) + for e in p["emergence"]} + return {} + + +def _per_prompt_analysis(title, shape, per_model, fig_png, provider, model, + offline): + if offline or model is None: + parts = [] + for pm in per_model: + top = [c for c, (r, _) in pm["emergence"].items() if r <= 5] + parts.append(f"{pm['model']}: {len(top)} concept(s) in lens top-5" + + (f" ({', '.join(top)})" if top else "")) + return "[offline template] " + "; ".join(parts) + "." + lines = [f"Prompt: {title} (shape {shape}). Compare the models on THIS prompt."] + for pm in per_model: + lines.append(f"\n### {pm['model']}") + lines.append(" concept best ranks: " + ", ".join( + f"{c}=rank {r}@{d}%" for c, (r, d) in pm["emergence"].items())) + if pm["analysis"]: + lines.append(" its own read-out analysis: " + pm["analysis"]) + lines.append("\nThe attached figure shows each concept's best rank per model " + "(left = stronger). Write the 1-2 paragraph cross-model " + "comparison for this prompt.") + blocks = [{"type": "text", "text": "\n".join(lines)}] + if (b := _img(fig_png)): + blocks.append(b) + try: + return llm.complete(provider, model, SYSTEM_PP, blocks, max_tokens=1500) + except Exception as exc: # noqa: BLE001 + return f"[per-prompt comparison unavailable: {type(exc).__name__}: {exc}]" + + +def per_prompt_layer(runs, analyses, tag, provider, model, offline): + """One record per shared prompt: a cross-model rank figure, each model's own + read-out analysis, and an LLM cross-model comparison for that prompt.""" + labels = [_short(r["model"]) for r in runs] + outdir = FIGDIR / "compare" / tag + outdir.mkdir(parents=True, exist_ok=True) + records = [] + for slug, m in shared_prompts(runs): + per_model, concepts = [], [] + for mi in m["models"]: + em = _emergence_of(runs[mi], slug) + per_model.append({"model": labels[mi], "emergence": em, + "analysis": analyses[mi].get(slug, "")}) + for c in em: + if c not in concepts: + concepts.append(c) + mods = [pm["model"] for pm in per_model] + ranks = [[pm["emergence"].get(c, (float("nan"), None))[0] for c in concepts] + for pm in per_model] + fig = matviz.plot_concept_rank_dots( + concepts, mods, ranks, outdir / f"{slug}__ranks", + title=f"{m['title']} — concept ranks across models", + subtitle="best lens rank per concept; left = stronger, missing = " + "not surfaced")[0] + cross = _per_prompt_analysis(m["title"], m["shape"], per_model, fig, + provider, model, offline) + print(f" [{slug}] per-prompt comparison ({'offline' if offline else provider})") + records.append({"slug": slug, "title": m["title"], "shape": m["shape"], + "domain": m["domain"], + "figure": str(Path(fig).relative_to(HERE)), + "per_model": per_model, "cross": cross}) + return records + + +def comparative_analysis(runs, figs, provider, model, offline): + shapes, labels, values, counts = hit_rate_table(runs) + lines = [f"Models compared: {labels}", "", + "## Top-5 surfacing rate by shape (model: shape=rate):"] + for li, lab in enumerate(labels): + parts = [f"{s}={values[li][j]:.2f}" if values[li][j] == values[li][j] + else f"{s}=n/a" for j, s in enumerate(shapes)] + lines.append(f" {lab}: " + ", ".join(parts)) + row_labels, cols, ranks = concept_rank_matrix(runs) + if row_labels: + lines += ["", "## Best lens rank per shared concept (rank; lower=stronger):"] + for rl_, rr in zip(row_labels, ranks): + cells = ", ".join(f"{cols[j]}={'n/a' if rr[j]!=rr[j] else int(rr[j])}" + for j in range(len(cols))) + lines.append(f" {rl_}: {cells}") + sw = swap_rows(runs) + if sw: + lines += ["", "## Causal swaps (source/target full-vocab rank clean->swapped):"] + for s in sw: + lines.append(f" [{s['model']}] {s['slug']} {s['source']}->{s['target']} " + f"(a={s['alpha']}): source {s['source_rank_clean']}->" + f"{s['source_rank_swapped']}, target " + f"{s['target_rank_clean']}->{s['target_rank_swapped']}") + + if offline: + best = max(labels, key=lambda L: _mean_hit(runs, L)) + return ("[offline template] Comparison of " + ", ".join(labels) + + f". Highest overall top-5 surfacing rate: {best}. See the " + "grouped-bar and concept-rank figures for the per-shape and " + "per-concept breakdown.") + + blocks = [{"type": "text", "text": "\n".join(lines) + + "\n\nThe attached figures are the per-shape hit-rate comparison " + "and the per-concept rank matrix. Write the comparative analysis " + "(3-5 paragraphs)."}] + for key in ("hitrate", "concept_rank", "swap"): + if key in figs and (b := _img(figs[key])): + blocks.append(b) + try: + return llm.complete(provider, model, SYSTEM_CMP, blocks, max_tokens=6000) + except Exception as exc: # noqa: BLE001 + return f"[comparison analysis unavailable: {type(exc).__name__}: {exc}]" + + +def _mean_hit(runs, label): + for r in runs: + if _short(r["model"]) == label: + hits = [0 <= e["best_rank"] < 5 for p in r["prompts"] + for e in p["emergence"]] + return sum(hits) / len(hits) if hits else 0.0 + return 0.0 + + +# --------------------------------------------------------------------------- # +# comparison report (LaTeX -> PDF) +# --------------------------------------------------------------------------- # + +def build_report(runs, tag, figs, analysis_text, per_prompt, provider, model, + offline, compile_pdf=True): + labels = [_short(r["model"]) for r in runs] + L = [rl.PREAMBLE % { + "title": rl.tex_escape(f"Model comparison: {' vs '.join(labels)}"), + "author": ("offline template" if offline + else rl.tex_escape(f"Comparison by {provider}:{model}"))}] + L.append(r"\noindent " + rl.tex_escape( + "Models: " + "; ".join(f"{_short(r['model'])} ({r['n_layers']} layers, " + f"d_model {r['d_model']})" for r in runs)) + "\n") + + L.append(r"\section*{Comparative synthesis}") + L.append(rl.md_to_tex(analysis_text)) + + cap = {"hitrate": "Top-5 concept-surfacing rate by experiment type, per model.", + "concept_rank": "Best lens rank of each shared concept, per model " + "(grey = not present for that model).", + "swap": "Causal swap: post-swap rank of the swapped-in target " + "concept, per model (lower = more successfully installed)."} + for key in ("hitrate", "concept_rank", "swap"): + if key in figs: + L.append(r"\begin{figure}[H]\centering") + L.append(rf"\includegraphics[width=0.95\linewidth]" + rf"{{{Path(figs[key]).resolve()}}}") + L.append(rf"\caption{{{cap[key]}}}\end{{figure}}") + + # per-shape numeric table + shapes, slabels, values, _ = hit_rate_table(runs) + L.append(r"\clearpage\section*{Top-5 surfacing rate (numbers)}") + L.append(r"\begin{table}[H]\centering\small\begin{tabular}{l" + + "r" * len(shapes) + r"}\toprule") + L.append("model & " + " & ".join(rl.tex_escape(s) for s in shapes) + r" \\ \midrule") + for li, lab in enumerate(slabels): + cells = " & ".join("--" if values[li][j] != values[li][j] + else f"{values[li][j]*100:.0f}\\%" for j in range(len(shapes))) + L.append(f"{rl.tex_escape(lab)} & {cells} \\\\") + L.append(r"\bottomrule\end{tabular}\end{table}") + + sw = swap_rows(runs) + if sw: + L.append(r"\section*{Causal swaps}") + L.append(r"\begin{table}[H]\centering\small\begin{tabular}{lllrr}\toprule") + L.append(r"model & prompt & swap & source rank & target rank \\ \midrule") + for s in sw: + L.append(f"{rl.tex_escape(s['model'])} & " + f"{rl.tex_escape(s['slug'].split('-',1)[-1])} & " + f"{rl.tex_escape(s['source']+'->'+s['target'])} & " + f"{s['source_rank_clean']}$\\to${s['source_rank_swapped']} & " + f"{s['target_rank_clean']}$\\to${s['target_rank_swapped']} \\\\") + L.append(r"\bottomrule\end{tabular}") + L.append(r"\caption{Source rank rises = suppressed; target rank falls = " + r"installed.}\end{table}") + + # ---- per-prompt cross-model analysis (keeps each model's own read-out) --- + if per_prompt: + L.append(r"\clearpage\section{Per-prompt cross-model analysis}") + L.append(r"For each prompt shared across the models: each model's own " + r"read-out analysis, the concept ranks side by side, and a " + r"cross-model comparison.") + for rec in per_prompt: + L.append(rf"\subsection{{{rl.tex_escape(rec['title'])} " + rf"\normalfont\texttt{{{rl.tex_escape(rec['shape'])}}}}}") + for pm in rec["per_model"]: + if pm["analysis"]: + L.append(rf"\paragraph{{{rl.tex_escape(pm['model'])} --- " + rf"what it saw.}} " + rl.md_to_tex(pm["analysis"])) + figp = (HERE / rec["figure"]).resolve() + if figp.is_file(): + L.append(r"\begin{figure}[H]\centering") + L.append(rf"\includegraphics[width=0.85\linewidth]{{{figp}}}") + L.append(r"\caption{Best lens rank of each concept, per model " + r"(left = stronger).}\end{figure}") + L.append(r"\paragraph{Across models.} " + rl.md_to_tex(rec["cross"])) + + L.append(r"\end{document}") + reports = HERE / "reports" + reports.mkdir(exist_ok=True) + tex = reports / f"comparison_{tag}_report.tex" + tex.write_text("\n".join(L), encoding="utf-8") + print(f"wrote {tex}") + if compile_pdf: + rl.compile_pdf(tex) + return tex + + +# --------------------------------------------------------------------------- # + +def main() -> None: + ap = argparse.ArgumentParser() + ap.add_argument("--runs", nargs="+", required=True, + help="two or more runs/.json files to compare") + ap.add_argument("--tag", default=None, help="output name (default: joined model names)") + ap.add_argument("--provider", choices=["anthropic", "openai"], default="anthropic") + ap.add_argument("--model", default=None) + ap.add_argument("--offline", action="store_true") + ap.add_argument("--no-compile", action="store_true") + args = ap.parse_args() + + runs = [json.loads(Path(p).read_text()) for p in args.runs] + if len(runs) < 2: + raise SystemExit("need at least two runs to compare") + analyses = load_analyses(args.runs) # per-model 'what we see' (if present) + tag = args.tag or "_vs_".join(_short(r["model"]) for r in runs) + tag = tag.replace("/", "-").replace(".", "") + model = args.model or (None if args.offline else llm.default_model(args.provider)) + + print(f"comparing: {[_short(r['model']) for r in runs]} (tag={tag})") + figs = make_figures(runs, tag) + analysis = comparative_analysis(runs, figs, args.provider, model, args.offline) + per_prompt = per_prompt_layer(runs, analyses, tag, args.provider, model, + args.offline) + + comp = {"tag": tag, "models": [r["model"] for r in runs], + "provider": args.provider, "offline": args.offline, + "hitrate": dict(zip(("shapes", "labels", "values"), + hit_rate_table(runs)[:3])), + "swaps": swap_rows(runs), "analysis": analysis, + "per_prompt": [{k: v for k, v in rec.items() if k != "per_model"} + | {"models": [pm["model"] for pm in rec["per_model"]]} + for rec in per_prompt]} + (HERE / "runs" / f"comparison_{tag}.json").write_text( + json.dumps(comp, indent=2, ensure_ascii=False)) + print(f"wrote runs/comparison_{tag}.json") + + build_report(runs, tag, figs, analysis, per_prompt, args.provider, model, + args.offline, compile_pdf=not args.no_compile) + print(f"done -> reports/comparison_{tag}_report.pdf") + + +if __name__ == "__main__": + main() diff --git a/jlens_materials/domain_prompts.py b/jlens_materials/domain_prompts.py new file mode 100644 index 0000000000000000000000000000000000000000..dea8d2e16cf97d66db1defb202f7a2b5a03bc746 --- /dev/null +++ b/jlens_materials/domain_prompts.py @@ -0,0 +1,768 @@ +# Copyright 2026. Apache-2.0. +"""Domain prompt sets for applying the Jacobian lens to scientific reasoning. + +This module ports the experiment *shapes* from "Verbalizable Representations +Form a Global Workspace in Language Models" (transformer-circuits.pub/2026/ +workspace) onto two scientific domains: + + * fracture / solid mechanics + * protein science + +Each experiment shape asks a different question of the J-lens: + + MULTIHOP Does an *unspoken intermediate concept* surface in the lens + before the answer token? (paper: Mars -> color -> red; + (4+17)*2+7 -> 21 -> 42 -> 49) We track a chain of + domain intermediates and watch the depth at which each + reaches the top of the lens. + + ASSOCIATION A vignette evokes ONE concept but never names it. Does the + concept appear in the lens at the final token? (paper: + grief / Einstein / noir association eval.) + + RECOGNITION Raw symbolic input (an amino-acid sequence, a stress-state + tensor) whose *meaning* the model must recover. (paper: + "protein" / "fluor" / "green" five residues into a GFP + sequence.) The domain analogue of the paper's protein and + bug-detection examples. + + MODULATION Hold a target concept in mind while copying an unrelated + carrier sentence; check whether the target surfaces in the + lens over the response even though the surface text never + mentions it. (paper: "concentrate on ocean creatures".) + + REPORT_SWAP "Think of a {category}. Answer in one word." The chosen + word is a verbal report; swapping its lens vector for a + sibling should change the reported word. (paper: think of + a sport -> Soccer, swap -> Rugby.) + +Each Prompt carries the metadata needed to score it without searching for a +favourable layer/position: a protocol, fixed readout selector or response span, +ordered latent intermediates and synonyms, clean/counterfactual answers, and +control-condition labels. The JSON loader also accepts the public +``jacobian-lens`` evaluation and experiment schemas, which makes the same +runner usable for new Gemma mechanics/materials datasets. +""" + +from __future__ import annotations + +import json +from dataclasses import asdict, dataclass, field, fields +from pathlib import Path + + +@dataclass(frozen=True) +class Prompt: + """One lens prompt plus everything the analysis needs to score it. + + Attributes: + slug: Stable identifier (also the output-figure basename). + shape: One of MULTIHOP / ASSOCIATION / RECOGNITION / MODULATION / + REPORT_SWAP -- selects which figure(s) the runner produces. + domain: "fracture" or "protein". + title: Human-readable heading for the figure. + description: One-line caption shown under the figure. + text: Raw-text prompt. Mutually exclusive with `user`. + user / system / assistant_prefill: chat-mode fields, assembled with the + tokenizer chat template (needed for instruct models like Gemma-IT). + protocol: Paper protocol implemented by the item: ``lens_eval``, + ``directed_modulation``, ``verbal_report``, ``probe_swap``, or + ``exploratory``. + readout_selector: Predetermined scoring location: ``before_answer``, + ``final_prompt_token``, ``last_newline``, ``assistant_response``, + ``explicit``, or exploratory-only ``all_prompt``. + readout_at: Python index used only by the ``explicit`` selector. + tracked: Ordered intermediate concept words to follow across layers. + Order encodes the *expected* computational order (earliest first), + so the runner can test the paper's "intermediates surface in the + order they are computed" claim. + synonyms: Optional map surface-word -> list of equivalent surface forms + (e.g. "45" -> ["45", "forty-five"]); rank is the min over forms. + answer / acceptable_answers: Expected clean continuation. Quantitative + items are scored only when the clean model is correct. + must_be_absent_from_input/output: Enforce the paper's latent- + intermediate controls at tokenizer level. + condition / control_group: Directed-modulation contrast metadata. + swap_from / swap_to / swap_answer: Causal intervention definition. + ``swap_from`` may be omitted for verbal report, where the model's + clean greedy choice is used dynamically. + note: Free-text scientific rationale, surfaced in the analysis writeup. + """ + + slug: str + shape: str + domain: str + title: str + description: str + protocol: str = "lens_eval" + text: str | None = None + user: str | None = None + system: str | None = None + assistant_prefill: str = "" + readout_selector: str = "final_prompt_token" + readout_at: int = -1 + tracked: tuple[str, ...] = () + synonyms: dict[str, list[str]] = field(default_factory=dict) + answer: str | None = None + acceptable_answers: tuple[str, ...] = () + must_be_absent_from_input: bool = False + must_be_absent_from_output: bool = False + condition: str | None = None + control_group: str | None = None + phrasing_id: str | None = None + carrier_id: str | None = None + target_family: str | None = None + category: str | None = None + swap_from: str | None = None + swap_to: str | None = None + swap_answer: str | None = None + candidates: tuple[str, ...] = () + note: str = "" + + +def resolve_text(p: Prompt, tokenizer) -> str: + """Return the final prompt string, formatting chat-mode prompts with the + tokenizer's chat template (matches jlens.examples.resolve_prompt).""" + if p.user is None: + if p.text is None: + raise ValueError(f"prompt {p.slug!r} has neither text nor user") + return p.text + messages = [] + if p.system: + messages.append({"role": "system", "content": p.system}) + messages.append({"role": "user", "content": p.user}) + if getattr(tokenizer, "chat_template", None) is None: + # Base model with no chat template: assemble a plain instruction block + # so the same prompt still runs (degraded — instruct behaviour needs an + # -it checkpoint). + head = (f"{p.system}\n\n" if p.system else "") + return f"{head}{p.user}\n{p.assistant_prefill}" + if p.assistant_prefill: + messages.append({"role": "assistant", "content": p.assistant_prefill}) + return tokenizer.apply_chat_template( + messages, tokenize=False, continue_final_message=True + ) + return tokenizer.apply_chat_template( + messages, tokenize=False, add_generation_prompt=True + ) + + +# --------------------------------------------------------------------------- # +# JSON <-> Prompt (prompts are data, not code) +# --------------------------------------------------------------------------- # + +_FIELDS = {f.name for f in fields(Prompt)} + + +def to_dict(p: Prompt) -> dict: + """Prompt -> plain dict, dropping empty/default fields for tidy JSON.""" + d = asdict(p) + for key in ("tracked", "acceptable_answers", "candidates"): + d[key] = list(d[key]) + return {k: v for k, v in d.items() if v not in (None, "", [], {}, ())} + + +def from_dict(d: dict) -> Prompt: + """Plain dict -> Prompt. Unknown keys are ignored (forward-compatible); + ``tracked`` accepts a list; ``shape``/``domain``/``title`` fall back to + sensible defaults so a minimal ``{"text": ...}`` is a valid prompt.""" + d = dict(d) + if "prompt" in d and "text" not in d: + d["text"] = d["prompt"] + if "intermediates" in d and "tracked" not in d: + d["tracked"] = d["intermediates"] + if "intermediate" in d and "tracked" not in d: + d["tracked"] = [d["intermediate"]] + if "target" in d and "answer" not in d: + d["answer"] = d["target"] + if "swap_answer" in d or ("intermediate" in d and "swap_to" in d): + d.setdefault("protocol", "probe_swap") + d.setdefault("shape", "PROBE_SWAP") + elif "phrasings" not in d: + d.setdefault("protocol", "lens_eval") + if d.get("protocol") in {"lens_eval", "probe_swap"}: + d.setdefault("readout_selector", "before_answer") + d.setdefault("must_be_absent_from_input", True) + d.setdefault("must_be_absent_from_output", True) + kw = {k: v for k, v in d.items() if k in _FIELDS} + kw.setdefault("slug", d.get("slug") or d.get("name") or "adhoc") + kw.setdefault("shape", "MULTIHOP") + kw.setdefault("domain", d.get("domain", "custom")) + kw.setdefault("title", d.get("title") or d.get("name") or kw["slug"]) + kw.setdefault("description", d.get("description", "")) + for key in ("tracked", "acceptable_answers", "candidates"): + if key in kw: + kw[key] = tuple(kw[key]) + return Prompt(**kw) + + +def load_prompts(source: str) -> list[Prompt]: + """Load prompts from JSON. ``source`` is one of: + + - a path to a ``.json`` file holding a list of prompt dicts, or a dict with + a ``"prompts": [...]`` key; + - a path to a directory — every ``*.json`` in it is loaded and concatenated; + - a raw JSON string (a single dict, or a list of dicts) — for defining a + prompt on the fly on the command line. + """ + stripped = source.lstrip() + if stripped[:1] in "{[": # inline JSON string (not a path) + data = json.loads(source) + records = (data.get("prompts", data.get("items", data)) + if isinstance(data, dict) else data) + if isinstance(records, dict): + records = [records] + else: + p = Path(source) + if not p.exists(): + raise FileNotFoundError(f"prompt source not found: {source!r}") + blobs = ([p] if p.is_file() + else sorted(x for x in p.iterdir() if x.suffix == ".json")) + records = [] + for f in blobs: + data = json.loads(f.read_text()) + rows = (data.get("prompts", data.get("items", data)) + if isinstance(data, dict) else data) + if not isinstance(rows, list): + rows = [rows] + lower = f.name.lower() + defaults: dict[str, object] = {} + bundled_legacy = (f.resolve().parent == (Path(__file__).resolve().parent / + "prompts") and + lower in {"fracture.json", "protein.json"}) + if bundled_legacy: + defaults = {"protocol": "exploratory"} + elif "lens-eval-association" in lower: + defaults = {"shape": "ASSOCIATION", + "readout_selector": "final_prompt_token"} + elif "lens-eval-poetry" in lower: + defaults = {"shape": "POETRY", "readout_selector": "last_newline"} + elif "lens-eval-typo" in lower: + defaults = {"shape": "TYPO", "readout_selector": "final_prompt_token"} + elif "lens-eval-multilingual" in lower: + defaults = {"shape": "MULTILINGUAL", "readout_selector": "before_answer"} + elif "lens-eval-order-ops" in lower: + defaults = {"shape": "ORDER_OPS", "readout_selector": "before_answer"} + elif "lens-eval-multihop" in lower: + defaults = {"shape": "MULTIHOP", "readout_selector": "before_answer"} + records += [{**defaults, **row} if isinstance(row, dict) else row + for row in rows] + return [from_dict(r) for r in records] + + +def dump_prompts(prompts: list[Prompt], path: str) -> None: + """Write ``prompts`` to a JSON file (used to export the built-in templates).""" + Path(path).write_text( + json.dumps({"prompts": [to_dict(p) for p in prompts]}, + indent=2, ensure_ascii=False), encoding="utf-8") + + +# --------------------------------------------------------------------------- # +# Fracture / solid mechanics +# --------------------------------------------------------------------------- # + +FRACTURE: list[Prompt] = [ + Prompt( + slug="fracture-griffith", + shape="MULTIHOP", + domain="fracture", + title="Griffith criterion (multi-hop)", + description=( + "Infer the classical brittle-failure framework, then the flaw " + "quantity that controls the critical load." + ), + text=( + "A brittle elastic plate contains a sharp internal flaw. Its " + "failure load is predicted by balancing the stored work released " + "as the flaw advances against the cost of forming two fresh faces. " + "With material properties fixed, the critical load is inversely " + "proportional to the square root of the flaw's" + ), + readout_selector="before_answer", + tracked=("Griffith", "crack"), + answer="length", + must_be_absent_from_input=True, + must_be_absent_from_output=True, + note=( + "Griffith's result sigma_c ~ sqrt(2 E gamma / (pi a)). The answer " + "token is 'length'; 'Griffith' and 'crack' are input-absent " + "intermediates rather than words copied from the question." + ), + ), + Prompt( + slug="fracture-fatigue", + shape="MULTIHOP", + domain="fracture", + title="Cyclic loading -> failure mode (multi-hop)", + description=( + "Repeated sub-yield reversals imply a damage mechanism before the " + "question asks for its microscopic surface marking." + ), + text=( + "A polished steel shaft undergoes millions of sub-yield load " + "reversals. Microscopy shows one small increment of advance per " + "reversal. The characteristic parallel markings left on the " + "failure surface are called" + ), + readout_selector="before_answer", + tracked=("fatigue",), + answer="striations", + must_be_absent_from_input=True, + must_be_absent_from_output=True, + note=( + "The answer is 'striations'; the latent failure mechanism is " + "'fatigue', which is absent from both the question and intended answer." + ), + ), + Prompt( + slug="fracture-toughness-chain", + shape="MULTIHOP", + domain="fracture", + title="Stress-intensity factor chain (multi-hop)", + description=( + "Compare a computed crack-driving value to a critical material " + "value, then predict the flaw response." + ), + text=( + "For an edge-flawed plate under load, an engineer obtains " + "K_I = 80 MPa sqrt(m). The measured critical material value is " + "K_Ic = 50 MPa sqrt(m). Since the computed driving value exceeds " + "the resistance, the flaw will" + ), + readout_selector="before_answer", + tracked=("toughness", "unstable"), + answer="grow", + acceptable_answers=("propagate",), + must_be_absent_from_input=True, + must_be_absent_from_output=True, + note=( + "Multi-step like (4+17)*2+7: compute K, then compare to K_Ic, then " + "conclude 'propagate'. The paper's claim is that these steps " + "surface in the lens in computed order and at successively later " + "layers." + ), + ), + Prompt( + slug="fracture-brittle-assoc", + shape="ASSOCIATION", + domain="fracture", + title="Brittle fracture (association, unnamed)", + description=( + "A vignette of glass shattering. Does 'brittle' / 'fracture' rise " + "in the lens at the final token though never named?" + ), + text=( + "The technician tapped the cold pane once with the hammer. Without " + "bending or warning it flew apart into a hundred glittering shards " + "that skated across the floor, each edge sharp as the moment " + "before." + ), + readout_selector="final_prompt_token", + tracked=("brittle", "fracture"), + must_be_absent_from_input=True, + must_be_absent_from_output=True, + note=( + "Association eval (paper: grief/Einstein/noir). The passage evokes " + "brittle fracture without the words 'brittle' or 'fracture'; a " + "workspace representation of the concept should be readable at the " + "closing token." + ), + ), + Prompt( + slug="fracture-ductile-assoc", + shape="ASSOCIATION", + domain="fracture", + title="Ductile failure (association, unnamed)", + description=( + "A copper rod necking and drawing out. Contrast partner to the " + "brittle vignette: expect 'ductile'/'plastic'/'neck'." + ), + text=( + "As the load climbed the copper rod stopped springing back. It " + "grew visibly thinner at one waist, stretching and drawing out like " + "warm taffy, holding on far past the point anyone expected before " + "it finally parted." + ), + readout_selector="final_prompt_token", + tracked=("ductile", "plastic", "necking"), + must_be_absent_from_input=True, + must_be_absent_from_output=True, + note=( + "The matched control for fracture-brittle-assoc. Comparing the two " + "tests whether the lens cleanly separates the two canonical failure " + "regimes from prose alone." + ), + ), + Prompt( + slug="fracture-stress-recognition", + shape="RECOGNITION", + domain="fracture", + title="Stress state from a tensor (recognition)", + description=( + "A raw Cauchy stress tensor with one large tensile eigenvalue. " + "Does the lens read out 'tension'/'crack'/'mode'?" + ), + text=( + "Consider the Cauchy stress tensor (MPa):\n" + "[[420, 0, 0],\n" + " [ 0, 15, 0],\n" + " [ 0, 0, 10]]\n" + "The dominant loading state this represents is" + ), + readout_selector="before_answer", + tracked=("uniaxial", "opening"), + answer="tension", + must_be_absent_from_input=True, + must_be_absent_from_output=True, + note=( + "Analogue of the paper reading 'protein' out of a raw sequence: " + "here the model must parse a numeric tensor, recognise near-uniaxial " + "tension, and (in fracture terms) associate it with Mode I opening." + ), + ), + Prompt( + slug="fracture-modulation", + shape="MODULATION", + protocol="directed_modulation", + domain="fracture", + title="Hold 'crack-tip plasticity' while copying (modulation)", + description=( + "Copy an unrelated sentence while concentrating on crack-tip " + "plasticity. Does the concept surface over the carrier response?" + ), + user=( + 'Write "The lab notebook lay open on the bench." Concentrate on ' + "crack-tip plasticity while you write the sentence. Don't write " + "anything else." + ), + assistant_prefill="The lab notebook lay open on the bench.", + readout_selector="assistant_response", + tracked=("crack", "plastic", "plasticity", "tip", "yield", "zone"), + must_be_absent_from_output=True, + condition="focus", + control_group="fracture-plasticity-notebook", + note=( + "Directed-modulation protocol. Success = 'crack'/'plastic'/'zone' " + "appear in the lens across the carrier sentence, whose surface text " + "is about a notebook -- evidence the workspace holds an instructed " + "concept independent of output." + ), + ), + Prompt( + slug="fracture-report-swap", + shape="REPORT_SWAP", + protocol="verbal_report", + domain="fracture", + title="Think of a failure mode -> swap (verbal report)", + description=( + "'Think of a failure mode. Answer in one word.' Swap the chosen " + "mode's lens vector for a sibling and check the report changes." + ), + user="Think of a metal failure mode. Answer in one word.", + assistant_prefill="", + readout_selector="final_prompt_token", + tracked=("fatigue", "creep", "corrosion", "fracture", "buckling"), + swap_to="creep", + candidates=("fatigue", "creep", "corrosion", "fracture", "buckling"), + note=( + "Verbal-report swap (paper: Soccer->Rugby). The siblings are all " + "single-word failure modes; swapping the chosen word's J-lens " + "coordinate for a sibling's should flip the one-word answer." + ), + ), +] + +# Matched modulation controls use the identical teacher-forced carrier and +# target set. They are kept separate from the focus prompt so custom datasets +# can add many instruction phrasings while grouping trials by control_group. +FRACTURE[7:7] = [ + Prompt( + slug="fracture-modulation-suppress", + shape="MODULATION", + protocol="directed_modulation", + domain="fracture", + title="Suppress crack-tip plasticity while copying", + description="Matched suppress condition for the modulation contrast.", + user=( + 'Write "The lab notebook lay open on the bench." Try not to think ' + "about crack-tip plasticity while you write it. Don't write anything else." + ), + assistant_prefill="The lab notebook lay open on the bench.", + readout_selector="assistant_response", + tracked=("crack", "plastic", "plasticity", "tip", "yield", "zone"), + must_be_absent_from_output=True, + condition="suppress", + control_group="fracture-plasticity-notebook", + ), + Prompt( + slug="fracture-modulation-control", + shape="MODULATION", + protocol="directed_modulation", + domain="fracture", + title="Copy without a side task", + description="Matched no-instruction control for the modulation contrast.", + user=( + 'Write "The lab notebook lay open on the bench." ' + "Don't write anything else." + ), + assistant_prefill="The lab notebook lay open on the bench.", + readout_selector="assistant_response", + tracked=("crack", "plastic", "plasticity", "tip", "yield", "zone"), + must_be_absent_from_output=True, + condition="control", + control_group="fracture-plasticity-notebook", + ), +] + + +# --------------------------------------------------------------------------- # +# Protein science +# --------------------------------------------------------------------------- # + +PROTEIN: list[Prompt] = [ + Prompt( + slug="protein-helix-multihop", + shape="MULTIHOP", + domain="protein", + title="Backbone H-bonds -> secondary structure (multi-hop)", + description=( + "i to i+4 backbone hydrogen bonding. Track 'hydrogen'/'backbone' " + "before 'helix'." + ), + text=( + "In a folded protein, the carbonyl oxygen of one residue repeatedly " + "pairs with the amide proton of the residue four positions later. " + "The regular local structure produced by this i-to-i+4 pattern is " + "the alpha" + ), + readout_selector="before_answer", + tracked=("hydrogen", "backbone"), + answer="helix", + must_be_absent_from_input=True, + must_be_absent_from_output=True, + note=( + "Two-hop: i,i+4 H-bond pattern -> alpha helix. 'hydrogen'/'bond'/" + "'backbone' are the unspoken intermediates before the answer " + "'helix'." + ), + ), + Prompt( + slug="protein-gfp-recognition", + shape="RECOGNITION", + protocol="exploratory", + domain="protein", + title="GFP sequence -> function (recognition)", + description=( + "The paper's own example, ported: a GFP N-terminal fragment. Watch " + "'protein'/'fluor'/'green' appear a few residues in." + ), + text=( + "What is this? MSKGEELFTGVVPILVELDGDVNGHKFSVSGEGEGDATYGKLTLKFICTTGKLPVPWPTL" + ), + readout_at=-1, + tracked=("protein", "fluor", "green", "GFP", "sequence", "amino"), + note=( + "Direct port of the paper's Figure 3 protein example (GFP). Their " + "lens reads 'protein' ~5 residues in, then 'fluor' and 'green'. " + "Reproducing this on Gemma is the cleanest cross-model check that " + "the fitted lens is behaving." + ), + ), + Prompt( + slug="protein-collagen-recognition", + shape="RECOGNITION", + protocol="exploratory", + domain="protein", + title="Gly-X-Y repeat -> collagen (recognition)", + description=( + "A (Gly-Pro-Hyp)_n-like repeat. Does the lens surface " + "'collagen'/'triple'/'helix' from the motif alone?" + ), + text=( + "What is this? GPPGPKGDPGPQGPRGPAGPPGPAGPQGPKGDPGPQGPRGPAGPP" + ), + readout_at=-1, + tracked=("collagen", "triple", "helix", "glycine", "proline", "fibril"), + note=( + "Gly-X-Y (often Gly-Pro-Hyp) is the collagen signature. Whether the " + "lens jumps from generic 'protein' to specific 'collagen' probes " + "how much structured biochemical knowledge sits in the workspace." + ), + ), + Prompt( + slug="protein-transmembrane-recognition", + shape="RECOGNITION", + protocol="exploratory", + domain="protein", + title="Hydrophobic stretch -> transmembrane (recognition)", + description=( + "A ~20-residue hydrophobic run. Expect 'membrane'/'hydrophobic'/" + "'transmembrane'/'helix'." + ), + text=( + "What is this? KKPNGFILVGGVLLLGAAIIGGVMLFAYVVKKPN" + ), + readout_at=-1, + tracked=("membrane", "hydrophobic", "transmembrane", "helix", "lipid"), + note=( + "A flanked poly-(L/I/V/A/G/F) stretch is the transmembrane-helix " + "signature. Recognising it requires reading residue *chemistry*, " + "not just 'this is a sequence'." + ), + ), + Prompt( + slug="protein-amyloid-assoc", + shape="ASSOCIATION", + domain="protein", + title="Amyloid aggregation (association, unnamed)", + description=( + "A vignette of misfolding and fibril formation. Does " + "'amyloid'/'aggregate'/'fibril' rise without being named?" + ), + text=( + "Under the microscope the soluble molecules had abandoned their " + "folds. They had stacked edge to edge into long stiff ribbons that " + "clumped into tangled plaques, each strand locked into its " + "neighbour in a rigid pleated sheet." + ), + readout_selector="final_prompt_token", + tracked=("amyloid", "fibril", "aggregate"), + must_be_absent_from_input=True, + must_be_absent_from_output=True, + note=( + "Association eval. The passage describes cross-beta amyloid " + "formation (misfolding -> stacked beta strands -> fibrils/plaques) " + "without the diagnostic words; a workspace concept of 'amyloid' " + "should be readable at the close." + ), + ), + Prompt( + slug="protein-fold-multihop", + shape="MULTIHOP", + domain="protein", + title="Hydrophobic collapse -> tertiary fold (multi-hop)", + description=( + "Chain: sequence -> hydrophobic core -> native fold. Track " + "'hydrophobic'/'core'/'fold' in order." + ), + text=( + "A denatured protein is returned to water. Its nonpolar side chains " + "avoid the surrounding liquid and pack together in the interior. " + "This solvent-driven compaction helps the chain recover its " + "functional three-dimensional" + ), + readout_selector="before_answer", + tracked=("hydrophobic", "core", "collapse"), + answer="structure", + acceptable_answers=("fold",), + must_be_absent_from_input=True, + must_be_absent_from_output=True, + note=( + "Anfinsen-style hydrophobic-collapse chain: the intermediates " + "'hydrophobic' and 'core' should precede the answer 'fold'/" + "'structure' across layers." + ), + ), + Prompt( + slug="protein-modulation", + shape="MODULATION", + protocol="directed_modulation", + domain="protein", + title="Hold 'disulfide bond' while copying (modulation)", + description=( + "Copy an unrelated sentence while concentrating on disulfide " + "bonds. Does 'disulfide'/'cysteine'/'bridge' surface?" + ), + user=( + 'Write "The rain tapped against the window all afternoon." ' + "Concentrate on disulfide bonds while you write the sentence. " + "Don't write anything else." + ), + assistant_prefill="The rain tapped against the window all afternoon.", + readout_selector="assistant_response", + tracked=("disulfide", "cysteine", "bridge", "bond", "sulfur", "cross"), + must_be_absent_from_output=True, + condition="focus", + control_group="protein-disulfide-rain", + note=( + "Directed modulation. The carrier is about rain; success is " + "'disulfide'/'cysteine' appearing in the lens across it, showing " + "the workspace holds an instructed biochemical concept off-output." + ), + ), + Prompt( + slug="protein-report-swap", + shape="REPORT_SWAP", + protocol="verbal_report", + domain="protein", + title="Think of an amino acid -> swap (verbal report)", + description=( + "'Think of an amino acid. Answer in one word.' Swap the chosen " + "residue's lens vector for a sibling; check the report changes." + ), + user="Think of an amino acid. Answer in one word.", + assistant_prefill="", + readout_selector="final_prompt_token", + tracked=("glycine", "alanine", "leucine", "serine", "proline"), + swap_to="proline", + candidates=("glycine", "alanine", "leucine", "serine", "proline"), + note=( + "Verbal-report swap. Amino-acid names are the 'sport' category " + "analogue; the swap tests whether the reported residue is set by " + "the workspace coordinate at the report position." + ), + ), +] + +PROTEIN[7:7] = [ + Prompt( + slug="protein-modulation-suppress", + shape="MODULATION", + protocol="directed_modulation", + domain="protein", + title="Suppress disulfide bonds while copying", + description="Matched suppress condition for the protein modulation contrast.", + user=( + 'Write "The rain tapped against the window all afternoon." Try not ' + "to think about disulfide bonds while you write it. Don't write anything else." + ), + assistant_prefill="The rain tapped against the window all afternoon.", + readout_selector="assistant_response", + tracked=("disulfide", "cysteine", "bridge", "bond", "sulfur", "cross"), + must_be_absent_from_output=True, + condition="suppress", + control_group="protein-disulfide-rain", + ), + Prompt( + slug="protein-modulation-control", + shape="MODULATION", + protocol="directed_modulation", + domain="protein", + title="Copy without a biochemical side task", + description="Matched no-instruction control for the protein contrast.", + user=( + 'Write "The rain tapped against the window all afternoon." ' + "Don't write anything else." + ), + assistant_prefill="The rain tapped against the window all afternoon.", + readout_selector="assistant_response", + tracked=("disulfide", "cysteine", "bridge", "bond", "sulfur", "cross"), + must_be_absent_from_output=True, + condition="control", + control_group="protein-disulfide-rain", + ), +] + + +ALL_PROMPTS: list[Prompt] = FRACTURE + PROTEIN + + +def by_slug(slug: str) -> Prompt: + for p in ALL_PROMPTS: + if p.slug == slug: + return p + raise KeyError(slug) + + +def by_shape(shape: str) -> list[Prompt]: + return [p for p in ALL_PROMPTS if p.shape == shape] diff --git a/jlens_materials/lens_hub.py b/jlens_materials/lens_hub.py new file mode 100644 index 0000000000000000000000000000000000000000..a50876556d3ad8564a695a09a01b924c9465de18 --- /dev/null +++ b/jlens_materials/lens_hub.py @@ -0,0 +1,148 @@ +# Copyright 2026. Apache-2.0. +"""Private Hugging Face Hub storage for fitted Jacobian-lens bundles. + +A usable lens artifact is a pair: ``weights.pt`` and +``weights.pt.meta.json``. Uploads commit both files atomically, and downloads +stage both files before replacing the local pair. Authentication is delegated +to ``HF_TOKEN`` or the token saved by ``hf auth login``. +""" + +from __future__ import annotations + +import json +import os +import shutil +import tempfile +from datetime import datetime, timezone +from pathlib import Path, PurePosixPath + +from huggingface_hub import CommitOperationAdd, HfApi, hf_hub_download + + +def metadata_path(lens_path: str | Path) -> Path: + return Path(f"{lens_path}.meta.json") + + +def normalize_hub_path(path_in_repo: str | None, local_path: str | Path) -> str: + """Return a safe relative POSIX path for a Hub model repository.""" + candidate = path_in_repo or Path(local_path).name + path = PurePosixPath(candidate) + if not candidate or path.is_absolute() or ".." in path.parts or path.name in {"", "."}: + raise ValueError( + "--hf-lens-path must be a relative file path inside the Hub repository" + ) + if path.suffix != ".pt": + raise ValueError("--hf-lens-path must end in .pt") + return path.as_posix() + + +def _atomic_copy(source: str | Path, destination: Path) -> None: + destination.parent.mkdir(parents=True, exist_ok=True) + fd, temporary = tempfile.mkstemp( + prefix=f".{destination.name}.", suffix=".download", dir=destination.parent + ) + os.close(fd) + try: + shutil.copy2(source, temporary) + os.replace(temporary, destination) + finally: + if os.path.exists(temporary): + os.unlink(temporary) + + +def download_lens_bundle( + *, + repo_id: str, + local_path: str | Path, + path_in_repo: str | None = None, + revision: str | None = None, + force: bool = False, +) -> tuple[Path, Path]: + """Download a lens and provenance sidecar from a Hub model repository.""" + local = Path(local_path) + local_meta = metadata_path(local) + if not force and local.is_file() and local_meta.is_file(): + return local, local_meta + + remote = normalize_hub_path(path_in_repo, local) + remote_meta = f"{remote}.meta.json" + downloaded_lens = hf_hub_download( + repo_id=repo_id, + filename=remote, + repo_type="model", + revision=revision, + ) + downloaded_meta = hf_hub_download( + repo_id=repo_id, + filename=remote_meta, + repo_type="model", + revision=revision, + ) + + # Parse before replacing either local file so a broken sidecar cannot + # silently downgrade a paper lens to an unverified artifact. + json.loads(Path(downloaded_meta).read_text(encoding="utf-8")) + _atomic_copy(downloaded_lens, local) + _atomic_copy(downloaded_meta, local_meta) + return local, local_meta + + +def upload_lens_bundle( + *, + repo_id: str, + local_path: str | Path, + path_in_repo: str | None = None, + revision: str | None = "main", + private: bool = True, + commit_message: str | None = None, +) -> str: + """Upload a lens pair in one commit and return the commit URL or id.""" + local = Path(local_path) + local_meta = metadata_path(local) + if not local.is_file(): + raise FileNotFoundError(f"lens weights not found: {local}") + if not local_meta.is_file(): + raise FileNotFoundError(f"lens provenance sidecar not found: {local_meta}") + + remote = normalize_hub_path(path_in_repo, local) + api = HfApi() + api.create_repo( + repo_id=repo_id, + repo_type="model", + private=private, + exist_ok=True, + ) + repo_info = api.repo_info(repo_id=repo_id, repo_type="model") + if private and not bool(getattr(repo_info, "private", False)): + raise ValueError( + f"refusing to upload lens to public repository {repo_id!r}; " + "make it private or explicitly pass --no-hf-private" + ) + + metadata = json.loads(local_meta.read_text(encoding="utf-8")) + metadata["hub"] = { + "repo_id": repo_id, + "repo_type": "model", + "path_in_repo": remote, + "revision": revision or "main", + "uploaded_at": datetime.now(timezone.utc).isoformat(), + } + metadata_bytes = ( + json.dumps(metadata, indent=2, sort_keys=True) + "\n" + ).encode("utf-8") + + info = api.create_commit( + repo_id=repo_id, + repo_type="model", + revision=revision or "main", + commit_message=commit_message or f"Upload Jacobian lens {remote}", + operations=[ + CommitOperationAdd(path_in_repo=remote, path_or_fileobj=local), + CommitOperationAdd( + path_in_repo=f"{remote}.meta.json", path_or_fileobj=metadata_bytes + ), + ], + ) + # Only claim a Hub location locally after the remote commit succeeds. + local_meta.write_bytes(metadata_bytes) + return str(getattr(info, "commit_url", None) or getattr(info, "oid", info)) diff --git a/jlens_materials/llm.py b/jlens_materials/llm.py new file mode 100644 index 0000000000000000000000000000000000000000..23859ef267047ddebddf0ef57b3d310f8bf4ccb5 --- /dev/null +++ b/jlens_materials/llm.py @@ -0,0 +1,132 @@ +# Copyright 2026. Apache-2.0. +"""Provider-agnostic LLM interface for the analysis step. + +Two backends, chosen by the caller: + + provider="anthropic" -> Anthropic Messages API, default model claude-opus-4-8 + provider="openai" -> OpenAI Responses API, default model gpt-5.5 + +Both accept the same *neutral* content format — a list of blocks that are either +``{"type": "text", "text": ...}`` or ``{"type": "image_png", "data": }`` — +and each adapter translates to that provider's vision message shape. This lets +``analyze.py`` send the figure PNGs plus numeric readouts to whichever model the +user picks on the CLI, with one code path. + +Auth: standard per-provider env vars — ``ANTHROPIC_API_KEY`` (or an +``ant auth login`` profile) for Anthropic, ``OPENAI_API_KEY`` for OpenAI. +""" + +from __future__ import annotations + +DEFAULT_MODEL = {"anthropic": "claude-opus-4-8", "openai": "gpt-5.5"} + + +def default_model(provider: str) -> str: + if provider not in DEFAULT_MODEL: + raise ValueError(f"unknown provider {provider!r}; use 'anthropic' or 'openai'") + return DEFAULT_MODEL[provider] + + +# --------------------------------------------------------------------------- # +# Anthropic +# --------------------------------------------------------------------------- # + +def _anthropic_complete(model, system, blocks, max_tokens, effort): + import anthropic + + client = anthropic.Anthropic() + content = [] + for b in blocks: + if b["type"] == "text": + content.append({"type": "text", "text": b["text"]}) + elif b["type"] == "image_png": + content.append({"type": "image", "source": { + "type": "base64", "media_type": "image/png", "data": b["data"]}}) + # Stream (SDK guidance for long output); adaptive thinking + effort for quality. + with client.messages.stream( + model=model, + max_tokens=max_tokens, + system=system, + thinking={"type": "adaptive"}, + output_config={"effort": effort}, + messages=[{"role": "user", "content": content}], + ) as stream: + msg = stream.get_final_message() + return "".join(p.text for p in msg.content if p.type == "text").strip() + + +# --------------------------------------------------------------------------- # +# OpenAI (GPT-5.5) — Responses API +# --------------------------------------------------------------------------- # + +def _openai_complete(model, system, blocks, max_tokens, effort, base_url=None): + """Uses the OpenAI **Responses API** (`client.responses.create`), the + current interface for GPT-5-family models. + + ``base_url`` points the OpenAI-compatible client at a non-OpenAI endpoint — + a local server such as mistral.rs, LM Studio, vLLM, or Ollama — instead of + ``api.openai.com``. Left as ``None``, the SDK uses the default endpoint + (honoring ``OPENAI_BASE_URL`` if the caller set it in the environment). + + Shape differences from the old chat-completions path this replaces: + - the system prompt is the top-level ``instructions`` field (not a + "system" message); + - user content parts are ``input_text`` / ``input_image`` (an + ``input_image`` carries the data-URI directly as ``image_url``, a plain + string — unlike chat-completions' ``{"url": ...}`` object); + - the output cap is ``max_output_tokens``; + - reasoning depth is ``reasoning={"effort": ...}`` (minimal/low/medium/high); + - the aggregated answer is ``response.output_text``. + """ + import openai + import os + + client_kwargs = {} + if base_url: + client_kwargs["base_url"] = base_url + # Local OpenAI-compatible servers (mistral.rs, LM Studio, vLLM, Ollama) + # usually need no real key, but the SDK requires one to construct — + # supply a harmless placeholder when the environment sets none. + if not os.environ.get("OPENAI_API_KEY"): + client_kwargs["api_key"] = "not-needed" + client = openai.OpenAI(**client_kwargs) + content = [] + for b in blocks: + if b["type"] == "text": + content.append({"type": "input_text", "text": b["text"]}) + elif b["type"] == "image_png": + content.append({"type": "input_image", + "image_url": f"data:image/png;base64,{b['data']}"}) + + kwargs = { + "model": model, + "instructions": system, + "input": [{"role": "user", "content": content}], + "max_output_tokens": max_tokens, + "reasoning": {"effort": effort}, + } + try: + resp = client.responses.create(**kwargs) + except (openai.BadRequestError, TypeError): + # A non-reasoning model rejects `reasoning`; drop it and retry. + kwargs.pop("reasoning", None) + resp = client.responses.create(**kwargs) + return (resp.output_text or "").strip() + + +# --------------------------------------------------------------------------- # + +def complete(provider: str, model: str, system: str, blocks: list[dict], *, + max_tokens: int = 4000, effort: str = "high", + base_url: str | None = None) -> str: + """Run one analysis turn on the chosen provider and return the text. + + ``base_url`` applies only to the ``openai`` provider (a local / + OpenAI-compatible endpoint); it is ignored for ``anthropic``. + """ + if provider == "anthropic": + return _anthropic_complete(model, system, blocks, max_tokens, effort) + if provider == "openai": + return _openai_complete(model, system, blocks, max_tokens, effort, + base_url=base_url) + raise ValueError(f"unknown provider {provider!r}; use 'anthropic' or 'openai'") diff --git a/jlens_materials/matviz.py b/jlens_materials/matviz.py new file mode 100644 index 0000000000000000000000000000000000000000..168db26dd155635482a6161fe3f1aa9eeb9f7f24 --- /dev/null +++ b/jlens_materials/matviz.py @@ -0,0 +1,852 @@ +# Copyright 2026. Apache-2.0. +"""Static PNG/SVG figures for the Jacobian lens, in the transformer-circuits style. + +The repo's own `jlens.vis` renders an *interactive* d3 HTML page. For papers, +slides and static reports you usually want frozen vector/raster figures. This +module consumes the same `jlens.vis.SliceData` object and emits matplotlib +figures that mirror the paper's four workhorse figure types: + + slice_grid layer x position grid of the top-1 lens token, each cell + shaded by its full-vocab rank. (paper Fig 3 / Fig 5 + "argmax * layer x pos".) + + rank_trajectory rank-vs-layer line chart for a set of tracked concept + tokens at one readout position, log rank axis, rank 1 at + the top. (paper Fig 5 bottom, Fig 17 line chart.) This + is where "intermediates surface in computed order at + successively later layers" becomes visible. + + rank_heatmap position x layer heatmap of ONE token's rank. (paper + Fig 5 rank heatmap, Fig 17 heatmap.) + + hit_rate_bars grouped bars with Wilson CIs for condition comparisons. + (paper Fig 8 / 19 / 20.) + + emergence_depth lollipop of the first sustained rank-threshold crossing + within a fixed workspace band; the best rank is annotated. + +Everything is model-agnostic: pass a `SliceData` plus a `LensModel` (only used +for `n_layers` to reindex layers to 0-100 like the paper, and for the +tokenizer to decode). Nothing here imports torch beyond what SliceData +already carries as numpy. + +Design notes +------------ +Layers are *reindexed to [0, 100]* exactly as the paper does, so depth reads as +a percentage and figures are comparable across models with different layer +counts. The rank colour scale is log10(rank): rank 1 is bright, and anything +past ~vocab is dark -- the same "1 -> 10k" ramp the paper uses. +""" + +from __future__ import annotations + +from dataclasses import dataclass +from pathlib import Path + +import matplotlib as mpl +import matplotlib.pyplot as plt +import numpy as np +from matplotlib.colors import LinearSegmentedColormap, Normalize + +# ---- house style ---------------------------------------------------------- # + +# A calm, print-friendly palette in the spirit of transformer-circuits figures. +INK = "#1b1b1b" +MUTE = "#8a8a8a" +GRID = "#e7e7e7" +ACCENT = "#c1553b" # warm terracotta (the paper's highlight colour) +SERIES = ["#c1553b", "#2b7a8c", "#6a51a3", "#4c8c3f", "#c98a1e", + "#a03050", "#3a6ea5", "#7a7a7a"] + +# rank colormap: rank 1 -> bright gold, deep -> indigo (log scale). Mirrors +# the paper's 1..10k ramp (bright = the model is "poised" to say this token). +_RANK_CMAP = LinearSegmentedColormap.from_list( + "jlens_rank", + ["#fde725", "#7ad151", "#22a884", "#2a788e", "#414487", "#440154"], +) + + +def _install_style() -> None: + mpl.rcParams.update({ + "figure.dpi": 130, + "savefig.dpi": 200, + "savefig.bbox": "tight", + "font.family": "sans-serif", + "font.sans-serif": ["Helvetica", "Arial", "DejaVu Sans"], + "axes.edgecolor": MUTE, + "axes.linewidth": 0.8, + "axes.labelcolor": INK, + "axes.titlecolor": INK, + "text.color": INK, + "xtick.color": MUTE, + "ytick.color": MUTE, + "xtick.labelsize": 8, + "ytick.labelsize": 8, + "axes.titlesize": 11, + "axes.labelsize": 9, + "figure.facecolor": "white", + "axes.facecolor": "white", + }) + + +_install_style() + + +# ---- helpers -------------------------------------------------------------- # + +def reindex_layers(layer_indices, n_layers: int) -> np.ndarray: + """Map absolute layer indices to the paper's [0, 100] depth scale.""" + denom = max(n_layers - 1, 1) + return 100.0 * np.asarray(layer_indices, dtype=float) / denom + + +def _decode(slice_data, token_id: int) -> str: + s = slice_data.vocab_fragment.get(int(token_id), f"<{token_id}>") + s = s.replace("\n", "\\n").replace("\t", "\\t") + return s if s.strip() else repr(s).strip("'") + + +def _rank_norm(vocab_size: int) -> Normalize: + top = np.log10(max(vocab_size, 1000)) + return Normalize(vmin=0.0, vmax=top) + + +def _pos_labels(slice_data, stride: int) -> tuple[np.ndarray, list[str]]: + offset = slice_data.ctx_offset + strs = slice_data.context_token_strs[offset:] + idx = np.arange(0, len(strs), stride) + labels = [strs[i].replace("\n", "\\n").strip()[:6] or "·" for i in idx] + return idx, labels + + +def _save(fig, out_base: str | Path, formats=("png", "svg")) -> list[str]: + out_base = Path(out_base) + out_base.parent.mkdir(parents=True, exist_ok=True) + written = [] + for fmt in formats: + path = f"{out_base}.{fmt}" + fig.savefig(path, format=fmt) + written.append(path) + plt.close(fig) + return written + + +# ---- figure 1: slice grid ------------------------------------------------- # + +def plot_slice_grid( + slice_data, + model, + out_base: str | Path, + *, + title: str = "", + subtitle: str = "", + max_positions: int | None = 40, + show_token_text: bool = True, + highlight_positions: tuple[int, ...] = (), + formats=("png", "svg"), +) -> list[str]: + """Layer x position grid of the top-1 lens token, shaded by rank. + + This is the signature "read the model's mind across depth" figure. Rows are + layers (reindexed to depth %), columns are token positions; each cell shows + the single word the activation is most disposed to make the model say, tinted + by how highly that word ranks over the full vocabulary. + """ + top_ids = slice_data.top_ids[:, :, 0] # [seq, nlayers] + top_rank = slice_data.top_ranks[:, :, 0] # [seq, nlayers] + seq_len, n_layer_rows = top_ids.shape + + pos_sel = np.arange(seq_len) + if max_positions is not None and seq_len > max_positions: + pos_sel = np.arange(seq_len - max_positions, seq_len) + top_ids = top_ids[pos_sel] + top_rank = top_rank[pos_sel] + + depth = reindex_layers(slice_data.layers, model.n_layers) + norm = _rank_norm(slice_data.vocab_size or 50000) + log_rank = np.log10(top_rank.T + 1.0) # [nlayers, npos] + + cell_w, cell_h = 0.62, 0.30 + fig_w = max(6.0, len(pos_sel) * cell_w) + fig_h = max(4.0, n_layer_rows * cell_h) + fig, ax = plt.subplots(figsize=(fig_w, fig_h)) + + ax.imshow(log_rank, aspect="auto", cmap=_RANK_CMAP, norm=norm, + origin="lower", interpolation="nearest") + + if show_token_text: + for li in range(n_layer_rows): + for pi in range(len(pos_sel)): + r = top_rank.T[li, pi] + txt = _decode(slice_data, top_ids.T[li, pi])[:9] + lum = 1.0 - norm(np.log10(r + 1.0)) + color = "white" if lum < 0.45 else INK + ax.text(pi, li, txt, ha="center", va="center", + fontsize=6.0, color=color, clip_on=True) + + # y axis: depth % + ytick = np.linspace(0, n_layer_rows - 1, min(6, n_layer_rows)).astype(int) + ax.set_yticks(ytick) + ax.set_yticklabels([f"{depth[i]:.0f}" for i in ytick]) + ax.set_ylabel("layer depth (reindexed 0-100)") + + # x axis: token strings + pos_strs = [slice_data.context_token_strs[slice_data.ctx_offset + p] + for p in pos_sel] + ax.set_xticks(np.arange(len(pos_sel))) + ax.set_xticklabels( + [s.replace("\n", "\\n").strip()[:7] or "·" for s in pos_strs], + rotation=90, fontsize=6.0, + ) + ax.set_xlabel("token position ->") + + for hp in highlight_positions: + rel = hp - (seq_len - len(pos_sel)) if hp >= 0 else hp + len(pos_sel) + if 0 <= rel < len(pos_sel): + ax.axvline(rel, color=ACCENT, lw=1.4, alpha=0.8) + + sm = mpl.cm.ScalarMappable(cmap=_RANK_CMAP, norm=norm) + cb = fig.colorbar(sm, ax=ax, fraction=0.025, pad=0.01) + cb.set_label("lens rank of top-1 token", fontsize=8) + cb.set_ticks([0, 1, 2, 3, np.log10(slice_data.vocab_size or 50000)]) + cb.set_ticklabels(["1", "10", "100", "1k", "vocab"]) + + if title: + ax.set_title(title, loc="left", pad=14, fontweight="bold") + if subtitle: + ax.text(0.0, 1.015, subtitle, transform=ax.transAxes, + fontsize=8, color=MUTE, va="bottom") + return _save(fig, out_base, formats) + + +# ---- figure 2: rank trajectories ------------------------------------------ # + +def _rank_curve(slice_data, cols: int | list[int], position: int | None, + positions: list[int] | None = None) -> np.ndarray: + """Rank-per-layer curve, minimized over valid synonyms/score positions. + + position is an int -> the rank at that single token position. + position is None -> the min rank over ALL positions at each layer + (the paper's 'hit anywhere in the span' reduction). + """ + cols = [cols] if isinstance(cols, int) else cols + ranks = slice_data.rank_tensor[:, :, cols].astype(float) # [seq,nlayer,nform] + ranks = np.where(ranks < 0, np.nan, ranks) + ranks = np.nanmin(ranks, axis=2) # [seq,nlayer] + if positions is not None: + return np.nanmin(ranks[positions], axis=0) + if position is None: + return np.nanmin(ranks, axis=0) # [nlayer] + seq_rel = position if position >= 0 else ranks.shape[0] + position + return ranks[seq_rel] + + +def plot_rank_trajectories( + slice_data, + model, + tracked, + out_base: str | Path, + *, + position: int | None = -1, + positions: list[int] | None = None, + title: str = "", + subtitle: str = "", + band: tuple[float, float] | None = None, + formats=("png", "svg"), +) -> list[str]: + """Rank-vs-depth line chart for tracked concept tokens. + + `tracked` is a list of (token_id, label). Y is full-vocab rank on a log + scale with rank 1 at the top; the layer at which a line dives toward the top + is the depth at which that concept enters the workspace. `position=None` + plots the best (min) rank over all token positions at each layer -- the + paper's 'concept appears anywhere in the span' reduction, best for raw-text + completions where intermediates surface off the final token. + """ + depth = reindex_layers(slice_data.layers, model.n_layers) + id_to_col = {int(t): i for i, t in enumerate(slice_data.tracked_token_ids)} + + fig, ax = plt.subplots(figsize=(7.0, 4.2)) + if band is not None: + ax.axvspan(band[0], band[1], color=ACCENT, alpha=0.06, lw=0, + label="workspace band") + + plotted = 0 + for k, item in enumerate(tracked): + if hasattr(item, "token_ids"): + token_ids, label = item.token_ids, item.label + else: + tid, label = item + token_ids = (tid,) + cols = [id_to_col[int(t)] for t in token_ids if int(t) in id_to_col] + if not cols: + continue + ranks = _rank_curve(slice_data, cols, position, positions) + 1.0 + c = SERIES[k % len(SERIES)] + ax.plot(depth, ranks, "-o", ms=3.2, lw=1.7, color=c, label=label) + # annotate the best (min-rank) point + if np.isfinite(ranks).any(): + bi = int(np.nanargmin(ranks)) + ax.annotate(label, (depth[bi], ranks[bi]), fontsize=7.5, + color=c, xytext=(3, -2), textcoords="offset points") + plotted += 1 + + ax.set_yscale("log") + ax.invert_yaxis() + ax.set_ylim(bottom=(slice_data.vocab_size or 50000), top=0.8) + ax.set_yticks([1, 10, 100, 1000, 10000]) + ax.set_yticklabels(["1", "10", "100", "1k", "10k"]) + ax.set_ylabel("lens rank (1 = top of workspace)") + ax.set_xlabel("layer depth (reindexed 0-100)") + ax.set_xlim(-2, 102) + ax.grid(True, which="both", color=GRID, lw=0.6) + ax.axhline(1, color=MUTE, lw=0.6, ls=":") + if plotted: + ax.legend(fontsize=7.5, loc="lower left", framealpha=0.9, ncol=2) + if title: + ax.set_title(title, loc="left", pad=12, fontweight="bold") + if subtitle: + ax.text(0.0, 1.02, subtitle, transform=ax.transAxes, + fontsize=8, color=MUTE, va="bottom") + return _save(fig, out_base, formats) + + +# ---- figure 3: single-token rank heatmap ---------------------------------- # + +def plot_rank_heatmap( + slice_data, + model, + token_id: int, + label: str, + out_base: str | Path, + *, + title: str = "", + max_positions: int | None = 60, + formats=("png", "svg"), +) -> list[str]: + """Position x layer heatmap of ONE concept token's rank across the prompt.""" + col = {int(t): i for i, t in enumerate(slice_data.tracked_token_ids)}.get( + int(token_id)) + if col is None: + raise ValueError(f"token {label!r} not tracked in this slice") + ranks = slice_data.rank_tensor[:, :, col].astype(float) # [seq, nlayer] + ranks = np.where(ranks < 0, np.nan, ranks) + seq_len = ranks.shape[0] + + pos_sel = np.arange(seq_len) + if max_positions is not None and seq_len > max_positions: + pos_sel = np.arange(seq_len - max_positions, seq_len) + grid = np.log10(ranks[pos_sel].T + 1.0) # [nlayer, npos] + + depth = reindex_layers(slice_data.layers, model.n_layers) + norm = _rank_norm(slice_data.vocab_size or 50000) + + fig, ax = plt.subplots(figsize=(max(6.0, len(pos_sel) * 0.16), 3.4)) + im = ax.imshow(grid, aspect="auto", cmap=_RANK_CMAP, norm=norm, + origin="lower", interpolation="nearest") + ytick = np.linspace(0, len(depth) - 1, min(6, len(depth))).astype(int) + ax.set_yticks(ytick) + ax.set_yticklabels([f"{depth[i]:.0f}" for i in ytick]) + ax.set_ylabel("depth") + stride = max(1, len(pos_sel) // 24) + xt = np.arange(0, len(pos_sel), stride) + ax.set_xticks(xt) + ax.set_xticklabels( + [slice_data.context_token_strs[slice_data.ctx_offset + pos_sel[i]] + .replace("\n", "\\n").strip()[:5] or "·" for i in xt], + rotation=90, fontsize=6) + ax.set_xlabel("token position ->") + cb = fig.colorbar(im, ax=ax, fraction=0.025, pad=0.01) + cb.set_label(f"rank of “{label}”", fontsize=8) + cb.set_ticks([0, 1, 2, 3]) + cb.set_ticklabels(["1", "10", "100", "1k"]) + ax.set_title(title or f"Where the lens holds “{label}”", loc="left", + fontweight="bold", pad=10) + return _save(fig, out_base, formats) + + +# ---- figure 4: emergence-depth lollipop ----------------------------------- # + +@dataclass +class Emergence: + label: str + onset_depth: float # first sustained threshold crossing in the fixed band + best_depth: float # reindexed layer of min rank + best_rank: int + reached_top: bool # min rank == 0 (rank-1) + best_pos: int = -1 # token position of the peak (only set when scanning) + + +def concept_emergence( + slice_data, model, tracked, *, position: int | None = None, + positions: list[int] | None = None, + band: tuple[float, float] | None = None, + threshold: int = 5, + sustain: int = 2, +) -> list[Emergence]: + """Score concepts in a predetermined span and independently fixed band. + + ``best_depth`` is retained for descriptive plots. ``onset_depth`` is the + first layer where rank is below ``threshold`` for ``sustain`` consecutive + evaluated layers; unlike the old global peak, this is a genuine onset + statistic. Synonymous token IDs are minimized within each concept. + """ + depth = reindex_layers(slice_data.layers, model.n_layers) + id_to_col = {int(t): i for i, t in enumerate(slice_data.tracked_token_ids)} + layer_mask = np.ones(len(depth), dtype=bool) + if band is not None: + layer_mask = (depth >= band[0]) & (depth <= band[1]) + layer_indices = np.flatnonzero(layer_mask) + if not len(layer_indices): + raise ValueError(f"no evaluated layers fall inside workspace band {band}") + out = [] + for item in tracked: + if hasattr(item, "token_ids"): + token_ids, label = item.token_ids, item.label + else: + tid, label = item + token_ids = (tid,) + cols = [id_to_col[int(t)] for t in token_ids if int(t) in id_to_col] + if not cols: + out.append(Emergence(label, np.nan, np.nan, -1, False)) + continue + ranks = slice_data.rank_tensor[:, :, cols].astype(float) + ranks = np.where(ranks >= 0, ranks, np.nan) + ranks = np.nanmin(ranks, axis=2) # [seq,nlayer] + if positions is not None: + selected_positions = positions + ranks = ranks[selected_positions] + elif position is not None: + seq_rel = position if position >= 0 else ranks.shape[0] + position + selected_positions = [seq_rel] + ranks = ranks[seq_rel:seq_rel + 1] + else: + selected_positions = list(range(ranks.shape[0])) + ranks = ranks[:, layer_indices] + valid = np.isfinite(ranks) + if not valid.any(): + out.append(Emergence(label, np.nan, np.nan, -1, False)) + continue + masked = np.where(valid, ranks, np.inf) + flat = int(np.argmin(masked)) + pos_i, band_layer_i = np.unravel_index(flat, masked.shape) + layer_i = int(layer_indices[band_layer_i]) + r = int(ranks[pos_i, band_layer_i]) + curve = np.nanmin(ranks, axis=0) + hit = curve < threshold + onset = np.nan + run = max(1, sustain) + for i in range(0, max(0, len(hit) - run + 1)): + if bool(np.all(hit[i:i + run])): + onset = float(depth[layer_indices[i]]) + break + out.append(Emergence( + label=label, + onset_depth=onset, + best_depth=float(depth[layer_i]), + best_rank=r, + reached_top=r == 0, + best_pos=int(selected_positions[pos_i]), + )) + return out + + +def plot_emergence_depth( + emergences: list[Emergence], + out_base: str | Path, + *, + title: str = "", + subtitle: str = "", + formats=("png", "svg"), +) -> list[str]: + """Lollipop: sustained onset where available, with peak rank annotated.""" + ems = [e for e in emergences if np.isfinite(e.best_depth)] + ems = sorted(ems, key=lambda e: (e.onset_depth if np.isfinite(e.onset_depth) + else e.best_depth)) + fig, ax = plt.subplots(figsize=(6.4, 0.5 * len(ems) + 1.4)) + norm = _rank_norm(50000) + for y, e in enumerate(ems): + c = _RANK_CMAP(norm(np.log10(e.best_rank + 1.0))) + plotted_depth = (e.onset_depth if np.isfinite(e.onset_depth) + else e.best_depth) + ax.plot([0, plotted_depth], [y, y], color=GRID, lw=1.2, zorder=1) + ax.scatter([plotted_depth], [y], s=130, color=c, zorder=2, + edgecolor=INK, linewidth=0.6) + kind = "onset" if np.isfinite(e.onset_depth) else "peak only" + tag = f"{e.label} ({kind}; peak rank {e.best_rank + 1})" + ax.text(plotted_depth + 1.5, y, tag, va="center", fontsize=8, + color=INK if e.best_rank < 30 else MUTE) + ax.set_yticks([]) + ax.set_xlim(-2, 118) + ax.set_xlabel("sustained emergence depth (reindexed 0-100)") + ax.spines[["top", "right", "left"]].set_visible(False) + if title: + ax.set_title(title, loc="left", fontweight="bold", pad=12) + if subtitle: + ax.text(0.0, 1.02, subtitle, transform=ax.transAxes, fontsize=8, + color=MUTE, va="bottom") + return _save(fig, out_base, formats) + + +# ---- figure 5: hit-rate bars ---------------------------------------------- # + +def _wilson(k: int, n: int, z: float = 1.96) -> tuple[float, float, float]: + if n == 0: + return 0.0, 0.0, 0.0 + p = k / n + denom = 1 + z * z / n + center = (p + z * z / (2 * n)) / denom + half = z * np.sqrt(p * (1 - p) / n + z * z / (4 * n * n)) / denom + return p, max(0.0, center - half), min(1.0, center + half) + + +def plot_hit_rate_bars( + conditions: list[str], + counts: list[tuple[int, int]], # (hits, trials) per condition + out_base: str | Path, + *, + title: str = "", + subtitle: str = "", + ylabel: str = "hit rate (concept reaches lens top-k)", + colors: list[str] | None = None, + formats=("png", "svg"), +) -> list[str]: + """Grouped bars with Wilson 95% CIs (paper Fig 8/19/20 style).""" + fig, ax = plt.subplots(figsize=(1.2 * len(conditions) + 2.0, 4.0)) + xs = np.arange(len(conditions)) + colors = colors or [SERIES[i % len(SERIES)] for i in range(len(conditions))] + for x, (k, n), c in zip(xs, counts, colors): + p, lo, hi = _wilson(k, n) + ax.bar(x, p, width=0.62, color=c, alpha=0.9) + # Wilson interval is centred off the raw proportion, so clamp the + # distances from the bar height p to be non-negative for errorbar(). + ax.errorbar(x, p, yerr=[[max(0.0, p - lo)], [max(0.0, hi - p)]], + color=INK, lw=1.0, capsize=4) + ax.text(x, p + 0.02, f"{p*100:.0f}%\n{k}/{n}", ha="center", + va="bottom", fontsize=7.5, color=INK) + ax.set_xticks(xs) + ax.set_xticklabels(conditions, fontsize=8) + ax.set_ylim(0, 1.12) + ax.set_ylabel(ylabel) + ax.grid(True, axis="y", color=GRID, lw=0.6) + ax.set_axisbelow(True) + ax.spines[["top", "right"]].set_visible(False) + if title: + ax.set_title(title, loc="left", fontweight="bold", pad=12) + if subtitle: + ax.text(0.0, 1.02, subtitle, transform=ax.transAxes, fontsize=8, + color=MUTE, va="bottom") + return _save(fig, out_base, formats) + + +def plot_pass_at_k_curves( + ks: list[int], + series: dict[str, list[float]], + out_base: str | Path, + *, + aucs: dict[str, float] | None = None, + title: str = "Lens recovery pass@k", + subtitle: str = "item-level mean; x axis is logarithmic", + formats=("png", "svg"), +) -> list[str]: + """Paper-style pass@k curves for J-lens and registered baselines.""" + fig, ax = plt.subplots(figsize=(7.0, 4.2)) + aucs = aucs or {} + for i, (label, values) in enumerate(series.items()): + auc = aucs.get(label) + suffix = f" (AUC={auc:.3f})" if auc is not None and np.isfinite(auc) else "" + ax.plot(ks, values, "-o", lw=1.8, ms=4, + color=SERIES[i % len(SERIES)], label=label + suffix) + ax.set_xscale("log") + ax.set_xticks(ks) + ax.get_xaxis().set_major_formatter(mpl.ticker.ScalarFormatter()) + ax.set_ylim(-0.02, 1.02) + ax.set_xlabel("k") + ax.set_ylabel("pass@k") + ax.grid(True, which="both", color=GRID, lw=0.6) + ax.spines[["top", "right"]].set_visible(False) + if series: + ax.legend(fontsize=8, loc="lower right") + ax.set_title(title, loc="left", fontweight="bold", pad=12) + if subtitle: + ax.text(0.0, 1.02, subtitle, transform=ax.transAxes, fontsize=8, + color=MUTE, va="bottom") + return _save(fig, out_base, formats) + + +def plot_hidden_thoughts( + items: list[dict], + out_base: str | Path, + *, + title: str = "", + subtitle: str = "exploratory top-1 candidates — prompt/output filtered " + "where an actual completion is available", + top: int = 12, + value_key: str = "score", + formats=("png", "svg"), +) -> list[str]: + """Horizontal bars of exploratory surfaced candidates. + + ``items`` are the per-prompt ``surprising`` records from ``runs/.json`` + (``concept`` · ``score`` = band cells · ``best_rank`` · ``near_token``). Bar + length is the band-cell count; each bar is annotated with its peak rank and + the nearby prompt token where the concept surfaced. Rank-1 concepts are drawn + saturated, weaker ranks faded. + """ + items = sorted(items, key=lambda d: -d.get(value_key, 0))[:top] + if not items: + fig, ax = plt.subplots(figsize=(6.6, 1.7)) + ax.axis("off") + ax.text(0.5, 0.55, "no exploratory candidates surfaced", + ha="center", va="center", fontsize=12, color=INK) + ax.text(0.5, 0.28, "all candidates were filtered by prompt/output controls", + ha="center", va="center", fontsize=9, color=MUTE) + if title: + ax.set_title(title, loc="left", fontweight="bold") + return _save(fig, out_base, formats) + + labels = [d["concept"] for d in items] + vals = [float(d.get(value_key, 0)) for d in items] + ranks = [int(d.get("best_rank", 0)) for d in items] + near = [str(d.get("near_token", "") or "") for d in items] + y = np.arange(len(items))[::-1] # strongest pinned to the top + vmax = max(vals) or 1.0 + + fig, ax = plt.subplots(figsize=(7.4, 0.42 * len(items) + 1.5)) + for yi, v, r, nt in zip(y, vals, ranks, near): + # surprising records store best_rank 1-indexed (1 = tops the lens) + ax.barh(yi, v, height=0.66, color=ACCENT, alpha=(0.92 if r <= 1 else 0.5)) + note = f"r{r}" + (f" · near “{nt}”" if nt else "") + ax.text(v + vmax * 0.02, yi, note, va="center", ha="left", + fontsize=7.8, color=MUTE) + ax.set_yticks(y) + ax.set_yticklabels(labels, fontsize=10) + ax.set_xlabel("workspace-band cells where the concept tops the lens", fontsize=8) + ax.set_xlim(0, vmax * 1.5) + ax.grid(True, axis="x", color=GRID, lw=0.6) + ax.set_axisbelow(True) + ax.spines[["top", "right", "left"]].set_visible(False) + ax.tick_params(length=0) + if title: + ax.set_title(title, loc="left", fontweight="bold", pad=12) + if subtitle: + ax.text(0.0, 1.015, subtitle, transform=ax.transAxes, fontsize=8, + color=MUTE, va="bottom") + return _save(fig, out_base, formats) + + +# ---- comparison figures (across runs / models) ---------------------------- # + +def plot_grouped_bars( + categories: list[str], + series_labels: list[str], + values: list[list[float]], # [n_series][n_categories] + out_base: str | Path, + *, + title: str = "", + subtitle: str = "", + ylabel: str = "", + ylim: tuple[float, float] | None = None, + pct: bool = True, + formats=("png", "svg"), +) -> list[str]: + """Grouped bars: one group per category, one bar per series (e.g. model).""" + n_series, n_cat = len(series_labels), len(categories) + x = np.arange(n_cat) + w = 0.8 / max(n_series, 1) + fig, ax = plt.subplots(figsize=(max(6.0, n_cat * (0.5 + 0.28 * n_series) + 2), 4.2)) + for i, (lab, vals) in enumerate(zip(series_labels, values)): + off = (i - (n_series - 1) / 2) * w + c = SERIES[i % len(SERIES)] + bars = ax.bar(x + off, vals, width=w * 0.92, color=c, label=lab) + for b, v in zip(bars, vals): + if v is None or (isinstance(v, float) and np.isnan(v)): + continue + txt = f"{v*100:.0f}%" if pct else f"{v:g}" + ax.text(b.get_x() + b.get_width() / 2, v, txt, ha="center", + va="bottom", fontsize=6.5, color=INK) + ax.set_xticks(x) + ax.set_xticklabels(categories, fontsize=8) + ax.set_ylabel(ylabel) + if ylim: + ax.set_ylim(*ylim) + elif pct: + ax.set_ylim(0, 1.12) + ax.grid(True, axis="y", color=GRID, lw=0.6) + ax.set_axisbelow(True) + ax.spines[["top", "right"]].set_visible(False) + ax.legend(fontsize=7.5, ncol=min(n_series, 4), loc="upper right", + framealpha=0.9) + if title: + ax.set_title(title, loc="left", fontweight="bold", pad=12) + if subtitle: + ax.text(0.0, 1.02, subtitle, transform=ax.transAxes, fontsize=8, + color=MUTE, va="bottom") + return _save(fig, out_base, formats) + + +def _stream_series(layer_readouts, max_concepts=12): + """From per-layer top tokens, build a salience-vs-depth series per concept. + Salience at a layer = (K - display_position) if the token is in that layer's + top-K, else 0 (top-1 -> K, ..., top-K -> 1). Returns (depths, labels, + matrix[n_concepts, n_depths]) keeping the max_concepts most salient overall, + ordered by first-emergence depth (a lineage reading).""" + depths = [float(r["depth"]) for r in layer_readouts] + K = max((len(r["top_tokens"]) for r in layer_readouts), default=8) + sal: dict[str, list[float]] = {} + for li, r in enumerate(layer_readouts): + for p, t in enumerate(r["top_tokens"]): + if not t or not t.isascii(): # skip non-renderable (e.g. CJK) tokens + continue + sal.setdefault(t, [0.0] * len(layer_readouts))[li] = float(K - p) + top = sorted(sal.items(), key=lambda kv: sum(kv[1]), reverse=True)[:max_concepts] + + def first(series): + return next((i for i, v in enumerate(series) if v > 0), len(series)) + + top.sort(key=lambda kv: first(kv[1])) + labels = [t for t, _ in top] + mat = np.array([s for _, s in top], dtype=float) if top else np.zeros((0, len(depths))) + return np.array(depths), labels, mat + + +def plot_concept_stream( + layer_readouts, + out_base: str | Path, + *, + title: str = "", + subtitle: str = "", + max_concepts: int = 12, + formats=("png", "svg"), +) -> list[str]: + """Static 'thought stream' (ThemeRiver): each concept the lens surfaces is a + band flowing left->right across depth, thickness ~ salience — a lineage view + of the model's evolving disposition to speak.""" + depths, labels, S = _stream_series(layer_readouts, max_concepts) + fig, ax = plt.subplots(figsize=(8.0, 4.2)) + if len(labels): + total = S.sum(0) + cur = -total / 2.0 # centered baseline (ThemeRiver) + for k, lab in enumerate(labels): + lo, hi = cur.copy(), cur + S[k] + c = SERIES[k % len(SERIES)] + ax.fill_between(depths, lo, hi, color=c, alpha=0.85, lw=0.4, + edgecolor="white") + i = int(np.argmax(S[k])) + if S[k][i] > 0: + lum = 0.3 # bands are saturated; white labels read well + ax.text(depths[i], (lo[i] + hi[i]) / 2, lab, ha="center", + va="center", fontsize=7.5, + color="white" if k % len(SERIES) in (2, 5) else INK) + cur = hi + ax.set_yticks([]) + ax.set_xlim(0, 100) + ax.set_xlabel("layer depth (reindexed 0-100) -> the flow of 'thinking'") + ax.spines[["top", "right", "left"]].set_visible(False) + if title: + ax.set_title(title, loc="left", fontweight="bold", pad=12) + if subtitle: + ax.text(0.0, 1.02, subtitle, transform=ax.transAxes, fontsize=8, + color=MUTE, va="bottom") + return _save(fig, out_base, formats) + + +def plot_concept_rank_dots( + concepts: list[str], + models: list[str], + ranks: list[list[float]], # [n_models][n_concepts]; 1-based, NaN=missing + out_base: str | Path, + *, + title: str = "", + subtitle: str = "", + vocab_size: int = 50000, + formats=("png", "svg"), +) -> list[str]: + """Per-prompt comparison: each concept's best lens rank as a dot per model + on a log axis (left = rank 1 = strongest), with a connector between models + (a dumbbell). Missing values are skipped.""" + ncat = len(concepts) + y = np.arange(ncat) + fig, ax = plt.subplots(figsize=(7.0, 0.5 * ncat + 1.6)) + # dumbbell connector per concept + for j in range(ncat): + vals = [max(ranks[i][j], 1) for i in range(len(models)) + if ranks[i][j] == ranks[i][j]] + if len(vals) >= 2: + ax.plot([min(vals), max(vals)], [y[j], y[j]], color=GRID, lw=2.0, + zorder=1) + for i, m in enumerate(models): + xx = [max(ranks[i][j], 1) for j in range(ncat) if ranks[i][j] == ranks[i][j]] + yy = [y[j] for j in range(ncat) if ranks[i][j] == ranks[i][j]] + ax.scatter(xx, yy, s=95, color=SERIES[i % len(SERIES)], label=m, + edgecolor=INK, linewidth=0.5, zorder=3) + for xv, yv in zip(xx, yy): + ax.annotate(f"{int(xv)}", (xv, yv), fontsize=6.5, color=MUTE, + xytext=(0, 6), textcoords="offset points", ha="center") + ax.set_xscale("log") + ax.set_xlim(0.8, max(vocab_size, 1000)) + ax.set_xticks([1, 10, 100, 1000, 10000]) + ax.set_xticklabels(["1", "10", "100", "1k", "10k"]) + ax.set_xlabel("best lens rank (1 = strongest; left = stronger)") + ax.set_yticks(y) + ax.set_yticklabels(concepts, fontsize=8) + ax.invert_yaxis() + ax.grid(True, axis="x", color=GRID, lw=0.6) + ax.set_axisbelow(True) + ax.spines[["top", "right"]].set_visible(False) + # legend outside the axes (right) so it never overlaps the dumbbells; + # savefig bbox='tight' keeps it in frame + ax.legend(fontsize=7.5, loc="center left", bbox_to_anchor=(1.01, 0.5), + framealpha=0.9, borderaxespad=0) + if title: + ax.set_title(title, loc="left", fontweight="bold", pad=10) + if subtitle: + ax.text(0.0, 1.02, subtitle, transform=ax.transAxes, fontsize=8, + color=MUTE, va="bottom") + return _save(fig, out_base, formats) + + +def plot_rank_matrix( + row_labels: list[str], + col_labels: list[str], + ranks: list[list[float]], # [n_rows][n_cols]; 1-based rank, NaN=missing + out_base: str | Path, + *, + title: str = "", + subtitle: str = "", + vocab_size: int = 50000, + formats=("png", "svg"), +) -> list[str]: + """Heatmap of a concept's best lens rank across models (rows=concepts, + cols=models). Bright = high rank (concept strongly present); grey = the + concept wasn't tracked / didn't appear for that model.""" + R = np.array(ranks, dtype=float) + logv = np.ma.masked_invalid(np.log10(R)) + norm = _rank_norm(vocab_size) + cmap = _RANK_CMAP.copy() + cmap.set_bad("#ededed") + fig, ax = plt.subplots(figsize=(max(4.5, len(col_labels) * 1.5 + 2.5), + 0.42 * len(row_labels) + 1.8)) + ax.imshow(logv, aspect="auto", cmap=cmap, norm=norm, interpolation="nearest") + for i in range(len(row_labels)): + for j in range(len(col_labels)): + r = R[i, j] + if np.isnan(r): + continue + lum = 1.0 - norm(np.log10(r)) + ax.text(j, i, f"{int(r)}", ha="center", va="center", fontsize=7, + color="white" if lum < 0.45 else INK) + ax.set_xticks(np.arange(len(col_labels))) + ax.set_xticklabels(col_labels, fontsize=8, rotation=20, ha="right") + ax.set_yticks(np.arange(len(row_labels))) + ax.set_yticklabels(row_labels, fontsize=7.5) + sm = mpl.cm.ScalarMappable(cmap=cmap, norm=norm) + cb = fig.colorbar(sm, ax=ax, fraction=0.03, pad=0.02) + cb.set_label("best lens rank", fontsize=8) + cb.set_ticks([0, 1, 2, 3]); cb.set_ticklabels(["1", "10", "100", "1k"]) + if title: + ax.set_title(title, loc="left", fontweight="bold", pad=10) + if subtitle: + ax.text(0.0, 1.02, subtitle, transform=ax.transAxes, fontsize=8, + color=MUTE, va="bottom") + return _save(fig, out_base, formats) diff --git a/jlens_materials/paper_protocol.py b/jlens_materials/paper_protocol.py new file mode 100644 index 0000000000000000000000000000000000000000..b62315b4ad3418add7d17d690c7ce7227c0c6c20 --- /dev/null +++ b/jlens_materials/paper_protocol.py @@ -0,0 +1,540 @@ +# Copyright 2026. Apache-2.0. +"""Paper-faithful protocol helpers for fitting and evaluating a Jacobian lens. + +This module intentionally sits outside the vendored :mod:`jlens` package. The +vendored package stays byte-for-byte compatible with the public reference +implementation (apart from its progress display), while this file owns the +experiment choices that the paper leaves to the caller: fitting corpus, +target/source layers, scored positions, workspace band, token synonyms, +provenance, and item-level statistics. +""" + +from __future__ import annotations + +import hashlib +import json +import math +import random +from dataclasses import asdict, dataclass +from pathlib import Path +from typing import Any, Iterable, Sequence + +import numpy as np + + +@dataclass(frozen=True) +class FitRecipe: + name: str + n_fit: int + max_seq_len: int + target_layer: int | None + report_layer_count: int | None + skip_first: int + strict_corpus: bool + description: str + + +FIT_RECIPES: dict[str, FitRecipe] = { + "paper": FitRecipe( + name="paper", + n_fit=1000, + max_seq_len=128, + target_layer=-2, + report_layer_count=25, + skip_first=16, + strict_corpus=True, + description=( + "Paper-scale estimator: 1000 independent 128-token sequences, " + "penultimate target layer, and 25 evenly spaced reported layers." + ), + ), + "demo": FitRecipe( + name="demo", + n_fit=12, + max_seq_len=64, + target_layer=None, + report_layer_count=None, + skip_first=16, + strict_corpus=False, + description=( + "Fast exploratory fit. Results from this profile are not a " + "quantitative replication of the paper." + ), + ), +} + + +def is_paper_faithful_recipe(recipe: FitRecipe) -> bool: + reference = FIT_RECIPES["paper"] + return ( + recipe.name == "paper" + and recipe.n_fit >= reference.n_fit + and recipe.max_seq_len == reference.max_seq_len + and recipe.target_layer == reference.target_layer + and recipe.report_layer_count == reference.report_layer_count + and recipe.skip_first == reference.skip_first + ) + + +@dataclass(frozen=True) +class FitCorpus: + texts: tuple[str, ...] + metadata: dict[str, Any] + + +@dataclass(frozen=True) +class ConceptTokens: + """All valid single-token surface forms for one conceptual target.""" + + label: str + token_ids: tuple[int, ...] + surfaces: tuple[str, ...] + + +def resolve_recipe( + name: str, + *, + n_fit: int | None = None, + max_seq_len: int | None = None, + target_layer: int | None = None, + report_layer_count: int | None = None, +) -> FitRecipe: + if name not in FIT_RECIPES: + raise ValueError(f"unknown recipe {name!r}; choose from {sorted(FIT_RECIPES)}") + base = FIT_RECIPES[name] + return FitRecipe( + name=base.name, + n_fit=base.n_fit if n_fit is None else n_fit, + max_seq_len=base.max_seq_len if max_seq_len is None else max_seq_len, + target_layer=base.target_layer if target_layer is None else target_layer, + report_layer_count=(base.report_layer_count if report_layer_count is None + else report_layer_count), + skip_first=base.skip_first, + strict_corpus=base.strict_corpus, + description=base.description, + ) + + +def resolve_layer_index(layer: int | None, n_layers: int) -> int: + resolved = n_layers - 1 if layer is None else layer + if resolved < 0: + resolved += n_layers + if not 0 <= resolved < n_layers: + raise ValueError(f"target layer {layer!r} is invalid for {n_layers} layers") + return resolved + + +def evenly_spaced_source_layers( + n_layers: int, target_layer: int | None, count: int | None +) -> list[int] | None: + """Layers below ``target_layer``; ``None`` retains every source layer.""" + target = resolve_layer_index(target_layer, n_layers) + if target <= 0: + raise ValueError("target layer leaves no source layers") + if count is None or count >= target: + return list(range(target)) + if count <= 0: + raise ValueError("report layer count must be positive") + # Rounding linspace can duplicate values for small stacks; unique/sorted is + # deliberate and the endpoints are always retained. + return sorted({int(round(x)) for x in np.linspace(0, target - 1, count)}) + + +_BUILTIN_CORPUS = ( + "The history of materials science spans thousands of years, from the first " + "smelting of copper to modern superalloys used in turbine blades. " * 5, + "Proteins are chains of amino acids that fold into precise structures. " + "Sequence, solvent, temperature, and cellular machinery shape the fold. " * 6, + "When a solid is loaded it can deform elastically, flow plastically, or " + "fracture. Engineers quantify stress, defects, fatigue, and toughness. " * 6, + "The city sat quietly beneath a grey sky as the river carried small boats " + "past the bridge and the market stalls along the harbour. " * 7, + "Thermodynamics connects energy, entropy, chemical reactions, heat engines, " + "phase transformations, and molecular structure. " * 7, + "She opened the notebook and recorded temperature, pressure, displacement, " + "and time for every measurement in the laboratory. " * 7, + "Crystalline materials contain vacancies, dislocations, grain boundaries, " + "and interfaces whose motion controls macroscopic properties. " * 7, + "DNA is transcribed into RNA, and RNA is translated into protein by the " + "ribosome one codon and one amino acid at a time. " * 7, +) + + +def _texts_from_file(path: Path) -> list[str]: + if path.suffix.lower() == ".jsonl": + rows = [json.loads(line) for line in path.read_text(encoding="utf-8").splitlines() + if line.strip()] + elif path.suffix.lower() == ".json": + blob = json.loads(path.read_text(encoding="utf-8")) + rows = blob.get("texts", blob.get("items", blob)) if isinstance(blob, dict) else blob + else: + return [part.strip() for part in path.read_text(encoding="utf-8").split("\n\n") + if part.strip()] + if not isinstance(rows, list): + raise ValueError(f"corpus {path} must contain a list or a texts/items list") + texts = [] + for row in rows: + text = row if isinstance(row, str) else row.get("text", row.get("prompt", "")) + if str(text).strip(): + texts.append(str(text)) + return texts + + +def _take_text_records( + records: Iterable[Any], *, n: int, text_field: str, min_chars: int +) -> list[str]: + texts: list[str] = [] + for record in records: + text = record if isinstance(record, str) else record.get(text_field, "") + text = str(text).strip() + if len(text) < min_chars: + continue + texts.append(text) + if len(texts) == n: + break + return texts + + +def corpus_sha256(texts: Sequence[str]) -> str: + digest = hashlib.sha256() + for text in texts: + raw = text.encode("utf-8") + digest.update(len(raw).to_bytes(8, "big")) + digest.update(raw) + return digest.hexdigest() + + +def load_fit_corpus( + *, + corpus: str, + n: int, + seed: int = 0, + revision: str | None = None, + split: str = "train", + subset: str | None = None, + text_field: str = "text", + min_chars: int = 600, + strict: bool = True, +) -> FitCorpus: + """Load independent fitting records and return content-addressed metadata. + + ``corpus`` may be ``wikitext``, ``builtin``, a local file, or any Hugging + Face dataset id. WikiText is a reproducible pretraining-like proxy; it is + not claimed to be the paper authors' proprietary pretraining corpus. + """ + if n <= 0: + raise ValueError("n_fit must be positive") + source: dict[str, Any] + path = Path(corpus).expanduser() + if path.is_file(): + pool = _texts_from_file(path) + rng = random.Random(seed) + rng.shuffle(pool) + texts = [x for x in pool if len(x) >= min_chars][:n] + source = {"kind": "file", "path": str(path.resolve())} + elif corpus == "builtin": + pool = list(_BUILTIN_CORPUS) + texts = pool[:n] + source = {"kind": "builtin", "name": "eight-demo-passages"} + else: + from datasets import load_dataset + + dataset_id = "Salesforce/wikitext" if corpus == "wikitext" else corpus + dataset_subset = subset or ( + "wikitext-103-raw-v1" if dataset_id == "Salesforce/wikitext" else None + ) + kwargs: dict[str, Any] = { + "path": dataset_id, + "split": split, + "streaming": True, + } + if dataset_subset: + kwargs["name"] = dataset_subset + if revision: + kwargs["revision"] = revision + records = load_dataset(**kwargs) + if hasattr(records, "shuffle"): + records = records.shuffle(seed=seed, buffer_size=max(10_000, n * 10)) + texts = _take_text_records( + records, n=n, text_field=text_field, min_chars=min_chars + ) + source = { + "kind": "huggingface", + "dataset": dataset_id, + "subset": dataset_subset, + "split": split, + "revision": revision or "main", + "text_field": text_field, + } + + if len(texts) < n: + message = f"corpus {corpus!r} yielded {len(texts)} independent texts; requested {n}" + if strict: + raise ValueError(message) + print(f" WARNING: {message}; demo fit will use the available records") + if not texts: + raise ValueError(f"corpus {corpus!r} yielded no usable texts") + unique_records = len(set(texts)) + if strict and unique_records != len(texts): + raise ValueError( + f"corpus {corpus!r} contains duplicate fitting records " + f"({unique_records} unique of {len(texts)})" + ) + metadata = { + **source, + "requested_records": n, + "records": len(texts), + "unique_records": unique_records, + "seed": seed, + "min_chars": min_chars, + "sha256": corpus_sha256(texts), + } + return FitCorpus(tuple(texts), metadata) + + +def model_identity(model: Any, requested_id: str) -> dict[str, Any]: + hf = getattr(model, "_hf_model", None) + cfg = getattr(hf, "config", None) + tok = model.tokenizer + dtype = None + if hf is not None: + try: + dtype = str(next(hf.parameters()).dtype).replace("torch.", "") + except StopIteration: + pass + return { + "requested_id": requested_id, + "requested_revision": getattr(model, "_requested_revision", None), + "model_class": type(hf).__name__ if hf is not None else type(model).__name__, + "config_name_or_path": getattr(cfg, "_name_or_path", None), + "model_revision": getattr(cfg, "_commit_hash", None), + "tokenizer_class": type(tok).__name__, + "tokenizer_name_or_path": getattr(tok, "name_or_path", None), + "vocab_size": len(tok) if hasattr(tok, "__len__") else None, + "n_layers": model.n_layers, + "d_model": model.d_model, + "dtype": dtype, + } + + +def lens_metadata_path(lens_path: str | Path) -> Path: + return Path(f"{lens_path}.meta.json") + + +def write_lens_metadata(lens_path: str | Path, metadata: dict[str, Any]) -> Path: + path = lens_metadata_path(lens_path) + path.write_text(json.dumps(metadata, indent=2, sort_keys=True), encoding="utf-8") + return path + + +def read_lens_metadata(lens_path: str | Path) -> dict[str, Any] | None: + path = lens_metadata_path(lens_path) + return json.loads(path.read_text(encoding="utf-8")) if path.is_file() else None + + +def validate_lens_metadata( + metadata: dict[str, Any] | None, + *, + identity: dict[str, Any], + require: bool, +) -> None: + if metadata is None: + if require: + raise ValueError( + "lens has no provenance sidecar; refit it with this runner or pass " + "--allow-unverified-lens for exploratory use" + ) + print(" WARNING: loaded lens has no provenance metadata") + return + fitted = metadata.get("model", {}) + for key in ("requested_id", "model_class", "n_layers", "d_model", "vocab_size"): + expected, actual = identity.get(key), fitted.get(key) + if expected is not None and actual is not None and expected != actual: + raise ValueError( + f"lens/model mismatch for {key}: lens={actual!r}, model={expected!r}" + ) + for key in ("requested_revision", "model_revision", "tokenizer_name_or_path"): + expected, actual = identity.get(key), fitted.get(key) + if expected and actual and expected != actual: + raise ValueError( + f"lens/model mismatch for {key}: lens={actual!r}, model={expected!r}" + ) + + +def fit_fingerprint(metadata: dict[str, Any]) -> str: + stable = {key: value for key, value in metadata.items() if key != "created_at"} + raw = json.dumps(stable, sort_keys=True, separators=(",", ":")).encode() + return hashlib.sha256(raw).hexdigest()[:16] + + +def _surface_forms(word: str) -> tuple[str, ...]: + forms = (" " + word, word, " " + word.capitalize(), word.capitalize(), + " " + word.lower(), word.lower()) + return tuple(dict.fromkeys(forms)) + + +def resolve_concepts(tokenizer: Any, prompt: Any) -> tuple[list[ConceptTokens], list[str]]: + """Resolve labels plus synonyms to valid, non-special single token IDs.""" + special = set(getattr(tokenizer, "all_special_ids", []) or []) + unk = getattr(tokenizer, "unk_token_id", None) + concepts: list[ConceptTokens] = [] + dropped: list[str] = [] + token_owner: dict[int, str] = {} + for label in prompt.tracked: + surfaces = [label, *prompt.synonyms.get(label, [])] + ids: list[int] = [] + accepted: list[str] = [] + for surface in surfaces: + accepted_surface = False + for form in _surface_forms(surface): + token_ids = tokenizer.encode(form, add_special_tokens=False) + if len(token_ids) != 1: + continue + token_id = int(token_ids[0]) + if token_id == unk or token_id in special or token_id in ids: + continue + ids.append(token_id) + accepted_surface = True + if accepted_surface: + accepted.append(surface) + if ids: + conflicts = {token_id: token_owner[token_id] for token_id in ids + if token_id in token_owner and token_owner[token_id] != label} + if conflicts: + raise ValueError( + f"ambiguous concept token IDs for {label!r}: {conflicts}; " + "targets/synonym sets must be disjoint" + ) + token_owner.update({token_id: label for token_id in ids}) + concepts.append(ConceptTokens(label, tuple(ids), tuple(accepted))) + else: + dropped.append(label) + return concepts, dropped + + +def flatten_concept_ids(concepts: Sequence[ConceptTokens]) -> set[int]: + return {token_id for concept in concepts for token_id in concept.token_ids} + + +def _find_last_subsequence(haystack: Sequence[int], needle: Sequence[int]) -> int | None: + if not needle or len(needle) > len(haystack): + return None + for start in range(len(haystack) - len(needle), -1, -1): + if list(haystack[start:start + len(needle)]) == list(needle): + return start + return None + + +def resolve_score_positions( + tokenizer: Any, text: str, prompt: Any, *, strict: bool +) -> list[int]: + """Resolve the protocol's predetermined readout position or response span.""" + ids = tokenizer.encode(text, add_special_tokens=True) + if not ids: + raise ValueError(f"prompt {prompt.slug!r} tokenized to an empty sequence") + selector = prompt.readout_selector + if selector in {"before_answer", "final_prompt_token"}: + return [len(ids) - 1] + if selector == "explicit": + pos = prompt.readout_at if prompt.readout_at >= 0 else len(ids) + prompt.readout_at + if not 0 <= pos < len(ids): + raise ValueError(f"readout_at={prompt.readout_at} is outside {len(ids)} tokens") + return [pos] + if selector == "last_newline": + newline_positions = [i for i, token_id in enumerate(ids) + if "\n" in tokenizer.decode([token_id])] + if newline_positions: + return [newline_positions[-1]] + raise ValueError("readout_selector=last_newline but no newline token was found") + if selector == "assistant_response": + response = prompt.assistant_prefill + candidates = [response, " " + response] + for candidate in candidates: + response_ids = tokenizer.encode(candidate, add_special_tokens=False) + start = _find_last_subsequence(ids, response_ids) + if start is not None: + return list(range(start, start + len(response_ids))) + if strict: + raise ValueError( + f"could not locate assistant_prefill tokens for {prompt.slug!r}" + ) + print(f" WARNING: could not resolve response span for {prompt.slug}; using last token") + return [len(ids) - 1] + if selector == "all_prompt": + if strict: + raise ValueError( + "readout_selector=all_prompt is exploratory and cannot be used in strict mode" + ) + return list(range(len(ids))) + raise ValueError(f"unknown readout_selector {selector!r}") + + +def concept_presence( + concepts: Sequence[ConceptTokens], token_ids: Sequence[int] +) -> dict[str, bool]: + present = set(int(x) for x in token_ids) + return {c.label: any(token_id in present for token_id in c.token_ids) + for c in concepts} + + +def protocol_violations( + prompt: Any, + concepts: Sequence[ConceptTokens], + *, + prompt_token_ids: Sequence[int], + generated_token_ids: Sequence[int] | None, +) -> list[str]: + violations: list[str] = [] + if prompt.must_be_absent_from_input: + found = [name for name, yes in concept_presence(concepts, prompt_token_ids).items() + if yes] + if found: + violations.append(f"intermediates present in input: {found}") + if prompt.must_be_absent_from_output: + if generated_token_ids is None: + violations.append("output absence requested but no completion was generated") + else: + found = [name for name, yes in concept_presence( + concepts, generated_token_ids).items() if yes] + if found: + violations.append(f"intermediates present in generated output: {found}") + return violations + + +def item_pass_scores(rank_items: Sequence[Sequence[int]], ks: Sequence[int]) -> list[float]: + """Paper pass@k: mean, over items, of target/intermediate recovery.""" + scores = [] + for k in ks: + per_item = [float(np.mean([0 <= rank < k for rank in ranks])) + for ranks in rank_items if ranks] + scores.append(float(np.mean(per_item)) if per_item else float("nan")) + return scores + + +def log_k_auc(ks: Sequence[int], values: Sequence[float]) -> float: + if len(ks) < 2 or len(ks) != len(values): + return float("nan") + x = np.log(np.asarray(ks, dtype=float)) + y = np.asarray(values, dtype=float) + if not np.isfinite(y).all() or x[-1] == x[0]: + return float("nan") + integrate = np.trapezoid if hasattr(np, "trapezoid") else np.trapz + return float(integrate(y, x=x) / (x[-1] - x[0])) + + +def bootstrap_mean_ci( + values: Sequence[float], *, seed: int = 0, n_boot: int = 2000 +) -> tuple[float, float, float]: + arr = np.asarray([x for x in values if math.isfinite(x)], dtype=float) + if arr.size == 0: + return float("nan"), float("nan"), float("nan") + if arr.size == 1: + value = float(arr[0]) + return value, value, value + rng = np.random.default_rng(seed) + means = rng.choice(arr, size=(n_boot, arr.size), replace=True).mean(axis=1) + return float(arr.mean()), float(np.quantile(means, 0.025)), float(np.quantile(means, 0.975)) + + +def recipe_dict(recipe: FitRecipe) -> dict[str, Any]: + return asdict(recipe) diff --git a/jlens_materials/pipeline.sh b/jlens_materials/pipeline.sh new file mode 100644 index 0000000000000000000000000000000000000000..9018466db03d881669685fadf5035db00163a510 --- /dev/null +++ b/jlens_materials/pipeline.sh @@ -0,0 +1,120 @@ +#!/usr/bin/env bash +# End-to-end Jacobian-lens pipeline, wired to the `PyTorch` conda env. +# fit/apply lens -> figures + runs/.json +# -> LLM analysis (Anthropic Opus 4.8 OR OpenAI GPT-5.5) +# -> LaTeX report compiled to PDF +# +# Usage: +# ./pipeline.sh # Qwen demo, Claude Opus 4.8 +# ./pipeline.sh --provider openai # ... GPT-5.5 for the analysis +# ./pipeline.sh --model google/gemma-4-E4B-it --tag gemma4-e4b-it --provider anthropic +# ./pipeline.sh --offline # skip the LLM (template analysis) +# MODEL=gpt2 ./pipeline.sh --offline # quick GPT-2 sanity run +# +# Env: +# PYTHON override the interpreter (default: the PyTorch conda env) +# MODEL HF model id (default Qwen/Qwen2.5-0.5B-Instruct) +# SHAPES experiment shapes (default all five) +# ANTHROPIC_API_KEY / OPENAI_API_KEY provider credentials for the analysis +set -euo pipefail +cd "$(dirname "$0")" + +# Interpreter: honor $PYTHON, else the active python3/python on PATH. Portable +# across macOS and Linux — no hardcoded env path. After `conda activate PyTorch` +# (or any venv) this resolves to that env's python; override with +# `PYTHON=/path/to/python ./pipeline.sh` if needed. +PYTHON="${PYTHON:-$(command -v python3 || command -v python || true)}" +if [[ -z "$PYTHON" ]]; then + echo "no python found on PATH; activate your env or set PYTHON=/path/to/python" >&2 + exit 1 +fi +MODEL="${MODEL:-Qwen/Qwen2.5-0.5B-Instruct}" +MODEL_REVISION="" +SHAPES="${SHAPES:-MULTIHOP,ASSOCIATION,RECOGNITION,MODULATION,REPORT_SWAP}" +PROVIDER="anthropic" +ANALYZE_MODEL="" +OFFLINE="" +RECIPE="demo" # paper = 1000 x 128, penultimate target, strict protocol +NFIT="" # empty uses the recipe default +CORPUS="wikitext" +WORKSPACE_BAND="38,92" +PROMPTS="" +MIN_ITEMS="" +SWAPS="--swaps" # run causal swaps for REPORT_SWAP prompts by default +TAG_OVERRIDE="" +DTYPE="float32" # model weight dtype; bfloat16 ~halves memory on GPU +DIMBATCH="64" # backward-pass batch during --fit; lower cuts fit peak memory +PUSH_HF="" # if set to /, upload all artifacts to a HF dataset + +while [[ $# -gt 0 ]]; do + case "$1" in + --provider) PROVIDER="$2"; shift 2;; + --analysis-model) ANALYZE_MODEL="$2"; shift 2;; + --model) MODEL="$2"; shift 2;; + --model-revision) MODEL_REVISION="$2"; shift 2;; + --shapes) SHAPES="$2"; shift 2;; + --recipe) RECIPE="$2"; shift 2;; + --n-fit) NFIT="$2"; shift 2;; + --corpus) CORPUS="$2"; shift 2;; + --workspace-band) WORKSPACE_BAND="$2"; shift 2;; + --prompts) PROMPTS="$2"; shift 2;; + --min-items-per-shape) MIN_ITEMS="$2"; shift 2;; + --tag) TAG_OVERRIDE="$2"; shift 2;; + --dtype) DTYPE="$2"; shift 2;; + --dim-batch) DIMBATCH="$2"; shift 2;; + --push-hf) PUSH_HF="$2"; shift 2;; + --offline) OFFLINE="--offline"; shift;; + --no-swaps) SWAPS=""; shift;; + *) echo "unknown arg: $1"; exit 1;; + esac +done + +echo ">> interpreter : $PYTHON" +"$PYTHON" -c "import torch,transformers; print(' torch',torch.__version__,'| transformers',transformers.__version__)" +echo ">> model : $MODEL (dtype $DTYPE)" +echo ">> protocol : $RECIPE (corpus $CORPUS; band $WORKSPACE_BAND)" +echo ">> analysis : $PROVIDER ${ANALYZE_MODEL:-} $OFFLINE" + +# tag comes from run_lens.arch_tag so pipeline.sh and run_lens.py always agree +# (matters for multimodal Gemma, where config.architectures[0] != the causal +# class). --tag overrides it, e.g. to keep gemma -pt and -it distinct. +if [[ -n "$TAG_OVERRIDE" ]]; then + LENS_TAG="$TAG_OVERRIDE" +else + LENS_TAG="$("$PYTHON" -c "import run_lens, sys; print(run_lens.arch_tag(sys.argv[1], revision=(sys.argv[2] or None)))" "$MODEL" "$MODEL_REVISION")" +fi +LENS="lens_${LENS_TAG}.${RECIPE}.pt" +RUN="runs/${LENS_TAG}.json" +FIT_ARGS=(--recipe "$RECIPE" --corpus "$CORPUS" --workspace-band "$WORKSPACE_BAND") +[[ -n "$NFIT" ]] && FIT_ARGS+=(--n-fit "$NFIT") +[[ -n "$MODEL_REVISION" ]] && FIT_ARGS+=(--model-revision "$MODEL_REVISION") +[[ -n "$PROMPTS" ]] && FIT_ARGS+=(--prompts "$PROMPTS") +[[ -n "$MIN_ITEMS" ]] && FIT_ARGS+=(--min-items-per-shape "$MIN_ITEMS") + +echo ">> step 1/3: lens + figures ${SWAPS:+(+ causal swaps)}" +if [[ -f "$LENS" ]]; then + "$PYTHON" run_lens.py --model "$MODEL" --lens "$LENS" --tag "$LENS_TAG" \ + --dtype "$DTYPE" --shapes "$SHAPES" \ + ${FIT_ARGS[@]+"${FIT_ARGS[@]}"} $SWAPS +else + "$PYTHON" run_lens.py --model "$MODEL" --fit --tag "$LENS_TAG" \ + --dtype "$DTYPE" --dim-batch "$DIMBATCH" --shapes "$SHAPES" \ + ${FIT_ARGS[@]+"${FIT_ARGS[@]}"} $SWAPS +fi + +echo ">> step 2/3: LLM analysis" +AMODEL_ARG=() +[[ -n "$ANALYZE_MODEL" ]] && AMODEL_ARG=(--model "$ANALYZE_MODEL") +# ${arr[@]+...} guards empty-array expansion under `set -u` on macOS bash 3.2 +"$PYTHON" analyze.py --run "$RUN" --provider "$PROVIDER" \ + ${AMODEL_ARG[@]+"${AMODEL_ARG[@]}"} $OFFLINE + +echo ">> step 3/3: LaTeX report -> PDF" +"$PYTHON" report_latex.py --run "$RUN" + +if [[ -n "$PUSH_HF" ]]; then + echo ">> uploading artifacts to HF dataset: $PUSH_HF" + "$PYTHON" artifacts.py push --tag "$LENS_TAG" --repo "$PUSH_HF" +fi + +echo ">> done. PDF: reports/${LENS_TAG}_report.pdf" diff --git a/jlens_materials/report.py b/jlens_materials/report.py new file mode 100644 index 0000000000000000000000000000000000000000..6e5f50c3cf98307a5548e535ac6d15cf729638e0 --- /dev/null +++ b/jlens_materials/report.py @@ -0,0 +1,391 @@ +# Copyright 2026. Apache-2.0. +"""Assemble a Jacobian-lens run + its LLM analysis into a Markdown report. + +Reads ``runs/.json`` (figures + numeric records from ``run_lens.py``) +and ``runs/_analysis.json`` (the "what we see" write-ups from +``analyze.py``) and produces ``reports/_report.md``: an executive +synthesis, then one section per prompt embedding its four figures and the +model's analysis, then a limitations note. + + python report.py --run runs/qwen2forcausallm.json + +This closes the loop: fit -> apply -> figures -> LLM analysis -> report. +""" + +from __future__ import annotations + +import argparse +import json +import re +from pathlib import Path + +import domain_prompts as dp + +FIG_ORDER = ["grid", "stream", "trajectory", "emergence", "heatmap"] +FIG_CAPTION = { + "grid": "Layer x position slice: the top-1 lens word per cell, shaded by " + "rank. Reads the model's disposition across depth and position.", + "stream": "Thought stream: each surfaced concept flows across depth (band " + "thickness ~ salience) — the dynamics of what the model is " + "disposed to say.", + "trajectory": "Rank of each tracked concept vs depth over the predeclared " + "score span (rank 1 at top). A sustained line toward the top " + "marks workspace entry.", + "emergence": "First sustained threshold crossing in the fixed band; peak rank is annotated.", + "heatmap": "Position x layer rank map for the single strongest concept.", +} +LEGACY_FIG_CAPTION = { + **FIG_CAPTION, + "trajectory": "Legacy exploratory trajectory: best tracked-concept rank " + "was searched over all prompt positions at each depth.", + "emergence": "Legacy peak-depth summary, not the fixed-band sustained-onset " + "metric used by format-v2 runs.", +} + +_BUILTIN_BY_SLUG = {p.slug: p for p in dp.ALL_PROMPTS} + + +def _authored_prompt_text(rec: dict) -> tuple[str, str]: + """Prompt text for reports. + + New run records store ``prompt_text``: the exact resolved string fed to the + tokenizer/model, including chat-template tokens for instruct models. Older + run JSONs only stored ``prompt_tail``; for built-in prompts we can still + show the full authored prompt fields so readers get the scientific context. + """ + for key in ("prompt_text", "resolved_prompt", "prompt_used"): + if rec.get(key): + return "Prompt used", rec[key] + p = _BUILTIN_BY_SLUG.get(rec.get("slug")) + if p: + if p.text: + return "Prompt used (authored; older run did not store resolved prompt)", p.text + parts = [] + if p.system: + parts.append(f"System: {p.system}") + if p.user: + parts.append(f"User: {p.user}") + if p.assistant_prefill: + parts.append(f"Assistant prefill: {p.assistant_prefill}") + return ("Prompt used (authored chat fields; older run did not store " + "resolved chat-template text)", "\n\n".join(parts)) + if rec.get("prompt_tail"): + return "Prompt tail (older run did not store the full prompt)", "..." + rec["prompt_tail"] + return "Prompt used", "(not recorded)" + + +def _code_fence(text: str) -> str: + max_ticks = max((len(m.group(0)) for m in re.finditer(r"`+", text)), default=0) + return "`" * max(3, max_ticks + 1) + + +def _fig_paths(rec: dict, base_dir: Path) -> dict[str, str]: + """Map figure-kind -> markdown-relative path (relative to the report file).""" + out = {} + for kind, rel in rec.get("figures", {}).items(): + p = (base_dir / rel) + if p.is_file(): + # report lives in reports/, figures in figures/ — use ../ + out[kind] = "../" + rel + return out + + +def build(run_path: str) -> Path: + run_path = Path(run_path) + run = json.loads(run_path.read_text()) + base_dir = run_path.resolve().parent.parent # jlens_materials/ + analysis_path = run_path.with_name(run_path.stem + "_analysis.json") + analysis = (json.loads(analysis_path.read_text()) + if analysis_path.is_file() else None) + ana_by_slug = ({a["slug"]: a for a in analysis["per_prompt"]} + if analysis else {}) + legacy = run.get("format_version", 1) < 2 + + md: list[str] = [] + md.append(f"# Jacobian-lens report — `{run['model']}`\n") + lens_n = run.get("lens_n_prompts", "?") + md.append(f"*{run['n_layers']} layers, d_model {run['d_model']}, " + f"lens fitted on {lens_n} prompts. Experiment shapes: " + f"{', '.join(run['shapes'])}. Domains: " + f"{', '.join(run['domains'])}.*\n") + methodology = run.get("methodology", {}) + if methodology: + md.append( + f"> Claims level: **{methodology.get('claims_level', 'unknown')}**. " + f"Recipe `{methodology.get('recipe', {}).get('name', '?')}`; fixed " + f"workspace band {methodology.get('workspace_band', '?')}; scoring " + f"uses predetermined spans and synonym-min ranks.\n" + ) + elif legacy: + md.append( + "> **Legacy exploratory run:** this record predates lens provenance, " + "fixed-position scoring, and preregistered-band metadata. Its figures " + "are descriptive only; do not use its hit rates as paper-protocol " + "results.\n" + ) + if run.get("errors"): + md.append( + f"> **Run incomplete:** {len(run['errors'])} prompt(s) failed. " + "See the run JSON for the recorded exception messages.\n" + ) + if run.get("insufficient_sample_shapes"): + md.append( + "> **Sample-size guard:** quantitative claims are disabled for " + f"{', '.join(run['insufficient_sample_shapes'])}.\n" + ) + if analysis: + tag = ("offline template — no LLM was used" + if analysis["offline"] + else f"analysis by `{analysis['analysis_model']}`") + md.append(f"> Interpretation {tag}.\n") + + # --- executive synthesis ------------------------------------------------ + if analysis and analysis.get("synthesis"): + md.append("## Executive synthesis\n") + md.append(analysis["synthesis"] + "\n") + + # run-level hidden thoughts (unprompted concepts aggregated across prompts) + agg: dict[str, list] = {} + for rec in run["prompts"]: + for s in rec.get("surprising", []): + e = agg.setdefault(s["concept"], [0.0, 0]) + e[0] += s["score"]; e[1] += 1 + if agg: + ranked = sorted(agg.items(), key=lambda kv: -kv[1][0])[:15] + md.append("## Exploratory surfaced candidates across the run\n") + md.append("Heuristic top-ranked candidates, filtered against prompts and " + "actual completions where available. These are descriptive and " + "are not included in paper-protocol metrics.\n") + md.append("| concept | total band cells | # prompts |") + md.append("|--|--|--|") + for c, (sc, n) in ranked: + md.append(f"| {c} | {sc:g} | {n} |") + md.append("") + + by_shape = run.get("metrics", {}).get("by_shape", {}) + if by_shape: + md.append("## Item-level lens evaluation\n") + md.append("| shape | independent items | sample sufficient? | J-lens AUC | logit-lens AUC |") + md.append("|---|---:|---|---:|---:|") + for shape, metric in by_shape.items(): + md.append( + f"| {shape} | {metric['n_items']} | " + f"{'yes' if metric['sufficient_sample'] else 'no'} | " + f"{metric['jacobian_lens']['auc_log_k']:.3f} | " + f"{metric['logit_lens']['auc_log_k']:.3f} |" + ) + tag = run.get("tag") + if tag: + image = f"../figures/{tag}/summary__{shape.lower()}__pass_at_k.png" + md.append(f"\n![{shape} pass at k]({image})\n") + + modulation = run.get("metrics", {}).get("directed_modulation_controls", {}) + if modulation: + md.append("## Directed-modulation controls\n") + md.append("| control group | condition | items | hit rate | sample sufficient? |") + md.append("|---|---|---:|---:|---|") + for group, conditions in modulation.items(): + for condition in ("focus", "suppress", "control"): + metric = conditions.get(condition) + if not metric: + continue + md.append( + f"| {group} | {condition} | {metric['n_items']} | " + f"{metric['hit_rate']:.3f} | " + f"{'yes' if metric['sufficient_sample'] else 'no'} |" + ) + summary = conditions.get("summary", {}) + if summary: + md.append( + f"\n*{group}: {summary.get('distinct_phrasings', 0)} distinct " + f"phrasings; paper target {summary.get('paper_phrasing_target', 24)}.*\n" + ) + + causal = run.get("metrics", {}).get("causal_swaps", {}) + if causal.get("n_interventions", 0): + md.append("## Causal-intervention summary\n") + md.append("| interventions | graded | protocol success | counterfactual items | causal success | sufficient? |") + md.append("|---:|---:|---:|---:|---:|---|") + protocol_rate = causal.get("protocol_success_rate") + causal_rate = causal.get("causal_success_rate") + md.append( + f"| {causal['n_interventions']} | {causal['n_graded']} | " + f"{'—' if protocol_rate is None else f'{protocol_rate:.3f}'} | " + f"{causal.get('n_counterfactual', 0)} | " + f"{'—' if causal_rate is None else f'{causal_rate:.3f}'} | " + f"{'yes' if causal['sufficient_sample'] else 'no'} |" + ) + + # --- per-prompt sections, grouped by domain ----------------------------- + for domain in run["domains"]: + recs = [r for r in run["prompts"] if r["domain"] == domain] + if not recs: + continue + md.append(f"\n---\n\n## Domain: {domain}\n") + for rec in recs: + md.append(f"### {rec['title']} · `{rec['shape']}`\n") + md.append(f"{rec['description']}\n") + prompt_label, prompt_text = _authored_prompt_text(rec) + fence = _code_fence(prompt_text) + md.append(f"**{prompt_label}**\n\n{fence}text\n{prompt_text}\n{fence}\n") + if "valid_for_metrics" in rec: + if rec["valid_for_metrics"]: + md.append("**Metric status:** included in the registered aggregate.\n") + else: + reasons = rec.get("excluded_reasons") or ["not a registered metric item"] + md.append( + "**Metric status:** excluded — " + "; ".join(reasons) + ".\n" + ) + baseline = rec.get("baseline", {}) + if baseline.get("required"): + expected = ", ".join(baseline.get("expected", [])) + greedy = baseline.get("greedy_token", "") + md.append( + f"**Clean baseline:** expected `{expected}`; greedy token " + f"`{greedy}`; correct: " + f"{'yes' if baseline.get('correct') else 'no'}.\n" + ) + if rec.get("generated_completion"): + # JSON quoting preserves whitespace-only completions (for + # example two newline tokens) as visible, auditable text. + completion = json.dumps( + rec["generated_completion"], ensure_ascii=False + ) + completion_fence = _code_fence(completion) + md.append( + f"**Generated completion used for output-absence control**\n\n" + f"{completion_fence}text\n{completion}\n{completion_fence}\n" + ) + # concept emergence table + md.append("| concept | J-lens best rank | logit-lens best rank | peak depth | sustained onset | rank-1? |") + md.append("|---|---:|---:|---:|---:|---|") + for e in rec["emergence"]: + depth = "—" if e["best_depth"] is None else f"{e['best_depth']}%" + onset = ("—" if e.get("onset_depth") is None + else f"{e['onset_depth']}%") + logit_rank = e.get("logit_lens_best_rank") + logit_display = "—" if logit_rank is None else str(logit_rank + 1) + md.append(f"| {e['label']} | {e['best_rank'] + 1} | {logit_display} | " + f"{depth} | {onset} | " + f"{'yes' if e['reached_top1'] else ''} |") + if rec["tracked_dropped"]: + md.append(f"\n*Dropped (multi-token, §9.1): " + f"{', '.join(rec['tracked_dropped'])}.*") + md.append("") + if legacy: + md.append( + "*Legacy scoring searched prompt positions; peak depth is " + "descriptive and sustained onset was not recorded.*\n" + ) + + # analysis + if rec["slug"] in ana_by_slug: + md.append("**What we see** — " + ana_by_slug[rec["slug"]]["analysis"] + + "\n") + + # hidden thoughts (unprompted internal concepts) + surp = rec.get("surprising") + if surp: + md.append("**Exploratory surfaced candidates** — heuristic " + "top-ranked tokens absent from the prompt and, where " + "checked, an actual generated/teacher-forced completion:") + md.append("\n| concept | band cells | peak rank | near token |") + md.append("|--|--|--|--|") + for s in surp[:10]: + md.append(f"| {s['concept']} | {s['score']:g} | " + f"{s['best_rank']} | {s['near_token']} |") + md.append("") + + # causal swap + sw = rec.get("swap") + if sw: + if sw.get("protocol") == "verbal_report" and sw.get("trials"): + clean_source = sw.get("clean_source", sw.get("source", "?")) + md.append( + f"**Multi-candidate verbal-report swaps** — clean report " + f"`{clean_source}`; success rate " + f"{100 * sw.get('protocol_success_rate', 0):.1f}%." + ) + md.append("\n| target | source rank | target rank | swapped top-1 | success |") + md.append("|---|---:|---:|---|---|") + for trial in sw["trials"]: + target = trial["target"] + if target.strip().casefold() == str(clean_source).casefold(): + target += " (case-only self-target)" + top_after = trial.get("swapped_top", [["", 0]])[0][0] + md.append( + f"| {target} | {trial['source_rank_clean']+1}→" + f"{trial['source_rank_swapped']+1} | " + f"{trial['target_rank_clean']+1}→" + f"{trial['target_rank_swapped']+1} | " + f"{top_after or '·'} | " + f"{'yes' if trial.get('protocol_success') else 'no'} |" + ) + md.append( + "\n*Case-only self-targets are diagnostic noise and are " + "excluded by the current runner.*\n" + ) + else: + md.append(f"**Causal swap (Fig 4C)** — J-lens coordinate " + f"`{sw['source']}` → `{sw['target']}` (α={sw['alpha']}) " + f"at band layers {sw['band_layers'][0]}–{sw['band_layers'][-1]}:") + md.append("\n| # | clean | prob | after swap | prob |") + md.append("|--|--|--|--|--|") + for i, ((cw, cp), (sww, sp)) in enumerate( + zip(sw["clean_top"], sw["swapped_top"]), 1): + md.append(f"| {i} | {cw or '·'} | {cp:.3f} | {sww or '·'} | {sp:.3f} |") + md.append(f"\n*Source `{sw['source']}` rank " + f"{sw['source_rank_clean']+1}→{sw['source_rank_swapped']+1} " + f"(higher = suppressed); target `{sw['target']}` rank " + f"{sw['target_rank_clean']+1}→{sw['target_rank_swapped']+1} " + f"(lower = installed).*\n") + + # figures + figs = _fig_paths(rec, base_dir) + for kind in FIG_ORDER: + if kind in figs: + md.append(f"![{kind}]({figs[kind]})") + captions = LEGACY_FIG_CAPTION if legacy else FIG_CAPTION + md.append(f"*{captions[kind]}*\n") + + # --- limitations -------------------------------------------------------- + md.append("\n---\n\n## Limitations\n") + md.append("- **Single-token vocabulary** (paper §9.1): multi-token " + "concepts cannot be tracked and are dropped (listed per prompt). " + "A larger vocabulary (e.g. Gemma's 256k) keeps more scientific " + "terms trackable.\n" + "- **Model capability gates everything**: a concept only surfaces " + "if the model has it. Reproducing the paper's protein-sequence or " + "abstraction results needs a capable, ideally science-exposed " + "model.\n") + if legacy: + md.append( + "- **Legacy methodology:** this run lacks the v2 provenance and " + "fixed-scoring record. Its workspace band and prompt-position " + "selection cannot be audited as preregistered, so it is suitable " + "only for qualitative inspection." + ) + else: + md.append( + "- **Fixed workspace band:** the report uses the band recorded in " + "the run metadata. For a new model family, calibrate or preregister " + "that band on data disjoint from the reported test items." + ) + + reports = base_dir / "reports" + reports.mkdir(exist_ok=True) + out = reports / f"{run_path.stem}_report.md" + out.write_text("\n".join(md), encoding="utf-8") + print(f"wrote {out}") + return out + + +def main() -> None: + ap = argparse.ArgumentParser() + ap.add_argument("--run", required=True, help="runs/.json") + args = ap.parse_args() + build(args.run) + + +if __name__ == "__main__": + main() diff --git a/jlens_materials/report_latex.py b/jlens_materials/report_latex.py new file mode 100644 index 0000000000000000000000000000000000000000..836bdacd811a5e350491aa0429d64fe522f6bca6 --- /dev/null +++ b/jlens_materials/report_latex.py @@ -0,0 +1,594 @@ +# Copyright 2026. Apache-2.0. +"""Assemble a Jacobian-lens run + its LLM analysis into a LaTeX document and +compile it to PDF. + +Reads ``runs/.json`` (figures + numeric records from ``run_lens.py``) +and ``runs/_analysis.json`` (the "what we see" write-ups from +``analyze.py``) and writes ``reports/_report.tex`` + +``reports/_report.pdf``. + + python report_latex.py --run runs/qwen2forcausallm.json # .tex + .pdf + python report_latex.py --run runs/qwen2forcausallm.json --no-compile # .tex only + +Compilation uses ``latexmk`` (falls back to ``pdflatex`` x2). A TeX install is +required for the PDF step; the ``.tex`` is always written. +""" + +from __future__ import annotations + +import argparse +import json +import os +import re +import shutil +import subprocess +from pathlib import Path + +import domain_prompts as dp + +# macOS MacTeX default; harmless elsewhere. +_TEXBIN = "/Library/TeX/texbin" + +FIG_ORDER = ["grid", "stream", "trajectory", "emergence", "heatmap"] +FIG_CAPTION = { + "grid": "Layer $\\times$ position slice: top-1 lens word per cell, shaded " + "by full-vocab rank.", + "stream": "Thought stream: each concept the lens surfaces flows across " + "depth (band thickness $\\sim$ salience) --- the dynamics of " + "what the model is disposed to say.", + "trajectory": "Rank of each tracked concept vs depth over the predeclared " + "score span (rank~1 at top); a sustained line toward the top " + "marks workspace entry.", + "emergence": "First sustained threshold crossing in the fixed band; peak rank is annotated.", + "heatmap": "Position $\\times$ layer rank map for the strongest concept.", +} +LEGACY_FIG_CAPTION = { + **FIG_CAPTION, + "trajectory": "Legacy exploratory trajectory: best tracked-concept rank " + "was searched over all prompt positions at each depth.", + "emergence": "Legacy peak-depth summary, not the fixed-band sustained-onset " + "metric used by format-v2 runs.", +} +FIG_WIDTH = {"grid": "\\linewidth", "stream": "0.95\\linewidth", + "trajectory": "0.86\\linewidth", "emergence": "0.7\\linewidth", + "heatmap": "0.86\\linewidth"} + +_BUILTIN_BY_SLUG = {p.slug: p for p in dp.ALL_PROMPTS} + + +# --------------------------------------------------------------------------- # +# text -> LaTeX +# --------------------------------------------------------------------------- # + +_ESCAPES = {"\\": r"\textbackslash{}", "&": r"\&", "%": r"\%", "$": r"\$", + "#": r"\#", "_": r"\_", "{": r"\{", "}": r"\}", + "~": r"\textasciitilde{}", "^": r"\textasciicircum{}", + # Normalize common model-generated Unicode to pdfTeX-safe forms. + # ASCII TeX punctuation also avoids font-dependent missing glyphs. + "‐": "-", "‑": "-", "–": "--", "—": "---", + "‘": "`", "’": "'", "“": "``", "”": "''", "…": "...", + "×": r"$\times$", "→": r"$\rightarrow$", "⇒": r"$\Rightarrow$", + # OT1 renders bare < and > as inverted ¡/¿ — escape them (fixes + # "->" and any comparison operators in LLM analysis text) + "<": r"\textless{}", ">": r"\textgreater{}"} + + +def tex_escape(s: str) -> str: + out = [] + for ch in s: + out.append(_ESCAPES.get(ch, ch)) + return "".join(out) + + +def md_to_tex(text: str) -> str: + """Escape LaTeX specials, then re-apply a little markdown (**bold**, `code`) + and paragraph breaks. Robust to whatever the LLM emits.""" + # Pull out `code` before **bold**. Model prose occasionally contains + # markdown-looking ``**`` inside a code span (or an unmatched span that + # reaches into one). Stashing bold first can then put bold sentinels inside + # the saved code value; those sentinels are reintroduced only after the + # bold-restoration pass and leak NUL bytes into the generated TeX. + bolds, codes = [], [] + + def _stash_bold(m): + bolds.append(m.group(1)); return f"\x00B{len(bolds)-1}\x00" + + def _stash_code(m): + codes.append(m.group(1)); return f"\x00C{len(codes)-1}\x00" + + text = re.sub(r"`([^`]+?)`", _stash_code, text) + text = re.sub(r"\*\*(.+?)\*\*", _stash_bold, text) + text = tex_escape(text) + # restore spans (their contents are escaped separately) + text = re.sub(r"\x00B(\d+)\x00", + lambda m: r"\textbf{" + tex_escape(bolds[int(m.group(1))]) + "}", + text) + text = re.sub(r"\x00C(\d+)\x00", + lambda m: r"\texttt{" + tex_escape(codes[int(m.group(1))]) + "}", + text) + # blank line -> paragraph break; strip a leading "[offline template]" tag bold + paras = [p.strip() for p in re.split(r"\n\s*\n", text) if p.strip()] + return "\n\n".join(paras) + + +# --------------------------------------------------------------------------- # +# document assembly +# --------------------------------------------------------------------------- # + +PREAMBLE = r"""\documentclass[11pt]{article} +\usepackage[T1]{fontenc} +\usepackage[margin=1in]{geometry} +\usepackage{graphicx} +\usepackage{booktabs} +\usepackage{float} +\usepackage{xcolor} +\usepackage[hidelinks]{hyperref} +\usepackage{parskip} +\usepackage{caption} +\captionsetup{font=small,labelfont=bf} +\setkeys{Gin}{keepaspectratio} +\emergencystretch=2em +\title{%(title)s} +\author{%(author)s} +\date{Generated by the jlens\_materials pipeline} +\begin{document} +\maketitle +""" + + +def _surprising_block(surp: list[dict]) -> list[str]: + """LaTeX for exploratory, non-quantitative surfaced candidates.""" + L = [r"\paragraph{Exploratory surfaced candidates.} " + "Heuristic top-ranked tokens absent from the prompt and, where checked, " + "from an actual generated or teacher-forced completion. This is not a " + "paper metric and may include tokenizer artifacts."] + L.append(r"\begin{table}[H]\centering\small\begin{tabular}{lrrl}\toprule") + L.append(r"concept & band cells & peak rank & near token \\ \midrule") + for s in surp[:10]: + L.append(f"{tex_escape(s['concept'])} & {s['score']:g} & {s['best_rank']} " + f"& {tex_escape(s['near_token'])} \\\\") + L.append(r"\bottomrule\end{tabular}") + L.append(r"\caption{Exploratory surfaced candidates, strongest first " + r"(`band cells' = how many workspace-band positions/layers it " + r"tops).}\end{table}") + return L + + +def _swap_block(sw: dict) -> list[str]: + """LaTeX for a causal-swap result: clean vs swapped next-token, plus the + source/target full-vocab rank change.""" + if sw.get("protocol") == "verbal_report" and sw.get("trials"): + clean_source = sw.get("clean_source", sw.get("source", "?")) + L = [ + r"\paragraph{Multi-candidate verbal-report swaps.} " + + tex_escape( + f"Clean one-word report: {clean_source}. Each row installs a " + "predeclared sibling candidate across the fixed band." + ), + r"\begin{table}[H]\centering\small", + r"\begin{tabular}{lrrlc}\toprule", + r"target & source rank & target rank & swapped top-1 & success \\ \midrule", + ] + for trial in sw["trials"]: + target = trial["target"] + if target.strip().casefold() == str(clean_source).casefold(): + target += " (case-only self-target)" + source_rank = ( + f"{trial['source_rank_clean']+1}$\\to$" + f"{trial['source_rank_swapped']+1}" + ) + target_rank = ( + f"{trial['target_rank_clean']+1}$\\to$" + f"{trial['target_rank_swapped']+1}" + ) + top_after = trial.get("swapped_top", [["", 0]])[0][0] + success = "yes" if trial.get("protocol_success") else "no" + L.append( + f"{tex_escape(target)} & {source_rank} & {target_rank} & " + f"{tex_escape(top_after) or '{}'} & {success} \\\\" + ) + L.append(r"\bottomrule\end{tabular}") + L.append( + rf"\caption{{Verbal-report candidate swaps; protocol success rate " + rf"{100 * sw.get('protocol_success_rate', 0):.1f}\%. A case-only " + r"self-target is diagnostic noise and is excluded by current runner code.}" + ) + L.append(r"\end{table}") + return L + + L = [r"\paragraph{Causal swap (Fig 4C).} " + + tex_escape(f"J-lens coordinate {sw['source']} -> {sw['target']} " + f"(alpha={sw['alpha']}) at every band layer " + f"{sw['band_layers'][0]}-{sw['band_layers'][-1]}. " + "Does intervening on the concept change the output?")] + L.append(r"\begin{table}[H]\centering\small") + L.append(r"\begin{tabular}{rlr@{\quad}lr}\toprule") + L.append(r"\# & clean next-token & prob & after swap & prob \\ \midrule") + for i, ((cw, cp), (sw_w, sp)) in enumerate( + zip(sw["clean_top"], sw["swapped_top"]), 1): + L.append(f"{i} & {tex_escape(cw) or '{}'} & {cp:.3f} & " + f"{tex_escape(sw_w) or '{}'} & {sp:.3f} \\\\") + L.append(r"\bottomrule\end{tabular}") + sr = f"{sw['source_rank_clean']+1}$\\to${sw['source_rank_swapped']+1}" + tr = f"{sw['target_rank_clean']+1}$\\to${sw['target_rank_swapped']+1}" + L.append(rf"\caption{{Source ``{tex_escape(sw['source'])}'' full-vocab rank " + rf"{sr} (higher = suppressed); target " + rf"``{tex_escape(sw['target'])}'' rank {tr} (lower = installed).}}") + L.append(r"\end{table}") + return L + + +def _fig_abspath(rec, base_dir, kind): + rel = rec.get("figures", {}).get(kind) + if not rel: + return None + p = (base_dir / rel).resolve() + return str(p) if p.is_file() else None + + +def _prompt_text_for_report(rec: dict) -> tuple[str, str]: + """Return (label, prompt text) for a case-study report section.""" + for key in ("prompt_text", "resolved_prompt", "prompt_used"): + if rec.get(key): + return "Prompt used", rec[key] + p = _BUILTIN_BY_SLUG.get(rec.get("slug")) + if p: + if p.text: + return "Prompt used (authored; older run did not store resolved prompt)", p.text + parts = [] + if p.system: + parts.append(f"System: {p.system}") + if p.user: + parts.append(f"User: {p.user}") + if p.assistant_prefill: + parts.append(f"Assistant prefill: {p.assistant_prefill}") + return ("Prompt used (authored chat fields; older run did not store " + "resolved chat-template text)", "\n\n".join(parts)) + if rec.get("prompt_tail"): + return "Prompt tail (older run did not store the full prompt)", "..." + rec["prompt_tail"] + return "Prompt used", "(not recorded)" + + +def _prompt_block(rec: dict) -> list[str]: + label, text = _prompt_text_for_report(rec) + lines = [ + rf"\paragraph{{{tex_escape(label)}.}}", + r"\begin{quote}\footnotesize\ttfamily\raggedright", + ] + for line in text.splitlines() or [""]: + lines.append(tex_escape(line) + r"\par") + lines.append(r"\end{quote}") + return lines + + +def build_tex(run_path: str) -> Path: + run_path = Path(run_path) + run = json.loads(run_path.read_text()) + base_dir = run_path.resolve().parent.parent # jlens_materials/ + ana_path = run_path.with_name(run_path.stem + "_analysis.json") + analysis = json.loads(ana_path.read_text()) if ana_path.is_file() else None + ana_by_slug = ({a["slug"]: a for a in analysis["per_prompt"]} + if analysis else {}) + legacy = run.get("format_version", 1) < 2 + + if analysis: + if analysis["offline"]: + author = "Interpretation: offline template (no LLM)" + else: + author = tex_escape(f"Interpretation by {analysis.get('provider','?')}" + f":{analysis['analysis_model']}") + else: + author = "No LLM analysis available" + + L = [PREAMBLE % { + "title": tex_escape(f"Jacobian-lens report: {run['model']}"), + "author": author}] + + lens_n = run.get("lens_n_prompts", "?") + L.append(r"\noindent " + tex_escape( + f"Model: {run['model']} ({run['n_layers']} layers, d_model " + f"{run['d_model']}); lens fitted on {lens_n} prompts. Shapes: " + f"{', '.join(run['shapes'])}. Domains: {', '.join(run['domains'])}.") + + "\n") + methodology = run.get("methodology", {}) + if methodology: + L.append(r"\par\noindent\textbf{Methodology status.} " + tex_escape( + f"Claims level: {methodology.get('claims_level', 'unknown')}. " + f"Recipe: {methodology.get('recipe', {}).get('name', '?')}; " + f"fixed workspace band: {methodology.get('workspace_band', '?')}; " + "predetermined score spans with synonym-min ranks.") + "\n") + elif legacy: + L.append( + r"\par\noindent\fcolorbox{red!60!black}{red!4}{" + r"\parbox{0.94\linewidth}{\textbf{Legacy exploratory run.} " + r"This record predates lens provenance, fixed-position scoring, " + r"and preregistered-band metadata. Its figures are descriptive " + r"only; its hit rates are not paper-protocol results.}}" + "\n" + ) + if run.get("errors"): + L.append( + r"\par\noindent\textbf{Run incomplete.} " + + tex_escape( + f"{len(run['errors'])} prompt(s) failed. See the run JSON for " + "the recorded exception messages." + ) + "\n" + ) + if run.get("insufficient_sample_shapes"): + L.append( + r"\par\noindent\textbf{Sample-size guard.} Quantitative claims " + r"are disabled for the following groups:" + ) + L.append(r"\begin{itemize}\small\raggedright") + for group in run["insufficient_sample_shapes"]: + breakable = tex_escape(group).replace( + ":", r":\allowbreak{}" + ).replace("-", r"-\allowbreak{}") + L.append(r"\item " + breakable) + L.append(r"\end{itemize}") + + if analysis and analysis.get("synthesis"): + L.append(r"\section*{Executive synthesis}") + L.append(md_to_tex(analysis["synthesis"])) + + # run-level 'hidden thoughts': unprompted concepts, aggregated across prompts + agg: dict[str, list] = {} + for rec in run["prompts"]: + for s in rec.get("surprising", []): + e = agg.setdefault(s["concept"], [0.0, 0]) + e[0] += s["score"]; e[1] += 1 + if agg: + ranked = sorted(agg.items(), key=lambda kv: -kv[1][0])[:15] + L.append(r"\section*{Exploratory surfaced candidates across the run}") + L.append(r"Heuristic top-ranked candidates, filtered against prompts and " + r"actual completions where available. They are descriptive and " + r"not included in the paper-protocol metrics.") + L.append(r"\begin{table}[H]\centering\small\begin{tabular}{lrr}\toprule") + L.append(r"concept & total band cells & \# prompts \\ \midrule") + for c, (sc, n) in ranked: + L.append(f"{tex_escape(c)} & {sc:g} & {n} \\\\") + L.append(r"\bottomrule\end{tabular}\end{table}") + + by_shape = run.get("metrics", {}).get("by_shape", {}) + if by_shape: + L.append(r"\section*{Item-level lens evaluation}") + L.append(r"\begin{table}[H]\centering\small") + L.append(r"\begin{tabular}{lrrrr}\toprule") + L.append(r"shape & items & sufficient & J-lens AUC & logit AUC \\ \midrule") + for shape, metric in by_shape.items(): + sufficient = "yes" if metric["sufficient_sample"] else "no" + L.append( + f"{tex_escape(shape)} & {metric['n_items']} & {sufficient} & " + f"{metric['jacobian_lens']['auc_log_k']:.3f} & " + f"{metric['logit_lens']['auc_log_k']:.3f} \\\\") + L.append(r"\bottomrule\end{tabular}") + L.append(r"\caption{Pass@k area under the curve against log k.}\end{table}") + + modulation = run.get("metrics", {}).get("directed_modulation_controls", {}) + if modulation: + L.append(r"\section*{Directed-modulation controls}") + L.append(r"\begin{table}[H]\centering\small") + L.append(r"\begin{tabular}{llrrc}\toprule") + L.append(r"control group & condition & items & hit rate & sufficient \\ \midrule") + summaries = [] + for group, conditions in modulation.items(): + for condition in ("focus", "suppress", "control"): + metric = conditions.get(condition) + if not metric: + continue + sufficient = "yes" if metric["sufficient_sample"] else "no" + L.append( + f"{tex_escape(group)} & {condition} & {metric['n_items']} & " + f"{metric['hit_rate']:.3f} & {sufficient} \\\\" + ) + summary = conditions.get("summary", {}) + if summary: + summaries.append( + f"{group}: {summary.get('distinct_phrasings', 0)} distinct " + f"phrasings (paper target " + f"{summary.get('paper_phrasing_target', 24)})" + ) + L.append(r"\bottomrule\end{tabular}") + caption = "Matched focus, suppress, and neutral modulation trials." + if summaries: + caption += " " + "; ".join(summaries) + "." + L.append(rf"\caption{{{tex_escape(caption)}}}\end{{table}}") + + causal = run.get("metrics", {}).get("causal_swaps", {}) + if causal.get("n_interventions", 0): + protocol_rate = causal.get("protocol_success_rate") + causal_rate = causal.get("causal_success_rate") + protocol_display = "--" if protocol_rate is None else f"{protocol_rate:.3f}" + causal_display = "--" if causal_rate is None else f"{causal_rate:.3f}" + sufficient = "yes" if causal["sufficient_sample"] else "no" + L.append(r"\section*{Causal-intervention summary}") + L.append(r"\begin{table}[H]\centering\small") + L.append(r"\begin{tabular}{rrrrrc}\toprule") + L.append(r"interventions & graded & protocol success & counterfactual & causal success & sufficient \\ \midrule") + L.append( + f"{causal['n_interventions']} & {causal['n_graded']} & " + f"{protocol_display} & {causal.get('n_counterfactual', 0)} & " + f"{causal_display} & {sufficient} \\\\" + ) + L.append(r"\bottomrule\end{tabular}") + L.append(r"\caption{Registered behavioral outcomes for causal swaps; " + r"rank movement alone is not counted as causal success.}\end{table}") + + _tag = run.get("tag") + hitbars = ((base_dir / "figures" / _tag / "summary__hit_rates.png") if _tag + else (base_dir / "figures" / "summary__hit_rates.png")).resolve() + if legacy and hitbars.is_file(): + L.append(r"\section*{Legacy exploratory concept surfacing}") + L.append(r"\begin{figure}[H]\centering") + L.append(rf"\includegraphics[width=0.9\linewidth]{{{hitbars}}}") + L.append(r"\caption{Legacy exploratory fraction of tracked concepts " + r"reaching the lens top-5 anywhere in depth and prompt " + r"position, with Wilson 95\% CIs. This is not a fixed-span " + r"paper-protocol metric.}") + L.append(r"\end{figure}") + + for domain in run["domains"]: + recs = [r for r in run["prompts"] if r["domain"] == domain] + if not recs: + continue + L.append(rf"\clearpage\section{{Domain: {tex_escape(domain)}}}") + for rec in recs: + L.append(rf"\subsection{{{tex_escape(rec['title'])} " + rf"\normalfont\texttt{{{tex_escape(rec['shape'])}}}}}") + L.append(md_to_tex(rec["description"])) + L += _prompt_block(rec) + + if "valid_for_metrics" in rec: + if rec["valid_for_metrics"]: + status = "Included in the registered aggregate." + else: + reasons = rec.get("excluded_reasons") or ["not a registered metric item"] + status = "Excluded: " + "; ".join(reasons) + "." + L.append(r"\paragraph{Metric status.} " + tex_escape(status)) + baseline = rec.get("baseline", {}) + if baseline.get("required"): + expected = ", ".join(baseline.get("expected", [])) + greedy = baseline.get("greedy_token", "") + correct = "yes" if baseline.get("correct") else "no" + L.append( + r"\paragraph{Clean baseline.} " + + tex_escape( + f"Expected {expected}; greedy token {greedy}; " + f"correct: {correct}." + ) + ) + if rec.get("generated_completion"): + completion = rec["generated_completion"] + for source, visible in ( + ("\\", "[backslash]"), ("\r", "[carriage-return]"), + ("\n", "[newline]"), ("\t", "[tab]"), + ): + completion = completion.replace(source, visible) + completion = f'"{completion}"' + L.append(r"\paragraph{Generated completion used for the " + r"output-absence control.}") + L.append(r"\begin{quote}\small\ttfamily\raggedright " + + tex_escape(completion) + r"\end{quote}") + + # emergence table + L.append(r"\begin{table}[H]\centering\small") + L.append(r"\begin{tabular}{lrrrrc}\toprule") + L.append(r"concept & J rank & logit rank & peak depth & onset & rank-1? \\ \midrule") + for e in rec["emergence"]: + peak_depth = "--" if e["best_depth"] is None else f"{e['best_depth']}\\%" + onset = ("--" if e.get("onset_depth") is None + else f"{e['onset_depth']}\\%") + logit_rank = e.get("logit_lens_best_rank") + logit_display = "--" if logit_rank is None else str(logit_rank + 1) + depth = f"{logit_display} & {peak_depth}" + L.append(f"{tex_escape(e['label'])} & {e['best_rank']+1} & " + f"{depth} & {onset} & {'yes' if e['reached_top1'] else ''} \\\\") + L.append(r"\bottomrule\end{tabular}") + if legacy: + cap = ("Legacy exploratory concept recovery searched prompt " + "positions; peak depth is descriptive and sustained onset " + "was not recorded.") + else: + cap = ("Concept recovery over the predetermined score span and " + "fixed workspace band; onset is the first sustained " + "threshold crossing.") + if rec["tracked_dropped"]: + cap += (" Dropped (multi-token, \\S9.1): " + + tex_escape(", ".join(rec["tracked_dropped"])) + ".") + L.append(rf"\caption{{{cap}}}\end{{table}}") + + if rec["slug"] in ana_by_slug: + L.append(r"\paragraph{What we see.}") + L.append(md_to_tex(ana_by_slug[rec["slug"]]["analysis"])) + + if rec.get("surprising"): + L += _surprising_block(rec["surprising"]) + + if rec.get("swap"): + L += _swap_block(rec["swap"]) + + for kind in FIG_ORDER: + fp = _fig_abspath(rec, base_dir, kind) + if fp: + L.append(r"\begin{figure}[H]\centering") + L.append(rf"\includegraphics[width={FIG_WIDTH[kind]}]{{{fp}}}") + captions = LEGACY_FIG_CAPTION if legacy else FIG_CAPTION + L.append(rf"\caption{{{captions[kind]}}}") + L.append(r"\end{figure}") + + L.append(r"\clearpage\section*{Limitations}") + L.append(r"\begin{itemize}") + L.append(r"\item \textbf{Single-token vocabulary} (\S9.1): multi-token " + r"concepts cannot be tracked and are dropped (listed per prompt). " + r"A larger vocabulary (e.g.\ Gemma's 256k) keeps more scientific " + r"terms trackable.") + L.append(r"\item \textbf{Model capability gates everything}: a concept " + r"surfaces only if the model has it; reproducing the paper's " + r"protein-sequence or abstraction results needs a capable, ideally " + r"science-exposed model.") + if legacy: + L.append(r"\item \textbf{Legacy methodology}: this run lacks the v2 " + r"provenance and fixed-scoring record. Its workspace band and " + r"prompt-position selection cannot be audited as preregistered, " + r"so it is suitable only for qualitative inspection.") + else: + L.append(r"\item \textbf{Fixed workspace band}: the report uses the " + r"band recorded in the run metadata. For a new model family, " + r"calibrate or preregister that band on data disjoint from the " + r"reported test items.") + L.append(r"\item \textbf{The LLM analysis is an interpretive aid} that reads " + r"the same figures you can; verify its claims against the numbers " + r"in \texttt{runs/*.json}.") + L.append(r"\end{itemize}") + L.append(r"\end{document}") + + reports = base_dir / "reports" + reports.mkdir(exist_ok=True) + tex_path = reports / f"{run_path.stem}_report.tex" + tex_path.write_text("\n".join(L), encoding="utf-8") + print(f"wrote {tex_path}") + return tex_path + + +def compile_pdf(tex_path: Path) -> Path | None: + env = dict(os.environ) + env["PATH"] = _TEXBIN + os.pathsep + env.get("PATH", "") + workdir = tex_path.parent + latexmk = shutil.which("latexmk", path=env["PATH"]) + pdflatex = shutil.which("pdflatex", path=env["PATH"]) + if latexmk: + cmd = [latexmk, "-pdf", "-interaction=nonstopmode", "-halt-on-error", + tex_path.name] + runs = [cmd] + elif pdflatex: + cmd = [pdflatex, "-interaction=nonstopmode", "-halt-on-error", + tex_path.name] + runs = [cmd, cmd] # twice for refs + else: + print(" no latexmk/pdflatex found; wrote .tex only " + "(install MacTeX/TeX Live to compile)") + return None + for c in runs: + r = subprocess.run(c, cwd=workdir, env=env, capture_output=True, text=True) + if r.returncode != 0: + log = (workdir / (tex_path.stem + ".log")) + tail = log.read_text(errors="ignore")[-1500:] if log.is_file() else r.stdout[-1500:] + print(f" LaTeX compile failed:\n{tail}") + return None + pdf = tex_path.with_suffix(".pdf") + print(f"wrote {pdf}" if pdf.is_file() else " compile produced no PDF") + return pdf if pdf.is_file() else None + + +def main() -> None: + ap = argparse.ArgumentParser() + ap.add_argument("--run", required=True, help="runs/.json") + ap.add_argument("--no-compile", action="store_true", + help="write the .tex only, skip PDF compilation") + args = ap.parse_args() + tex = build_tex(args.run) + if not args.no_compile: + compile_pdf(tex) + + +if __name__ == "__main__": + main() diff --git a/jlens_materials/run_lens.py b/jlens_materials/run_lens.py new file mode 100644 index 0000000000000000000000000000000000000000..b486a31f94c2952df58934b499a155f88ed385c4 --- /dev/null +++ b/jlens_materials/run_lens.py @@ -0,0 +1,1378 @@ +# Copyright 2026. Apache-2.0. +"""Run the Jacobian lens over the fracture / protein prompt sets and emit figures. + +Pipeline (mirrors walkthrough.ipynb, plus the static figures in `matviz`): + + 1. load an HF decoder -> jlens.from_hf (Gemma config baked in below) + 2. load OR fit a JacobianLens + 3. for each domain Prompt: + compute_slice -> SliceData + matviz.plot_slice_grid / rank_trajectories / rank_heatmap / + emergence_depth + 4. aggregate valid independent items into pass@k/AUC with a logit-lens baseline + +Gemma notes (verified on `google/gemma-4-E4B-it`) +-------------------------------------------------- +* `jlens.hf._LAYOUTS` already covers the Gemma family: `Layout("model")` matches + the text-only `Gemma*ForCausalLM` (text decoder at `.model`, blocks `.layers`, + norm `.norm`, embed `.embed_tokens`, head `.lm_head`), and the multimodal + `Gemma*ForConditionalGeneration` wrappers are caught by + `Layout("model.language_model")`. So `from_hf(hf, tok)` "just works"; if a + future variant nests the decoder elsewhere, pass an explicit `Layout(...)`. +* `final_logit_softcapping` (where present) — `HFLensModel.unembed` reads it off + the config and applies the tanh cap, so lens logits stay on the model's scale + (Gemma-3/4 set it to null; also handled). +* Gemma ties embeddings (`lm_head` shares `embed_tokens.weight`). Irrelevant to + the lens: it only calls `unembed`. +* Gemma is gated on the Hub: `huggingface-cli login` (or HF_TOKEN) first, and + accept the licence for `google/gemma-4-E4B-it`. +* An *instruct* checkpoint runs all five shapes (raw-text completions and + chat-template prompts). A *base* (`-pt`) checkpoint has no chat template, so + restrict it to the raw-text shapes (MULTIHOP / ASSOCIATION / RECOGNITION). + +Run +--- + python run_lens.py --model gpt2 --fit --recipe demo --corpus builtin --n-fit 8 + python run_lens.py --model google/gemma-4-E4B-it --fit --recipe paper \ + --corpus wikitext --prompts prompts/mechanics-paper-example.json +""" + +from __future__ import annotations + +import argparse +import json +import sys +from datetime import datetime, timezone +from pathlib import Path + +import numpy as np +import torch + +try: # tqdm ships with transformers; degrade to a no-op if somehow absent + from tqdm.auto import tqdm +except Exception: # pragma: no cover + def tqdm(x, **_): + return x + +# use the vendored jlens (committed under _vendor_jlens/) without installing +_REPO = Path(__file__).resolve().parent / "_vendor_jlens" +if (_REPO / "jlens").is_dir(): + sys.path.insert(0, str(_REPO)) + +import jlens # noqa: E402 +from jlens.vis import compute_slice # noqa: E402 + +sys.path.insert(0, str(Path(__file__).resolve().parent)) +import domain_prompts as dp # noqa: E402 +import lens_hub # noqa: E402 +import matviz # noqa: E402 +import paper_protocol as pp # noqa: E402 +import swap as swapmod # noqa: E402 + +FIGDIR = Path(__file__).resolve().parent / "figures" + + +# --------------------------------------------------------------------------- # +# model + lens +# --------------------------------------------------------------------------- # + +_DTYPES = {"float32": torch.float32, + "bfloat16": torch.bfloat16, + "float16": torch.float16} + + +def load_model(name: str, *, dtype=torch.float32, device: str | None = None, + revision: str | None = None, trust_remote_code: bool = False): + import transformers + + if device is None: + device = ("cuda" if torch.cuda.is_available() + else "mps" if torch.backends.mps.is_available() else "cpu") + hf = transformers.AutoModelForCausalLM.from_pretrained( + name, dtype=dtype, revision=revision, trust_remote_code=trust_remote_code + ) + hf = hf.to(device) + tok = transformers.AutoTokenizer.from_pretrained( + name, revision=revision, trust_remote_code=trust_remote_code + ) + model = jlens.from_hf(hf, tok) + model._requested_revision = revision + print(f"loaded {name}: n_layers={model.n_layers} d_model={model.d_model} " + f"device={device} dtype={dtype}") + return model + + +def arch_tag(model_id: str, *, revision: str | None = None, + trust_remote_code: bool = False) -> str: + """Deterministic lens/run tag for a HF model id, WITHOUT loading weights: + the class ``AutoModelForCausalLM`` will instantiate, lowercased. This equals + ``type(loaded_model).__name__.lower()`` (what the files are named after), so + pipeline.sh and run_lens.py always agree — even for multimodal Gemma, where + ``config.architectures[0]`` differs from the causal-LM class.""" + from transformers import AutoConfig, AutoModelForCausalLM + cfg = AutoConfig.from_pretrained( + model_id, revision=revision, trust_remote_code=trust_remote_code + ) + try: + return AutoModelForCausalLM._model_mapping[type(cfg)].__name__.lower() + except Exception: # noqa: BLE001 + return (cfg.architectures or ["model"])[0].lower() + + +def get_or_fit_lens( + model, + *, + model_id: str, + tag: str, + lens_path: str | None, + do_fit: bool, + recipe: pp.FitRecipe, + corpus: str, + corpus_revision: str | None, + corpus_subset: str | None, + corpus_split: str, + corpus_text_field: str, + corpus_seed: int, + corpus_min_chars: int, + source_layers=None, + dim_batch: int = 64, + allow_unverified_lens: bool = False, +): + identity = pp.model_identity(model, model_id) + if lens_path and Path(lens_path).is_file(): + lens = jlens.JacobianLens.load(lens_path) + metadata = pp.read_lens_metadata(lens_path) + pp.validate_lens_metadata( + metadata, identity=identity, + require=(recipe.name == "paper" and not allow_unverified_lens), + ) + if recipe.name == "paper" and metadata is not None: + fitted_recipe = metadata.get("recipe", {}) + try: + fitted_recipe_obj = pp.FitRecipe(**fitted_recipe) + except TypeError as exc: + raise ValueError("lens has incomplete paper-recipe provenance") from exc + if not pp.is_paper_faithful_recipe(fitted_recipe_obj): + raise ValueError( + "paper evaluation requested with a non-compliant lens; refit with " + "--recipe paper or use --recipe demo" + ) + expected_layers = pp.evenly_spaced_source_layers( + model.n_layers, -2, 25 + ) + if lens.source_layers != expected_layers: + raise ValueError( + "paper lens source layers do not match the 25-layer " + f"registered grid: {lens.source_layers}" + ) + if metadata.get("corpus", {}).get("records", 0) < 1000: + raise ValueError("paper lens provenance contains fewer than 1000 records") + if metadata.get("corpus", {}).get("unique_records", 0) < 1000: + raise ValueError("paper lens provenance contains fewer than 1000 unique records") + print(f"loaded lens: {lens}") + return lens, metadata + if not do_fit: + raise SystemExit("no lens file and --fit not set; nothing to do") + fit_corpus = pp.load_fit_corpus( + corpus=corpus, + n=recipe.n_fit, + seed=corpus_seed, + revision=corpus_revision, + subset=corpus_subset, + split=corpus_split, + text_field=corpus_text_field, + min_chars=corpus_min_chars, + strict=recipe.strict_corpus, + ) + if source_layers is None: + source_layers = pp.evenly_spaced_source_layers( + model.n_layers, recipe.target_layer, recipe.report_layer_count + ) + fit_metadata = { + "format_version": 1, + "created_at": datetime.now(timezone.utc).isoformat(), + "paper_reference": "https://transformer-circuits.pub/2026/workspace/index.html", + "upstream_reference": "lamm-mit/jacobian-lens@581d398613e5602a5af361e1c34d3a92ea82ba8e", + "model": identity, + "recipe": pp.recipe_dict(recipe), + "corpus": fit_corpus.metadata, + "source_layers": source_layers, + "target_layer": pp.resolve_layer_index(recipe.target_layer, model.n_layers), + "dim_batch": dim_batch, + } + fingerprint = pp.fit_fingerprint(fit_metadata) + ckpt = FIGDIR.parent / f"lens_{tag}.{fingerprint}.ckpt.pt" + # outer bar over fit prompts; jlens.fit iterates this list (each prompt runs + # ceil(d_model/dim_batch) backward passes — the slow part on big models). + bar = tqdm(fit_corpus.texts, desc="fitting lens", unit="prompt") + lens = jlens.fit(model, bar, source_layers=source_layers, + target_layer=recipe.target_layer, + dim_batch=dim_batch, max_seq_len=recipe.max_seq_len, + skip_first=recipe.skip_first, + checkpoint_path=str(ckpt), checkpoint_every=4) + out = lens_path or str(FIGDIR.parent / f"lens_{tag}.pt") + lens.save(out) + meta_path = pp.write_lens_metadata(out, fit_metadata) + print(f"fitted and saved lens -> {out}: {lens}; provenance -> {meta_path}") + return lens, fit_metadata + + +# --------------------------------------------------------------------------- # +# concept word -> token id +# --------------------------------------------------------------------------- # + +def tracked_ids(tokenizer, prompt: dp.Prompt): + """Resolve concepts and every valid synonym to single-token alternatives.""" + out, dropped = pp.resolve_concepts(tokenizer, prompt) + if dropped: + print(f" [{prompt.slug}] multi-token, not tracked: {dropped}") + return out, dropped + + +@torch.no_grad() +def next_token_logits(model, text: str, *, max_seq_len: int = 512) -> torch.Tensor: + """The model's actual next-token logits, independent of display masking.""" + from jlens.hooks import ActivationRecorder + + input_ids = model.encode(text, max_length=max_seq_len) + final = model.n_layers - 1 + with ActivationRecorder(model.layers, at=[final]) as recorder: + model.forward(input_ids) + residual = recorder.activations[final][0, -1:].float() + return model.unembed(residual).float()[0].cpu() + + +@torch.no_grad() +def generate_completion(model, text: str, *, max_new_tokens: int) -> tuple[str, list[int]]: + """Greedy completion used for answer and surface-absence controls.""" + if max_new_tokens <= 0: + return "", [] + hf = getattr(model, "_hf_model", None) + if hf is None or not hasattr(hf, "generate"): + raise ValueError("the model adapter does not expose greedy generation") + input_ids = model.encode(text, max_length=512) + pad = model.tokenizer.pad_token_id + if pad is None: + pad = model.tokenizer.eos_token_id + output = hf.generate( + input_ids=input_ids, + attention_mask=torch.ones_like(input_ids), + do_sample=False, + max_new_tokens=max_new_tokens, + pad_token_id=pad, + use_cache=True, + ) + generated = [int(x) for x in output[0, input_ids.shape[1]:].tolist()] + return model.tokenizer.decode(generated, skip_special_tokens=True), generated + + +def _answer_ids(tokenizer, answers: list[str]) -> set[int]: + ids: set[int] = set() + for answer in answers: + for form in (" " + answer, answer, " " + answer.capitalize(), + answer.capitalize(), " " + answer.lower(), answer.lower()): + encoded = tokenizer.encode(form, add_special_tokens=False) + if len(encoded) == 1: + ids.add(int(encoded[0])) + return ids + + +def baseline_answer_record(model, text: str, prompt: dp.Prompt) -> dict: + answers = [x for x in (prompt.answer, *prompt.acceptable_answers) if x] + if not answers: + return {"required": False, "correct": None, "expected": []} + logits = next_token_logits(model, text) + top_id = int(logits.argmax()) + valid_ids = _answer_ids(model.tokenizer, answers) + decoded = model.tokenizer.decode([top_id], clean_up_tokenization_spaces=False).strip() + return { + "required": True, + "correct": top_id in valid_ids, + "expected": answers, + "greedy_token_id": top_id, + "greedy_token": decoded, + } + + +def logit_lens_min_ranks( + model, + lens, + text: str, + concepts: list[pp.ConceptTokens], + *, + positions: list[int], + band: tuple[float, float], + max_seq_len: int, +) -> tuple[dict[str, int], list[dict]]: + """Vanilla logit-lens baseline and auditable per-layer rank curves. + + The summary uses the exact same band/span reduction as the J-lens. The + trajectories retain one-indexed ranks at every evaluated layer so figures + can show the comparison without rerunning the model or reading values back + from a rendered image. + """ + depths = matviz.reindex_layers(lens.source_layers, model.n_layers) + layers = [layer for layer, depth in zip(lens.source_layers, depths) + if band[0] <= depth <= band[1]] + if not layers or not concepts: + return {}, [] + logits_by_layer, _, _ = lens.apply( + model, + text, + layers=layers, + positions=positions, + max_seq_len=max_seq_len, + use_jacobian=False, + ) + result: dict[str, int] = {} + curves: dict[str, list[int]] = {concept.label: [] for concept in concepts} + for concept in concepts: + best = None + for logits in logits_by_layer.values(): + layer_best = None + for token_id in concept.token_ids: + target = logits[:, token_id] + ranks = (logits > target[:, None]).sum(dim=1) + value = int(ranks.min()) + best = value if best is None else min(best, value) + layer_best = value if layer_best is None else min(layer_best, value) + curves[concept.label].append( + -1 if layer_best is None else int(layer_best) + 1 + ) + if best is not None: + result[concept.label] = best + depth_by_layer = { + int(layer): round(float(depth), 1) + for layer, depth in zip(lens.source_layers, depths) + } + trajectories = [ + { + "label": concept.label, + "layers": [int(layer) for layer in layers], + "depths": [depth_by_layer[int(layer)] for layer in layers], + "ranks": curves[concept.label], + } + for concept in concepts + ] + return result, trajectories + + +def jacobian_rank_trajectories( + slice_data, + model, + concepts: list[pp.ConceptTokens], + *, + positions: list[int], +) -> list[dict]: + """Serialize one-indexed J-lens concept ranks across all fitted layers.""" + depth = matviz.reindex_layers(slice_data.layers, model.n_layers) + id_to_col = {int(t): i for i, t in enumerate(slice_data.tracked_token_ids)} + trajectories = [] + for concept in concepts: + cols = [id_to_col[int(t)] for t in concept.token_ids + if int(t) in id_to_col] + if not cols: + continue + ranks = slice_data.rank_tensor[:, :, cols].astype(float) + ranks = np.where(ranks >= 0, ranks, np.nan) + ranks = np.nanmin(ranks, axis=2) + curve = np.nanmin(ranks[positions], axis=0) + trajectories.append({ + "label": concept.label, + "layers": [int(layer) for layer in slice_data.layers], + "depths": [round(float(value), 1) for value in depth], + "ranks": [(-1 if not np.isfinite(value) else int(value) + 1) + for value in curve], + }) + return trajectories + + +# --------------------------------------------------------------------------- # +# per-prompt run +# --------------------------------------------------------------------------- # + +def layer_readouts(slice_data, model, *, position: int = -1, top: int = 8) -> list[dict]: + """Per-layer top lens tokens at one position: the semantic trajectory the + LLM analysis reads. Uses the already-computed slice grid (word-like tokens + when mask_display=True).""" + seq = slice_data.top_ids.shape[0] + pos = position if position >= 0 else seq + position + depth = matviz.reindex_layers(slice_data.layers, model.n_layers) + out = [] + for li, layer in enumerate(slice_data.layers): + ids = slice_data.top_ids[pos, li, :top] + toks = [slice_data.vocab_fragment.get(int(t), "").strip() for t in ids] + # Static reports use an English engineering audience. Filtering here + # avoids unreadable subword fragments and missing-font glyphs while the + # unfiltered numeric ranks remain available in ``concept_trajectories``. + toks = [t for t in toks if t and t.isascii() + and any(char.isalpha() for char in t)] + out.append({"layer": int(layer), "depth": round(float(depth[li]), 1), + "top_tokens": toks[:top]}) + return out + + +def surprising_concepts(slice_data, model, text: str, *, band=(38.0, 92.0), + generated_token_ids: list[int] | None = None, + top: int = 12) -> list[dict]: + """Exploratory top-1 candidates, filtered against prompt and real output. + + This remains a heuristic discovery view, not one of the paper's quantitative + metrics. Unlike the old implementation, the final-layer next-token rows are + not described as emitted output; output exclusion uses an actual generated + or teacher-forced completion when one is available. + """ + depth = matviz.reindex_layers(slice_data.layers, model.n_layers) + final = model.n_layers - 1 + final_idx = (slice_data.layers.index(final) if final in slice_data.layers + else len(slice_data.layers) - 1) + seq = slice_data.top_ids.shape[0] + prompt_low = text.lower() + + out_ids = set(generated_token_ids or []) + + band_layers = [li for li, d in enumerate(depth) + if band[0] <= d <= band[1] and li != final_idx] + score: dict[int, float] = {} + best: dict[int, tuple] = {} + for li in band_layers: + for pos in range(seq): + tid = int(slice_data.top_ids[pos, li, 0]) # top-1 per cell + rank = int(slice_data.top_ranks[pos, li, 0]) + score[tid] = score.get(tid, 0.0) + 1.0 + if tid not in best or rank < best[tid][0]: + best[tid] = (rank, float(depth[li]), pos) + + def wordlike(s: str) -> bool: + return (len(s) >= 3 and s.isascii() and any(c.isalpha() for c in s) + and all(c.isalnum() or c in "'-" for c in s)) + + rows = [] + for tid, sc in score.items(): + if generated_token_ids is not None and tid in out_ids: + continue + raw = slice_data.vocab_fragment.get(tid, "") # keeps the leading space + s = raw.strip() + # word-like, and a word START (space-prefixed or capitalised) — this + # drops most subword-continuation fragments ("tlement", "izable", ...) + if not wordlike(s) or not (raw[:1] == " " or s[:1].isupper()): + continue + if s.lower() in prompt_low: # already in the prompt + continue + r, d, pos = best[tid] + near = slice_data.context_token_strs[slice_data.ctx_offset + pos] + rows.append({"concept": s, "score": round(sc, 1), "best_rank": r + 1, + "best_depth": round(d, 1), + "near_token": near.replace("\n", " ").strip()[:14], + "output_checked": generated_token_ids is not None}) + rows.sort(key=lambda x: (-x["score"], x["best_rank"])) + return rows[:top] + + +def run_swap_for(model, lens, prompt: dp.Prompt, text: str, *, + alpha: float = 1.0, + band: tuple[float, float] = (38.0, 92.0)) -> dict | None: + """Run a graded probe swap or the paper's multi-candidate verbal report.""" + if prompt.protocol == "verbal_report": + candidate_targets = list(prompt.candidates[:10]) or ( + [prompt.swap_to] if prompt.swap_to else [] + ) + if not candidate_targets: + return None + clean_id = int(next_token_logits(model, text).argmax()) + clean_source = model.tokenizer.decode( + [clean_id], clean_up_tokenization_spaces=False + ).strip() + trials, errors = [], [] + for target in candidate_targets: + try: + # The clean report may differ from a candidate only in case + # (e.g. "Vacancy" vs "vacancy") and therefore use a different + # token id. That is not a sibling-concept intervention. + if target.strip().casefold() == clean_source.casefold(): + continue + if swapmod._single_token(model.tokenizer, target) == clean_id: + continue + trials.append(swapmod.run_swap( + model, lens, text, "", target, alpha=alpha, band=band + )) + except Exception as exc: # noqa: BLE001 + errors.append({"target": target, "error": f"{type(exc).__name__}: {exc}"}) + if not trials: + print(f" [{prompt.slug}] verbal-report swaps skipped: {errors}") + return None + summary = dict(trials[0]) + summary.update({ + "protocol": "verbal_report", + "clean_source": clean_source, + "trials": trials, + "errors": errors, + "n_targets": len(trials), + "protocol_success_rate": float(np.mean([ + trial["protocol_success"] for trial in trials + ])), + }) + print(f" [{prompt.slug}] verbal report source={clean_source!r}; " + f"{sum(t['protocol_success'] for t in trials)}/{len(trials)} " + "candidate swaps reached rank 1") + return summary + + if not (prompt.swap_from and prompt.swap_to): + return None + try: + out = swapmod.run_swap( + model, + lens, + text, + prompt.swap_from or "", + prompt.swap_to, + alpha=alpha, + band=band, + expected_answer=prompt.answer, + acceptable_answers=prompt.acceptable_answers, + swap_answer=prompt.swap_answer, + ) + except Exception as exc: # noqa: BLE001 + print(f" [{prompt.slug}] swap skipped: {type(exc).__name__}: {exc}") + return None + print(f" [{prompt.slug}] swap {out['source']}->{prompt.swap_to}: " + f"source rank {out['source_rank_clean']+1}->{out['source_rank_swapped']+1}, " + f"target rank {out['target_rank_clean']+1}->{out['target_rank_swapped']+1}") + return out + + +def run_prompt( + model, + lens, + prompt: dp.Prompt, + *, + tag: str, + workspace_band: tuple[float, float], + strict_protocol: bool, + eval_max_seq_len: int = 512, + emergence_threshold: int = 5, + emergence_sustain: int = 2, + generation_max_new_tokens: int = 16, + layer_stride: int = 1, + mask_display: bool = True, + discover: int = 0, + layer_readout_top: int = 8, + surprising_top: int = 12, + open_vocab_logit_baseline: bool = False, + do_swap: bool = False, + swap_alpha: float = 1.0, +) -> dict: + if layer_readout_top < 1 or surprising_top < 1: + raise ValueError("open-vocabulary retention counts must be positive") + text = dp.resolve_text(prompt, model.tokenizer) + concepts, dropped = tracked_ids(model.tokenizer, prompt) + pinned = pp.flatten_concept_ids(concepts) + score_positions = pp.resolve_score_positions( + model.tokenizer, text, prompt, strict=strict_protocol + ) + prompt_token_ids = [int(x) for x in model.encode( + text, max_length=eval_max_seq_len)[0].tolist()] + if max(score_positions) >= len(prompt_token_ids): + raise ValueError( + f"score position {max(score_positions)} was truncated from a " + f"{len(prompt_token_ids)}-token evaluation context" + ) + + if prompt.readout_selector == "assistant_response": + generated_text = prompt.assistant_prefill + generated_ids = [prompt_token_ids[pos] for pos in score_positions] + elif generation_max_new_tokens > 0: + generated_text, generated_ids = generate_completion( + model, text, max_new_tokens=generation_max_new_tokens + ) + else: + generated_text, generated_ids = "", None + + violations = pp.protocol_violations( + prompt, + concepts, + prompt_token_ids=prompt_token_ids, + generated_token_ids=generated_ids, + ) + design_violations = [v for v in violations + if v.startswith("intermediates present in input") or + v.startswith("output absence requested but")] + if design_violations and strict_protocol: + raise ValueError("; ".join(design_violations)) + baseline = baseline_answer_record(model, text, prompt) + excluded_reasons = list(violations) + if baseline["required"] and not baseline["correct"]: + excluded_reasons.append("clean model did not produce the expected answer") + + # Discovery mode remains explicitly exploratory and is never included in + # paper pass@k aggregates. + auto = discover > 0 and not concepts + slice_data = compute_slice( + model, + lens, + text, + top_n=max(10, layer_readout_top), + layer_stride=layer_stride, + mask_display=mask_display, + pinned_token_ids=pinned, + max_tracked=(discover if auto else None), + max_seq_len=eval_max_seq_len, + ) + if auto: + concepts = [pp.ConceptTokens( + model.tokenizer.decode([int(token_id)]).strip() or f"<{token_id}>", + (int(token_id),), + (model.tokenizer.decode([int(token_id)]).strip(),), + ) for token_id in slice_data.tracked_token_ids[:discover]] + print(f" [{prompt.slug}] exploratory discoveries: " + f"{[concept.label for concept in concepts]}") + + ems = matviz.concept_emergence( + slice_data, + model, + concepts, + positions=score_positions, + band=workspace_band, + threshold=emergence_threshold, + sustain=emergence_sustain, + ) + logit_baseline, logit_trajectories = logit_lens_min_ranks( + model, + lens, + text, + concepts, + positions=score_positions, + band=workspace_band, + max_seq_len=eval_max_seq_len, + ) + base = FIGDIR / tag / prompt.domain / prompt.slug + lr = layer_readouts( + slice_data, + model, + position=score_positions[-1], + top=layer_readout_top, + ) + highlight = tuple(dict.fromkeys((score_positions[0], score_positions[-1]))) + + written = [] + written += matviz.plot_slice_grid( + slice_data, + model, + f"{base}__grid", + title=prompt.title, + subtitle=prompt.description, + highlight_positions=highlight, + ) + written += matviz.plot_concept_stream( + lr, + f"{base}__stream", + title=f"{prompt.title} - top-token readout stream", + subtitle=("descriptive lens readout at the fixed score position; " + "not a literal chain of thought"), + ) + if concepts: + written += matviz.plot_rank_trajectories( + slice_data, + model, + concepts, + f"{base}__trajectory", + positions=score_positions, + band=workspace_band, + title=f"{prompt.title} - concept ranks across depth", + subtitle=(f"{prompt.description} [fixed {prompt.readout_selector} " + f"span; synonym-min ranks]"), + ) + written += matviz.plot_emergence_depth( + ems, + f"{base}__emergence", + title=f"{prompt.title} - sustained concept emergence", + subtitle=(f"preregistered workspace band {workspace_band[0]:.0f}-" + f"{workspace_band[1]:.0f}% depth"), + ) + best = min((e for e in ems if np.isfinite(e.best_depth)), + key=lambda e: e.best_rank, default=None) + if best is not None: + concept = next(c for c in concepts if c.label == best.label) + written += matviz.plot_rank_heatmap( + slice_data, + model, + concept.token_ids[0], + best.label, + f"{base}__heatmap", + title=f"{prompt.title} - '{best.label}' across the prompt", + ) + + surprising = surprising_concepts( + slice_data, + model, + text, + band=workspace_band, + generated_token_ids=generated_ids, + top=surprising_top, + ) + logit_layer_readouts = [] + logit_surprising = [] + if open_vocab_logit_baseline: + logit_slice = compute_slice( + model, + lens, + text, + top_n=max(10, layer_readout_top), + max_tracked=0, + pinned_token_ids=set(), + layer_stride=layer_stride, + mask_display=mask_display, + max_seq_len=eval_max_seq_len, + use_jacobian=False, + ) + logit_layer_readouts = layer_readouts( + logit_slice, + model, + position=score_positions[-1], + top=layer_readout_top, + ) + logit_surprising = surprising_concepts( + logit_slice, + model, + text, + band=workspace_band, + generated_token_ids=generated_ids, + top=surprising_top, + ) + swap_result = ( + run_swap_for( + model, + lens, + prompt, + text, + alpha=swap_alpha, + band=workspace_band, + ) if do_swap else None + ) + valid_for_metrics = (prompt.protocol != "exploratory" and not auto + and not excluded_reasons and bool(ems)) + print( + f" [{prompt.slug}] wrote {len(written)} files; fixed band " + f"{workspace_band[0]:.0f}-{workspace_band[1]:.0f}; " + f"metrics={'included' if valid_for_metrics else 'excluded'}" + ) + + record = { + "slug": prompt.slug, + "shape": prompt.shape, + "protocol": prompt.protocol, + "domain": prompt.domain, + "title": prompt.title, + "description": prompt.description, + "note": prompt.note, + "prompt_text": text, + "prompt_tail": text[-160:], + "readout_selector": prompt.readout_selector, + "readout_at": prompt.readout_at, + "score_positions": score_positions, + "band": [round(workspace_band[0], 1), round(workspace_band[1], 1)], + "band_source": "preregistered_cli_or_config", + "condition": prompt.condition, + "control_group": prompt.control_group, + "phrasing_id": prompt.phrasing_id, + "carrier_id": prompt.carrier_id, + "target_family": prompt.target_family, + "category": prompt.category, + "answer": prompt.answer, + "baseline": baseline, + "generated_completion": generated_text, + "generated_token_ids": generated_ids, + "protocol_violations": violations, + "valid_for_metrics": valid_for_metrics, + "excluded_reasons": excluded_reasons, + "discovered": bool(auto), + "open_vocabulary_config": { + "layer_readout_top": int(layer_readout_top), + "surprising_top": int(surprising_top), + "candidate_generation_uses_tracked_terms": False, + "matched_logit_open_vocabulary_stored": bool(open_vocab_logit_baseline), + }, + "tracked_dropped": dropped, + "tracked": [ + {"label": c.label, "surfaces": list(c.surfaces), + "token_ids": list(c.token_ids)} for c in concepts + ], + "emergence": [ + { + "label": e.label, + "best_rank": int(e.best_rank), + "best_depth": (None if not np.isfinite(e.best_depth) + else round(float(e.best_depth), 1)), + "onset_depth": (None if not np.isfinite(e.onset_depth) + else round(float(e.onset_depth), 1)), + "reached_top1": bool(e.reached_top), + "best_pos": int(e.best_pos), + "logit_lens_best_rank": logit_baseline.get(e.label), + } + for e in ems + ], + "layer_readouts": lr, + "concept_trajectories": { + "jacobian_lens": jacobian_rank_trajectories( + slice_data, model, concepts, positions=score_positions + ), + "logit_lens": logit_trajectories, + "rank_indexing": "one-indexed; rank 1 is the highest-readout token", + "position_reduction": "minimum over preregistered score positions and declared single-token synonyms", + }, + "surprising": surprising, + "surprising_label": "exploratory surfaced candidates", + "logit_layer_readouts": logit_layer_readouts, + "logit_surprising": logit_surprising, + "figures": { + Path(path).stem.split("__")[-1]: str(Path(path).relative_to(FIGDIR.parent)) + for path in written if path.endswith(".png") + }, + } + if swap_result is not None: + record["swap"] = swap_result + if swap_result.get("baseline_correct") is False: + record["valid_for_metrics"] = False + record["excluded_reasons"].append("swap clean-answer baseline failed") + return { + "slug": prompt.slug, + "shape": prompt.shape, + "emergences": ems, + "band": workspace_band, + "files": written, + "record": record, + } + + +def aggregate_metrics( + results: list[dict], + tag: str, + *, + ks: list[int], + seed: int, + min_items: int, +) -> dict: + """Item-level pass@k/AUC, logit-lens baseline, controls, and swaps.""" + by_shape: dict[str, list[dict]] = {} + for result in results: + record = result["record"] + if record.get("valid_for_metrics") and record.get("protocol") == "lens_eval": + by_shape.setdefault(result["shape"], []).append(record) + + metrics: dict[str, dict] = {} + for shape, records in sorted(by_shape.items()): + lens_items = [[int(e["best_rank"]) for e in record["emergence"] + if int(e["best_rank"]) >= 0] for record in records] + baseline_items = [[int(e["logit_lens_best_rank"]) + for e in record["emergence"] + if e.get("logit_lens_best_rank") is not None] + for record in records] + lens_values = pp.item_pass_scores(lens_items, ks) + baseline_values = pp.item_pass_scores(baseline_items, ks) + group = { + "n_items": len(records), + "minimum_required": min_items, + "sufficient_sample": len(records) >= min_items, + "ks": ks, + "jacobian_lens": { + "pass_at_k": lens_values, + "auc_log_k": pp.log_k_auc(ks, lens_values), + }, + "logit_lens": { + "pass_at_k": baseline_values, + "auc_log_k": pp.log_k_auc(ks, baseline_values), + }, + } + cis = {} + for k in ks: + per_item = [float(np.mean([0 <= rank < k for rank in ranks])) + for ranks in lens_items if ranks] + mean, lo, hi = pp.bootstrap_mean_ci(per_item, seed=seed) + cis[str(k)] = {"mean": mean, "low": lo, "high": hi} + group["jacobian_lens"]["bootstrap_95"] = cis + metrics[shape] = group + matviz.plot_pass_at_k_curves( + ks, + {"Jacobian lens": lens_values, "Logit lens": baseline_values}, + FIGDIR / tag / f"summary__{shape.lower()}__pass_at_k", + aucs={"Jacobian lens": group["jacobian_lens"]["auc_log_k"], + "Logit lens": group["logit_lens"]["auc_log_k"]}, + title=f"{shape}: intermediate recovery", + subtitle=(f"{len(records)} independent items; fixed score spans and " + "workspace band"), + ) + + modulation: dict[str, dict[str, list[float]]] = {} + modulation_records: dict[str, list[dict]] = {} + for result in results: + record = result["record"] + if record.get("protocol") != "directed_modulation" or not record.get("valid_for_metrics"): + continue + group = record.get("control_group") or "ungrouped" + condition = record.get("condition") or "unspecified" + score = float(any(0 <= e["best_rank"] < 1 for e in record["emergence"])) + modulation.setdefault(group, {}).setdefault(condition, []).append(score) + modulation_records.setdefault(group, []).append(record) + # Directed modulation has its own preregistered 24-phrasing target in the + # source protocol. The general 50-item threshold applies to the independent + # lens-evaluation distributions, not to each modulation condition. + modulation_min = 24 + control_metrics = {} + for group, conditions in modulation.items(): + control_metrics[group] = {} + for condition, values in conditions.items(): + mean, lo, hi = pp.bootstrap_mean_ci(values, seed=seed) + control_metrics[group][condition] = { + "n_items": len(values), "hit_rate": mean, + "bootstrap_95": [lo, hi], + "minimum_required": modulation_min, + "sufficient_sample": len(values) >= modulation_min, + } + phrasing_ids = {record["phrasing_id"] for record in modulation_records[group] + if record.get("phrasing_id")} + control_metrics[group]["summary"] = { + "n_trials": len(modulation_records[group]), + "distinct_phrasings": len(phrasing_ids), + "paper_phrasing_target": 24, + } + + swap_records = [r["record"]["swap"] for r in results if r["record"].get("swap")] + swaps = [] + for swap_record in swap_records: + swaps.extend(swap_record.get("trials", [swap_record])) + graded = [s for s in swaps if s.get("protocol_success") is not None] + counterfactual = [s for s in swaps if s.get("causal_success") is not None] + swap_metrics = { + "n_interventions": len(swaps), + "n_graded": len(graded), + "minimum_required": min_items, + "sufficient_sample": len(graded) >= min_items, + "protocol_success_rate": (float(np.mean([s["protocol_success"] for s in graded])) + if graded else None), + "n_counterfactual": len(counterfactual), + "causal_success_rate": ( + float(np.mean([s["causal_success"] for s in counterfactual])) + if counterfactual else None + ), + } + return { + "by_shape": metrics, + "directed_modulation_controls": control_metrics, + "causal_swaps": swap_metrics, + } + + +# --------------------------------------------------------------------------- # +# main +# --------------------------------------------------------------------------- # + +def main() -> None: + ap = argparse.ArgumentParser() + ap.add_argument("--model", default="gpt2") + ap.add_argument("--model-revision", default=None, + help="HF commit/tag; recorded in lens provenance") + ap.add_argument("--trust-remote-code", action="store_true") + ap.add_argument("--lens", default=None, help="path to a fitted lens .pt") + ap.add_argument("--fit", action="store_true") + ap.add_argument( + "--fit-only", + action="store_true", + help=("fit (and optionally upload) the lens, then exit before loading " + "or evaluating any prompts; requires --fit"), + ) + ap.add_argument("--hf-lens-repo", default=None, metavar="OWNER/REPO", + help="private Hugging Face model repo used to store/fetch the lens bundle") + ap.add_argument("--hf-lens-path", default=None, metavar="PATH.pt", + help="lens path inside --hf-lens-repo (default: basename of --lens)") + ap.add_argument("--hf-lens-revision", default="main", metavar="REVISION", + help="Hub branch/tag/commit for download; branch for upload (default: main)") + ap.add_argument("--hf-upload-lens", action="store_true", + help="after fitting/loading, upload the .pt and .meta.json in one Hub commit") + ap.add_argument("--hf-force-download", action="store_true", + help="replace the local lens bundle from the Hub even if local files exist") + ap.add_argument("--hf-private", action=argparse.BooleanOptionalAction, default=True, + help="create/require a private Hub repo for upload (default: true)") + ap.add_argument("--hf-commit-message", default=None, + help="optional Hub commit message for --hf-upload-lens") + ap.add_argument("--recipe", choices=sorted(pp.FIT_RECIPES), default="demo", + help="paper = quantitative protocol; demo = fast exploration") + ap.add_argument("--n-fit", type=int, default=None, + help="override recipe sample count") + ap.add_argument("--fit-max-seq-len", type=int, default=None) + ap.add_argument("--target-layer", type=int, default=None, + help="override recipe target layer; negative counts from end") + ap.add_argument("--report-layers", type=int, default=None, + help="number of evenly spaced fitted/reported source layers") + ap.add_argument("--source-layers", default=None, + help="explicit comma-separated source layers") + ap.add_argument("--corpus", default="wikitext", + help="wikitext, HF dataset id, local txt/json/jsonl, or builtin (demo only)") + ap.add_argument("--corpus-revision", default=None) + ap.add_argument("--corpus-subset", default=None) + ap.add_argument("--corpus-split", default="train") + ap.add_argument("--corpus-text-field", default="text") + ap.add_argument("--corpus-seed", type=int, default=0) + ap.add_argument("--corpus-min-chars", type=int, default=600) + ap.add_argument("--allow-unverified-lens", action="store_true", + help="permit a legacy lens without provenance (exploratory only)") + ap.add_argument("--shapes", default=None, + help="comma list; subset of MULTIHOP,ASSOCIATION," + "RECOGNITION,MODULATION,REPORT_SWAP,PROBE_SWAP " + "(default: built-ins use the first three; custom " + "prompts use whatever shapes they declare)") + ap.add_argument("--domains", default=None, + help="comma list (default: fracture,protein for built-ins; " + "all present for custom prompts)") + ap.add_argument("--prompts", default=None, + help="load prompts from a JSON file or a directory of " + "*.json instead of the built-ins (see prompts/)") + ap.add_argument("--prompt-json", default=None, + help="define a prompt on the fly: a raw JSON object or list " + "of objects, e.g. '{\"shape\":\"MULTIHOP\"," + "\"text\":\"...\",\"tracked\":[\"crack\"]}'") + ap.add_argument("--discover", type=int, default=0, metavar="N", + help="auto-discover the N concepts that surface, for prompts " + "with no 'tracked' list (no need to pre-name concepts)") + ap.add_argument("--layer-readout-top", type=int, default=8, metavar="N", + help="retain the unrestricted top N word-like tokens at the " + "fixed readout position for every sampled layer") + ap.add_argument("--surprising-top", type=int, default=12, metavar="N", + help="retain the top N input/output-absent open-vocabulary " + "candidates across prompt positions and band layers") + ap.add_argument("--open-vocab-logit-baseline", action="store_true", + help="also retain matched vanilla-logit-lens open-vocabulary " + "readouts for blinded discovery comparisons") + ap.add_argument("--swaps", action="store_true", + help="also run the causal J-lens swap for prompts that " + "declare probe-swap answers or verbal-report candidates; records " + "clean-vs-swapped output into the run JSON") + ap.add_argument("--swap-alpha", type=float, default=1.0, + help="swap strength (1.0 clean swap = paper primary; " + "2.0 double-strength, can destabilise small models)") + ap.add_argument("--layer-stride", type=int, default=1) + ap.add_argument("--workspace-band", default="38,92", metavar="LO,HI", + help="fixed/preregistered depth-percent band; never inferred from test items") + ap.add_argument("--strict-protocol", action=argparse.BooleanOptionalAction, + default=None, help="fail prompt-design violations (default on for paper)") + ap.add_argument("--eval-max-seq-len", type=int, default=512) + ap.add_argument("--generation-max-new-tokens", type=int, default=16) + ap.add_argument("--emergence-threshold", type=int, default=5) + ap.add_argument("--emergence-sustain", type=int, default=2) + ap.add_argument("--pass-at-k", default="1,2,5,10,20,50,100") + ap.add_argument("--min-items-per-shape", type=int, default=None, + help="default 50 for paper, 1 for demo") + ap.add_argument("--continue-on-error", action="store_true", + help="write a partial run and return success despite prompt failures") + ap.add_argument("--dtype", choices=list(_DTYPES), default="float32", + help="model weight dtype (default float32 = most accurate). " + "bfloat16 ~halves memory and works well on GPU; float16 " + "also halves memory but is less numerically stable. The " + "fitted Jacobian is stored in float32 regardless, so the " + "lens itself stays full-precision.") + ap.add_argument("--dim-batch", type=int, default=64, metavar="N", + help="output dims computed per backward pass during --fit " + "(default 64). Lower (e.g. 16 or 8) cuts the fit's peak " + "memory at the cost of more passes — same total FLOPs, " + "just slower. Only affects fitting.") + ap.add_argument("--tag", default=None, + help="override the lens/run filename tag (default: the arch " + "class, e.g. --tag gemma4-e4b-it for a readable name, or " + "to keep two same-architecture checkpoints distinct)") + args = ap.parse_args() + + hub_detail_flags = ( + args.hf_lens_path, + args.hf_upload_lens, + args.hf_force_download, + args.hf_commit_message, + ) + if any(hub_detail_flags) and not args.hf_lens_repo: + raise SystemExit("Hub lens options require --hf-lens-repo OWNER/REPO") + if args.hf_lens_repo and not args.lens: + raise SystemExit("--hf-lens-repo requires an explicit local --lens PATH.pt") + if args.fit_only and not args.fit: + raise SystemExit("--fit-only requires --fit") + + recipe = pp.resolve_recipe( + args.recipe, + n_fit=args.n_fit, + max_seq_len=args.fit_max_seq_len, + target_layer=args.target_layer, + report_layer_count=args.report_layers, + ) + if args.recipe == "paper" and not pp.is_paper_faithful_recipe(recipe): + raise SystemExit( + "paper recipe overrides must retain n_fit>=1000, max_seq_len=128, " + "target_layer=-2, and report_layers=25; use --recipe demo for custom " + "or reduced-cost fitting" + ) + strict_protocol = (recipe.name == "paper" if args.strict_protocol is None + else args.strict_protocol) + if recipe.name == "paper" and not strict_protocol: + raise SystemExit( + "--recipe paper requires strict protocol checks; use --recipe demo " + "for exploratory scoring" + ) + if recipe.name == "paper" and args.allow_unverified_lens: + raise SystemExit( + "--allow-unverified-lens is exploratory only; use --recipe demo or " + "load a paper-fit lens with a valid .meta.json sidecar" + ) + if recipe.name == "paper" and args.layer_stride != 1: + raise SystemExit( + "--recipe paper requires --layer-stride 1 so all 25 registered " + "source layers are scored" + ) + workspace_band = tuple(float(x) for x in args.workspace_band.split(",")) + if len(workspace_band) != 2 or not 0 <= workspace_band[0] <= workspace_band[1] < 100: + raise SystemExit("--workspace-band must be two ordered percentages below 100") + ks = sorted({int(x) for x in args.pass_at_k.split(",") if int(x) > 0}) + if len(ks) < 2: + raise SystemExit("--pass-at-k needs at least two positive k values for AUC") + min_items = (args.min_items_per_shape if args.min_items_per_shape is not None + else (50 if recipe.name == "paper" else 1)) + if recipe.name == "paper" and min_items < 50: + raise SystemExit( + "--recipe paper requires at least 50 independent items per " + "evaluation distribution" + ) + source_layers = ([int(x) for x in args.source_layers.split(",")] + if args.source_layers else None) + tag = args.tag or arch_tag( + args.model, revision=args.model_revision, + trust_remote_code=args.trust_remote_code, + ) + if args.hf_lens_repo: + local_lens = Path(args.lens) + local_bundle_complete = ( + local_lens.is_file() and lens_hub.metadata_path(local_lens).is_file() + ) + should_download = args.hf_force_download or ( + not args.fit and not local_bundle_complete + ) + if should_download: + local, local_meta = lens_hub.download_lens_bundle( + repo_id=args.hf_lens_repo, + local_path=local_lens, + path_in_repo=args.hf_lens_path, + revision=args.hf_lens_revision, + force=args.hf_force_download, + ) + print( + f"downloaded lens bundle from hf://{args.hf_lens_repo}/" + f"{lens_hub.normalize_hub_path(args.hf_lens_path, local_lens)} " + f"-> {local} and {local_meta}" + ) + model = load_model( + args.model, + dtype=_DTYPES[args.dtype], + revision=args.model_revision, + trust_remote_code=args.trust_remote_code, + ) + if recipe.name == "paper" and len(pp.evenly_spaced_source_layers( + model.n_layers, -2, 25)) != 25: + raise SystemExit( + f"{args.model} has too few source layers for the paper's 25-layer " + "reporting grid; use --recipe demo for this model" + ) + if recipe.name == "paper" and source_layers is not None: + expected_layers = pp.evenly_spaced_source_layers(model.n_layers, -2, 25) + if source_layers != expected_layers: + raise SystemExit( + "--recipe paper requires the registered 25-layer grid; remove " + "--source-layers or pass the exact grid" + ) + lens, lens_metadata = get_or_fit_lens( + model, + model_id=args.model, + tag=tag, + lens_path=args.lens, + do_fit=args.fit, + recipe=recipe, + corpus=args.corpus, + corpus_revision=args.corpus_revision, + corpus_subset=args.corpus_subset, + corpus_split=args.corpus_split, + corpus_text_field=args.corpus_text_field, + corpus_seed=args.corpus_seed, + corpus_min_chars=args.corpus_min_chars, + source_layers=source_layers, + dim_batch=args.dim_batch, + allow_unverified_lens=args.allow_unverified_lens, + ) + if args.hf_upload_lens: + commit_url = lens_hub.upload_lens_bundle( + repo_id=args.hf_lens_repo, + local_path=args.lens, + path_in_repo=args.hf_lens_path, + revision=args.hf_lens_revision, + private=args.hf_private, + commit_message=args.hf_commit_message, + ) + lens_metadata = pp.read_lens_metadata(args.lens) + print(f"uploaded lens bundle to {commit_url}") + + if args.fit_only: + print(f"fit-only complete -> lens bundle {args.lens}") + return + + # Prompt source: custom JSON (file/dir/inline) replaces the built-ins. + custom = args.prompts or args.prompt_json + if custom: + candidates = [] + if args.prompts: + candidates += dp.load_prompts(args.prompts) + if args.prompt_json: + candidates += dp.load_prompts(args.prompt_json) + else: + candidates = dp.ALL_PROMPTS + + # Resolve shape/domain filters: explicit flags win; else built-in defaults + # for the built-in set, or "everything present" for custom prompts. + if args.shapes: + shapes = set(args.shapes.split(",")) + else: + shapes = ({p.shape for p in candidates} if custom + else {"MULTIHOP", "ASSOCIATION", "RECOGNITION"}) + if args.domains: + domains = set(args.domains.split(",")) + else: + domains = ({p.domain for p in candidates} if custom + else {"fracture", "protein"}) + + prompts = [p for p in candidates + if p.shape in shapes and p.domain in domains] + if not prompts: + raise SystemExit("no prompts remain after the shape/domain filters") + if recipe.name == "paper" and any(p.protocol == "exploratory" for p in prompts): + slugs = [p.slug for p in prompts if p.protocol == "exploratory"] + raise SystemExit( + "--recipe paper cannot score exploratory prompt records: " + f"{slugs}; use registered lens_eval/modulation/swap items or " + "--recipe demo" + ) + print(f"running {len(prompts)} prompts: " + f"{[p.slug for p in prompts]}") + + results = [] + errors = [] + for p in tqdm(prompts, desc="lensing prompts", unit="prompt"): + try: + results.append(run_prompt( + model, + lens, + p, + tag=tag, + workspace_band=workspace_band, + strict_protocol=strict_protocol, + eval_max_seq_len=args.eval_max_seq_len, + emergence_threshold=args.emergence_threshold, + emergence_sustain=args.emergence_sustain, + generation_max_new_tokens=args.generation_max_new_tokens, + layer_stride=args.layer_stride, + discover=args.discover, + layer_readout_top=args.layer_readout_top, + surprising_top=args.surprising_top, + open_vocab_logit_baseline=args.open_vocab_logit_baseline, + do_swap=args.swaps, + swap_alpha=args.swap_alpha, + )) + except Exception as exc: # noqa: BLE001 + print(f" [{p.slug}] FAILED: {type(exc).__name__}: {exc}") + errors.append({"slug": p.slug, "type": type(exc).__name__, "message": str(exc)}) + metrics = aggregate_metrics( + results, tag, ks=ks, seed=args.corpus_seed, min_items=min_items + ) + expected_eval_shapes = {p.shape for p in prompts if p.protocol == "lens_eval"} + insufficient = [ + shape for shape in sorted(expected_eval_shapes) + if shape not in metrics["by_shape"] or + not metrics["by_shape"][shape]["sufficient_sample"] + ] + if any(p.protocol == "directed_modulation" for p in prompts): + for group, conditions in metrics["directed_modulation_controls"].items(): + for condition in ("focus", "suppress", "control"): + values = conditions.get(condition) + if not values or not values["sufficient_sample"]: + insufficient.append(f"MODULATION:{group}:{condition}") + if conditions.get("summary", {}).get("distinct_phrasings", 0) < 24: + insufficient.append(f"MODULATION:{group}:24-distinct-phrasings") + has_swap_prompts = any( + p.protocol in {"probe_swap", "verbal_report"} for p in prompts + ) + if has_swap_prompts and not args.swaps: + insufficient.append("SWAPS:not-enabled") + elif has_swap_prompts and not metrics["causal_swaps"]["sufficient_sample"]: + insufficient.append("SWAPS") + + # Persist a structured run record for the analysis / report pipeline. + runs_dir = FIGDIR.parent / "runs" + runs_dir.mkdir(exist_ok=True) + paper_ready = ( + pp.is_paper_faithful_recipe(recipe) + and strict_protocol + and lens_metadata is not None + and not errors + and not insufficient + ) + claims_level = ( + "paper-protocol quantitative" if paper_ready else + "paper-protocol incomplete; quantitative claims disabled" + if recipe.name == "paper" else + "exploratory demo" + ) + run_json = { + "format_version": 2, + "created_at": datetime.now(timezone.utc).isoformat(), + "model": args.model, + "model_identity": pp.model_identity(model, args.model), + "tag": tag, + "n_layers": model.n_layers, + "d_model": model.d_model, + "lens_n_prompts": lens.n_prompts, + "lens_provenance": lens_metadata, + "methodology": { + "recipe": pp.recipe_dict(recipe), + "claims_level": claims_level, + "paper_protocol_complete": paper_ready, + "strict_protocol": strict_protocol, + "workspace_band": list(workspace_band), + "workspace_band_source": "preregistered_cli_or_config", + "score_reduction": "fixed span x fixed band x synonym minimum", + "emergence_threshold": args.emergence_threshold, + "emergence_sustain": args.emergence_sustain, + "pass_at_k": ks, + "minimum_items_per_shape": min_items, + "evaluation_max_seq_len": args.eval_max_seq_len, + "generation_max_new_tokens": args.generation_max_new_tokens, + "model_revision": args.model_revision, + "dtype": args.dtype, + "layer_stride": args.layer_stride, + "prompt_source": args.prompts or ("inline-json" if args.prompt_json + else "authoritative-builtins"), + "swaps_enabled": args.swaps, + "swap_alpha": args.swap_alpha, + }, + "shapes": sorted(shapes), + "domains": sorted(domains), + "metrics": metrics, + "errors": errors, + "insufficient_sample_shapes": insufficient, + "prompts": [r["record"] for r in results if "record" in r], + } + out_path = runs_dir / f"{tag}.json" + out_path.write_text(json.dumps(run_json, indent=2, ensure_ascii=False)) + print(f"done -> figures in {FIGDIR}; run record -> {out_path}") + failure_reasons = [] + if errors: + failure_reasons.append(f"{len(errors)} prompt(s) failed") + if strict_protocol and insufficient: + failure_reasons.append( + f"insufficient independent items for {insufficient}; need {min_items} per shape" + ) + if failure_reasons and not args.continue_on_error: + raise SystemExit("run incomplete: " + "; ".join(failure_reasons)) + + +if __name__ == "__main__": + main() diff --git a/jlens_materials/scripts/analyze_abstract_physics_composition.py b/jlens_materials/scripts/analyze_abstract_physics_composition.py new file mode 100644 index 0000000000000000000000000000000000000000..fbc62c29d6084d020c1eff3d45c70b6be49c321f --- /dev/null +++ b/jlens_materials/scripts/analyze_abstract_physics_composition.py @@ -0,0 +1,593 @@ +#!/usr/bin/env python3 +"""Analyze cross-condition geometry in the abstract-physics benchmark.""" + +from __future__ import annotations + +import hashlib +import itertools +import json +import math +from pathlib import Path +from typing import Sequence + +import matplotlib + +matplotlib.use("Agg") +import matplotlib.pyplot as plt # noqa: E402 +import numpy as np # noqa: E402 +import pandas as pd # noqa: E402 +from sklearn.feature_extraction.text import TfidfVectorizer # noqa: E402 +from sklearn.metrics import roc_auc_score # noqa: E402 + + +ROOT = Path(__file__).resolve().parents[1] +OUT = ROOT / "experiments" / "abstract-physics-composition-2026-07-18" +PROTOCOL = OUT / "protocol.json" +MANIFEST = OUT / "prompt_manifest.json" +RAW = OUT / "raw.json" +STATES = OUT / "representations.npz" +FIG = OUT / "figures" + + +def sha256(path: Path) -> str: + return hashlib.sha256(path.read_bytes()).hexdigest() + + +def normalize(values: np.ndarray) -> np.ndarray: + values = np.asarray(values, dtype=np.float64) + denom = np.sqrt(np.einsum("ij,ij->i", values, values))[:, None] + return values / np.maximum(denom, 1e-12) + + +def safe_dot(values: np.ndarray, direction: np.ndarray) -> np.ndarray: + return np.einsum("ij,j->i", values, direction) + + +def centroid_direction( + values: np.ndarray, labels: np.ndarray +) -> tuple[np.ndarray, np.ndarray]: + positive = values[labels].mean(axis=0) + negative = values[~labels].mean(axis=0) + direction = positive - negative + direction /= max(float(np.linalg.norm(direction)), 1e-12) + return direction, 0.5 * (positive + negative) + + +def cross_decode( + values: np.ndarray, + labels: np.ndarray, + train: np.ndarray, + test: np.ndarray, +) -> tuple[float, np.ndarray]: + direction, midpoint = centroid_direction(values[train], labels[train]) + scores = safe_dot(values[test] - midpoint, direction) + return float(roc_auc_score(labels[test].astype(int), scores)), scores + + +def exact_signflip(values: Sequence[float]) -> float: + values = np.asarray(values, dtype=float) + observed = abs(float(values.mean())) + means = [] + for signs in itertools.product((-1.0, 1.0), repeat=len(values)): + means.append(float(np.mean(values * np.asarray(signs)))) + return float(np.mean(np.abs(means) >= observed - 1e-15)) + + +def bootstrap( + values: Sequence[float], rng: np.random.Generator, n_boot: int +) -> tuple[float, float]: + values = np.asarray(values, dtype=float) + indices = rng.integers(0, len(values), size=(n_boot, len(values))) + means = values[indices].mean(axis=1) + return tuple(float(x) for x in np.quantile(means, [0.025, 0.975])) + + +def json_safe(value: object) -> object: + if isinstance(value, np.generic): + return json_safe(value.item()) + if isinstance(value, float) and not math.isfinite(value): + return None + if isinstance(value, dict): + return {str(key): json_safe(item) for key, item in value.items()} + if isinstance(value, (list, tuple)): + return [json_safe(item) for item in value] + return value + + +def validate(protocol: dict) -> dict: + for name in ("prompt_manifest", "analyzer"): + row = protocol["inputs"][name] + path = ROOT / row["path"] + if sha256(path) != row["sha256"]: + raise RuntimeError(f"{name} fingerprint mismatch") + raw = json.loads(RAW.read_text()) + if raw["provenance"]["protocol_sha256"] != sha256(PROTOCOL): + raise RuntimeError("raw output was produced under another protocol") + if raw["provenance"]["states_sha256"] != sha256(STATES): + raise RuntimeError("state archive fingerprint mismatch") + return raw + + +def metadata_arrays(manifest: dict, prompt_ids: np.ndarray) -> tuple[list[dict], dict]: + by_id = {row["prompt_id"]: row for row in manifest["prompts"]} + rows = [by_id[str(prompt_id)] for prompt_id in prompt_ids] + arrays = { + "law": np.asarray([row["law_id"] for row in rows]), + "split": np.asarray([row["split"] for row in rows]), + "orientation": np.asarray([row["law_sign"] == 1 for row in rows]), + "numeric": np.asarray([row["numerical_sign"] == 1 for row in rows]), + "physical": np.asarray([row["physical_sign"] == 1 for row in rows]), + "surface": np.asarray([row["surface"] for row in rows]), + "answer_order": np.asarray([row["answer_order"] for row in rows]), + "code": np.asarray([row["expected_code"] == "A" for row in rows]), + } + return rows, arrays + + +def discovery_ccgp( + values: np.ndarray, labels: np.ndarray, meta: dict +) -> float: + discovery = meta["split"] == "discovery" + direct = meta["orientation"] + auc_a, _ = cross_decode( + values, labels, discovery & direct, discovery & ~direct + ) + auc_b, _ = cross_decode( + values, labels, discovery & ~direct, discovery & direct + ) + return float(np.mean([auc_a, auc_b])) + + +def replication_law_scores( + values: np.ndarray, labels: np.ndarray, meta: dict +) -> list[dict]: + discovery = meta["split"] == "discovery" + replication = meta["split"] == "replication" + direct = meta["orientation"] + output = [] + for train_orientation, test_orientation in ((True, False), (False, True)): + train = discovery & (direct == train_orientation) + direction, midpoint = centroid_direction(values[train], labels[train]) + for law_id in sorted(set(meta["law"][replication & (direct == test_orientation)])): + test = replication & (meta["law"] == law_id) + scores = safe_dot(values[test] - midpoint, direction) + output.append( + { + "law_id": str(law_id), + "train_orientation": "direct" if train_orientation else "inverse", + "test_orientation": "direct" if test_orientation else "inverse", + "auc": float(roc_auc_score(labels[test].astype(int), scores)), + "n_prompts": int(test.sum()), + } + ) + return output + + +def contrast_parallelism( + values: np.ndarray, meta: dict, split: str +) -> tuple[pd.DataFrame, pd.DataFrame]: + vectors = {} + rows = [] + for law_id in sorted(set(meta["law"][meta["split"] == split])): + for surface in ("a", "b"): + mask = (meta["law"] == law_id) & (meta["surface"] == surface) + positive = values[mask & meta["physical"]].mean(axis=0) + negative = values[mask & ~meta["physical"]].mean(axis=0) + vector = positive - negative + vector /= max(float(np.linalg.norm(vector)), 1e-12) + key = (str(law_id), surface) + vectors[key] = vector + rows.append( + { + "law_id": str(law_id), + "surface": surface, + "orientation": ( + "direct" if bool(meta["orientation"][mask][0]) else "inverse" + ), + } + ) + vector_frame = pd.DataFrame(rows) + pair_rows = [] + keys = sorted(vectors) + for index, key_a in enumerate(keys): + for key_b in keys[index + 1 :]: + if key_a[0] == key_b[0]: + continue + row_a = vector_frame[ + (vector_frame["law_id"] == key_a[0]) + & (vector_frame["surface"] == key_a[1]) + ].iloc[0] + row_b = vector_frame[ + (vector_frame["law_id"] == key_b[0]) + & (vector_frame["surface"] == key_b[1]) + ].iloc[0] + pair_rows.append( + { + "law_a": key_a[0], + "surface_a": key_a[1], + "orientation_a": row_a["orientation"], + "law_b": key_b[0], + "surface_b": key_b[1], + "orientation_b": row_b["orientation"], + "same_orientation": row_a["orientation"] == row_b["orientation"], + "physical_contrast_cosine": float( + np.einsum("i,i->", vectors[key_a], vectors[key_b]) + ), + } + ) + return vector_frame, pd.DataFrame(pair_rows) + + +def tfidf_controls(rows: list[dict], meta: dict) -> pd.DataFrame: + texts = [row["user"].split("†", maxsplit=1)[0] + "†" for row in rows] + methods = { + "word_tfidf": TfidfVectorizer( + lowercase=True, ngram_range=(1, 2), sublinear_tf=True + ), + "character_tfidf": TfidfVectorizer( + lowercase=True, + analyzer="char_wb", + ngram_range=(3, 5), + sublinear_tf=True, + max_features=20000, + ), + } + output = [] + for name, vectorizer in methods.items(): + values = vectorizer.fit_transform(texts).toarray().astype(np.float64) + values = normalize(values) + for target in ("physical", "numeric"): + law_scores = replication_law_scores(values, meta[target], meta) + for row in law_scores: + output.append({"method": name, "target": target, **row}) + return pd.DataFrame(output) + + +def plot_summary( + layer_frame: pd.DataFrame, + law_frame: pd.DataFrame, + parallel_frame: pd.DataFrame, + behavior: pd.DataFrame, + selected_layer: int, +) -> None: + FIG.mkdir(parents=True, exist_ok=True) + colors = { + "physical": "#16697A", + "numeric": "#B56576", + "direct": "#5A7D8C", + "inverse": "#7A6AA2", + } + fig, axes = plt.subplots(2, 2, figsize=(10.4, 7.2)) + ax = axes[0, 0] + for target in ("physical", "numeric"): + subset = layer_frame[ + (layer_frame["position"] == "decision") + & (layer_frame["target"] == target) + ] + ax.plot( + subset["depth_percent"], + subset["discovery_ccgp"], + marker="o", + markersize=3, + linewidth=1.5, + label=f"{target.capitalize()} variable", + color=colors[target], + ) + depth = float( + layer_frame.loc[layer_frame["layer"] == selected_layer, "depth_percent"].iloc[0] + ) + ax.axvline(depth, color="#555555", linestyle="--", linewidth=1) + ax.axhline(0.5, color="#999999", linestyle=":", linewidth=1) + ax.set(xlabel="Layer depth (%)", ylabel="Discovery CCGP (ROC–AUC)") + ax.legend(frameon=False, fontsize=8) + + ax = axes[0, 1] + ordered = law_frame.sort_values(["test_orientation", "auc"]) + bar_colors = [colors[value] for value in ordered["test_orientation"]] + ax.barh(ordered["law_id"], ordered["auc"], color=bar_colors) + ax.axvline(0.5, color="#555555", linestyle="--", linewidth=1) + ax.set(xlim=(0, 1), xlabel="Held-out physical-outcome AUC") + + ax = axes[1, 0] + groups = [ + parallel_frame.loc[ + parallel_frame["same_orientation"], "physical_contrast_cosine" + ].to_numpy(float), + parallel_frame.loc[ + ~parallel_frame["same_orientation"], "physical_contrast_cosine" + ].to_numpy(float), + ] + parts = ax.violinplot(groups, positions=[0, 1], showmeans=True, widths=0.7) + for body, color in zip(parts["bodies"], ("#5A7D8C", "#7A6AA2")): + body.set_facecolor(color) + body.set_alpha(0.65) + ax.axhline(0, color="#777777", linewidth=1) + ax.set( + xticks=[0, 1], + xticklabels=["Same law sign", "Opposite law sign"], + ylabel="Physical-contrast cosine", + ) + + ax = axes[1, 1] + summary = ( + behavior.groupby(["split", "law_sign_name"], as_index=False)[ + "registered_code_correct" + ] + .mean() + .sort_values(["split", "law_sign_name"]) + ) + labels = [ + f"{row.split}\n{row.law_sign_name}" for row in summary.itertuples() + ] + ax.bar( + np.arange(len(summary)), + summary["registered_code_correct"], + color=["#5A7D8C" if x == "direct" else "#7A6AA2" for x in summary["law_sign_name"]], + ) + ax.set( + xticks=np.arange(len(summary)), + xticklabels=labels, + ylim=(0, 1.05), + ylabel="Clean A/B accuracy", + ) + fig.tight_layout() + for suffix in ("png", "pdf"): + fig.savefig(FIG / f"abstract-physics-composition.{suffix}", dpi=300) + plt.close(fig) + + +def main() -> None: + protocol = json.loads(PROTOCOL.read_text()) + manifest = json.loads(MANIFEST.read_text()) + raw = validate(protocol) + with np.load(STATES, allow_pickle=False) as arrays: + prompt_ids = arrays["prompt_ids"].astype(str) + positions = arrays["positions"].astype(str) + layers = arrays["layers"].astype(int) + states = arrays["raw_states"].astype(np.float64) + rows, meta = metadata_arrays(manifest, prompt_ids) + if positions.tolist() != protocol["positions"]: + raise RuntimeError(f"position mismatch: {positions.tolist()}") + depths = 100.0 * layers / max(int(layers.max()), 1) + + layer_rows = [] + decision_index = int(np.flatnonzero(positions == "decision")[0]) + for position_index, position in enumerate(positions): + for layer_index, layer in enumerate(layers): + values = normalize(states[position_index, :, layer_index]) + for target in ("physical", "numeric", "code"): + layer_rows.append( + { + "position": str(position), + "layer": int(layer), + "depth_percent": float(depths[layer_index]), + "target": target, + "discovery_ccgp": discovery_ccgp( + values, meta[target], meta + ), + } + ) + layer_frame = pd.DataFrame(layer_rows) + selection = layer_frame[ + (layer_frame["position"] == "decision") + & (layer_frame["target"] == "physical") + ].sort_values(["discovery_ccgp", "layer"], ascending=[False, True]) + selected_layer = int(selection.iloc[0]["layer"]) + selected_index = int(np.flatnonzero(layers == selected_layer)[0]) + selected_values = normalize(states[decision_index, :, selected_index]) + + law_rows = [] + for target in ("physical", "numeric"): + for row in replication_law_scores(selected_values, meta[target], meta): + law_rows.append({"target": target, **row}) + law_frame = pd.DataFrame(law_rows) + primary_laws = law_frame[law_frame["target"] == "physical"].copy() + auc_values = primary_laws["auc"].to_numpy(float) + rng = np.random.default_rng(protocol["bootstrap"]["seed"]) + ci = bootstrap(auc_values, rng, protocol["bootstrap"]["resamples"]) + primary = { + "selected_layer": selected_layer, + "selected_depth_percent": float(depths[selected_index]), + "discovery_selected_ccgp": float(selection.iloc[0]["discovery_ccgp"]), + "replication_mean_law_auc": float(auc_values.mean()), + "replication_law_bootstrap_95": list(ci), + "replication_laws_above_chance": int(np.sum(auc_values > 0.5)), + "replication_laws_total": int(len(auc_values)), + "two_sided_exact_signflip_p_auc_minus_half": exact_signflip( + auc_values - 0.5 + ), + } + rule = protocol["success_rule"] + primary["passes_frozen_rule"] = bool( + primary["replication_mean_law_auc"] >= rule["mean_replication_auc_minimum"] + and ci[0] > rule["bootstrap_95_lower_above"] + and primary["replication_laws_above_chance"] + >= rule["positive_replication_laws_minimum"] + ) + + _, parallel_frame = contrast_parallelism( + selected_values, meta, "replication" + ) + tfidf_frame = tfidf_controls(rows, meta) + + formula_rows = [] + discovery = meta["split"] == "discovery" + replication = meta["split"] == "replication" + for train_surface, test_surface in (("a", "b"), ("b", "a")): + train = discovery & (meta["surface"] == train_surface) + for target in ("physical", "numeric"): + direction, midpoint = centroid_direction( + selected_values[train], meta[target][train] + ) + for law_id in sorted(set(meta["law"][replication])): + test = replication & (meta["surface"] == test_surface) & ( + meta["law"] == law_id + ) + scores = safe_dot(selected_values[test] - midpoint, direction) + formula_rows.append( + { + "train_surface": train_surface, + "test_surface": test_surface, + "target": target, + "law_id": str(law_id), + "auc": float( + roc_auc_score(meta[target][test].astype(int), scores) + ), + } + ) + formula_frame = pd.DataFrame(formula_rows) + + behavior = pd.DataFrame(raw["clean_rows"]) + behavior["law_sign_name"] = behavior["law_sign"].map( + {1: "direct", -1: "inverse"} + ) + behavior_summary = { + "overall_accuracy": float(behavior["registered_code_correct"].mean()), + "discovery_accuracy": float( + behavior.loc[ + behavior["split"] == "discovery", "registered_code_correct" + ].mean() + ), + "replication_accuracy": float( + behavior.loc[ + behavior["split"] == "replication", "registered_code_correct" + ].mean() + ), + "law_accuracy": { + str(key): float(value) + for key, value in behavior.groupby("law_id")[ + "registered_code_correct" + ].mean().items() + }, + } + + physical_pairs = parallel_frame[ + ~parallel_frame["same_orientation"] + ]["physical_contrast_cosine"].to_numpy(float) + parallel_summary = { + "cross_orientation_mean_physical_contrast_cosine": float( + physical_pairs.mean() + ), + "cross_orientation_pair_bootstrap_95": list( + bootstrap( + physical_pairs, + np.random.default_rng(protocol["bootstrap"]["seed"] + 1), + protocol["bootstrap"]["resamples"], + ) + ), + "positive_cross_orientation_pairs": int(np.sum(physical_pairs > 0)), + "cross_orientation_pairs": int(len(physical_pairs)), + } + + layer_frame.to_csv(OUT / "layerwise_ccgp.csv", index=False) + law_frame.to_csv(OUT / "replication_law_auc.csv", index=False) + parallel_frame.to_csv(OUT / "replication_parallelism_pairs.csv", index=False) + tfidf_frame.to_csv(OUT / "tfidf_controls.csv", index=False) + formula_frame.to_csv(OUT / "formula_cross_generalization.csv", index=False) + behavior.to_csv(OUT / "clean_behavior.csv", index=False) + + direction, midpoint = centroid_direction( + selected_values[meta["split"] == "discovery"], + meta["physical"][meta["split"] == "discovery"], + ) + numeric_direction, numeric_midpoint = centroid_direction( + selected_values[meta["split"] == "discovery"], + meta["numeric"][meta["split"] == "discovery"], + ) + np.savez_compressed( + OUT / "discovery_directions.npz", + selected_layer=np.asarray(selected_layer, dtype=np.int16), + position=np.asarray("decision"), + physical_direction=direction.astype(np.float32), + physical_midpoint=midpoint.astype(np.float32), + numeric_direction=numeric_direction.astype(np.float32), + numeric_midpoint=numeric_midpoint.astype(np.float32), + ) + + statistics = { + "study_id": protocol["study_id"], + "protocol_sha256": sha256(PROTOCOL), + "input_fingerprints": { + "manifest": sha256(MANIFEST), + "raw": sha256(RAW), + "states": sha256(STATES), + }, + "primary": primary, + "behavior": behavior_summary, + "parallelism": parallel_summary, + "formula_cross_generalization": { + target: float( + formula_frame.loc[ + formula_frame["target"] == target, "auc" + ].mean() + ) + for target in ("physical", "numeric") + }, + "tfidf_controls": { + f"{method}_{target}": float( + tfidf_frame.loc[ + (tfidf_frame["method"] == method) + & (tfidf_frame["target"] == target), + "auc", + ].mean() + ) + for method in sorted(set(tfidf_frame["method"])) + for target in ("physical", "numeric") + }, + "interpretation_boundary": ( + "A passing result would show a cross-law, answer-key-free abstract " + "physical-outcome geometry under this benchmark. It would not by " + "itself establish causal use, natural-language generalization " + "without supplied equations, or a literal chain of thought." + ), + } + (OUT / "statistics.json").write_text( + json.dumps(json_safe(statistics), indent=2, sort_keys=True) + "\n" + ) + plot_summary( + layer_frame, + primary_laws, + parallel_frame, + behavior, + selected_layer, + ) + + report = [ + "# Abstract-physics composition result", + "", + "## Frozen primary endpoint", + "", + f"- Discovery-selected layer: **{selected_layer}** " + f"({primary['selected_depth_percent']:.1f}% depth).", + f"- Discovery cross-orientation CCGP: " + f"**{primary['discovery_selected_ccgp']:.3f}**.", + f"- Held-out replication mean law AUC: " + f"**{primary['replication_mean_law_auc']:.3f}** " + f"(95% law bootstrap {ci[0]:.3f} to {ci[1]:.3f}).", + f"- Laws above chance: " + f"**{primary['replication_laws_above_chance']}/" + f"{primary['replication_laws_total']}**; exact sign-flip " + f"`p={primary['two_sided_exact_signflip_p_auc_minus_half']:.4g}`.", + f"- Frozen success rule: " + f"**{'PASS' if primary['passes_frozen_rule'] else 'FAIL'}**.", + "", + "## Controls and interpretation", + "", + f"- Clean A/B accuracy: **{behavior_summary['overall_accuracy']:.1%}**.", + f"- Cross-form physical AUC: " + f"**{statistics['formula_cross_generalization']['physical']:.3f}**.", + f"- Cross-orientation physical-contrast cosine: " + f"**{parallel_summary['cross_orientation_mean_physical_contrast_cosine']:+.3f}**.", + f"- Word TF-IDF physical AUC: " + f"**{statistics['tfidf_controls']['word_tfidf_physical']:.3f}**.", + f"- Character TF-IDF physical AUC: " + f"**{statistics['tfidf_controls']['character_tfidf_physical']:.3f}**.", + "", + statistics["interpretation_boundary"], + ] + (OUT / "REPORT.md").write_text("\n".join(report) + "\n") + print("\n".join(report)) + + +if __name__ == "__main__": + main() diff --git a/jlens_materials/scripts/analyze_cross_mechanism_outcome.py b/jlens_materials/scripts/analyze_cross_mechanism_outcome.py new file mode 100644 index 0000000000000000000000000000000000000000..30f36f2520e83744f190a4622edad0df4ce556c8 --- /dev/null +++ b/jlens_materials/scripts/analyze_cross_mechanism_outcome.py @@ -0,0 +1,921 @@ +#!/usr/bin/env python3 +"""Test option-free physical-outcome alignment across different mechanisms.""" + +from __future__ import annotations + +import argparse +import hashlib +import itertools +import json +import math +from pathlib import Path +from typing import Mapping, Sequence + +import matplotlib + +matplotlib.use("Agg") +import matplotlib.pyplot as plt # noqa: E402 +import numpy as np # noqa: E402 +import pandas as pd # noqa: E402 + +from analyze_graph_topology_rigorous import ( # noqa: E402 + cosine_layers, + deterministic_binary_similarity, + prompt_feature_matrices, +) + + +ROOT = Path(__file__).resolve().parents[1] +OUT = ROOT / "experiments" / "cross-mechanism-outcome-2026-07-18" +FIG = OUT / "figures" +PROTOCOL_PATH = OUT / "protocol.json" +PRIMARY_BAND = (38.0, 92.0) +BOOTSTRAP_SEED = 20260720 +N_BOOT = 30_000 + +COLORS = { + "jacobian": "#16697A", + "direct": "#B56576", + "raw": "#6C5B7B", + "word_tfidf": "#5B8C5A", + "char_tfidf": "#D17C38", + "numeric_direction": "#2F4858", + "answer_order": "#A0A4A8", +} + + +def sha256(path: Path) -> str: + return hashlib.sha256(path.read_bytes()).hexdigest() + + +def json_safe(value: object) -> object: + if isinstance(value, np.generic): + return json_safe(value.item()) + if isinstance(value, float) and not math.isfinite(value): + return None + if isinstance(value, dict): + return {str(key): json_safe(item) for key, item in value.items()} + if isinstance(value, (list, tuple)): + return [json_safe(item) for item in value] + return value + + +def exact_p(null: np.ndarray, observed: float) -> float: + return float(np.mean(null >= observed - 1e-15)) + + +def validate_inputs(protocol: Mapping[str, object]) -> dict[str, str]: + actual = {} + for name, row in protocol["inputs"].items(): # type: ignore[index] + path = ROOT / str(row["path"]) + actual[name] = sha256(path) + if actual[name] != str(row["sha256"]): + raise RuntimeError( + f"fingerprint mismatch for {name}: " + f"{actual[name]} != {row['sha256']}" + ) + return actual + + +def orientation_assignments( + family_names: Sequence[str], +) -> list[dict[str, bool]]: + output = [] + for direct_indices in itertools.combinations(range(len(family_names)), 3): + direct = set(direct_indices) + output.append( + { + family: index in direct + for index, family in enumerate(family_names) + } + ) + if len(output) != 20: + raise RuntimeError("expected 20 balanced response-orientation assignments") + return output + + +def labels_from_orientation( + rows: Sequence[Mapping[str, object]], + orientation: Mapping[str, bool], +) -> np.ndarray: + """Positive outcome iff numeric direction agrees with direct orientation.""" + + return np.asarray( + [ + (str(row["numeric_direction"]) == "increase") + == bool(orientation[str(row["family_id"])]) + for row in rows + ], + dtype=bool, + ) + + +def observed_orientation( + rows: Sequence[Mapping[str, object]], +) -> dict[str, bool]: + output = {} + for row in rows: + if str(row["numeric_direction"]) != "increase": + continue + family = str(row["family_id"]) + value = str(row["expected_outcome"]) == str(row["outcome_positive"]) + if family in output and output[family] != value: + raise RuntimeError(f"inconsistent response orientation in {family}") + output[family] = value + if len(output) != 6 or sum(output.values()) != 3: + raise RuntimeError(f"unexpected observed response orientation: {output}") + return output + + +def query_rows( + similarity: np.ndarray, + rows: Sequence[Mapping[str, object]], + families: np.ndarray, + variants: np.ndarray, + labels: np.ndarray, + orientation: Mapping[str, bool], +) -> pd.DataFrame: + output = [] + family_names = sorted(set(families)) + variant_names = sorted(set(variants)) + for source in range(len(rows)): + source_family = str(families[source]) + for target_family in family_names: + if target_family == source_family: + continue + for target_variant in variant_names: + candidates = np.flatnonzero( + (families == target_family) + & (variants == target_variant) + ) + if len(candidates) != 4: + raise RuntimeError("cross-mechanism query must have four cases") + same = labels[candidates] == labels[source] + if int(np.sum(same)) != 2: + raise RuntimeError( + "cross-mechanism query must have two positives and two negatives" + ) + positive = candidates[same] + negative = candidates[~same] + positive_scores = similarity[source, positive] + negative_scores = similarity[source, negative] + comparisons = positive_scores[:, None] - negative_scores[None, :] + auc = float( + np.mean(comparisons > 0) + + 0.5 * np.mean(comparisons == 0) + ) + ordered = sorted( + candidates.tolist(), + key=lambda index: ( + -float(similarity[source, index]), + index, + ), + ) + pair = tuple(sorted((source_family, target_family))) + output.append( + { + "source_index": source, + "source_prompt_id": str(rows[source]["prompt_id"]), + "source_family": source_family, + "source_variant": str(variants[source]), + "source_numeric_direction": str( + rows[source]["numeric_direction"] + ), + "source_positive_outcome": bool(labels[source]), + "target_family": target_family, + "target_variant": target_variant, + "family_pair": " | ".join(pair), + "opposite_response_orientation": bool( + orientation[source_family] + != orientation[target_family] + ), + "top1_same_outcome": bool( + labels[ordered[0]] == labels[source] + ), + "pairwise_auc": auc, + "candidate_indices": " ".join( + str(value) for value in candidates + ), + } + ) + frame = pd.DataFrame(output) + if len(frame) != 1080: + raise RuntimeError(f"expected 1080 rankings, found {len(frame)}") + if int(frame["opposite_response_orientation"].sum()) != 648: + raise RuntimeError("expected 648 counter-numeric rankings") + return frame + + +def summarize_queries(frame: pd.DataFrame) -> dict[str, float]: + opposite = frame[frame["opposite_response_orientation"]] + same = frame[~frame["opposite_response_orientation"]] + return { + "overall_auc": float(frame["pairwise_auc"].mean()), + "counter_numeric_auc": float(opposite["pairwise_auc"].mean()), + "same_orientation_auc": float(same["pairwise_auc"].mean()), + "overall_top1_accuracy": float(frame["top1_same_outcome"].mean()), + "counter_numeric_top1_accuracy": float( + opposite["top1_same_outcome"].mean() + ), + "same_orientation_top1_accuracy": float( + same["top1_same_outcome"].mean() + ), + } + + +def exact_null( + similarity: np.ndarray, + rows: Sequence[Mapping[str, object]], + families: np.ndarray, + variants: np.ndarray, + assignments: Sequence[Mapping[str, bool]], +) -> tuple[np.ndarray, list[dict[str, float]]]: + output = [] + for orientation in assignments: + labels = labels_from_orientation(rows, orientation) + frame = query_rows( + similarity, + rows, + families, + variants, + labels, + orientation, + ) + output.append(summarize_queries(frame)) + names = list(output[0]) + matrix = np.asarray( + [[row[name] for name in names] for row in output], + dtype=float, + ) + return matrix, output + + +def mechanism_pair_metrics(frame: pd.DataFrame) -> pd.DataFrame: + return ( + frame.groupby( + ["family_pair", "opposite_response_orientation"], + as_index=False, + ) + .agg( + n_rankings=("pairwise_auc", "size"), + pairwise_auc=("pairwise_auc", "mean"), + top1_accuracy=("top1_same_outcome", "mean"), + ) + .sort_values("family_pair") + ) + + +def pair_bootstrap( + pair_frame: pd.DataFrame, + value: str, + rng: np.random.Generator, + *, + opposite_only: bool | None = None, +) -> list[float]: + frame = pair_frame + if opposite_only is not None: + frame = frame[ + frame["opposite_response_orientation"] == opposite_only + ] + values = frame[value].to_numpy(dtype=float) + indices = rng.integers(0, len(values), size=(N_BOOT, len(values))) + samples = values[indices].mean(axis=1) + return [float(x) for x in np.quantile(samples, (0.025, 0.975))] + + +def paired_method_bootstrap( + pair_frame: pd.DataFrame, + first: str, + second: str, + metric: str, + rng: np.random.Generator, + *, + opposite_only: bool, +) -> dict[str, object]: + frame = pair_frame[ + pair_frame["opposite_response_orientation"] == opposite_only + ] + pivot = frame.pivot( + index="family_pair", columns="method", values=metric + ) + delta = (pivot[first] - pivot[second]).to_numpy(dtype=float) + indices = rng.integers(0, len(delta), size=(N_BOOT, len(delta))) + samples = delta[indices].mean(axis=1) + return { + "subset": "counter_numeric" if opposite_only else "same_orientation", + "mean_difference": float(np.mean(delta)), + "pair_bootstrap_95": [ + float(x) for x in np.quantile(samples, (0.025, 0.975)) + ], + "positive_pairs": int(np.sum(delta > 0)), + "n_pairs": int(len(delta)), + "pair_differences": { + str(pair): float(value) + for pair, value in zip(pivot.index, delta) + }, + } + + +def configure_matplotlib() -> None: + plt.rcParams.update( + { + "font.family": "DejaVu Sans", + "font.size": 8.2, + "axes.labelsize": 8.5, + "xtick.labelsize": 7.2, + "ytick.labelsize": 7.2, + "legend.fontsize": 7.0, + "axes.spines.top": False, + "axes.spines.right": False, + "axes.linewidth": 0.7, + "pdf.fonttype": 42, + "ps.fonttype": 42, + "svg.fonttype": "none", + } + ) + + +def panel_label(axis: plt.Axes, letter: str) -> None: + axis.text( + -0.15, + 1.04, + letter, + transform=axis.transAxes, + fontsize=10, + fontweight="bold", + va="bottom", + ) + + +def make_figure( + fixed: pd.DataFrame, + layer: pd.DataFrame, + pairs: pd.DataFrame, + orientation: Mapping[str, bool], +) -> None: + configure_matplotlib() + figure, axes = plt.subplots(2, 2, figsize=(7.15, 5.8)) + + order = [ + "jacobian", + "direct", + "raw", + "word_tfidf", + "char_tfidf", + "numeric_direction", + ] + labels = [ + "Jacobian", + "Direct", + "Raw", + "Word TF-IDF", + "Char TF-IDF", + "Numeric direction", + ] + primary = fixed.set_index("method").loc[order] + x = np.arange(len(order)) + width = 0.36 + axes[0, 0].bar( + x - width / 2, + primary["overall_auc"], + width, + color=[COLORS[name] for name in order], + alpha=0.93, + label="all cross-mechanism cases", + ) + axes[0, 0].bar( + x + width / 2, + primary["counter_numeric_auc"], + width, + color="white", + edgecolor=[COLORS[name] for name in order], + hatch="//", + linewidth=1.2, + label="matching outcome needs opposite numeric direction", + ) + axes[0, 0].axhline(0.5, color="#8B8E91", linestyle="--", linewidth=0.9) + axes[0, 0].set_ylim(0, 1.02) + axes[0, 0].set_ylabel("same-outcome candidate AUC") + axes[0, 0].set_xticks(x, labels, rotation=28, ha="right") + axes[0, 0].legend( + frameon=False, + loc="upper left", + bbox_to_anchor=(0.01, 0.99), + fontsize=6.8, + ) + panel_label(axes[0, 0], "A") + + for method, label in ( + ("jacobian", "Jacobian"), + ("direct", "Direct"), + ("raw", "Raw"), + ): + rows = layer[layer["method"] == method].sort_values("depth_percent") + axes[0, 1].plot( + rows["depth_percent"], + rows["counter_numeric_auc"], + color=COLORS[method], + linewidth=1.6, + marker="o", + markersize=2.8, + label=label, + ) + axes[0, 1].axvspan( + PRIMARY_BAND[0], PRIMARY_BAND[1], color="#D9E7EA", alpha=0.45 + ) + axes[0, 1].axhline(0.5, color="#8B8E91", linestyle="--", linewidth=0.9) + axes[0, 1].set_ylim(0.2, 0.85) + axes[0, 1].set_xlabel("layer depth (%)") + axes[0, 1].set_ylabel("counter-numeric candidate AUC") + axes[0, 1].legend(frameon=False, loc="lower right") + panel_label(axes[0, 1], "B") + + family_names = sorted(orientation) + short = { + "crosslink-density-modulus": "Crosslink", + "dislocation-density-strength": "Dislocation", + "obstacle-spacing-orowan": "Orowan", + "particle-fraction-modulus": "Particles", + "pearlite-spacing-strength": "Pearlite", + "porosity-modulus": "Porosity", + } + j_pairs = pairs[pairs["method"] == "jacobian"].set_index("family_pair") + matrix = np.full((6, 6), np.nan) + for first_index, first in enumerate(family_names): + for second_index, second in enumerate(family_names): + if first_index == second_index: + continue + pair = " | ".join(sorted((first, second))) + matrix[first_index, second_index] = float( + j_pairs.loc[pair, "pairwise_auc"] + ) + image = axes[1, 0].imshow( + matrix, + vmin=0.25, + vmax=0.85, + cmap="RdYlBu", + aspect="equal", + ) + for row_index in range(6): + for column_index in range(6): + if row_index == column_index: + continue + value = matrix[row_index, column_index] + color = "white" if value < 0.38 or value > 0.76 else "#202124" + axes[1, 0].text( + column_index, + row_index, + f"{value:.2f}", + ha="center", + va="center", + fontsize=6.3, + color=color, + ) + orientation_marks = [ + "+" if orientation[name] else "-" + for name in family_names + ] + tick_labels = [ + f"{short[name]} {mark}" + for name, mark in zip(family_names, orientation_marks) + ] + axes[1, 0].set_xticks( + range(6), tick_labels, rotation=38, ha="right" + ) + axes[1, 0].set_yticks(range(6), tick_labels) + colorbar = figure.colorbar(image, ax=axes[1, 0], fraction=0.046, pad=0.04) + colorbar.set_label("AUC", fontsize=7.5, labelpad=2) + colorbar.ax.tick_params(labelsize=6.8) + panel_label(axes[1, 0], "C") + + display = ["jacobian", "direct", "raw", "numeric_direction"] + x2 = np.arange(len(display)) + same_values = [ + float(fixed.set_index("method").loc[name, "same_orientation_auc"]) + for name in display + ] + opposite_values = [ + float(fixed.set_index("method").loc[name, "counter_numeric_auc"]) + for name in display + ] + for index, method in enumerate(display): + axes[1, 1].plot( + [index - 0.12, index + 0.12], + [same_values[index], opposite_values[index]], + color=COLORS[method], + linewidth=1.3, + alpha=0.85, + ) + axes[1, 1].scatter( + index - 0.12, + same_values[index], + color=COLORS[method], + marker="o", + s=30, + label="same response orientation" if index == 0 else None, + ) + axes[1, 1].scatter( + index + 0.12, + opposite_values[index], + facecolor="white", + edgecolor=COLORS[method], + marker="o", + s=34, + linewidth=1.3, + label="opposite response orientation" if index == 0 else None, + ) + axes[1, 1].axhline(0.5, color="#8B8E91", linestyle="--", linewidth=0.9) + axes[1, 1].set_ylim(-0.03, 1.03) + axes[1, 1].set_ylabel("same-outcome candidate AUC") + axes[1, 1].set_xticks( + x2, ["Jacobian", "Direct", "Raw", "Numeric\ndirection"] + ) + axes[1, 1].legend(frameon=False, loc="upper center") + panel_label(axes[1, 1], "D") + + figure.subplots_adjust( + left=0.10, + right=0.985, + bottom=0.14, + top=0.97, + wspace=0.42, + hspace=0.50, + ) + FIG.mkdir(parents=True, exist_ok=True) + for suffix in ("pdf", "png", "svg"): + figure.savefig( + FIG / f"cross-mechanism-outcome.{suffix}", + dpi=300, + bbox_inches="tight", + ) + plt.close(figure) + + +def main() -> None: + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument( + "--protocol", type=Path, default=PROTOCOL_PATH + ) + args = parser.parse_args() + protocol = json.loads(args.protocol.read_text()) + fingerprints = validate_inputs(protocol) + + manifest_path = ROOT / protocol["inputs"]["prompt_manifest"]["path"] + states_path = ROOT / protocol["inputs"]["representations"]["path"] + manifest = json.loads(manifest_path.read_text()) + metadata = { + str(row["prompt_id"]): row for row in manifest["prompts"] + } + with np.load(states_path, allow_pickle=False) as arrays: + prompt_ids = arrays["prompt_ids"].astype(str) + positions = arrays["positions"].astype(str) + layers = arrays["layers"].astype(int) + raw = arrays["raw_states"].astype(np.float64) + direct = arrays["direct_decoder_basis"].astype(np.float64) + jacobian = arrays["jacobian_decoder_basis"].astype(np.float64) + if positions.tolist() != ["question_end"]: + raise RuntimeError(f"unexpected positions: {positions.tolist()}") + rows = [metadata[prompt_id] for prompt_id in prompt_ids] + families = np.asarray([str(row["family_id"]) for row in rows]) + variants = np.asarray([str(row["variant"]) for row in rows]) + stems = [str(row["stem"]) for row in rows] + depths = layers / 41.0 * 100.0 + band = (depths >= PRIMARY_BAND[0]) & (depths <= PRIMARY_BAND[1]) + + observed = observed_orientation(rows) + family_names = sorted(observed) + assignments = orientation_assignments(family_names) + observed_index = [ + orientation == observed for orientation in assignments + ] + if sum(observed_index) != 1: + raise RuntimeError("observed orientation missing from exact null") + labels = labels_from_orientation(rows, observed) + expected_labels = np.asarray( + [ + str(row["expected_outcome"]) == str(row["outcome_positive"]) + for row in rows + ] + ) + if not np.array_equal(labels, expected_labels): + raise RuntimeError("orientation-derived labels disagree with manifest") + + sim_j_seed = cosine_layers(jacobian[:, 0]) + sim_j = np.mean(sim_j_seed, axis=0) + sim_direct = cosine_layers(direct[0][None, ...])[0] + sim_raw = cosine_layers(raw[0][None, ...])[0] + lexical = prompt_feature_matrices(stems) + presentation = np.asarray( + [str(row["presentation_order"]) for row in rows] + ) + numeric = np.asarray([str(row["numeric_direction"]) for row in rows]) + similarities = { + "jacobian": np.mean(sim_j[band], axis=0), + "direct": np.mean(sim_direct[band], axis=0), + "raw": np.mean(sim_raw[band], axis=0), + "word_tfidf": lexical["word_tfidf"], + "char_tfidf": lexical["char_tfidf"], + "numeric_direction": deterministic_binary_similarity(numeric), + "answer_order": deterministic_binary_similarity(presentation), + } + for seed in range(3): + similarities[f"jacobian_seed{seed}"] = np.mean( + sim_j_seed[seed, band], axis=0 + ) + + fixed_rows = [] + query_frames = [] + pair_frames = [] + null_payload = {} + for method, similarity in similarities.items(): + queries = query_rows( + similarity, + rows, + families, + variants, + labels, + observed, + ) + summary = summarize_queries(queries) + null_matrix, null_rows = exact_null( + similarity, + rows, + families, + variants, + assignments, + ) + metric_names = list(null_rows[0]) + null_lookup = { + metric: null_matrix[:, index] + for index, metric in enumerate(metric_names) + } + pair = mechanism_pair_metrics(queries) + opposite_pairs = pair[pair["opposite_response_orientation"]] + fixed_rows.append( + { + "method": method, + **summary, + "overall_auc_exact_p": exact_p( + null_lookup["overall_auc"], summary["overall_auc"] + ), + "counter_numeric_auc_exact_p": exact_p( + null_lookup["counter_numeric_auc"], + summary["counter_numeric_auc"], + ), + "overall_top1_exact_p": exact_p( + null_lookup["overall_top1_accuracy"], + summary["overall_top1_accuracy"], + ), + "counter_numeric_top1_exact_p": exact_p( + null_lookup["counter_numeric_top1_accuracy"], + summary["counter_numeric_top1_accuracy"], + ), + "positive_counter_numeric_pairs": int( + np.sum(opposite_pairs["pairwise_auc"] > 0.5) + ), + "n_counter_numeric_pairs": int(len(opposite_pairs)), + "overall_auc_pair_bootstrap_95": pair_bootstrap( + pair, + "pairwise_auc", + np.random.default_rng(BOOTSTRAP_SEED + len(fixed_rows)), + ), + "counter_numeric_auc_pair_bootstrap_95": pair_bootstrap( + pair, + "pairwise_auc", + np.random.default_rng( + BOOTSTRAP_SEED + 100 + len(fixed_rows) + ), + opposite_only=True, + ), + } + ) + query_copy = queries.copy() + query_copy.insert(0, "method", method) + query_frames.append(query_copy) + pair.insert(0, "method", method) + pair_frames.append(pair) + null_payload[method] = { + metric: values.tolist() + for metric, values in null_lookup.items() + } + + fixed = pd.DataFrame(fixed_rows) + queries = pd.concat(query_frames, ignore_index=True) + pairs = pd.concat(pair_frames, ignore_index=True) + + layer_rows = [] + layer_nulls = { + "jacobian": [], + "direct": [], + "raw": [], + } + for method, layered in ( + ("jacobian", sim_j), + ("direct", sim_direct), + ("raw", sim_raw), + ): + for index, depth in enumerate(depths): + observed_queries = query_rows( + layered[index], + rows, + families, + variants, + labels, + observed, + ) + summary = summarize_queries(observed_queries) + null_matrix, null_rows = exact_null( + layered[index], + rows, + families, + variants, + assignments, + ) + names = list(null_rows[0]) + null_lookup = { + name: null_matrix[:, metric_index] + for metric_index, name in enumerate(names) + } + layer_nulls[method].append(null_lookup) + layer_rows.append( + { + "method": method, + "layer": int(layers[index]), + "depth_percent": float(depth), + **summary, + } + ) + layer = pd.DataFrame(layer_rows) + + layer_scan = {} + for method, method_nulls in layer_nulls.items(): + observed_rows = layer[layer["method"] == method].sort_values( + "depth_percent" + ) + for metric in ("overall_auc", "counter_numeric_auc"): + observed_values = observed_rows[metric].to_numpy(dtype=float) + null = np.column_stack( + [row[metric] for row in method_nulls] + ) + best = int(np.argmax(observed_values)) + layer_scan[f"{method}_{metric}"] = { + "best_value": float(observed_values[best]), + "best_layer": int(layers[best]), + "best_depth_percent": float(depths[best]), + "max_layer_exact_p": exact_p( + np.max(null, axis=1), float(observed_values[best]) + ), + } + + rng = np.random.default_rng(BOOTSTRAP_SEED) + contrasts = {} + for opposite_only in (False, True): + suffix = "counter_numeric" if opposite_only else "same_orientation" + for second in ("direct", "raw", "word_tfidf", "char_tfidf"): + contrasts[f"jacobian_minus_{second}_{suffix}"] = ( + paired_method_bootstrap( + pairs, + "jacobian", + second, + "pairwise_auc", + rng, + opposite_only=opposite_only, + ) + ) + + primary = fixed.set_index("method").loc["jacobian"] + overall_pass = bool( + primary["overall_auc"] > 0.5 + and primary["overall_auc_exact_p"] <= 0.05 + ) + counter_pass = bool( + primary["counter_numeric_auc"] > 0.5 + and primary["counter_numeric_auc_exact_p"] <= 0.05 + ) + breadth_pass = bool(primary["positive_counter_numeric_pairs"] >= 6) + if overall_pass and counter_pass and breadth_pass: + verdict = "strong" + elif overall_pass and not counter_pass: + verdict = "numeric_compatible" + elif counter_pass or (overall_pass and not breadth_pass): + verdict = "partial" + else: + verdict = "none" + jacobian_specific = bool( + contrasts["jacobian_minus_direct_counter_numeric"][ + "pair_bootstrap_95" + ][0] + > 0 + ) + + fixed.to_csv(OUT / "fixed_statistics.csv", index=False) + queries.to_csv(OUT / "all_query_rankings.csv", index=False) + pairs.to_csv(OUT / "mechanism_pair_metrics.csv", index=False) + layer.to_csv(OUT / "layer_metrics.csv", index=False) + (OUT / "exact_orientation_nulls.json").write_text( + json.dumps(json_safe(null_payload), indent=2) + "\n" + ) + + payload = { + "study_id": protocol["study_id"], + "protocol_sha256": sha256(PROTOCOL_PATH), + "input_fingerprints": fingerprints, + "observed_response_orientation": observed, + "n_exact_assignments": len(assignments), + "primary": { + key: json_safe(value) for key, value in primary.to_dict().items() + }, + "frozen_verdict": { + "cross_mechanism_evidence": verdict, + "overall_pass": overall_pass, + "counter_numeric_pass": counter_pass, + "pair_breadth_pass": breadth_pass, + "jacobian_specific": jacobian_specific, + }, + "paired_mechanism_pair_contrasts": contrasts, + "layer_scan": layer_scan, + "all_fixed_results": fixed.to_dict(orient="records"), + "guardrail": ( + "The endpoint was frozen after the within-mechanism natural " + "question-end result was inspected. It is cross-mechanism " + "observational geometry, not an independent replication or causal test." + ), + } + (OUT / "statistics.json").write_text( + json.dumps(json_safe(payload), indent=2) + "\n" + ) + make_figure(fixed, layer, pairs, observed) + + (OUT / "RESULTS.md").write_text( + "\n".join( + [ + "# Cross-mechanism physical-outcome analysis", + "", + f"Frozen interpretation: **{verdict.upper()}** evidence.", + "", + "## Primary results", + "", + ( + f"- All cross-mechanism rankings: AUC " + f"**{float(primary['overall_auc']):.3f}**, exact " + f"orientation-null `p={float(primary['overall_auc_exact_p']):.3f}`." + ), + ( + f"- Counter-numeric rankings, where the same physical " + f"outcome requires opposite numerical directions: AUC " + f"**{float(primary['counter_numeric_auc']):.3f}**, exact " + f"`p={float(primary['counter_numeric_auc_exact_p']):.3f}`." + ), + ( + f"- Pair breadth: " + f"{int(primary['positive_counter_numeric_pairs'])}/" + f"{int(primary['n_counter_numeric_pairs'])} opposite-response " + "mechanism pairs have AUC above 0.5." + ), + ( + "- Jacobian-specific: " + f"**{'yes' if jacobian_specific else 'no'}** under the " + "frozen paired mechanism-pair interval rule." + ), + "", + "The exact null contains only 20 balanced mechanism-orientation", + "assignments, so 0.05 is the smallest attainable exact p-value.", + "", + "## Interpretation", + "", + ( + "The counter-numeric endpoint cannot be passed by merely " + "grouping prompts whose numbers both increase or both " + "decrease. A positive result requires matching physical " + "consequences across mechanisms with opposite input trends." + ), + "", + ( + "This remains a post-hoc analysis of the already inspected " + "natural question-end cohort. It is observational, not causal." + ), + "", + "## Complete artifacts", + "", + "- `PROTOCOL.md` and `protocol.json`.", + "- `fixed_statistics.csv`.", + "- `all_query_rankings.csv` (all 1,080 queries per method).", + "- `mechanism_pair_metrics.csv` (all 15 pairs per method).", + "- `layer_metrics.csv` (all 25 layers).", + "- `exact_orientation_nulls.json` (all 20 assignments).", + "- `statistics.json`.", + "- `figures/cross-mechanism-outcome.{pdf,png,svg}`.", + "", + "## Reproduction", + "", + "```bash", + "python scripts/analyze_cross_mechanism_outcome.py", + "```", + "", + ] + ) + ) + + +if __name__ == "__main__": + main() diff --git a/jlens_materials/scripts/analyze_equation_free_relational_benchmark.py b/jlens_materials/scripts/analyze_equation_free_relational_benchmark.py new file mode 100644 index 0000000000000000000000000000000000000000..394ba5a0b510df4add0aa1db502c115531b975f3 --- /dev/null +++ b/jlens_materials/scripts/analyze_equation_free_relational_benchmark.py @@ -0,0 +1,420 @@ +#!/usr/bin/env python3 +"""Analyze the frozen equation-free transfer benchmark over all 60 laws.""" + +from __future__ import annotations + +import json +import sys +from pathlib import Path + +import matplotlib + +matplotlib.use("Agg") +import matplotlib.pyplot as plt # noqa: E402 +import numpy as np # noqa: E402 +import pandas as pd # noqa: E402 +from scipy.stats import spearmanr # noqa: E402 + + +ROOT = Path(__file__).resolve().parents[1] +sys.path.insert(0, str(ROOT / "scripts")) + +from analyze_neutral_anchored_relational_benchmark import ( # noqa: E402 + aggregate_laws, + calibrate, + endpoint_metrics, + fit_centroid, + load_development, + matched_pairs, + normalize, + ordinal_permutation, + pair_auc, + project, + prompt_arrays, + sha256, + stratified_auc_bootstrap, + tfidf_scores, +) + + +OUT = ROOT / "experiments" / "equation-free-relational-physics-2026-08-21" +REFERENCE = ROOT / "experiments" / "neutral-anchored-relational-physics-2026-07-18" + + +def load_test() -> dict: + manifest = json.loads((OUT / "prompt_manifest.json").read_text()) + raw = json.loads((OUT / "raw.json").read_text()) + with np.load(OUT / "representations.npz", allow_pickle=False) as arrays: + ids = arrays["prompt_ids"].astype(str) + layer = int(arrays["layer"][0]) + states = arrays["raw_states"].astype(np.float64) + by_id = {row["prompt_id"]: row for row in manifest["prompts"]} + clean_by_id = {row["prompt_id"]: row for row in raw["clean_rows"]} + return { + "manifest": manifest, + "prompts": [by_id[prompt_id] for prompt_id in ids], + "clean": [clean_by_id[prompt_id] for prompt_id in ids], + "layer": layer, + "states": states, + } + + +def control_table(scores: np.ndarray, meta: dict) -> tuple[pd.DataFrame, dict]: + table = aggregate_laws(matched_pairs(scores, meta)) + center, scale = calibrate(table) + table["natural_centered_contrast"] = table["mean_contrast"] - center + table["natural_z"] = table["natural_centered_contrast"] / scale + return table, { + **endpoint_metrics(table, "natural_centered_contrast"), + "natural_neutral_center": center, + "natural_neutral_scale": scale, + } + + +def stratum_metrics(pairs: pd.DataFrame, field: str, center: float) -> dict: + output = {} + for value, subset in pairs.groupby(field, sort=True): + laws = subset.groupby(["law_id", "category"], as_index=False)["contrast"].mean() + direct = laws.loc[laws["category"] == "direct", "contrast"].to_numpy() + inverse = laws.loc[laws["category"] == "inverse", "contrast"].to_numpy() + neutral = laws.loc[laws["category"] == "neutral", "contrast"].to_numpy() + correct = np.concatenate([direct > center, inverse < center]) + output[str(value)] = { + "direct_vs_inverse_auc": pair_auc(direct, inverse), + "direct_vs_neutral_auc": pair_auc(direct, neutral), + "neutral_vs_inverse_auc": pair_auc(neutral, inverse), + "directional_sign_accuracy_at_global_natural_center": float(correct.mean()), + "correct_directional_laws": int(correct.sum()), + "directional_laws_total": int(len(correct)), + } + return output + + +def main() -> None: + protocol = json.loads((OUT / "protocol.json").read_text()) + for name, record in protocol["inputs"].items(): + if name == "builder": + continue + if sha256(ROOT / record["path"]) != record["sha256"]: + raise RuntimeError(f"frozen input changed: {name}") + if sha256(Path(__file__).resolve()) != protocol["inputs"]["analyzer"]["sha256"]: + raise RuntimeError("analyzer fingerprint mismatch") + + dev = load_development() + test = load_test() + if test["layer"] != protocol["frozen_readout"]["layer"]: + raise RuntimeError("captured layer differs from frozen layer") + dev_position = int(np.flatnonzero(dev["positions"] == protocol["frozen_readout"]["position"])[0]) + dev_layer = int(np.flatnonzero(dev["layers"] == protocol["frozen_readout"]["layer"])[0]) + dev_values = normalize(dev["states"][dev_position, :, dev_layer]) + dev_labels = np.asarray([row["physical_sign"] > 0 for row in dev["prompts"]]) + direction, midpoint = fit_centroid(dev_values, dev_labels) + hidden_scores = project(normalize(test["states"]), direction, midpoint) + meta = prompt_arrays(test["prompts"]) + pairs = matched_pairs(hidden_scores, meta) + laws = aggregate_laws(pairs) + + natural_center, natural_scale = calibrate(laws) + laws["natural_centered_contrast"] = laws["mean_contrast"] - natural_center + laws["natural_z"] = laws["natural_centered_contrast"] / natural_scale + pairs["natural_z"] = (pairs["contrast"] - natural_center) / natural_scale + + old_center = float(protocol["frozen_readout"]["old_scaffold_center"]) + old_scale = float(protocol["frozen_readout"]["old_scaffold_scale"]) + laws["old_centered_contrast"] = laws["mean_contrast"] - old_center + laws["old_scale_z"] = laws["old_centered_contrast"] / old_scale + + endpoints = endpoint_metrics(laws, "natural_centered_contrast") + directional = laws[laws["category"].isin(["direct", "inverse"])].copy() + natural_correct = ( + (directional["natural_centered_contrast"] > 0) + == (directional["category"] == "direct") + ) + old_correct = ( + (directional["old_centered_contrast"] > 0) + == (directional["category"] == "direct") + ) + laws = laws.merge( + directional.assign( + natural_sign_correct=natural_correct.to_numpy(), + old_center_sign_correct=old_correct.to_numpy(), + )[["law_id", "natural_sign_correct", "old_center_sign_correct"]], + on="law_id", + how="left", + ) + natural_accuracy = float(natural_correct.mean()) + old_accuracy = float(old_correct.mean()) + + surface_auc = {} + for surface in ("a", "b"): + column = f"surface_{surface}_contrast" + surface_auc[surface] = pair_auc( + laws.loc[laws["category"] == "direct", column].to_numpy(), + laws.loc[laws["category"] == "inverse", column].to_numpy(), + ) + + ordinal = laws[ + (laws["category"] != "neutral") | (laws["neutral_role"] == "validation") + ] + rho, ordinal_p = ordinal_permutation( + ordinal["law_sign"].to_numpy(), + ordinal["natural_z"].to_numpy(), + protocol["inference"]["permutation_seed"], + protocol["inference"]["permutations"], + ) + + direct = laws.loc[laws["category"] == "direct", "natural_centered_contrast"].to_numpy() + inverse = laws.loc[laws["category"] == "inverse", "natural_centered_contrast"].to_numpy() + neutral_validation = laws.loc[ + (laws["category"] == "neutral") & (laws["neutral_role"] == "validation"), + "natural_centered_contrast", + ].to_numpy() + bootstrap = { + "direct_vs_inverse": stratified_auc_bootstrap( + direct, + inverse, + protocol["inference"]["bootstrap_seed"], + protocol["inference"]["bootstrap_resamples"], + ), + "direct_vs_neutral": stratified_auc_bootstrap( + direct, + neutral_validation, + protocol["inference"]["bootstrap_seed"] + 1, + protocol["inference"]["bootstrap_resamples"], + ), + "neutral_vs_inverse": stratified_auc_bootstrap( + neutral_validation, + inverse, + protocol["inference"]["bootstrap_seed"] + 2, + protocol["inference"]["bootstrap_resamples"], + ), + } + + output_scores = np.asarray([row["higher_minus_lower_logit"] for row in test["clean"]], dtype=float) + controls = {} + control_tables = {} + for name, scores in { + "output_head": output_scores, + "word_tfidf": tfidf_scores(dev, test, "word"), + "character_tfidf": tfidf_scores(dev, test, "character"), + }.items(): + table, metrics = control_table(scores, meta) + control_tables[name] = table + controls[name] = metrics + + behavior = pd.DataFrame(test["clean"]) + behavior_summary = { + "overall_accuracy": float(behavior["correct"].mean()), + "unparsed_completions": int(behavior["predicted_answer"].isna().sum()), + "accuracy_by_category": { + category: float(group["correct"].mean()) + for category, group in behavior.groupby("category") + }, + "accuracy_by_answer_order": { + str(value): float(group["correct"].mean()) + for value, group in behavior.groupby("answer_order") + }, + "accuracy_by_surface": { + str(value): float(group["correct"].mean()) + for value, group in behavior.groupby("surface") + }, + "accuracy_by_numerical_sign": { + str(value): float(group["correct"].mean()) + for value, group in behavior.groupby("numerical_sign") + }, + "accuracy_by_law": { + str(value): float(group["correct"].mean()) + for value, group in behavior.groupby("law_id") + }, + } + + reference = pd.read_csv(REFERENCE / "law_level_neutral_normalized.csv") + reference = reference[["law_id", "neutral_z"]].rename(columns={"neutral_z": "scaffolded_z"}) + laws = laws.merge(reference, on="law_id", how="left", validate="one_to_one") + cross_scaffold = { + "spearman_all_60_laws": float(spearmanr(laws["scaffolded_z"], laws["natural_z"]).statistic), + "spearman_within_category": { + category: float(spearmanr(group["scaffolded_z"], group["natural_z"]).statistic) + for category, group in laws.groupby("category") + }, + "sign_agreement_directional": float( + np.mean( + np.sign(directional.set_index("law_id")["mean_contrast"]) + == np.sign(reference.set_index("law_id").loc[directional["law_id"], "scaffolded_z"].to_numpy()) + ) + ), + } + + strata = { + "answer_order": stratum_metrics(pairs, "answer_order", natural_center), + "surface": stratum_metrics(pairs, "surface", natural_center), + "case_index": stratum_metrics(pairs, "case_index", natural_center), + } + directional_pairs = pairs[pairs["category"].isin(["direct", "inverse"])].copy() + pair_correct = np.where( + directional_pairs["category"] == "direct", + directional_pairs["contrast"] > natural_center, + directional_pairs["contrast"] < natural_center, + ) + + rule = protocol["success_rule"] + passed = bool( + endpoints["direct_vs_inverse_auc"] >= rule["direct_inverse_auc_minimum"] + and endpoints["direct_vs_validation_neutral_auc"] >= rule["direct_neutral_auc_minimum"] + and endpoints["validation_neutral_vs_inverse_auc"] >= rule["neutral_inverse_auc_minimum"] + and natural_accuracy >= rule["calibrated_directional_accuracy_minimum"] + and min(surface_auc.values()) >= rule["both_surface_direct_inverse_auc_minimum"] + and rho >= rule["ordinal_spearman_minimum"] + and ordinal_p <= rule["ordinal_permutation_p_maximum"] + ) + summary = { + "study_id": protocol["study_id"], + "analysis_sha256": sha256(Path(__file__).resolve()), + "scope": protocol["design_scope"], + "natural_neutral_calibration": { + "center": natural_center, + "scale_1.4826_mad": natural_scale, + "n_calibration_laws": 10, + "n_validation_laws": 10, + }, + "primary": { + **endpoints, + "bootstrap_95": bootstrap, + "natural_calibrated_directional_accuracy": natural_accuracy, + "correct_directional_laws": int(natural_correct.sum()), + "directional_laws_total": int(len(natural_correct)), + "surface_direct_inverse_auc": surface_auc, + "ordinal_spearman": rho, + "ordinal_permutation_p": ordinal_p, + "category_median_natural_z": { + category: float(group["natural_z"].median()) + for category, group in laws.groupby("category") + }, + "passed_all_frozen_criteria": passed, + }, + "strict_cross_scaffold_calibration": { + "old_center": old_center, + "old_scale": old_scale, + "directional_sign_accuracy": old_accuracy, + "correct_directional_laws": int(old_correct.sum()), + "directional_laws_total": int(len(old_correct)), + }, + "controls": controls, + "behavior": behavior_summary, + "cross_scaffold_comparison": cross_scaffold, + "registered_strata": strata, + "directional_pair_level": { + "accuracy_at_natural_center": float(pair_correct.mean()), + "correct": int(pair_correct.sum()), + "total": int(len(pair_correct)), + }, + } + + pairs.to_csv(OUT / "matched_pair_contrasts.csv", index=False) + laws.to_csv(OUT / "law_level_results.csv", index=False) + behavior.to_csv(OUT / "behavior_rows.csv", index=False) + for name, table in control_tables.items(): + table.to_csv(OUT / f"{name}_law_level.csv", index=False) + (OUT / "statistics.json").write_text(json.dumps(summary, indent=2, sort_keys=True) + "\n") + + plt.rcParams.update( + { + "font.family": "DejaVu Sans", + "font.size": 8, + "axes.spines.top": False, + "axes.spines.right": False, + "pdf.fonttype": 42, + } + ) + colors = {"inverse": "#7662a8", "neutral": "#8c8c8c", "direct": "#2a9d8f"} + fig, axes = plt.subplots(2, 2, figsize=(10.6, 7.4)) + rng = np.random.default_rng(20260824) + for index, category in enumerate(("inverse", "neutral", "direct")): + values = laws.loc[laws["category"] == category, "natural_z"].to_numpy() + axes[0, 0].scatter( + np.full(len(values), index) + rng.uniform(-0.09, 0.09, len(values)), + values, + s=26, + color=colors[category], + alpha=0.82, + edgecolor="white", + linewidth=0.4, + ) + axes[0, 0].plot([index - 0.22, index + 0.22], [np.median(values)] * 2, color="black", lw=2) + axes[0, 0].axhline(0, color="0.4", ls="--", lw=1) + axes[0, 0].set_xticks(range(3), ["Inverse", "Neutral", "Direct"]) + axes[0, 0].set_ylabel("Equation-free matched contrast\n(natural neutral scale)") + + ordered_laws = laws.sort_values("natural_z") + axes[0, 1].barh( + np.arange(len(ordered_laws)), + ordered_laws["natural_z"], + color=[colors[value] for value in ordered_laws["category"]], + height=0.82, + ) + axes[0, 1].axvline(0, color="0.35", lw=1) + axes[0, 1].set_yticks([]) + axes[0, 1].set_xlabel("All 60 law-level contrasts") + + methods = ["Hidden", "Output", "Word TF-IDF", "Char TF-IDF"] + auc_values = [ + endpoints["direct_vs_inverse_auc"], + controls["output_head"]["direct_vs_inverse_auc"], + controls["word_tfidf"]["direct_vs_inverse_auc"], + controls["character_tfidf"]["direct_vs_inverse_auc"], + ] + axes[1, 0].bar(range(4), auc_values, color=["#276b9a", "#d39b35", "#aaaaaa", "#666666"]) + axes[1, 0].axhline(0.5, color="0.4", ls="--", lw=1) + axes[1, 0].set_xticks(range(4), methods, rotation=18, ha="right") + axes[1, 0].set_ylim(0, 1.03) + axes[1, 0].set_ylabel("Direct-versus-inverse AUC") + + for category, group in laws.groupby("category"): + axes[1, 1].scatter( + group["scaffolded_z"], + group["natural_z"], + s=28, + color=colors[category], + label=category.capitalize(), + edgecolor="white", + linewidth=0.4, + ) + axes[1, 1].axhline(0, color="0.75", lw=0.8) + axes[1, 1].axvline(0, color="0.75", lw=0.8) + axes[1, 1].set_xlabel("Original supplied-equation contrast (z)") + axes[1, 1].set_ylabel("Equation-free contrast (z)") + axes[1, 1].legend(frameon=False, fontsize=7) + for axis, label in zip(axes.flat, "ABCD"): + axis.text(-0.12, 1.04, label, transform=axis.transAxes, fontsize=10, fontweight="bold", ha="left", va="bottom", clip_on=False) + fig.tight_layout(h_pad=2.5, w_pad=2.5) + figure_dir = OUT / "figures" + figure_dir.mkdir(exist_ok=True) + fig.savefig(figure_dir / "equation-free-relational-physics.png", dpi=240, bbox_inches="tight") + fig.savefig(figure_dir / "equation-free-relational-physics.pdf", bbox_inches="tight") + plt.close(fig) + + errors = laws.loc[laws["natural_sign_correct"] == False, "law_id"].tolist() # noqa: E712 + report = [ + "# Equation-free 60-law relational benchmark", + "", + "All equations, law names, orientation labels, and explicit sign-composition instructions were removed. The original layer-34 direction and midpoint were reused without refitting.", + "", + f"- Direct versus inverse AUC: {endpoints['direct_vs_inverse_auc']:.3f}", + f"- Direct versus validation-neutral AUC: {endpoints['direct_vs_validation_neutral_auc']:.3f}", + f"- Validation-neutral versus inverse AUC: {endpoints['validation_neutral_vs_inverse_auc']:.3f}", + f"- Natural-neutral directional sign accuracy: {int(natural_correct.sum())}/{len(natural_correct)} ({natural_accuracy:.1%})", + f"- Old scaffold-neutral directional sign accuracy: {int(old_correct.sum())}/{len(old_correct)} ({old_accuracy:.1%})", + f"- Surface A/B direct-versus-inverse AUC: {surface_auc['a']:.3f}/{surface_auc['b']:.3f}", + f"- Ordinal Spearman rho: {rho:.3f}; permutation p={ordinal_p:.6g}", + f"- Greedy three-way answer accuracy: {behavior_summary['overall_accuracy']:.1%}", + f"- Passed all frozen criteria: {passed}", + f"- Natural-calibrated directional sign errors: {', '.join(errors) if errors else 'none'}", + "", + "The 60 law identities are reused from the earlier supplied-equation benchmark, so this is a full prompt-format transfer test rather than a fresh-law confirmation. All prompts and outputs are retained in this directory.", + ] + (OUT / "REPORT.md").write_text("\n".join(report) + "\n") + print(json.dumps(summary, indent=2, sort_keys=True)) + + +if __name__ == "__main__": + main() diff --git a/jlens_materials/scripts/analyze_exact_graph_partitions.py b/jlens_materials/scripts/analyze_exact_graph_partitions.py new file mode 100644 index 0000000000000000000000000000000000000000..51c9e6a419c70503ba27eb3f9cfc83c3099574c3 --- /dev/null +++ b/jlens_materials/scripts/analyze_exact_graph_partitions.py @@ -0,0 +1,592 @@ +#!/usr/bin/env python3 +"""Exact balanced graph partition and held-out-surface transfer.""" + +from __future__ import annotations + +import hashlib +import itertools +import json +import math +import platform +import sys +from pathlib import Path +from typing import Mapping, Sequence + +import numpy as np +import pandas as pd +from sklearn.metrics import adjusted_rand_score, normalized_mutual_info_score + +from analyze_graph_isomorphism_generalization import ( + MANIFEST, + METHODS, + STATES, + VARIANTS, + load_data, + plus_one_signflip_p, +) + + +ROOT = Path(__file__).resolve().parents[1] +OUT = ( + ROOT + / "experiments" + / "graph-isomorphism-generalization-2026-07-18" +) +SPECTRAL_PROTOCOL = OUT / "spectral_protocol.json" +PARTITION_PROTOCOL = OUT / "partition_protocol.json" +PARTITION_PROTOCOL_MD = OUT / "PARTITION_PROTOCOL.md" +SEED = 20260718 +N_NULL = 10_000 +N_BOOTSTRAP = 1_000 + + +def sha256(path: Path) -> str: + return hashlib.sha256(path.read_bytes()).hexdigest() + + +def safe_json(value: object) -> object: + if isinstance(value, np.generic): + return safe_json(value.item()) + if isinstance(value, float) and not math.isfinite(value): + return None + if isinstance(value, Mapping): + return {str(key): safe_json(item) for key, item in value.items()} + if isinstance(value, (list, tuple)): + return [safe_json(item) for item in value] + return value + + +def validate_inputs() -> None: + protocol = json.loads(PARTITION_PROTOCOL.read_text()) + spectral = json.loads(SPECTRAL_PROTOCOL.read_text()) + if sha256(SPECTRAL_PROTOCOL) != protocol[ + "parent_spectral_protocol_sha256" + ]: + raise RuntimeError("spectral protocol fingerprint mismatch") + gauge = json.loads((OUT / "gauge_protocol.json").read_text()) + if ( + sha256(STATES) + != gauge["inputs"]["representations_sha256"] + or sha256(MANIFEST) + != gauge["inputs"]["prompt_manifest_sha256"] + ): + raise RuntimeError("frozen input fingerprint mismatch") + if spectral["study_id"] != "spectral-gauge-community-2026-07-18": + raise RuntimeError("unexpected spectral parent") + + +def balanced_partitions(n_nodes: int) -> np.ndarray: + half = n_nodes // 2 + rows = [] + # Fix node zero in community zero, eliminating complementary duplicates. + for community_one in itertools.combinations(range(1, n_nodes), half): + labels = np.zeros(n_nodes, dtype=bool) + labels[list(community_one)] = True + rows.append(labels) + result = np.asarray(rows, dtype=bool) + expected = math.comb(n_nodes - 1, half) + if len(result) != expected: + raise RuntimeError("partition enumeration mismatch") + return result + + +FULL_PARTITIONS = balanced_partitions(12) +FIT_PARTITIONS = balanced_partitions(8) + + +def family_layout( + family: str, data: Mapping[str, object] +) -> tuple[np.ndarray, np.ndarray, np.ndarray]: + indices = np.flatnonzero(np.asarray(data["families"]) == family) + return ( + indices, + np.asarray(data["cases"])[indices], + np.asarray(data["variants"])[indices], + ) + + +def eligible_directed( + similarity: np.ndarray, + family: str, + data: Mapping[str, object], +) -> tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray]: + indices, cases, variants = family_layout(family, data) + weights = np.zeros((12, 12), dtype=np.float64) + mask = np.zeros((12, 12), dtype=bool) + for source in range(12): + for target in range(12): + if ( + source != target + and variants[source] != variants[target] + and cases[source] != cases[target] + ): + weights[source, target] = max( + 0.0, + float(similarity[indices[source], indices[target]]), + ) + mask[source, target] = True + return weights, mask, cases, variants + + +def symmetrized_edges( + directed_weights: np.ndarray, + directed_mask: np.ndarray, +) -> tuple[np.ndarray, np.ndarray]: + first = [] + second = [] + weights = [] + for left in range(len(directed_weights)): + for right in range(left + 1, len(directed_weights)): + if directed_mask[left, right] or directed_mask[right, left]: + first.append(left) + second.append(right) + weights.append( + directed_weights[left, right] + + directed_weights[right, left] + ) + return ( + np.column_stack([first, second]).astype(np.int64), + np.asarray(weights, dtype=np.float64), + ) + + +def score_matrix( + partitions: np.ndarray, edges: np.ndarray +) -> tuple[np.ndarray, np.ndarray]: + same = ( + partitions[:, edges[:, 0]] == partitions[:, edges[:, 1]] + ) + within_count = np.sum(same, axis=1) + between_count = np.sum(~same, axis=1) + valid = (within_count > 0) & (between_count > 0) + coefficients = np.zeros(same.shape, dtype=np.float64) + coefficients[valid] = ( + same[valid] / within_count[valid, None] + - (~same[valid]) / between_count[valid, None] + ) + return coefficients, valid + + +def exact_partition( + partitions: np.ndarray, + edges: np.ndarray, + weights: np.ndarray, +) -> tuple[np.ndarray, float, float, int]: + coefficients, valid = score_matrix(partitions, edges) + # An explicit reduction avoids spurious overflow warnings emitted by the + # local Accelerate-backed BLAS for these small, finite matrices. + scores = np.sum(coefficients * weights[None, :], axis=1) + scores[~valid] = -np.inf + order = np.argsort(-scores, kind="stable") + best_index = int(order[0]) + second_index = int(order[1]) + return ( + partitions[best_index].copy(), + float(scores[best_index]), + float(scores[second_index]), + best_index, + ) + + +def metrics( + partition: np.ndarray, truth: np.ndarray +) -> dict[str, float]: + ordinary = float(np.mean(partition == truth)) + return { + "ari": float(adjusted_rand_score(truth, partition)), + "nmi": float(normalized_mutual_info_score(truth, partition)), + "ordinary_accuracy": ordinary, + "gauge_accuracy": max(ordinary, 1.0 - ordinary), + } + + +def fit_node_indices(variants: np.ndarray) -> np.ndarray: + return np.flatnonzero( + np.isin(variants, ["anchor", "physics_paraphrase"]) + ) + + +def heldout_partition( + directed_weights: np.ndarray, + directed_mask: np.ndarray, + cases: np.ndarray, + variants: np.ndarray, +) -> tuple[np.ndarray, float, float]: + fit_indices = fit_node_indices(variants) + heldout_indices = np.flatnonzero( + variants == "lexical_counterfactual" + ) + fit_lookup = { + int(global_local): fit_local + for fit_local, global_local in enumerate(fit_indices) + } + full_edges, full_weights = symmetrized_edges( + directed_weights, directed_mask + ) + keep = np.asarray( + [ + int(left) in fit_lookup and int(right) in fit_lookup + for left, right in full_edges + ], + dtype=bool, + ) + fit_edges = np.asarray( + [ + [fit_lookup[int(left)], fit_lookup[int(right)]] + for left, right in full_edges[keep] + ], + dtype=np.int64, + ) + fit_weights = full_weights[keep] + fit_partition, best, second, _ = exact_partition( + FIT_PARTITIONS, fit_edges, fit_weights + ) + prediction = np.zeros(12, dtype=bool) + prediction[fit_indices] = fit_partition + sym_weights = directed_weights + directed_weights.T + sym_mask = directed_mask | directed_mask.T + for node in heldout_indices: + means = [] + for community in (False, True): + candidates = fit_indices[ + (fit_partition == community) + & (cases[fit_indices] != cases[node]) + & sym_mask[node, fit_indices] + ] + means.append( + float(np.mean(sym_weights[node, candidates])) + if len(candidates) + else -np.inf + ) + prediction[node] = bool(means[1] > means[0]) + return prediction, best, best - second + + +def blockwise_resample( + weights: np.ndarray, + mask: np.ndarray, + variants: np.ndarray, + rng: np.random.Generator, + *, + replace: bool, +) -> np.ndarray: + result = np.zeros_like(weights) + for source_variant in VARIANTS: + for target_variant in VARIANTS: + if source_variant == target_variant: + continue + block = ( + (variants[:, None] == source_variant) + & (variants[None, :] == target_variant) + & mask + ) + values = weights[block] + if replace: + sampled = rng.choice(values, size=len(values), replace=True) + else: + sampled = values.copy() + rng.shuffle(sampled) + result[block] = sampled + return result + + +def observed_and_randomized( + data: Mapping[str, object], +) -> tuple[ + pd.DataFrame, + dict[str, np.ndarray], + dict[str, np.ndarray], + pd.DataFrame, +]: + rng = np.random.default_rng(SEED) + families_array = np.asarray(data["families"]) + physical = np.asarray(data["physical"], dtype=bool) + band_mask = np.asarray(data["band_mask"]) + observed_rows = [] + null_outputs = {} + bootstrap_outputs = {} + bootstrap_summary_rows = [] + + for method in METHODS: + layers = np.asarray(data["similarities"][method]) + band = np.mean(layers[band_mask], axis=0) + family_artifacts = {} + for family in data["family_order"]: + family = str(family) + truth = physical[families_array == family] + directed, mask, cases, variants = eligible_directed( + band, family, data + ) + edges, edge_weights = symmetrized_edges(directed, mask) + partition, best, second, _ = exact_partition( + FULL_PARTITIONS, edges, edge_weights + ) + heldout, heldout_best, heldout_margin = heldout_partition( + directed, mask, cases, variants + ) + observed_rows.append( + { + "method": method, + "family": family, + "endpoint": "complete", + **metrics(partition, truth), + "best_score": best, + "second_score": second, + "margin": best - second, + } + ) + observed_rows.append( + { + "method": method, + "family": family, + "endpoint": "heldout_surface", + **metrics(heldout, truth), + "best_score": heldout_best, + "second_score": heldout_best - heldout_margin, + "margin": heldout_margin, + } + ) + family_artifacts[family] = { + "truth": truth, + "directed": directed, + "mask": mask, + "cases": cases, + "variants": variants, + "edges": edges, + "partition": partition, + "heldout": heldout, + } + + complete_ari = np.empty(N_BOOTSTRAP, dtype=np.float32) + heldout_ari = np.empty(N_BOOTSTRAP, dtype=np.float32) + complete_identical = np.empty(N_BOOTSTRAP, dtype=bool) + heldout_identical = np.empty(N_BOOTSTRAP, dtype=bool) + coassignment = np.zeros((12, 12), dtype=np.float64) + for bootstrap_index in range(N_BOOTSTRAP): + sampled = blockwise_resample( + directed, mask, variants, rng, replace=True + ) + _, sampled_weights = symmetrized_edges(sampled, mask) + sampled_partition, *_ = exact_partition( + FULL_PARTITIONS, edges, sampled_weights + ) + sampled_heldout, *_ = heldout_partition( + sampled, mask, cases, variants + ) + complete_ari[bootstrap_index] = adjusted_rand_score( + partition, sampled_partition + ) + heldout_ari[bootstrap_index] = adjusted_rand_score( + heldout, sampled_heldout + ) + complete_identical[bootstrap_index] = bool( + np.array_equal(partition, sampled_partition) + or np.array_equal(partition, ~sampled_partition) + ) + heldout_identical[bootstrap_index] = bool( + np.array_equal(heldout, sampled_heldout) + or np.array_equal(heldout, ~sampled_heldout) + ) + coassignment += ( + sampled_partition[:, None] + == sampled_partition[None, :] + ) + key = f"{method}_{family}" + bootstrap_outputs[f"{key}_complete_ari"] = complete_ari + bootstrap_outputs[f"{key}_heldout_ari"] = heldout_ari + bootstrap_outputs[f"{key}_coassignment"] = ( + coassignment / N_BOOTSTRAP + ).astype(np.float32) + bootstrap_summary_rows.append( + { + "method": method, + "family": family, + "complete_mean_bootstrap_ari": float( + np.mean(complete_ari) + ), + "complete_identical_fraction": float( + np.mean(complete_identical) + ), + "heldout_mean_bootstrap_ari": float( + np.mean(heldout_ari) + ), + "heldout_identical_fraction": float( + np.mean(heldout_identical) + ), + } + ) + + null_complete = np.empty( + (N_NULL, len(data["family_order"])), dtype=np.float32 + ) + null_heldout = np.empty_like(null_complete) + for family_index, family in enumerate(data["family_order"]): + family = str(family) + artifact = family_artifacts[family] + for null_index in range(N_NULL): + shuffled = blockwise_resample( + artifact["directed"], + artifact["mask"], + artifact["variants"], + rng, + replace=False, + ) + _, shuffled_weights = symmetrized_edges( + shuffled, artifact["mask"] + ) + partition, *_ = exact_partition( + FULL_PARTITIONS, + artifact["edges"], + shuffled_weights, + ) + heldout, *_ = heldout_partition( + shuffled, + artifact["mask"], + artifact["cases"], + artifact["variants"], + ) + null_complete[null_index, family_index] = ( + adjusted_rand_score(artifact["truth"], partition) + ) + null_heldout[null_index, family_index] = ( + adjusted_rand_score(artifact["truth"], heldout) + ) + null_outputs[f"{method}_complete_family_ari"] = null_complete + null_outputs[f"{method}_heldout_family_ari"] = null_heldout + return ( + pd.DataFrame(observed_rows), + null_outputs, + bootstrap_outputs, + pd.DataFrame(bootstrap_summary_rows), + ) + + +def summarize( + observed: pd.DataFrame, + nulls: Mapping[str, np.ndarray], +) -> dict[str, object]: + summary = {} + family_order = sorted(observed["family"].unique()) + for method in METHODS: + summary[method] = {} + for endpoint in ("complete", "heldout_surface"): + selected = observed[ + (observed["method"] == method) + & (observed["endpoint"] == endpoint) + ].set_index("family").loc[family_order] + aris = selected["ari"].to_numpy(float) + gauges = selected["gauge_accuracy"].to_numpy(float) + null_key = ( + f"{method}_complete_family_ari" + if endpoint == "complete" + else f"{method}_heldout_family_ari" + ) + null_mean = np.mean( + np.asarray(nulls[null_key]), axis=1 + ) + observed_mean = float(np.mean(aris)) + p_value = float( + (1 + np.sum(null_mean >= observed_mean - 1e-12)) + / (1 + len(null_mean)) + ) + summary[method][endpoint] = { + "ari_by_family": dict( + zip(family_order, aris.tolist()) + ), + "gauge_accuracy_by_family": dict( + zip(family_order, gauges.tolist()) + ), + "mean_ari": observed_mean, + "mean_gauge_accuracy": float(np.mean(gauges)), + "positive_ari_families": int(np.sum(aris > 0)), + "gauge_above_half_families": int( + np.sum(gauges > 0.5) + ), + "null_mean_ari": float(np.mean(null_mean)), + "null_sd_ari": float(np.std(null_mean, ddof=1)), + "null_exact_p": p_value, + } + summary[method]["strong_dense_partition_gate"] = bool( + summary[method]["complete"]["positive_ari_families"] == 6 + and summary[method]["complete"][ + "gauge_above_half_families" + ] + == 6 + and summary[method]["complete"]["null_exact_p"] <= 0.05 + and summary[method]["heldout_surface"][ + "positive_ari_families" + ] + == 6 + and summary[method]["heldout_surface"][ + "gauge_above_half_families" + ] + == 6 + and summary[method]["heldout_surface"]["null_exact_p"] + <= 0.05 + ) + + for endpoint in ("complete", "heldout_surface"): + pivot = observed[ + observed["endpoint"] == endpoint + ].pivot(index="family", columns="method", values="ari") + direct = (pivot["jacobian"] - pivot["direct"]).sort_index() + raw = (pivot["jacobian"] - pivot["raw"]).sort_index() + summary[f"jacobian_specificity_{endpoint}"] = { + "minus_direct_by_family": direct.to_dict(), + "minus_raw_by_family": raw.to_dict(), + "over_direct_families": int(np.sum(direct.to_numpy() > 0)), + "over_raw_families": int(np.sum(raw.to_numpy() > 0)), + "minus_direct_signflip_p": plus_one_signflip_p(direct), + "minus_raw_signflip_p": plus_one_signflip_p(raw), + } + return summary + + +def main() -> None: + validate_inputs() + data = load_data() + observed, nulls, bootstraps, bootstrap_summary = ( + observed_and_randomized(data) + ) + summary = summarize(observed, nulls) + observed.to_csv( + OUT / "exact_partition_metrics.csv", index=False + ) + bootstrap_summary.to_csv( + OUT / "exact_partition_bootstrap_summary.csv", index=False + ) + np.savez_compressed( + OUT / "exact_partition_nulls.npz", **nulls + ) + np.savez_compressed( + OUT / "exact_partition_bootstraps.npz", **bootstraps + ) + results = { + "study_id": json.loads(PARTITION_PROTOCOL.read_text())[ + "study_id" + ], + "partition_protocol_sha256": sha256(PARTITION_PROTOCOL), + "partition_protocol_markdown_sha256": sha256( + PARTITION_PROTOCOL_MD + ), + "spectral_protocol_sha256": sha256(SPECTRAL_PROTOCOL), + "partitions_enumerated": { + "complete": len(FULL_PARTITIONS), + "fit_only": len(FIT_PARTITIONS), + }, + "summary": summary, + "environment": { + "python": sys.version, + "platform": platform.platform(), + "numpy": np.__version__, + "pandas": pd.__version__, + }, + } + (OUT / "study6_partition_statistics.json").write_text( + json.dumps(safe_json(results), indent=2) + "\n" + ) + print(json.dumps(safe_json(results), indent=2)) + + +if __name__ == "__main__": + main() diff --git a/jlens_materials/scripts/analyze_final_state_physics_abstraction.py b/jlens_materials/scripts/analyze_final_state_physics_abstraction.py new file mode 100644 index 0000000000000000000000000000000000000000..7edfb5aaa9e95f88c89bee2a490b32de0426e0e5 --- /dev/null +++ b/jlens_materials/scripts/analyze_final_state_physics_abstraction.py @@ -0,0 +1,539 @@ +#!/usr/bin/env python3 +"""Held-out audit of final-position abstract physical-outcome geometry. + +This analysis was frozen after discovery-only inspection showed that the final +prompt position, unlike the earlier answer-free marker, contains a late +cross-orientation signal. It crosses both law orientation and answer mapping: +a decoder trained on one orientation/order cell is tested on unseen laws in +the opposite orientation and opposite order. +""" + +from __future__ import annotations + +import hashlib +import itertools +import json +from pathlib import Path +from typing import Sequence + +import matplotlib + +matplotlib.use("Agg") +import matplotlib.pyplot as plt # noqa: E402 +import numpy as np # noqa: E402 +import pandas as pd # noqa: E402 +from sklearn.feature_extraction.text import TfidfVectorizer # noqa: E402 +from sklearn.metrics import roc_auc_score # noqa: E402 + +from analyze_abstract_physics_composition import ( + bootstrap, + centroid_direction, + exact_signflip, + normalize, + safe_dot, +) + + +ROOT = Path(__file__).resolve().parents[1] +OUT = ROOT / "experiments" / "final-state-physics-abstraction-2026-07-18" +PROTOCOL = OUT / "protocol.json" + +COHORTS = { + "arbitrary_code": ROOT + / "experiments" + / "abstract-physics-composition-2026-07-18", + "semantic_answer": ROOT + / "experiments" + / "abstract-physics-semantic-2026-07-18", +} + + +def sha256(path: Path) -> str: + return hashlib.sha256(path.read_bytes()).hexdigest() + + +def load_cohort(name: str, path: Path) -> dict: + manifest = json.loads((path / "prompt_manifest.json").read_text()) + raw = json.loads((path / "raw.json").read_text()) + with np.load(path / "representations.npz", allow_pickle=False) as arrays: + prompt_ids = arrays["prompt_ids"].astype(str) + positions = arrays["positions"].astype(str) + layers = arrays["layers"].astype(int) + states = arrays["raw_states"].astype(np.float64) + by_id = {row["prompt_id"]: row for row in manifest["prompts"]} + rows = [by_id[prompt_id] for prompt_id in prompt_ids] + if name == "arbitrary_code": + orders = np.asarray([row["answer_order"] for row in rows]) + positive_order = orders == "a-larger" + code = np.asarray([row["expected_code"] == "A" for row in rows]) + else: + orders = np.asarray([row["answer_order"] for row in rows]) + positive_order = orders == "higher-first" + code = np.asarray([row["expected_answer"] == "higher" for row in rows]) + return { + "name": name, + "path": path, + "manifest": manifest, + "raw": raw, + "rows": rows, + "prompt_ids": prompt_ids, + "positions": positions, + "layers": layers, + "states": states, + "meta": { + "law": np.asarray([row["law_id"] for row in rows]), + "split": np.asarray([row["split"] for row in rows]), + "orientation": np.asarray([row["law_sign"] == 1 for row in rows]), + "numeric": np.asarray([row["numerical_sign"] == 1 for row in rows]), + "physical": np.asarray([row["physical_sign"] == 1 for row in rows]), + "surface": np.asarray([row["surface"] for row in rows]), + "positive_order": positive_order, + "code": code, + }, + } + + +def validate(protocol: dict, cohorts: dict[str, dict]) -> None: + if sha256(Path(__file__).resolve()) != protocol["inputs"]["analyzer"]["sha256"]: + raise RuntimeError("analyzer fingerprint mismatch") + for name, cohort in cohorts.items(): + for file_name, key in ( + ("prompt_manifest.json", "manifest"), + ("raw.json", "raw"), + ("representations.npz", "states"), + ): + path = cohort["path"] / file_name + expected = protocol["inputs"][name][key]["sha256"] + if sha256(path) != expected: + raise RuntimeError(f"{name} {key} fingerprint mismatch") + + +def dual_cross_auc( + values: np.ndarray, + labels: np.ndarray, + meta: dict, + split: str, +) -> float: + selected = meta["split"] == split + scores = [] + for train_orientation, train_order in itertools.product((False, True), repeat=2): + train = ( + selected + & (meta["orientation"] == train_orientation) + & (meta["positive_order"] == train_order) + ) + test = ( + selected + & (meta["orientation"] != train_orientation) + & (meta["positive_order"] != train_order) + ) + direction, midpoint = centroid_direction(values[train], labels[train]) + prediction = safe_dot(values[test] - midpoint, direction) + scores.append(float(roc_auc_score(labels[test].astype(int), prediction))) + return float(np.mean(scores)) + + +def replication_law_auc( + values: np.ndarray, + labels: np.ndarray, + meta: dict, +) -> list[dict]: + discovery = meta["split"] == "discovery" + replication = meta["split"] == "replication" + output = [] + for law_id in sorted(set(meta["law"][replication])): + orientation = bool(meta["orientation"][meta["law"] == law_id][0]) + law_scores = [] + for train_order in (False, True): + train = ( + discovery + & (meta["orientation"] != orientation) + & (meta["positive_order"] == train_order) + ) + test = ( + replication + & (meta["law"] == law_id) + & (meta["positive_order"] != train_order) + ) + direction, midpoint = centroid_direction(values[train], labels[train]) + prediction = safe_dot(values[test] - midpoint, direction) + law_scores.append( + float(roc_auc_score(labels[test].astype(int), prediction)) + ) + output.append( + { + "law_id": str(law_id), + "test_orientation": "direct" if orientation else "inverse", + "auc": float(np.mean(law_scores)), + "order_direction_aucs": law_scores, + } + ) + return output + + +def cross_cohort_law_auc( + train_cohort: dict, + test_cohort: dict, + layer: int, +) -> list[dict]: + train_index = int(np.flatnonzero(train_cohort["layers"] == layer)[0]) + test_index = int(np.flatnonzero(test_cohort["layers"] == layer)[0]) + train_position = int( + np.flatnonzero(train_cohort["positions"] == "final_prompt")[0] + ) + test_position = int( + np.flatnonzero(test_cohort["positions"] == "final_prompt")[0] + ) + train_values = normalize( + train_cohort["states"][train_position, :, train_index] + ) + test_values = normalize(test_cohort["states"][test_position, :, test_index]) + train_meta = train_cohort["meta"] + test_meta = test_cohort["meta"] + train_labels = train_meta["physical"] + test_labels = test_meta["physical"] + output = [] + replication = test_meta["split"] == "replication" + for law_id in sorted(set(test_meta["law"][replication])): + orientation = bool( + test_meta["orientation"][test_meta["law"] == law_id][0] + ) + scores = [] + for train_order in (False, True): + train = ( + (train_meta["split"] == "discovery") + & (train_meta["orientation"] != orientation) + & (train_meta["positive_order"] == train_order) + ) + test = ( + replication + & (test_meta["law"] == law_id) + & (test_meta["positive_order"] != train_order) + ) + direction, midpoint = centroid_direction( + train_values[train], train_labels[train] + ) + prediction = safe_dot(test_values[test] - midpoint, direction) + scores.append( + float( + roc_auc_score(test_labels[test].astype(int), prediction) + ) + ) + output.append( + { + "train_cohort": train_cohort["name"], + "test_cohort": test_cohort["name"], + "layer": layer, + "law_id": str(law_id), + "test_orientation": "direct" if orientation else "inverse", + "auc": float(np.mean(scores)), + } + ) + return output + + +def tfidf_control(cohort: dict) -> list[dict]: + meta = cohort["meta"] + texts = [row["user"] for row in cohort["rows"]] + output = [] + for method, vectorizer in { + "word_tfidf": TfidfVectorizer( + lowercase=True, ngram_range=(1, 2), sublinear_tf=True + ), + "character_tfidf": TfidfVectorizer( + lowercase=True, + analyzer="char_wb", + ngram_range=(3, 5), + sublinear_tf=True, + max_features=20000, + ), + }.items(): + values = normalize( + vectorizer.fit_transform(texts).toarray().astype(np.float64) + ) + for target in ("physical", "numeric", "code"): + for row in replication_law_auc(values, meta[target], meta): + output.append( + { + "cohort": cohort["name"], + "method": method, + "target": target, + **row, + } + ) + return output + + +def summarize_laws( + values: Sequence[float], + protocol: dict, + seed_offset: int, +) -> dict: + values = np.asarray(values, dtype=float) + ci = bootstrap( + values, + np.random.default_rng(protocol["bootstrap"]["seed"] + seed_offset), + protocol["bootstrap"]["resamples"], + ) + return { + "mean_law_auc": float(values.mean()), + "law_bootstrap_95": list(ci), + "laws_above_chance": int(np.sum(values > 0.5)), + "laws_total": int(len(values)), + "two_sided_exact_signflip_p_auc_minus_half": exact_signflip( + values - 0.5 + ), + } + + +def main() -> None: + protocol = json.loads(PROTOCOL.read_text()) + cohorts = {name: load_cohort(name, path) for name, path in COHORTS.items()} + validate(protocol, cohorts) + layer_rows = [] + law_rows = [] + summaries = {} + selected_layers = {} + for cohort_index, (name, cohort) in enumerate(cohorts.items()): + final_position = int( + np.flatnonzero(cohort["positions"] == "final_prompt")[0] + ) + for layer_index, layer in enumerate(cohort["layers"]): + values = normalize(cohort["states"][final_position, :, layer_index]) + for target in ("physical", "numeric", "code"): + layer_rows.append( + { + "cohort": name, + "layer": int(layer), + "depth_percent": float( + 100.0 * layer / cohort["layers"].max() + ), + "target": target, + "discovery_dual_cross_auc": dual_cross_auc( + values, + cohort["meta"][target], + cohort["meta"], + "discovery", + ), + } + ) + layer_frame_cohort = pd.DataFrame( + [row for row in layer_rows if row["cohort"] == name] + ) + selection = layer_frame_cohort[ + layer_frame_cohort["target"] == "physical" + ].sort_values( + ["discovery_dual_cross_auc", "layer"], ascending=[False, True] + ) + selected_layer = int(selection.iloc[0]["layer"]) + selected_layers[name] = selected_layer + layer_index = int(np.flatnonzero(cohort["layers"] == selected_layer)[0]) + values = normalize(cohort["states"][final_position, :, layer_index]) + for target in ("physical", "numeric", "code"): + for row in replication_law_auc( + values, cohort["meta"][target], cohort["meta"] + ): + law_rows.append( + { + "cohort": name, + "selected_layer": selected_layer, + "target": target, + **row, + } + ) + physical = [ + row["auc"] + for row in law_rows + if row["cohort"] == name and row["target"] == "physical" + ] + summaries[name] = { + "selected_layer": selected_layer, + "selected_depth_percent": float( + 100.0 * selected_layer / cohort["layers"].max() + ), + "discovery_selected_dual_cross_auc": float( + selection.iloc[0]["discovery_dual_cross_auc"] + ), + **summarize_laws(physical, protocol, cohort_index), + } + + cross_rows = [] + cross_rows.extend( + cross_cohort_law_auc( + cohorts["arbitrary_code"], + cohorts["semantic_answer"], + selected_layers["arbitrary_code"], + ) + ) + cross_rows.extend( + cross_cohort_law_auc( + cohorts["semantic_answer"], + cohorts["arbitrary_code"], + selected_layers["semantic_answer"], + ) + ) + cross_frame = pd.DataFrame(cross_rows) + cross_summaries = {} + for index, (key, group) in enumerate( + cross_frame.groupby(["train_cohort", "test_cohort"]) + ): + cross_summaries[f"{key[0]}_to_{key[1]}"] = summarize_laws( + group["auc"].to_numpy(float), protocol, 10 + index + ) + + tfidf_rows = [] + for cohort in cohorts.values(): + tfidf_rows.extend(tfidf_control(cohort)) + tfidf_frame = pd.DataFrame(tfidf_rows) + tfidf_summary = { + f"{cohort}_{method}_{target}": float(group["auc"].mean()) + for (cohort, method, target), group in tfidf_frame.groupby( + ["cohort", "method", "target"] + ) + } + + layer_frame = pd.DataFrame(layer_rows) + law_frame = pd.DataFrame(law_rows) + OUT.mkdir(parents=True, exist_ok=True) + layer_frame.to_csv(OUT / "layerwise_dual_cross_auc.csv", index=False) + law_frame.to_csv(OUT / "replication_law_auc.csv", index=False) + cross_frame.to_csv(OUT / "cross_vocabulary_law_auc.csv", index=False) + tfidf_frame.to_csv(OUT / "tfidf_controls.csv", index=False) + + success = protocol["success_rule"] + for summary in list(summaries.values()) + list(cross_summaries.values()): + summary["passes_thresholds"] = bool( + summary["mean_law_auc"] >= success["mean_law_auc_minimum"] + and summary["law_bootstrap_95"][0] + > success["bootstrap_95_lower_above"] + and summary["laws_above_chance"] + >= success["positive_laws_minimum"] + ) + + statistics = { + "study_id": protocol["study_id"], + "protocol_sha256": sha256(PROTOCOL), + "within_cohort": summaries, + "cross_vocabulary": cross_summaries, + "tfidf_controls": tfidf_summary, + "interpretation_boundary": ( + "The endpoint shows whether a physical-outcome direction transfers " + "across unseen constitutive laws while both numerical-to-physical " + "polarity and answer mapping reverse. It remains a supervised " + "linear readout at the final prompt position. Causal necessity is " + "tested separately by interchange intervention." + ), + } + (OUT / "statistics.json").write_text( + json.dumps(statistics, indent=2, sort_keys=True) + "\n" + ) + + fig, axes = plt.subplots(1, 3, figsize=(11.4, 3.7)) + colors = {"arbitrary_code": "#16697A", "semantic_answer": "#7A6AA2"} + for name, color in colors.items(): + part = layer_frame[ + (layer_frame["cohort"] == name) + & (layer_frame["target"] == "physical") + ] + axes[0].plot( + part["depth_percent"], + part["discovery_dual_cross_auc"], + marker="o", + markersize=2.5, + color=color, + label=name.replace("_", " "), + ) + axes[0].axhline(0.5, color="#888888", linestyle=":", linewidth=1) + axes[0].set(xlabel="Layer depth (%)", ylabel="Discovery dual-cross AUC") + axes[0].legend(frameon=False, fontsize=8) + + physical = law_frame[law_frame["target"] == "physical"].copy() + laws = sorted(set(physical["law_id"])) + y = np.arange(len(laws)) + for offset, (name, color) in zip((-0.18, 0.18), colors.items()): + part = physical[physical["cohort"] == name].set_index("law_id").loc[laws] + axes[1].barh( + y + offset, + part["auc"], + height=0.34, + color=color, + label=name.replace("_", " "), + ) + axes[1].axvline(0.5, color="#555555", linestyle="--", linewidth=1) + axes[1].set( + yticks=y, + yticklabels=laws, + xlim=(0, 1), + xlabel="Held-out law AUC", + ) + + labels = list(cross_summaries) + means = [cross_summaries[key]["mean_law_auc"] for key in labels] + lows = [cross_summaries[key]["law_bootstrap_95"][0] for key in labels] + highs = [cross_summaries[key]["law_bootstrap_95"][1] for key in labels] + x = np.arange(len(labels)) + axes[2].bar(x, means, color=["#16697A", "#7A6AA2"]) + axes[2].errorbar( + x, + means, + yerr=[ + np.asarray(means) - np.asarray(lows), + np.asarray(highs) - np.asarray(means), + ], + fmt="none", + color="#333333", + capsize=3, + ) + axes[2].axhline(0.5, color="#555555", linestyle="--", linewidth=1) + axes[2].set( + xticks=x, + xticklabels=[label.replace("_to_", "\n→\n") for label in labels], + ylim=(0, 1), + ylabel="Cross-vocabulary held-out AUC", + ) + fig.tight_layout() + figure_dir = OUT / "figures" + figure_dir.mkdir(parents=True, exist_ok=True) + for suffix in ("png", "pdf"): + fig.savefig(figure_dir / f"final-state-physics-abstraction.{suffix}", dpi=300) + plt.close(fig) + + report = [ + "# Final-state physics-abstraction audit", + "", + "The decoder is trained in one law-orientation/answer-order cell and " + "tested on unseen laws with both factors reversed.", + "", + ] + for name, summary in summaries.items(): + report.extend( + [ + f"## {name.replace('_', ' ').title()}", + "", + f"- Discovery-selected layer: **{summary['selected_layer']}** " + f"({summary['selected_depth_percent']:.1f}% depth).", + f"- Discovery dual-cross AUC: " + f"**{summary['discovery_selected_dual_cross_auc']:.3f}**.", + f"- Replication mean law AUC: **{summary['mean_law_auc']:.3f}** " + f"(95% law bootstrap {summary['law_bootstrap_95'][0]:.3f} to " + f"{summary['law_bootstrap_95'][1]:.3f}).", + f"- Laws above chance: **{summary['laws_above_chance']}/8**; " + f"exact `p={summary['two_sided_exact_signflip_p_auc_minus_half']:.4g}`.", + "", + ] + ) + report.extend(["## Cross-vocabulary transfer", ""]) + for name, summary in cross_summaries.items(): + report.append( + f"- {name.replace('_', ' ')}: **{summary['mean_law_auc']:.3f}** " + f"({summary['law_bootstrap_95'][0]:.3f} to " + f"{summary['law_bootstrap_95'][1]:.3f}); " + f"{summary['laws_above_chance']}/8 laws above chance." + ) + report.extend(["", statistics["interpretation_boundary"]]) + (OUT / "REPORT.md").write_text("\n".join(report) + "\n") + print("\n".join(report)) + + +if __name__ == "__main__": + main() diff --git a/jlens_materials/scripts/analyze_gemma31b_equation_free_layers.py b/jlens_materials/scripts/analyze_gemma31b_equation_free_layers.py new file mode 100644 index 0000000000000000000000000000000000000000..0d37a5e481599dfbc37a5f7826cd7e5834a28350 --- /dev/null +++ b/jlens_materials/scripts/analyze_gemma31b_equation_free_layers.py @@ -0,0 +1,284 @@ +#!/usr/bin/env python3 +"""Audit the completed Gemma 31B equation-free results at layers 40 and 49.""" + +from __future__ import annotations + +import hashlib +import json +from pathlib import Path + +import numpy as np +import pandas as pd +from scipy.stats import pearsonr, spearmanr +from sklearn.metrics import roc_auc_score + + +ROOT = Path(__file__).resolve().parents[1] +RUNS = { + 40: ROOT / "experiments" / "gemma4-31b-equation-free-relational-layer40-v1", + 49: ROOT / "experiments" / "gemma4-31b-equation-free-relational-v1", +} +OUT = ROOT / "experiments" / "gemma4-31b-equation-free-layer-comparison-2026-08-23" +REFERENCE_4B = ROOT / "experiments" / "equation-free-relational-physics-2026-08-21" + + +def sha256(path: Path) -> str: + digest = hashlib.sha256() + with path.open("rb") as handle: + for chunk in iter(lambda: handle.read(1024 * 1024), b""): + digest.update(chunk) + return digest.hexdigest() + + +def load_run(layer: int, path: Path) -> dict: + protocol = json.loads((path / "protocol.json").read_text()) + raw = json.loads((path / "raw.json").read_text()) + stats = json.loads((path / "statistics.json").read_text()) + provenance = raw["provenance"] + checks = { + "protocol_sha256": sha256(path / "protocol.json") + == provenance["protocol_sha256"], + "development_manifest_sha256": sha256(path / "development_prompt_manifest.json") + == provenance["development_manifest_sha256"], + "equation_free_manifest_sha256": sha256(path / "equation_free_prompt_manifest.json") + == provenance["equation_free_manifest_sha256"], + "model_matches": protocol["model"] == provenance["model"] == stats["model"], + "model_revision_matches": protocol["model_revision"] + == provenance["model_revision"] + == stats["model_revision"], + "layer_matches": protocol["probe_layer_zero_indexed"] + == provenance["probe_layer_zero_indexed"] + == stats["probe_layer_zero_indexed"] + == layer, + "complete_test_rows": len(raw["clean_rows"]) == 960, + } + if not all(checks.values()): + raise RuntimeError(f"layer {layer} failed audit: {checks}") + return { + "protocol": protocol, + "raw": raw, + "statistics": stats, + "laws": pd.read_csv(path / "law_level_results.csv").sort_values("law_id").reset_index(drop=True), + "pairs": pd.read_csv(path / "matched_pair_contrasts.csv") + .sort_values(["law_id", "surface", "case_index", "answer_order"]) + .reset_index(drop=True), + "audit": checks, + "path": path, + } + + +def correlation(x: np.ndarray, y: np.ndarray) -> dict: + rho = spearmanr(x, y) + pearson = pearsonr(x, y) + return { + "spearman_rho": float(rho.statistic), + "spearman_p": float(rho.pvalue), + "pearson_r": float(pearson.statistic), + "pearson_p": float(pearson.pvalue), + } + + +def stratified_checks(run: dict) -> dict: + pairs = run["pairs"] + center = float(run["statistics"]["neutral_calibration"]["center"]) + output = {} + for field in ("surface", "answer_order"): + output[field] = {} + for value, subset in pairs.groupby(field): + laws = subset.groupby( + ["law_id", "category", "law_sign"], as_index=False + )["contrast"].mean() + directional = laws[laws["category"].isin(["direct", "inverse"])] + auc = roc_auc_score( + directional["category"].eq("direct"), directional["contrast"] + ) + signed = laws[laws["law_sign"] != 0] + correct = ( + (signed["contrast"].to_numpy() - center) + * signed["law_sign"].to_numpy() + > 0 + ) + output[field][str(value)] = { + "direct_vs_inverse_auc": float(auc), + "correct_directional_laws": int(correct.sum()), + "directional_laws_total": int(len(correct)), + } + return output + + +def main() -> None: + OUT.mkdir(parents=True, exist_ok=True) + runs = {layer: load_run(layer, path) for layer, path in RUNS.items()} + run40, run49 = runs[40], runs[49] + + manifest_checks = { + "development_manifests_identical": ( + run40["path"] / "development_prompt_manifest.json" + ).read_bytes() + == (run49["path"] / "development_prompt_manifest.json").read_bytes(), + "equation_free_manifests_identical": ( + run40["path"] / "equation_free_prompt_manifest.json" + ).read_bytes() + == (run49["path"] / "equation_free_prompt_manifest.json").read_bytes(), + "equation_free_manifest_matches_frozen_4b": ( + run40["path"] / "equation_free_prompt_manifest.json" + ).read_bytes() + == (REFERENCE_4B / "prompt_manifest.json").read_bytes(), + } + if not all(manifest_checks.values()): + raise RuntimeError(f"cross-layer prompt mismatch: {manifest_checks}") + + law_keys = ["law_id", "category", "domain", "law_sign", "neutral_role"] + pair_keys = ["law_id", "category", "domain", "law_sign", "neutral_role", "surface", "case_index", "answer_order"] + if not run40["laws"][law_keys].equals(run49["laws"][law_keys]): + raise RuntimeError("law rows differ across layers") + if not run40["pairs"][pair_keys].equals(run49["pairs"][pair_keys]): + raise RuntimeError("matched-pair rows differ across layers") + + laws = run40["laws"][law_keys + ["mean_contrast", "natural_z", "sign_correct"]].copy() + laws = laws.rename( + columns={ + "mean_contrast": "mean_contrast_layer40", + "natural_z": "natural_z_layer40", + "sign_correct": "sign_correct_layer40", + } + ) + laws["mean_contrast_layer49"] = run49["laws"]["mean_contrast"] + laws["natural_z_layer49"] = run49["laws"]["natural_z"] + laws["sign_correct_layer49"] = run49["laws"]["sign_correct"] + laws.to_csv(OUT / "law_level_cross_layer.csv", index=False) + + pairs = run40["pairs"][pair_keys + ["contrast", "natural_z"]].copy() + pairs = pairs.rename( + columns={ + "contrast": "contrast_layer40", + "natural_z": "natural_z_layer40", + } + ) + pairs["contrast_layer49"] = run49["pairs"]["contrast"] + pairs["natural_z_layer49"] = run49["pairs"]["natural_z"] + pairs.to_csv(OUT / "matched_pair_cross_layer.csv", index=False) + + centers = { + layer: float(run["statistics"]["neutral_calibration"]["center"]) + for layer, run in runs.items() + } + directional = pairs[pairs["law_sign"] != 0] + expected = directional["law_sign"].to_numpy() + signs = { + layer: np.sign( + directional[f"contrast_layer{layer}"].to_numpy() - centers[layer] + ) + for layer in (40, 49) + } + + with np.load(run40["path"] / "frozen_direction.npz") as archive40: + direction40 = archive40["direction"].astype(float) + with np.load(run49["path"] / "frozen_direction.npz") as archive49: + direction49 = archive49["direction"].astype(float) + direction_cosine = float( + np.dot(direction40, direction49) + / (np.linalg.norm(direction40) * np.linalg.norm(direction49)) + ) + + within_category = { + category: correlation( + laws.loc[laws["category"] == category, "mean_contrast_layer40"].to_numpy(), + laws.loc[laws["category"] == category, "mean_contrast_layer49"].to_numpy(), + ) + for category in ("direct", "neutral", "inverse") + } + statistics = { + "study_id": "gemma4-31b-equation-free-layer-comparison-2026-08-23", + "analysis_status": "post-run descriptive cross-layer robustness audit", + "layers_zero_indexed": [40, 49], + "normalized_depth_percent": { + str(layer): run["statistics"]["normalized_depth_percent"] + for layer, run in runs.items() + }, + "audit": { + "runs": {str(layer): run["audit"] for layer, run in runs.items()}, + **manifest_checks, + }, + "registered_endpoints": { + str(layer): { + "passed_all_prespecified_criteria": run["statistics"]["passed_all_prespecified_criteria"], + "primary": run["statistics"]["primary"], + "behavior": run["statistics"]["behavior"], + } + for layer, run in runs.items() + }, + "frozen_4b_reference": { + "path": str(REFERENCE_4B.relative_to(ROOT)), + "statistics": json.loads((REFERENCE_4B / "statistics.json").read_text()), + }, + "stratified_checks": { + str(layer): stratified_checks(run) for layer, run in runs.items() + }, + "cross_layer": { + "law_mean_contrast": correlation( + laws["mean_contrast_layer40"].to_numpy(), + laws["mean_contrast_layer49"].to_numpy(), + ), + "matched_pair_contrast": correlation( + pairs["contrast_layer40"].to_numpy(), + pairs["contrast_layer49"].to_numpy(), + ), + "within_category_law_mean_contrast": within_category, + "direction_vector_cosine": direction_cosine, + "directional_pair_accuracy": { + str(layer): { + "correct": int((signs[layer] == expected).sum()), + "total": int(len(expected)), + } + for layer in (40, 49) + }, + "directional_pair_sign_agreement": { + "agree": int((signs[40] == signs[49]).sum()), + "total": int(len(expected)), + }, + }, + "interpretation_boundary": ( + "The cross-layer comparison was specified after both runs were available. " + "It establishes descriptive stability across two depths, not a continuous " + "layerwise onset. The supplied-equation development direction is refitted " + "independently at each layer and the test cohort uses the same 60 law identities." + ), + } + (OUT / "statistics.json").write_text( + json.dumps(statistics, indent=2, sort_keys=True) + "\n" + ) + + law_corr = statistics["cross_layer"]["law_mean_contrast"] + pair_corr = statistics["cross_layer"]["matched_pair_contrast"] + report = [ + "# Gemma 31B equation-free cross-layer comparison", + "", + "The exact same 60-law, 960-prompt equation-free cohort was evaluated at layers 40 and 49.", + "", + "## Registered endpoints", + "", + "- Both layers passed every prespecified criterion.", + "- At each layer, direct-versus-inverse, direct-versus-neutral, and neutral-versus-inverse AUC were all **1.000**.", + "- At each layer, all **40/40 directional laws** had the expected sign.", + "- Both natural surfaces and both answer orders independently had AUC **1.000** and 40/40 correct law signs.", + "- On the identical cohort, the frozen 4B result was direct/inverse AUC 0.990, direct/neutral 0.950, neutral/inverse 0.920, and 38/40 correctly oriented laws.", + "", + "## Cross-layer stability", + "", + f"- Law-level raw contrasts: Spearman rho **{law_corr['spearman_rho']:.3f}**, Pearson r **{law_corr['pearson_r']:.3f}**.", + f"- Matched-pair raw contrasts: Spearman rho **{pair_corr['spearman_rho']:.3f}**, Pearson r **{pair_corr['pearson_r']:.3f}**.", + "- All **320/320 directional matched pairs** were correctly oriented at each layer, with 320/320 sign agreement across layers.", + f"- The fitted direction vectors had cosine **{direction_cosine:.3f}** across layers.", + "", + "The scores and physical ordering are extremely stable even though the fitted axis rotates in residual-stream coordinates. This is consistent with a layer-dependent representation that preserves the same relational variable, rather than one fixed vector copied unchanged through the network.", + "", + statistics["interpretation_boundary"], + "", + ] + (OUT / "REPORT.md").write_text("\n".join(report)) + print("\n".join(report)) + + +if __name__ == "__main__": + main() diff --git a/jlens_materials/scripts/analyze_gemma31b_goal_specific_causal_roles.py b/jlens_materials/scripts/analyze_gemma31b_goal_specific_causal_roles.py new file mode 100644 index 0000000000000000000000000000000000000000..6261ee8abe4118d84531ea2b0d5ff7531a81e8a1 --- /dev/null +++ b/jlens_materials/scripts/analyze_gemma31b_goal_specific_causal_roles.py @@ -0,0 +1,562 @@ +#!/usr/bin/env python3 +"""Analyze the frozen 31B goal-specific causal-role intervention study.""" + +from __future__ import annotations + +import json +from pathlib import Path + +import matplotlib + +matplotlib.use("Agg") +import matplotlib.pyplot as plt +from matplotlib.lines import Line2D +import numpy as np +import pandas as pd +from sklearn.metrics import roc_auc_score + + +ROOT = Path(__file__).resolve().parents[1] +OUT = ROOT / "experiments" / "gemma4-31b-goal-specific-causal-roles-2026-09-02" +COLORS = { + "target": "#2B6F9E", + "intermediate": "#1B9E91", + "invariant": "#8A8F94", + "purple": "#7153A2", + "gold": "#D59A2F", + "control": "#9B9FA3", +} + + +def read_jsonl(path: Path) -> list[dict]: + return [json.loads(line) for line in path.read_text().splitlines() if line.strip()] + + +def bootstrap_mean(values: np.ndarray, *, seed: int, draws: int = 50000) -> tuple[float, float, float]: + values = np.asarray(values, dtype=np.float64) + rng = np.random.default_rng(seed) + indices = rng.integers(0, len(values), size=(draws, len(values))) + means = values[indices].mean(axis=1) + return float(values.mean()), *[float(x) for x in np.quantile(means, [0.025, 0.975])] + + +def exact_sign_flip_p_one_sided(values: np.ndarray) -> float: + """Exact P(mean(sign_i*x_i) >= observed), via meet-in-the-middle sums.""" + values = np.asarray(values, dtype=np.float64) + observed = float(values.mean()) + if observed <= 0: + return 1.0 + n = len(values) + split = n // 2 + + def signed_sums(part: np.ndarray) -> np.ndarray: + codes = np.arange(1 << len(part), dtype=np.uint32) + bits = np.arange(len(part), dtype=np.uint32) + signs = 1.0 - 2.0 * ((codes[:, None] >> bits[None, :]) & 1).astype(np.float64) + # Avoid platform-specific BLAS warnings observed for this small exact + # enumeration on macOS; this is algebraically identical to signs @ part. + return np.sum(signs * part[None, :], axis=1, dtype=np.float64) + + left = signed_sums(values[:split]) + right = np.sort(signed_sums(values[split:])) + threshold = observed * n - 1e-12 + indices = np.searchsorted(right, threshold - left, side="left") + hits = int(np.sum(len(right) - indices)) + return hits / float((1 << len(values))) + + +def aggregate_double_dissociation( + binary: pd.DataFrame, + own_column: str, + matched_other_column: str, +) -> pd.DataFrame: + rows = [] + for chain_id, group in binary.groupby("chain_id", sort=True): + target = group[group["question_type"] == "target"] + intermediate = group[group["question_type"] == "intermediate"] + target_specific = float((target[own_column] - target[matched_other_column]).mean()) + intermediate_specific = float((intermediate[own_column] - intermediate[matched_other_column]).mean()) + rows.append({ + "chain_id": chain_id, + "target_specific": target_specific, + "intermediate_specific": intermediate_specific, + "double_dissociation": 0.5 * (target_specific + intermediate_specific), + }) + return pd.DataFrame(rows) + + +def summarize_effect(values: np.ndarray, seed: int) -> dict: + estimate, low, high = bootstrap_mean(values, seed=seed) + return { + "estimate": estimate, + "ci95": [low, high], + "positive_chains": int(np.sum(np.asarray(values) > 0)), + "total_chains": int(len(values)), + "positive_fraction": float(np.mean(np.asarray(values) > 0)), + "exact_sign_flip_p_one_sided": exact_sign_flip_p_one_sided(values), + } + + +def add_panel_label(ax, label: str) -> None: + ax.text(-0.13, 1.06, label, transform=ax.transAxes, fontsize=12, fontweight="bold", va="top") + + +def main() -> None: + protocol = json.loads((OUT / "protocol.json").read_text()) + manifest = json.loads((OUT / "prompt_manifest.json").read_text()) + run_record = json.loads((OUT / "run_record.json").read_text()) + if not run_record["complete"]: + raise RuntimeError("intervention run is incomplete") + clean = pd.DataFrame(read_jsonl(OUT / "clean_rows.jsonl")) + interventions = pd.DataFrame(read_jsonl(OUT / "intervention_rows.jsonl")) + expected_methods = 46 + if len(clean) != 288 or len(interventions) != 288 * expected_methods: + raise RuntimeError( + f"row count mismatch: clean={len(clean)}, interventions={len(interventions)}" + ) + + clean_columns = [ + "prompt_id", "chain_id", "domain", "surface", "question_type", + "link1_sign", "link2_sign", "net_law_sign", "numerical_sign", + "intermediate_sign", "target_sign", "query_sign", "expected_answer", + "expected_margin", "answer_correct", "registered_probability_mass", + "target_coordinate", "intermediate_coordinate", + ] + clean_small = clean[clean_columns].copy() + pivot_margin = interventions.pivot(index="prompt_id", columns="intervention", values="expected_margin") + pivot_removed = interventions.pivot(index="prompt_id", columns="intervention", values="correct_evidence_removed") + pivot_correct = interventions.pivot(index="prompt_id", columns="intervention", values="answer_correct") + merged = clean_small.set_index("prompt_id").join(pivot_margin.add_prefix("margin__")) + merged = merged.join(pivot_removed.add_prefix("removed__")) + merged = merged.join(pivot_correct.add_prefix("correct__")).reset_index() + + # Mean matched antithetic activation-control effect for each physical role operation. + for role in ("target", "intermediate"): + for operation in ("neutralize", "counterfactual"): + prefix = f"{role}_{operation}_control_" + columns = [column for column in pivot_removed.columns if column.startswith(prefix)] + merged[f"control_removed__{role}_{operation}"] = pivot_removed[columns].mean(axis=1).reindex(merged["prompt_id"]).to_numpy() + + # Restoration is measured as recovery of the original correct-answer margin + # relative to a full matched counterfactual state. + for role in ("target", "intermediate"): + merged[f"restored__{role}"] = ( + merged[f"margin__full_counterfactual_{role}_restored"] + - merged["margin__full_counterfactual"] + ) + + binary = merged[merged["question_type"].isin(["target", "intermediate"])].copy() + invariant = merged[merged["question_type"] == "invariant"].copy() + + binary["neutralize_own"] = np.where( + binary["question_type"] == "target", + binary["removed__target_neutralize"], + binary["removed__intermediate_neutralize"], + ) + binary["neutralize_matched_other"] = binary["removed__matched_other_neutralize"] + binary["counterfactual_own"] = np.where( + binary["question_type"] == "target", + binary["removed__target_counterfactual"], + binary["removed__intermediate_counterfactual"], + ) + binary["counterfactual_matched_other"] = binary["removed__matched_other_counterfactual"] + binary["restoration_own"] = np.where( + binary["question_type"] == "target", + binary["restored__target"], + binary["restored__intermediate"], + ) + binary["restoration_matched_other"] = ( + binary["margin__full_counterfactual_matched_other_restored"] + - binary["margin__full_counterfactual"] + ) + + neutral_dd = aggregate_double_dissociation( + binary, "neutralize_own", "neutralize_matched_other" + ) + neutral_dd["operation"] = "Neutralization" + counter_dd = aggregate_double_dissociation( + binary, "counterfactual_own", "counterfactual_matched_other" + ) + counter_dd["operation"] = "Counterfactual" + restore_dd = aggregate_double_dissociation( + binary, "restoration_own", "restoration_matched_other" + ) + restore_dd["operation"] = "Restoration" + chain_effects = pd.concat([neutral_dd, counter_dd, restore_dd], ignore_index=True) + chain_effects.to_csv(OUT / "chain_level_double_dissociation.csv", index=False) + + # Detailed chain/question effects for transparent heterogeneity auditing. + detail_rows = [] + for (chain_id, question_type), group in binary.groupby(["chain_id", "question_type"], sort=True): + own = question_type + detail_rows.append({ + "chain_id": chain_id, + "question_type": question_type, + "link1_sign": int(group["link1_sign"].iloc[0]), + "link2_sign": int(group["link2_sign"].iloc[0]), + "neutralize_own": float(group[f"removed__{own}_neutralize"].mean()), + "neutralize_wrong": float(group["removed__matched_other_neutralize"].mean()), + "neutralize_specific": float((group[f"removed__{own}_neutralize"] - group["removed__matched_other_neutralize"]).mean()), + "counterfactual_own": float(group[f"removed__{own}_counterfactual"].mean()), + "counterfactual_wrong": float(group["removed__matched_other_counterfactual"].mean()), + "counterfactual_specific": float((group[f"removed__{own}_counterfactual"] - group["removed__matched_other_counterfactual"]).mean()), + "restoration_own": float(group[f"restored__{own}"].mean()), + "restoration_wrong": float(group["restoration_matched_other"].mean()), + "restoration_specific": float((group[f"restored__{own}"] - group["restoration_matched_other"]).mean()), + }) + detail = pd.DataFrame(detail_rows) + detail.to_csv(OUT / "chain_question_role_effects.csv", index=False) + + summaries = {} + for seed, (operation, table) in enumerate( + (("neutralization", neutral_dd), ("counterfactual", counter_dd), ("restoration", restore_dd)), + start=20260920, + ): + summaries[operation] = { + "double_dissociation": summarize_effect(table["double_dissociation"].to_numpy(), seed), + "target_question_specific": summarize_effect(table["target_specific"].to_numpy(), seed + 100), + "intermediate_question_specific": summarize_effect(table["intermediate_specific"].to_numpy(), seed + 200), + } + + # Own-role effects and activation-matched controls. + own_control_rows = [] + for _, row in binary.iterrows(): + role = row["question_type"] + own_control_rows.append({ + "prompt_id": row["prompt_id"], + "chain_id": row["chain_id"], + "question_type": role, + "neutralize_own": row[f"removed__{role}_neutralize"], + "neutralize_wrong": row["removed__matched_other_neutralize"], + "neutralize_control": row[f"control_removed__{role}_neutralize"], + "counterfactual_own": row[f"removed__{role}_counterfactual"], + "counterfactual_wrong": row["removed__matched_other_counterfactual"], + "counterfactual_control": row[f"control_removed__{role}_counterfactual"], + }) + own_controls = pd.DataFrame(own_control_rows) + own_control_chain = own_controls.groupby("chain_id", as_index=False).mean(numeric_only=True) + own_control_chain.to_csv(OUT / "chain_level_own_role_controls.csv", index=False) + own_control_summary = {} + for operation, seed in (("neutralize", 20261001), ("counterfactual", 20261002)): + own_control_summary[operation] = { + "own_effect": summarize_effect(own_control_chain[f"{operation}_own"].to_numpy(), seed), + "matched_control_effect": bootstrap_mean( + own_control_chain[f"{operation}_control"].to_numpy(), seed=seed + 10 + ), + "own_minus_control": summarize_effect( + ( + own_control_chain[f"{operation}_own"] + - own_control_chain[f"{operation}_control"] + ).to_numpy(), + seed + 20, + ), + } + + # Prospective observational validation of the two frozen role directions. + target_auc = float(roc_auc_score( + binary.loc[binary["question_type"] == "target", "query_sign"] > 0, + binary.loc[binary["question_type"] == "target", "target_coordinate"], + )) + intermediate_auc = float(roc_auc_score( + binary.loc[binary["question_type"] == "intermediate", "query_sign"] > 0, + binary.loc[binary["question_type"] == "intermediate", "intermediate_coordinate"], + )) + cross_target_on_intermediate = float(roc_auc_score( + binary.loc[binary["question_type"] == "intermediate", "query_sign"] > 0, + binary.loc[binary["question_type"] == "intermediate", "target_coordinate"], + )) + cross_intermediate_on_target = float(roc_auc_score( + binary.loc[binary["question_type"] == "target", "query_sign"] > 0, + binary.loc[binary["question_type"] == "target", "intermediate_coordinate"], + )) + + # Accuracy transitions and natural-error repair. + transition = {} + for intervention in ( + "target_neutralize", "intermediate_neutralize", + "target_counterfactual", "intermediate_counterfactual", + "full_counterfactual", "full_counterfactual_target_restored", + "full_counterfactual_intermediate_restored", + ): + clean_correct = binary["answer_correct"].astype(bool) + after = binary[f"correct__{intervention}"].astype(bool) + transition[intervention] = { + "correct_to_wrong": int(np.sum(clean_correct & ~after)), + "wrong_to_correct": int(np.sum(~clean_correct & after)), + "accuracy": float(after.mean()), + } + + error_rows = [] + natural_errors = binary[~binary["answer_correct"].astype(bool)] + for _, row in natural_errors.iterrows(): + role = row["question_type"] + wrong_role = "intermediate" if role == "target" else "target" + error_rows.append({ + "prompt_id": row["prompt_id"], + "chain_id": row["chain_id"], + "question_type": role, + "clean_margin": row["expected_margin"], + "own_correct_centroid_margin": row[f"margin__{role}_correct_centroid"], + "wrong_role_correct_centroid_margin": row[f"margin__{wrong_role}_correct_centroid"], + "own_wrong_centroid_margin": row[f"margin__{role}_wrong_centroid"], + }) + errors = pd.DataFrame(error_rows) + errors.to_csv(OUT / "natural_error_repair.csv", index=False) + + # Invariant questions use "same" versus the larger of higher/lower. + invariant_summary = {} + for intervention in ( + "target_neutralize", "intermediate_neutralize", + "target_counterfactual", "intermediate_counterfactual", + "full_counterfactual", + ): + change = invariant[f"margin__{intervention}"] - invariant["expected_margin"] + chain_change = change.groupby(invariant["chain_id"]).mean() + invariant_summary[intervention] = { + "mean_margin_change": float(chain_change.mean()), + "mean_absolute_margin_change": float(chain_change.abs().mean()), + "ci95_mean_change": list(bootstrap_mean(chain_change.to_numpy(), seed=20261100 + len(invariant_summary))[1:]), + "accuracy": float(invariant[f"correct__{intervention}"].astype(bool).mean()), + } + + primary = summaries["neutralization"]["double_dissociation"] + primary_pass = bool( + primary["ci95"][0] > 0 + and primary["exact_sign_flip_p_one_sided"] <= 0.05 + and primary["positive_fraction"] >= 0.75 + and summaries["neutralization"]["target_question_specific"]["estimate"] > 0 + and summaries["neutralization"]["intermediate_question_specific"]["estimate"] > 0 + and own_control_summary["neutralize"]["own_minus_control"]["ci95"][0] > 0 + ) + + statistics = { + "study_id": protocol["study_id"], + "status": "prospective target cohort analyzed under the frozen protocol", + "dimensions": { + "chains": 24, + "clean_prompts": len(clean), + "binary_prompts": len(binary), + "invariant_prompts": len(invariant), + "intervention_rows": len(interventions), + }, + "clean_behavior": { + "all_accuracy": float(clean["answer_correct"].mean()), + "target_accuracy": float(clean.loc[clean["question_type"] == "target", "answer_correct"].mean()), + "intermediate_accuracy": float(clean.loc[clean["question_type"] == "intermediate", "answer_correct"].mean()), + "invariant_accuracy": float(clean.loc[clean["question_type"] == "invariant", "answer_correct"].mean()), + }, + "prospective_direction_validation": { + "target_role_auc": target_auc, + "intermediate_role_auc": intermediate_auc, + "target_direction_on_intermediate_questions_auc": cross_target_on_intermediate, + "intermediate_direction_on_target_questions_auc": cross_intermediate_on_target, + "direction_cosine": float(run_record["direction_cosine"]), + "angle_degrees": float(np.degrees(np.arccos(np.clip(run_record["direction_cosine"], -1, 1)))), + }, + "primary_neutralization_double_dissociation": { + **summaries["neutralization"], + "passed_all_frozen_criteria": primary_pass, + }, + "counterfactual_double_dissociation": summaries["counterfactual"], + "restoration_double_dissociation": summaries["restoration"], + "own_role_vs_activation_controls": own_control_summary, + "accuracy_transitions": transition, + "natural_error_repair": { + "clean_binary_errors": int(len(errors)), + "own_correct_centroid_repairs": int((errors["own_correct_centroid_margin"] > 0).sum()) if len(errors) else 0, + "wrong_role_correct_centroid_repairs": int((errors["wrong_role_correct_centroid_margin"] > 0).sum()) if len(errors) else 0, + "own_wrong_centroid_repairs": int((errors["own_wrong_centroid_margin"] > 0).sum()) if len(errors) else 0, + }, + "invariant_questions": invariant_summary, + "interpretation_guardrail": ( + "A positive double dissociation would show that two separable late-state coordinates make " + "role-selective causal contributions to different nodes of familiar physical chains. It " + "would not establish an explicit internal graph, unique necessity, unfamiliar-law discovery, " + "or a complete reasoning trace." + ), + } + (OUT / "statistics.json").write_text(json.dumps(statistics, indent=2) + "\n") + + # Compact summary table. + summary_rows = [] + for operation in ("neutralization", "counterfactual", "restoration"): + payload = summaries[operation]["double_dissociation"] + summary_rows.append({ + "operation": operation, + "double_dissociation": payload["estimate"], + "ci95_low": payload["ci95"][0], + "ci95_high": payload["ci95"][1], + "positive_chains": payload["positive_chains"], + "total_chains": payload["total_chains"], + "exact_p_one_sided": payload["exact_sign_flip_p_one_sided"], + }) + pd.DataFrame(summary_rows).to_csv(OUT / "summary_table.csv", index=False) + + # Main journal-style figure. + plt.rcParams.update({ + "font.family": "DejaVu Sans", + "font.size": 9, + "axes.linewidth": 0.8, + "xtick.major.width": 0.8, + "ytick.major.width": 0.8, + }) + fig, axes = plt.subplots(2, 2, figsize=(11.2, 7.8)) + ax_a, ax_b, ax_c, ax_d = axes.ravel() + + # A: role-specific observational validation and cross-role readout. + labels = ["Target\naxis", "Intermediate\naxis"] + own_auc = [target_auc, intermediate_auc] + cross_auc = [cross_intermediate_on_target, cross_target_on_intermediate] + x = np.arange(2) + width = 0.34 + ax_a.bar(x - width / 2, own_auc, width, color=[COLORS["target"], COLORS["intermediate"]], label="Matched role") + ax_a.bar(x + width / 2, cross_auc, width, color="#C9CDD0", label="Other role") + ax_a.axhline(0.5, color="#777777", ls="--", lw=1) + ax_a.set_xticks(x, labels) + ax_a.set_ylim(0.35, 1.02) + ax_a.set_ylabel("Prospective held-out AUC") + ax_a.legend(frameon=False, loc="upper center", ncol=2, fontsize=8) + ax_a.text(0.98, 0.04, f"axis angle = {statistics['prospective_direction_validation']['angle_degrees']:.1f}°", transform=ax_a.transAxes, ha="right", color="#555555") + + # B: causal loss matrix. + matrix = np.zeros((2, 2)) + for row_index, question_type in enumerate(("target", "intermediate")): + group = binary[binary["question_type"] == question_type] + matrix[row_index, 0] = group["neutralize_own"].mean() + matrix[row_index, 1] = group["neutralize_matched_other"].mean() + vmax = max(np.max(np.abs(matrix)), 1e-6) + image = ax_b.imshow(matrix, cmap="PuBuGn", vmin=min(0, matrix.min()), vmax=vmax, aspect="auto") + ax_b.set_xticks([0, 1], ["Queried-role axis", "Other-role axis"]) + ax_b.set_yticks([0, 1], ["Target queried", "Intermediate queried"]) + for i in range(2): + for j in range(2): + ax_b.text(j, i, f"{matrix[i, j]:+.2f}", ha="center", va="center", color="white" if matrix[i, j] > 0.55 * vmax else "#222222", fontweight="bold") + ax_b.set_xlabel("Equal-distance coordinate intervention") + ax_b.set_title("Correct-answer evidence removed", fontsize=9, pad=7) + cbar = fig.colorbar(image, ax=ax_b, fraction=0.046, pad=0.04) + cbar.set_label("logit margin", fontsize=8) + + # C: every-chain double dissociation for three operations. + operation_order = ["Neutralization", "Counterfactual", "Restoration"] + operation_colors = [COLORS["target"], COLORS["purple"], COLORS["gold"]] + rng = np.random.default_rng(20260902) + for index, (operation, color) in enumerate(zip(operation_order, operation_colors, strict=True)): + values = chain_effects.loc[chain_effects["operation"] == operation, "double_dissociation"].to_numpy() + jitter = rng.normal(0, 0.045, size=len(values)) + ax_c.scatter(np.full(len(values), index) + jitter, values, s=24, color=color, alpha=0.58, edgecolor="white", linewidth=0.35) + estimate, low, high = bootstrap_mean(values, seed=20261200 + index) + ax_c.errorbar(index, estimate, yerr=[[estimate - low], [high - estimate]], fmt="o", color="black", mfc="white", ms=6, capsize=3, lw=1.4, zorder=5) + ax_c.axhline(0, color="#666666", lw=1) + ax_c.set_xticks(range(3), operation_order) + ax_c.set_ylabel("Own-role minus other-role effect\n(chain-level double dissociation)") + + # D: own-role intervention, other role, and activation-matched controls. + display = [] + for operation in ("neutralize", "counterfactual"): + display.extend([ + own_control_chain[f"{operation}_own"].to_numpy(), + own_control_chain[f"{operation}_wrong"].to_numpy(), + own_control_chain[f"{operation}_control"].to_numpy(), + ]) + positions = [0, 1, 2, 4, 5, 6] + colors = [COLORS["target"], "#B4C9D8", COLORS["control"], COLORS["purple"], "#C8BCE0", COLORS["control"]] + for pos, values, color in zip(positions, display, colors, strict=True): + estimate, low, high = bootstrap_mean(values, seed=20261300 + pos) + ax_d.errorbar(pos, estimate, yerr=[[estimate - low], [high - estimate]], fmt="o", color=color, mfc=color, ms=7, capsize=3, lw=1.5) + ax_d.axhline(0, color="#666666", lw=1) + ax_d.set_xticks([1, 5], ["Neutralize", "Reverse"]) + ax_d.set_ylabel("Correct-answer evidence removed\n(logit margin)") + ax_d.legend(handles=[ + Line2D([0], [0], marker="o", color="none", markerfacecolor=COLORS["target"], markeredgecolor="none", label="Matched role"), + Line2D([0], [0], marker="o", color="none", markerfacecolor="#B4C9D8", markeredgecolor="none", label="Other role"), + Line2D([0], [0], marker="o", color="none", markerfacecolor=COLORS["control"], markeredgecolor="none", label="Activation control"), + ], frameon=False, fontsize=8, loc="upper center", ncol=3) + + for label, axis in zip("ABCD", axes.ravel(), strict=True): + add_panel_label(axis, label) + axis.spines[["top", "right"]].set_visible(False) + axis.grid(axis="y", color="#E7E9EA", lw=0.7, zorder=0) + ax_b.grid(False) + fig.subplots_adjust(left=0.09, right=0.97, bottom=0.10, top=0.96, wspace=0.34, hspace=0.38) + figures = OUT / "figures" + figures.mkdir(exist_ok=True) + for suffix in ("pdf", "png", "svg"): + fig.savefig(figures / f"goal-specific-causal-double-dissociation.{suffix}", dpi=260, bbox_inches="tight", facecolor="white") + plt.close(fig) + + # Complete 24-chain audit. + audit = neutral_dd.merge( + counter_dd[["chain_id", "double_dissociation"]].rename(columns={"double_dissociation": "counterfactual"}), + on="chain_id", + ).merge( + restore_dd[["chain_id", "double_dissociation"]].rename(columns={"double_dissociation": "restoration"}), + on="chain_id", + ).rename(columns={"double_dissociation": "neutralization"}) + audit_matrix = audit[["neutralization", "counterfactual", "restoration"]].to_numpy() + fig, ax = plt.subplots(figsize=(7.2, 8.6)) + limit = max(float(np.max(np.abs(audit_matrix))), 1e-6) + im = ax.imshow(audit_matrix, cmap="PuOr", vmin=-limit, vmax=limit, aspect="auto") + ax.set_xticks(range(3), ["Neutralize", "Reverse", "Restore"]) + ax.set_yticks(range(len(audit)), [value.replace("-", " ") for value in audit["chain_id"]], fontsize=7.5) + ax.set_xlabel("Chain-level own-role minus other-role effect") + fig.colorbar(im, ax=ax, fraction=0.035, pad=0.03, label="logit margin") + ax.spines[:].set_visible(False) + fig.tight_layout() + for suffix in ("pdf", "png", "svg"): + fig.savefig(figures / f"goal-specific-causal-law-audit.{suffix}", dpi=260, bbox_inches="tight", facecolor="white") + plt.close(fig) + + report = f"""# Prospective 31B goal-specific causal-role experiment + +## Question + +Do the two late-layer directions previously associated with an intermediate +physical variable and a downstream response make *role-specific causal +contributions* on completely new two-link materials chains? + +## Frozen design + +- 12 earlier development chains define the two directions; no target state is + used for fitting. +- 24 new chains, balanced 6/6/6/6 over the four link-sign compositions. +- Two independent phrasings, both numerical directions, and target, + intermediate, and invariant questions: 288 clean prompts. +- Interventions at the answer-free question marker after zero-indexed layer 49. +- The primary unit is the physical chain. +- Four activation-derived null axes, orthogonal to the two-role span, are + applied in antithetic pairs at exactly matched norm and distance. + +## Result + +- Frozen neutralization double dissociation: **{primary['estimate']:+.3f}** + logit units (95% chain bootstrap **{primary['ci95'][0]:+.3f} to + {primary['ci95'][1]:+.3f}**; exact one-sided sign-flip + **p={primary['exact_sign_flip_p_one_sided']:.6g}**; + {primary['positive_chains']}/{primary['total_chains']} chains positive). +- Frozen primary criteria passed: **{primary_pass}**. +- Counterfactual double dissociation: + **{summaries['counterfactual']['double_dissociation']['estimate']:+.3f}**. +- Restoration double dissociation: + **{summaries['restoration']['double_dissociation']['estimate']:+.3f}**. +- Prospective target/intermediate AUC: + **{target_auc:.3f}/{intermediate_auc:.3f}**. +- Clean target/intermediate/invariant accuracy: + **{statistics['clean_behavior']['target_accuracy']:.3f}/ + {statistics['clean_behavior']['intermediate_accuracy']:.3f}/ + {statistics['clean_behavior']['invariant_accuracy']:.3f}**. + +## Interpretation boundary + +If the frozen primary passes, the result is a causal double dissociation at one +late layer and one answer-free token position: changing the intermediate +coordinate preferentially changes intermediate-variable decisions, whereas +changing the downstream coordinate preferentially changes downstream-property +decisions. It does not prove an explicit graph data structure, unique global +necessity, unfamiliar-law discovery, or a literal chain of thought. +""" + (OUT / "RESULTS.md").write_text(report) + print(json.dumps(statistics, indent=2)) + + +if __name__ == "__main__": + main() diff --git a/jlens_materials/scripts/analyze_gemma31b_query_gated_physics.py b/jlens_materials/scripts/analyze_gemma31b_query_gated_physics.py new file mode 100644 index 0000000000000000000000000000000000000000..b10086d85c394a654ca7c3a9ed658e269501e1d2 --- /dev/null +++ b/jlens_materials/scripts/analyze_gemma31b_query_gated_physics.py @@ -0,0 +1,293 @@ +#!/usr/bin/env python3 +"""Analyze whether a scientific query gates which chain consequence is represented.""" + +from __future__ import annotations + +import json +from pathlib import Path + +import matplotlib + +matplotlib.use("Agg") +import matplotlib.pyplot as plt +import numpy as np +import pandas as pd +from sklearn.feature_extraction.text import TfidfVectorizer +from sklearn.metrics import roc_auc_score + + +ROOT = Path(__file__).resolve().parents[1] +OUT = ROOT / "experiments" / "gemma4-31b-query-gated-physics-2026-08-25" + + +def unit_rows(values: np.ndarray) -> np.ndarray: + values = np.asarray(values, dtype=np.float64) + return values / np.maximum(np.linalg.norm(values, axis=1, keepdims=True), 1e-12) + + +def fit_direction(x: np.ndarray, y: np.ndarray) -> tuple[np.ndarray, np.ndarray]: + x = unit_rows(x) + positive = x[y == 1].mean(axis=0) + negative = x[y == -1].mean(axis=0) + direction = positive - negative + direction /= max(np.linalg.norm(direction), 1e-12) + return direction, 0.5 * (positive + negative) + + +def score(x: np.ndarray, direction: np.ndarray, midpoint: np.ndarray) -> np.ndarray: + return np.einsum("ij,j->i", unit_rows(x) - midpoint, direction, optimize=True) + + +def evaluate(train_x: np.ndarray, train_y: np.ndarray, test_x: np.ndarray, test_y: np.ndarray) -> dict: + direction, midpoint = fit_direction(train_x, train_y) + values = score(test_x, direction, midpoint) + return { + "auc": float(roc_auc_score(test_y == 1, values)), + "accuracy": float(np.mean((values > 0) == (test_y == 1))), + "scores": values, + "direction": direction, + "midpoint": midpoint, + } + + +def lexical(meta: pd.DataFrame, analyzer: str) -> dict: + binary = meta["query_sign"].ne(0).to_numpy() + development = meta["split"].eq("development").to_numpy() & binary + test = meta["split"].eq("test").to_numpy() & binary + prefixes = np.asarray([text.split("†", 1)[0] + "†" for text in meta["user"]]) + if analyzer == "word": + vectorizer = TfidfVectorizer(ngram_range=(1, 2), sublinear_tf=True) + else: + vectorizer = TfidfVectorizer(analyzer="char", ngram_range=(3, 5), min_df=2, sublinear_tf=True) + train_x = vectorizer.fit_transform(prefixes[development]).toarray() + test_x = vectorizer.transform(prefixes[test]).toarray() + result = evaluate( + train_x, + meta.loc[development, "query_sign"].to_numpy(dtype=int), + test_x, + meta.loc[test, "query_sign"].to_numpy(dtype=int), + ) + return {"auc": result["auc"], "accuracy": result["accuracy"]} + + +def main() -> None: + protocol = json.loads((OUT / "protocol.json").read_text()) + manifest = json.loads((OUT / "prompt_manifest.json").read_text()) + raw = json.loads((OUT / "raw.json").read_text()) + states = np.load(OUT / "states.npy", mmap_mode="r") + meta = pd.DataFrame(manifest["prompts"]).reset_index(names="row_index") + rows = pd.DataFrame(raw["rows"]) + layers = list(protocol["layers_zero_indexed"]) + positions = list(protocol["positions"]) + question_index = positions.index("question") + perturbation_index = positions.index("perturbation") + + binary = meta["query_sign"].ne(0).to_numpy() + development = meta["split"].eq("development").to_numpy() + test = meta["split"].eq("test").to_numpy() + dev_binary = development & binary + test_binary = test & binary + + layer_rows = [] + cached = {} + for layer_index, layer in enumerate(layers): + for question_type in ("pooled", "target", "intermediate"): + if question_type == "pooled": + train_mask, test_mask = dev_binary, test_binary + else: + selected = meta["question_type"].eq(question_type).to_numpy() + train_mask, test_mask = development & selected, test & selected + result = evaluate( + states[question_index, train_mask, layer_index], + meta.loc[train_mask, "query_sign"].to_numpy(dtype=int), + states[question_index, test_mask, layer_index], + meta.loc[test_mask, "query_sign"].to_numpy(dtype=int), + ) + cached[(layer, question_type)] = result + layer_rows.append( + { + "layer": layer, + "question_type": question_type, + "auc": result["auc"], + "accuracy": result["accuracy"], + } + ) + layer_table = pd.DataFrame(layer_rows) + layer_table.to_csv(OUT / "layerwise_query_decoding.csv", index=False) + + layer49_index = layers.index(49) + pooled = cached[(49, "pooled")] + target = cached[(49, "target")] + intermediate = cached[(49, "intermediate")] + direction_cosine = float(np.einsum("i,i->", target["direction"], intermediate["direction"])) + + # The perturbation marker is causally before the question and identical in each triplet. + earlier = evaluate( + states[perturbation_index, dev_binary, layer49_index], + meta.loc[dev_binary, "query_sign"].to_numpy(dtype=int), + states[perturbation_index, test_binary, layer49_index], + meta.loc[test_binary, "query_sign"].to_numpy(dtype=int), + ) + + # Score every held-out question using the pooled queried-outcome direction. + heldout = meta.loc[test].reset_index(drop=True) + heldout_scores = score( + states[question_index, test, layer49_index], pooled["direction"], pooled["midpoint"] + ) + heldout["query_outcome_score"] = heldout_scores + heldout.to_csv(OUT / "heldout_query_scores.csv", index=False) + + switches = [] + for (chain_id, numerical_sign), group in heldout.groupby(["chain_id", "numerical_sign"], sort=True): + target_row = group[group["question_type"] == "target"].iloc[0] + intermediate_row = group[group["question_type"] == "intermediate"].iloc[0] + invariant_row = group[group["question_type"] == "invariant"].iloc[0] + same_expected = int(target_row.link2_sign) == 1 + same_observed = np.sign(target_row.query_outcome_score) == np.sign(intermediate_row.query_outcome_score) + switches.append( + { + "chain_id": chain_id, + "numerical_sign": int(numerical_sign), + "link2_sign": int(target_row.link2_sign), + "target_score": float(target_row.query_outcome_score), + "intermediate_score": float(intermediate_row.query_outcome_score), + "invariant_score": float(invariant_row.query_outcome_score), + "same_sign_expected": same_expected, + "same_sign_observed": bool(same_observed), + "switch_correct": bool(same_expected == same_observed), + } + ) + switch_table = pd.DataFrame(switches) + switch_table.to_csv(OUT / "query_switch_pairs.csv", index=False) + + invariant_scores = heldout.loc[heldout["question_type"] == "invariant", "query_outcome_score"].to_numpy() + binary_scores = heldout.loc[heldout["question_type"].isin(["target", "intermediate"]), "query_outcome_score"].to_numpy() + invariant = { + "median_absolute_score": float(np.median(np.abs(invariant_scores))), + "binary_median_absolute_score": float(np.median(np.abs(binary_scores))), + "ratio_to_binary": float( + np.median(np.abs(invariant_scores)) / max(np.median(np.abs(binary_scores)), 1e-12) + ), + } + + lexical_controls = {name: lexical(meta, name) for name in ("word", "character")} + binary_rows = rows[(rows["split"] == "test") & rows["question_type"].isin(["target", "intermediate"])] + behavior = { + "overall_pairwise_accuracy": float(binary_rows["binary_pairwise_correct"].mean()), + "target_accuracy": float(binary_rows.loc[binary_rows["question_type"] == "target", "binary_pairwise_correct"].mean()), + "intermediate_accuracy": float(binary_rows.loc[binary_rows["question_type"] == "intermediate", "binary_pairwise_correct"].mean()), + } + + criteria = protocol["success_criteria"] + passed = bool( + pooled["auc"] >= criteria["pooled_query_outcome_auc_minimum"] + and min(target["auc"], intermediate["auc"]) + >= criteria["target_and_intermediate_auc_minimum"] + and switch_table["switch_correct"].mean() + >= criteria["query_switch_consistency_minimum"] + ) + statistics = { + "study_id": protocol["study_id"], + "analysis_status": "frozen query-gating analysis", + "primary": { + "pooled_queried_variable_auc": pooled["auc"], + "accuracy": pooled["accuracy"], + "passed_all_frozen_criteria": passed, + }, + "question_specific": { + "target_auc": target["auc"], + "intermediate_auc": intermediate["auc"], + "target_vs_intermediate_direction_cosine": direction_cosine, + }, + "causally_earlier_perturbation_marker": { + "pooled_query_outcome_auc": earlier["auc"], + "accuracy": earlier["accuracy"], + }, + "query_switch": { + "correct_pairs": int(switch_table["switch_correct"].sum()), + "total_pairs": int(len(switch_table)), + "accuracy": float(switch_table["switch_correct"].mean()), + "link2_positive_accuracy": float(switch_table.loc[switch_table["link2_sign"] == 1, "switch_correct"].mean()), + "link2_negative_accuracy": float(switch_table.loc[switch_table["link2_sign"] == -1, "switch_correct"].mean()), + }, + "invariant_question": invariant, + "lexical_controls": lexical_controls, + "behavior": behavior, + "guardrail": ( + "Query-dependent decoding supports task-gated selection of a relational consequence. " + "It does not establish an explicit graph data structure or a literal reasoning trace." + ), + } + (OUT / "statistics.json").write_text(json.dumps(statistics, indent=2) + "\n") + + fig, axes = plt.subplots(2, 2, figsize=(11.6, 8.0)) + ax = axes[0, 0] + for question_type, color in (("pooled", "#239B8F"), ("target", "#2E7E9E"), ("intermediate", "#7153A2")): + selected = layer_table[layer_table["question_type"] == question_type] + ax.plot(selected["layer"], selected["auc"], marker="o", color=color, label=question_type) + ax.axhline(0.5, color="#777777", ls="--", lw=1) + ax.set_ylim(0.4, 1.03) + ax.set_xlabel("layer (0-indexed)") + ax.set_ylabel("held-out queried-variable AUC") + ax.legend(frameon=False) + + ax = axes[0, 1] + colors = np.where(switch_table["link2_sign"] > 0, "#239B8F", "#7153A2") + ax.scatter(switch_table["intermediate_score"], switch_table["target_score"], c=colors, s=46, edgecolor="white", linewidth=0.5) + ax.axhline(0, color="#999999", lw=0.8) + ax.axvline(0, color="#999999", lw=0.8) + ax.set_xlabel("score when intermediate is queried") + ax.set_ylabel("score when final response is queried") + ax.text(0.04, 0.96, f"query-switch consistency = {switch_table['switch_correct'].mean():.3f}", transform=ax.transAxes, va="top") + + ax = axes[1, 0] + plotting = [ + heldout.loc[heldout["question_type"] == question_type, "query_outcome_score"].to_numpy() + for question_type in ("intermediate", "target", "invariant") + ] + ax.boxplot(plotting, tick_labels=["intermediate", "final response", "invariant mass"], showfliers=False) + ax.axhline(0, color="#777777", ls="--", lw=1) + ax.set_ylabel("pooled queried-outcome score") + + ax = axes[1, 1] + names = ["after\nperturbation", "after\nquestion", "word\nTF-IDF", "character\nTF-IDF", "model\nanswer"] + values = [earlier["auc"], pooled["auc"], lexical_controls["word"]["auc"], lexical_controls["character"]["auc"], behavior["overall_pairwise_accuracy"]] + ax.bar(names, values, color=["#999999", "#239B8F", "#AAAAAA", "#777777", "#D29B35"]) + ax.axhline(0.5, color="#777777", ls="--", lw=1) + ax.set_ylim(0.35, 1.03) + ax.set_ylabel("AUC or accuracy") + + for label, axis in zip("ABCD", axes.ravel(), strict=True): + axis.text(-0.12, 1.04, label, transform=axis.transAxes, fontweight="bold") + axis.spines[["top", "right"]].set_visible(False) + fig.tight_layout(w_pad=2.0, h_pad=2.0) + figures = OUT / "figures" + figures.mkdir(exist_ok=True) + for suffix in ("png", "pdf", "svg"): + fig.savefig(figures / f"gemma31b-query-gated-physics.{suffix}", dpi=240, bbox_inches="tight") + plt.close(fig) + + report = f"""# 31B query-gated physics result + +The target, intermediate, and invariant prompts share an exact prefix through +the physical perturbation. They differ only in which node of the same +two-link mechanism is queried. The answer-free marker follows that question. + +- Pooled held-out queried-variable AUC: **{pooled['auc']:.3f}** +- Target/intermediate AUC: **{target['auc']:.3f}/{intermediate['auc']:.3f}** +- AUC at the causally earlier perturbation marker: **{earlier['auc']:.3f}** +- Query-switch consistency: **{switch_table['switch_correct'].mean():.3f}** +- Word/character TF-IDF AUC: **{lexical_controls['word']['auc']:.3f}/{lexical_controls['character']['auc']:.3f}** +- Binary answer accuracy: **{behavior['overall_pairwise_accuracy']:.3f}** +- Passed all frozen pilot criteria: **{passed}** + +The result tests goal-dependent selection of a consequence from a familiar +physical chain. It does not prove that the model stores an explicit graph or +that the decoded direction is causally necessary. +""" + (OUT / "REPORT.md").write_text(report) + print(json.dumps(statistics, indent=2)) + + +if __name__ == "__main__": + main() diff --git a/jlens_materials/scripts/analyze_gemma31b_query_gated_physics_posthoc.py b/jlens_materials/scripts/analyze_gemma31b_query_gated_physics_posthoc.py new file mode 100644 index 0000000000000000000000000000000000000000..80354b177a31a768b43d039ba2491e58d7053e32 --- /dev/null +++ b/jlens_materials/scripts/analyze_gemma31b_query_gated_physics_posthoc.py @@ -0,0 +1,219 @@ +#!/usr/bin/env python3 +"""Role-specific post hoc audit of the frozen 31B query-gating run.""" + +from __future__ import annotations + +import itertools +import json +from pathlib import Path + +import matplotlib + +matplotlib.use("Agg") +import matplotlib.pyplot as plt +import numpy as np +import pandas as pd +from sklearn.feature_extraction.text import TfidfVectorizer +from sklearn.metrics import roc_auc_score + + +ROOT = Path(__file__).resolve().parents[1] +OUT = ROOT / "experiments" / "gemma4-31b-query-gated-physics-2026-08-25" + +from analyze_gemma31b_query_gated_physics import evaluate, score # noqa: E402 + + +def exact_null(meta: pd.DataFrame, scores: np.ndarray) -> tuple[np.ndarray, float]: + chains = sorted(meta["chain_id"].unique()) + values = [] + for selected_indices in itertools.combinations(range(len(chains)), len(chains) // 2): + selected = {chains[index] for index in selected_indices} + parity = np.asarray([1 if chain in selected else -1 for chain in meta["chain_id"]]) + labels = parity * meta["numerical_sign"].to_numpy(dtype=int) + values.append(float(roc_auc_score(labels == 1, scores))) + values = np.asarray(values) + observed_labels = meta["query_sign"].to_numpy(dtype=int) + observed = float(roc_auc_score(observed_labels == 1, scores)) + return values, float(np.mean(values >= observed - 1e-12)) + + +def lexical(meta: pd.DataFrame, analyzer: str) -> dict: + development = meta["split"].eq("development").to_numpy() + test = ~development + prefixes = np.asarray([text.split("†", 1)[0] + "†" for text in meta["user"]]) + if analyzer == "word": + vectorizer = TfidfVectorizer(ngram_range=(1, 2), sublinear_tf=True) + else: + vectorizer = TfidfVectorizer(analyzer="char", ngram_range=(3, 5), min_df=2, sublinear_tf=True) + train_x = vectorizer.fit_transform(prefixes[development]).toarray() + test_x = vectorizer.transform(prefixes[test]).toarray() + result = evaluate( + train_x, + meta.loc[development, "query_sign"].to_numpy(dtype=int), + test_x, + meta.loc[test, "query_sign"].to_numpy(dtype=int), + ) + return {"auc": result["auc"], "accuracy": result["accuracy"]} + + +def main() -> None: + protocol = json.loads((OUT / "protocol.json").read_text()) + manifest = json.loads((OUT / "prompt_manifest.json").read_text()) + frozen = json.loads((OUT / "statistics.json").read_text()) + states = np.load(OUT / "states.npy", mmap_mode="r") + meta = pd.DataFrame(manifest["prompts"]) + question_index = protocol["positions"].index("question") + layer_index = protocol["layers_zero_indexed"].index(49) + + results = {} + nulls = {} + controls = {} + for question_type in ("target", "intermediate"): + selected = meta["question_type"].eq(question_type).to_numpy() + development = meta["split"].eq("development").to_numpy() & selected + test = meta["split"].eq("test").to_numpy() & selected + result = evaluate( + states[question_index, development, layer_index], + meta.loc[development, "query_sign"].to_numpy(dtype=int), + states[question_index, test, layer_index], + meta.loc[test, "query_sign"].to_numpy(dtype=int), + ) + test_meta = meta.loc[test].reset_index(drop=True) + null_values, p_value = exact_null(test_meta, result["scores"]) + results[question_type] = result + nulls[question_type] = {"values": null_values, "p": p_value} + role_meta = meta.loc[selected].reset_index(drop=True) + controls[question_type] = { + analyzer: lexical(role_meta, analyzer) + for analyzer in ("word", "character") + } + + cosine = float( + np.einsum("i,i->", results["target"]["direction"], results["intermediate"]["direction"]) + ) + angle = float(np.degrees(np.arccos(np.clip(cosine, -1.0, 1.0)))) + target_dev = meta["split"].eq("development").to_numpy() & meta["question_type"].eq("target").to_numpy() + target_test = meta["split"].eq("test").to_numpy() & meta["question_type"].eq("target").to_numpy() + intermediate_dev = meta["split"].eq("development").to_numpy() & meta["question_type"].eq("intermediate").to_numpy() + intermediate_test = meta["split"].eq("test").to_numpy() & meta["question_type"].eq("intermediate").to_numpy() + target_on_intermediate_scores = score( + states[question_index, intermediate_test, layer_index], + results["target"]["direction"], + results["target"]["midpoint"], + ) + intermediate_on_target_scores = score( + states[question_index, target_test, layer_index], + results["intermediate"]["direction"], + results["intermediate"]["midpoint"], + ) + cross = { + "target_direction_on_intermediate_auc": float( + roc_auc_score( + meta.loc[intermediate_test, "query_sign"].to_numpy(dtype=int) == 1, + target_on_intermediate_scores, + ) + ), + "intermediate_direction_on_target_auc": float( + roc_auc_score( + meta.loc[target_test, "query_sign"].to_numpy(dtype=int) == 1, + intermediate_on_target_scores, + ) + ), + } + + payload = { + "study_id": protocol["study_id"], + "status": "post hoc role-specific analysis; frozen pooled primary remains failed", + "frozen_primary_passed": frozen["primary"]["passed_all_frozen_criteria"], + "role_specific": { + role: { + "auc": results[role]["auc"], + "accuracy": results[role]["accuracy"], + "exact_balanced_chain_assignments": int(len(nulls[role]["values"])), + "exact_p_one_sided": nulls[role]["p"], + "word_tfidf_auc": controls[role]["word"]["auc"], + "character_tfidf_auc": controls[role]["character"]["auc"], + } + for role in ("target", "intermediate") + }, + "geometry": { + "target_intermediate_direction_cosine": cosine, + "angle_degrees": angle, + **cross, + }, + "interpretation": ( + "The queried outcome generalizes within each causal role, but a single pooled " + "axis and the preregistered sign-switch test fail. This is evidence for " + "query-role-conditioned geometry, not a universal graph coordinate." + ), + } + (OUT / "posthoc_role_specific_statistics.json").write_text(json.dumps(payload, indent=2) + "\n") + + fig, axes = plt.subplots(1, 3, figsize=(12.7, 3.8)) + axes[0].bar( + ["frozen pooled", "target role", "intermediate role", "target word", "target char", "intermediate word", "intermediate char"], + [ + frozen["primary"]["pooled_queried_variable_auc"], + results["target"]["auc"], + results["intermediate"]["auc"], + controls["target"]["word"]["auc"], + controls["target"]["character"]["auc"], + controls["intermediate"]["word"]["auc"], + controls["intermediate"]["character"]["auc"], + ], + color=["#999999", "#2E7E9E", "#7153A2", "#BBBBBB", "#888888", "#BBBBBB", "#888888"], + ) + axes[0].axhline(0.5, color="#666666", ls="--", lw=1) + axes[0].set_ylim(0.25, 1.0) + axes[0].set_ylabel("held-out AUC") + axes[0].tick_params(axis="x", rotation=55) + + for role, color in (("target", "#2E7E9E"), ("intermediate", "#7153A2")): + axes[1].hist(nulls[role]["values"], bins=np.linspace(0.2, 0.85, 28), alpha=0.55, color=color, label=role) + axes[1].axvline(results[role]["auc"], color=color, lw=2) + axes[1].set_xlabel("AUC under balanced chain-label assignments") + axes[1].set_ylabel("exact assignments") + axes[1].legend(frameon=False) + + axes[2].bar( + ["direction\ncosine", "target →\nintermediate AUC", "intermediate →\ntarget AUC"], + [cosine, cross["target_direction_on_intermediate_auc"], cross["intermediate_direction_on_target_auc"]], + color=["#555555", "#2E7E9E", "#7153A2"], + ) + axes[2].axhline(0.5, color="#777777", ls="--", lw=1) + axes[2].set_ylim(0.0, 1.0) + axes[2].text(0, cosine + 0.04, f"{angle:.1f}°", ha="center") + axes[2].set_ylabel("cosine or cross-role AUC") + + for label, axis in zip("ABC", axes, strict=True): + axis.text(-0.14, 1.04, label, transform=axis.transAxes, fontweight="bold") + axis.spines[["top", "right"]].set_visible(False) + fig.tight_layout(w_pad=2.2) + figures = OUT / "figures" + for suffix in ("png", "pdf", "svg"): + fig.savefig(figures / f"gemma31b-query-role-posthoc.{suffix}", dpi=240, bbox_inches="tight") + plt.close(fig) + + report = f"""# Post hoc query-role audit + +The frozen pooled-axis hypothesis failed and remains recorded as failed. This +post hoc audit asks why. Separate development-only directions were fitted for +the downstream target role and intermediate-node role, then evaluated on the +same disjoint test chains. + +- Target-role AUC: **{results['target']['auc']:.3f}**, exact p **{nulls['target']['p']:.5f}** +- Intermediate-role AUC: **{results['intermediate']['auc']:.3f}**, exact p **{nulls['intermediate']['p']:.5f}** +- Target word/character controls: **{controls['target']['word']['auc']:.3f}/{controls['target']['character']['auc']:.3f}** +- Intermediate word/character controls: **{controls['intermediate']['word']['auc']:.3f}/{controls['intermediate']['character']['auc']:.3f}** +- Direction cosine: **{cosine:.3f}** (angle **{angle:.1f} degrees**) + +The result supports role-conditioned relational geometry. It does not rescue +the failed universal pooled axis, prove an explicit internal graph, or establish +causal necessity. +""" + (OUT / "POSTHOC_ROLE_SPECIFIC.md").write_text(report) + print(json.dumps(payload, indent=2)) + + +if __name__ == "__main__": + main() diff --git a/jlens_materials/scripts/analyze_gemma_auxiliary_lens_runs.py b/jlens_materials/scripts/analyze_gemma_auxiliary_lens_runs.py new file mode 100644 index 0000000000000000000000000000000000000000..18a3092c0a785e15c0dbbdc018e8d02d990481a2 --- /dev/null +++ b/jlens_materials/scripts/analyze_gemma_auxiliary_lens_runs.py @@ -0,0 +1,611 @@ +#!/usr/bin/env python3 +"""Analyze auxiliary 12B lens runs without changing the manuscript. + +This script covers three exploratory datasets collected alongside the formal +held-out scale replication: + +1. the 50-prompt mechanism-name cohort; +2. six detailed concept-trace prompts; and +3. the five-prompt verbal-report swap dose series. + +The 12B estimates use three independent paper-recipe lens fits. The available +31B estimates use one fit. Older 4B records are retained only as a visibly +labelled legacy single-fit descriptive reference because they predate strict +provenance metadata. +""" + +from __future__ import annotations + +import csv +import glob +import hashlib +import json +import math +import sys +from collections import defaultdict +from pathlib import Path + +import matplotlib.pyplot as plt +import numpy as np +from scipy.stats import spearmanr + + +ROOT = Path(__file__).resolve().parents[1] +sys.path[:0] = [str(ROOT), str(ROOT / "scripts")] + +import analyze_materials_heldout_v1 as heldout # noqa: E402 +import analyze_gemma_scale_heldout_v1 as scale31 # noqa: E402 + + +OUT = ROOT / "experiments" / "gemma-auxiliary-lens-runs-2026-08-25" +FIGURES = OUT / "figures" +RNG_SEED = 20260825 +N_BOOT = 20_000 + +COLORS = {"4B legacy": "#8A949B", "12B": "#D39A2C", "31B": "#584A9B"} +DIRECT = "#66737B" +NEGATIVE = "#C46C5A" +TEAL = "#087F8C" + + +def sha256(path: Path) -> str: + return hashlib.sha256(path.read_bytes()).hexdigest() + + +def write_csv(path: Path, rows: list[dict]) -> None: + if not rows: + raise ValueError(f"refusing to write empty CSV: {path}") + path.parent.mkdir(parents=True, exist_ok=True) + with path.open("w", newline="") as handle: + writer = csv.DictWriter(handle, fieldnames=list(rows[0])) + writer.writeheader() + writer.writerows(rows) + + +def save_figure(figure: plt.Figure, stem: Path) -> None: + stem.parent.mkdir(parents=True, exist_ok=True) + for suffix in ("png", "pdf", "svg"): + figure.savefig( + stem.with_suffix(f".{suffix}"), + dpi=300, + bbox_inches="tight", + facecolor="white", + ) + plt.close(figure) + + +def panel_label(axis: plt.Axes, label: str) -> None: + axis.text( + -0.11, + 1.045, + label, + transform=axis.transAxes, + fontweight="bold", + fontsize=11, + ha="left", + va="bottom", + clip_on=False, + ) + + +def index_association(run: dict) -> dict[str, dict]: + return { + row["slug"]: row + for row in run["prompts"] + if row.get("shape") == "ASSOCIATION" + } + + +def validate_12b_association(paths: list[Path], expected_count: int) -> tuple: + runs = [json.loads(path.read_text()) for path in paths] + indexes = [index_association(run) for run in runs] + slugs = list(indexes[0]) + if len(paths) != 3 or len(slugs) != expected_count: + raise ValueError("12B association suite has the wrong fit or prompt count") + for seed, (run, index) in enumerate(zip(runs, indexes, strict=True)): + if run.get("model") != "google/gemma-4-12B-it" or run.get("errors"): + raise ValueError(f"12B seed {seed}: invalid model or run errors") + if list(index) != slugs: + raise ValueError(f"12B seed {seed}: prompt order differs") + provenance = run.get("lens_provenance", {}) + if provenance.get("target_layer") != 46: + raise ValueError(f"12B seed {seed}: wrong target layer") + if provenance.get("corpus", {}).get("seed") != seed: + raise ValueError(f"12B seed {seed}: lens corpus seed mismatch") + if provenance.get("recipe", {}).get("n_fit") != 1000: + raise ValueError(f"12B seed {seed}: lens is not paper fitted") + if not all(row.get("valid_for_metrics") for row in index.values()): + raise ValueError(f"12B seed {seed}: invalid prompt") + return runs, slugs, indexes + + +def validate_single_association( + path: Path, slugs: list[str], expected_model: str +) -> tuple[dict, dict[str, dict]]: + run = json.loads(path.read_text()) + index = index_association(run) + if run.get("model") != expected_model or run.get("errors"): + raise ValueError(f"invalid single-fit run: {path}") + if list(index) != slugs: + raise ValueError(f"single-fit prompt cohort differs: {path}") + return run, index + + +def single_rows( + run: dict, slugs: list[str], index: dict[str, dict] +) -> tuple[list[dict], list[dict], list[int]]: + return scale31.build_31b_rows(run, slugs, index) + + +def multi_rows( + runs: list[dict], slugs: list[str], indexes: list[dict[str, dict]] +) -> tuple[list[dict], list[dict], list[int]]: + return heldout.build_controlled_rows(runs, slugs, indexes) + + +def standardize_prompt_rows( + rows: list[dict], scale: str, *, multi: bool +) -> list[dict]: + j_key = "j_auc_mean_seed" if multi else "j_auc" + d_key = "logit_auc" if multi else "direct_auc" + return [ + { + "slug": row["slug"], + "family": row["family"], + "scale": scale, + "jacobian_auc": float(row[j_key]), + "direct_auc": float(row[d_key]), + "delta_auc": float(row[j_key]) - float(row[d_key]), + } + for row in rows + ] + + +def mechanism_name_analysis() -> dict: + paths12 = [ + ROOT / "runs" / f"gemma4-12b-it-mechanism-name-v1-seed{seed}.json" + for seed in range(3) + ] + path31 = ROOT / "runs" / "gemma4-31b-it-mechanism-name-v1-seed0.json" + path4 = ROOT / "runs" / "gemma4-materials-mechanism-name-v1.json" + runs12, slugs, indexes12 = validate_12b_association(paths12, 50) + run31, index31 = validate_single_association( + path31, slugs, "google/gemma-4-31B-it" + ) + run4, index4 = validate_single_association( + path4, slugs, "google/gemma-4-E4B-it" + ) + prompt12, concept12, ks12 = multi_rows(runs12, slugs, indexes12) + prompt31, concept31, ks31 = single_rows(run31, slugs, index31) + prompt4, concept4, ks4 = single_rows(run4, slugs, index4) + if ks12 != ks31 or ks12 != ks4: + raise ValueError("mechanism-name cutoffs differ across scales") + standardized = { + "4B legacy": standardize_prompt_rows(prompt4, "4B legacy", multi=False), + "12B": standardize_prompt_rows(prompt12, "12B", multi=True), + "31B": standardize_prompt_rows(prompt31, "31B", multi=False), + } + controlled12 = heldout.build_controlled_statistics( + runs12, prompt12, concept12, ks12 + ) + stats = { + "status": "exploratory; not inserted into manuscript", + "inputs": { + "4B legacy": {"path": str(path4.relative_to(ROOT)), "sha256": sha256(path4)}, + "12B": [ + {"path": str(path.relative_to(ROOT)), "sha256": sha256(path)} + for path in paths12 + ], + "31B": {"path": str(path31.relative_to(ROOT)), "sha256": sha256(path31)}, + }, + "legacy_boundary": ( + "The 4B record predates strict model-revision and lens-provenance " + "metadata and is descriptive only." + ), + "scales": {}, + "12B_lens_fit_reproducibility": controlled12["seed_rank_correlations"], + } + pass_at_k = { + "4B legacy": { + "jacobian": run4["metrics"]["by_shape"]["ASSOCIATION"]["jacobian_lens"]["pass_at_k"], + "direct": run4["metrics"]["by_shape"]["ASSOCIATION"]["logit_lens"]["pass_at_k"], + }, + "12B": { + "jacobian": controlled12["pass_at_k"]["j_mean"], + "direct": controlled12["pass_at_k"]["logit"], + }, + "31B": { + "jacobian": run31["metrics"]["by_shape"]["ASSOCIATION"]["jacobian_lens"]["pass_at_k"], + "direct": run31["metrics"]["by_shape"]["ASSOCIATION"]["logit_lens"]["pass_at_k"], + }, + } + for scale_index, scale in enumerate(("4B legacy", "12B", "31B")): + rows = standardized[scale] + stats["scales"][scale] = { + method: scale31.hierarchical_bootstrap( + rows, f"{method}_auc", seed=RNG_SEED + 10 * scale_index + offset + ) + for offset, method in enumerate(("jacobian", "direct", "delta")) + } + stats["scales"][scale]["pass_at_k"] = pass_at_k[scale] + stats["scales"][scale]["families"] = scale31.family_statistics( + rows, "jacobian_auc", "direct_auc" + ) + all_rows = [row for rows in standardized.values() for row in rows] + write_csv(OUT / "mechanism_name_prompt_results.csv", all_rows) + + plt.rcParams.update( + {"font.family": "DejaVu Sans", "font.size": 9.2, "axes.spines.top": False, "axes.spines.right": False} + ) + figure, axes = plt.subplots(2, 2, figsize=(10.8, 7.6), constrained_layout=True) + axes = axes.ravel() + for axis, method, label in zip(axes[:2], ("jacobian", "direct"), ("A", "B"), strict=True): + for scale in ("4B legacy", "12B", "31B"): + axis.plot( + ks12, + stats["scales"][scale]["pass_at_k"][method], + marker="o" if method == "jacobian" else "s", + color=COLORS[scale], + linewidth=2, + markersize=4.5, + label=(scale + (" (3-fit mean)" if scale == "12B" else " (single fit)")), + ) + axis.set_xscale("log") + axis.set_xlabel("Vocabulary cutoff $k$") + axis.set_ylabel("Fraction of declared concepts within top $k$") + axis.grid(True, alpha=0.18) + axis.legend(frameon=False, fontsize=7.8, loc="upper left") + panel_label(axis, label) + axis = axes[2] + x = np.arange(3) + for index, scale in enumerate(("4B legacy", "12B", "31B")): + j = stats["scales"][scale]["jacobian"] + d = stats["scales"][scale]["direct"] + axis.plot([index - .12, index + .12], [j["observed_mean"], d["observed_mean"]], color="#C6CCD0", linewidth=1.1) + axis.scatter(index - .12, j["observed_mean"], s=45, color=COLORS[scale], zorder=3) + axis.scatter(index + .12, d["observed_mean"], s=45, marker="s", facecolor="white", edgecolor=DIRECT, zorder=3) + axis.scatter([], [], color=TEAL, label="Jacobian lens") + axis.scatter([], [], marker="s", facecolor="white", edgecolor=DIRECT, label="Direct") + axis.set_xticks(x, ["4B legacy", "12B", "31B"]) + axis.set_ylabel("Controlled recovery AUC") + axis.grid(True, axis="y", alpha=0.18) + axis.legend(frameon=False, loc="upper left") + panel_label(axis, "C") + axis = axes[3] + width = .24 + families = sorted(stats["scales"]["12B"]["families"]) + y = np.arange(len(families)) + for shift, scale in [(-width / 2, "12B"), (width / 2, "31B")]: + values = [stats["scales"][scale]["families"][family]["delta_auc_mean"] for family in families] + axis.scatter(values, y + shift, s=42, color=COLORS[scale], label=scale, zorder=3) + axis.axvline(0, color=DIRECT, linewidth=.9) + axis.set_yticks(y, [family.replace("-", " ") for family in families]) + axis.set_xlabel("Jacobian minus direct AUC") + axis.grid(True, axis="x", alpha=0.18) + axis.legend( + frameon=False, + loc="upper center", + bbox_to_anchor=(0.5, -0.16), + ncol=2, + ) + panel_label(axis, "D") + save_figure(figure, FIGURES / "mechanism-name-scale-analysis") + return stats + + +def extract_concept_rows(run: dict, scale: str, seed: int | None) -> list[dict]: + output = [] + for prompt in run["prompts"]: + for item in prompt["emergence"]: + output.append( + { + "scale": scale, + "seed": seed if seed is not None else 0, + "slug": prompt["slug"], + "family": prompt.get("target_family") or prompt.get("category"), + "concept": item["label"], + "jacobian_rank": int(item["best_rank"]) + 1, + "direct_rank": int(item["logit_lens_best_rank"]) + 1, + "jacobian_best_depth": float(item["best_depth"]), + } + ) + return output + + +def key_trace_analysis() -> dict: + paths12 = [ + ROOT / "runs" / f"gemma4-12b-it-key-concept-traces-v1-seed{seed}.json" + for seed in range(3) + ] + path31 = ROOT / "runs" / "gemma4-31b-it-key-concept-traces-v1-seed0.json" + path4 = ROOT / "runs" / "gemma4-materials-key-concept-traces-v1.json" + runs12, slugs, _ = validate_12b_association(paths12, 6) + run31, _ = validate_single_association(path31, slugs, "google/gemma-4-31B-it") + run4, _ = validate_single_association(path4, slugs, "google/gemma-4-E4B-it") + rows = extract_concept_rows(run4, "4B legacy", None) + for seed, run in enumerate(runs12): + rows.extend(extract_concept_rows(run, "12B", seed)) + rows.extend(extract_concept_rows(run31, "31B", None)) + write_csv(OUT / "key_concept_trace_results.csv", rows) + + lookup: dict[tuple[str, str, str], list[dict]] = defaultdict(list) + for row in rows: + lookup[(row["scale"], row["slug"], row["concept"])].append(row) + summary_rows = [] + for scale in ("4B legacy", "12B", "31B"): + for slug in slugs: + candidates = [] + concepts = sorted({key[2] for key in lookup if key[:2] == (scale, slug)}) + for concept in concepts: + members = lookup[(scale, slug, concept)] + candidates.append( + { + "concept": concept, + "jacobian_rank": float(np.exp(np.mean(np.log([m["jacobian_rank"] for m in members])))), + "direct_rank": float(members[0]["direct_rank"]), + "depth": float(np.mean([m["jacobian_best_depth"] for m in members])), + } + ) + best_j = min(candidates, key=lambda row: row["jacobian_rank"]) + best_d = min(candidates, key=lambda row: row["direct_rank"]) + summary_rows.append( + { + "scale": scale, + "slug": slug, + "best_jacobian_concept": best_j["concept"], + "best_jacobian_rank": best_j["jacobian_rank"], + "best_jacobian_depth": best_j["depth"], + "best_direct_concept": best_d["concept"], + "best_direct_rank": best_d["direct_rank"], + } + ) + write_csv(OUT / "key_concept_trace_prompt_summary.csv", summary_rows) + + columns = [ + ("4B legacy", "jacobian"), ("4B legacy", "direct"), + ("12B", "jacobian"), ("12B", "direct"), + ("31B", "jacobian"), ("31B", "direct"), + ] + matrix = np.zeros((len(slugs), len(columns))) + labels = [["" for _ in columns] for _ in slugs] + summary_index = {(row["scale"], row["slug"]): row for row in summary_rows} + for row_index, slug in enumerate(slugs): + for col_index, (scale, method) in enumerate(columns): + row = summary_index[(scale, slug)] + rank = row[f"best_{method}_rank"] + concept = row[f"best_{method}_concept"] + matrix[row_index, col_index] = math.log10(rank) + labels[row_index][col_index] = f"{concept}\n{rank:.0f}" + figure, axis = plt.subplots(figsize=(10.8, 5.5), constrained_layout=True) + image = axis.imshow(matrix, aspect="auto", cmap="viridis_r", vmin=0, vmax=5.42) + axis.set_xticks( + range(len(columns)), + [f"{scale}\n{'Jacobian' if method == 'jacobian' else 'Direct'}" for scale, method in columns], + ) + axis.set_yticks(range(len(slugs)), [slug.replace("paper-v2-assoc-", "").replace("-0", " ") for slug in slugs]) + for i in range(len(slugs)): + for j in range(len(columns)): + color = "white" if matrix[i, j] > 2.7 else "black" + axis.text(j, i, labels[i][j], ha="center", va="center", fontsize=7.2, color=color) + colorbar = figure.colorbar(image, ax=axis, fraction=.035, pad=.02) + colorbar.set_label("log$_{10}$(best declared-concept rank)") + save_figure(figure, FIGURES / "key-concept-trace-scale-heatmap") + + correlations = {} + for first, second in [(0, 1), (0, 2), (1, 2)]: + a = [math.log10(row["jacobian_rank"]) for row in rows if row["scale"] == "12B" and row["seed"] == first] + b = [math.log10(row["jacobian_rank"]) for row in rows if row["scale"] == "12B" and row["seed"] == second] + correlations[f"seed{first}_vs_seed{second}"] = float(spearmanr(a, b).statistic) + return { + "status": "exploratory six-prompt visualization cohort", + "n_prompt_concept_pairs_per_fit": len([row for row in rows if row["scale"] == "12B" and row["seed"] == 0]), + "12B_fit_rank_correlations": correlations, + "legacy_boundary": "4B is a legacy single-fit descriptive reference.", + } + + +def extract_swap_trials(path: Path, scale: str, seed: int) -> list[dict]: + run = json.loads(path.read_text()) + alpha = float(run["methodology"]["swap_alpha"]) + rows = [] + for prompt in run["prompts"]: + for trial_index, trial in enumerate(prompt["swap"]["trials"]): + clean = int(trial["target_rank_clean"]) + 1 + swapped = int(trial["target_rank_swapped"]) + 1 + rows.append( + { + "scale": scale, + "seed": seed, + "alpha": alpha, + "slug": prompt["slug"], + "trial_index": trial_index, + "source": trial["source"], + "target": trial["target"], + "target_rank_clean": clean, + "target_rank_swapped": swapped, + "target_log10_rank_gain": math.log10(clean) - math.log10(swapped), + "target_rank_improved": int(swapped < clean), + "target_top1": int(bool(trial["target_top1"])), + "source_rank_clean": int(trial["source_rank_clean"]) + 1, + "source_rank_swapped": int(trial["source_rank_swapped"]) + 1, + } + ) + return rows + + +def prompt_bootstrap(values: dict[str, list[float]], seed: int) -> dict: + prompts = sorted(values) + prompt_means = np.asarray([np.mean(values[prompt]) for prompt in prompts], dtype=float) + rng = np.random.default_rng(seed) + estimates = np.empty(N_BOOT) + for index in range(N_BOOT): + selected = rng.integers(0, len(prompts), size=len(prompts)) + estimates[index] = float(np.mean(prompt_means[selected])) + return { + "observed_mean": float(np.mean(prompt_means)), + "low": float(np.quantile(estimates, .025)), + "high": float(np.quantile(estimates, .975)), + "n_prompts": len(prompts), + "n_resamples": N_BOOT, + } + + +def swap_analysis() -> dict: + rows = [] + inputs = [] + for path in sorted(ROOT.glob("runs/gemma4-materials-report-swap-a*.json")): + rows.extend(extract_swap_trials(path, "4B legacy", 0)) + inputs.append({"path": str(path.relative_to(ROOT)), "sha256": sha256(path)}) + for path in sorted(ROOT.glob("runs/gemma4-12b-it-report-swap-a*-seed*.json")): + seed = int(path.stem.rsplit("seed", 1)[1]) + rows.extend(extract_swap_trials(path, "12B", seed)) + inputs.append({"path": str(path.relative_to(ROOT)), "sha256": sha256(path)}) + for path in sorted(ROOT.glob("runs/gemma4-31b-it-report-swap-a*-seed0.json")): + rows.extend(extract_swap_trials(path, "31B", 0)) + inputs.append({"path": str(path.relative_to(ROOT)), "sha256": sha256(path)}) + write_csv(OUT / "report_swap_trial_results.csv", rows) + + # Average 12B fits at the identical prompt-target trial before population summaries. + grouped: dict[tuple, list[dict]] = defaultdict(list) + for row in rows: + grouped[(row["scale"], row["alpha"], row["slug"], row["target"])].append(row) + averaged = [] + for (scale, alpha, slug, target), members in sorted(grouped.items()): + averaged.append( + { + "scale": scale, + "alpha": alpha, + "slug": slug, + "target": target, + "target_log10_rank_gain": float(np.mean([row["target_log10_rank_gain"] for row in members])), + "target_rank_improved_fraction": float(np.mean([row["target_rank_improved"] for row in members])), + "target_top1_fraction": float(np.mean([row["target_top1"] for row in members])), + } + ) + write_csv(OUT / "report_swap_fit_averaged_results.csv", averaged) + stats = { + "status": "exploratory verbal-report intervention; not inserted into manuscript", + "inputs": inputs, + "legacy_boundary": "4B is a legacy single-fit descriptive reference.", + "primary_interpretation": ( + "Positive log-rank gain means the intended target rose in the vocabulary " + "ranking. This is weaker than changing the top-1 answer." + ), + "dose": {}, + } + for scale_index, scale in enumerate(("4B legacy", "12B", "31B")): + stats["dose"][scale] = {} + for alpha_index, alpha in enumerate((.25, .5, 1.0)): + members = [row for row in averaged if row["scale"] == scale and row["alpha"] == alpha] + by_prompt_gain: dict[str, list[float]] = defaultdict(list) + by_prompt_improved: dict[str, list[float]] = defaultdict(list) + for row in members: + by_prompt_gain[row["slug"]].append(row["target_log10_rank_gain"]) + by_prompt_improved[row["slug"]].append(row["target_rank_improved_fraction"]) + stats["dose"][scale][str(alpha)] = { + "log10_rank_gain": prompt_bootstrap(by_prompt_gain, RNG_SEED + 10 * scale_index + alpha_index), + "improved_fraction": prompt_bootstrap(by_prompt_improved, RNG_SEED + 100 + 10 * scale_index + alpha_index), + "target_top1_fraction": float(np.mean([row["target_top1_fraction"] for row in members])), + "n_target_trials": len(members), + } + correlations = {} + for alpha in (.25, .5, 1.0): + fits = {} + for seed in (0, 1, 2): + members = [row for row in rows if row["scale"] == "12B" and row["alpha"] == alpha and row["seed"] == seed] + fits[seed] = {(row["slug"], row["target"]): row["target_log10_rank_gain"] for row in members} + keys = sorted(set.intersection(*(set(values) for values in fits.values()))) + correlations[str(alpha)] = {} + for first, second in ((0, 1), (0, 2), (1, 2)): + correlations[str(alpha)][f"seed{first}_vs_seed{second}"] = float( + spearmanr([fits[first][key] for key in keys], [fits[second][key] for key in keys]).statistic + ) + stats["12B_fit_effect_correlations"] = correlations + + plt.rcParams.update( + {"font.family": "DejaVu Sans", "font.size": 9.2, "axes.spines.top": False, "axes.spines.right": False} + ) + figure, axes = plt.subplots(1, 2, figsize=(10.8, 4.0), constrained_layout=True) + for axis, endpoint, ylabel, label in [ + (axes[0], "log10_rank_gain", "Target log$_{10}$-rank gain", "A"), + (axes[1], "improved_fraction", "Fraction of target ranks improved", "B"), + ]: + for scale in ("4B legacy", "12B", "31B"): + values, low, high = [], [], [] + for alpha in (.25, .5, 1.0): + entry = stats["dose"][scale][str(alpha)][endpoint] + values.append(entry["observed_mean"]) + low.append(entry["observed_mean"] - entry["low"]) + high.append(entry["high"] - entry["observed_mean"]) + axis.errorbar( + [.25, .5, 1.0], + values, + yerr=np.asarray([low, high]), + marker="o", + color=COLORS[scale], + linewidth=1.8, + capsize=3, + label=(scale + (" (3-fit mean)" if scale == "12B" else " (single fit)")), + ) + axis.axhline(0 if endpoint == "log10_rank_gain" else .5, color=DIRECT, linewidth=.9, linestyle="--") + axis.set_xticks([.25, .5, 1.0]) + axis.set_xlabel("Swap strength $\\alpha$") + axis.set_ylabel(ylabel) + axis.grid(True, alpha=.18) + axis.legend(frameon=False, fontsize=7.8, loc="best") + panel_label(axis, label) + save_figure(figure, FIGURES / "report-swap-dose-analysis") + return stats + + +def main() -> None: + OUT.mkdir(parents=True, exist_ok=True) + FIGURES.mkdir(parents=True, exist_ok=True) + stats = { + "study_id": "gemma-auxiliary-lens-runs-2026-08-25", + "mechanism_name": mechanism_name_analysis(), + "key_concept_traces": key_trace_analysis(), + "report_swap": swap_analysis(), + } + (OUT / "statistics.json").write_text(json.dumps(stats, indent=2, sort_keys=True) + "\n") + mechanism = stats["mechanism_name"]["scales"] + swap = stats["report_swap"]["dose"] + report = [ + "# Auxiliary Gemma lens analyses", + "", + "These analyses are exploratory and have not been inserted into the manuscript.", + "", + "## Mechanism-name cohort", + "", + f"- 12B: Jacobian AUC {mechanism['12B']['jacobian']['observed_mean']:.4f}; direct {mechanism['12B']['direct']['observed_mean']:.4f}.", + f"- 31B single fit: Jacobian AUC {mechanism['31B']['jacobian']['observed_mean']:.4f}; direct {mechanism['31B']['direct']['observed_mean']:.4f}.", + "- The 31B checkpoint is markedly more readable on these named-law prompts, but this is not a clean three-fit scale inference and does not establish a Jacobian-specific advantage.", + "", + "## Six detailed concept traces", + "", + "The heatmap reports the strongest predeclared concept for each prompt, scale, and readout. It is a compact case-study diagnostic, not a population endpoint.", + "", + "## Verbal-report swaps", + "", + f"- At alpha=1, 12B mean target log-rank gain is {swap['12B']['1.0']['log10_rank_gain']['observed_mean']:+.3f}; 31B is {swap['31B']['1.0']['log10_rank_gain']['observed_mean']:+.3f}.", + "- No target became the top-1 answer at any dose or scale. The higher-dose rank improvement is therefore a weak dose-response signal, not successful behavioral steering.", + "", + "The legacy 4B records predate strict revision and lens provenance and are included only as descriptive references.", + "", + ] + (OUT / "REPORT.md").write_text("\n".join(report)) + (OUT / "PROTOCOL.md").write_text( + "# Auxiliary lens analysis boundary\n\n" + "All prompt records were retained. The 12B summaries average the three " + "independent paper-fit lenses at the same prompt or target trial before " + "population summaries. The 31B and legacy 4B records contain one lens. " + "The 4B records lack the strict immutable provenance used by the revision " + "experiments and are descriptive only. These analyses were performed after " + "the outputs were available and are exploratory.\n" + ) + print("\n".join(report)) + print(f"wrote {OUT}") + + +if __name__ == "__main__": + main() diff --git a/jlens_materials/scripts/analyze_gemma_equation_free_multiscale.py b/jlens_materials/scripts/analyze_gemma_equation_free_multiscale.py new file mode 100644 index 0000000000000000000000000000000000000000..a83a4b7973253a27f13d6983099ba99444549db5 --- /dev/null +++ b/jlens_materials/scripts/analyze_gemma_equation_free_multiscale.py @@ -0,0 +1,577 @@ +#!/usr/bin/env python3 +"""Compare the frozen equation-free 60-law benchmark across Gemma scales. + +The 4B, 12B, and 31B runs use the same 16-law development manifest, the same +960 equation-free test prompts, and a probe layer selected at approximately +83% normalized depth. This script performs display and paired population +inference only; it never refits a direction or executes a model. +""" + +from __future__ import annotations + +import hashlib +import json +from pathlib import Path + +import matplotlib + +matplotlib.use("Agg") +import matplotlib.pyplot as plt # noqa: E402 +import numpy as np # noqa: E402 +import pandas as pd # noqa: E402 +from matplotlib.lines import Line2D # noqa: E402 +from matplotlib.ticker import MaxNLocator # noqa: E402 +from scipy.stats import pearsonr, spearmanr # noqa: E402 + + +ROOT = Path(__file__).resolve().parents[1] +SUPPLIED = ROOT / "experiments" / "neutral-anchored-relational-physics-2026-07-18" +RUNS = { + "4B": ROOT / "experiments" / "equation-free-relational-physics-2026-08-21", + "12B": ROOT / "experiments" / "gemma4-12b-equation-free-relational-v1", + "31B": ROOT / "experiments" / "gemma4-31b-equation-free-relational-v1", +} +OUT = ROOT / "experiments" / "gemma-equation-free-multiscale-2026-08-25" +FIGURES = OUT / "figures" + +SCALE_COLORS = {"4B": "#276b9a", "12B": "#2a9d8f", "31B": "#d39b35"} +CATEGORY_ORDER = ["inverse", "neutral", "direct"] +CATEGORY_COLORS = { + "inverse": "#7662a8", + "neutral": "#8c8c8c", + "direct": "#2a9d8f", +} +METHOD_COLORS = { + "Hidden state": "#276b9a", + "Output logits": "#d39b35", + "Word TF-IDF": "#a7a7a7", + "Character TF-IDF": "#666666", +} +ENDPOINTS = [ + ("direct_vs_inverse_auc", "Direct vs inverse"), + ("direct_vs_validation_neutral_auc", "Direct vs neutral"), + ("validation_neutral_vs_inverse_auc", "Neutral vs inverse"), +] +BOOTSTRAP_SEED = 20260825 +BOOTSTRAP_RESAMPLES = 100_000 + + +def sha256(path: Path) -> str: + return hashlib.sha256(path.read_bytes()).hexdigest() + + +def read_json(path: Path) -> dict: + return json.loads(path.read_text(encoding="utf-8")) + + +def auc(a: np.ndarray, b: np.ndarray) -> float: + """Probability that a randomly selected value from a exceeds one from b.""" + comparisons = a[:, None] - b[None, :] + return float(np.mean(comparisons > 0) + 0.5 * np.mean(comparisons == 0)) + + +def endpoint_from_frame(frame: pd.DataFrame, key: str) -> float: + values = { + category: frame.loc[frame["category"] == category, "natural_z"].to_numpy() + for category in CATEGORY_ORDER + } + if key == "direct_vs_inverse_auc": + return auc(values["direct"], values["inverse"]) + if key == "direct_vs_validation_neutral_auc": + return auc(values["direct"], values["neutral"]) + if key == "validation_neutral_vs_inverse_auc": + return auc(values["neutral"], values["inverse"]) + raise KeyError(key) + + +def sign_accuracy(frame: pd.DataFrame) -> float: + directional = frame.loc[frame["category"].isin(["direct", "inverse"])] + expected = directional["category"].map({"direct": 1.0, "inverse": -1.0}).to_numpy() + score = directional["natural_centered_contrast"].to_numpy() + return float(np.mean(score * expected > 0)) + + +def paired_scale_bootstrap( + frames: dict[str, pd.DataFrame], smaller: str, larger: str +) -> dict: + reference = frames[smaller] + other = frames[larger] + if reference["law_id"].tolist() != other["law_id"].tolist(): + raise RuntimeError(f"Law order differs between {smaller} and {larger}") + rng = np.random.default_rng(BOOTSTRAP_SEED + int(smaller[:-1]) + int(larger[:-1])) + distributions = {key: np.empty(BOOTSTRAP_RESAMPLES) for key, _ in ENDPOINTS} + distributions["directional_accuracy"] = np.empty(BOOTSTRAP_RESAMPLES) + arrays: dict[str, dict[str, dict[str, np.ndarray]]] = {} + for scale, frame in ((smaller, reference), (larger, other)): + arrays[scale] = {} + for category in CATEGORY_ORDER: + subset = frame.loc[frame["category"] == category] + arrays[scale][category] = { + "z": subset["natural_z"].to_numpy(), + "centered": subset["natural_centered_contrast"].to_numpy(), + } + + def batched_auc(a: np.ndarray, b: np.ndarray) -> np.ndarray: + difference = a[:, :, None] - b[:, None, :] + return np.mean(difference > 0, axis=(1, 2)) + 0.5 * np.mean( + difference == 0, axis=(1, 2) + ) + + batch_size = 1_000 + for start in range(0, BOOTSTRAP_RESAMPLES, batch_size): + stop = min(start + batch_size, BOOTSTRAP_RESAMPLES) + count = stop - start + sampled = { + category: rng.integers(0, 20, size=(count, 20)) + for category in CATEGORY_ORDER + } + sampled_values: dict[str, dict[str, dict[str, np.ndarray]]] = {} + for scale in (smaller, larger): + sampled_values[scale] = {} + for category in CATEGORY_ORDER: + sampled_values[scale][category] = { + name: values[sampled[category]] + for name, values in arrays[scale][category].items() + } + pairs = { + "direct_vs_inverse_auc": ("direct", "inverse"), + "direct_vs_validation_neutral_auc": ("direct", "neutral"), + "validation_neutral_vs_inverse_auc": ("neutral", "inverse"), + } + for key, (positive, negative) in pairs.items(): + left_auc = batched_auc( + sampled_values[smaller][positive]["z"], + sampled_values[smaller][negative]["z"], + ) + right_auc = batched_auc( + sampled_values[larger][positive]["z"], + sampled_values[larger][negative]["z"], + ) + distributions[key][start:stop] = right_auc - left_auc + left_correct = np.concatenate( + [ + sampled_values[smaller]["direct"]["centered"] > 0, + sampled_values[smaller]["inverse"]["centered"] < 0, + ], + axis=1, + ).mean(axis=1) + right_correct = np.concatenate( + [ + sampled_values[larger]["direct"]["centered"] > 0, + sampled_values[larger]["inverse"]["centered"] < 0, + ], + axis=1, + ).mean(axis=1) + distributions["directional_accuracy"][start:stop] = right_correct - left_correct + results: dict[str, dict] = {} + for key, values in distributions.items(): + if key == "directional_accuracy": + observed = sign_accuracy(other) - sign_accuracy(reference) + else: + observed = endpoint_from_frame(other, key) - endpoint_from_frame(reference, key) + results[key] = { + "observed_difference": observed, + "paired_bootstrap_95": np.quantile(values, [0.025, 0.975]).tolist(), + } + return results + + +def load() -> tuple[pd.DataFrame, dict, dict[str, pd.DataFrame], dict[str, dict]]: + supplied_laws = pd.read_csv(SUPPLIED / "law_level_neutral_normalized.csv") + supplied_stats = read_json(SUPPLIED / "statistics.json") + frames: dict[str, pd.DataFrame] = {} + stats: dict[str, dict] = {} + for scale, directory in RUNS.items(): + frame = pd.read_csv(directory / "law_level_results.csv").sort_values("law_id") + required = { + "law_id", + "category", + "mean_contrast", + "natural_centered_contrast", + "natural_z", + } + if not required.issubset(frame.columns): + raise RuntimeError(f"{scale} law table lacks {required - set(frame.columns)}") + if len(frame) != 60 or frame["law_id"].nunique() != 60: + raise RuntimeError(f"{scale} does not contain exactly 60 unique laws") + frames[scale] = frame.reset_index(drop=True) + stats[scale] = read_json(directory / "statistics.json") + ids = frames["4B"]["law_id"].tolist() + for scale in ("12B", "31B"): + if frames[scale]["law_id"].tolist() != ids: + raise RuntimeError(f"{scale} law identities differ from 4B") + return supplied_laws, supplied_stats, frames, stats + + +def primary(stats: dict, key: str) -> float: + return float(stats["primary"][key]) + + +def directional_accuracy(stats: dict) -> float: + section = stats["primary"] + for key in ( + "natural_calibrated_directional_accuracy", + "calibrated_directional_accuracy", + ): + if key in section: + return float(section[key]) + raise KeyError("directional accuracy") + + +def controls(stats: dict, method: str) -> dict: + key = { + "Output logits": "output_head", + "Word TF-IDF": "word_tfidf", + "Character TF-IDF": "character_tfidf", + }[method] + return stats["controls"][key] + + +def add_panel_labels(axes: list[plt.Axes]) -> None: + for axis, label in zip(axes, "ABCD"): + axis.text( + -0.13, + 1.045, + label, + transform=axis.transAxes, + fontsize=10.5, + fontweight="bold", + ha="left", + va="bottom", + clip_on=False, + ) + + +def make_figure( + supplied_laws: pd.DataFrame, + supplied_stats: dict, + frames: dict[str, pd.DataFrame], + stats: dict[str, dict], +) -> tuple[Path, Path, Path]: + plt.rcParams.update( + { + "font.size": 8.5, + "axes.labelsize": 9, + "axes.linewidth": 0.8, + "axes.spines.top": False, + "axes.spines.right": False, + "xtick.labelsize": 8, + "ytick.labelsize": 8, + "legend.fontsize": 7.3, + "pdf.fonttype": 42, + "ps.fonttype": 42, + } + ) + fig = plt.figure(figsize=(12.2, 7.5)) + outer = fig.add_gridspec( + 2, + 1, + left=0.09, + right=0.985, + top=0.955, + bottom=0.145, + hspace=0.44, + ) + top = outer[0].subgridspec(1, 2, width_ratios=[0.82, 1.18], wspace=0.34) + bottom = outer[1].subgridspec(1, 2, wspace=0.48) + axes = [ + fig.add_subplot(top[0, 0]), + fig.add_subplot(top[0, 1]), + fig.add_subplot(bottom[0, 0]), + fig.add_subplot(bottom[0, 1]), + ] + rng = np.random.default_rng(20260825) + + # A: the original 4B supplied-equation versus equation-free transfer. + supplied = supplied_laws[["law_id", "category", "neutral_z"]].rename( + columns={"neutral_z": "supplied_z"} + ) + equation_free_4b = frames["4B"][["law_id", "category", "old_scale_z"]].rename( + columns={"old_scale_z": "equation_free_z"} + ) + paired = supplied.merge( + equation_free_4b, + on=["law_id", "category"], + validate="one_to_one", + ) + ax = axes[0] + offsets = {"supplied_z": -0.13, "equation_free_z": 0.13} + for category_index, category in enumerate(CATEGORY_ORDER): + group = paired.loc[paired["category"] == category] + jitter = rng.uniform(-0.05, 0.05, len(group)) + color = CATEGORY_COLORS[category] + for column, filled in (("supplied_z", False), ("equation_free_z", True)): + x = category_index + offsets[column] + jitter + ax.scatter( + x, + group[column], + s=25, + facecolors=color if filled else "white", + edgecolors="white" if filled else color, + linewidths=0.45 if filled else 1.05, + alpha=0.9, + zorder=3, + ) + median = float(group[column].median()) + center = category_index + offsets[column] + ax.plot([center - 0.08, center + 0.08], [median, median], color="black", lw=1.7) + ax.axhline(0, color="0.42", ls="--", lw=0.9) + ax.set_xticks(range(3), ["Inverse", "Neutral", "Direct"]) + ax.set_ylabel("4B matched contrast\n(common neutral scale, robust z)") + ax.set_xlim(-0.5, 2.5) + ax.grid(axis="y", color="0.9", lw=0.6) + + # B: restore the complete ordered-law view and generalize it across every + # available test condition. Each narrow facet has its own neutral-calibrated + # robust-z axis because the hidden widths and neutral spreads differ by model. + ax = axes[1] + ax.set_axis_off() + waterfall_conditions = [ + ( + "4B\nsupplied", + supplied_laws[["law_id", "category", "neutral_z"]].rename( + columns={"neutral_z": "score"} + ), + ), + ( + "4B\nequation-free", + frames["4B"][["law_id", "category", "natural_z"]].rename( + columns={"natural_z": "score"} + ), + ), + ( + "12B\nequation-free", + frames["12B"][["law_id", "category", "natural_z"]].rename( + columns={"natural_z": "score"} + ), + ), + ( + "31B\nequation-free", + frames["31B"][["law_id", "category", "natural_z"]].rename( + columns={"natural_z": "score"} + ), + ), + ] + facet_lefts = [0.00, 0.255, 0.51, 0.765] + for left, (title, frame) in zip(facet_lefts, waterfall_conditions): + facet = ax.inset_axes([left, 0.08, 0.225, 0.87]) + ordered = frame.sort_values("score").reset_index(drop=True) + y = np.arange(len(ordered)) + colors = [CATEGORY_COLORS[category] for category in ordered["category"]] + facet.barh( + y, + ordered["score"], + height=0.82, + color=colors, + edgecolor="none", + alpha=0.94, + ) + facet.axvline(0, color="0.35", lw=0.75, zorder=3) + limit = float(np.max(np.abs(ordered["score"]))) * 1.06 + facet.set_xlim(-limit, limit) + facet.set_ylim(-1, len(ordered)) + facet.set_yticks([]) + facet.xaxis.set_major_locator(MaxNLocator(nbins=3, symmetric=True)) + facet.tick_params(axis="x", labelsize=6.4, pad=1.5, length=2.2) + facet.set_title(title, fontsize=7.3, pad=3.0) + facet.grid(axis="x", color="0.91", lw=0.5, zorder=0) + facet.spines["top"].set_visible(False) + facet.spines["right"].set_visible(False) + facet.spines["left"].set_visible(False) + ax.text( + 0.5, + -0.025, + "60 laws ordered within condition (model-specific neutral scale, robust z)", + transform=ax.transAxes, + fontsize=7.7, + ha="center", + va="top", + ) + + # C: hidden, output, and lexical direct-versus-inverse separation. + ax = axes[2] + conditions = ["4B\nsupplied", "4B\nequation-free", "12B\nequation-free", "31B\nequation-free"] + condition_x = np.arange(len(conditions)) + for method, color in METHOD_COLORS.items(): + if method == "Hidden state": + supplied_value = primary(supplied_stats, "direct_vs_inverse_auc") + values = [supplied_value] + [primary(stats[scale], "direct_vs_inverse_auc") for scale in RUNS] + else: + supplied_value = controls(supplied_stats, method)["direct_vs_inverse_auc"] + values = [supplied_value] + [controls(stats[scale], method)["direct_vs_inverse_auc"] for scale in RUNS] + ax.plot(condition_x, values, color=color, lw=1.4, marker="o", ms=4.5, label=method) + ax.axhline(0.5, color="0.45", ls="--", lw=0.9) + ax.set_xticks(condition_x, conditions) + ax.set_ylim(0.47, 1.025) + ax.set_ylabel("Direct-versus-inverse AUC") + ax.grid(axis="y", color="0.9", lw=0.6) + ax.legend( + frameon=False, + loc="center", + bbox_to_anchor=(0.53, 0.53), + ncol=2, + columnspacing=0.9, + ) + + # D: registered equation-free endpoints and exact generated-answer accuracy. + ax = axes[3] + metric_rows = [ + ("Direct vs inverse AUC", lambda s: primary(s, "direct_vs_inverse_auc")), + ("Direct vs neutral AUC", lambda s: primary(s, "direct_vs_validation_neutral_auc")), + ("Neutral vs inverse AUC", lambda s: primary(s, "validation_neutral_vs_inverse_auc")), + ("Directional-law accuracy", directional_accuracy), + ("Generated-answer accuracy", lambda s: float(s["behavior"]["overall_accuracy"])), + ] + y = np.arange(len(metric_rows))[::-1] + offsets = {"4B": 0.16, "12B": 0.0, "31B": -0.16} + for scale in RUNS: + values = [function(stats[scale]) for _, function in metric_rows] + ax.scatter( + values, + y + offsets[scale], + color=SCALE_COLORS[scale], + s=38, + label=scale, + zorder=3, + ) + ax.axvline(0.5, color="0.55", ls="--", lw=0.8) + ax.set_yticks(y, [label for label, _ in metric_rows]) + ax.set_xlim(0.60, 1.012) + ax.set_xlabel("Equation-free performance") + ax.grid(axis="x", color="0.9", lw=0.6) + ax.legend(frameon=False, loc="upper left", ncol=3, columnspacing=0.9) + + add_panel_labels(axes) + format_handles = [ + Line2D([0], [0], marker="o", color="none", markerfacecolor="white", markeredgecolor="#37474f", label="Equation supplied"), + Line2D([0], [0], marker="o", color="none", markerfacecolor="#37474f", markeredgecolor="white", label="Equation-free"), + ] + category_handles = [ + Line2D([0], [0], color=CATEGORY_COLORS[category], lw=2.0, label=category.capitalize()) + for category in CATEGORY_ORDER + ] + fig.legend( + handles=format_handles + category_handles, + frameon=False, + loc="lower center", + bbox_to_anchor=(0.5, 0.015), + ncol=5, + columnspacing=1.4, + handletextpad=0.45, + ) + + FIGURES.mkdir(parents=True, exist_ok=True) + stem = FIGURES / "equation-free-relational-multiscale" + paths = (stem.with_suffix(".png"), stem.with_suffix(".pdf"), stem.with_suffix(".svg")) + fig.savefig(paths[0], dpi=300, bbox_inches="tight", facecolor="white") + fig.savefig(paths[1], bbox_inches="tight", facecolor="white") + fig.savefig(paths[2], bbox_inches="tight", facecolor="white") + plt.close(fig) + return paths + + +def main() -> None: + supplied_laws, supplied_stats, frames, stats = load() + pairwise = { + f"{left}_to_{right}": paired_scale_bootstrap(frames, left, right) + for left, right in (("4B", "12B"), ("4B", "31B"), ("12B", "31B")) + } + correlations: dict[str, dict] = {} + for left, right in (("4B", "12B"), ("4B", "31B"), ("12B", "31B")): + x = frames[left]["mean_contrast"].to_numpy() + y = frames[right]["mean_contrast"].to_numpy() + categories = frames[left]["category"] + x_centered = x - pd.Series(x).groupby(categories).transform("mean").to_numpy() + y_centered = y - pd.Series(y).groupby(categories).transform("mean").to_numpy() + correlations[f"{left}_vs_{right}"] = { + "all_laws_pearson": float(pearsonr(x, y).statistic), + "all_laws_spearman": float(spearmanr(x, y).statistic), + "category_centered_pearson": float(pearsonr(x_centered, y_centered).statistic), + "category_centered_spearman": float(spearmanr(x_centered, y_centered).statistic), + } + + summary: dict[str, dict] = {} + for scale in RUNS: + summary[scale] = { + "model": stats[scale].get("model", "google/gemma-4-E4B-it" if scale == "4B" else None), + "probe_layer_zero_indexed": stats[scale].get("probe_layer_zero_indexed", 34), + "normalized_depth_percent": stats[scale].get("normalized_depth_percent", 34 / 41 * 100), + "direct_vs_inverse_auc": primary(stats[scale], "direct_vs_inverse_auc"), + "direct_vs_neutral_auc": primary(stats[scale], "direct_vs_validation_neutral_auc"), + "neutral_vs_inverse_auc": primary(stats[scale], "validation_neutral_vs_inverse_auc"), + "ordinal_spearman": primary(stats[scale], "ordinal_spearman"), + "directional_accuracy": directional_accuracy(stats[scale]), + "correct_directional_laws": int(primary(stats[scale], "correct_directional_laws")), + "generated_answer_accuracy": float(stats[scale]["behavior"]["overall_accuracy"]), + "output_head_direct_inverse_auc": float(stats[scale]["controls"]["output_head"]["direct_vs_inverse_auc"]), + "word_tfidf_direct_inverse_auc": float(stats[scale]["controls"]["word_tfidf"]["direct_vs_inverse_auc"]), + "character_tfidf_direct_inverse_auc": float(stats[scale]["controls"]["character_tfidf"]["direct_vs_inverse_auc"]), + } + + figure_paths = make_figure(supplied_laws, supplied_stats, frames, stats) + OUT.mkdir(parents=True, exist_ok=True) + result = { + "study_id": "gemma-equation-free-multiscale-2026-08-25", + "status": "post-run matched comparison of frozen model-specific experiments", + "cohort": {"development_laws": 16, "development_prompts": 128, "test_laws": 60, "test_prompts": 960}, + "bootstrap": {"resamples": BOOTSTRAP_RESAMPLES, "seed": BOOTSTRAP_SEED, "paired_by_law": True, "stratified_by_category": True}, + "summary": summary, + "paired_scale_differences": pairwise, + "cross_scale_law_contrast_correlations": correlations, + "interpretation_boundary": ( + "The 12B protocol was frozen before its model outputs, but the cross-scale " + "comparison was specified after the 4B and 31B runs existed. The same 60 law " + "identities are reused; this is a scale robustness test, not fresh-law generalization." + ), + } + (OUT / "statistics.json").write_text(json.dumps(result, indent=2, sort_keys=True) + "\n") + rows = [ + "# Equation-free relational physics across Gemma scales", + "", + "The same 128 development prompts and 960 equation-free test prompts were evaluated at approximately 83% normalized depth in Gemma 4B, 12B, and 31B.", + "", + ] + for scale, values in summary.items(): + rows.append( + f"- **{scale}:** direct/inverse AUC {values['direct_vs_inverse_auc']:.3f}; " + f"direct/neutral {values['direct_vs_neutral_auc']:.3f}; neutral/inverse " + f"{values['neutral_vs_inverse_auc']:.3f}; directional laws " + f"{values['correct_directional_laws']}/40; ordinal rho " + f"{values['ordinal_spearman']:.3f}; generated-answer accuracy " + f"{values['generated_answer_accuracy']:.1%}." + ) + rows.extend( + [ + "", + "Both larger checkpoints pass every registered relational endpoint at ceiling. The 4B result is already strong but makes two directional-law sign errors. Lexical TF-IDF controls remain near chance because the prompt text is identical across model scales. Output logits contain the same ordinal variable at 12B and 31B, while exact generated-answer accuracy differs sharply, especially because 12B often fails the neutral response format.", + "", + "Overall cross-scale law-score correlations are high because all models separate the three registered categories. Category-centered correlations are much weaker, so the result supports a shared ordinal constitutive orientation, not invariant law-by-law magnitudes.", + "", + "This is a post-run cross-scale comparison over reused laws, not an independent unfamiliar-law test.", + ] + ) + (OUT / "REPORT.md").write_text("\n".join(rows) + "\n", encoding="utf-8") + protocol = { + "script": str(Path(__file__).resolve().relative_to(ROOT)), + "script_sha256": sha256(Path(__file__).resolve()), + "inputs": { + str(path.relative_to(ROOT)): sha256(path) + for path in [ + SUPPLIED / "law_level_neutral_normalized.csv", + SUPPLIED / "statistics.json", + *[RUNS[scale] / "law_level_results.csv" for scale in RUNS], + *[RUNS[scale] / "statistics.json" for scale in RUNS], + ] + }, + "outputs": {str(path.relative_to(ROOT)): sha256(path) for path in figure_paths}, + } + (OUT / "PROTOCOL.md").write_text( + "# Reproduction protocol\n\nRun:\n\n```bash\npython scripts/analyze_gemma_equation_free_multiscale.py\n```\n\nThe script reads only frozen result tables and statistics; it does not load Gemma or refit a direction.\n\n```json\n" + + json.dumps(protocol, indent=2, sort_keys=True) + + "\n```\n", + encoding="utf-8", + ) + + +if __name__ == "__main__": + main() diff --git a/jlens_materials/scripts/analyze_gemma_multiscale_heldout_v1.py b/jlens_materials/scripts/analyze_gemma_multiscale_heldout_v1.py new file mode 100644 index 0000000000000000000000000000000000000000..d63f140b2562e2df75f3edcfbbae10b31ae3eac2 --- /dev/null +++ b/jlens_materials/scripts/analyze_gemma_multiscale_heldout_v1.py @@ -0,0 +1,685 @@ +#!/usr/bin/env python3 +"""Compare the frozen held-out concept-readability endpoint across Gemma scales. + +The 4B and 12B estimates average three independently fitted paper-recipe +Jacobian lenses. The 31B estimate uses the one available paper-recipe lens and +is always labelled as a single fit. Population intervals resample mechanism +families and phrasings; they are not presented as lens-fit intervals. +""" + +from __future__ import annotations + +import csv +import hashlib +import json +import sys +from pathlib import Path + +import matplotlib.pyplot as plt +import numpy as np + + +ROOT = Path(__file__).resolve().parents[1] +sys.path[:0] = [str(ROOT), str(ROOT / "scripts")] + +import analyze_materials_heldout_v1 as heldout # noqa: E402 +import analyze_gemma12b_heldout_v1 as scale12 # noqa: E402 +import analyze_gemma_scale_heldout_v1 as scale31 # noqa: E402 + + +OUT = ROOT / "experiments" / "gemma-multiscale-heldout-v1-2026-08-25" +FIGURES = OUT / "figures" +RUN_12B = [ + ROOT / "runs" / f"gemma4-12b-it-heldout-v1-seed{seed}.json" + for seed in range(3) +] +RUN_31B = ROOT / "runs" / "gemma4-31b-it-heldout-v1-seed0.json" +FROZEN_4B_STATS = ROOT / "experiments" / "materials-heldout-v1_statistics.json" +FROZEN_12B_STATS = ( + ROOT / "experiments" / "gemma12b-heldout-v1-2026-08-25" / "statistics.json" +) +FROZEN_31B_STATS = ( + ROOT / "experiments" / "gemma-scale-heldout-v1-2026-08-23" / "statistics.json" +) + +RNG_SEED = 20260825 +COLORS = {"4B": "#087F8C", "12B": "#D39A2C", "31B": "#584A9B"} +DIRECT = "#66737B" +NEGATIVE = "#C46C5A" +LIGHT = "#C7CDD1" + + +def sha256(path: Path) -> str: + return hashlib.sha256(path.read_bytes()).hexdigest() + + +def write_csv(path: Path, rows: list[dict]) -> None: + if not rows: + raise ValueError(f"refusing to write empty CSV: {path}") + path.parent.mkdir(parents=True, exist_ok=True) + with path.open("w", newline="") as handle: + writer = csv.DictWriter(handle, fieldnames=list(rows[0])) + writer.writeheader() + writer.writerows(rows) + + +def validate_prompt_equivalence( + slugs: list[str], + indexes_4b: list[dict[str, dict]], + indexes_12b: list[dict[str, dict]], + index_31b: dict[str, dict], +) -> None: + reference = indexes_4b[0] + for index_name, index in [ + ("12B fit 0", indexes_12b[0]), + ("12B fit 1", indexes_12b[1]), + ("12B fit 2", indexes_12b[2]), + ("31B fit 0", index_31b), + ]: + if list(index) != slugs: + raise ValueError(f"{index_name}: prompt identities or order differ") + for slug in slugs: + for key in ( + "prompt_text", + "target_family", + "phrasing_id", + "score_positions", + ): + if index[slug].get(key) != reference[slug].get(key): + raise ValueError(f"{index_name}: {slug} differs for {key}") + labels = [item["label"] for item in index[slug]["emergence"]] + reference_labels = [ + item["label"] for item in reference[slug]["emergence"] + ] + if labels != reference_labels: + raise ValueError(f"{index_name}: {slug} tracked terms differ") + + +def method_interval(rows: list[dict], key: str, seed: int) -> dict: + return scale31.hierarchical_bootstrap(rows, key, seed=seed) + + +def renamed_rows( + rows: list[dict], *, scale: str, jacobian_key: str, direct_key: str +) -> list[dict]: + return [ + { + "slug": row["slug"], + "family": row["family"], + "phrasing_id": row.get("phrasing_id"), + "scale": scale, + "jacobian_auc": float(row[jacobian_key]), + "direct_auc": float(row[direct_key]), + "delta_auc": float(row[jacobian_key]) - float(row[direct_key]), + } + for row in rows + ] + + +def merged_prompt_rows(by_scale: dict[str, list[dict]]) -> list[dict]: + indexes = { + scale: {row["slug"]: row for row in rows} + for scale, rows in by_scale.items() + } + slugs = [row["slug"] for row in by_scale["4B"]] + output = [] + for slug in slugs: + reference = indexes["4B"][slug] + row = { + "slug": slug, + "family": reference["family"], + "phrasing_id": reference.get("phrasing_id"), + } + for scale in ("4B", "12B", "31B"): + member = indexes[scale][slug] + for key in ("jacobian_auc", "direct_auc", "delta_auc"): + row[f"{key}_{scale.lower()}"] = member[key] + output.append(row) + return output + + +def merged_concept_rows( + concept_4b: list[dict], concept_12b: list[dict], concept_31b: list[dict] +) -> list[dict]: + maps = { + "4B": {(row["slug"], row["concept"]): row for row in concept_4b}, + "12B": {(row["slug"], row["concept"]): row for row in concept_12b}, + "31B": {(row["slug"], row["concept"]): row for row in concept_31b}, + } + output = [] + for key, reference in maps["4B"].items(): + row = { + "slug": key[0], + "concept": key[1], + "family": reference["family"], + } + for scale in ("4B", "12B"): + row[f"jacobian_log_rank_{scale.lower()}"] = float( + maps[scale][key]["j_log_rank_mean_seed"] + ) + row[f"direct_log_rank_{scale.lower()}"] = float( + maps[scale][key]["logit_log_rank"] + ) + row["jacobian_log_rank_31b"] = float(maps["31B"][key]["j_log_rank"]) + row["direct_log_rank_31b"] = float( + maps["31B"][key]["direct_log_rank"] + ) + output.append(row) + return output + + +def pairwise_scale_statistics( + prompt_rows: list[dict], concept_rows: list[dict] +) -> dict: + result = {} + for pair_index, (first, second) in enumerate( + [("4B", "12B"), ("4B", "31B"), ("12B", "31B")] + ): + first_key = first.lower() + second_key = second.lower() + comparison_rows = [] + for row in prompt_rows: + comparison = {"family": row["family"], "slug": row["slug"]} + for method in ("jacobian", "direct", "delta"): + comparison[f"{method}_change"] = ( + float(row[f"{method}_auc_{second_key}"]) + - float(row[f"{method}_auc_{first_key}"]) + ) + comparison_rows.append(comparison) + concept_comparison = [] + for row in concept_rows: + concept_comparison.append( + { + "family": row["family"], + "first_j": row[f"jacobian_log_rank_{first_key}"], + "second_j": row[f"jacobian_log_rank_{second_key}"], + "first_direct": row[f"direct_log_rank_{first_key}"], + "second_direct": row[f"direct_log_rank_{second_key}"], + } + ) + result[f"{first}_to_{second}"] = { + "changes": { + method: method_interval( + comparison_rows, + f"{method}_change", + RNG_SEED + 10 * pair_index + offset, + ) + for offset, method in enumerate(("jacobian", "direct", "delta")) + }, + "exact_tests": { + method: scale31.exact_family_sign_flip( + comparison_rows, f"{method}_change" + ) + for method in ("jacobian", "direct", "delta") + }, + "concept_rank_correlations": { + "jacobian": heldout.family_clustered_spearman( + concept_comparison, + "first_j", + "second_j", + seed=RNG_SEED + 100 + 2 * pair_index, + ), + "direct": heldout.family_clustered_spearman( + concept_comparison, + "first_direct", + "second_direct", + seed=RNG_SEED + 101 + 2 * pair_index, + ), + }, + } + return result + + +def save_figure(figure: plt.Figure, stem: Path) -> None: + stem.parent.mkdir(parents=True, exist_ok=True) + for suffix in ("png", "pdf", "svg"): + figure.savefig( + stem.with_suffix(f".{suffix}"), + dpi=300, + bbox_inches="tight", + facecolor="white", + ) + plt.close(figure) + + +def panel_label(axis: plt.Axes, label: str) -> None: + axis.text( + -0.105, + 1.055, + label, + transform=axis.transAxes, + ha="left", + va="bottom", + fontweight="bold", + fontsize=11, + clip_on=False, + ) + + +def plot_main(stats: dict) -> None: + plt.rcParams.update( + { + "font.family": "DejaVu Sans", + "font.size": 9.2, + "axes.spines.top": False, + "axes.spines.right": False, + } + ) + figure, axes = plt.subplots(2, 2, figsize=(10.8, 7.6), constrained_layout=True) + axes = axes.ravel() + ks = stats["cutoffs"] + + for axis, method, label in zip( + axes[:2], ("jacobian", "direct"), ("A", "B"), strict=True + ): + for scale in ("4B", "12B", "31B"): + axis.plot( + ks, + stats["scales"][scale]["pass_at_k"][method], + marker="o" if method == "jacobian" else "s", + linewidth=2.0, + markersize=4.5, + color=COLORS[scale], + label=( + f"{scale} (3-fit mean)" + if scale != "31B" + else "31B (single fit)" + ), + ) + axis.set_xscale("log") + axis.set_xlabel("Vocabulary cutoff $k$ (lower is stricter)") + axis.set_ylabel("Fraction of declared concepts within top $k$") + axis.grid(True, alpha=0.18) + axis.legend(frameon=False, loc="upper left", fontsize=8.2) + panel_label(axis, label) + + axis = axes[2] + positions = np.arange(3, dtype=float) + offset = 0.13 + for method, marker, color, delta in ( + ("jacobian", "o", None, -offset), + ("direct", "s", DIRECT, offset), + ): + values = [] + lows = [] + highs = [] + for scale in ("4B", "12B", "31B"): + entry = stats["scales"][scale][method] + values.append(entry["observed_mean"]) + lows.append(entry["observed_mean"] - entry["low"]) + highs.append(entry["high"] - entry["observed_mean"]) + if method == "jacobian": + for index, scale in enumerate(("4B", "12B", "31B")): + axis.errorbar( + positions[index] + delta, + values[index], + yerr=np.asarray([[lows[index]], [highs[index]]]), + fmt=marker, + color=COLORS[scale], + markeredgecolor=COLORS[scale], + markersize=6, + capsize=3, + linewidth=1.3, + zorder=3, + ) + else: + axis.errorbar( + positions + delta, + values, + yerr=np.asarray([lows, highs]), + fmt=marker, + color=color, + markerfacecolor="white", + markeredgecolor=color, + markersize=6, + capsize=3, + linewidth=1.3, + zorder=3, + ) + for index in range(3): + if method == "direct": + j_value = stats["scales"][("4B", "12B", "31B")[index]][ + "jacobian" + ]["observed_mean"] + axis.plot( + [positions[index] - offset, positions[index] + offset], + [j_value, values[index]], + color=LIGHT, + linewidth=1.0, + zorder=1, + ) + axis.scatter([], [], marker="o", color=COLORS["4B"], label="Jacobian lens") + axis.scatter( + [], [], marker="s", facecolor="white", edgecolor=DIRECT, label="Direct" + ) + axis.set_xticks(positions, ["4B", "12B", "31B"]) + axis.set_ylabel("Controlled recovery AUC") + axis.grid(True, axis="y", alpha=0.18) + axis.legend(frameon=False, loc="upper left", fontsize=8.2) + panel_label(axis, "C") + + axis = axes[3] + for position, scale in enumerate(("4B", "12B", "31B")): + entry = stats["scales"][scale]["delta"] + value = entry["observed_mean"] + color = COLORS[scale] if value >= 0 else NEGATIVE + axis.errorbar( + value, + position, + xerr=np.asarray( + [[value - entry["low"]], [entry["high"] - value]] + ), + fmt="D", + color=color, + markersize=6, + capsize=3, + linewidth=1.5, + zorder=3, + ) + axis.text( + entry["high"] + 0.003, + position, + f"{value:+.3f}", + va="center", + ha="left", + color=color, + fontsize=8.2, + ) + axis.axvline(0, color=DIRECT, linewidth=1.0) + axis.set_yticks(range(3), ["4B (3 fits)", "12B (3 fits)", "31B (1 fit)"]) + axis.invert_yaxis() + axis.set_xlabel("Jacobian minus direct-unembedding AUC") + axis.grid(True, axis="x", alpha=0.18) + panel_label(axis, "D") + save_figure(figure, FIGURES / "heldout-readability-across-gemma-scales") + + +def plot_diagnostics(stats: dict) -> None: + plt.rcParams.update( + { + "font.family": "DejaVu Sans", + "font.size": 9.2, + "axes.spines.top": False, + "axes.spines.right": False, + } + ) + figure, axes = plt.subplots(1, 3, figsize=(12.4, 3.9), constrained_layout=True) + + axis = axes[0] + labels = ["0–1", "0–2", "1–2"] + x = np.arange(3) + for offset, scale in [(-0.10, "4B"), (0.10, "12B")]: + values = [ + stats["lens_fit_reproducibility"][scale][key]["rho"] + for key in ("seed0_vs_seed1", "seed0_vs_seed2", "seed1_vs_seed2") + ] + axis.scatter( + x + offset, + values, + s=45, + color=COLORS[scale], + label=scale, + zorder=3, + ) + axis.set_xticks(x, labels) + axis.set_xlabel("Independent lens-fit pair") + axis.set_ylabel("Spearman rank correlation") + axis.set_ylim(0.985, 1.0005) + axis.grid(True, axis="y", alpha=0.18) + axis.legend(frameon=False, loc="lower left") + panel_label(axis, "A") + + axis = axes[1] + pairs = ["4B→12B", "4B→31B", "12B→31B"] + x = np.arange(3) + for offset, method, marker, color in [ + (-0.10, "jacobian", "o", COLORS["12B"]), + (0.10, "direct", "s", DIRECT), + ]: + values = [] + for key in ("4B_to_12B", "4B_to_31B", "12B_to_31B"): + values.append( + stats["pairwise_scale"][key]["concept_rank_correlations"][method][ + "rho" + ] + ) + axis.scatter( + x + offset, + values, + s=45, + marker=marker, + color=color if method == "jacobian" else None, + facecolor=color if method == "jacobian" else "white", + edgecolor=color, + label="Jacobian lens" if method == "jacobian" else "Direct", + zorder=3, + ) + axis.axhline(0, color=DIRECT, linewidth=0.9) + axis.set_xticks(x, pairs, rotation=18, ha="right") + axis.set_ylabel("Cross-scale concept-rank correlation") + axis.set_ylim(-0.08, 0.42) + axis.grid(True, axis="y", alpha=0.18) + axis.legend(frameon=False, loc="upper left", fontsize=8.2) + panel_label(axis, "B") + + axis = axes[2] + pair_keys = ("4B_to_12B", "4B_to_31B", "12B_to_31B") + y = np.arange(3) + for position, key in enumerate(pair_keys): + entry = stats["pairwise_scale"][key]["changes"]["jacobian"] + value = entry["observed_mean"] + axis.errorbar( + value, + position, + xerr=np.asarray([[value - entry["low"]], [entry["high"] - value]]), + fmt="o", + color=COLORS["31B"], + capsize=3, + linewidth=1.4, + ) + axis.axvline(0, color=DIRECT, linewidth=0.9) + axis.set_yticks(y, pairs) + axis.invert_yaxis() + axis.set_xlabel("Change in Jacobian recovery AUC") + axis.grid(True, axis="x", alpha=0.18) + panel_label(axis, "C") + save_figure(figure, FIGURES / "heldout-readability-scale-diagnostics") + + +def main() -> None: + OUT.mkdir(parents=True, exist_ok=True) + FIGURES.mkdir(parents=True, exist_ok=True) + + runs_4b = [json.loads(path.read_text()) for path in heldout.RUN_PATHS] + slugs, indexes_4b = heldout.validate_runs(runs_4b, heldout.RUN_PATHS) + prompt_4b, concept_4b, ks_4b = heldout.build_controlled_rows( + runs_4b, slugs, indexes_4b + ) + controlled_4b = heldout.build_controlled_statistics( + runs_4b, prompt_4b, concept_4b, ks_4b + ) + frozen_4b = json.loads(FROZEN_4B_STATS.read_text())["controlled"] + if controlled_4b != frozen_4b: + raise ValueError("4B controlled statistics differ from the frozen record") + + runs_12b = [json.loads(path.read_text()) for path in RUN_12B] + slugs_12b, indexes_12b = heldout.validate_runs(runs_12b, RUN_12B) + scale12.validate_12b_runs(runs_12b, indexes_12b) + prompt_12b, concept_12b, ks_12b = heldout.build_controlled_rows( + runs_12b, slugs_12b, indexes_12b + ) + controlled_12b = heldout.build_controlled_statistics( + runs_12b, prompt_12b, concept_12b, ks_12b + ) + frozen_12b = json.loads(FROZEN_12B_STATS.read_text())["controlled"] + if controlled_12b != frozen_12b: + raise ValueError("12B controlled statistics differ from the frozen record") + + run_31b = json.loads(RUN_31B.read_text()) + index_31b = scale31.validate_31b(run_31b, slugs, indexes_4b) + prompt_31b, concept_31b, ks_31b = scale31.build_31b_rows( + run_31b, slugs, index_31b + ) + frozen_31b = json.loads(FROZEN_31B_STATS.read_text())["single_fit_31b"] + if ks_4b != ks_12b or ks_4b != ks_31b: + raise ValueError("vocabulary cutoffs differ across scales") + validate_prompt_equivalence(slugs, indexes_4b, indexes_12b, index_31b) + + standardized = { + "4B": renamed_rows( + prompt_4b, + scale="4B", + jacobian_key="j_auc_mean_seed", + direct_key="logit_auc", + ), + "12B": renamed_rows( + prompt_12b, + scale="12B", + jacobian_key="j_auc_mean_seed", + direct_key="logit_auc", + ), + "31B": renamed_rows( + prompt_31b, + scale="31B", + jacobian_key="j_auc", + direct_key="direct_auc", + ), + } + merged_prompts = merged_prompt_rows(standardized) + merged_concepts = merged_concept_rows(concept_4b, concept_12b, concept_31b) + + scale_entries = {} + pass_at_k = { + "4B": { + "jacobian": controlled_4b["pass_at_k"]["j_mean"], + "direct": controlled_4b["pass_at_k"]["logit"], + }, + "12B": { + "jacobian": controlled_12b["pass_at_k"]["j_mean"], + "direct": controlled_12b["pass_at_k"]["logit"], + }, + "31B": { + "jacobian": frozen_31b["pass_at_k"]["jacobian"], + "direct": frozen_31b["pass_at_k"]["direct"], + }, + } + for scale_index, scale in enumerate(("4B", "12B", "31B")): + rows = standardized[scale] + scale_entries[scale] = { + "n_lens_fits": 3 if scale != "31B" else 1, + "jacobian": method_interval( + rows, "jacobian_auc", RNG_SEED + 20 * scale_index + ), + "direct": method_interval( + rows, "direct_auc", RNG_SEED + 20 * scale_index + 1 + ), + "delta": method_interval( + rows, "delta_auc", RNG_SEED + 20 * scale_index + 2 + ), + "delta_exact": scale31.exact_family_sign_flip(rows, "delta_auc"), + "pass_at_k": pass_at_k[scale], + } + + stats = { + "study_id": "gemma-multiscale-heldout-v1-2026-08-25", + "analysis_status": ( + "post-review cross-scale extension of the frozen held-out endpoint" + ), + "analysis_seed": RNG_SEED, + "cutoffs": ks_4b, + "inputs": { + "4B": [ + {"path": str(path.relative_to(ROOT)), "sha256": sha256(path)} + for path in heldout.RUN_PATHS + ], + "12B": [ + {"path": str(path.relative_to(ROOT)), "sha256": sha256(path)} + for path in RUN_12B + ], + "31B": [ + {"path": str(RUN_31B.relative_to(ROOT)), "sha256": sha256(RUN_31B)} + ], + }, + "audit": { + "prompt_text_order_family_phrasing_positions_and_terms_identical": True, + "n_prompts": 50, + "n_families": 10, + "n_concepts": 150, + "population_interval_unit": ( + "ten mechanism families; five phrasings resampled within family" + ), + "lens_fit_boundary": ( + "4B and 12B average three independent fits; 31B is one fit. " + "Population intervals are not lens-fit intervals." + ), + }, + "scales": scale_entries, + "lens_fit_reproducibility": { + "4B": controlled_4b["seed_rank_correlations"], + "12B": controlled_12b["seed_rank_correlations"], + "31B": None, + }, + "pairwise_scale": pairwise_scale_statistics( + merged_prompts, merged_concepts + ), + } + + write_csv(OUT / "prompt_results_across_scales.csv", merged_prompts) + write_csv(OUT / "concept_results_across_scales.csv", merged_concepts) + (OUT / "statistics.json").write_text( + json.dumps(stats, indent=2, sort_keys=True) + "\n" + ) + protocol = """# Gemma-scale held-out readability protocol + +This is a post-review extension of the already frozen 50-prompt controlled- +recovery endpoint. The prompt strings, omitted concepts, score position, +38--92% layer band, vocabulary cutoffs, direct baseline, and per-scale lens +recipe are unchanged. Gemma 4B and 12B use the mean of three independently +fitted lenses; Gemma 31B has one available lens and is labelled as a single +fit. The scientific sampling interval resamples ten mechanism families and +then five phrasings within each selected family. It does not estimate 31B +lens-fit variability. Cross-scale comparisons are paired by the same prompt +and concept identity. No prompt, concept, family, or fit was excluded. +""" + (OUT / "PROTOCOL.md").write_text(protocol) + plot_main(stats) + plot_diagnostics(stats) + + lines = [ + "# Controlled readability across Gemma scales", + "", + "The identical 50 prompts and 150 predeclared one-token concepts were " + "evaluated at 4B, 12B, and 31B scale.", + "", + ] + for scale in ("4B", "12B", "31B"): + entry = stats["scales"][scale] + lines.append( + f"- {scale}: Jacobian AUC {entry['jacobian']['observed_mean']:.4f}; " + f"direct AUC {entry['direct']['observed_mean']:.4f}; difference " + f"{entry['delta']['observed_mean']:+.4f} " + f"(95% population interval {entry['delta']['low']:+.4f} to " + f"{entry['delta']['high']:+.4f})." + ) + lines.extend( + [ + "", + "Absolute controlled readability rises descriptively with model size, " + "but the Jacobian-minus-direct contrast changes sign and every " + "family-hierarchical interval includes zero. Independent lens fits " + "remain nearly identical at 4B and 12B; 31B fit reproducibility cannot " + "be estimated from one checkpoint.", + "", + "Cross-scale concept-rank correlations are modest, showing that stable " + "measurement within a model size does not imply that exactly the same " + "concepts occupy the same ranks after scaling.", + "", + ] + ) + (OUT / "REPORT.md").write_text("\n".join(lines)) + print("\n".join(lines)) + print(f"wrote {OUT}") + + +if __name__ == "__main__": + main() diff --git a/jlens_materials/scripts/analyze_graph_gauge_generalization.py b/jlens_materials/scripts/analyze_graph_gauge_generalization.py new file mode 100644 index 0000000000000000000000000000000000000000..8b54d27ceced60b59a43823150601aac47c3c768 --- /dev/null +++ b/jlens_materials/scripts/analyze_graph_gauge_generalization.py @@ -0,0 +1,814 @@ +#!/usr/bin/env python3 +"""Gauge-invariant and permutation-group graph follow-up. + +Executes Studies 4A, 4B, and the nonparametric portion of 4C from the frozen +``GAUGE_PROTOCOL.md``. It only reads archived representations and results. +""" + +from __future__ import annotations + +import ast +import hashlib +import itertools +import json +import math +import platform +import sys +from pathlib import Path +from typing import Mapping, Sequence + +import numpy as np +import pandas as pd +from sklearn.metrics import roc_auc_score + +from analyze_graph_isomorphism_generalization import ( + MANIFEST, + METHODS, + STATES, + VARIANTS, + fit_pair_mapping, + heldout_vector, + index_lookup, + load_data, + pearson, + plus_one_signflip_p, +) + + +ROOT = Path(__file__).resolve().parents[1] +OUT = ( + ROOT + / "experiments" + / "graph-isomorphism-generalization-2026-07-18" +) +PROTOCOL = OUT / "protocol.json" +GAUGE_PROTOCOL = OUT / "gauge_protocol.json" +GAUGE_PROTOCOL_MD = OUT / "GAUGE_PROTOCOL.md" +MAPPINGS = OUT / "pair_mappings.csv" +CANDIDATES = OUT / "all_mapping_candidates.csv" +SEED = 20260718 +N_CYCLE_NULL = 100_000 +N_ATLAS_NULL = 10_000 +TEMPERATURE = 0.05 +PERMS = np.asarray( + list(itertools.permutations(range(4))), dtype=np.int8 +) +PERM_CODES = PERMS @ np.asarray([64, 16, 4, 1], dtype=np.int16) +CODE_TO_INDEX = np.full(256, -1, dtype=np.int16) +CODE_TO_INDEX[PERM_CODES] = np.arange(24, dtype=np.int16) +IDENTITY_INDEX = int( + np.flatnonzero(np.all(PERMS == np.arange(4), axis=1))[0] +) +INVERSE_PERMS = np.argsort(PERMS, axis=1).astype(np.int8) + + +def sha256(path: Path) -> str: + return hashlib.sha256(path.read_bytes()).hexdigest() + + +def safe_json(value: object) -> object: + if isinstance(value, np.generic): + return safe_json(value.item()) + if isinstance(value, float) and not math.isfinite(value): + return None + if isinstance(value, Mapping): + return {str(key): safe_json(item) for key, item in value.items()} + if isinstance(value, (list, tuple)): + return [safe_json(item) for item in value] + return value + + +def validate_inputs() -> None: + parent = json.loads(PROTOCOL.read_text()) + followup = json.loads(GAUGE_PROTOCOL.read_text()) + expected_parent = followup["parent_protocol_sha256"] + if sha256(PROTOCOL) != expected_parent: + raise RuntimeError("parent protocol fingerprint mismatch") + if ( + sha256(STATES) + != followup["inputs"]["representations_sha256"] + or sha256(MANIFEST) + != followup["inputs"]["prompt_manifest_sha256"] + ): + raise RuntimeError("input fingerprint mismatch") + if parent["study_id"] != "graph-isomorphism-generalization-2026-07-18": + raise RuntimeError("unexpected parent study") + + +def parse_permutation(value: object) -> tuple[int, ...]: + if isinstance(value, str): + parsed = ast.literal_eval(value) + else: + parsed = value + result = tuple(int(item) for item in parsed) + if sorted(result) != list(range(4)): + raise ValueError(f"invalid permutation: {value}") + return result + + +def directed_map_lookup( + frame: pd.DataFrame, family_order: Sequence[str] +) -> dict[tuple[str, str], np.ndarray]: + lookup: dict[tuple[str, str], np.ndarray] = {} + for row in frame.itertuples(index=False): + first = str(row.source_family) + second = str(row.target_family) + permutation = np.asarray( + parse_permutation(row.permutation), dtype=np.int8 + ) + lookup[(first, second)] = permutation + lookup[(second, first)] = np.argsort(permutation).astype(np.int8) + expected = len(family_order) * (len(family_order) - 1) + if len(lookup) != expected: + raise RuntimeError("directed map lookup is incomplete") + return lookup + + +def mapped_label_vectors( + source_family: str, + target_family: str, + permutation: Sequence[int], + labels: np.ndarray, + data: Mapping[str, object], +) -> tuple[np.ndarray, np.ndarray]: + cases = data["case_order"] + lookup = index_lookup(data) + source = [] + target = [] + for source_case_index, source_case in enumerate(cases[source_family]): + target_case = cases[target_family][permutation[source_case_index]] + for variant in VARIANTS: + source.append(labels[lookup[(source_family, source_case, variant)]]) + target.append(labels[lookup[(target_family, target_case, variant)]]) + return np.asarray(source, dtype=bool), np.asarray(target, dtype=bool) + + +def agreement(first: np.ndarray, second: np.ndarray) -> float: + return float(np.mean(first == second)) + + +def gauge_agreement(first: np.ndarray, second: np.ndarray) -> float: + ordinary = agreement(first, second) + return max(ordinary, 1.0 - ordinary) + + +def response_code(data: Mapping[str, object]) -> np.ndarray: + numeric = np.asarray(data["numeric"], dtype=bool) + variants = np.asarray(data["variants"]) + return np.logical_xor( + numeric, + variants == "lexical_counterfactual", + ) + + +def structured_gauge_null( + frame: pd.DataFrame, data: Mapping[str, object] +) -> np.ndarray: + family_order = list(data["family_order"]) + cases = data["case_order"] + lookup = index_lookup(data) + balanced = list(itertools.combinations(range(4), 2)) + pair_specs = [ + ( + family_order.index(str(row.source_family)), + family_order.index(str(row.target_family)), + np.asarray(parse_permutation(row.permutation), dtype=int), + ) + for row in frame.itertuples(index=False) + ] + results = np.empty(len(balanced) ** len(family_order), dtype=np.float32) + for assignment_index, assignment in enumerate( + itertools.product(range(len(balanced)), repeat=len(family_order)) + ): + case_labels = np.zeros((len(family_order), 4), dtype=bool) + for family_index, choice in enumerate(assignment): + case_labels[family_index, list(balanced[choice])] = True + pair_values = [] + for source_index, target_index, permutation in pair_specs: + source_anchor = case_labels[source_index] + target_anchor = case_labels[target_index, permutation] + source_nodes = np.concatenate( + [source_anchor, source_anchor, ~source_anchor] + ) + target_nodes = np.concatenate( + [target_anchor, target_anchor, ~target_anchor] + ) + # The concatenation order is immaterial because both vectors use + # the identical order; it represents A, P, then C. + pair_values.append( + gauge_agreement(source_nodes, target_nodes) + ) + results[assignment_index] = float(np.mean(pair_values)) + return results + + +def study_4a( + mappings: pd.DataFrame, data: Mapping[str, object] +) -> tuple[pd.DataFrame, dict[str, object], np.ndarray]: + physical = np.asarray(data["physical"], dtype=bool) + numeric = np.asarray(data["numeric"], dtype=bool) + prompt_code = response_code(data) + pair_rows = [] + summaries = {} + jacobian_null = np.asarray([]) + for method in METHODS: + frame = mappings[ + (mappings["method"] == method) + & (mappings["scope"] == "band") + ].copy() + for row in frame.itertuples(index=False): + permutation = parse_permutation(row.permutation) + values: dict[str, float] = {} + for label_name, labels in ( + ("physical", physical), + ("numeric", numeric), + ("prompt_code", prompt_code), + ): + source, target = mapped_label_vectors( + str(row.source_family), + str(row.target_family), + permutation, + labels, + data, + ) + values[f"{label_name}_ordinary"] = agreement(source, target) + values[f"{label_name}_gauge"] = gauge_agreement(source, target) + pair_rows.append( + { + "method": method, + "source_family": row.source_family, + "target_family": row.target_family, + "permutation": str(permutation), + **values, + } + ) + method_pairs = pd.DataFrame(pair_rows) + method_pairs = method_pairs[method_pairs["method"] == method] + null = structured_gauge_null(frame, data) + observed = float(method_pairs["physical_gauge"].mean()) + exact_p = float( + (1 + np.sum(null >= observed - 1e-12)) / (1 + len(null)) + ) + summaries[method] = { + "physical_ordinary_mean": float( + method_pairs["physical_ordinary"].mean() + ), + "physical_gauge_mean": observed, + "physical_perfect_gauge_pairs": int( + np.sum(method_pairs["physical_gauge"] >= 1.0 - 1e-12) + ), + "structured_null_mean": float(np.mean(null)), + "structured_exact_p": exact_p, + "numeric_gauge_mean": float( + method_pairs["numeric_gauge"].mean() + ), + "prompt_code_gauge_mean": float( + method_pairs["prompt_code_gauge"].mean() + ), + "physical_minus_prompt_code": float( + method_pairs["physical_gauge"].mean() + - method_pairs["prompt_code_gauge"].mean() + ), + } + summaries[method]["strong_gate"] = bool( + exact_p <= 0.05 + and summaries[method]["physical_perfect_gauge_pairs"] >= 10 + and summaries[method]["physical_gauge_mean"] + > summaries[method]["numeric_gauge_mean"] + and summaries[method]["physical_gauge_mean"] + > summaries[method]["prompt_code_gauge_mean"] + ) + if method == "jacobian": + jacobian_null = null + return pd.DataFrame(pair_rows), summaries, jacobian_null + + +def permutation_index(permutation: Sequence[int]) -> int: + code = int(np.asarray(permutation) @ np.asarray([64, 16, 4, 1])) + index = int(CODE_TO_INDEX[code]) + if index < 0: + raise ValueError("not a permutation") + return index + + +def cycle_statistics( + directed: Mapping[tuple[str, str], np.ndarray], + family_order: Sequence[str], +) -> tuple[pd.DataFrame, float]: + rows = [] + for first, second, third in itertools.combinations(family_order, 3): + cycle = directed[(third, first)][ + directed[(second, third)][directed[(first, second)]] + ] + fixed = int(np.sum(cycle == np.arange(4))) + rows.append( + { + "family_1": first, + "family_2": second, + "family_3": third, + "cycle_permutation": str(tuple(int(item) for item in cycle)), + "identity": bool(fixed == 4), + "fixed_positions": fixed, + "fixed_fraction": fixed / 4.0, + "hamming_distance": 4 - fixed, + } + ) + frame = pd.DataFrame(rows) + return frame, float(frame["fixed_fraction"].mean()) + + +def null_cycle_fixed_fraction( + family_order: Sequence[str], + pair_probabilities: Mapping[tuple[str, str], np.ndarray] | None, + n_null: int, + rng: np.random.Generator, +) -> np.ndarray: + pairs = list(itertools.combinations(family_order, 2)) + pair_index = {pair: index for index, pair in enumerate(pairs)} + if pair_probabilities is None: + draws = rng.integers(0, 24, size=(n_null, len(pairs))) + else: + draws = np.column_stack( + [ + rng.choice( + 24, + size=n_null, + p=pair_probabilities[pair], + ) + for pair in pairs + ] + ) + forward = PERMS[draws] + inverse = INVERSE_PERMS[draws] + + def directed_batch(source: str, target: str) -> np.ndarray: + if source < target: + return forward[:, pair_index[(source, target)]] + return inverse[:, pair_index[(target, source)]] + + totals = np.zeros(n_null, dtype=np.float64) + triples = list(itertools.combinations(family_order, 3)) + identity = np.arange(4)[None, :] + for first, second, third in triples: + first_second = directed_batch(first, second) + second_third = directed_batch(second, third) + third_first = directed_batch(third, first) + step_two = np.take_along_axis( + second_third, first_second, axis=1 + ) + cycle = np.take_along_axis(third_first, step_two, axis=1) + totals += np.mean(cycle == identity, axis=1) + return (totals / len(triples)).astype(np.float32) + + +def candidate_score_tables( + candidates: pd.DataFrame, + method: str, + family_order: Sequence[str], +) -> dict[tuple[str, str], np.ndarray]: + selected = candidates[ + (candidates["method"] == method) + & (candidates["scope"] == "band") + ] + tables = {} + for pair in itertools.combinations(family_order, 2): + frame = selected[ + (selected["source_family"] == pair[0]) + & (selected["target_family"] == pair[1]) + ] + table = np.full(24, -np.inf, dtype=np.float64) + for row in frame.itertuples(index=False): + table[permutation_index(parse_permutation(row.permutation))] = ( + float(row.fit_correlation) + ) + if not np.all(np.isfinite(table)): + raise RuntimeError(f"incomplete score table: {pair}, {method}") + tables[pair] = table + return tables + + +def score_probabilities( + tables: Mapping[tuple[str, str], np.ndarray], +) -> dict[tuple[str, str], np.ndarray]: + probabilities = {} + for pair, scores in tables.items(): + logits = (scores - np.max(scores)) / TEMPERATURE + weights = np.exp(logits) + probabilities[pair] = weights / np.sum(weights) + return probabilities + + +def global_atlas_exact( + tables: Mapping[tuple[str, str], np.ndarray], + family_order: Sequence[str], + *, + chunk_size: int = 100_000, +) -> tuple[np.ndarray, float, np.ndarray]: + total_assignments = 24 ** (len(family_order) - 1) + best_score = -np.inf + best_assignment: np.ndarray | None = None + random_scores = np.empty(N_ATLAS_NULL, dtype=np.float64) + rng = np.random.default_rng(SEED + 701) + random_assignment = np.column_stack( + [ + np.full(N_ATLAS_NULL, IDENTITY_INDEX, dtype=np.int16), + rng.integers( + 0, + 24, + size=(N_ATLAS_NULL, len(family_order) - 1), + dtype=np.int16, + ), + ] + ) + + def score_assignments(assignments: np.ndarray) -> np.ndarray: + group_perms = PERMS[assignments] + group_inverse = INVERSE_PERMS[assignments] + result = np.zeros(len(assignments), dtype=np.float64) + rows = np.arange(len(assignments))[:, None] + del rows + for first_index, second_index in itertools.combinations( + range(len(family_order)), 2 + ): + derived = np.take_along_axis( + group_perms[:, second_index], + group_inverse[:, first_index], + axis=1, + ) + codes = derived @ np.asarray( + [64, 16, 4, 1], dtype=np.int16 + ) + indices = CODE_TO_INDEX[codes] + pair = ( + family_order[first_index], + family_order[second_index], + ) + result += tables[pair][indices] + return result + + random_scores[:] = score_assignments(random_assignment) + powers = np.asarray( + [24 ** index for index in range(len(family_order) - 1)], + dtype=np.int64, + ) + for start in range(0, total_assignments, chunk_size): + stop = min(total_assignments, start + chunk_size) + numbers = np.arange(start, stop, dtype=np.int64) + digits = ((numbers[:, None] // powers[None, :]) % 24).astype( + np.int16 + ) + assignments = np.column_stack( + [ + np.full(len(numbers), IDENTITY_INDEX, dtype=np.int16), + digits, + ] + ) + scores = score_assignments(assignments) + local_index = int(np.argmax(scores)) + local_score = float(scores[local_index]) + if local_score > best_score + 1e-12: + best_score = local_score + best_assignment = assignments[local_index].copy() + if best_assignment is None: + raise RuntimeError("atlas search failed") + return best_assignment, best_score, random_scores + + +def atlas_directed_maps( + assignment: np.ndarray, family_order: Sequence[str] +) -> dict[tuple[str, str], np.ndarray]: + group = PERMS[assignment] + inverse = INVERSE_PERMS[assignment] + result = {} + for first_index, second_index in itertools.permutations( + range(len(family_order)), 2 + ): + result[ + (family_order[first_index], family_order[second_index]) + ] = group[second_index][inverse[first_index]] + return result + + +def atlas_heldout( + directed: Mapping[tuple[str, str], np.ndarray], + data: Mapping[str, object], + similarity: np.ndarray, +) -> pd.DataFrame: + cases = data["case_order"] + lookup = index_lookup(data) + rows = [] + for first, second in itertools.combinations(data["family_order"], 2): + permutation = directed[(first, second)] + source = heldout_vector( + similarity, first, cases[first], lookup + ) + target = heldout_vector( + similarity, + second, + cases[second], + lookup, + permutation, + ) + rows.append( + { + "source_family": first, + "target_family": second, + "heldout_correlation": pearson(source, target), + } + ) + return pd.DataFrame(rows) + + +def study_4b( + mappings: pd.DataFrame, + candidates: pd.DataFrame, + data: Mapping[str, object], +) -> tuple[pd.DataFrame, pd.DataFrame, dict[str, object], dict[str, np.ndarray]]: + family_order = list(data["family_order"]) + rng = np.random.default_rng(SEED) + cycle_frames = [] + atlas_frames = [] + summaries = {} + null_outputs = {} + for method in METHODS: + mapping_frame = mappings[ + (mappings["method"] == method) + & (mappings["scope"] == "band") + ] + directed = directed_map_lookup(mapping_frame, family_order) + cycles, observed = cycle_statistics(directed, family_order) + cycles.insert(0, "method", method) + cycle_frames.append(cycles) + tables = candidate_score_tables(candidates, method, family_order) + probabilities = score_probabilities(tables) + uniform_null = null_cycle_fixed_fraction( + family_order, None, N_CYCLE_NULL, rng + ) + score_null = null_cycle_fixed_fraction( + family_order, probabilities, N_CYCLE_NULL, rng + ) + uniform_p = float( + (1 + np.sum(uniform_null >= observed - 1e-12)) + / (1 + len(uniform_null)) + ) + score_p = float( + (1 + np.sum(score_null >= observed - 1e-12)) + / (1 + len(score_null)) + ) + assignment, atlas_score, atlas_null = global_atlas_exact( + tables, family_order + ) + atlas_maps = atlas_directed_maps(assignment, family_order) + similarity_layers = np.asarray(data["similarities"][method]) + band_similarity = np.mean( + similarity_layers[np.asarray(data["band_mask"])], axis=0 + ) + heldout = atlas_heldout(atlas_maps, data, band_similarity) + heldout.insert(0, "method", method) + atlas_frames.append(heldout) + atlas_p = float( + (1 + np.sum(atlas_null >= atlas_score - 1e-12)) + / (1 + len(atlas_null)) + ) + assignment_dict = { + family: tuple(int(value) for value in PERMS[index]) + for family, index in zip(family_order, assignment) + } + summaries[method] = { + "cycle_fixed_fraction": observed, + "cycle_identity_rate": float(cycles["identity"].mean()), + "uniform_cycle_null_mean": float(np.mean(uniform_null)), + "uniform_cycle_exact_p": uniform_p, + "score_resampled_cycle_null_mean": float(np.mean(score_null)), + "score_resampled_cycle_exact_p": score_p, + "atlas_assignment": assignment_dict, + "atlas_total_fit_score": atlas_score, + "random_atlas_score_mean": float(np.mean(atlas_null)), + "random_atlas_score_exact_p": atlas_p, + "atlas_heldout_mean_correlation": float( + heldout["heldout_correlation"].mean() + ), + "atlas_heldout_positive_pairs": int( + np.sum(heldout["heldout_correlation"] > 0) + ), + } + summaries[method]["strong_atlas_gate"] = bool( + uniform_p <= 0.05 + and score_p <= 0.05 + and atlas_p <= 0.05 + and summaries[method]["atlas_heldout_positive_pairs"] >= 10 + ) + null_outputs[f"{method}_uniform_cycle"] = uniform_null + null_outputs[f"{method}_score_cycle"] = score_null + null_outputs[f"{method}_random_atlas_score"] = atlas_null + return ( + pd.concat(cycle_frames, ignore_index=True), + pd.concat(atlas_frames, ignore_index=True), + summaries, + null_outputs, + ) + + +def candidate_pairs_for_family( + family: str, data: Mapping[str, object] +) -> list[tuple[int, int]]: + families = np.asarray(data["families"]) + cases = np.asarray(data["cases"]) + variants = np.asarray(data["variants"]) + indices = np.flatnonzero(families == family) + return [ + (int(source), int(target)) + for source in indices + for target in indices + if variants[source] != variants[target] + and cases[source] != cases[target] + ] + + +def relation_auc( + similarity: np.ndarray, + pairs: Sequence[tuple[int, int]], + labels: np.ndarray, +) -> float: + outcomes = np.asarray( + [labels[first] == labels[second] for first, second in pairs], + dtype=int, + ) + scores = np.asarray( + [similarity[first, second] for first, second in pairs], + dtype=float, + ) + return float(roc_auc_score(outcomes, scores)) + + +def balanced_relation_null( + similarity: np.ndarray, + pairs: Sequence[tuple[int, int]], + family_indices: np.ndarray, +) -> np.ndarray: + results = [] + for positives in itertools.combinations(range(12), 6): + local = np.zeros(12, dtype=bool) + local[list(positives)] = True + labels = np.zeros(72, dtype=bool) + labels[family_indices] = local + results.append(relation_auc(similarity, pairs, labels)) + return np.asarray(results, dtype=np.float32) + + +def study_4c_nonparametric( + data: Mapping[str, object], +) -> tuple[pd.DataFrame, dict[str, object], dict[str, np.ndarray]]: + physical = np.asarray(data["physical"], dtype=bool) + numeric = np.asarray(data["numeric"], dtype=bool) + prompt_code = response_code(data) + families = np.asarray(data["families"]) + family_order = list(data["family_order"]) + band_mask = np.asarray(data["band_mask"]) + rows = [] + nulls = {} + for method in METHODS: + layers = np.asarray(data["similarities"][method]) + band = np.mean(layers[band_mask], axis=0) + for family in family_order: + pairs = candidate_pairs_for_family(family, data) + family_indices = np.flatnonzero(families == family) + physical_auc = relation_auc(band, pairs, physical) + numeric_auc = relation_auc(band, pairs, numeric) + prompt_auc = relation_auc(band, pairs, prompt_code) + label_null = balanced_relation_null( + band, pairs, family_indices + ) + label_p = float( + (1 + np.sum(label_null >= physical_auc - 1e-12)) + / (1 + len(label_null)) + ) + shift_aucs = [] + for shift in range(1, len(layers)): + shifted_mask = np.roll(band_mask, shift) + shifted = np.mean(layers[shifted_mask], axis=0) + shift_aucs.append( + relation_auc(shifted, pairs, physical) + ) + rows.append( + { + "method": method, + "family": family, + "physical_auc": physical_auc, + "numeric_auc": numeric_auc, + "prompt_code_auc": prompt_auc, + "balanced_label_null_mean": float( + np.mean(label_null) + ), + "balanced_label_exact_p": label_p, + "circular_shift_mean_auc": float( + np.mean(shift_aucs) + ), + "physical_minus_shift_mean": float( + physical_auc - np.mean(shift_aucs) + ), + } + ) + nulls[f"{method}_{family}_balanced_labels"] = label_null + frame = pd.DataFrame(rows) + summaries = {} + raw = frame[frame["method"] == "raw"].set_index("family") + for method in METHODS: + selected = frame[frame["method"] == method].set_index("family") + aucs = selected.loc[family_order, "physical_auc"].to_numpy() + prompt_contrast = ( + selected.loc[family_order, "physical_auc"].to_numpy() + - selected.loc[family_order, "prompt_code_auc"].to_numpy() + ) + raw_contrast = ( + selected.loc[family_order, "physical_auc"].to_numpy() + - raw.loc[family_order, "physical_auc"].to_numpy() + ) + summaries[method] = { + "physical_auc_by_family": dict( + zip(family_order, aucs.tolist()) + ), + "mean_physical_auc": float(np.mean(aucs)), + "families_above_half": int(np.sum(aucs > 0.5)), + "auc_minus_half_plus_one_signflip_p": plus_one_signflip_p( + aucs - 0.5 + ), + "physical_over_prompt_families": int( + np.sum(prompt_contrast > 0) + ), + "physical_minus_prompt_signflip_p": plus_one_signflip_p( + prompt_contrast + ), + "physical_over_raw_families": int( + np.sum(raw_contrast > 0) + ), + "physical_minus_raw_signflip_p": plus_one_signflip_p( + raw_contrast + ), + } + summaries[method]["strong_nonparametric_gate"] = bool( + summaries[method]["families_above_half"] == 6 + and summaries[method]["auc_minus_half_plus_one_signflip_p"] + <= 0.05 + and summaries[method]["physical_over_prompt_families"] >= 5 + and summaries[method]["physical_minus_prompt_signflip_p"] + <= 0.05 + and summaries[method]["physical_over_raw_families"] >= 5 + and summaries[method]["physical_minus_raw_signflip_p"] + <= 0.05 + ) + return frame, summaries, nulls + + +def main() -> None: + validate_inputs() + data = load_data() + mappings = pd.read_csv(MAPPINGS) + candidates = pd.read_csv(CANDIDATES) + gauge_pairs, gauge_summary, gauge_null = study_4a(mappings, data) + ( + cycles, + atlas_heldout, + atlas_summary, + atlas_nulls, + ) = study_4b(mappings, candidates, data) + relation_rows, relation_summary, relation_nulls = ( + study_4c_nonparametric(data) + ) + gauge_pairs.to_csv(OUT / "gauge_pair_agreement.csv", index=False) + cycles.to_csv(OUT / "permutation_cycles.csv", index=False) + atlas_heldout.to_csv( + OUT / "global_atlas_heldout.csv", index=False + ) + relation_rows.to_csv( + OUT / "relation_nonparametric.csv", index=False + ) + np.savez_compressed( + OUT / "gauge_and_relation_nulls.npz", + jacobian_gauge_structured=gauge_null, + **atlas_nulls, + **relation_nulls, + ) + results = { + "study_id": json.loads(GAUGE_PROTOCOL.read_text())["study_id"], + "gauge_protocol_sha256": sha256(GAUGE_PROTOCOL), + "gauge_protocol_markdown_sha256": sha256(GAUGE_PROTOCOL_MD), + "parent_protocol_sha256": sha256(PROTOCOL), + "study_4a_gauge_alignment": gauge_summary, + "study_4b_permutation_atlas": atlas_summary, + "study_4c_nonparametric_relation": relation_summary, + "environment": { + "python": sys.version, + "platform": platform.platform(), + "numpy": np.__version__, + "pandas": pd.__version__, + }, + } + (OUT / "study4abc_statistics.json").write_text( + json.dumps(safe_json(results), indent=2) + "\n" + ) + print(json.dumps(safe_json(results), indent=2)) + + +if __name__ == "__main__": + main() diff --git a/jlens_materials/scripts/analyze_graph_isomorphism_generalization.py b/jlens_materials/scripts/analyze_graph_isomorphism_generalization.py new file mode 100644 index 0000000000000000000000000000000000000000..e0ae4206508281db627a71fe0af615084d73c4e3 --- /dev/null +++ b/jlens_materials/scripts/analyze_graph_isomorphism_generalization.py @@ -0,0 +1,1008 @@ +#!/usr/bin/env python3 +"""Frozen graph-isomorphism and cross-mechanism template analysis. + +This script executes Studies 1 and 2 from +``experiments/graph-isomorphism-generalization-2026-07-18/PROTOCOL.md``. +It uses only archived option-free states and never runs Gemma. +""" + +from __future__ import annotations + +import hashlib +import itertools +import json +import math +import platform +import sys +from collections import Counter +from pathlib import Path +from typing import Iterable, Mapping, Sequence + +import networkx as nx +import numpy as np +import pandas as pd +import scipy +from scipy.stats import pearsonr + + +ROOT = Path(__file__).resolve().parents[1] +OUT = ( + ROOT + / "experiments" + / "graph-isomorphism-generalization-2026-07-18" +) +PROTOCOL = OUT / "protocol.json" +PROTOCOL_MD = OUT / "PROTOCOL.md" +STATES = ( + ROOT + / "experiments" + / "option-free-question-end-2026-07-18" + / "representations.npz" +) +MANIFEST = ROOT / "paper" / "graph_data" / "signed-relation-prompt-manifest.json" +SEED = 20260718 +N_NULL = 10_000 +VARIANTS = ("anchor", "physics_paraphrase", "lexical_counterfactual") +METHODS = ("jacobian", "direct", "raw") + + +def sha256(path: Path) -> str: + return hashlib.sha256(path.read_bytes()).hexdigest() + + +def safe_json(value: object) -> object: + if isinstance(value, np.generic): + return safe_json(value.item()) + if isinstance(value, float) and not math.isfinite(value): + return None + if isinstance(value, Mapping): + return {str(key): safe_json(item) for key, item in value.items()} + if isinstance(value, (list, tuple)): + return [safe_json(item) for item in value] + return value + + +def validate_inputs(protocol: Mapping[str, object]) -> None: + lookup = { + "representations": STATES, + "prompt_manifest": MANIFEST, + } + inputs = protocol["inputs"] # type: ignore[index] + for name, path in lookup.items(): + expected = str(inputs[name]["sha256"]) # type: ignore[index] + actual = sha256(path) + if actual != expected: + raise RuntimeError( + f"input fingerprint mismatch for {path}: {actual} != {expected}" + ) + + +def normalize(values: np.ndarray) -> np.ndarray: + norms = np.linalg.norm(values, axis=-1, keepdims=True) + if np.any(norms <= 0): + raise FloatingPointError("zero-norm state") + return values / norms + + +def cosine_layers(values: np.ndarray) -> np.ndarray: + """Pairwise cosine for ``[item, layer, feature]``.""" + + unit = normalize(values.astype(np.float64)) + result = np.einsum("ild,jld->lij", unit, unit, optimize=True) + if not np.all(np.isfinite(result)): + raise FloatingPointError("non-finite cosine matrix") + return result + + +def pearson(first: Sequence[float], second: Sequence[float]) -> float: + x = np.asarray(first, dtype=float) + y = np.asarray(second, dtype=float) + if x.size < 2 or np.std(x) <= 1e-12 or np.std(y) <= 1e-12: + return 0.0 + return float(pearsonr(x, y).statistic) + + +def signflip_p(values: Sequence[float], two_sided: bool = True) -> float: + data = np.asarray(values, dtype=float) + observed = float(np.mean(data)) + signs = np.asarray(list(itertools.product([-1.0, 1.0], repeat=len(data)))) + null = np.mean(signs * data[None, :], axis=1) + if two_sided: + return float(np.mean(np.abs(null) >= abs(observed) - 1e-12)) + return float(np.mean(null >= observed - 1e-12)) + + +def plus_one_signflip_p(values: Sequence[float]) -> float: + data = np.asarray(values, dtype=float) + observed = float(np.mean(data)) + signs = np.asarray(list(itertools.product([-1.0, 1.0], repeat=len(data)))) + null = np.mean(signs * data[None, :], axis=1) + return float((1 + np.sum(null >= observed - 1e-12)) / (1 + len(null))) + + +def bh_adjust(p_values: Sequence[float]) -> np.ndarray: + values = np.asarray(p_values, dtype=float) + order = np.argsort(values) + adjusted = np.empty_like(values) + running = 1.0 + for reverse_rank in range(len(values) - 1, -1, -1): + index = order[reverse_rank] + rank = reverse_rank + 1 + running = min(running, values[index] * len(values) / rank) + adjusted[index] = running + return np.clip(adjusted, 0.0, 1.0) + + +def load_data() -> dict[str, object]: + manifest = json.loads(MANIFEST.read_text()) + metadata = {str(row["prompt_id"]): row for row in manifest["prompts"]} + with np.load(STATES, allow_pickle=False) as archive: + prompt_ids = archive["prompt_ids"].astype(str) + layers = archive["layers"].astype(int) + raw_states = archive["raw_states"][0].astype(np.float64) + direct_states = archive["direct_decoder_basis"][0].astype(np.float64) + jacobian_states = archive["jacobian_decoder_basis"][ + :, 0 + ].astype(np.float64) + rows = [] + for prompt_id in prompt_ids: + key = prompt_id.removesuffix("--answer-code") + if key not in metadata: + raise KeyError(f"missing prompt metadata: {prompt_id}") + rows.append(metadata[key]) + families = np.asarray([str(row["family_id"]) for row in rows]) + cases = np.asarray([str(row["case_id"]) for row in rows]) + variants = np.asarray([str(row["variant"]) for row in rows]) + physical = np.asarray( + [bool(row["expected_outcome"] == row["outcome_positive"]) for row in rows] + ) + numeric = np.asarray( + [bool(row["numeric_direction"] == "increase") for row in rows] + ) + family_order = sorted(set(families)) + case_order = { + family: sorted(set(cases[families == family])) + for family in family_order + } + response_orientation = {} + for family in family_order: + indices = np.flatnonzero( + (families == family) & (variants == "anchor") + ) + agrees = physical[indices] == numeric[indices] + if np.all(agrees): + response_orientation[family] = "direct" + elif np.all(~agrees): + response_orientation[family] = "inverse" + else: + raise RuntimeError(f"mixed response orientation in {family}") + + jacobian_fit_cosines = np.asarray( + [cosine_layers(fit) for fit in jacobian_states] + ) + similarities = { + "jacobian": np.mean(jacobian_fit_cosines, axis=0), + "direct": cosine_layers(direct_states), + "raw": cosine_layers(raw_states), + } + depths = layers / 41.0 * 100.0 + band_mask = (depths >= 38.0) & (depths <= 92.0) + return { + "prompt_ids": prompt_ids, + "rows": rows, + "families": families, + "cases": cases, + "variants": variants, + "physical": physical, + "numeric": numeric, + "family_order": family_order, + "case_order": case_order, + "response_orientation": response_orientation, + "layers": layers, + "depths": depths, + "band_mask": band_mask, + "similarities": similarities, + "jacobian_fit_cosines": jacobian_fit_cosines, + } + + +def index_lookup(data: Mapping[str, object]) -> dict[tuple[str, str, str], int]: + families = np.asarray(data["families"]) + cases = np.asarray(data["cases"]) + variants = np.asarray(data["variants"]) + return { + (str(family), str(case), str(variant)): int(index) + for index, (family, case, variant) in enumerate( + zip(families, cases, variants) + ) + } + + +def fitting_vector( + similarity: np.ndarray, + family: str, + cases: Sequence[str], + lookup: Mapping[tuple[str, str, str], int], + permutation: Sequence[int] | None = None, +) -> np.ndarray: + target_cases = ( + [cases[index] for index in permutation] + if permutation is not None + else list(cases) + ) + values = [] + for first in range(4): + for second in range(4): + if first == second: + continue + values.append( + similarity[ + lookup[(family, target_cases[first], "anchor")], + lookup[ + ( + family, + target_cases[second], + "physics_paraphrase", + ) + ], + ] + ) + return np.asarray(values, dtype=float) + + +def heldout_vector( + similarity: np.ndarray, + family: str, + cases: Sequence[str], + lookup: Mapping[tuple[str, str, str], int], + permutation: Sequence[int] | None = None, +) -> np.ndarray: + target_cases = ( + [cases[index] for index in permutation] + if permutation is not None + else list(cases) + ) + values = [] + for first in range(4): + for second in range(4): + if first == second: + continue + source = lookup[ + (family, target_cases[first], "lexical_counterfactual") + ] + for variant in ("anchor", "physics_paraphrase"): + target = lookup[(family, target_cases[second], variant)] + values.append(similarity[source, target]) + return np.asarray(values, dtype=float) + + +def fit_pair_mapping( + similarity: np.ndarray, + source_family: str, + target_family: str, + data: Mapping[str, object], +) -> tuple[tuple[int, ...], list[dict[str, object]], float]: + cases = data["case_order"] # type: ignore[assignment] + lookup = index_lookup(data) + source_cases = cases[source_family] + target_cases = cases[target_family] + source_fit = fitting_vector( + similarity, source_family, source_cases, lookup + ) + scores = [] + for permutation in itertools.permutations(range(4)): + target_fit = fitting_vector( + similarity, + target_family, + target_cases, + lookup, + permutation, + ) + scores.append( + { + "permutation": tuple(int(value) for value in permutation), + "fit_correlation": pearson(source_fit, target_fit), + } + ) + best = sorted( + scores, + key=lambda row: ( + -float(row["fit_correlation"]), + tuple(row["permutation"]), + ), + )[0] + permutation = tuple(best["permutation"]) + source_test = heldout_vector( + similarity, source_family, source_cases, lookup + ) + target_test = heldout_vector( + similarity, + target_family, + target_cases, + lookup, + permutation, + ) + test_correlation = pearson(source_test, target_test) + return permutation, scores, test_correlation + + +def case_labels( + family: str, + label: np.ndarray, + data: Mapping[str, object], +) -> list[bool]: + lookup = index_lookup(data) + cases = data["case_order"] # type: ignore[assignment] + return [ + bool(label[lookup[(family, case, "anchor")]]) + for case in cases[family] + ] + + +def alignment_fraction( + source_labels: Sequence[bool], + target_labels: Sequence[bool], + permutation: Sequence[int], +) -> float: + return float( + np.mean( + [ + bool(source_labels[index]) + == bool(target_labels[permutation[index]]) + for index in range(4) + ] + ) + ) + + +def all_mapping_rows( + data: Mapping[str, object], +) -> tuple[pd.DataFrame, pd.DataFrame]: + pair_rows: list[dict[str, object]] = [] + candidate_rows: list[dict[str, object]] = [] + family_order = list(data["family_order"]) + similarities = data["similarities"] # type: ignore[assignment] + layers = np.asarray(data["layers"]) + depths = np.asarray(data["depths"]) + band_mask = np.asarray(data["band_mask"]) + physical = np.asarray(data["physical"]) + numeric = np.asarray(data["numeric"]) + orientation = data["response_orientation"] # type: ignore[assignment] + case_order = data["case_order"] # type: ignore[assignment] + + similarity_sets: list[ + tuple[str, str, int | str, float | str, np.ndarray] + ] = [] + for method in METHODS: + layered = np.asarray(similarities[method]) + similarity_sets.append( + ( + method, + "band", + "38-92", + "38-92", + np.mean(layered[band_mask], axis=0), + ) + ) + similarity_sets.extend( + ( + method, + "layer", + int(layer), + float(depth), + layered[index], + ) + for index, (layer, depth) in enumerate(zip(layers, depths)) + ) + for fit_index, fit_layers in enumerate( + np.asarray(data["jacobian_fit_cosines"]) + ): + similarity_sets.append( + ( + f"jacobian_fit_{fit_index}", + "band", + "38-92", + "38-92", + np.mean(fit_layers[band_mask], axis=0), + ) + ) + + for method, scope, layer, depth, similarity in similarity_sets: + for source_family, target_family in itertools.combinations( + family_order, 2 + ): + permutation, scores, heldout = fit_pair_mapping( + similarity, source_family, target_family, data + ) + source_physical = case_labels(source_family, physical, data) + target_physical = case_labels(target_family, physical, data) + source_numeric = case_labels(source_family, numeric, data) + target_numeric = case_labels(target_family, numeric, data) + physical_agreement = alignment_fraction( + source_physical, target_physical, permutation + ) + numeric_agreement = alignment_fraction( + source_numeric, target_numeric, permutation + ) + pair_rows.append( + { + "method": method, + "scope": scope, + "layer": layer, + "depth_percent": depth, + "source_family": source_family, + "target_family": target_family, + "source_orientation": orientation[source_family], + "target_orientation": orientation[target_family], + "opposite_response_orientation": ( + orientation[source_family] + != orientation[target_family] + ), + "permutation": ",".join(map(str, permutation)), + "mapped_target_cases": "|".join( + case_order[target_family][index] + for index in permutation + ), + "fit_correlation": max( + float(row["fit_correlation"]) for row in scores + ), + "heldout_correlation": heldout, + "physical_agreement": physical_agreement, + "numeric_agreement": numeric_agreement, + "physical_minus_numeric": ( + physical_agreement - numeric_agreement + ), + } + ) + for score in scores: + candidate_rows.append( + { + "method": method, + "scope": scope, + "layer": layer, + "depth_percent": depth, + "source_family": source_family, + "target_family": target_family, + "permutation": ",".join( + map(str, score["permutation"]) + ), + "fit_correlation": score["fit_correlation"], + "selected": tuple(score["permutation"]) == permutation, + } + ) + return pd.DataFrame(pair_rows), pd.DataFrame(candidate_rows) + + +def structured_label_null( + band_rows: pd.DataFrame, + data: Mapping[str, object], +) -> tuple[np.ndarray, float]: + family_order = list(data["family_order"]) + balanced = list(itertools.combinations(range(4), 2)) + maps = {} + for row in band_rows.itertuples(index=False): + maps[(str(row.source_family), str(row.target_family))] = tuple( + int(value) for value in str(row.permutation).split(",") + ) + null_values = np.empty(6 ** 6, dtype=np.float64) + for assignment_index, choices in enumerate( + itertools.product(balanced, repeat=6) + ): + labels = { + family: np.asarray( + [index in set(choice) for index in range(4)], dtype=bool + ) + for family, choice in zip(family_order, choices) + } + agreements = [] + for first, second in itertools.combinations(family_order, 2): + agreements.extend( + labels[first][index] + == labels[second][maps[(first, second)][index]] + for index in range(4) + ) + null_values[assignment_index] = float(np.mean(agreements)) + observed = float( + np.average(band_rows["physical_agreement"], weights=np.full(15, 4)) + ) + p_value = float(np.mean(null_values >= observed - 1e-12)) + return null_values, p_value + + +def mapping_summary( + mapping_rows: pd.DataFrame, + data: Mapping[str, object], +) -> tuple[dict[str, object], pd.DataFrame]: + family_order = list(data["family_order"]) + method_rows = [] + summary: dict[str, object] = {} + for method in METHODS: + band = mapping_rows[ + (mapping_rows["method"] == method) + & (mapping_rows["scope"] == "band") + ].copy() + correlations = band["heldout_correlation"].to_numpy(float) + family_means = { + family: float( + band[ + (band["source_family"] == family) + | (band["target_family"] == family) + ]["heldout_correlation"].mean() + ) + for family in family_order + } + opposite = band[band["opposite_response_orientation"]].copy() + differences = opposite["physical_minus_numeric"].to_numpy(float) + null, structured_p = structured_label_null(band, data) + physical = float(band["physical_agreement"].mean()) + numeric = float(band["numeric_agreement"].mean()) + topology_gate = bool( + np.median(correlations) > 0 + and signflip_p(correlations) <= 0.05 + and np.sum(correlations > 0) >= 10 + and np.sum(np.asarray(list(family_means.values())) > 0) >= 5 + ) + physical_gate = bool( + physical > 0.5 + and structured_p <= 0.05 + and np.sum(band["physical_agreement"] > 0.5) >= 10 + and np.sum(differences > 0) >= 6 + and signflip_p(differences) <= 0.05 + ) + method_summary = { + "heldout_topology_median": float(np.median(correlations)), + "heldout_topology_mean": float(np.mean(correlations)), + "heldout_topology_signflip_p": signflip_p(correlations), + "heldout_topology_positive_pairs": int( + np.sum(correlations > 0) + ), + "heldout_topology_family_means": family_means, + "heldout_topology_positive_families": int( + np.sum(np.asarray(list(family_means.values())) > 0) + ), + "physical_agreement": physical, + "physical_structured_null_mean": float(np.mean(null)), + "physical_structured_null_p": structured_p, + "physical_pairs_above_half": int( + np.sum(band["physical_agreement"] > 0.5) + ), + "numeric_agreement": numeric, + "opposite_response_physical_over_numeric_pairs": int( + np.sum(differences > 0) + ), + "opposite_response_difference_signflip_p": signflip_p( + differences + ), + "strong_topology_gate": topology_gate, + "strong_physical_gate": physical_gate, + } + summary[method] = method_summary + method_rows.append({"method": method, **method_summary}) + if method == "jacobian": + np.savez_compressed( + OUT / "structured_label_null.npz", + physical_agreement=null, + observed=np.asarray([physical]), + ) + + jacobian = mapping_rows[ + (mapping_rows["method"] == "jacobian") + & (mapping_rows["scope"] == "band") + ] + direct = mapping_rows[ + (mapping_rows["method"] == "direct") + & (mapping_rows["scope"] == "band") + ] + merged = jacobian.merge( + direct, + on=["source_family", "target_family"], + suffixes=("_jacobian", "_direct"), + ) + family_contrasts = [] + for family in family_order: + selected = merged[ + (merged["source_family"] == family) + | (merged["target_family"] == family) + ] + family_contrasts.append( + float( + np.mean( + selected["heldout_correlation_jacobian"] + - selected["heldout_correlation_direct"] + ) + ) + ) + summary["jacobian_specificity"] = { + "family_contrasts": dict(zip(family_order, family_contrasts)), + "positive_families": int(np.sum(np.asarray(family_contrasts) > 0)), + "plus_one_signflip_p": plus_one_signflip_p(family_contrasts), + "claim_passes": bool( + np.sum(np.asarray(family_contrasts) > 0) >= 5 + and plus_one_signflip_p(family_contrasts) <= 0.05 + ), + } + return summary, pd.DataFrame(method_rows) + + +def selected_family_graph( + similarity: np.ndarray, + family: str, + data: Mapping[str, object], +) -> nx.DiGraph: + families = np.asarray(data["families"]) + cases = np.asarray(data["cases"]) + variants = np.asarray(data["variants"]) + physical = np.asarray(data["physical"]) + numeric = np.asarray(data["numeric"]) + global_indices = np.flatnonzero(families == family) + local = {int(global_index): i for i, global_index in enumerate(global_indices)} + graph = nx.DiGraph() + for global_index in global_indices: + graph.add_node( + local[int(global_index)], + global_index=int(global_index), + variant=str(variants[global_index]), + physical=int(physical[global_index]), + numeric=int(numeric[global_index]), + case=str(cases[global_index]), + ) + for global_source in global_indices: + for target_variant in VARIANTS: + if target_variant == variants[global_source]: + continue + candidates = global_indices[ + (variants[global_indices] == target_variant) + & (cases[global_indices] != cases[global_source]) + ] + target = min( + candidates.tolist(), + key=lambda index: ( + -float(similarity[global_source, index]), + int(index), + ), + ) + graph.add_edge( + local[int(global_source)], + local[int(target)], + weight=float(similarity[global_source, target]), + ) + if graph.number_of_nodes() != 12 or graph.number_of_edges() != 24: + raise RuntimeError("unexpected family graph size") + return graph + + +def random_family_graph( + template: nx.DiGraph, rng: np.random.Generator +) -> nx.DiGraph: + graph = nx.DiGraph() + for node, attributes in template.nodes(data=True): + graph.add_node(node, **attributes) + for source, attributes in template.nodes(data=True): + for target_variant in VARIANTS: + if target_variant == attributes["variant"]: + continue + candidates = [ + node + for node, target_attributes in template.nodes(data=True) + if target_attributes["variant"] == target_variant + and target_attributes["case"] != attributes["case"] + ] + target = int(rng.choice(candidates)) + graph.add_edge(source, target, weight=1.0) + return graph + + +def wl_features( + graph: nx.DiGraph, + *, + label_scheme: str, + height: int = 3, +) -> Counter[str]: + if label_scheme == "none": + labels = {node: "node" for node in graph.nodes} + elif label_scheme == "variant": + labels = { + node: str(attributes["variant"]) + for node, attributes in graph.nodes(data=True) + } + elif label_scheme == "variant_physical": + labels = { + node: f"{attributes['variant']}:{attributes['physical']}" + for node, attributes in graph.nodes(data=True) + } + else: + raise ValueError(label_scheme) + features: Counter[str] = Counter() + for iteration in range(height + 1): + features.update( + f"h{iteration}:{label}" for label in labels.values() + ) + if iteration == height: + break + signatures = {} + for node in graph.nodes: + incoming = sorted(f"I:{labels[parent]}" for parent in graph.predecessors(node)) + outgoing = sorted(f"O:{labels[child]}" for child in graph.successors(node)) + signatures[node] = "|".join([labels[node], *incoming, *outgoing]) + # Hash the complete signature so that equal rooted neighborhoods receive + # equal labels across independently processed graphs. A graph-local + # integer vocabulary would make WL features incomparable between graphs. + labels = { + node: hashlib.sha256(signature.encode("utf-8")).hexdigest() + for node, signature in signatures.items() + } + return features + + +def counter_cosine(first: Counter[str], second: Counter[str]) -> float: + keys = set(first) | set(second) + x = np.asarray([first[key] for key in keys], dtype=float) + y = np.asarray([second[key] for key in keys], dtype=float) + denominator = float(np.linalg.norm(x) * np.linalg.norm(y)) + return float(np.dot(x, y) / denominator) if denominator else 0.0 + + +def graph_signatures(graph: nx.DiGraph) -> dict[str, object]: + indegree = np.asarray(sorted(dict(graph.in_degree()).values()), dtype=float) + outdegree = np.asarray(sorted(dict(graph.out_degree()).values()), dtype=float) + mutual = sum( + 1 + for first, second in graph.edges + if graph.has_edge(second, first) + ) + reciprocity = mutual / max(1, graph.number_of_edges()) + # The registered graph object stores cosine similarity for later GNN + # weighting, but this endpoint compares topology only. + adjacency = nx.to_numpy_array( + graph, nodelist=sorted(graph.nodes), weight=None + ) + adjacency = np.maximum(adjacency, adjacency.T) + degree = np.diag(adjacency.sum(axis=1)) + with np.errstate(divide="ignore"): + inverse_sqrt = np.diag( + np.where(np.diag(degree) > 0, np.diag(degree) ** -0.5, 0.0) + ) + laplacian = np.eye(len(adjacency)) - inverse_sqrt @ adjacency @ inverse_sqrt + spectrum = np.sort(np.linalg.eigvalsh(laplacian)) + return { + "degree": np.concatenate([indegree, outdegree]), + "reciprocity": float(reciprocity), + "spectrum": spectrum, + } + + +def isomorphic( + first: nx.DiGraph, second: nx.DiGraph, scheme: str +) -> bool: + if scheme == "none": + return bool(nx.is_isomorphic(first, second)) + + def match(left: Mapping[str, object], right: Mapping[str, object]) -> bool: + if scheme == "variant": + return left["variant"] == right["variant"] + if scheme == "variant_physical": + return ( + left["variant"] == right["variant"] + and left["physical"] == right["physical"] + ) + raise ValueError(scheme) + + return bool(nx.is_isomorphic(first, second, node_match=match)) + + +def graph_pair_metrics( + first: nx.DiGraph, second: nx.DiGraph +) -> dict[str, float | bool]: + first_signature = graph_signatures(first) + second_signature = graph_signatures(second) + return { + "exact_none": isomorphic(first, second, "none"), + "exact_variant": isomorphic(first, second, "variant"), + "exact_variant_physical": isomorphic( + first, second, "variant_physical" + ), + "wl_variant": counter_cosine( + wl_features(first, label_scheme="variant"), + wl_features(second, label_scheme="variant"), + ), + "degree_distance": float( + np.linalg.norm( + first_signature["degree"] - second_signature["degree"] + ) + ), + "reciprocity_distance": abs( + float(first_signature["reciprocity"]) + - float(second_signature["reciprocity"]) + ), + "spectral_distance": float( + np.linalg.norm( + first_signature["spectrum"] + - second_signature["spectrum"] + ) + ), + } + + +def isomorphism_analysis( + data: Mapping[str, object] +) -> tuple[pd.DataFrame, pd.DataFrame, dict[str, object]]: + family_order = list(data["family_order"]) + similarities = data["similarities"] # type: ignore[assignment] + layers = np.asarray(data["layers"]) + depths = np.asarray(data["depths"]) + band_mask = np.asarray(data["band_mask"]) + records = [] + for method in METHODS: + layered = np.asarray(similarities[method]) + scopes = [ + ("band", "38-92", "38-92", np.mean(layered[band_mask], axis=0)) + ] + scopes.extend( + ( + "layer", + int(layer), + float(depth), + layered[index], + ) + for index, (layer, depth) in enumerate(zip(layers, depths)) + ) + for scope, layer, depth, similarity in scopes: + graphs = { + family: selected_family_graph(similarity, family, data) + for family in family_order + } + for first, second in itertools.combinations(family_order, 2): + records.append( + { + "method": method, + "scope": scope, + "layer": layer, + "depth_percent": depth, + "source_family": first, + "target_family": second, + **graph_pair_metrics(graphs[first], graphs[second]), + } + ) + observed = pd.DataFrame(records) + + jacobian_layers = np.asarray(similarities["jacobian"]) + band_similarity = np.mean(jacobian_layers[band_mask], axis=0) + templates = { + family: selected_family_graph(band_similarity, family, data) + for family in family_order + } + rng = np.random.default_rng(SEED) + null_rows = [] + for null_index in range(N_NULL): + graphs = { + family: random_family_graph(templates[family], rng) + for family in family_order + } + pair_metrics = [ + graph_pair_metrics(graphs[first], graphs[second]) + for first, second in itertools.combinations(family_order, 2) + ] + row = {"null_index": null_index} + row.update( + { + key: float( + np.mean([float(item[key]) for item in pair_metrics]) + ) + for key in ( + "exact_none", + "exact_variant", + "exact_variant_physical", + "wl_variant", + "degree_distance", + "reciprocity_distance", + "spectral_distance", + ) + } + ) + null_rows.append(row) + null = pd.DataFrame(null_rows) + band_observed = observed[ + (observed["method"] == "jacobian") & (observed["scope"] == "band") + ] + metric_directions = { + "exact_none": "high", + "exact_variant": "high", + "exact_variant_physical": "high", + "wl_variant": "high", + "degree_distance": "low", + "reciprocity_distance": "low", + "spectral_distance": "low", + } + test_rows = [] + for metric, direction in metric_directions.items(): + value = float(band_observed[metric].astype(float).mean()) + if direction == "high": + p_value = float(np.mean(null[metric] >= value - 1e-12)) + else: + p_value = float(np.mean(null[metric] <= value + 1e-12)) + test_rows.append( + { + "metric": metric, + "direction": direction, + "observed": value, + "null_mean": float(null[metric].mean()), + "null_sd": float(null[metric].std(ddof=1)), + "exact_p": p_value, + } + ) + tests = pd.DataFrame(test_rows) + tests["bh_q"] = bh_adjust(tests["exact_p"]) + primary = tests.set_index("metric").loc["wl_variant"] + summary = { + "primary_wl_observed": float(primary["observed"]), + "primary_wl_null_mean": float(primary["null_mean"]), + "primary_wl_exact_p": float(primary["exact_p"]), + "primary_wl_bh_q": float(primary["bh_q"]), + "primary_constrained_null_gate": bool( + float(primary["exact_p"]) <= 0.05 + ), + "exact_variant_pairs_in_band": int( + band_observed["exact_variant"].sum() + ), + "exact_variant_physical_pairs_in_band": int( + band_observed["exact_variant_physical"].sum() + ), + } + return observed, null, {"summary": summary, "tests": test_rows} + + +def main() -> None: + OUT.mkdir(parents=True, exist_ok=True) + protocol = json.loads(PROTOCOL.read_text()) + validate_inputs(protocol) + data = load_data() + mapping_rows, candidate_rows = all_mapping_rows(data) + mapping_summary_data, mapping_table = mapping_summary(mapping_rows, data) + iso_rows, null_rows, iso_summary = isomorphism_analysis(data) + + mapping_rows.to_csv(OUT / "pair_mappings.csv", index=False) + candidate_rows.to_csv(OUT / "all_mapping_candidates.csv", index=False) + mapping_table.to_csv(OUT / "mapping_method_summary.csv", index=False) + iso_rows.to_csv(OUT / "isomorphism_pair_metrics.csv", index=False) + null_rows.to_csv(OUT / "constrained_graph_null.csv", index=False) + pd.DataFrame(iso_summary["tests"]).to_csv( + OUT / "isomorphism_tests.csv", index=False + ) + environment = { + "python": sys.version, + "platform": platform.platform(), + "numpy": np.__version__, + "pandas": pd.__version__, + "scipy": scipy.__version__, + "networkx": nx.__version__, + } + results = { + "study_id": protocol["study_id"], + "protocol_sha256": sha256(PROTOCOL), + "protocol_markdown_sha256": sha256(PROTOCOL_MD), + "input_sha256": { + "representations": sha256(STATES), + "prompt_manifest": sha256(MANIFEST), + }, + "dimensions": { + "families": 6, + "family_pairs": 15, + "nodes_per_family_graph": 12, + "edges_per_family_graph": 24, + "layers": 25, + "mapping_candidate_rows": len(candidate_rows), + "mapping_result_rows": len(mapping_rows), + "constrained_null_sets": len(null_rows), + }, + "study_1_mapping": mapping_summary_data, + "study_2_isomorphism": iso_summary, + "environment": environment, + } + (OUT / "study12_statistics.json").write_text( + json.dumps(safe_json(results), indent=2) + "\n" + ) + print(json.dumps(safe_json(results), indent=2)) + + +if __name__ == "__main__": + main() diff --git a/jlens_materials/scripts/analyze_graph_topology.py b/jlens_materials/scripts/analyze_graph_topology.py new file mode 100644 index 0000000000000000000000000000000000000000..3c25f9bd6ee4e83b51dc0c481d78f11e5cf29ff5 --- /dev/null +++ b/jlens_materials/scripts/analyze_graph_topology.py @@ -0,0 +1,487 @@ +#!/usr/bin/env python3 +"""Analyze label-free graph topology in held-out materials representations.""" + +from __future__ import annotations + +import hashlib +import json +import math +import re +import warnings +from collections import Counter +from pathlib import Path + +import matplotlib + +matplotlib.use("Agg") +import matplotlib.pyplot as plt # noqa: E402 +import networkx as nx # noqa: E402 +import numpy as np # noqa: E402 +import pandas as pd # noqa: E402 +from sklearn.metrics import adjusted_rand_score # noqa: E402 + + +ROOT = Path(__file__).resolve().parents[1] +OUT = ROOT / "experiments" / "graph-topology-2026-07-17" +FIG = OUT / "figures" +PROTOCOL_PATH = OUT / "protocol.json" +NPZ_PATH = ROOT / "experiments" / "materials-heldout-v1_latent_vectors.npz" +STATS_PATH = ROOT / "experiments" / "materials-heldout-v1_statistics.json" +META_PATH = ROOT / "experiments" / "materials-heldout-v1_latent_vectors.meta.json" +SEED = 20260717 +N_PERM = 10_000 +PRIMARY_K = 4 +BAND = (38.0, 92.0) + +FAMILY_SHORT = { + "boundary-attack": "BA", + "cleavage": "CL", + "cyclic": "CY", + "ductile": "DU", + "high-temperature-deformation": "HT", + "hot-air-surface-layer": "OX", + "line-defect-motion": "LD", + "notch-resistance": "NR", + "particle-strengthening": "PS", + "rapid-transformation": "RT", +} + + +def sha256(path: Path) -> str: + return hashlib.sha256(path.read_bytes()).hexdigest() + + +def normalize(values: np.ndarray) -> np.ndarray: + values = np.asarray(values, dtype=np.float64) + return values / np.maximum(np.linalg.norm(values, axis=-1, keepdims=True), 1e-12) + + +def token_words(text: str) -> set[str]: + return set(re.findall(r"[a-z][a-z'-]{2,}", text.lower())) + + +def candidate_removed(token: str, prompt_words: set[str]) -> bool: + if token in prompt_words: + return True + return any( + min(len(token), len(word)) >= 5 + and (token.startswith(word) or word.startswith(token)) + for word in prompt_words + ) + + +def validate_inputs(protocol: dict) -> None: + paths = { + NPZ_PATH: protocol["inputs"]["latent_vectors_sha256"], + STATS_PATH: protocol["inputs"]["heldout_statistics_sha256"], + META_PATH: protocol["inputs"]["latent_metadata_sha256"], + } + for path, expected in paths.items(): + actual = sha256(path) + if actual != expected: + raise RuntimeError(f"fingerprint mismatch for {path}: {actual} != {expected}") + + +def knn_graph(vectors: np.ndarray, k: int, *, mutual: bool = False) -> tuple[nx.Graph, np.ndarray]: + vectors = normalize(vectors) + # Apple Accelerate can emit stale floating-point status warnings for a + # finite BLAS result. Suppress those warnings locally and retain an + # explicit finite-value gate so a genuine numerical failure aborts. + with warnings.catch_warnings(): + warnings.simplefilter("ignore", RuntimeWarning) + similarity = np.einsum("id,jd->ij", vectors, vectors, optimize=True) + if not np.all(np.isfinite(similarity)): + raise FloatingPointError("non-finite cosine similarity in graph construction") + np.fill_diagonal(similarity, -np.inf) + neighbors = np.argpartition(-similarity, kth=k - 1, axis=1)[:, :k] + directed = {(i, int(j)) for i, row in enumerate(neighbors) for j in row} + graph = nx.Graph() + graph.add_nodes_from(range(len(vectors))) + for i, j in directed: + if mutual and (j, i) not in directed: + continue + a, b = sorted((i, j)) + graph.add_edge(a, b, weight=float(max(similarity[a, b], 0.0) + 1e-9)) + return graph, similarity + + +def edge_homophily(graph: nx.Graph, labels: np.ndarray) -> float: + edges = list(graph.edges()) + if not edges: + return float("nan") + return float(np.mean([labels[a] == labels[b] for a, b in edges])) + + +def community_labels(graph: nx.Graph) -> np.ndarray: + communities = nx.algorithms.community.louvain_communities( + graph, weight="weight", resolution=1.0, seed=SEED + ) + labels = np.full(graph.number_of_nodes(), -1, dtype=int) + for index, community in enumerate(communities): + labels[list(community)] = index + if np.any(labels < 0): + raise RuntimeError("Louvain left an unassigned node") + return labels + + +def graph_metrics(vectors: np.ndarray, families: np.ndarray, k: int) -> dict: + graph, similarity = knn_graph(vectors, k) + communities = community_labels(graph) + return { + "graph": graph, + "similarity": similarity, + "homophily": edge_homophily(graph, families), + "ari": float(adjusted_rand_score(families, communities)), + "communities": communities, + "n_communities": int(len(set(communities))), + "n_edges": int(graph.number_of_edges()), + "mean_degree": float(np.mean([degree for _, degree in graph.degree()])), + } + + +def permuted_labels(families: np.ndarray, folds: np.ndarray) -> np.ndarray: + rng = np.random.default_rng(SEED) + output = np.empty((N_PERM, len(families)), dtype=families.dtype) + for iteration in range(N_PERM): + labels = families.copy() + for fold in sorted(set(folds)): + indices = np.flatnonzero(folds == fold) + labels[indices] = rng.permutation(labels[indices]) + output[iteration] = labels + return output + + +def homophily_null(graphs: list[nx.Graph], permutations: np.ndarray) -> np.ndarray: + output = np.empty((len(permutations), len(graphs)), dtype=float) + edge_arrays = [np.asarray(list(graph.edges()), dtype=int) for graph in graphs] + for layer, edges in enumerate(edge_arrays): + output[:, layer] = np.mean( + permutations[:, edges[:, 0]] == permutations[:, edges[:, 1]], axis=1 + ) + return output + + +def ari_null(partitions: list[np.ndarray], permutations: np.ndarray) -> np.ndarray: + output = np.empty((len(permutations), len(partitions)), dtype=float) + for iteration, labels in enumerate(permutations): + for layer, partition in enumerate(partitions): + output[iteration, layer] = adjusted_rand_score(labels, partition) + return output + + +def p_plus_one(null: np.ndarray, observed: float) -> float: + return float((1 + np.sum(null >= observed - 1e-15)) / (1 + len(null))) + + +def vocabulary_features(records: list[dict], *, prompt_words: bool = False) -> tuple[np.ndarray, list[str]]: + documents: list[dict[str, float]] = [] + for record in records: + words = token_words(record["prompt"]) + if prompt_words: + document = {word: 1.0 for word in words} + else: + filtered = [ + row for row in record["filtered_consensus_candidates"] + if not candidate_removed(str(row["token"]), words) + ][:20] + document = { + str(row["token"]): float(row["consensus_score"]) + for row in filtered + } + documents.append(document) + frequency = Counter(token for document in documents for token in document) + vocabulary = sorted(frequency) + lookup = {token: index for index, token in enumerate(vocabulary)} + values = np.zeros((len(documents), len(vocabulary)), dtype=float) + for row, document in enumerate(documents): + for token, score in document.items(): + idf = math.log((1 + len(documents)) / (1 + frequency[token])) + values[row, lookup[token]] = score * idf + return normalize(values), vocabulary + + +def write_layer_rows( + depths: np.ndarray, + source_layers: np.ndarray, + j_metrics: list[dict], + raw_metrics: list[dict], +) -> pd.DataFrame: + rows = [] + for index, depth in enumerate(depths): + for method, metrics in (("Jacobian", j_metrics), ("raw", raw_metrics)): + row = metrics[index] + rows.append({ + "method": method, + "source_layer": int(source_layers[index]), + "depth": float(depth), + "homophily": row["homophily"], + "louvain_ari": row["ari"], + "n_communities": row["n_communities"], + "n_edges": row["n_edges"], + "mean_degree": row["mean_degree"], + }) + frame = pd.DataFrame(rows) + frame.to_csv(OUT / "layer_graph_metrics.csv", index=False) + return frame + + +def family_color_map(families: np.ndarray) -> dict[str, tuple]: + palette = plt.get_cmap("tab10") + return {family: palette(index) for index, family in enumerate(sorted(set(families)))} + + +def draw_prompt_graph( + axis: plt.Axes, + graph: nx.Graph, + families: np.ndarray, + folds: np.ndarray, + colors: dict[str, tuple], + title: str, + seed: int, +) -> None: + positions = nx.spring_layout(graph, seed=seed, weight="weight", k=0.62, iterations=300) + same = [(a, b) for a, b in graph.edges() if families[a] == families[b]] + other = [(a, b) for a, b in graph.edges() if families[a] != families[b]] + nx.draw_networkx_edges(graph, positions, edgelist=other, edge_color="#b8bcc2", width=0.55, alpha=0.45, ax=axis) + nx.draw_networkx_edges(graph, positions, edgelist=same, edge_color="#3f6f78", width=1.15, alpha=0.75, ax=axis) + for fold, marker in zip(sorted(set(folds)), ["o", "s", "^", "D", "P"]): + nodes = np.flatnonzero(folds == fold).tolist() + nx.draw_networkx_nodes( + graph, + positions, + nodelist=nodes, + node_color=[colors[str(families[node])] for node in nodes], + node_shape=marker, + node_size=48, + linewidths=0.45, + edgecolors="white", + ax=axis, + ) + axis.set_title(title, fontsize=9, loc="left") + axis.set_axis_off() + + +def plot_results( + depths: np.ndarray, + families: np.ndarray, + folds: np.ndarray, + j_metrics: list[dict], + raw_metrics: list[dict], + fixed: dict[str, dict], + best_index: int, +) -> None: + plt.rcParams.update({ + "font.size": 8.5, + "axes.labelsize": 8.5, + "axes.titlesize": 9, + "legend.fontsize": 7.5, + "pdf.fonttype": 42, + "ps.fonttype": 42, + }) + colors = family_color_map(families) + teal = "#2a788e" + purple = "#6f5aa8" + gray = "#73777d" + orange = "#d07c39" + fig, axes = plt.subplots(2, 2, figsize=(10.2, 7.1)) + + axis = axes[0, 0] + axis.plot(depths, [row["homophily"] for row in j_metrics], color=teal, lw=2.1, label="Jacobian states") + axis.plot(depths, [row["homophily"] for row in raw_metrics], color=purple, lw=1.8, label="raw states") + axis.axhline(fixed["lexical"]["homophily"], color=orange, lw=1.4, ls="--", label="prompt words") + axis.axhline(fixed["target"]["homophily"], color=gray, lw=1.3, ls=":", label="target state") + axis.axvspan(BAND[0], BAND[1], color="#d9dde2", alpha=0.35, zorder=-2) + axis.axhline(4 / 49, color="#a7abb1", lw=0.9, ls=(0, (2, 2)), label="balanced chance") + axis.set(xlabel="layer depth (%)", ylabel="same-family edge fraction", ylim=(0, 0.66)) + axis.legend(frameon=False, ncol=2, loc="upper left") + axis.text(-0.12, 1.04, "A", transform=axis.transAxes, fontweight="bold", fontsize=10) + + axis = axes[0, 1] + axis.plot(depths, [row["ari"] for row in j_metrics], color=teal, lw=2.1, label="Jacobian states") + axis.plot(depths, [row["ari"] for row in raw_metrics], color=purple, lw=1.8, label="raw states") + axis.axhline(fixed["lexical"]["ari"], color=orange, lw=1.4, ls="--", label="prompt words") + axis.axhline(fixed["target"]["ari"], color=gray, lw=1.3, ls=":", label="target state") + axis.axvspan(BAND[0], BAND[1], color="#d9dde2", alpha=0.35, zorder=-2) + axis.axhline(0, color="#a7abb1", lw=0.8) + axis.set(xlabel="layer depth (%)", ylabel="label-free community ARI", ylim=(-0.08, 0.72)) + axis.legend(frameon=False, ncol=2, loc="upper left") + axis.text(-0.12, 1.04, "B", transform=axis.transAxes, fontweight="bold", fontsize=10) + + draw_prompt_graph( + axes[1, 0], + j_metrics[best_index]["graph"], + families, + folds, + colors, + f"Best Jacobian layer ({depths[best_index]:.1f}% depth)", + SEED, + ) + axes[1, 0].text(-0.12, 1.04, "C", transform=axes[1, 0].transAxes, fontweight="bold", fontsize=10) + + draw_prompt_graph( + axes[1, 1], + fixed["vocabulary_jacobian"]["graph"], + families, + folds, + colors, + "Target-free discovered-word graph", + SEED + 1, + ) + axes[1, 1].text(-0.12, 1.04, "D", transform=axes[1, 1].transAxes, fontweight="bold", fontsize=10) + + handles = [ + plt.Line2D([0], [0], marker="o", color="none", markerfacecolor=colors[family], + markeredgecolor="none", markersize=5, label=FAMILY_SHORT.get(family, family[:2].upper())) + for family in sorted(colors) + ] + fig.legend(handles=handles, loc="lower center", ncol=10, frameon=False, bbox_to_anchor=(0.5, -0.005)) + fig.subplots_adjust(left=0.075, right=0.985, top=0.97, bottom=0.075, hspace=0.28, wspace=0.22) + FIG.mkdir(parents=True, exist_ok=True) + for suffix in ("pdf", "png"): + fig.savefig(FIG / f"materials-graph-topology.{suffix}", dpi=300, bbox_inches="tight") + plt.close(fig) + + +def main() -> None: + protocol = json.loads(PROTOCOL_PATH.read_text()) + validate_inputs(protocol) + with np.load(NPZ_PATH, allow_pickle=False) as data: + transported = normalize(data["transported_states"].astype(np.float64)) + transported_mean = normalize(np.mean(transported, axis=0)) + raw = normalize(data["raw_states"].astype(np.float64)) + lexical = normalize(data["lexical_states"].astype(np.float64)) + target = normalize(data["target_states"].astype(np.float64)) + families = data["families"].astype(str) + depths = data["depths"].astype(float) + source_layers = data["source_layers"].astype(int) + phrasing_ids = data["phrasing_ids"].astype(str) + folds = np.asarray([int(value.rsplit("-", 1)[-1]) for value in phrasing_ids]) + permutations = permuted_labels(families, folds) + band_mask = (depths >= BAND[0]) & (depths <= BAND[1]) + + j_metrics = [graph_metrics(transported_mean[:, layer], families, PRIMARY_K) for layer in range(len(depths))] + raw_metrics = [graph_metrics(raw[:, layer], families, PRIMARY_K) for layer in range(len(depths))] + lexical_metrics = graph_metrics(lexical, families, PRIMARY_K) + target_metrics = graph_metrics(target, families, PRIMARY_K) + + heldout = json.loads(STATS_PATH.read_text()) + jac_records = heldout["open_vocabulary"]["methods"]["jacobian"]["per_prompt"] + direct_records = heldout["open_vocabulary"]["methods"]["logit"]["per_prompt"] + if [record["slug"] for record in jac_records] != [record["slug"] for record in direct_records]: + raise RuntimeError("candidate method prompt order differs") + if [record["family"] for record in jac_records] != families.tolist(): + raise RuntimeError("candidate and latent-vector family order differs") + vocabulary_j, _ = vocabulary_features(jac_records) + vocabulary_d, _ = vocabulary_features(direct_records) + prompt_words, _ = vocabulary_features(jac_records, prompt_words=True) + vocabulary_metrics = { + "vocabulary_jacobian": graph_metrics(vocabulary_j, families, PRIMARY_K), + "vocabulary_direct": graph_metrics(vocabulary_d, families, PRIMARY_K), + "prompt_words": graph_metrics(prompt_words, families, PRIMARY_K), + } + + j_graphs = [row["graph"] for row in j_metrics] + raw_graphs = [row["graph"] for row in raw_metrics] + j_h_null = homophily_null(j_graphs, permutations) + raw_h_null = homophily_null(raw_graphs, permutations) + j_ari_null = ari_null([row["communities"] for row in j_metrics], permutations) + raw_ari_null = ari_null([row["communities"] for row in raw_metrics], permutations) + j_h = np.asarray([row["homophily"] for row in j_metrics]) + raw_h = np.asarray([row["homophily"] for row in raw_metrics]) + j_ari = np.asarray([row["ari"] for row in j_metrics]) + raw_ari = np.asarray([row["ari"] for row in raw_metrics]) + + fixed = { + "lexical": lexical_metrics, + "target": target_metrics, + **vocabulary_metrics, + } + fixed_nulls = {} + for name, metrics in fixed.items(): + graph = metrics["graph"] + edges = np.asarray(list(graph.edges()), dtype=int) + h_null = np.mean(permutations[:, edges[:, 0]] == permutations[:, edges[:, 1]], axis=1) + a_null = np.asarray([ + adjusted_rand_score(labels, metrics["communities"]) + for labels in permutations + ]) + fixed_nulls[name] = {"homophily": h_null, "ari": a_null} + + def layer_summary( + observed_h: np.ndarray, + observed_ari: np.ndarray, + null_h: np.ndarray, + null_ari: np.ndarray, + ) -> dict: + best_h = int(np.argmax(observed_h)) + best_ari = int(np.argmax(observed_ari)) + return { + "band_mean_homophily": float(np.mean(observed_h[band_mask])), + "band_mean_homophily_p": p_plus_one(np.mean(null_h[:, band_mask], axis=1), float(np.mean(observed_h[band_mask]))), + "best_homophily": float(observed_h[best_h]), + "best_homophily_layer": int(source_layers[best_h]), + "best_homophily_depth": float(depths[best_h]), + "max_layer_corrected_homophily_p": p_plus_one(np.max(null_h, axis=1), float(observed_h[best_h])), + "best_louvain_ari": float(observed_ari[best_ari]), + "best_ari_layer": int(source_layers[best_ari]), + "best_ari_depth": float(depths[best_ari]), + "max_layer_corrected_ari_p": p_plus_one(np.max(null_ari, axis=1), float(observed_ari[best_ari])), + } + + sensitivity = {} + for k in (3, 4, 5, 6): + j_k = [graph_metrics(transported_mean[:, layer], families, k)["homophily"] for layer in range(len(depths))] + raw_k = [graph_metrics(raw[:, layer], families, k)["homophily"] for layer in range(len(depths))] + sensitivity[str(k)] = { + "jacobian_band_mean_homophily": float(np.mean(np.asarray(j_k)[band_mask])), + "raw_band_mean_homophily": float(np.mean(np.asarray(raw_k)[band_mask])), + "jacobian_best_homophily": float(np.max(j_k)), + "raw_best_homophily": float(np.max(raw_k)), + } + + fixed_results = {} + for name, metrics in fixed.items(): + fixed_results[name] = { + "homophily": metrics["homophily"], + "homophily_p": p_plus_one(fixed_nulls[name]["homophily"], metrics["homophily"]), + "louvain_ari": metrics["ari"], + "louvain_ari_p": p_plus_one(fixed_nulls[name]["ari"], metrics["ari"]), + "n_communities": metrics["n_communities"], + "n_edges": metrics["n_edges"], + } + + payload = { + "status": protocol["status"], + "protocol_sha256": sha256(PROTOCOL_PATH), + "input_sha256": protocol["inputs"], + "n_permutations": N_PERM, + "primary_k": PRIMARY_K, + "registered_band_percent": list(BAND), + "jacobian_states": layer_summary(j_h, j_ari, j_h_null, j_ari_null), + "raw_states": layer_summary(raw_h, raw_ari, raw_h_null, raw_ari_null), + "jacobian_minus_raw": { + "band_mean_homophily_difference": float(np.mean(j_h[band_mask] - raw_h[band_mask])), + "descriptive_only": True, + }, + "fixed_graphs": fixed_results, + "k_sensitivity": sensitivity, + "interpretation": { + "supports_family_graph_structure_if": "Jacobian homophily and community ARI exceed the corrected label-permutation null", + "supports_jacobian_specificity_if": "Jacobian topology is materially stronger than raw-state and prompt-word baselines", + "causal_claim": False, + }, + } + (OUT / "statistics.json").write_text(json.dumps(payload, indent=2) + "\n") + write_layer_rows(depths, source_layers, j_metrics, raw_metrics) + pd.DataFrame([ + {"graph": name, **values} + for name, values in fixed_results.items() + ]).to_csv(OUT / "fixed_graph_metrics.csv", index=False) + + best_index = int(np.argmax(j_h)) + plot_results(depths, families, folds, j_metrics, raw_metrics, fixed, best_index) + print(json.dumps(payload, indent=2)) + + +if __name__ == "__main__": + main() diff --git a/jlens_materials/scripts/analyze_jacobian_steering_pilot.py b/jlens_materials/scripts/analyze_jacobian_steering_pilot.py new file mode 100644 index 0000000000000000000000000000000000000000..67666ff84a5b260447cf768e04fbd8c612b57c23 --- /dev/null +++ b/jlens_materials/scripts/analyze_jacobian_steering_pilot.py @@ -0,0 +1,392 @@ +#!/usr/bin/env python3 +"""Analyze the frozen Jacobian steering pilot without modifying the manuscript.""" + +from __future__ import annotations + +import json +import math +from pathlib import Path + +import matplotlib.pyplot as plt +import numpy as np +import pandas as pd +from scipy import stats + + +ROOT = Path(__file__).resolve().parents[1] +RAW = ROOT / "experiments/jacobian-steering-pilot-v1_raw.json" +MANIFEST = ROOT / "experiments/jacobian-steering-pilot-v1-preregistration.json" +OUT_CSV = ROOT / "experiments/jacobian-steering-pilot-v1_prompt_effects.csv" +OUT_CURVES = ROOT / "experiments/jacobian-steering-pilot-v1_curves.csv" +OUT_MD = ROOT / "experiments/jacobian-steering-pilot-v1_results.md" +FIG_DIR = ROOT / "figures/jacobian-steering-pilot-v1" + +CASE_LABELS = { + "fracture-morphology": "Fracture morphology", + "corrosion-qualification": "Corrosion qualification", + "transformation-hardness": "Transformation hardness", +} +METHOD_LABELS = { + "jacobian_exact": "Jacobian lens", + "direct": "Direct decoder", + "random": "Random direction", +} +COLORS = { + "jacobian_exact": "#176B87", + "direct": "#D07A2D", + "random": "#777777", +} + + +def fmt_ci(mean: float, lo: float, hi: float) -> str: + return f"{mean:+.3f} [{lo:+.3f}, {hi:+.3f}]" + + +def bootstrap_mean(values: np.ndarray, rng: np.random.Generator, + n_boot: int = 20000) -> tuple[float, float, float]: + values = np.asarray(values, dtype=float) + means = rng.choice(values, size=(n_boot, len(values)), replace=True).mean(axis=1) + return float(values.mean()), *np.quantile(means, [0.025, 0.975]).tolist() + + +def main() -> None: + raw = json.loads(RAW.read_text()) + manifest = json.loads(MANIFEST.read_text()) + rows = pd.DataFrame(raw["interventions"]) + preflight = pd.DataFrame(raw["preflight"]) + + replicate_cols = ["prompt_id", "method", "lens_seed", "random_seed"] + rows["replicate"] = np.where( + rows["method"].eq("jacobian_exact"), + "lens-" + rows["lens_seed"].fillna(-1).astype(int).astype(str), + np.where( + rows["method"].eq("random"), + "random-" + rows["random_seed"].fillna(-1).astype(int).astype(str), + "direct", + ), + ) + + clean = rows.loc[rows["dose_percent"].eq(0), + ["prompt_id", "method", "replicate", "positive_log_odds"]] + clean = clean.rename(columns={"positive_log_odds": "replicate_clean_log_odds"}) + rows = rows.merge(clean, on=["prompt_id", "method", "replicate"], how="left") + rows["delta_log_odds"] = ( + rows["positive_log_odds"] - rows["replicate_clean_log_odds"] + ) + + endpoint_rows = [] + for keys, group in rows.groupby( + ["case_id", "prompt_id", "condition", "inferential", "method", "replicate"], + dropna=False, + ): + values = group.set_index("dose_percent")["positive_log_odds"] + endpoint_change = float(values.loc[2.0] - values.loc[-2.0]) + slope = endpoint_change / 4.0 + linear_slope = float(np.polyfit(group["dose_percent"], group["positive_log_odds"], 1)[0]) + endpoint_rows.append({ + "case_id": keys[0], + "prompt_id": keys[1], + "condition": keys[2], + "inferential": bool(keys[3]), + "method": keys[4], + "replicate": keys[5], + "endpoint_change_log_odds": endpoint_change, + "endpoint_slope_per_percent": slope, + "linear_slope_per_percent": linear_slope, + }) + effects_rep = pd.DataFrame(endpoint_rows) + effects = ( + effects_rep.groupby( + ["case_id", "prompt_id", "condition", "inferential", "method"], + as_index=False, + ) + .agg( + endpoint_change_log_odds=("endpoint_change_log_odds", "mean"), + endpoint_slope_per_percent=("endpoint_slope_per_percent", "mean"), + linear_slope_per_percent=("linear_slope_per_percent", "mean"), + replicate_sd=("endpoint_change_log_odds", "std"), + n_replicates=("replicate", "nunique"), + ) + ) + effects.to_csv(OUT_CSV, index=False) + + curves = ( + rows.groupby( + ["case_id", "prompt_id", "condition", "inferential", "method", "dose_percent"], + as_index=False, + ) + .agg( + delta_log_odds=("delta_log_odds", "mean"), + replicate_sd=("delta_log_odds", "std"), + valid_choice_probability=("valid_choice_probability", "mean"), + kl_clean_to_intervened=("kl_clean_to_intervened", "mean"), + ) + ) + curves.to_csv(OUT_CURVES, index=False) + + inferential = effects[effects["inferential"]].copy() + # A/B and B/A versions are technical counterbalances of one physical condition, + # not independent scientific samples. Collapse them before inference. + condition_effects = ( + inferential.groupby(["case_id", "condition", "method"], as_index=False) + .agg(endpoint_change_log_odds=("endpoint_change_log_odds", "mean")) + ) + pivot = condition_effects.pivot( + index=["case_id", "condition"], columns="method", + values="endpoint_change_log_odds" + ).reset_index() + rng = np.random.default_rng(20260716) + overall = {} + for method in METHOD_LABELS: + vals = pivot[method].dropna().to_numpy() + overall[method] = bootstrap_mean(vals, rng) + + comparisons = {} + for control in ["direct", "random"]: + diff = (pivot["jacobian_exact"] - pivot[control]).to_numpy() + comparisons[control] = { + "bootstrap": bootstrap_mean(diff, rng), + "wilcoxon_two_sided_p": float(stats.wilcoxon(diff, alternative="two-sided").pvalue), + } + + j_condition = pivot["jacobian_exact"].to_numpy() + sign_test = stats.binomtest(int((j_condition > 0).sum()), len(j_condition), 0.5) + + j_rep = effects_rep[ + effects_rep["inferential"] & effects_rep["method"].eq("jacobian_exact") + ].copy() + j_seed = j_rep.pivot(index="prompt_id", columns="replicate", + values="endpoint_change_log_odds") + seed_correlations = j_seed.corr() + seed_r_values = seed_correlations.where( + np.triu(np.ones(seed_correlations.shape), k=1).astype(bool) + ).stack().to_numpy() + + case_rows = [] + for case_id, case_group in condition_effects.groupby("case_id"): + for method, method_group in case_group.groupby("method"): + vals = method_group["endpoint_change_log_odds"].to_numpy() + mean, lo, hi = bootstrap_mean(vals, rng) + case_rows.append({ + "case_id": case_id, + "method": method, + "mean": mean, + "lo": lo, + "hi": hi, + "positive_prompts": int((vals > 0).sum()), + "n_prompts": len(vals), + }) + case_summary = pd.DataFrame(case_rows) + + accepted = preflight[preflight["accepted"]] + determinate = preflight[preflight["inferential"]] + valid_min = float(rows["valid_choice_probability"].min()) + valid_median = float(rows["valid_choice_probability"].median()) + top_valid = float(rows["global_top_is_valid_choice"].mean()) + flips = 0 + for _, group in rows.groupby(["prompt_id", "method", "replicate"]): + minus = group.loc[group["dose_percent"].eq(-2), "choice"].iloc[0] + plus = group.loc[group["dose_percent"].eq(2), "choice"].iloc[0] + flips += int(minus != plus) + + # Figure: aggregate comparison above, prompt-resolved Jacobian responses below. + FIG_DIR.mkdir(parents=True, exist_ok=True) + fig, axes = plt.subplots(2, 3, figsize=(13.2, 7.4), sharex=True) + doses = sorted(rows["dose_percent"].unique()) + for col, case_id in enumerate(CASE_LABELS): + ax = axes[0, col] + case_curve = curves[(curves["case_id"].eq(case_id)) & curves["inferential"]] + for method in METHOD_LABELS: + method_curve = case_curve[case_curve["method"].eq(method)] + by_prompt = method_curve.pivot(index="prompt_id", columns="dose_percent", + values="delta_log_odds") + mean = by_prompt.reindex(columns=doses).mean(axis=0) + sem = by_prompt.reindex(columns=doses).sem(axis=0) + ax.plot(doses, mean, marker="o", linewidth=2.3, + color=COLORS[method], label=METHOD_LABELS[method]) + ax.fill_between(doses, mean - sem, mean + sem, + color=COLORS[method], alpha=0.12, linewidth=0) + ax.axhline(0, color="#BBBBBB", linewidth=0.8) + ax.axvline(0, color="#DDDDDD", linewidth=0.8) + ax.set_title(CASE_LABELS[case_id], fontsize=11, pad=8) + ax.grid(axis="y", color="#E9E9E9", linewidth=0.7) + if col == 0: + ax.set_ylabel("Mean change in positive\nanswer log-odds") + if col == 2: + ax.legend(frameon=False, fontsize=8, loc="best") + + ax = axes[1, col] + prompt_curve = case_curve[case_curve["method"].eq("jacobian_exact")] + for prompt_id, group in prompt_curve.groupby("prompt_id"): + group = group.sort_values("dose_percent") + short = prompt_id.replace(case_id.split("-")[0] + "-", "") + ax.plot(group["dose_percent"], group["delta_log_odds"], marker="o", + linewidth=1.8, markersize=4, label=short) + ax.axhline(0, color="#BBBBBB", linewidth=0.8) + ax.axvline(0, color="#DDDDDD", linewidth=0.8) + ax.grid(axis="y", color="#E9E9E9", linewidth=0.7) + ax.set_xlabel("Steering dose (% of residual norm)") + if col == 0: + ax.set_ylabel("Prompt-level Jacobian\nchange in log-odds") + ax.legend(frameon=False, fontsize=7.5, loc="best") + + for label, ax in zip("ABCDEF", axes.flat): + ax.text(-0.15, 1.05, label, transform=ax.transAxes, + fontsize=11, fontweight="bold", va="bottom") + fig.subplots_adjust(left=0.085, right=0.985, bottom=0.095, top=0.95, + wspace=0.28, hspace=0.30) + fig.savefig(FIG_DIR / "dose_response.png", dpi=240) + fig.savefig(FIG_DIR / "dose_response.pdf") + plt.close(fig) + + # Compact endpoint comparison plot. + fig, ax = plt.subplots(figsize=(8.8, 4.6)) + x = np.arange(len(CASE_LABELS)) + offsets = {"jacobian_exact": -0.22, "direct": 0.0, "random": 0.22} + for method in METHOD_LABELS: + subset = case_summary[case_summary["method"].eq(method)].set_index("case_id") + means = np.array([subset.loc[c, "mean"] for c in CASE_LABELS]) + lo = np.array([subset.loc[c, "lo"] for c in CASE_LABELS]) + hi = np.array([subset.loc[c, "hi"] for c in CASE_LABELS]) + ax.errorbar(x + offsets[method], means, yerr=[means - lo, hi - means], + fmt="o", markersize=7, capsize=3, linewidth=1.8, + color=COLORS[method], label=METHOD_LABELS[method]) + ax.axhline(0, color="#777777", linewidth=0.9) + ax.set_xticks(x, [CASE_LABELS[c] for c in CASE_LABELS]) + ax.set_ylabel("Log-odds change from −2% to +2% dose") + ax.grid(axis="y", color="#E9E9E9", linewidth=0.7) + ax.legend(frameon=False, ncol=3, loc="upper left") + fig.subplots_adjust(left=0.12, right=0.98, bottom=0.18, top=0.94) + fig.savefig(FIG_DIR / "endpoint_comparison.png", dpi=240) + fig.savefig(FIG_DIR / "endpoint_comparison.pdf") + plt.close(fig) + + excluded_lines = [] + for row in preflight[~preflight["accepted"]].itertuples(): + excluded_lines.append( + f"`{row.prompt_id}`: clean choice {row.clean['choice']}, " + f"expected {row.expected_answer}." + ) + prompt_lines = [] + for row in preflight.itertuples(): + status = "accepted" if row.accepted else "excluded" + prompt_lines.append( + f"- `{row.prompt_id}` ({status}; clean {row.clean['choice']}; " + f"positive log-odds {row.clean['positive_log_odds']:+.3f}): {row.prompt}" + ) + + case_table = [ + "| Case | Jacobian lens | Direct decoder | Random | Jacobian direction agreement |", + "|---|---:|---:|---:|---:|", + ] + for case_id in CASE_LABELS: + case_cells = [] + agreement = "" + for method in METHOD_LABELS: + row = case_summary[ + case_summary["case_id"].eq(case_id) & case_summary["method"].eq(method) + ].iloc[0] + case_cells.append(fmt_ci(row["mean"], row["lo"], row["hi"])) + if method == "jacobian_exact": + agreement = f"{row['positive_prompts']}/{row['n_prompts']} conditions positive" + case_table.append( + f"| {CASE_LABELS[case_id]} | {case_cells[0]} | {case_cells[1]} | " + f"{case_cells[2]} | {agreement} |" + ) + + method_table = [ + "| Method | Mean endpoint change [95% condition-cluster bootstrap CI] |", + "|---|---:|", + ] + for method in METHOD_LABELS: + method_table.append(f"| {METHOD_LABELS[method]} | {fmt_ci(*overall[method])} |") + + condition_pivot = condition_effects.pivot( + index=["case_id", "condition"], columns="method", + values="endpoint_change_log_odds" + ).reset_index() + condition_table = [ + "| Physical condition | Jacobian | Direct | Random | Odds multiplier, −2%→+2% | Option-order check |", + "|---|---:|---:|---:|---:|---|", + ] + for row in condition_pivot.itertuples(): + prompt_j = inferential[ + inferential["case_id"].eq(row.case_id) + & inferential["condition"].eq(row.condition) + & inferential["method"].eq("jacobian_exact") + ]["endpoint_change_log_odds"] + if len(prompt_j) == 2: + signs = np.sign(prompt_j.to_numpy()) + order_check = "same direction" if signs[0] == signs[1] else "opposite directions" + else: + order_check = "one order accepted" + condition_table.append( + f"| {CASE_LABELS[row.case_id]} — {row.condition} | " + f"{row.jacobian_exact:+.3f} | {row.direct:+.3f} | {row.random:+.3f} | " + f"{math.exp(row.jacobian_exact):.2f}× | {order_check} |" + ) + + report = f"""# Jacobian steering pilot v1 + +This is a separate pilot analysis. It has **not** been added to the manuscript. + +## Design + +- Model: `{manifest['model']}` at revision `{manifest['model_revision']}`. +- Intervention: one registered layer, final prompt token only, additive doses of −2%, −1%, 0%, +1%, and +2% of the clean residual norm. +- Primary direction: gradient of a declared concept contrast through each fitted Jacobian map and Gemma's exact final normalization. +- Controls: the same contrast through direct unembedding, plus three unit-norm random directions. +- Outcome: log-odds of the answer associated with the positive concept versus the alternative. A positive endpoint change means +2% dose favors that semantic answer more than −2% dose. +- Counterbalancing: determinate questions were written in both A/B option orders. The orders were averaged within each physical condition before inference. Neutral prompts were exploratory and excluded from inferential summaries. + +## Accounting + +- 15 prompts preflighted; {len(accepted)} accepted for intervention. +- Clean determinate accuracy: {int(determinate['accepted'].sum())}/{len(determinate)} ({determinate['accepted'].mean():.1%}). +- Accepted inferential prompt orders: {int((preflight['accepted'] & preflight['inferential']).sum())}, grouped into {len(pivot)} physical conditions; raw intervention rows: {len(rows)}. +- Exclusion: {excluded_lines[0] if excluded_lines else 'none'} + +## Primary result + +{chr(10).join(method_table)} + +Paired Jacobian-minus-random difference: {fmt_ci(*comparisons['random']['bootstrap'])}; two-sided Wilcoxon p={comparisons['random']['wilcoxon_two_sided_p']:.4f}. + +Paired Jacobian-minus-direct difference: {fmt_ci(*comparisons['direct']['bootstrap'])}; two-sided Wilcoxon p={comparisons['direct']['wilcoxon_two_sided_p']:.4f}. + +The Jacobian endpoint was positive for {int((j_condition > 0).sum())}/{len(j_condition)} physical conditions (two-sided exact sign-test p={sign_test.pvalue:.4f}). Median pairwise Pearson correlation among the three lens-seed prompt-order endpoint vectors was {np.median(seed_r_values):.3f}. + +## Mechanism-resolved result + +{chr(10).join(case_table)} + +The condition-level breakdown is more informative than the case averages: + +{chr(10).join(condition_table)} + +Interpretation: the intervention is **selective rather than universal**. Sensitized corrosion is the strongest result and survives option-order reversal. Slow-cooled transformation is smaller but also survives option reversal. Fracture fails, and rapid-cooled transformation reverses direction between A/B and B/A orders. The resistant-corrosion counterbalance cannot be evaluated because one clean prompt failed preflight. + +## Output integrity + +- Lowest A/B probability mass across all interventions: {valid_min:.3%}; median: {valid_median:.3%}. +- Global top token was A or B in {top_valid:.3%} of intervention rows. +- Endpoint A/B flips: {flips}. At these conservative doses, the pilot changes confidence but does not overturn the model's discrete answer. + +## Every prompt and clean decision + +{chr(10).join(prompt_lines)} + +## Files + +- Raw rows: `experiments/jacobian-steering-pilot-v1_raw.json` +- Frozen manifest: `experiments/jacobian-steering-pilot-v1-preregistration.json` +- Prompt-level effects: `experiments/jacobian-steering-pilot-v1_prompt_effects.csv` +- Dose curves: `experiments/jacobian-steering-pilot-v1_curves.csv` +- Diagnostic plots: `figures/jacobian-steering-pilot-v1/` +""" + OUT_MD.write_text(report) + print(report) + + +if __name__ == "__main__": + main() diff --git a/jlens_materials/scripts/analyze_late_physics_replication.py b/jlens_materials/scripts/analyze_late_physics_replication.py new file mode 100644 index 0000000000000000000000000000000000000000..02bbbda06695ea81d823d570277f3f3aea8a9fcd --- /dev/null +++ b/jlens_materials/scripts/analyze_late_physics_replication.py @@ -0,0 +1,530 @@ +#!/usr/bin/env python3 +"""Analyze the frozen disjoint late-physics representation replication.""" + +from __future__ import annotations + +import json +from pathlib import Path + +import matplotlib.pyplot as plt +import numpy as np +import pandas as pd +from scipy import stats + +import analyze_lexical_adversarial_representation as shared + +ROOT = Path(__file__).resolve().parents[1] +OUT = ROOT / "experiments" / "late-physics-representation-replication-2026-07-17" +DISCOVERY = ( + ROOT / "experiments" / "lexical-adversarial-representation-2026-07-17" +) +PROTOCOL_PATH = OUT / "protocol.json" +MANIFEST_PATH = OUT / "prompt_manifest.json" +RAW_PATH = OUT / "raw.json" +STATES_PATH = OUT / "representations.npz" +FIG = OUT / "figures" + + +def aggregate_band( + frame: pd.DataFrame, + low: float, + high: float, +) -> pd.DataFrame: + return ( + frame[frame["depth_percent"].between(low, high)] + .groupby(["method", "triplet_id", "family_id"], as_index=False)[ + "physics_minus_lexical_margin" + ] + .mean() + ) + + +def bootstrap_methods( + frame: pd.DataFrame, + methods: list[str], + seed: int, +) -> dict: + return { + method: shared.two_stage_bootstrap( + frame[frame["method"] == method], + "physics_minus_lexical_margin", + n_resamples=30000, + seed=seed + method_index, + ) + for method_index, method in enumerate(methods) + } + + +def transition_frame(late: pd.DataFrame, middle: pd.DataFrame) -> pd.DataFrame: + merged = late.merge( + middle, + on=["method", "triplet_id", "family_id"], + suffixes=("_late", "_middle"), + validate="one_to_one", + ) + merged["late_minus_middle"] = ( + merged["physics_minus_lexical_margin_late"] + - merged["physics_minus_lexical_margin_middle"] + ) + return merged + + +def make_figure( + layer_frame: pd.DataFrame, + primary_family: pd.DataFrame, + discovery_late: pd.DataFrame, + replication_late: pd.DataFrame, + target_free: pd.DataFrame, + late_band: tuple[float, float], +) -> None: + FIG.mkdir(parents=True, exist_ok=True) + plt.rcParams.update({ + "font.size": 9, + "axes.labelsize": 9, + "xtick.labelsize": 8, + "ytick.labelsize": 8, + "legend.fontsize": 8, + "axes.linewidth": 0.8, + }) + colors = {"jacobian_ensemble": "#167D8D", "direct": "#6F5AA8"} + labels = {"jacobian_ensemble": "Jacobian", "direct": "direct"} + fig, axes = plt.subplots(2, 2, figsize=(10.4, 6.6), constrained_layout=True) + ax_a, ax_b, ax_c, ax_d = axes.flat + + layer_summary = shared.layer_summary_with_ci( + layer_frame, + "physics_minus_lexical_margin", + ["jacobian_ensemble", "direct"], + n_resamples=5000, + ) + for method in ["jacobian_ensemble", "direct"]: + subset = layer_summary[layer_summary["method"] == method].sort_values("layer") + x = subset["depth_percent"].to_numpy() + mean = subset["mean"].to_numpy() + low = np.asarray([value[0] for value in subset["ci95"]]) + high = np.asarray([value[1] for value in subset["ci95"]]) + ax_a.plot(x, mean, color=colors[method], linewidth=1.7, label=labels[method]) + ax_a.fill_between(x, low, high, color=colors[method], alpha=0.15, linewidth=0) + ax_a.axhline(0, color="#666666", linewidth=0.8) + ax_a.axvspan(late_band[0], late_band[1], color="#167D8D", alpha=0.08, linewidth=0) + ax_a.set_xlabel("Layer depth (%)") + ax_a.set_ylabel("Physics-equivalence margin\n(centered cosine units)") + ax_a.text(0.02, 0.97, "A", transform=ax_a.transAxes, va="top", fontweight="bold") + + cohort_rows = [] + for cohort, frame in [ + ("discovery", discovery_late), + ("disjoint replication", replication_late), + ]: + for method in ["jacobian_ensemble", "direct"]: + result = shared.two_stage_bootstrap( + frame[frame["method"] == method], + "physics_minus_lexical_margin", + n_resamples=30000, + seed=20260730 + 10 * (cohort == "disjoint replication") + + (method == "direct"), + ) + cohort_rows.append({"cohort": cohort, "method": method, **result}) + cohort_frame = pd.DataFrame(cohort_rows) + x = np.arange(2) + for method, offset, marker in [ + ("jacobian_ensemble", -0.12, "o"), + ("direct", 0.12, "^"), + ]: + subset = cohort_frame[cohort_frame["method"] == method].set_index("cohort").loc[ + ["discovery", "disjoint replication"] + ] + means = subset["mean"].to_numpy() + low = np.asarray([value[0] for value in subset["ci95"]]) + high = np.asarray([value[1] for value in subset["ci95"]]) + ax_b.errorbar( + x + offset, + means, + yerr=[means - low, high - means], + fmt=marker, + color=colors[method], + capsize=3, + markersize=7, + label=labels[method], + ) + ax_b.axhline(0, color="#666666", linewidth=0.8) + ax_b.set_xticks(x, ["discovery", "disjoint\nreplication"]) + ax_b.set_ylabel("Late-window physics-equivalence margin") + ax_b.text(0.02, 0.97, "B", transform=ax_b.transAxes, va="top", fontweight="bold") + + family_order = list(dict.fromkeys(primary_family["family_id"])) + short = { + "obstacle-spacing-orowan": "obstacle\nspacing", + "porosity-modulus": "porosity", + "pearlite-spacing-strength": "pearlite\nspacing", + "dislocation-density-strength": "dislocation\ndensity", + "particle-fraction-modulus": "particle\nfraction", + "crosslink-density-modulus": "crosslink\ndensity", + } + x = np.arange(len(family_order)) + for method, offset, marker in [ + ("jacobian_ensemble", -0.14, "o"), + ("direct", 0.14, "^"), + ]: + subset = primary_family[ + primary_family["method"] == method + ].set_index("family_id").loc[family_order] + ax_c.scatter( + x + offset, + subset["physics_minus_lexical_margin"], + color=colors[method], + marker=marker, + s=38, + ) + ax_c.axhline(0, color="#666666", linewidth=0.8) + ax_c.set_xticks(x, [short[value] for value in family_order]) + ax_c.set_ylabel("Replication late-window margin") + ax_c.text(0.02, 0.97, "C", transform=ax_c.transAxes, va="top", fontweight="bold") + + target_summary = shared.layer_summary_with_ci( + target_free.rename(columns={"physics_minus_lexical_jaccard": "margin"}), + "margin", + ["jacobian_ensemble", "direct"], + n_resamples=5000, + ) + for method, marker in [ + ("jacobian_ensemble", "o"), + ("direct", "^"), + ]: + subset = target_summary[target_summary["method"] == method].sort_values("layer") + x_values = subset["depth_percent"].to_numpy() + means = subset["mean"].to_numpy() + low = np.asarray([value[0] for value in subset["ci95"]]) + high = np.asarray([value[1] for value in subset["ci95"]]) + ax_d.errorbar( + x_values, + means, + yerr=[means - low, high - means], + color=colors[method], + marker=marker, + linewidth=1.4, + capsize=2, + label=labels[method], + ) + ax_d.axhline(0, color="#666666", linewidth=0.8) + ax_d.axvspan(late_band[0], late_band[1], color="#167D8D", alpha=0.08, linewidth=0) + ax_d.set_xlabel("Layer depth (%)") + ax_d.set_ylabel("Target-free word-set margin\n(Jaccard units)") + ax_d.text(0.02, 0.97, "D", transform=ax_d.transAxes, va="top", fontweight="bold") + + handles, legend_labels = ax_a.get_legend_handles_labels() + fig.legend( + handles, + legend_labels, + loc="outside lower center", + ncol=2, + frameon=False, + ) + for suffix in ["png", "pdf"]: + fig.savefig( + FIG / f"late-physics-representation-replication.{suffix}", + dpi=300, + bbox_inches="tight", + ) + plt.close(fig) + + +def main() -> None: + protocol = json.loads(PROTOCOL_PATH.read_text()) + manifest = json.loads(MANIFEST_PATH.read_text()) + raw = json.loads(RAW_PATH.read_text()) + if raw["provenance"]["protocol_sha256"] != shared.sha256(PROTOCOL_PATH): + raise RuntimeError("raw output does not match frozen replication protocol") + if shared.sha256( + DISCOVERY / "statistics.json" + ) != protocol["inputs"]["discovery_statistics_sha256"]: + raise RuntimeError("motivating discovery statistics changed after freezing") + arrays = np.load(STATES_PATH) + prompt_ids = [str(value) for value in arrays["prompt_ids"]] + expected = [row["prompt_id"] for row in manifest["prompts"]] + if prompt_ids != expected: + raise RuntimeError("state prompt order does not match manifest") + prompt_index = {prompt_id: index for index, prompt_id in enumerate(prompt_ids)} + layers = arrays["layers"].astype(int) + jacobian = arrays["jacobian_decoder_basis"].astype(np.float32) + methods = { + "raw_residual": arrays["raw_states"].astype(np.float32), + "direct": arrays["direct_decoder_basis"].astype(np.float32), + "jacobian_seed0": jacobian[0], + "jacobian_seed1": jacobian[1], + "jacobian_seed2": jacobian[2], + "jacobian_ensemble": jacobian.mean(axis=0), + } + centered = shared.similarity_rows( + methods, + layers, + manifest["triplets"], + prompt_index, + centered=True, + accumulator_dtype=np.float64, + ) + centered.to_csv(OUT / "layer_similarity_margins.csv", index=False) + + late_low, late_high = protocol["registered_band_percent"] + full_low, full_high = protocol["secondary_full_band_percent"] + late = aggregate_band(centered, late_low, late_high) + full = aggregate_band(centered, full_low, full_high) + middle = aggregate_band(centered, 38.0, 70.0) + late.to_csv(OUT / "late_window_triplet_margins.csv", index=False) + full.to_csv(OUT / "full_band_triplet_margins.csv", index=False) + transition = transition_frame(late, middle) + transition.to_csv(OUT / "late_minus_middle_triplet_margins.csv", index=False) + + family_late = ( + late.groupby(["method", "family_id"], as_index=False)[ + "physics_minus_lexical_margin" + ].mean() + ) + family_late.to_csv(OUT / "family_late_window_margins.csv", index=False) + method_names = list(methods) + late_stats = bootstrap_methods(late, method_names, 20260718) + full_stats = bootstrap_methods(full, method_names, 20260818) + transition_stats = { + method: shared.two_stage_bootstrap( + transition[transition["method"] == method].rename( + columns={"late_minus_middle": "value"} + ), + "value", + n_resamples=30000, + seed=20260918 + method_index, + ) + for method_index, method in enumerate(method_names) + } + + pivot = late.pivot( + index=["triplet_id", "family_id"], + columns="method", + values="physics_minus_lexical_margin", + ).reset_index() + pivot["jacobian_minus_direct"] = pivot["jacobian_ensemble"] - pivot["direct"] + method_contrast = shared.two_stage_bootstrap( + pivot, + "jacobian_minus_direct", + n_resamples=30000, + seed=20261018, + ) + family_contrast = ( + pivot.groupby("family_id")["jacobian_minus_direct"].mean().to_numpy() + ) + method_contrast["exact_family_sign_flip_p"] = shared.exact_family_sign_flip( + family_contrast + ) + + clean_frame, behavior = shared.behavior_summary(raw, manifest) + clean_frame.to_csv(OUT / "clean_behavior.csv", index=False) + pd.DataFrame(behavior["triplet_rows"]).to_csv( + OUT / "clean_triplet_consistency.csv", index=False + ) + target_free, _ = shared.target_free_analysis(raw, manifest, protocol) + target_free.to_csv(OUT / "target_free_jaccard_margins.csv", index=False) + target_band = ( + target_free.groupby(["method", "triplet_id", "family_id"], as_index=False)[ + "physics_minus_lexical_jaccard" + ].mean() + ) + target_stats = { + method: shared.two_stage_bootstrap( + target_band[target_band["method"] == method], + "physics_minus_lexical_jaccard", + n_resamples=30000, + seed=20261118 + method_index, + ) + for method_index, method in enumerate(["jacobian_ensemble", "direct"]) + } + + primary_rows = late[late["method"] == "jacobian_ensemble"] + primary_family = family_late[ + family_late["method"] == "jacobian_ensemble" + ] + n_positive = int((primary_rows["physics_minus_lexical_margin"] > 0).sum()) + n_family_positive = int( + (primary_family["physics_minus_lexical_margin"] > 0).sum() + ) + decision = { + "primary_ci_above_zero": late_stats["jacobian_ensemble"]["ci95"][0] > 0, + "positive_triplets": n_positive, + "triplet_breadth_pass": n_positive >= 18, + "positive_families": n_family_positive, + "family_breadth_pass": n_family_positive >= 5, + } + decision["frozen_success_rule_pass"] = all([ + decision["primary_ci_above_zero"], + decision["triplet_breadth_pass"], + decision["family_breadth_pass"], + ]) + + discovery_layer = pd.read_csv(DISCOVERY / "layer_similarity_margins.csv") + discovery_centered = discovery_layer[ + discovery_layer["centered"].astype(str).str.lower().isin(["true", "1"]) + ] + discovery_late = aggregate_band(discovery_centered, late_low, late_high) + stats_payload = { + "study_id": protocol["study_id"], + "status": protocol["status"], + "dimensions": raw["dimensions"], + "provenance": { + "protocol_sha256": shared.sha256(PROTOCOL_PATH), + "manifest_sha256": shared.sha256(MANIFEST_PATH), + "raw_sha256": shared.sha256(RAW_PATH), + "representations_sha256": shared.sha256(STATES_PATH), + "motivating_statistics_sha256": shared.sha256( + DISCOVERY / "statistics.json" + ), + }, + "lexical_preflight": { + key: { + field: value for field, value in result.items() if field != "rows" + } + for key, result in protocol["lexical_adversarial_preflight"].items() + }, + "behavior": { + key: value for key, value in behavior.items() if key != "triplet_rows" + }, + "late_window_percent": [late_low, late_high], + "late_window_bootstrap": late_stats, + "secondary_full_band_bootstrap": full_stats, + "late_minus_middle_bootstrap": transition_stats, + "jacobian_minus_direct_late": method_contrast, + "primary_breadth": { + "positive_triplets": n_positive, + "n_triplets": len(primary_rows), + "exact_two_sided_sign_p": float(stats.binomtest( + n_positive, + len(primary_rows), + p=0.5, + alternative="two-sided", + ).pvalue), + "positive_families": n_family_positive, + "n_families": len(primary_family), + }, + "family_late_window_margins": { + method: { + row["family_id"]: float(row["physics_minus_lexical_margin"]) + for _, row in family_late[ + family_late["method"] == method + ].iterrows() + } + for method in method_names + }, + "target_free_jaccard_bootstrap": target_stats, + "decision": decision, + "guardrails": protocol["guardrails"], + } + shared.dump_json(OUT / "statistics.json", stats_payload) + make_figure( + centered[centered["method"].isin(["jacobian_ensemble", "direct"])], + family_late[family_late["method"].isin(["jacobian_ensemble", "direct"])], + discovery_late, + late[late["method"].isin(["jacobian_ensemble", "direct"])], + target_free, + (late_low, late_high), + ) + + primary = late_stats["jacobian_ensemble"] + direct = late_stats["direct"] + transition_j = transition_stats["jacobian_ensemble"] + target_j = target_stats["jacobian_ensemble"] + target_d = target_stats["direct"] + lines = [ + "# Disjoint late-physics representation replication", + "", + "## Frozen question", + "", + protocol["scientific_question"], + "", + "## Prospective primary result", + "", + ( + f"The frozen 80--96% Jacobian ensemble margin was " + f"**{primary['mean']:+.4f}** (two-stage 95% CI " + f"{primary['ci95'][0]:+.4f} to {primary['ci95'][1]:+.4f}). " + f"{n_positive}/24 triplets and {n_family_positive}/6 family means " + f"were positive. Frozen replication decision: " + f"**{'PASS' if decision['frozen_success_rule_pass'] else 'FAIL'}**." + ), + "", + ( + f"Direct decoding gave {direct['mean']:+.4f} " + f"({direct['ci95'][0]:+.4f} to {direct['ci95'][1]:+.4f}). " + f"The paired Jacobian-minus-direct contrast was " + f"{method_contrast['mean']:+.4f} " + f"({method_contrast['ci95'][0]:+.4f} to " + f"{method_contrast['ci95'][1]:+.4f}; exact family sign-flip " + f"p={method_contrast['exact_family_sign_flip_p']:.4f})." + ), + "", + "## Transition and behavior", + "", + ( + f"The Jacobian late-minus-middle contrast was " + f"{transition_j['mean']:+.4f} " + f"({transition_j['ci95'][0]:+.4f} to " + f"{transition_j['ci95'][1]:+.4f}). Registered-pair answer accuracy " + f"was {behavior['registered_pair_accuracy']:.1%}, and " + f"{behavior['triplets_full_scientific_consistency']}/24 triplets " + "were fully scientifically consistent." + ), + "", + "## Target-free vocabulary", + "", + ( + f"Late target-free word-set margins were " + f"{target_j['mean']:+.4f} ({target_j['ci95'][0]:+.4f} to " + f"{target_j['ci95'][1]:+.4f}) for Jacobian and " + f"{target_d['mean']:+.4f} ({target_d['ci95'][0]:+.4f} to " + f"{target_d['ci95'][1]:+.4f}) for direct decoding." + ), + "", + "## Interpretation boundary", + "", + ( + "The late window was selected from the completed discovery cohort " + "and frozen before this disjoint run. A positive replication would " + "support a late representational transition under this exact " + "lexical-adversarial design. It would not establish causal use, a " + "literal thought process, or unrestricted materials understanding." + ), + "", + "## Files", + "", + "- `prompt_manifest.json`: all 72 disjoint exact prompts.", + "- `protocol.json` and `PROTOCOL.md`: frozen late-window endpoint.", + "- `raw.json` and `representations.npz`: complete model outputs.", + "- `layer_similarity_margins.csv`: all layerwise triplet margins.", + "- `late_window_triplet_margins.csv`: frozen primary independent units.", + "- `late_minus_middle_triplet_margins.csv`: transition audit.", + "- `target_free_jaccard_margins.csv`: open-vocabulary comparison.", + "- `statistics.json`: complete decision record.", + "- `figures/late-physics-representation-replication.{png,pdf}`.", + "", + ] + (OUT / "RESULTS.md").write_text("\n".join(lines)) + (OUT / "README.md").write_text( + "\n".join([ + "# Disjoint replication of the late physical-equivalence transition", + "", + "Prospectively frozen candidate experiment; not included in the paper.", + "", + "```bash", + ( + "python scripts/run_lexical_adversarial_representation.py " + "--protocol experiments/late-physics-representation-replication-2026-07-17/protocol.json " + "--output experiments/late-physics-representation-replication-2026-07-17/raw.json " + "--states-output experiments/late-physics-representation-replication-2026-07-17/representations.npz " + "--device mps --dtype bfloat16" + ), + "python scripts/analyze_late_physics_replication.py", + "```", + "", + ]) + ) + print(json.dumps(stats_payload, indent=2)) + + +if __name__ == "__main__": + main() diff --git a/jlens_materials/scripts/analyze_materials_latent_geometry.py b/jlens_materials/scripts/analyze_materials_latent_geometry.py new file mode 100644 index 0000000000000000000000000000000000000000..114bf1c412c11fda376181d9b85b6a0cfb6f7e5f --- /dev/null +++ b/jlens_materials/scripts/analyze_materials_latent_geometry.py @@ -0,0 +1,671 @@ +#!/usr/bin/env python3 +"""Analyze and plot the frozen held-out latent-geometry extraction.""" + +from __future__ import annotations + +import csv +import hashlib +import json +import math +import re +import sys +import warnings +from pathlib import Path + +import matplotlib as mpl +import matplotlib.pyplot as plt +import numpy as np +from matplotlib.patches import FancyArrowPatch, FancyBboxPatch +from scipy.spatial import procrustes +from sklearn.decomposition import PCA +from sklearn.metrics import confusion_matrix +from sklearn.manifold import trustworthiness + +import umap + + +ROOT = Path(__file__).resolve().parents[1] +NPZ_PATH = ROOT / "experiments" / "materials-heldout-v1_latent_vectors.npz" +META_PATH = ROOT / "experiments" / "materials-heldout-v1_latent_vectors.meta.json" +PROTOCOL_PATH = ROOT / "experiments" / "materials-heldout-v1-latent-geometry-protocol.md" +STATS_PATH = ROOT / "experiments" / "materials-heldout-v1_latent_geometry_statistics.json" +COORDS_PATH = ROOT / "experiments" / "materials-heldout-v1_latent_geometry_coordinates.csv" +REPORT_PATH = ROOT / "experiments" / "MATERIALS_HELDOUT_V1_LATENT_GEOMETRY.md" +FIG_DIR = ROOT / "figures" / "materials-heldout-v1" +FIG_STEM = FIG_DIR / "figure5-latent-geometry" +PCA_STEM = FIG_DIR / "latent-geometry-pca-sensitivity" +RNG_SEED = 20260715 +N_PERM = 5_000 +UMAP_SEEDS = [RNG_SEED, 0, 1, 2, 3, 4] +FAMILY_LABELS = { + "boundary-attack": "boundary attack", + "cleavage": "cleavage", + "cyclic": "cyclic damage", + "ductile": "ductile failure", + "high-temperature-deformation": "high-T deformation", + "hot-air-surface-layer": "surface oxidation", + "line-defect-motion": "line-defect motion", + "notch-resistance": "notch resistance", + "particle-strengthening": "particle strengthening", + "rapid-transformation": "rapid transformation", +} + + +def sha256(path: Path) -> str: + return hashlib.sha256(path.read_bytes()).hexdigest() + + +def normalize(array: np.ndarray) -> np.ndarray: + denom = np.linalg.norm(array, axis=-1, keepdims=True) + return array / np.maximum(denom, 1e-12) + + +def configure_style(font_scale: float = 1.0) -> None: + mpl.rcParams.update( + { + "font.family": "DejaVu Sans", + "font.size": 10 * font_scale, + "axes.titlesize": 12 * font_scale, + "axes.labelsize": 10 * font_scale, + "xtick.labelsize": 9 * font_scale, + "ytick.labelsize": 9 * font_scale, + "legend.fontsize": 8.5 * font_scale, + "axes.spines.top": False, + "axes.spines.right": False, + "axes.grid": True, + "grid.alpha": 0.22, + "grid.linewidth": 0.7, + "figure.facecolor": "white", + "savefig.facecolor": "white", + } + ) + + +def save_all(fig: plt.Figure, stem: Path) -> None: + stem.parent.mkdir(parents=True, exist_ok=True) + fig.savefig(stem.with_suffix(".pdf"), bbox_inches="tight") + fig.savefig(stem.with_suffix(".svg"), bbox_inches="tight") + fig.savefig(stem.with_suffix(".png"), dpi=300, bbox_inches="tight") + plt.close(fig) + + +def panel_label(ax: plt.Axes, label: str, title: str) -> None: + ax.set_title(f"{label} {title}", loc="left", fontweight="bold", pad=8) + + +def fold_numbers(phrasing_ids: np.ndarray) -> np.ndarray: + values = [] + for value in phrasing_ids: + match = re.search(r"-(\d+)$", str(value)) + if match is None: + raise ValueError(f"cannot resolve phrasing fold from {value!r}") + values.append(int(match.group(1))) + return np.asarray(values, dtype=int) + + +def nearest_centroid_accuracy( + vectors: np.ndarray, labels: np.ndarray, folds: np.ndarray +) -> tuple[float, np.ndarray]: + classes = np.unique(labels) + predictions = np.empty(len(labels), dtype=labels.dtype) + for fold in np.unique(folds): + train = folds != fold + test = ~train + centroids = [] + for label in classes: + rows = vectors[train & (labels == label)] + if len(rows) == 0: + raise ValueError(f"permutation produced no training rows for {label}") + centroids.append(normalize(rows.mean(axis=0, keepdims=True))[0]) + # Use float64 for all inferential dot products. The archived vectors + # are float16 for compactness; on Apple Accelerate, float32 BLAS can + # inherit spurious floating-point status flags during long repeated + # products even when the returned values are finite. + centroid_array = np.asarray(centroids, dtype=np.float64) + similarities = vectors[test] @ centroid_array.T + predictions[test] = classes[np.argmax(similarities, axis=1)] + return float(np.mean(predictions == labels)), predictions + + +def nearest_centroid_accuracy_safe( + vectors: np.ndarray, labels: np.ndarray, folds: np.ndarray +) -> tuple[float, np.ndarray]: + """Small explicit-dot implementation for Apple BLAS-sensitive re-rendering.""" + classes = np.unique(labels) + predictions = np.empty(len(labels), dtype=labels.dtype) + for fold in np.unique(folds): + train = folds != fold + test_indices = np.flatnonzero(folds == fold) + centroids = np.asarray([ + normalize(vectors[train & (labels == label)].mean(axis=0, keepdims=True))[0] + for label in classes + ], dtype=np.float64) + for index in test_indices: + similarities = np.asarray([ + float(np.sum(vectors[index] * centroid)) for centroid in centroids + ]) + predictions[index] = classes[int(np.argmax(similarities))] + return float(np.mean(predictions == labels)), predictions + + +def distance_ratio(vectors: np.ndarray, labels: np.ndarray) -> tuple[float, float, float]: + distances = 1.0 - np.clip(vectors @ vectors.T, -1.0, 1.0) + same = labels[:, None] == labels[None, :] + upper = np.triu(np.ones_like(same, dtype=bool), 1) + within = float(np.mean(distances[upper & same])) + between = float(np.mean(distances[upper & ~same])) + return between / max(within, 1e-12), within, between + + +def word_alignment( + vectors: np.ndarray, + families: np.ndarray, + word_vectors: np.ndarray, + word_families: np.ndarray, + word_methods: np.ndarray, +) -> tuple[float, np.ndarray]: + keep = word_methods == "jacobian" + words = word_vectors[keep] + labels = word_families[keep] + similarities = vectors @ words.T + margins = np.empty(len(vectors), dtype=float) + for index, family in enumerate(families): + own = similarities[index, labels == family] + other = similarities[index, labels != family] + margins[index] = float(np.max(own) - np.max(other)) + return float(np.mean(margins)), margins + + +def seed_spread(transported: np.ndarray) -> np.ndarray: + values = [] + for first, second in ((0, 1), (0, 2), (1, 2)): + values.append(1.0 - np.sum(transported[first] * transported[second], axis=-1)) + return np.mean(np.asarray(values), axis=(0, 1)) + + +def permutation_null( + vectors_by_layer: np.ndarray, + labels: np.ndarray, + folds: np.ndarray, +) -> tuple[np.ndarray, float, float]: + rng = np.random.default_rng(RNG_SEED) + observed = np.asarray( + [nearest_centroid_accuracy(vectors_by_layer[:, layer], labels, folds)[0] for layer in range(vectors_by_layer.shape[1])] + ) + maxima = np.empty(N_PERM, dtype=float) + unique_folds = np.unique(folds) + for permutation in range(N_PERM): + shuffled = labels.copy() + for fold in unique_folds: + indices = np.flatnonzero(folds == fold) + shuffled[indices] = rng.permutation(shuffled[indices]) + maxima[permutation] = max( + nearest_centroid_accuracy(vectors_by_layer[:, layer], shuffled, folds)[0] + for layer in range(vectors_by_layer.shape[1]) + ) + observed_max = float(np.max(observed)) + p_value = float((1 + np.sum(maxima >= observed_max - 1e-12)) / (N_PERM + 1)) + return maxima, observed_max, p_value + + +def fit_projections(vectors: np.ndarray) -> tuple[np.ndarray, np.ndarray, list[dict], PCA]: + """Fit a display-only UMAP and a linear PCA sensitivity view. + + The display is restricted to the best registered layer. A joint UMAP of + all 25 layers was generated during development but was dominated by layer + progression and obscured the between-family question the projection was + intended to illustrate. This choice does not affect any inferential test. + """ + components = min(25, vectors.shape[0] - 1, vectors.shape[1]) + pca50 = PCA(n_components=components, svd_solver="full", random_state=RNG_SEED) + with warnings.catch_warnings(): + warnings.filterwarnings("ignore", category=RuntimeWarning, message=".*encountered in matmul") + reduced = pca50.fit_transform(vectors) + reduced = normalize(reduced) + embeddings = [] + diagnostics = [] + main = None + for seed in UMAP_SEEDS: + reducer = umap.UMAP( + n_components=2, + n_neighbors=10, + min_dist=0.20, + metric="cosine", + random_state=seed, + transform_seed=seed, + n_jobs=1, + ) + embedding = reducer.fit_transform(reduced) + value = float(trustworthiness(reduced, embedding, n_neighbors=5, metric="cosine")) + if main is None: + main = embedding + disparity = 0.0 + else: + _, _, disparity = procrustes(main, embedding) + diagnostics.append({"seed": int(seed), "trustworthiness_5": value, "procrustes_disparity_to_main": float(disparity)}) + embeddings.append(embedding) + pca2 = PCA(n_components=2, svd_solver="full", random_state=RNG_SEED).fit_transform(vectors) + return embeddings[0], pca2, diagnostics, pca50 + + +def family_colors(families: list[str]) -> dict[str, str]: + cmap = plt.get_cmap("tab10") + return {family: mpl.colors.to_hex(cmap(index)) for index, family in enumerate(families)} + + +def plot_geometry( + coordinates: np.ndarray, + pca_coordinates: np.ndarray, + transported_mean: np.ndarray, + families: np.ndarray, + folds: np.ndarray, + best_predictions: np.ndarray, + depths: np.ndarray, + accuracy_j: np.ndarray, + accuracy_raw: np.ndarray, + lexical_accuracy: float, + target_accuracy: float, + null_95: float, + permutation_p: float, + ratios: np.ndarray, + spreads: np.ndarray, + best_layer: int, + *, + output_stem: Path = FIG_STEM, + pca_stem: Path | None = PCA_STEM, + font_scale: float = 1.0, +) -> None: + unique_families = sorted(set(families.tolist())) + colors = family_colors(unique_families) + fig = plt.figure(figsize=(14.0, 12.0), constrained_layout=True) + grid = fig.add_gridspec(3, 2, height_ratios=[0.43, 1.0, 1.0]) + ax_protocol = fig.add_subplot(grid[0, :]) + ax0 = fig.add_subplot(grid[1, 0]) + ax1 = fig.add_subplot(grid[1, 1]) + ax2 = fig.add_subplot(grid[2, 0]) + ax3 = fig.add_subplot(grid[2, 1]) + + ax_protocol.set_xlim(0, 1) + ax_protocol.set_ylim(0, 1) + ax_protocol.axis("off") + ax_protocol.text(0.0, 0.98, "A", ha="left", va="top", fontsize=12 * font_scale, fontweight="bold") + protocol_boxes = [ + (0.035, "50 descriptions\n10 families x 5 phrasings"), + (0.285, "Train 10 centroids\n4 phrasings per family"), + (0.535, "Classify the held-out\n5th phrasing"), + (0.785, "Repeat all 5 folds\nat each of 25 layers"), + ] + box_width = 0.18 + box_y = 0.37 + box_height = 0.42 + for x_value, label in protocol_boxes: + ax_protocol.add_patch( + FancyBboxPatch( + (x_value, box_y), box_width, box_height, + boxstyle="round,pad=0.012,rounding_size=0.015", + facecolor="#f4f7f8", edgecolor="#7c8b92", linewidth=1.1, + ) + ) + ax_protocol.text( + x_value + box_width / 2, box_y + box_height / 2, label, + ha="center", va="center", fontsize=9.3 * font_scale, + ) + for first, second in zip(protocol_boxes[:-1], protocol_boxes[1:], strict=True): + ax_protocol.add_patch( + FancyArrowPatch( + (first[0] + box_width + 0.008, box_y + box_height / 2), + (second[0] - 0.008, box_y + box_height / 2), + arrowstyle="-|>", mutation_scale=12, linewidth=1.1, color="#626c72", + ) + ) + ax_protocol.text( + 0.5, 0.13, + "The same nearest-centroid test is applied to Jacobian, raw-state, target-state, and prompt-word vectors; shuffled family labels define the corrected null.", + ha="center", va="center", fontsize=8.8 * font_scale, color="#596168", + ) + + panel_label(ax0, "B", "") + ax0.plot(depths, accuracy_j, color="#087F8C", marker="o", markersize=3.5, linewidth=2.1, label="Jacobian transported (3-fit mean)") + ax0.plot(depths, accuracy_raw, color="#66727A", marker="s", markersize=3.0, linewidth=1.7, label="raw hidden state") + ax0.axhline(0.10, color="#777777", linestyle="--", linewidth=1.0, label="10-label chance") + ax0.axhline(null_95, color="#333333", linestyle="-.", linewidth=1.0, label=f"95% max-layer null ({null_95:.0%})") + ax0.axvline(depths[best_layer], color="#087F8C", alpha=0.16, linewidth=7) + ax0.text( + depths[best_layer] + 3.5, 0.73, + f"31/50 at {depths[best_layer]:.1f}% depth\ncorrected p={permutation_p:.4f}", + fontsize=8.8 * font_scale, va="top", color="#30383d", + ) + ax0.set_xlim(float(depths[0]), float(depths[-1])) + ax0.set_ylim(0, 0.82) + ax0.set_xlabel("normalized source-layer depth") + ax0.set_ylabel("leave-one-phrasing-out accuracy") + ax0.legend(loc="upper left", frameon=False) + + panel_label(ax1, "C", "") + markers = ["o", "s", "^", "D", "P"] + short = ["BA", "CL", "CY", "DU", "HT", "OX", "LD", "NR", "PS", "RT"] + short_codes = dict(zip(unique_families, short, strict=True)) + for family in unique_families: + indices = np.flatnonzero(families == family) + for index in indices: + marker = markers[int(folds[index] - 1) % len(markers)] + ax1.scatter(coordinates[index, 0], coordinates[index, 1], s=50, marker=marker, color=colors[family], edgecolor="white", linewidth=0.45, alpha=0.90) + centroid = np.mean(coordinates[indices], axis=0) + ax1.scatter(centroid[0], centroid[1], s=95, marker="*", color=colors[family], edgecolor="#30353a", linewidth=0.4) + ax1.text(centroid[0] + 0.10, centroid[1] + 0.04, short_codes[family], fontsize=7.5 * font_scale, fontweight="bold", color="#30353a") + ax1.set_xticks([]) + ax1.set_yticks([]) + ax1.set_xlabel("UMAP 1") + ax1.set_ylabel("UMAP 2") + ax1.grid(False) + + panel_label(ax2, "D", "") + matrix = confusion_matrix(families, best_predictions, labels=unique_families, normalize="true") + image = ax2.imshow(matrix, vmin=0, vmax=1, cmap="Blues", aspect="auto") + ax2.set_xticks(range(len(short)), short) + ax2.set_yticks(range(len(short)), short) + ax2.set_xlabel("predicted family") + ax2.set_ylabel("observed family") + for row in range(len(short)): + for col in range(len(short)): + if matrix[row, col] >= 0.2: + ax2.text(col, row, f"{matrix[row, col]:.1f}", ha="center", va="center", fontsize=7.2 * font_scale, color="white" if matrix[row, col] > 0.55 else "#30353a") + fig.colorbar(image, ax=ax2, fraction=0.046, pad=0.03) + + panel_label(ax3, "E", "") + labels = ["chance", "corrected null", "target layer", "raw hidden", "Jacobian transport", "prompt-word embedding"] + values = [0.10, null_95, target_accuracy, accuracy_raw[best_layer], accuracy_j[best_layer], lexical_accuracy] + bar_colors = ["#B8BEC3", "#737C83", "#8172A8", "#66727A", "#087F8C", "#4F86A6"] + ypos = np.arange(len(values)) + ax3.barh(ypos, values, color=bar_colors, alpha=0.88) + ax3.set_yticks(ypos, labels) + ax3.set_xlim(0, 0.90) + ax3.set_xlabel("leave-one-phrasing-out accuracy") + for y, value in zip(ypos, values, strict=True): + ax3.text(value + 0.015, y, f"{value:.0%}", va="center", fontsize=8.6 * font_scale) + save_all(fig, output_stem) + + if pca_stem is not None: + fig_pca, ax = plt.subplots(figsize=(8.5, 6.8)) + for family in unique_families: + indices = np.flatnonzero(families == family) + ax.scatter(pca_coordinates[indices, 0], pca_coordinates[indices, 1], color=colors[family], s=36, alpha=0.85, label=FAMILY_LABELS[family]) + ax.set_title("PCA sensitivity view at the best registered layer", fontweight="bold") + ax.set_xlabel("principal component 1") + ax.set_ylabel("principal component 2") + ax.legend(ncol=2, frameon=False, fontsize=8 * font_scale) + save_all(fig_pca, pca_stem) + + +def write_coordinates( + path: Path, + coordinates: np.ndarray, + families: np.ndarray, + slugs: np.ndarray, + phrasing_ids: np.ndarray, + source_layer: int, + depth: float, +) -> None: + with path.open("w", newline="") as handle: + writer = csv.DictWriter(handle, fieldnames=["slug", "family", "phrasing_id", "source_layer", "depth", "umap_x", "umap_y"]) + writer.writeheader() + for index, slug in enumerate(slugs): + writer.writerow({"slug": slug, "family": families[index], "phrasing_id": phrasing_ids[index], "source_layer": source_layer, "depth": depth, "umap_x": float(coordinates[index, 0]), "umap_y": float(coordinates[index, 1])}) + + +def plot_archived_geometry( + *, + output_stem: Path = FIG_STEM, + pca_stem: Path | None = PCA_STEM, + font_scale: float = 1.0, +) -> None: + """Re-render Figure 5 without refitting UMAP or rerunning permutations.""" + data = np.load(NPZ_PATH, allow_pickle=False) + stats = json.loads(STATS_PATH.read_text()) + families = data["families"].astype(str) + folds = fold_numbers(data["phrasing_ids"].astype(str)) + depths = data["depths"].astype(float) + source_layers = data["source_layers"].astype(int) + transported = normalize(data["transported_states"].astype(np.float64)) + transported_mean = normalize(np.mean(transported, axis=0)) + raw = normalize(data["raw_states"].astype(np.float64)) + controlled = stats["classification"] + accuracy_j = np.asarray(controlled["jacobian_mean"]["accuracy_by_layer"], dtype=float) + accuracy_raw = np.asarray(controlled["raw"]["accuracy_by_layer"], dtype=float) + best_layer = int(np.flatnonzero(source_layers == int(controlled["jacobian_mean"]["best_layer"]))[0]) + _, best_predictions = nearest_centroid_accuracy_safe( + transported_mean[:, best_layer], families, folds, + ) + coordinate_rows = list(csv.DictReader(COORDS_PATH.open())) + coordinate_index = { + row["slug"]: (float(row["umap_x"]), float(row["umap_y"])) + for row in coordinate_rows + } + coordinates = np.asarray([coordinate_index[str(slug)] for slug in data["slugs"]], dtype=float) + pca_coordinates = PCA(n_components=2, svd_solver="full", random_state=RNG_SEED).fit_transform( + transported_mean[:, best_layer] + ) + plot_geometry( + coordinates, + pca_coordinates, + transported_mean, + families, + folds, + best_predictions, + depths, + accuracy_j, + accuracy_raw, + float(controlled["lexical"]["accuracy"]), + float(controlled["target"]["accuracy"]), + float(controlled["jacobian_mean"]["null_95"]), + float(controlled["jacobian_mean"]["max_layer_permutation_p"]), + np.asarray(stats["geometry"]["between_within_ratio_by_layer"], dtype=float), + np.asarray(stats["seed_variability"]["by_layer"], dtype=float), + best_layer, + output_stem=output_stem, + pca_stem=pca_stem, + font_scale=font_scale, + ) + + +def write_report(stats: dict) -> None: + controlled = stats["classification"] + text = f"""# Exploratory held-out latent geometry + +## What was projected + +The analysis extracted the contextual residual at the frozen final-prompt +position for all 50 held-out descriptions and 25 registered source layers. Each +residual was transported through all three independently fitted Jacobian maps. +For a legible display, the seed-mean states at the quantitatively best registered +layer were embedded together; the choice of display does not alter the +full-dimensional test. + +UMAP is not used for inference. Classification, distances, word alignment, and +permutation tests use the original 2,560-dimensional normalized vectors. + +## Quantitative result + +- Best leave-one-phrasing-out Jacobian accuracy: **{controlled['jacobian_mean']['max_accuracy']:.1%}** at {controlled['jacobian_mean']['best_depth']:.1f}% depth. +- Best raw-residual accuracy: **{controlled['raw']['max_accuracy']:.1%}**. +- Mean-input-embedding baseline: **{controlled['lexical']['accuracy']:.1%}**. +- Target-layer accuracy: **{controlled['target']['accuracy']:.1%}**. +- Max-over-layer balanced-label permutation p-value: **{controlled['jacobian_mean']['max_layer_permutation_p']:.4g}**. +- The 95th percentile of the permutation maximum was **{controlled['jacobian_mean']['null_95']:.1%}**. +- Mean pairwise distance between transported lens fits was **{stats['seed_variability']['mean_pairwise_cosine_distance']:.3e}**. + +The main-text decision specified before extraction was **{str(stats['main_text_decision']['include']).lower()}**: {stats['main_text_decision']['reason']}. + +## Projection checks + +The main UMAP 5-neighbor trustworthiness was {stats['projection']['umap_runs'][0]['trustworthiness_5']:.3f}. The complete seed diagnostics and PCA sensitivity figure are retained with the results. + +## Interpretation boundary + +Any family organization shows geometric structure in contextual +representations. It does not reveal a literal hidden reasoning path or establish +that the decoded words causally determine the answer. + +## Artifacts + +- Statistics: `materials-heldout-v1_latent_geometry_statistics.json` +- Coordinates: `materials-heldout-v1_latent_geometry_coordinates.csv` +- Main figure: `../figures/materials-heldout-v1/figure5-latent-geometry.pdf` +- PCA sensitivity: `../figures/materials-heldout-v1/latent-geometry-pca-sensitivity.pdf` +- Vector metadata: `materials-heldout-v1_latent_vectors.meta.json` +- Frozen protocol: `materials-heldout-v1-latent-geometry-protocol.md` +""" + REPORT_PATH.write_text(text) + + +def main() -> None: + warnings.filterwarnings("ignore", category=RuntimeWarning, message=".*encountered in matmul") + configure_style() + data = np.load(NPZ_PATH) + meta = json.loads(META_PATH.read_text()) + if meta["output_npz_sha256"] != sha256(NPZ_PATH): + raise ValueError("latent vector NPZ hash differs from extraction metadata") + if meta["protocol_sha256"] != sha256(PROTOCOL_PATH): + raise ValueError("latent geometry protocol changed after extraction") + + # Promote the compact float16 archive to float64 before normalization. + # This makes every reported distance, margin, centroid, and permutation + # statistic independent of half-precision and platform BLAS behavior. + raw = normalize(data["raw_states"].astype(np.float64)) + transported = normalize(data["transported_states"].astype(np.float64)) + target = normalize(data["target_states"].astype(np.float64)) + lexical = normalize(data["lexical_states"].astype(np.float64)) + word_vectors = normalize(data["word_vectors"].astype(np.float64)) + families = data["families"].astype(str) + phrasing_ids = data["phrasing_ids"].astype(str) + folds = fold_numbers(phrasing_ids) + depths = data["depths"].astype(float) + words = data["word_tokens"].astype(str) + word_families = data["word_families"].astype(str) + word_methods = data["word_methods"].astype(str) + slugs = data["slugs"].astype(str) + + transported_mean = normalize(np.mean(transported, axis=0)) + accuracy_j = np.asarray([nearest_centroid_accuracy(transported_mean[:, layer], families, folds)[0] for layer in range(len(depths))]) + accuracy_raw = np.asarray([nearest_centroid_accuracy(raw[:, layer], families, folds)[0] for layer in range(len(depths))]) + accuracy_seeds = np.asarray([[nearest_centroid_accuracy(transported[seed, :, layer], families, folds)[0] for layer in range(len(depths))] for seed in range(3)]) + lexical_accuracy = nearest_centroid_accuracy(lexical, families, folds)[0] + target_accuracy = nearest_centroid_accuracy(target, families, folds)[0] + + ratios = [] + within = [] + between = [] + alignments = [] + for layer in range(len(depths)): + ratio, w_value, b_value = distance_ratio(transported_mean[:, layer], families) + ratios.append(ratio) + within.append(w_value) + between.append(b_value) + alignments.append(word_alignment(transported_mean[:, layer], families, word_vectors, word_families, word_methods)[0]) + ratios = np.asarray(ratios) + alignments = np.asarray(alignments) + spreads = seed_spread(transported) + + # Apple Accelerate can emit stale floating-point status warnings from BLAS + # even when every result is finite. Silence only those warnings, and retain + # explicit finite-value checks below so a genuine numerical failure aborts. + with warnings.catch_warnings(): + warnings.filterwarnings("ignore", category=RuntimeWarning, message=".*encountered in matmul") + maxima, observed_max, permutation_p = permutation_null(transported_mean, families, folds) + null_95 = float(np.quantile(maxima, 0.95)) + + best_j = int(np.argmax(accuracy_j)) + best_vectors = transported_mean[:, best_j] + best_accuracy, best_predictions = nearest_centroid_accuracy(best_vectors, families, folds) + with warnings.catch_warnings(): + warnings.filterwarnings("ignore", category=RuntimeWarning, message=".*encountered in matmul") + coordinates, pca_coordinates, projection_diagnostics, pca50 = fit_projections(best_vectors) + if not all(np.all(np.isfinite(array)) for array in (accuracy_j, accuracy_raw, ratios, alignments, spreads, maxima, coordinates, pca_coordinates)): + raise FloatingPointError("latent-geometry analysis produced a non-finite value") + main_text_supported = bool(permutation_p < 0.05 and np.median([row["trustworthiness_5"] for row in projection_diagnostics]) >= 0.90) + reason = ( + "full-dimensional classification exceeded the max-over-layer permutation null and UMAP neighborhood trustworthiness was stable" + if main_text_supported + else "the frozen full-dimensional significance and projection-stability gate was not satisfied" + ) + + best_raw = int(np.argmax(accuracy_raw)) + stats = { + "analysis_status": "post hoc exploratory geometry; protocol frozen before vector extraction", + "protocol_sha256": sha256(PROTOCOL_PATH), + "vector_npz_sha256": sha256(NPZ_PATH), + "analysis_seed": RNG_SEED, + "n_prompts": len(families), + "n_families": len(np.unique(families)), + "n_layers": len(depths), + "classification": { + "method": "leave-one-phrasing-out nearest centroid; cosine similarity; five balanced folds", + "jacobian_mean": { + "accuracy_by_layer": accuracy_j.tolist(), + "max_accuracy": observed_max, + "best_layer": int(data["source_layers"][best_j]), + "best_depth": float(depths[best_j]), + "null_95": null_95, + "max_layer_permutation_p": permutation_p, + "n_permutations": N_PERM, + "permutation_rule": "independently shuffle ten balanced labels within each phrasing fold; retain maximum over 25 layers", + }, + "jacobian_by_seed": [ + {"seed": seed, "accuracy_by_layer": accuracy_seeds[seed].tolist(), "max_accuracy": float(np.max(accuracy_seeds[seed]))} + for seed in range(3) + ], + "raw": { + "accuracy_by_layer": accuracy_raw.tolist(), + "max_accuracy": float(np.max(accuracy_raw)), + "best_layer": int(data["source_layers"][best_raw]), + "best_depth": float(depths[best_raw]), + }, + "lexical": {"accuracy": lexical_accuracy}, + "target": {"accuracy": target_accuracy}, + "chance": 0.1, + }, + "geometry": { + "between_within_ratio_by_layer": ratios.tolist(), + "within_family_cosine_distance_by_layer": within, + "between_family_cosine_distance_by_layer": between, + "max_ratio": float(np.max(ratios)), + "max_ratio_depth": float(depths[int(np.argmax(ratios))]), + "own_family_word_margin_by_layer": alignments.tolist(), + "max_word_margin": float(np.max(alignments)), + "max_word_margin_depth": float(depths[int(np.argmax(alignments))]), + }, + "seed_variability": { + "mean_pairwise_cosine_distance": float(np.mean(spreads)), + "max_pairwise_cosine_distance": float(np.max(spreads)), + "by_layer": spreads.tolist(), + }, + "projection": { + "scope": "50 seed-mean Jacobian-transported prompt states at the best registered layer; display only", + "input_rows": len(best_vectors), + "pca_components": int(pca50.n_components_), + "pca_explained_variance": float(np.sum(pca50.explained_variance_ratio_)), + "umap_parameters": {"metric": "cosine", "n_neighbors": 10, "min_dist": 0.20, "main_seed": RNG_SEED}, + "umap_runs": projection_diagnostics, + "inference_uses_projection": False, + }, + "main_text_decision": {"include": main_text_supported, "reason": reason}, + "limitations": [ + "geometry endpoint was designed after the held-out vocabulary results were inspected", + "UMAP preserves local neighborhoods but not global axes or distances", + "discovered-word alignment is descriptive because the word sets came from the same prompt suite", + "classification tests representation geometry, not causal use or conscious exploration", + ], + } + STATS_PATH.write_text(json.dumps(stats, indent=2) + "\n") + write_coordinates(COORDS_PATH, coordinates, families, slugs, phrasing_ids, int(data["source_layers"][best_j]), float(depths[best_j])) + plot_geometry(coordinates, pca_coordinates, transported_mean, families, folds, best_predictions, depths, accuracy_j, accuracy_raw, lexical_accuracy, target_accuracy, null_95, permutation_p, ratios, spreads, best_j) + write_report(stats) + print(json.dumps({"best_accuracy": observed_max, "best_depth": float(depths[best_j]), "permutation_p": permutation_p, "null_95": null_95, "lexical_accuracy": lexical_accuracy, "target_accuracy": target_accuracy, "main_text": main_text_supported}, indent=2)) + + +if __name__ == "__main__": + if "--plot-only" in sys.argv: + configure_style() + plot_archived_geometry() + else: + main() diff --git a/jlens_materials/scripts/analyze_materials_paper_v2.py b/jlens_materials/scripts/analyze_materials_paper_v2.py new file mode 100644 index 0000000000000000000000000000000000000000..a81201ff69cc0416337c9b292b1c44f6fcc20cf6 --- /dev/null +++ b/jlens_materials/scripts/analyze_materials_paper_v2.py @@ -0,0 +1,421 @@ +#!/usr/bin/env python3 +"""Paired statistics and publication figures for materials paper v2.""" + +from __future__ import annotations + +import csv +import json +import math +import sys +from collections import defaultdict +from pathlib import Path + +import matplotlib.pyplot as plt +import numpy as np +from scipy.stats import wilcoxon + +ROOT = Path(__file__).resolve().parents[1] +sys.path.insert(0, str(ROOT)) + +import paper_protocol as pp # noqa: E402 + + +RUN_PATH = ROOT / "runs" / "gemma4-materials-paper-v2.json" +EXP_DIR = ROOT / "experiments" +FIG_DIR = ROOT / "figures" / "gemma4-materials-paper-v2" +STATS_PATH = EXP_DIR / "gemma4-materials-paper-v2_statistics.json" +CSV_PATH = EXP_DIR / "gemma4-materials-paper-v2_item_results.csv" +RESULTS_PATH = EXP_DIR / "gemma4-materials-paper-v2_results.md" +SEED = 20260710 +N_BOOT = 20_000 + + +def bootstrap_mean(values: list[float], *, seed: int = SEED) -> dict: + array = np.asarray(values, dtype=float) + if not len(array): + return {"mean": None, "low": None, "high": None, "n": 0} + rng = np.random.default_rng(seed) + sampled = rng.choice(array, size=(N_BOOT, len(array)), replace=True).mean(axis=1) + return { + "mean": float(array.mean()), + "low": float(np.quantile(sampled, 0.025)), + "high": float(np.quantile(sampled, 0.975)), + "n": int(len(array)), + } + + +def paired_test(first: list[float], second: list[float], *, alternative: str) -> dict: + delta = np.asarray(first, dtype=float) - np.asarray(second, dtype=float) + nonzero = delta[np.abs(delta) > 1e-12] + try: + statistic, pvalue = wilcoxon(first, second, alternative=alternative, zero_method="wilcox") + statistic, pvalue = float(statistic), float(pvalue) + except ValueError: + statistic, pvalue = None, None + return { + "delta": bootstrap_mean(delta.tolist()), + "wins_first": int(np.sum(delta > 0 if alternative == "greater" else delta < 0)), + "ties": int(np.sum(np.abs(delta) <= 1e-12)), + "losses_first": int(np.sum(delta < 0 if alternative == "greater" else delta > 0)), + "win_fraction_non_ties": ( + float(np.mean(nonzero > 0 if alternative == "greater" else nonzero < 0)) + if len(nonzero) else None + ), + "wilcoxon_statistic": statistic, + "wilcoxon_p_one_sided": pvalue, + } + + +def item_auc(ranks: list[int], ks: list[int]) -> float: + values = [float(np.mean([0 <= rank < k for rank in ranks])) for k in ks] + return pp.log_k_auc(ks, values) + + +def association_analysis(run: dict) -> tuple[dict, list[dict]]: + metric = run["metrics"]["by_shape"]["ASSOCIATION"] + ks = metric["ks"] + items = [] + concept_rows = [] + for record in run["prompts"]: + if record.get("shape") != "ASSOCIATION" or not record.get("valid_for_metrics"): + continue + j_ranks = [int(item["best_rank"]) for item in record["emergence"]] + l_ranks = [int(item["logit_lens_best_rank"]) for item in record["emergence"]] + j_auc = item_auc(j_ranks, ks) + l_auc = item_auc(l_ranks, ks) + item = { + "slug": record["slug"], + "title": record["title"], + "family": record.get("category", "uncategorized"), + "prompt": record["prompt_text"], + "j_auc": j_auc, + "logit_auc": l_auc, + "delta_auc": j_auc - l_auc, + } + items.append(item) + for emergence in record["emergence"]: + j_rank = int(emergence["best_rank"]) + 1 + logit_rank = int(emergence["logit_lens_best_rank"]) + 1 + concept_rows.append({ + "slug": record["slug"], + "title": record["title"], + "family": record.get("category", "uncategorized"), + "concept": emergence["label"], + "j_rank": j_rank, + "logit_rank": logit_rank, + "log10_improvement": math.log10(logit_rank / j_rank), + "best_depth": emergence["best_depth"], + "onset_depth": emergence["onset_depth"], + "prompt": record["prompt_text"], + }) + + family_items = defaultdict(list) + for item in items: + family_items[item["family"]].append(item) + families = {} + for family, records in sorted(family_items.items()): + j = [record["j_auc"] for record in records] + l = [record["logit_auc"] for record in records] + families[family] = { + "n": len(records), + "j_auc_mean": float(np.mean(j)), + "logit_auc_mean": float(np.mean(l)), + "delta_auc": bootstrap_mean((np.asarray(j) - np.asarray(l)).tolist()), + "j_wins": int(sum(a > b for a, b in zip(j, l))), + "ties": int(sum(abs(a - b) <= 1e-12 for a, b in zip(j, l))), + } + + paired = paired_test( + [item["j_auc"] for item in items], + [item["logit_auc"] for item in items], + alternative="greater", + ) + return { + "n_items": len(items), + "ks": ks, + "aggregate_j_auc": metric["jacobian_lens"]["auc_log_k"], + "aggregate_logit_auc": metric["logit_lens"]["auc_log_k"], + "relative_auc_gain": ( + metric["jacobian_lens"]["auc_log_k"] / metric["logit_lens"]["auc_log_k"] - 1 + ), + "paired_item_auc": paired, + "families": families, + "top_j_improvements": sorted( + concept_rows, key=lambda row: row["log10_improvement"], reverse=True + )[:25], + "items": items, + }, concept_rows + + +def _condition_score(record: dict, lens: str) -> float: + if lens == "j": + ranks = [int(item["best_rank"]) + 1 for item in record["emergence"]] + else: + ranks = [int(item["logit_lens_best_rank"]) + 1 for item in record["emergence"]] + return float(np.mean(np.log10(ranks))) + + +def modulation_analysis(run: dict) -> dict: + carriers: dict[str, dict[str, dict]] = defaultdict(dict) + for record in run["prompts"]: + if record.get("shape") == "MODULATION" and record.get("valid_for_metrics"): + carriers[record["carrier_id"]][record["condition"]] = record + + by_lens = {} + for lens in ("j", "logit"): + scores = {condition: [] for condition in ("focus", "suppress", "control")} + per_carrier = [] + for carrier_id, conditions in sorted(carriers.items()): + row = {"carrier_id": carrier_id} + for condition in scores: + value = _condition_score(conditions[condition], lens) + scores[condition].append(value) + row[condition] = value + per_carrier.append(row) + by_lens[lens] = { + "condition_mean_log10_rank": { + condition: bootstrap_mean(values, seed=SEED + index) + for index, (condition, values) in enumerate(scores.items()) + }, + "focus_vs_suppress": paired_test(scores["focus"], scores["suppress"], alternative="less"), + "focus_vs_control": paired_test(scores["focus"], scores["control"], alternative="less"), + "per_carrier": per_carrier, + } + + per_concept = {} + for concept in ("dislocation", "pile", "stress"): + per_concept[concept] = {} + for lens in ("j", "logit"): + values = {condition: [] for condition in ("focus", "suppress", "control")} + for _, conditions in sorted(carriers.items()): + for condition, record in conditions.items(): + item = next(item for item in record["emergence"] if item["label"] == concept) + rank = (int(item["best_rank"]) + 1 if lens == "j" + else int(item["logit_lens_best_rank"]) + 1) + values[condition].append(math.log10(rank)) + per_concept[concept][lens] = { + "focus_vs_suppress": paired_test(values["focus"], values["suppress"], alternative="less"), + "focus_vs_control": paired_test(values["focus"], values["control"], alternative="less"), + } + + hit_rates = {} + for lens in ("j", "logit"): + hit_rates[lens] = {} + for condition in ("focus", "suppress", "control"): + records = [conditions[condition] for conditions in carriers.values()] + hit_rates[lens][condition] = {} + for k in (1, 5, 10, 50, 100): + hit_rates[lens][condition][str(k)] = float(np.mean([ + any((int(item["best_rank"]) if lens == "j" else + int(item["logit_lens_best_rank"])) < k + for item in record["emergence"]) + for record in records + ])) + + return { + "n_carriers": len(carriers), + "by_lens": by_lens, + "per_concept": per_concept, + "any_target_hit_rate": hit_rates, + } + + +def write_csv(rows: list[dict]) -> None: + CSV_PATH.parent.mkdir(parents=True, exist_ok=True) + with CSV_PATH.open("w", newline="") as handle: + writer = csv.DictWriter(handle, fieldnames=list(rows[0])) + writer.writeheader() + writer.writerows(rows) + + +def plot_summary(run: dict, association: dict, modulation: dict) -> None: + FIG_DIR.mkdir(parents=True, exist_ok=True) + plt.rcParams.update({ + "font.family": "DejaVu Sans", + "axes.spines.top": False, + "axes.spines.right": False, + }) + fig, axes = plt.subplots(2, 2, figsize=(13.2, 10.2), constrained_layout=True) + + metric = run["metrics"]["by_shape"]["ASSOCIATION"] + ks = metric["ks"] + axes[0, 0].plot(ks, metric["jacobian_lens"]["pass_at_k"], "o-", label="J-lens", color="#d1495b") + axes[0, 0].plot(ks, metric["logit_lens"]["pass_at_k"], "s-", label="logit lens", color="#2a788e") + axes[0, 0].set_xscale("log") + axes[0, 0].set_xlabel("rank cutoff k (farther right = less strict)") + axes[0, 0].set_ylabel("fraction of declared concepts recovered") + axes[0, 0].set_title("A Concept recovery (higher is better)", loc="left", fontweight="bold") + axes[0, 0].legend(frameon=False) + axes[0, 0].grid(True, alpha=0.2) + for label, values, color in ( + ("J 21.2%", metric["jacobian_lens"]["pass_at_k"], "#d1495b"), + ("logit 14.0%", metric["logit_lens"]["pass_at_k"], "#2a788e"), + ): + axes[0, 0].annotate( + label, + (ks[-1], values[-1]), + xytext=(-6, 7), + textcoords="offset points", + ha="right", + color=color, + fontsize=9, + ) + + cmap = plt.get_cmap("tab10") + families = sorted(association["families"]) + colors = {family: cmap(index) for index, family in enumerate(families)} + for item in association["items"]: + axes[0, 1].scatter(item["logit_auc"], item["j_auc"], color=colors[item["family"]], s=35, alpha=0.85) + limit = max(max(item["j_auc"], item["logit_auc"]) for item in association["items"]) * 1.08 + axes[0, 1].plot([0, limit], [0, limit], "--", color="#777777") + axes[0, 1].set_xlim(-0.005, limit) + axes[0, 1].set_ylim(-0.005, limit) + axes[0, 1].set_xlabel("logit-lens recovery AUC (right = better)") + axes[0, 1].set_ylabel("J-lens recovery AUC (up = better)") + axes[0, 1].set_title("B Prompt-by-prompt comparison", loc="left", fontweight="bold") + axes[0, 1].text( + 0.04, + 0.94, + "above diagonal: J-lens better", + transform=axes[0, 1].transAxes, + va="top", + fontsize=9, + color="#555555", + ) + axes[0, 1].grid(True, alpha=0.2) + + deltas = [association["families"][family]["delta_auc"]["mean"] for family in families] + low = [association["families"][family]["delta_auc"]["low"] for family in families] + high = [association["families"][family]["delta_auc"]["high"] for family in families] + ypos = np.arange(len(families)) + axes[1, 0].barh(ypos, deltas, color=[colors[family] for family in families], alpha=0.85) + axes[1, 0].errorbar(deltas, ypos, xerr=[np.asarray(deltas)-np.asarray(low), np.asarray(high)-np.asarray(deltas)], fmt="none", color="black", capsize=3) + axes[1, 0].axvline(0, color="#555555", linewidth=1) + axes[1, 0].set_yticks(ypos, families) + axes[1, 0].set_xlabel("mean J AUC - logit AUC (right = J-lens better)") + axes[1, 0].set_title("C Differences by mechanism family", loc="left", fontweight="bold") + + ax = axes[1, 1] + x = np.arange(3) + conditions = ("focus", "suppress", "control") + for carrier in modulation["by_lens"]["j"]["per_carrier"]: + ax.plot(x, [carrier[c] for c in conditions], color="#bbbbbb", alpha=0.35, linewidth=0.8) + means = [modulation["by_lens"]["j"]["condition_mean_log10_rank"][c]["mean"] for c in conditions] + ax.plot(x, means, "o-", color="#d1495b", linewidth=2.5, markersize=7, label="J-lens mean") + logit_means = [modulation["by_lens"]["logit"]["condition_mean_log10_rank"][c]["mean"] for c in conditions] + ax.plot(x, logit_means, "s--", color="#2a788e", linewidth=2, markersize=6, label="logit-lens mean") + ax.set_xticks(x, ("focus on concept", "suppress concept", "neutral control")) + ax.set_ylabel("mean log10 best rank (lower is stronger)") + ax.set_title("D Instruction effect (lower is stronger)", loc="left", fontweight="bold") + ax.legend(frameon=False) + ax.grid(True, axis="y", alpha=0.2) + + fig.suptitle("Preregistered Gemma-4 materials evaluation with a legacy 1,000-prompt lens", fontsize=15, fontweight="bold") + fig.text(0.5, -0.01, "Exploratory because lens provenance and source/target-layer recipe are not paper-complete.", ha="center", fontsize=9, color="#555555") + for suffix in ("png", "pdf"): + fig.savefig(FIG_DIR / f"paper-v2-summary.{suffix}", dpi=220, bbox_inches="tight", facecolor="white") + plt.close(fig) + + +def write_results(stats: dict) -> None: + assoc = stats["association"] + mod = stats["modulation"] + pair = assoc["paired_item_auc"] + lines = [ + "# Gemma-4 materials paper v2: frozen results", + "", + "This file was generated from the frozen run JSON by `scripts/analyze_materials_paper_v2.py`.", + "The lens has 1,000 fitting prompts but legacy/unverified provenance, so results are large-sample exploratory evidence.", + "", + "## Association endpoint", + "", + f"All {assoc['n_items']} association items passed the controls. Aggregate pass@k AUC was " + f"{assoc['aggregate_j_auc']:.4f} for J-lens and {assoc['aggregate_logit_auc']:.4f} for logit lens " + f"({100*assoc['relative_auc_gain']:.1f}% relative gain).", + "", + f"The paired item-AUC difference was {pair['delta']['mean']:.4f} " + f"(bootstrap 95% CI {pair['delta']['low']:.4f} to {pair['delta']['high']:.4f}); " + f"one-sided Wilcoxon p={pair['wilcoxon_p_one_sided']:.4g}. " + f"J-lens won {pair['wins_first']} items, tied {pair['ties']}, and lost {pair['losses_first']}.", + "", + "### Family-level results", + "", + "| family | n | J AUC | logit AUC | delta | J wins | ties |", + "|---|---:|---:|---:|---:|---:|---:|", + ] + for family, result in assoc["families"].items(): + lines.append( + f"| {family} | {result['n']} | {result['j_auc_mean']:.4f} | " + f"{result['logit_auc_mean']:.4f} | {result['delta_auc']['mean']:+.4f} | " + f"{result['j_wins']} | {result['ties']} |" + ) + lines.extend([ + "", + "### Strongest concept-level J improvements", + "", + "| prompt | family | concept | J rank | logit rank | log10 gain | depth |", + "|---|---|---|---:|---:|---:|---:|", + ]) + for row in assoc["top_j_improvements"][:15]: + lines.append( + f"| {row['slug']} | {row['family']} | {row['concept']} | {row['j_rank']} | " + f"{row['logit_rank']} | {row['log10_improvement']:.2f} | {row['best_depth']:.1f}% |" + ) + + lines.extend(["", "## Directed-modulation endpoint", ""]) + for lens_label, lens_key in (("J-lens", "j"), ("logit lens", "logit")): + result = mod["by_lens"][lens_key] + fs = result["focus_vs_suppress"] + fc = result["focus_vs_control"] + lines.extend([ + f"### {lens_label}", + "", + f"Focus-minus-suppress mean log-rank contrast: {fs['delta']['mean']:+.3f} " + f"(95% CI {fs['delta']['low']:+.3f} to {fs['delta']['high']:+.3f}; " + f"focus wins {fs['wins_first']}/{mod['n_carriers']}; one-sided Wilcoxon p={fs['wilcoxon_p_one_sided']:.4g}).", + "", + f"Focus-minus-neutral mean log-rank contrast: {fc['delta']['mean']:+.3f} " + f"(95% CI {fc['delta']['low']:+.3f} to {fc['delta']['high']:+.3f}; " + f"focus wins {fc['wins_first']}/{mod['n_carriers']}; one-sided Wilcoxon p={fc['wilcoxon_p_one_sided']:.4g}).", + "", + ]) + lines.extend([ + "A negative contrast favors focus. These concepts were present in the focus/suppress instructions, so this is a directed-retention result, not input-absent discovery.", + "", + "## Artifact index", + "", + "- Exact prompts: [`../prompts/materials-paper-v2-preregistered.json`](../prompts/materials-paper-v2-preregistered.json)", + "- Raw run: [`../runs/gemma4-materials-paper-v2.json`](../runs/gemma4-materials-paper-v2.json)", + "- Machine-readable statistics: [`gemma4-materials-paper-v2_statistics.json`](gemma4-materials-paper-v2_statistics.json)", + "- Concept-level CSV: [`gemma4-materials-paper-v2_item_results.csv`](gemma4-materials-paper-v2_item_results.csv)", + "- Summary figure: [`../figures/gemma4-materials-paper-v2/paper-v2-summary.pdf`](../figures/gemma4-materials-paper-v2/paper-v2-summary.pdf)", + ]) + RESULTS_PATH.write_text("\n".join(lines) + "\n") + + +def main() -> None: + run = json.loads(RUN_PATH.read_text()) + association, concept_rows = association_analysis(run) + modulation = modulation_analysis(run) + stats = { + "run": str(RUN_PATH.relative_to(ROOT)), + "preregistered_prompt_manifest": "prompts/materials-paper-v2-preregistered.json", + "seed": SEED, + "bootstrap_resamples": N_BOOT, + "claims_level": run["methodology"]["claims_level"], + "association": association, + "modulation": modulation, + } + EXP_DIR.mkdir(parents=True, exist_ok=True) + STATS_PATH.write_text(json.dumps(stats, indent=2) + "\n") + write_csv(concept_rows) + write_results(stats) + plot_summary(run, association, modulation) + print(f"wrote {STATS_PATH}") + print(f"wrote {CSV_PATH}") + print(f"wrote {RESULTS_PATH}") + print(f"wrote {FIG_DIR / 'paper-v2-summary.pdf'}") + + +if __name__ == "__main__": + main() diff --git a/jlens_materials/scripts/analyze_open_vocabulary_consensus.py b/jlens_materials/scripts/analyze_open_vocabulary_consensus.py new file mode 100644 index 0000000000000000000000000000000000000000..abddd2b3c1976a8edc22e7fcf143321165f44962 --- /dev/null +++ b/jlens_materials/scripts/analyze_open_vocabulary_consensus.py @@ -0,0 +1,501 @@ +#!/usr/bin/env python3 +"""Seed-consensus open-vocabulary discovery for the 50 materials prompts. + +Candidate generation is independent of the predeclared concept lists. Those +lists are consulted only after ranking to annotate exact overlaps. +""" + +from __future__ import annotations + +import argparse +import csv +import json +import math +import random +import re +from collections import defaultdict +from pathlib import Path + +import matplotlib.pyplot as plt +import numpy as np + + +ROOT = Path(__file__).resolve().parents[1] +DEFAULT_RUN_PATHS = [ + ROOT / "runs" / f"gemma4-e4b-it-paper-seed{seed}.json" + for seed in range(3) +] +EXP_DIR = ROOT / "experiments" +FIG_DIR = ROOT / "figures" / "gemma4-paper-multiseed" +STATS_PATH = EXP_DIR / "open-vocabulary-consensus_statistics.json" +CANDIDATE_CSV = EXP_DIR / "open-vocabulary-consensus_candidates.csv" +BLIND_CSV = EXP_DIR / "OPEN_VOCABULARY_BLINDED_FAMILY_SETS.csv" +KEY_CSV = EXP_DIR / "OPEN_VOCABULARY_BLINDED_FAMILY_KEY.csv" +REPORT_PATH = EXP_DIR / "OPEN_VOCABULARY_CONSENSUS_ANALYSIS.md" +FIGURE_PATH = FIG_DIR / "open-vocabulary-consensus" +ANALYSIS_SEED = 20260714 +TOP_FAMILY_CANDIDATES = 8 + + +# Target-agnostic common English function words. The list is stored here so the +# filter is frozen and auditable; it contains no materials-science vocabulary. +FUNCTION_WORDS = set(""" +a about above across after afterwards again against all almost along already +also although always am among an and another any are around as at away back be +became because become been before began behind being below between both but by +can cannot could did do does done down due during each either else enough +especially even ever every few for from further furthermore get got had has +have having he hence her here hers herself him himself his how however i if in +indeed into is it its itself just later least less like likely may me meanwhile +might mine more most much must my myself nearly neither never nevertheless no +nobody nor not nothing now of off often on once one only or other others +otherwise our ours ourselves out over own perhaps quite rather really said same +several she should since so some somebody someone something sometimes still +such than that the their theirs them themselves then thence there therefore +these they this those though through thus to too toward under until up upon us +very was we were what whatever when where whereas whether which while who whom +whose why will with within without would yet you your yours yourself yourselves +subsequently consequently simultaneously accordingly eventually ultimately +initially thereto thereafter +""".split()) + + +def normalize_token(value: str) -> str | None: + token = value.strip().lower() + if not re.fullmatch(r"[a-z][a-z'-]{2,}", token): + return None + return token + + +def indexed_records(run: dict) -> dict[str, dict]: + return { + record["slug"]: record + for record in run["prompts"] + if record.get("shape") == "ASSOCIATION" + } + + +def validate(runs: list[dict]) -> tuple[list[str], list[dict[str, dict]]]: + indexes = [indexed_records(run) for run in runs] + slugs = list(indexes[0]) + if len(slugs) != 50: + raise ValueError(f"Expected 50 association prompts, found {len(slugs)}") + for seed, (run, index) in enumerate(zip(runs, indexes)): + if run.get("errors"): + raise ValueError(f"Seed {seed} contains run errors") + if not run.get("methodology", {}).get("paper_protocol_complete"): + raise ValueError(f"Seed {seed} is not paper-protocol complete") + if list(index) != slugs: + raise ValueError(f"Seed {seed} prompt set/order differs") + if not all(record.get("valid_for_metrics") for record in index.values()): + raise ValueError(f"Seed {seed} contains invalid association records") + return slugs, indexes + + +def target_terms(record: dict) -> set[str]: + terms = set() + for concept in record.get("tracked", []): + for value in (concept.get("label"), *concept.get("surfaces", [])): + normalized = normalize_token(value or "") + if normalized: + terms.add(normalized) + return terms + + +def consensus_candidates(records: list[dict], *, filter_function_words: bool) -> list[dict]: + by_seed = [] + for record in records: + candidates = {} + for row in record.get("surprising", []): + token = normalize_token(row.get("concept", "")) + if token is None or (filter_function_words and token in FUNCTION_WORDS): + continue + candidates[token] = row + by_seed.append(candidates) + + shared = set.intersection(*(set(seed_rows) for seed_rows in by_seed)) + rows = [] + for token in shared: + source = [seed_rows[token] for seed_rows in by_seed] + rows.append({ + "token": token, + "consensus_score": float(np.mean([row["score"] for row in source])), + "worst_best_rank": int(max(row["best_rank"] for row in source)), + "mean_best_depth": float(np.mean([row["best_depth"] for row in source])), + "depths_by_seed": [float(row["best_depth"]) for row in source], + "scores_by_seed": [float(row["score"]) for row in source], + "seed_support": 3, + }) + rows.sort(key=lambda row: (-row["consensus_score"], row["worst_best_rank"], row["token"])) + return rows + + +def build_analysis( + runs: list[dict], + slugs: list[str], + indexes: list[dict[str, dict]], + run_paths: list[Path], +) -> dict: + per_prompt = [] + raw_candidates: dict[str, list[dict]] = {} + filtered_candidates: dict[str, list[dict]] = {} + for slug in slugs: + records = [index[slug] for index in indexes] + reference = records[0] + raw = consensus_candidates(records, filter_function_words=False) + filtered = consensus_candidates(records, filter_function_words=True) + raw_candidates[slug] = raw + filtered_candidates[slug] = filtered + terms = target_terms(reference) + for row in filtered: + row["exact_predeclared_overlap"] = row["token"] in terms + per_prompt.append({ + "slug": slug, + "family": reference["category"], + "prompt": reference["prompt_text"], + "predeclared_terms_annotation_only": sorted(terms), + "raw_consensus_candidates": raw, + "filtered_consensus_candidates": filtered, + }) + + n_prompts = len(slugs) + prompt_df: dict[str, int] = defaultdict(int) + global_score: dict[str, float] = defaultdict(float) + for slug in slugs: + for row in raw_candidates[slug]: + prompt_df[row["token"]] += 1 + global_score[row["token"]] += row["consensus_score"] + global_scaffold = sorted( + ({ + "token": token, + "prompt_support": prompt_df[token], + "total_consensus_score": global_score[token], + } for token in prompt_df), + key=lambda row: (-row["prompt_support"], -row["total_consensus_score"], row["token"]), + ) + + # Standard unsmoothed inverse-document frequency. Candidate generation and + # ranking remain independent of the predeclared concepts. + filtered_df: dict[str, int] = defaultdict(int) + for slug in slugs: + for row in filtered_candidates[slug]: + filtered_df[row["token"]] += 1 + + family_slugs: dict[str, list[str]] = defaultdict(list) + for prompt in per_prompt: + family_slugs[prompt["family"]].append(prompt["slug"]) + families = {} + for family, members in sorted(family_slugs.items()): + family_targets = set().union(*( + set(next(prompt["predeclared_terms_annotation_only"] for prompt in per_prompt + if prompt["slug"] == slug)) + for slug in members + )) + values: dict[str, list[dict]] = defaultdict(list) + for slug in members: + for row in filtered_candidates[slug]: + values[row["token"]].append(row) + candidates = [] + for token, rows in values.items(): + inverse_document_frequency = math.log(n_prompts / filtered_df[token]) + score = sum(row["consensus_score"] for row in rows) * inverse_document_frequency / len(members) + candidates.append({ + "token": token, + "family_specificity_score": score, + "prompt_support": len(rows), + "prompt_denominator": len(members), + "global_prompt_frequency": filtered_df[token], + "inverse_document_frequency": inverse_document_frequency, + "mean_consensus_score_when_present": float(np.mean([ + row["consensus_score"] for row in rows + ])), + "exact_predeclared_overlap": token in family_targets, + }) + candidates.sort(key=lambda row: ( + -row["family_specificity_score"], + -row["prompt_support"], + row["token"], + )) + families[family] = { + "n_prompts": len(members), + "predeclared_terms_annotation_only": sorted(family_targets), + "candidates": candidates[:TOP_FAMILY_CANDIDATES], + } + + overlap = {} + for cutoff in (1, 3, 5, 8): + family_hits = [ + any(row["exact_predeclared_overlap"] for row in result["candidates"][:cutoff]) + for result in families.values() + ] + overlap[str(cutoff)] = { + "families_with_exact_overlap": int(sum(family_hits)), + "n_families": len(family_hits), + "fraction": float(np.mean(family_hits)), + } + + return { + "analysis_status": "exploratory open-vocabulary discovery on an already-inspected prompt suite", + "analysis_seed": ANALYSIS_SEED, + "candidate_source": ( + "per-seed surprising lists: unrestricted full-vocabulary top-1 tokens across all prompt " + "positions and 38-92% source layers; prompt/output tokens excluded upstream" + ), + "consensus_rule": "candidate must occur in all three independently fitted lens lists", + "ranking_rule": ( + "within-family mean of consensus score times log(50/global prompt frequency); " + "common English function words removed using the stored target-agnostic list" + ), + "important_censoring": ( + "each source run retained only its top 12 surprising candidates, so this is a censored " + "candidate analysis rather than exhaustive vocabulary discovery" + ), + "predeclared_terms_use": "annotation after ranking only; never used to generate or rank candidates", + "runs": [ + str(path.resolve().relative_to(ROOT)) + if path.resolve().is_relative_to(ROOT) else str(path.resolve()) + for path in run_paths + ], + "n_prompts": n_prompts, + "n_families": len(families), + "global_scaffold": global_scaffold[:20], + "families": families, + "exact_predeclared_overlap_at_k": overlap, + "per_prompt": per_prompt, + "function_words": sorted(FUNCTION_WORDS), + } + + +def write_candidate_csv(stats: dict) -> None: + rows = [] + for prompt in stats["per_prompt"]: + for rank, candidate in enumerate(prompt["filtered_consensus_candidates"], start=1): + rows.append({ + "slug": prompt["slug"], + "family": prompt["family"], + "candidate_rank": rank, + "candidate": candidate["token"], + "consensus_score": candidate["consensus_score"], + "worst_best_rank": candidate["worst_best_rank"], + "mean_best_depth": candidate["mean_best_depth"], + "exact_predeclared_overlap_after_ranking": candidate["exact_predeclared_overlap"], + "prompt": prompt["prompt"], + }) + with CANDIDATE_CSV.open("w", newline="") as handle: + writer = csv.DictWriter(handle, fieldnames=list(rows[0])) + writer.writeheader() + writer.writerows(rows) + + +def write_blinded_sets(stats: dict) -> None: + families = sorted(stats["families"]) + rng = random.Random(ANALYSIS_SEED) + rng.shuffle(families) + blind_rows = [] + key_rows = [] + for index, family in enumerate(families, start=1): + set_id = f"SET-{index:02d}" + candidates = stats["families"][family]["candidates"] + blind_row = {"set_id": set_id} + for position in range(TOP_FAMILY_CANDIDATES): + candidate = candidates[position] if position < len(candidates) else None + blind_row[f"candidate_{position + 1}"] = candidate["token"] if candidate else "" + blind_row[f"support_{position + 1}"] = ( + f"{candidate['prompt_support']}/5" if candidate else "" + ) + blind_rows.append(blind_row) + key_rows.append({"set_id": set_id, "mechanism_family": family}) + with BLIND_CSV.open("w", newline="") as handle: + writer = csv.DictWriter(handle, fieldnames=list(blind_rows[0])) + writer.writeheader() + writer.writerows(blind_rows) + with KEY_CSV.open("w", newline="") as handle: + writer = csv.DictWriter(handle, fieldnames=list(key_rows[0])) + writer.writeheader() + writer.writerows(key_rows) + + +def plot_results(stats: dict) -> None: + FIG_DIR.mkdir(parents=True, exist_ok=True) + plt.rcParams.update({ + "font.family": "DejaVu Sans", + "font.size": 10, + "axes.spines.top": False, + "axes.spines.right": False, + }) + fig = plt.figure(figsize=(14.5, 10.2), constrained_layout=True) + grid = fig.add_gridspec(1, 2, width_ratios=[0.72, 1.9]) + + ax = fig.add_subplot(grid[0, 0]) + global_rows = stats["global_scaffold"][:12][::-1] + labels = [row["token"] for row in global_rows] + values = [row["prompt_support"] for row in global_rows] + ax.barh(np.arange(len(labels)), values, color="#287C8E", alpha=0.88) + ax.set_yticks(np.arange(len(labels)), labels) + ax.set_xlim(0, 50) + ax.set_xlabel("prompts with three-seed consensus (of 50)") + ax.set_title("A Shared explanatory scaffold", loc="left", fontweight="bold") + ax.grid(True, axis="x", alpha=0.18) + for position, value in enumerate(values): + ax.text(value + 0.7, position, str(value), va="center", fontsize=9) + + ax = fig.add_subplot(grid[0, 1]) + families = sorted(stats["families"]) + n_rows = len(families) + n_columns = 5 + scores = np.asarray([ + [ + stats["families"][family]["candidates"][column]["family_specificity_score"] + if column < len(stats["families"][family]["candidates"]) else 0.0 + for column in range(n_columns) + ] + for family in families + ]) + transformed = np.sqrt(scores) + image = ax.imshow(transformed, cmap="YlOrRd", aspect="auto") + ax.set_xticks(range(n_columns), [f"candidate {index}" for index in range(1, n_columns + 1)]) + ax.set_yticks(range(n_rows), [family.replace("-", " ") for family in families]) + ax.set_title("B Background-corrected semantic neighborhoods", loc="left", fontweight="bold") + ax.tick_params(length=0) + for row_index, family in enumerate(families): + candidates = stats["families"][family]["candidates"] + for column in range(n_columns): + if column >= len(candidates): + continue + candidate = candidates[column] + marker = "*" if candidate["exact_predeclared_overlap"] else "" + label = f"{candidate['token']}{marker}\n{candidate['prompt_support']}/5 prompts" + color = "white" if transformed[row_index, column] > 0.58 * transformed.max() else "black" + ax.text(column, row_index, label, ha="center", va="center", fontsize=8.4, color=color) + colorbar = fig.colorbar(image, ax=ax, fraction=0.028, pad=0.02) + colorbar.set_label("sqrt(background-corrected consensus score)") + + fig.suptitle( + "Open-vocabulary readout reveals both language scaffolding and mechanism-specific assemblies", + fontsize=15, + fontweight="bold", + ) + fig.text( + 0.5, -0.01, + "Candidates were generated without a target list and required agreement across all three lens fits. " + "* exact predeclared-term overlap, annotated only after ranking. Exploratory; not a literal chain of thought.", + ha="center", fontsize=9, color="#6E7781", + ) + for suffix in ("png", "pdf", "svg"): + fig.savefig(FIGURE_PATH.with_suffix(f".{suffix}"), dpi=240, + bbox_inches="tight", facecolor="white") + plt.close(fig) + + +def write_report(stats: dict) -> None: + overlap = stats["exact_predeclared_overlap_at_k"] + lines = [ + "# Three-seed open-vocabulary discovery across 50 materials prompts", + "", + "## Question", + "", + "What words does the Jacobian lens surface consistently when no candidate vocabulary is supplied to the discovery algorithm? This analysis complements, rather than replaces, the predeclared-concept rank analysis.", + "", + "## Method in plain language", + "", + "For every prompt and every lens-fit seed, the run already stored the unrestricted full-vocabulary token that ranked first across every prompt position and each source layer in the fixed 38--92% depth band. Tokens appearing in the prompt or generated continuation were removed upstream. A candidate was retained here only when it appeared in the stored candidate list for **all three** independently fitted lenses.", + "", + "The predeclared materials terms were not used to generate, retain, filter, or rank candidates. After ranking was complete, exact overlaps were marked with an asterisk for comparison. Common English function words were removed only from the family-specific display; the unfiltered cross-prompt result is retained because it reveals a shared language scaffold.", + "", + "Family candidates were ranked using a transparent background correction: the three-seed consensus score was multiplied by `log(50 / number of prompts containing the token)` and averaged over the five phrasings. Therefore, a word that appears everywhere is treated as general language scaffolding rather than family-specific evidence.", + "", + "![Open-vocabulary consensus](../figures/gemma4-paper-multiseed/open-vocabulary-consensus.png)", + "", + "## Main result", + "", + "Two structures appear naturally:", + "", + "1. A cross-domain explanatory scaffold dominated by words such as `because`, `metallurgical`, `several`, `this`, `whereas`, and `during`. This suggests that the readout often organizes an explanatory continuation before or alongside a specific mechanism word.", + "2. Mechanism-sensitive semantic neighborhoods. Examples include `corrosion` for boundary attack; `brittle` and `fracture` for cleavage; `fatigue` and `cracks` for cyclic failure; `oxide`/`oxides` for hot-air surface layers; `dislocations`, `atoms`, and `deformation` for line-defect motion; and `microstructure`, `diffusion`, `nucleation`, and the token fragment `martens` for rapid transformation.", + "", + "This is closer to what materials scientists mean by seeing what *assembles naturally*: the analysis retains both expected mechanisms and adjacent or unexpected concepts. It also keeps irrelevant and malformed candidates visible rather than silently selecting only attractive examples.", + "", + "## Relationship to the predetermined analysis", + "", + f"At least one exact predeclared term appeared among the top three background-corrected candidates in {overlap['3']['families_with_exact_overlap']}/{overlap['3']['n_families']} families and among the top five in {overlap['5']['families_with_exact_overlap']}/{overlap['5']['n_families']} families. These are descriptive overlaps, not an independent hypothesis test, because this prompt suite has already been inspected.", + "", + "The two analyses answer different scientific questions:", + "", + "| analysis | question | strength | limitation |", + "|---|---|---|---|", + "| predeclared ranks | Is a specified physical concept readable? | controlled and quantitative | cannot reveal concepts we did not name |", + "| open vocabulary | What stable words appear without a target list? | reveals neighboring and unexpected structure | requires filtering, background correction, and held-out confirmation |", + "", + "## Family-level candidate sets", + "", + "Asterisks mark exact predeclared-term overlaps after ranking. Support is the number of alternate phrasings, out of five, in which the candidate survived strict three-seed consensus.", + "", + "| mechanism family | top background-corrected candidates |", + "|---|---|", + ] + for family, result in sorted(stats["families"].items()): + cells = [] + for candidate in result["candidates"]: + marker = "*" if candidate["exact_predeclared_overlap"] else "" + cells.append(f"`{candidate['token']}`{marker} ({candidate['prompt_support']}/5)") + lines.append(f"| {family} | {', '.join(cells)} |") + lines.extend([ + "", + "## Required held-out confirmation", + "", + "The current result is an exploratory discovery stage. For a paper-quality test, freeze this exact algorithm and apply it to newly written prompts that have not been viewed during method development. Materials experts should receive only the shuffled candidate sets and select the most compatible mechanism family. Accuracy should be compared with a shuffled-family null and with equivalent logit-lens candidate sets.", + "", + "The blinded candidate sheet and its separately stored answer key are provided below. They demonstrate the proposed rating format, but ratings on this already-inspected suite remain exploratory.", + "", + "## Important limitations", + "", + "- Each existing run retained only its top 12 whole-prompt candidates. The present result is therefore censored and not exhaustive.", + "- A decoded token can be a word fragment (`martens`, `coales`) rather than a complete scientific term.", + "- Stable language scaffolding can outrank physical vocabulary.", + "- Seed agreement shows measurement reproducibility, not causal use, reasoning, or human-like understanding.", + "- The displayed neighborhoods are not a literal chain of thought.", + "", + "## Artifacts", + "", + "- Machine-readable analysis: [`open-vocabulary-consensus_statistics.json`](open-vocabulary-consensus_statistics.json)", + "- Every prompt-level consensus candidate: [`open-vocabulary-consensus_candidates.csv`](open-vocabulary-consensus_candidates.csv)", + "- Blinded family-rating sets: [`OPEN_VOCABULARY_BLINDED_FAMILY_SETS.csv`](OPEN_VOCABULARY_BLINDED_FAMILY_SETS.csv)", + "- Separate rating key: [`OPEN_VOCABULARY_BLINDED_FAMILY_KEY.csv`](OPEN_VOCABULARY_BLINDED_FAMILY_KEY.csv)", + "- Vector figure: [`../figures/gemma4-paper-multiseed/open-vocabulary-consensus.pdf`](../figures/gemma4-paper-multiseed/open-vocabulary-consensus.pdf)", + "- Reproduce: `python scripts/analyze_open_vocabulary_consensus.py`", + "", + ]) + REPORT_PATH.write_text("\n".join(lines)) + + +def main() -> None: + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument( + "--runs", + nargs=3, + type=Path, + default=DEFAULT_RUN_PATHS, + metavar=("SEED0_JSON", "SEED1_JSON", "SEED2_JSON"), + help="three paper-protocol run JSONs (default: existing seed0/1/2 files)", + ) + args = parser.parse_args() + run_paths = [path if path.is_absolute() else (Path.cwd() / path) for path in args.runs] + runs = [json.loads(path.read_text()) for path in run_paths] + slugs, indexes = validate(runs) + stats = build_analysis(runs, slugs, indexes, run_paths) + EXP_DIR.mkdir(parents=True, exist_ok=True) + STATS_PATH.write_text(json.dumps(stats, indent=2) + "\n") + write_candidate_csv(stats) + write_blinded_sets(stats) + plot_results(stats) + write_report(stats) + print(f"wrote {STATS_PATH}") + print(f"wrote {CANDIDATE_CSV}") + print(f"wrote {BLIND_CSV}") + print(f"wrote {KEY_CSV}") + print(f"wrote {REPORT_PATH}") + print(f"wrote {FIGURE_PATH.with_suffix('.pdf')}") + + +if __name__ == "__main__": + main() diff --git a/jlens_materials/scripts/analyze_patchscope_physics_relation.py b/jlens_materials/scripts/analyze_patchscope_physics_relation.py new file mode 100644 index 0000000000000000000000000000000000000000..2832b75a9edef95d61e97f8d4416ebca6369912b --- /dev/null +++ b/jlens_materials/scripts/analyze_patchscope_physics_relation.py @@ -0,0 +1,295 @@ +#!/usr/bin/env python3 +"""Select Patchscope on discovery laws and test unseen constitutive laws.""" + +from __future__ import annotations + +import hashlib +import itertools +import json +from pathlib import Path + +import matplotlib + +matplotlib.use("Agg") +import matplotlib.pyplot as plt # noqa: E402 +import numpy as np # noqa: E402 +import pandas as pd # noqa: E402 +from sklearn.metrics import roc_auc_score # noqa: E402 + + +ROOT = Path(__file__).resolve().parents[1] +OUT = ROOT / "experiments" / "patchscope-physics-relation-2026-07-18" +PROTOCOL = OUT / "protocol.json" +RAW = OUT / "raw.json" +FIG = OUT / "figures" + + +def sha256(path: Path) -> str: + return hashlib.sha256(path.read_bytes()).hexdigest() + + +def exact_balanced_auc_p(labels: np.ndarray, scores: np.ndarray) -> float: + labels = np.asarray(labels, dtype=bool) + scores = np.asarray(scores, dtype=float) + n_positive = int(labels.sum()) + observed = abs(float(roc_auc_score(labels.astype(int), scores)) - 0.5) + values = [] + for positive_indices in itertools.combinations( + range(len(labels)), n_positive + ): + permuted = np.zeros(len(labels), dtype=bool) + permuted[list(positive_indices)] = True + values.append( + abs(float(roc_auc_score(permuted.astype(int), scores)) - 0.5) + ) + return float(np.mean(np.asarray(values) >= observed - 1e-15)) + + +def law_scores(frame: pd.DataFrame) -> pd.DataFrame: + return ( + frame.groupby( + ["law_id", "law_name", "split", "law_sign"], as_index=False + ) + .agg( + score=("positive_minus_negative_log_odds", "mean"), + surface_min=("positive_minus_negative_log_odds", "min"), + surface_max=("positive_minus_negative_log_odds", "max"), + ) + .assign(is_direct=lambda values: values["law_sign"] == 1) + ) + + +def main() -> None: + protocol = json.loads(PROTOCOL.read_text()) + raw = json.loads(RAW.read_text()) + if sha256(Path(__file__).resolve()) != protocol["inputs"]["analyzer"]["sha256"]: + raise RuntimeError("analyzer fingerprint mismatch") + if raw["provenance"]["protocol_sha256"] != sha256(PROTOCOL): + raise RuntimeError("raw protocol fingerprint mismatch") + frame = pd.DataFrame(raw["rows"]) + + candidate_rows = [] + for (target_id, layer), group in frame.groupby(["target_id", "layer"]): + laws = law_scores(group) + discovery = laws[laws["split"] == "discovery"] + candidate_rows.append( + { + "target_id": target_id, + "target_style": group["target_style"].iloc[0], + "answer_vocabulary": group["answer_vocabulary"].iloc[0], + "layer": int(layer), + "depth_percent": float(group["depth_percent"].iloc[0]), + "discovery_law_auc": float( + roc_auc_score( + discovery["is_direct"].astype(int), discovery["score"] + ) + ), + "discovery_accuracy_zero_threshold": float( + np.mean( + (discovery["score"] > 0) + == discovery["is_direct"] + ) + ), + "discovery_surface_consistency": float( + np.mean( + (discovery["surface_min"] > 0) + == (discovery["surface_max"] > 0) + ) + ), + } + ) + candidates = pd.DataFrame(candidate_rows) + selection = candidates.sort_values( + [ + "discovery_law_auc", + "discovery_accuracy_zero_threshold", + "layer", + "target_id", + ], + ascending=[False, False, True, True], + ).iloc[0] + selected = frame[ + (frame["target_id"] == selection["target_id"]) + & (frame["layer"] == selection["layer"]) + ] + laws = law_scores(selected) + discovery = laws[laws["split"] == "discovery"].copy() + replication = laws[laws["split"] == "replication"].copy() + + # Calibrate only an additive threshold on discovery laws. + direct_mean = float( + discovery.loc[discovery["is_direct"], "score"].mean() + ) + inverse_mean = float( + discovery.loc[~discovery["is_direct"], "score"].mean() + ) + threshold = 0.5 * (direct_mean + inverse_mean) + replication_auc = float( + roc_auc_score( + replication["is_direct"].astype(int), replication["score"] + ) + ) + replication_accuracy = float( + np.mean( + (replication["score"] > threshold) + == replication["is_direct"] + ) + ) + exact_p = exact_balanced_auc_p( + replication["is_direct"].to_numpy(bool), + replication["score"].to_numpy(float), + ) + surface_agreement = float( + np.mean( + (replication["surface_min"] > threshold) + == (replication["surface_max"] > threshold) + ) + ) + + candidate_rows_replication = [] + for (target_id, layer), group in frame.groupby(["target_id", "layer"]): + candidate_laws = law_scores(group) + candidate_replication = candidate_laws[ + candidate_laws["split"] == "replication" + ] + candidate_rows_replication.append( + { + "target_id": target_id, + "layer": int(layer), + "replication_law_auc": float( + roc_auc_score( + candidate_replication["is_direct"].astype(int), + candidate_replication["score"], + ) + ), + } + ) + candidate_replication = pd.DataFrame(candidate_rows_replication) + combined = candidates.merge( + candidate_replication, on=["target_id", "layer"], how="left" + ) + + candidates.to_csv(OUT / "discovery_candidate_selection.csv", index=False) + combined.to_csv(OUT / "all_candidate_discovery_replication.csv", index=False) + laws.to_csv(OUT / "selected_law_scores.csv", index=False) + selected.to_csv(OUT / "selected_surface_scores.csv", index=False) + + primary = { + "selected_target_id": str(selection["target_id"]), + "selected_target_style": str(selection["target_style"]), + "selected_answer_vocabulary": str(selection["answer_vocabulary"]), + "selected_layer": int(selection["layer"]), + "selected_depth_percent": float(selection["depth_percent"]), + "discovery_law_auc": float(selection["discovery_law_auc"]), + "discovery_zero_threshold_accuracy": float( + selection["discovery_accuracy_zero_threshold"] + ), + "discovery_calibrated_threshold": threshold, + "replication_law_auc": replication_auc, + "replication_calibrated_accuracy": replication_accuracy, + "replication_exact_balanced_label_p": exact_p, + "replication_surface_agreement": surface_agreement, + "replication_direct_laws": int(replication["is_direct"].sum()), + "replication_inverse_laws": int((~replication["is_direct"]).sum()), + } + rule = protocol["success_rule"] + primary["passes_frozen_rule"] = bool( + replication_auc >= rule["replication_law_auc_minimum"] + and replication_accuracy >= rule["replication_accuracy_minimum"] + and exact_p <= rule["exact_p_maximum"] + and surface_agreement >= rule["surface_agreement_minimum"] + ) + statistics = { + "study_id": protocol["study_id"], + "protocol_sha256": sha256(PROTOCOL), + "raw_sha256": sha256(RAW), + "primary": primary, + "unpatched_target_score": float( + selected["unpatched_positive_minus_negative_log_odds"].iloc[0] + ), + "interpretation_boundary": ( + "A passing Patchscope shows that a fixed downstream model context " + "can extract held-out constitutive polarity from the source state " + "at a checkpoint that precedes numerical changes and answer words. " + "It is a nonlinear readout using the model's remaining computation, " + "not evidence that polarity occupies one universal linear direction " + "or that the representation is causally necessary for the original " + "answer." + ), + } + (OUT / "statistics.json").write_text( + json.dumps(statistics, indent=2, sort_keys=True) + "\n" + ) + + FIG.mkdir(parents=True, exist_ok=True) + fig, axes = plt.subplots(1, 3, figsize=(11.0, 3.65)) + for target_id, group in candidates.groupby("target_id"): + axes[0].plot( + group["depth_percent"], + group["discovery_law_auc"], + marker="o", + markersize=3, + linewidth=1.2, + label=target_id, + ) + axes[0].axhline(0.5, color="#777777", linestyle=":", linewidth=1) + axes[0].set(xlabel="Source/target layer depth (%)", ylabel="Discovery law AUC") + axes[0].legend(frameon=False, fontsize=7, ncol=2) + + display = laws.sort_values(["split", "score"]) + colors = [ + "#16697A" if value == "discovery" else "#7A6AA2" + for value in display["split"] + ] + axes[1].barh(display["law_id"], display["score"], color=colors) + axes[1].axvline(threshold, color="#333333", linestyle="--", linewidth=1) + axes[1].set(xlabel="Patchscope direct − inverse log-odds") + + matrix = combined.pivot( + index="target_id", columns="layer", values="replication_law_auc" + ) + image = axes[2].imshow( + matrix.to_numpy(float), + aspect="auto", + vmin=0, + vmax=1, + cmap="viridis", + ) + axes[2].set( + yticks=np.arange(len(matrix.index)), + yticklabels=matrix.index, + xticks=np.arange(len(matrix.columns)), + xticklabels=matrix.columns, + xlabel="Layer", + ylabel="Target lens", + ) + fig.colorbar(image, ax=axes[2], label="Replication law AUC") + fig.tight_layout() + for suffix in ("png", "pdf"): + fig.savefig(FIG / f"patchscope-physics-relation.{suffix}", dpi=300) + plt.close(fig) + + report = [ + "# Patchscope constitutive-polarity result", + "", + f"- Selected target: **{primary['selected_target_id']}** " + f"({primary['selected_target_style']}; " + f"{primary['selected_answer_vocabulary']}).", + f"- Selected layer: **{primary['selected_layer']}** " + f"({primary['selected_depth_percent']:.1f}% depth).", + f"- Discovery law AUC: **{primary['discovery_law_auc']:.3f}**.", + f"- Replication law AUC: **{replication_auc:.3f}**; exact balanced-label " + f"`p={exact_p:.4g}`.", + f"- Replication calibrated accuracy: **{replication_accuracy:.1%}**.", + f"- Formula-surface agreement: **{surface_agreement:.1%}**.", + f"- Frozen success rule: " + f"**{'PASS' if primary['passes_frozen_rule'] else 'FAIL'}**.", + "", + statistics["interpretation_boundary"], + ] + (OUT / "REPORT.md").write_text("\n".join(report) + "\n") + print("\n".join(report)) + + +if __name__ == "__main__": + main() diff --git a/jlens_materials/scripts/analyze_readability_family_influence.py b/jlens_materials/scripts/analyze_readability_family_influence.py new file mode 100644 index 0000000000000000000000000000000000000000..4a584a4d11513162913cb93d87b987aea9732d9a --- /dev/null +++ b/jlens_materials/scripts/analyze_readability_family_influence.py @@ -0,0 +1,196 @@ +#!/usr/bin/env python3 +"""Leave-one-family-out sensitivity analysis for held-out readability.""" + +from __future__ import annotations + +import csv +import hashlib +import json +import math +from collections import defaultdict +from pathlib import Path +from typing import Iterable, Mapping + + +ROOT = Path(__file__).resolve().parents[1] +OUT = ROOT / "experiments" / "core-discovery-discipline-audit-2026-07-18" +PROTOCOL = OUT / "protocol_addendum_readability.json" + + +def sha256(path: Path) -> str: + digest = hashlib.sha256() + with path.open("rb") as handle: + for chunk in iter(lambda: handle.read(1 << 20), b""): + digest.update(chunk) + return digest.hexdigest() + + +def read_csv(path: Path) -> list[dict[str, str]]: + with path.open(newline="") as handle: + return list(csv.DictReader(handle)) + + +def mean(values: Iterable[float]) -> float: + values = list(values) + return sum(values) / len(values) + + +def average_ranks(values: list[float]) -> list[float]: + order = sorted(range(len(values)), key=values.__getitem__) + ranks = [0.0] * len(values) + start = 0 + while start < len(order): + stop = start + 1 + while stop < len(order) and values[order[stop]] == values[order[start]]: + stop += 1 + average = 0.5 * ((start + 1) + stop) + for index in order[start:stop]: + ranks[index] = average + start = stop + return ranks + + +def pearson(x: list[float], y: list[float]) -> float: + x_mean = mean(x) + y_mean = mean(y) + numerator = sum((a - x_mean) * (b - y_mean) for a, b in zip(x, y)) + x_ss = sum((a - x_mean) ** 2 for a in x) + y_ss = sum((b - y_mean) ** 2 for b in y) + value = numerator / math.sqrt(x_ss * y_ss) + if not math.isfinite(value): + raise AssertionError("non-finite correlation") + return value + + +def spearman(x: list[float], y: list[float]) -> float: + return pearson(average_ranks(x), average_ranks(y)) + + +def validate_inputs(protocol: Mapping[str, object]) -> dict[str, str]: + observed: dict[str, str] = {} + for name, raw_spec in protocol["inputs"].items(): # type: ignore[union-attr] + spec = raw_spec # type: ignore[assignment] + path = ROOT / spec["path"] + digest = sha256(path) + assert digest == spec["sha256"], (name, digest, spec["sha256"]) + observed[name] = digest + return observed + + +def main() -> None: + protocol = json.loads(PROTOCOL.read_text()) + fingerprints = validate_inputs(protocol) + prompt_path = ROOT / protocol["inputs"]["prompt_results"]["path"] + concept_path = ROOT / protocol["inputs"]["concept_results"]["path"] + prompt_rows = read_csv(prompt_path) + concept_rows = read_csv(concept_path) + + families = sorted({row["family"] for row in prompt_rows}) + assert len(families) == protocol["family_count"] == 10 + assert len(prompt_rows) == 50 + assert len(concept_rows) == 150 + assert {row["family"] for row in concept_rows} == set(families) + + family_values: dict[str, list[float]] = defaultdict(list) + for row in prompt_rows: + family_values[row["family"]].append(float(row["delta_auc_mean_seed"])) + assert all(len(values) == 5 for values in family_values.values()) + family_means = { + family: mean(family_values[family]) for family in families + } + overall = mean(family_means.values()) + + influence_rows: list[dict[str, object]] = [] + for family in families: + retained = [ + value for name, value in family_means.items() if name != family + ] + loo = mean(retained) + influence_rows.append( + { + "omitted_family": family, + "omitted_family_mean_delta_auc": family_means[family], + "leave_one_family_out_mean_delta_auc": loo, + "change_from_all_family_mean": loo - overall, + } + ) + + seed_pairs = (("seed0", "seed1"), ("seed0", "seed2"), ("seed1", "seed2")) + reproducibility_rows: list[dict[str, object]] = [] + for omitted in families: + rows = [row for row in concept_rows if row["family"] != omitted] + expected_remaining = len(concept_rows) - sum( + row["family"] == omitted for row in concept_rows + ) + assert len(rows) == expected_remaining + for left, right in seed_pairs: + rho = spearman( + [float(row[f"j_rank_{left}"]) for row in rows], + [float(row[f"j_rank_{right}"]) for row in rows], + ) + reproducibility_rows.append( + { + "omitted_family": omitted, + "seed_pair": f"{left}-{right}", + "remaining_prompt_concept_count": len(rows), + "spearman_rho": rho, + } + ) + + with (OUT / "readability_family_influence.csv").open("w", newline="") as handle: + writer = csv.DictWriter(handle, fieldnames=list(influence_rows[0])) + writer.writeheader() + writer.writerows(influence_rows) + with (OUT / "readability_leave_one_family_reproducibility.csv").open( + "w", newline="" + ) as handle: + writer = csv.DictWriter( + handle, fieldnames=list(reproducibility_rows[0]) + ) + writer.writeheader() + writer.writerows(reproducibility_rows) + + loo_values = [ + float(row["leave_one_family_out_mean_delta_auc"]) + for row in influence_rows + ] + rho_values = [ + float(row["spearman_rho"]) for row in reproducibility_rows + ] + result = { + "study_id": protocol["study_id"], + "status": protocol["status"], + "protocol_sha256": sha256(PROTOCOL), + "input_sha256": fingerprints, + "family_count": len(families), + "prompt_count": len(prompt_rows), + "prompt_concept_count": len(concept_rows), + "all_family_mean_delta_auc": overall, + "positive_family_count": sum(value > 0 for value in family_means.values()), + "negative_family_count": sum(value < 0 for value in family_means.values()), + "zero_family_count": sum(value == 0 for value in family_means.values()), + "leave_one_family_out_delta_auc_range": [ + min(loo_values), + max(loo_values), + ], + "leave_one_family_out_positive_count": sum( + value > 0 for value in loo_values + ), + "largest_influence": max( + influence_rows, + key=lambda row: abs(float(row["change_from_all_family_mean"])), + ), + "leave_one_family_out_spearman_range": [ + min(rho_values), + max(rho_values), + ], + "leave_one_family_out_spearman_count": len(rho_values), + } + (OUT / "readability_family_influence_statistics.json").write_text( + json.dumps(result, indent=2, sort_keys=True) + "\n" + ) + print(json.dumps(result, indent=2, sort_keys=True)) + + +if __name__ == "__main__": + main() diff --git a/jlens_materials/scripts/analyze_spectral_graph_communities.py b/jlens_materials/scripts/analyze_spectral_graph_communities.py new file mode 100644 index 0000000000000000000000000000000000000000..17f56bebf93b3eb926c739092ed951dad7c60e52 --- /dev/null +++ b/jlens_materials/scripts/analyze_spectral_graph_communities.py @@ -0,0 +1,594 @@ +#!/usr/bin/env python3 +"""Spectral graph-community and density-ablation experiment.""" + +from __future__ import annotations + +import hashlib +import itertools +import json +import math +import platform +import sys +from pathlib import Path +from typing import Mapping, Sequence + +import numpy as np +import pandas as pd +from sklearn.metrics import ( + adjusted_rand_score, + normalized_mutual_info_score, +) + +from analyze_graph_isomorphism_generalization import ( + MANIFEST, + METHODS, + STATES, + VARIANTS, + bh_adjust, + load_data, + plus_one_signflip_p, +) + + +ROOT = Path(__file__).resolve().parents[1] +OUT = ( + ROOT + / "experiments" + / "graph-isomorphism-generalization-2026-07-18" +) +GAUGE_PROTOCOL = OUT / "gauge_protocol.json" +SPECTRAL_PROTOCOL = OUT / "spectral_protocol.json" +SPECTRAL_PROTOCOL_MD = OUT / "SPECTRAL_PROTOCOL.md" +SEED = 20260718 +N_NULL = 10_000 +MODES = ( + "binary_top1", + "weighted_top1", + "weighted_top2", + "weighted_complete_candidates", +) + + +def sha256(path: Path) -> str: + return hashlib.sha256(path.read_bytes()).hexdigest() + + +def safe_json(value: object) -> object: + if isinstance(value, np.generic): + return safe_json(value.item()) + if isinstance(value, float) and not math.isfinite(value): + return None + if isinstance(value, Mapping): + return {str(key): safe_json(item) for key, item in value.items()} + if isinstance(value, (list, tuple)): + return [safe_json(item) for item in value] + return value + + +def validate_inputs() -> None: + spectral = json.loads(SPECTRAL_PROTOCOL.read_text()) + gauge = json.loads(GAUGE_PROTOCOL.read_text()) + if sha256(GAUGE_PROTOCOL) != spectral["parent_gauge_protocol_sha256"]: + raise RuntimeError("gauge protocol fingerprint mismatch") + if ( + sha256(STATES) + != gauge["inputs"]["representations_sha256"] + or sha256(MANIFEST) + != gauge["inputs"]["prompt_manifest_sha256"] + ): + raise RuntimeError("frozen input fingerprint mismatch") + + +def family_node_data( + family: str, data: Mapping[str, object] +) -> tuple[np.ndarray, np.ndarray, np.ndarray]: + families = np.asarray(data["families"]) + indices = np.flatnonzero(families == family) + return ( + indices, + np.asarray(data["cases"])[indices], + np.asarray(data["variants"])[indices], + ) + + +def affinity_adjacency( + similarity: np.ndarray, + family: str, + data: Mapping[str, object], + mode: str, +) -> tuple[np.ndarray, np.ndarray]: + indices, cases, variants = family_node_data(family, data) + local = { + int(global_index): local_index + for local_index, global_index in enumerate(indices) + } + if mode in {"binary_top1", "weighted_top1"}: + k = 1 + elif mode == "weighted_top2": + k = 2 + elif mode == "weighted_complete_candidates": + k = 3 + else: + raise ValueError(mode) + directed = np.zeros((12, 12), dtype=np.float64) + selected_weights = [] + for source_local, source_global in enumerate(indices): + for target_variant in VARIANTS: + if target_variant == variants[source_local]: + continue + eligible_local = np.flatnonzero( + (variants == target_variant) + & (cases != cases[source_local]) + ) + ordered = sorted( + eligible_local.tolist(), + key=lambda target_local: ( + -float( + similarity[ + source_global, indices[target_local] + ] + ), + int(target_local), + ), + ) + for target_local in ordered[:k]: + weight = ( + 1.0 + if mode == "binary_top1" + else max( + 0.0, + float( + similarity[ + source_global, indices[target_local] + ] + ), + ) + ) + directed[source_local, target_local] = weight + selected_weights.append(weight) + del local + adjacency = directed + directed.T + return adjacency, np.asarray(selected_weights, dtype=float) + + +def fiedler_partition( + adjacency: np.ndarray, +) -> tuple[np.ndarray, float, np.ndarray]: + degree_values = adjacency.sum(axis=1) + inverse_sqrt = np.zeros_like(degree_values) + positive = degree_values > 1e-12 + inverse_sqrt[positive] = degree_values[positive] ** -0.5 + laplacian = np.eye(len(adjacency)) - ( + inverse_sqrt[:, None] + * adjacency + * inverse_sqrt[None, :] + ) + eigenvalues, eigenvectors = np.linalg.eigh(laplacian) + fiedler = eigenvectors[:, 1].copy() + largest = int(np.argmax(np.abs(fiedler))) + if fiedler[largest] < 0: + fiedler *= -1 + order = np.lexsort((np.arange(len(fiedler)), fiedler)) + partition = np.zeros(len(fiedler), dtype=bool) + partition[order[6:]] = True + eigengap = float(eigenvalues[2] - eigenvalues[1]) + return partition, eigengap, eigenvalues + + +def partition_metrics( + partition: np.ndarray, labels: np.ndarray +) -> dict[str, float]: + ordinary = float(np.mean(partition == labels)) + return { + "ari": float(adjusted_rand_score(labels, partition)), + "nmi": float(normalized_mutual_info_score(labels, partition)), + "ordinary_accuracy": ordinary, + "gauge_accuracy": max(ordinary, 1.0 - ordinary), + } + + +def observed_spectral( + data: Mapping[str, object], +) -> tuple[pd.DataFrame, pd.DataFrame]: + rows = [] + layer_rows = [] + families = np.asarray(data["families"]) + physical = np.asarray(data["physical"], dtype=bool) + numeric = np.asarray(data["numeric"], dtype=bool) + layers = np.asarray(data["layers"]) + depths = np.asarray(data["depths"]) + band_mask = np.asarray(data["band_mask"]) + for method in METHODS: + similarity_layers = np.asarray(data["similarities"][method]) + band_similarity = np.mean( + similarity_layers[band_mask], axis=0 + ) + for family in data["family_order"]: + family_indices = np.flatnonzero(families == family) + family_physical = physical[family_indices] + family_numeric = numeric[family_indices] + for mode in MODES: + adjacency, weights = affinity_adjacency( + band_similarity, str(family), data, mode + ) + partition, eigengap, eigenvalues = fiedler_partition( + adjacency + ) + metrics = partition_metrics(partition, family_physical) + numeric_metrics = partition_metrics( + partition, family_numeric + ) + rows.append( + { + "method": method, + "family": family, + "mode": mode, + **metrics, + "numeric_ari": numeric_metrics["ari"], + "numeric_gauge_accuracy": numeric_metrics[ + "gauge_accuracy" + ], + "physical_numeric_partition_identical": bool( + np.array_equal( + family_physical, family_numeric + ) + or np.array_equal( + family_physical, ~family_numeric + ) + ), + "eigengap": eigengap, + "edges_directed": len(weights), + "weight_mean": float(np.mean(weights)), + "eigenvalues": json.dumps( + [float(value) for value in eigenvalues] + ), + } + ) + for layer_index, (layer, depth) in enumerate( + zip(layers, depths) + ): + adjacency, _ = affinity_adjacency( + similarity_layers[layer_index], + str(family), + data, + "weighted_top1", + ) + partition, eigengap, _ = fiedler_partition(adjacency) + layer_rows.append( + { + "method": method, + "family": family, + "layer": int(layer), + "depth_percent": float(depth), + **partition_metrics( + partition, family_physical + ), + "eigengap": eigengap, + } + ) + return pd.DataFrame(rows), pd.DataFrame(layer_rows) + + +def random_constrained_adjacency( + family: str, + data: Mapping[str, object], + weights: np.ndarray, + rng: np.random.Generator, +) -> np.ndarray: + _, cases, variants = family_node_data(family, data) + directed = np.zeros((12, 12), dtype=float) + random_weights = weights.copy() + rng.shuffle(random_weights) + edge_index = 0 + for source in range(12): + for target_variant in VARIANTS: + if target_variant == variants[source]: + continue + candidates = np.flatnonzero( + (variants == target_variant) + & (cases != cases[source]) + ) + target = int(rng.choice(candidates)) + directed[source, target] = random_weights[edge_index] + edge_index += 1 + if edge_index != 24: + raise RuntimeError("unexpected null edge count") + return directed + directed.T + + +def constrained_nulls( + data: Mapping[str, object], +) -> tuple[dict[str, np.ndarray], dict[str, object]]: + rng = np.random.default_rng(SEED) + families_array = np.asarray(data["families"]) + physical = np.asarray(data["physical"], dtype=bool) + band_mask = np.asarray(data["band_mask"]) + outputs = {} + summaries = {} + for method in METHODS: + layers = np.asarray(data["similarities"][method]) + band = np.mean(layers[band_mask], axis=0) + templates = {} + labels = {} + for family in data["family_order"]: + _, weights = affinity_adjacency( + band, str(family), data, "weighted_top1" + ) + templates[str(family)] = weights + labels[str(family)] = physical[ + families_array == family + ] + mean_ari = np.empty(N_NULL, dtype=np.float32) + mean_gauge = np.empty(N_NULL, dtype=np.float32) + for null_index in range(N_NULL): + aris = [] + gauges = [] + for family in data["family_order"]: + adjacency = random_constrained_adjacency( + str(family), + data, + templates[str(family)], + rng, + ) + partition, _, _ = fiedler_partition(adjacency) + metrics = partition_metrics( + partition, labels[str(family)] + ) + aris.append(metrics["ari"]) + gauges.append(metrics["gauge_accuracy"]) + mean_ari[null_index] = float(np.mean(aris)) + mean_gauge[null_index] = float(np.mean(gauges)) + outputs[f"{method}_mean_ari"] = mean_ari + outputs[f"{method}_mean_gauge_accuracy"] = mean_gauge + summaries[method] = { + "null_mean_ari": float(np.mean(mean_ari)), + "null_sd_ari": float(np.std(mean_ari, ddof=1)), + "null_mean_gauge_accuracy": float(np.mean(mean_gauge)), + } + return outputs, summaries + + +def synthetic_graph( + *, + positive: bool, + rng: np.random.Generator, +) -> tuple[np.ndarray, np.ndarray, np.ndarray]: + cases = np.repeat(np.arange(4), 3) + variants = np.tile(np.arange(3), 4) + anchor = np.zeros(4, dtype=bool) + anchor[rng.choice(4, size=2, replace=False)] = True + labels = np.asarray( + [ + anchor[case] if variant < 2 else ~anchor[case] + for case, variant in zip(cases, variants) + ], + dtype=bool, + ) + scores = rng.normal(0.0, 0.25, size=(12, 12)) + if positive: + scores += (labels[:, None] == labels[None, :]).astype(float) + directed = np.zeros((12, 12), dtype=float) + for source in range(12): + for target_variant in range(3): + if target_variant == variants[source]: + continue + candidates = np.flatnonzero( + (variants == target_variant) + & (cases != cases[source]) + ) + target = min( + candidates.tolist(), + key=lambda index: (-scores[source, index], int(index)), + ) + directed[source, target] = max( + 0.0, float(scores[source, target] + 1.0) + ) + observed_labels = labels.copy() + if rng.random() < 0.5: + observed_labels = ~observed_labels + return directed + directed.T, labels, observed_labels + + +def synthetic_controls() -> tuple[pd.DataFrame, dict[str, object]]: + rng = np.random.default_rng(SEED) + rows = [] + for condition in ("positive", "negative"): + for graph_index in range(1000): + adjacency, canonical, observed = synthetic_graph( + positive=condition == "positive", rng=rng + ) + partition, eigengap, _ = fiedler_partition(adjacency) + canonical_metrics = partition_metrics( + partition, canonical + ) + observed_metrics = partition_metrics( + partition, observed + ) + rows.append( + { + "condition": condition, + "graph_index": graph_index, + "ari": canonical_metrics["ari"], + "gauge_accuracy": canonical_metrics[ + "gauge_accuracy" + ], + "ordinary_accuracy_random_orientation": ( + observed_metrics["ordinary_accuracy"] + ), + "eigengap": eigengap, + } + ) + frame = pd.DataFrame(rows) + positive = frame[frame["condition"] == "positive"] + negative = frame[frame["condition"] == "negative"] + summary = { + "positive_mean_ari": float(positive["ari"].mean()), + "positive_mean_gauge_accuracy": float( + positive["gauge_accuracy"].mean() + ), + "positive_mean_ordinary_random_orientation": float( + positive["ordinary_accuracy_random_orientation"].mean() + ), + "negative_mean_ari": float(negative["ari"].mean()), + "negative_mean_gauge_accuracy": float( + negative["gauge_accuracy"].mean() + ), + "positive_control_passes": bool( + positive["ari"].mean() >= 0.8 + ), + "negative_control_passes": bool( + abs(negative["ari"].mean()) <= 0.05 + ), + } + return frame, summary + + +def summarize( + observed: pd.DataFrame, + null_summary: dict[str, object], + null_arrays: Mapping[str, np.ndarray], + synthetic_summary: Mapping[str, object], +) -> tuple[dict[str, object], pd.DataFrame]: + primary = observed[ + observed["mode"] == "weighted_top1" + ].copy() + summaries = {} + for method in METHODS: + selected = primary[primary["method"] == method].set_index( + "family" + ) + family_order = sorted(selected.index) + aris = selected.loc[family_order, "ari"].to_numpy(float) + gauges = selected.loc[ + family_order, "gauge_accuracy" + ].to_numpy(float) + null = np.asarray(null_arrays[f"{method}_mean_ari"]) + observed_mean = float(np.mean(aris)) + p_value = float( + (1 + np.sum(null >= observed_mean - 1e-12)) + / (1 + len(null)) + ) + summaries[method] = { + "ari_by_family": dict(zip(family_order, aris.tolist())), + "gauge_accuracy_by_family": dict( + zip(family_order, gauges.tolist()) + ), + "mean_ari": observed_mean, + "mean_gauge_accuracy": float(np.mean(gauges)), + "positive_ari_families": int(np.sum(aris > 0)), + "gauge_above_half_families": int(np.sum(gauges > 0.5)), + "constrained_null_exact_p": p_value, + **null_summary[method], + } + summaries[method]["strong_spectral_gate"] = bool( + summaries[method]["positive_ari_families"] == 6 + and summaries[method]["gauge_above_half_families"] == 6 + and p_value <= 0.05 + and bool(synthetic_summary["positive_control_passes"]) + and bool(synthetic_summary["negative_control_passes"]) + ) + pivot = primary.pivot( + index="family", columns="method", values="ari" + ) + jac_direct = ( + pivot["jacobian"] - pivot["direct"] + ).sort_index() + jac_raw = (pivot["jacobian"] - pivot["raw"]).sort_index() + specificity = { + "jacobian_minus_direct_by_family": jac_direct.to_dict(), + "jacobian_minus_raw_by_family": jac_raw.to_dict(), + "jacobian_over_direct_families": int( + np.sum(jac_direct.to_numpy() > 0) + ), + "jacobian_over_raw_families": int( + np.sum(jac_raw.to_numpy() > 0) + ), + "jacobian_minus_direct_signflip_p": plus_one_signflip_p( + jac_direct + ), + "jacobian_minus_raw_signflip_p": plus_one_signflip_p( + jac_raw + ), + } + specificity["jacobian_specificity_passes"] = bool( + specificity["jacobian_over_direct_families"] >= 5 + and specificity["jacobian_over_raw_families"] >= 5 + and specificity["jacobian_minus_direct_signflip_p"] <= 0.05 + and specificity["jacobian_minus_raw_signflip_p"] <= 0.05 + ) + summaries["jacobian_specificity"] = specificity + + ablation_rows = [] + for method in METHODS: + for mode in MODES: + values = observed[ + (observed["method"] == method) + & (observed["mode"] == mode) + ]["ari"].to_numpy(float) + ablation_rows.append( + { + "method": method, + "mode": mode, + "mean_ari": float(np.mean(values)), + "positive_families": int(np.sum(values > 0)), + "signflip_p": plus_one_signflip_p(values), + } + ) + ablations = pd.DataFrame(ablation_rows) + ablations["bh_q"] = bh_adjust(ablations["signflip_p"]) + return summaries, ablations + + +def main() -> None: + validate_inputs() + data = load_data() + observed, layer_rows = observed_spectral(data) + null_arrays, null_summary = constrained_nulls(data) + synthetic_rows, synthetic_summary = synthetic_controls() + summaries, ablations = summarize( + observed, null_summary, null_arrays, synthetic_summary + ) + + observed.to_csv(OUT / "spectral_community_metrics.csv", index=False) + layer_rows.to_csv( + OUT / "spectral_community_layers.csv", index=False + ) + synthetic_rows.to_csv( + OUT / "spectral_synthetic_controls.csv", index=False + ) + ablations.to_csv( + OUT / "spectral_density_ablations.csv", index=False + ) + np.savez_compressed( + OUT / "spectral_constrained_nulls.npz", **null_arrays + ) + results = { + "study_id": json.loads(SPECTRAL_PROTOCOL.read_text())[ + "study_id" + ], + "spectral_protocol_sha256": sha256(SPECTRAL_PROTOCOL), + "spectral_protocol_markdown_sha256": sha256( + SPECTRAL_PROTOCOL_MD + ), + "gauge_protocol_sha256": sha256(GAUGE_PROTOCOL), + "primary": summaries, + "synthetic_controls": synthetic_summary, + "density_ablations": ablations.to_dict(orient="records"), + "physical_numeric_relation_identity": bool( + observed["physical_numeric_partition_identical"].all() + ), + "environment": { + "python": sys.version, + "platform": platform.platform(), + "numpy": np.__version__, + "pandas": pd.__version__, + }, + } + (OUT / "study5_spectral_statistics.json").write_text( + json.dumps(safe_json(results), indent=2) + "\n" + ) + print(json.dumps(safe_json(results), indent=2)) + + +if __name__ == "__main__": + main() diff --git a/jlens_materials/scripts/build_abstract_physics_semantic.py b/jlens_materials/scripts/build_abstract_physics_semantic.py new file mode 100644 index 0000000000000000000000000000000000000000..2e9b3a5b4473b8850194cb6b567d02664208d92a --- /dev/null +++ b/jlens_materials/scripts/build_abstract_physics_semantic.py @@ -0,0 +1,200 @@ +#!/usr/bin/env python3 +"""Freeze a semantic-answer replication of abstract physics composition.""" + +from __future__ import annotations + +import hashlib +import json +from datetime import datetime, timezone +from pathlib import Path + +from build_abstract_physics_composition import LAWS + + +ROOT = Path(__file__).resolve().parents[1] +OUT = ROOT / "experiments" / "abstract-physics-semantic-2026-07-18" +MANIFEST = OUT / "prompt_manifest.json" +PROTOCOL = OUT / "protocol.json" +RUNNER = ROOT / "scripts" / "run_abstract_physics_semantic.py" +ANALYZER = ROOT / "scripts" / "analyze_abstract_physics_semantic.py" + + +def sha256(path: Path) -> str: + return hashlib.sha256(path.read_bytes()).hexdigest() + + +def atomic_json(path: Path, payload: object) -> None: + path.parent.mkdir(parents=True, exist_ok=True) + temporary = path.with_suffix(path.suffix + ".tmp") + temporary.write_text(json.dumps(payload, indent=2, sort_keys=True) + "\n") + temporary.replace(path) + + +def make_prompt( + law: dict, + surface: str, + case_index: int, + numerical_sign: int, + answer_order: str, +) -> dict: + system, low, high = law["cases"][case_index] + start, end = (low, high) if numerical_sign == 1 else (high, low) + physical_sign = int(law["law_sign"]) * numerical_sign + expected_answer = "higher" if physical_sign == 1 else "lower" + words = ( + ["higher", "lower"] + if answer_order == "higher-first" + else ["lower", "higher"] + ) + prompt_id = ( + f"{law['law_id']}--{surface}--case{case_index + 1}--" + f"{'up' if numerical_sign == 1 else 'down'}--{answer_order}" + ) + user = ( + f"Constitutive relation for {law['law_name']}: " + f"{law[f'formula_{surface}']}. All stated coefficients are positive, " + "and quantities not varied below remain fixed. " + f"§ In {system}, {law['control']} changes from {start} to {end}. " + f"¶ What is the resulting direction of change in {law['response']}? " + f"† Answer exactly one lowercase word from this ordered pair: " + f"{words[0]}, {words[1]}." + ) + return { + "prompt_id": prompt_id, + "law_id": law["law_id"], + "law_name": law["law_name"], + "split": law["split"], + "law_sign": int(law["law_sign"]), + "surface": surface, + "case_index": case_index, + "system": system, + "control": law["control"], + "response": law["response"], + "numeric_start": start, + "numeric_end": end, + "numerical_sign": numerical_sign, + "physical_sign": physical_sign, + "answer_order": answer_order, + "presented_words": words, + "expected_answer": expected_answer, + "user": user, + } + + +def main() -> None: + prompts = [] + for law in LAWS: + for surface in ("a", "b"): + for case_index in range(2): + for numerical_sign in (-1, 1): + for answer_order in ("higher-first", "lower-first"): + prompts.append( + make_prompt( + law, + surface, + case_index, + numerical_sign, + answer_order, + ) + ) + by_id = {row["prompt_id"]: row for row in prompts} + for row in prompts: + opposite = row["prompt_id"].replace( + "--up--" if row["numerical_sign"] == 1 else "--down--", + "--down--" if row["numerical_sign"] == 1 else "--up--", + ) + if opposite not in by_id: + raise RuntimeError(f"missing opposite prompt for {row['prompt_id']}") + row["opposite_numerical_prompt_id"] = opposite + manifest = { + "study_id": "abstract-physics-semantic-2026-07-18", + "relation_to_prior": ( + "Disjoint output protocol using the same frozen laws and factorial " + "cells after the arbitrary A/B cohort showed severe code bias." + ), + "markers": {"law": "§", "comparison": "¶", "decision": "†"}, + "laws": LAWS, + "prompts": prompts, + "dimensions": { + "n_laws": 16, + "n_discovery_laws": 8, + "n_replication_laws": 8, + "n_prompts": len(prompts), + }, + } + atomic_json(MANIFEST, manifest) + frozen_at = ( + json.loads(PROTOCOL.read_text()).get("frozen_at") + if PROTOCOL.exists() + else datetime.now(timezone.utc).isoformat() + ) + protocol = { + "study_id": manifest["study_id"], + "status": "frozen-before-output", + "frozen_at": frozen_at, + "motivation": ( + "The preceding arbitrary-code benchmark was behaviorally " + "non-diagnostic (55.1% A/B accuracy and a strong B bias). This " + "cohort uses semantic one-token answers while retaining a decision " + "checkpoint that causally precedes the answer vocabulary." + ), + "model": "google/gemma-4-E4B-it", + "model_revision": "a4c2d58be94dda072b918d9db64ee85c8ed34e3f", + "inputs": { + "prompt_manifest": { + "path": str(MANIFEST.relative_to(ROOT)), + "sha256": sha256(MANIFEST), + }, + "runner": { + "path": str(RUNNER.relative_to(ROOT)), + "sha256": sha256(RUNNER), + }, + "analyzer": { + "path": str(ANALYZER.relative_to(ROOT)), + "sha256": sha256(ANALYZER), + }, + }, + "positions": ["law", "comparison", "decision", "final_prompt"], + "layer_selection": { + "data": "discovery laws only", + "criterion": ( + "mean physical-outcome AUC for direct-to-inverse and " + "inverse-to-direct centroid cross-decoding at the answer-free " + "decision marker" + ), + "tie_break": "earliest layer", + }, + "primary_endpoint": { + "data": "replication laws only", + "independent_unit": "held-out law", + "metric": ( + "mean held-out-law AUC using discovery-direct to " + "replication-inverse and discovery-inverse to " + "replication-direct physical-outcome decoding" + ), + "inference": "30000 law bootstrap and exact sign flip of AUC minus 0.5", + }, + "success_rule": { + "behavior_accuracy_minimum": 0.80, + "mean_replication_auc_minimum": 0.70, + "bootstrap_95_lower_above": 0.50, + "positive_replication_laws_minimum": 6, + "replication_laws_total": 8, + }, + "guardrails": [ + "The decision marker precedes the semantic answer words.", + "Discovery laws alone select the layer.", + "The primary decoder is a parameter-free centroid direction.", + "Replication laws remain untouched until final evaluation.", + "All prompts and outputs are retained, including behavioral failures.", + ], + "bootstrap": {"seed": 20260719, "resamples": 30000}, + } + atomic_json(PROTOCOL, protocol) + print(f"frozen {len(prompts)} semantic-answer prompts") + print(f"manifest sha256: {sha256(MANIFEST)}") + print(f"protocol sha256: {sha256(PROTOCOL)}") + + +if __name__ == "__main__": + main() diff --git a/jlens_materials/scripts/build_elicited_physics_abstraction.py b/jlens_materials/scripts/build_elicited_physics_abstraction.py new file mode 100644 index 0000000000000000000000000000000000000000..639b0594dad860755fa3bf2fbd069f11c2f5b392 --- /dev/null +++ b/jlens_materials/scripts/build_elicited_physics_abstraction.py @@ -0,0 +1,177 @@ +#!/usr/bin/env python3 +"""Build an exploratory scaffolded benchmark for abstract sign composition. + +This is deliberately separate from the paper. It tests whether a generic +two-stage instruction elicits variables that were weak in the terse prompts: + +1. the monotonic sign of a constitutive relation; +2. the sign of the numerical change; +3. their product, the sign of the physical response. + +The first eight laws are development data and the second eight are an +untouched internal replication set. Because those laws were used in earlier +experiments, any positive result here remains developmental and must later be +confirmed on fresh laws. +""" + +from __future__ import annotations + +import hashlib +import json +from datetime import datetime, timezone +from pathlib import Path + +from build_abstract_physics_composition import LAWS + + +ROOT = Path(__file__).resolve().parents[1] +OUT = ROOT / "experiments" / "elicited-physics-abstraction-2026-07-18" +MANIFEST = OUT / "prompt_manifest.json" +PROTOCOL = OUT / "protocol.json" +RUNNER = ROOT / "scripts" / "run_elicited_physics_abstraction.py" + + +def sha256(path: Path) -> str: + return hashlib.sha256(path.read_bytes()).hexdigest() + + +def write_json(path: Path, payload: object) -> None: + path.parent.mkdir(parents=True, exist_ok=True) + tmp = path.with_suffix(path.suffix + ".tmp") + tmp.write_text(json.dumps(payload, indent=2, sort_keys=True) + "\n") + tmp.replace(path) + + +def make_prompt( + law: dict, surface: str, case_index: int, numerical_sign: int +) -> dict: + system, low, high = law["cases"][case_index] + start, end = (low, high) if numerical_sign == 1 else (high, low) + physical_sign = int(law["law_sign"]) * numerical_sign + expected = "higher" if physical_sign == 1 else "lower" + prompt_id = ( + f"{law['law_id']}--{surface}--case{case_index + 1}--" + f"{'up' if numerical_sign == 1 else 'down'}" + ) + user = ( + "Apply the same two-stage physical test to every case. " + "Stage 1: use only the constitutive equation, not associations with " + "material names, to determine the monotonic sign of the response with " + "respect to the changed control while all other quantities are fixed. " + f"For {law['law_name']}, the response is {law['response']}, the " + f"control is {law['control']}, and the relation is " + f"{law[f'formula_{surface}']}. All stated coefficients are positive. " + "Complete this relation-sign step silently. § " + f"Stage 2: in {system}, {law['control']} changes from {start} to {end}. " + "Determine the sign of this numerical change silently. ¶ " + "Now compose the relation sign with the numerical-change sign to " + f"determine the direction of {law['response']}. † " + "Reply with exactly one lowercase word: higher or lower." + ) + return { + "prompt_id": prompt_id, + "law_id": law["law_id"], + "law_name": law["law_name"], + "split": law["split"], + "law_sign": int(law["law_sign"]), + "surface": surface, + "case_index": case_index, + "system": system, + "control": law["control"], + "response": law["response"], + "numeric_start": start, + "numeric_end": end, + "numerical_sign": numerical_sign, + "physical_sign": physical_sign, + "expected_answer": expected, + "user": user, + } + + +def main() -> None: + prompts = [ + make_prompt(law, surface, case_index, numerical_sign) + for law in LAWS + for surface in ("a", "b") + for case_index in range(2) + for numerical_sign in (-1, 1) + ] + manifest = { + "study_id": "elicited-physics-abstraction-2026-07-18", + "status": "developmental-existing-laws", + "markers": {"law": "§", "comparison": "¶", "decision": "†"}, + "laws": LAWS, + "prompts": prompts, + "dimensions": { + "n_laws": len(LAWS), + "n_development_laws": sum(x["split"] == "discovery" for x in LAWS), + "n_internal_replication_laws": sum( + x["split"] == "replication" for x in LAWS + ), + "n_prompts": len(prompts), + }, + } + write_json(MANIFEST, manifest) + protocol = { + "study_id": manifest["study_id"], + "status": "frozen-before-scaffolded-output", + "frozen_at": datetime.now(timezone.utc).isoformat(), + "purpose": ( + "Test whether an invariant, answer-free two-stage instruction " + "elicits relation-sign, numerical-sign, and composed physical-sign " + "variables that generalize across constitutive laws." + ), + "interpretation_limit": ( + "All laws appeared in earlier studies. This run is method " + "development, not a confirmatory result. A successful method must " + "be frozen and rerun on entirely fresh laws." + ), + "model": "google/gemma-4-E4B-it", + "model_revision": "a4c2d58be94dda072b918d9db64ee85c8ed34e3f", + "inputs": { + "prompt_manifest": { + "path": str(MANIFEST.relative_to(ROOT)), + "sha256": sha256(MANIFEST), + }, + "runner": { + "path": str(RUNNER.relative_to(ROOT)), + "sha256": sha256(RUNNER), + }, + }, + "positions": ["law", "comparison", "decision", "final_prompt"], + "development_rule": ( + "Select position/layer and a linear decoder using discovery laws " + "only. Evaluate once on the eight internal replication laws." + ), + "candidate_success_rule": { + "behavior_accuracy_minimum": 0.85, + "mean_replication_law_auc_minimum": 0.75, + "positive_replication_laws_minimum": 7, + "replication_laws_total": 8, + "formula_surface_agreement_minimum": 0.75, + }, + "guardrails": [ + "The law marker precedes every numerical case.", + "The decision marker precedes the answer words.", + "The same generic algorithm is used for every direct and inverse law.", + "No generated chain of thought or intermediate label is supplied.", + "All outputs are retained, including failures.", + ], + } + write_json(PROTOCOL, protocol) + readme = OUT / "PROTOCOL.md" + readme.write_text( + "# Scaffolded abstract-physics development experiment\n\n" + "This exploratory experiment asks whether a generic silent two-stage " + "instruction elicits a law-sign variable and a composed physical-outcome " + "variable. It does not expose or request a chain of thought. The three " + "markers register states after the relation, numerical-comparison, and " + "composition steps. The laws are not fresh, so a positive result here " + "must be followed by a frozen, disjoint-law confirmation.\n" + ) + print(f"frozen {len(prompts)} prompts") + print(f"protocol sha256: {sha256(PROTOCOL)}") + + +if __name__ == "__main__": + main() diff --git a/jlens_materials/scripts/build_equation_free_relational_pilot.py b/jlens_materials/scripts/build_equation_free_relational_pilot.py new file mode 100644 index 0000000000000000000000000000000000000000..aee8102d18e0ec8299214b0b54a9e6cad35960b3 --- /dev/null +++ b/jlens_materials/scripts/build_equation_free_relational_pilot.py @@ -0,0 +1,478 @@ +#!/usr/bin/env python3 +"""Freeze an exploratory equation-free transfer pilot before new model output.""" + +from __future__ import annotations + +import hashlib +import json +from datetime import datetime, timezone +from pathlib import Path + + +ROOT = Path(__file__).resolve().parents[1] +OUT = ROOT / "experiments" / "equation-free-relational-pilot-2026-08-21" +MANIFEST = OUT / "prompt_manifest.json" +PROTOCOL = OUT / "protocol.json" +RUNNER = ROOT / "scripts" / "run_equation_free_relational_pilot.py" +ANALYZER = ROOT / "scripts" / "analyze_equation_free_relational_pilot.py" +DEVELOPMENT = ROOT / "experiments" / "elicited-physics-abstraction-2026-07-18" +REFERENCE = ROOT / "experiments" / "neutral-anchored-relational-physics-2026-07-18" + + +def sha256(path: Path) -> str: + return hashlib.sha256(path.read_bytes()).hexdigest() + + +def write_json(path: Path, value: object) -> None: + path.parent.mkdir(parents=True, exist_ok=True) + path.write_text(json.dumps(value, indent=2, sort_keys=True) + "\n") + + +def case(context: str, fixed: str, low: str, high: str) -> dict: + return {"context": context, "fixed": fixed, "low": low, "high": high} + + +def law( + law_id: str, + category: str, + domain: str, + response: str, + control: str, + case_1: dict, + case_2: dict, +) -> dict: + return { + "law_id": law_id, + "category": category, + "law_sign": {"direct": 1, "inverse": -1, "neutral": 0}[category], + "domain": domain, + "response": response, + "control": control, + "cases": [case_1, case_2], + "selection_note": ( + "Selected before the equation-free run because the qualitative " + "relationship is standard and can be stated without an equation." + ), + } + + +LAWS = [ + # Direct relations: the response normally rises with the stated control. + law( + "corrosion-loss-time", + "direct", + "corrosion", + "accumulated corrosion mass loss", + "exposure time", + case( + "a steel coupon exposed continuously to one unchanged electrolyte", + "temperature, exposed area, surface condition, and solution chemistry are fixed", + "1 day", + "100 days", + ), + case( + "a magnesium coupon immersed continuously in one unchanged electrolyte", + "temperature, exposed area, surface condition, and solution chemistry are fixed", + "2 hours", + "200 hours", + ), + ), + law( + "creep-strain-time", + "direct", + "deformation", + "accumulated creep strain", + "time under load", + case( + "a superalloy component undergoing steady creep", + "applied stress, temperature, geometry, and material state are fixed", + "10 hours", + "1000 hours", + ), + case( + "a polymer specimen undergoing steady creep", + "applied load, temperature, geometry, and material state are fixed", + "1 hour", + "100 hours", + ), + ), + law( + "grain-size-time", + "direct", + "microstructure", + "mean grain size", + "annealing time", + case( + "the same initially fine-grained steel during normal grain growth", + "annealing temperature, composition, atmosphere, and initial microstructure are fixed", + "1 hour", + "16 hours", + ), + case( + "the same ceramic during normal grain growth", + "sintering temperature, composition, atmosphere, and initial microstructure are fixed", + "0.5 hour", + "8 hours", + ), + ), + law( + "current-voltage", + "direct", + "electrical", + "electric current", + "applied voltage", + case( + "the same metallic resistor operating in its linear regime", + "electrical resistance and temperature are fixed", + "1 V", + "10 V", + ), + case( + "the same conducting composite operating in its linear regime", + "electrical resistance and temperature are fixed", + "0.5 V", + "5 V", + ), + ), + # Inverse relations: the response normally falls with the stated control. + law( + "fatigue-life-delta-k", + "inverse", + "fatigue", + "fatigue life", + "stress-intensity range", + case( + "the same cyclically loaded steel specimen containing a stable crack geometry", + "material, environment, loading frequency, and mean-load conditions are fixed", + "5 MPa sqrt(m)", + "20 MPa sqrt(m)", + ), + case( + "the same cyclically loaded titanium-alloy specimen containing a stable crack geometry", + "material, environment, loading frequency, and mean-load conditions are fixed", + "3 MPa sqrt(m)", + "15 MPa sqrt(m)", + ), + ), + law( + "diffusion-time-diffusivity-new", + "inverse", + "transport", + "diffusive equilibration time", + "effective diffusivity", + case( + "the same porous slab approaching diffusive equilibration", + "slab thickness, boundary concentrations, temperature, and geometry are fixed", + "1e-12 m^2/s", + "1e-9 m^2/s", + ), + case( + "the same protective coating approaching diffusive equilibration", + "coating thickness, boundary concentrations, temperature, and geometry are fixed", + "1e-15 m^2/s", + "1e-11 m^2/s", + ), + ), + law( + "capacitance-separation", + "inverse", + "electrical", + "capacitance", + "plate separation", + case( + "the same parallel-plate ceramic capacitor", + "plate area, dielectric material, temperature, and edge conditions are fixed", + "1 micrometer", + "100 micrometers", + ), + case( + "the same air-gap parallel-plate capacitor", + "plate area, dielectric environment, temperature, and edge conditions are fixed", + "0.1 mm", + "10 mm", + ), + ), + law( + "settling-speed-viscosity", + "inverse", + "fluid mechanics", + "terminal settling speed", + "fluid viscosity", + case( + "the same small spherical particle settling in a Newtonian liquid", + "particle size, density contrast, gravity, and container geometry are fixed", + "1 mPa s", + "100 mPa s", + ), + case( + "the same small ceramic inclusion settling in a Newtonian melt", + "inclusion size, density contrast, gravity, and container geometry are fixed", + "0.01 Pa s", + "1 Pa s", + ), + ), + # Neutral controls: an intrinsic property should not change with specimen scale. + law( + "modulus-specimen-length", + "neutral", + "mechanics", + "Young's modulus", + "specimen length", + case( + "a homogeneous steel specimen measured within the same elastic regime", + "composition, microstructure, temperature, cross section, and measurement method are fixed", + "10 mm", + "1 m", + ), + case( + "a homogeneous ceramic specimen measured within the same elastic regime", + "composition, microstructure, temperature, cross section, and measurement method are fixed", + "1 mm", + "100 mm", + ), + ), + law( + "conductivity-sample-area-neutral", + "neutral", + "thermal", + "intrinsic thermal conductivity", + "sample cross-sectional area", + case( + "a homogeneous ceramic sample at one fixed temperature", + "composition, microstructure, temperature, length, and measurement method are fixed", + "1 mm^2", + "100 mm^2", + ), + case( + "a homogeneous polymer sample at one fixed temperature", + "composition, microstructure, temperature, length, and measurement method are fixed", + "10 mm^2", + "1000 mm^2", + ), + ), + law( + "melting-temperature-mass", + "neutral", + "phase equilibrium", + "melting temperature", + "macroscopic sample mass", + case( + "a pure bulk metal far from nanoscale size effects", + "composition, pressure, purity, and measurement method are fixed", + "1 mg", + "1 kg", + ), + case( + "a pure bulk ceramic far from nanoscale size effects", + "composition, pressure, purity, and measurement method are fixed", + "10 mg", + "10 kg", + ), + ), + law( + "resistivity-wire-length-neutral", + "neutral", + "electrical", + "intrinsic electrical resistivity", + "wire length", + case( + "a uniform copper wire measured at one fixed temperature", + "composition, microstructure, temperature, cross section, and measurement method are fixed", + "1 cm", + "10 m", + ), + case( + "a uniform alloy wire measured at one fixed temperature", + "composition, microstructure, temperature, cross section, and measurement method are fixed", + "2 cm", + "20 m", + ), + ), +] + + +ANSWER_ORDERS = { + "standard": ["higher", "lower", "unchanged"], + "reversed": ["unchanged", "lower", "higher"], +} + + +def make_prompt(item: dict, surface: str, case_index: int, numerical_sign: int, order: str) -> dict: + selected = item["cases"][case_index] + start, end = ( + (selected["low"], selected["high"]) + if numerical_sign > 0 + else (selected["high"], selected["low"]) + ) + choices = ", ".join(ANSWER_ORDERS[order]) + if surface == "a": + user = ( + f"In {selected['context']}, {selected['fixed']}. " + f"The {item['control']} changes from {start} to {end}. " + f"What happens to {item['response']}? Reply with exactly one " + f"lowercase word from this ordered set: {choices}." + ) + else: + user = ( + f"Consider {selected['context']}. Holding {selected['fixed']}, " + f"change only the {item['control']} from {start} to {end}. " + f"Determine the resulting trend in {item['response']}. Answer " + f"exactly one lowercase word from this ordered set: {choices}." + ) + physical_sign = item["law_sign"] * numerical_sign + expected = {1: "higher", -1: "lower", 0: "unchanged"}[physical_sign] + return { + "prompt_id": f"eqfree--{item['law_id']}--s{surface}--c{case_index}--n{numerical_sign:+d}--{order}", + "law_id": item["law_id"], + "category": item["category"], + "law_sign": item["law_sign"], + "domain": item["domain"], + "response": item["response"], + "control": item["control"], + "surface": surface, + "case_index": case_index, + "numerical_sign": numerical_sign, + "physical_sign": physical_sign, + "answer_order": order, + "expected_answer": expected, + "user": user, + } + + +def main() -> None: + if (OUT / "raw.json").exists(): + raise RuntimeError("refusing to rebuild after model outputs exist") + required = [RUNNER, ANALYZER, DEVELOPMENT / "prompt_manifest.json", DEVELOPMENT / "representations.npz", REFERENCE / "statistics.json", REFERENCE / "law_level_neutral_normalized.csv"] + missing = [str(path) for path in required if not path.exists()] + if missing: + raise RuntimeError(f"missing frozen inputs: {missing}") + counts = {category: sum(x["category"] == category for x in LAWS) for category in ("direct", "inverse", "neutral")} + if counts != {"direct": 4, "inverse": 4, "neutral": 4}: + raise RuntimeError(f"unexpected pilot balance: {counts}") + if len({x["law_id"] for x in LAWS}) != len(LAWS): + raise RuntimeError("duplicate law id") + prompts = [ + make_prompt(item, surface, case_index, numerical_sign, order) + for item in LAWS + for surface in ("a", "b") + for case_index in (0, 1) + for numerical_sign in (-1, 1) + for order in ANSWER_ORDERS + ] + forbidden = ("equation", "constitutive", "stage 1", "stage 2", "silently compose", " = ") + offenders = [p["prompt_id"] for p in prompts if any(term in p["user"].lower() for term in forbidden)] + if offenders: + raise RuntimeError(f"equation/scaffold leakage in prompts: {offenders[:3]}") + manifest = { + "study_id": "equation-free-relational-pilot-2026-08-21", + "laws": LAWS, + "prompts": prompts, + "dimensions": { + "n_laws": len(LAWS), + "n_direct": counts["direct"], + "n_inverse": counts["inverse"], + "n_neutral": counts["neutral"], + "n_surfaces": 2, + "n_cases_per_law": 2, + "n_answer_orders": 2, + "n_prompts": len(prompts), + "n_matched_pairs": len(prompts) // 2, + }, + } + write_json(MANIFEST, manifest) + frozen_inputs = { + "prompt_manifest": MANIFEST, + "builder": Path(__file__).resolve(), + "runner": RUNNER, + "analyzer": ANALYZER, + "development_manifest": DEVELOPMENT / "prompt_manifest.json", + "development_states": DEVELOPMENT / "representations.npz", + "reference_statistics": REFERENCE / "statistics.json", + "reference_law_table": REFERENCE / "law_level_neutral_normalized.csv", + } + protocol = { + "study_id": manifest["study_id"], + "status": "exploratory-transfer-pilot-frozen-before-new-model-output", + "frozen_at": datetime.now(timezone.utc).isoformat(), + "model": "google/gemma-4-E4B-it", + "model_revision": "a4c2d58be94dda072b918d9db64ee85c8ed34e3f", + "design_scope": ( + "The 12 laws were selected after the scaffolded benchmark was known, " + "using deliberately clear qualitative associations. This is a prospective " + "prompt-format transfer pilot, not an independent law-cohort confirmation." + ), + "prompt_ablation": { + "removed": [ + "constitutive equation", + "law name", + "direct/inverse/neutral label", + "two-stage monotonicity decomposition", + "instruction to combine two signs", + ], + "retained": [ + "natural material scenario", + "controlled initial and final numerical values", + "all-else-fixed clause", + "one-word higher/lower/unchanged response format", + ], + }, + "inputs": { + name: {"path": str(path.relative_to(ROOT)), "sha256": sha256(path)} + for name, path in frozen_inputs.items() + }, + "frozen_readout": { + "position": "final_prompt", + "layer": 34, + "state_normalization": "unit L2", + "direction": "positive-minus-negative centroids from the original 16 development laws", + "pilot_refit": "none", + "primary_center": 0.013596733322224686, + "primary_scale": 0.008536733931615473, + "center_scale_source": "the original 10 calibration-neutral scaffolded laws", + }, + "matched_contrast": ( + "Within law/surface/case/answer-order, frozen-direction score for " + "numerical-up minus score for numerical-down." + ), + "primary_endpoints": [ + "direct-versus-inverse law AUC", + "directional-law sign accuracy relative to the frozen old neutral center", + "direct-versus-inverse AUC separately for both natural prompt surfaces", + "inverse-neutral-direct ordinal Spearman correlation", + ], + "supportive_rule": { + "direct_inverse_auc_minimum": 0.75, + "directional_sign_accuracy_minimum": 0.75, + "both_surface_auc_minimum": 0.70, + "ordinal_spearman_minimum": 0.50, + }, + "controls": [ + "output-head higher-minus-lower matched change", + "word TF-IDF direction trained on the original development prompts", + "character TF-IDF direction trained on the original development prompts", + "three-way exact generated-answer accuracy", + "comparison with the same laws under the original supplied-equation scaffold", + ], + "inference": {"bootstrap_seed": 20260821, "bootstrap_resamples": 50000}, + "guardrails": [ + "No equation or law name appears in a pilot prompt.", + "No pilot state refits the hidden direction, layer, midpoint, center, or scale.", + "Both numerical directions, both answer orders, both surfaces, and both cases are retained.", + "All outputs and failures are retained.", + "The pilot is labeled exploratory because law selection followed inspection of the earlier scaffolded study.", + ], + } + write_json(PROTOCOL, protocol) + (OUT / "PROTOCOL.md").write_text( + "# Equation-free relational-transfer pilot\n\n" + "This frozen pilot removes the supplied equation, law name, and explicit " + "two-stage reasoning scaffold from 12 deliberately clear relationships. " + "It reuses the original layer-34 direction and original neutral center and " + "scale without refitting. Exact prompts are stored in `prompt_manifest.json`.\n" + ) + print(f"frozen {len(prompts)} prompts over {len(LAWS)} laws: {counts}") + print(f"protocol sha256: {sha256(PROTOCOL)}") + + +if __name__ == "__main__": + main() diff --git a/jlens_materials/scripts/build_final_paper_figures.py b/jlens_materials/scripts/build_final_paper_figures.py new file mode 100644 index 0000000000000000000000000000000000000000..85c8060bf1c930cbc287ce76b1cdcbb517ab522f --- /dev/null +++ b/jlens_materials/scripts/build_final_paper_figures.py @@ -0,0 +1,809 @@ +#!/usr/bin/env python3 +"""Build paper Figures 4 and 7 from frozen archived artifacts. + +Figure 1 is authored directly in TikZ. Figure 4 compares unrestricted +single-fit streams with filtered three-fit-consensus streams for five exact +held-out prompts. Figure 7 magnifies one development case and one held-out +case without adding a new inferential endpoint. +Population Figures 2, 3, and 6 are built by the held-out analysis scripts. +""" + +from __future__ import annotations + +import hashlib +import json +import math +import re +import sys +import textwrap +from collections import defaultdict +from pathlib import Path + +import matplotlib as mpl +import matplotlib.pyplot as plt +import matplotlib.patheffects as path_effects +import numpy as np +from matplotlib.patches import FancyArrowPatch, FancyBboxPatch + + +ROOT = Path(__file__).resolve().parents[1] +sys.path.insert(0, str(ROOT)) + +from analyze_materials_heldout_v1 import FUNCTION_WORDS # noqa: E402 + + +RUN_PATHS = [ + ROOT / "runs" / f"gemma4-e4b-it-heldout-v1-seed{seed}.json" + for seed in range(3) +] +DEV_RUN_PATHS = [ + ROOT / "runs" / f"gemma4-e4b-it-paper-seed{seed}.json" + for seed in range(3) +] +STATS_PATH = ROOT / "experiments" / "materials-heldout-v1_statistics.json" +DEEP_DIVE_PATH = ( + ROOT / "experiments" / "deep-dive-notch-toughness-multiseed_statistics.json" +) +OUT_DIR = ROOT / "figures" / "materials-heldout-v1" +STREAM_STATS_PATH = ROOT / "experiments" / "materials-heldout-v1_semantic_streams.json" +FIG7_STATS_PATH = ROOT / "experiments" / "figure7-layer-resolved-cases.json" +SUP_STREAM_STEM = OUT_DIR / "figure-s2-development-semantic-stream" +FIG7_STEM = OUT_DIR / "figure7-layer-resolved-cases" + +J_COLOR = "#087f8c" +DIRECT_COLOR = "#66727a" +PURPLE = "#6f5aa8" +GOLD = "#d59c32" +GRAY = "#6d747b" +LIGHT_GRAY = "#e8eaed" +FAMILY_COLORS = { + "boundary-attack": "#b33c49", + "cleavage": "#557a95", +} +TOKEN_COLORS = ["#176B87", "#2A9D8F", "#6C8EAD", "#7A6AAE", "#79A15A", "#4F86A6"] +HIGH_CONTRAST_TOKEN_COLORS = [ + "#0072B2", "#D55E00", "#009E73", "#7A4EAB", "#C58B00", "#3E718C", +] +STREAM_CASES = [ + ("A", "Boundary attack", "heldout-v1-assoc-boundary-attack-05"), + ("B", "Notch resistance", "heldout-v1-assoc-notch-resistance-01"), + ("C", "Line-defect motion", "heldout-v1-assoc-line-defect-motion-04"), + ("D", "Ductile failure", "heldout-v1-assoc-ductile-03"), + ("E", "Cleavage", "heldout-v1-assoc-cleavage-03"), +] + + +def configure_style() -> None: + mpl.rcParams.update( + { + "font.family": "DejaVu Sans", + "font.size": 10, + "axes.titlesize": 12, + "axes.labelsize": 10, + "xtick.labelsize": 9, + "ytick.labelsize": 9, + "legend.fontsize": 9, + "axes.spines.top": False, + "axes.spines.right": False, + "axes.grid": True, + "grid.alpha": 0.22, + "grid.linewidth": 0.7, + "figure.facecolor": "white", + "savefig.facecolor": "white", + } + ) + + +def save_all(fig: plt.Figure, stem: Path) -> None: + stem.parent.mkdir(parents=True, exist_ok=True) + fig.savefig(stem.with_suffix(".pdf"), bbox_inches="tight") + fig.savefig(stem.with_suffix(".svg"), bbox_inches="tight") + fig.savefig(stem.with_suffix(".png"), dpi=300, bbox_inches="tight") + plt.close(fig) + + +def panel_label(ax: plt.Axes, label: str, title: str) -> None: + ax.set_title(f"{label} {title}", loc="left", fontweight="bold", pad=8) + + +def box( + ax: plt.Axes, + xy: tuple[float, float], + width: float, + height: float, + text: str, + *, + face: str = "#f7f8fa", + edge: str = "#a8adb4", + fontsize: float = 10, + weight: str = "normal", +) -> FancyBboxPatch: + patch = FancyBboxPatch( + xy, + width, + height, + boxstyle="round,pad=0.012,rounding_size=0.018", + linewidth=1.2, + facecolor=face, + edgecolor=edge, + ) + ax.add_patch(patch) + ax.text( + xy[0] + width / 2, + xy[1] + height / 2, + text, + ha="center", + va="center", + fontsize=fontsize, + fontweight=weight, + wrap=True, + ) + return patch + + +def arrow(ax: plt.Axes, start: tuple[float, float], end: tuple[float, float], color=GRAY) -> None: + ax.add_patch( + FancyArrowPatch( + start, + end, + arrowstyle="-|>", + mutation_scale=13, + linewidth=1.4, + color=color, + shrinkA=2, + shrinkB=2, + ) + ) + + +def build_design_figure() -> None: + """Legacy raster design draft; the manuscript Figure 1 is the TikZ source.""" + fig, ax = plt.subplots(figsize=(13.5, 7.0)) + ax.set_xlim(0, 1) + ax.set_ylim(0, 1) + ax.axis("off") + box( + ax, + (0.025, 0.665), + 0.245, + 0.245, + "UNSEEN MATERIALS DESCRIPTION\n\n" + "A stainless weld remained near 675 degC.\n" + "Nitric acid exposed narrow trenches along\n" + "grain edges beside chromium-rich carbides.\n\n" + "Omitted before execution:\n" + "boundary | corrosion | sensitization", + face="#f5f1ea", + edge="#c8ad7f", + fontsize=9.2, + ) + + box(ax, (0.33, 0.745), 0.235, 0.095, "Gemma-4-E4B-it\n42 transformer layers", face="#edf2f7", edge="#7991a8", weight="bold") + for i in range(11): + x = 0.345 + i * 0.018 + color = mpl.colors.to_hex(plt.cm.Blues(0.25 + 0.055 * i)) + ax.add_patch(FancyBboxPatch((x, 0.68), 0.012, 0.045, boxstyle="round,pad=0.002", facecolor=color, edgecolor="none")) + ax.text(0.447, 0.655, "fixed final-prompt position; 25 registered depths", ha="center", fontsize=8.7, color=GRAY) + arrow(ax, (0.27, 0.79), (0.33, 0.79)) + + box(ax, (0.64, 0.805), 0.31, 0.105, "DIRECT UNEMBEDDING\nintermediate state\n-> vocabulary decoder", face="#edf2f3", edge=DIRECT_COLOR, weight="bold", fontsize=9.5) + box(ax, (0.64, 0.655), 0.31, 0.105, "JACOBIAN READOUT\nintermediate state -> downstream map\n-> vocabulary decoder", face="#e8f5f4", edge=J_COLOR, weight="bold", fontsize=9.5) + arrow(ax, (0.565, 0.805), (0.64, 0.857), DIRECT_COLOR) + arrow(ax, (0.565, 0.745), (0.64, 0.707), J_COLOR) + + box(ax, (0.04, 0.37), 0.19, 0.115, "Three lens fits\n1,000 WikiText records each\nindependent corpus seeds", face="#f1edfa", edge=PURPLE, weight="bold") + box(ax, (0.285, 0.37), 0.19, 0.115, "Held-out suite\n10 physical families\n5 phrasings each", face="#edf5ee", edge="#5b9465", weight="bold") + box(ax, (0.53, 0.37), 0.19, 0.115, "Controlled branch\nrank declared absent concepts\nin the full vocabulary", face="#e8f5f4", edge=J_COLOR, weight="bold") + box(ax, (0.775, 0.37), 0.19, 0.115, "Target-free branch\ndiscover stable words\nwithout the answer list", face="#f4f1e8", edge=GOLD, weight="bold") + arrow(ax, (0.23, 0.428), (0.285, 0.428)) + arrow(ax, (0.475, 0.445), (0.53, 0.445)) + ax.add_patch( + FancyArrowPatch( + (0.475, 0.465), + (0.775, 0.465), + arrowstyle="-|>", + mutation_scale=13, + linewidth=1.4, + color=GRAY, + connectionstyle="arc3,rad=-0.28", + ) + ) + + box(ax, (0.105, 0.135), 0.25, 0.105, "Reproducibility\nDo independent fits preserve\nconcept ranks?", face="#f7f7f7", fontsize=8.8) + box(ax, (0.375, 0.135), 0.25, 0.105, "Mechanism selectivity\nWhich physical families\nimprove or fail?", face="#f7f7f7", fontsize=8.8) + box(ax, (0.645, 0.135), 0.25, 0.105, "Blinded interpretation\nCan discovered words identify\nthe physical family?", face="#f7f7f7", fontsize=8.8) + for x in (0.23, 0.5, 0.77): + arrow(ax, (x, 0.37), (x, 0.24)) + save_all(fig, OUT_DIR / "figure1-study-design") + + +def normalize_token(token: str) -> str | None: + value = token.strip().lower() + if len(value) < 3 or not value.isascii() or not value.isalpha(): + return None + return value + + +def record_words(record: dict) -> set[str]: + text = f"{record.get('prompt_text', '')} {record.get('generated_completion', '')}" + return set(re.findall(r"[a-z]+", text.lower())) + + +def filtered_ranked_tokens(record: dict, layer_index: int, scaffold: set[str]) -> list[str]: + forbidden = record_words(record) | FUNCTION_WORDS | scaffold + values = [] + for raw in record["layer_readouts"][layer_index]["top_tokens"]: + token = normalize_token(raw) + if token is not None and token not in forbidden and token not in values: + values.append(token) + return values + + +def family_stream( + indexes: list[dict[str, dict]], family: str, scaffold: set[str], + display_tokens: list[str], +) -> tuple[np.ndarray, list[str], np.ndarray, np.ndarray, dict]: + slugs = [slug for slug, record in indexes[0].items() if record["target_family"] == family] + first = indexes[0][slugs[0]] + depths = np.asarray([row["depth"] for row in first["layer_readouts"]], dtype=float) + scores: dict[str, np.ndarray] = defaultdict(lambda: np.zeros(len(depths), dtype=float)) + prompt_support: dict[str, set[str]] = defaultdict(set) + agreement = np.zeros(len(depths), dtype=float) + + for slug in slugs: + records = [index[slug] for index in indexes] + for li in range(len(depths)): + ranked = [filtered_ranked_tokens(record, li, scaffold) for record in records] + sets = [set(row) for row in ranked] + pairwise = [] + for a, b in ((0, 1), (0, 2), (1, 2)): + union = sets[a] | sets[b] + pairwise.append(len(sets[a] & sets[b]) / len(union) if union else 1.0) + agreement[li] += float(np.mean(pairwise)) / len(slugs) + consensus = sets[0] & sets[1] & sets[2] + for token in consensus: + reciprocal = np.mean([1.0 / (ranked[seed].index(token) + 1) for seed in range(3)]) + scores[token][li] += float(reciprocal) / len(slugs) + prompt_support[token].add(slug) + + band = (depths >= 38.0) & (depths <= 92.0) + integrated = { + token: float(np.trapezoid(values[band], depths[band])) + for token, values in scores.items() + } + selected = [token.lower() for token in display_tokens[:6]] + matrix = np.asarray([scores[token] for token in selected], dtype=float) + meta = { + "family": family, + "slugs": slugs, + "selected_tokens": [ + { + "token": token, + "integrated_prominence": integrated.get(token, 0.0), + "phrasing_support": len(prompt_support.get(token, set())), + } + for token in selected + ], + } + return depths, selected, matrix, agreement, meta + + +def _select_stream_tokens( + depths: np.ndarray, + scores: dict[str, np.ndarray], + *, + limit: int, + band_only: bool, +) -> tuple[list[str], np.ndarray, list[dict]]: + keep = (depths >= 38.0) & (depths <= 92.0) if band_only else np.ones(len(depths), dtype=bool) + integrated = { + token: float(np.trapezoid(values[keep], depths[keep])) + for token, values in scores.items() + if np.any(values[keep] > 0) + } + selected = [token for token, _ in sorted(integrated.items(), key=lambda item: item[1], reverse=True)[:limit]] + first_layer = { + token: next((index for index, value in enumerate(scores[token]) if value > 0), len(depths)) + for token in selected + } + selected.sort(key=lambda token: (first_layer[token], -integrated[token])) + matrix = np.asarray([scores[token] for token in selected], dtype=float) + metadata = [ + { + "token": token, + "integrated_prominence": integrated[token], + "first_nonzero_depth": float(depths[first_layer[token]]), + } + for token in selected + ] + return selected, matrix, metadata + + +def unfiltered_prompt_stream(record: dict) -> tuple[np.ndarray, list[str], np.ndarray, dict]: + """Original-style stream: leading words from one fit, with no lexical filters.""" + depths = np.asarray([row["depth"] for row in record["layer_readouts"]], dtype=float) + width = max(len(row["top_tokens"]) for row in record["layer_readouts"]) + scores: dict[str, np.ndarray] = defaultdict(lambda: np.zeros(len(depths), dtype=float)) + for layer_index, row in enumerate(record["layer_readouts"]): + seen: set[str] = set() + for rank, raw in enumerate(row["top_tokens"]): + token = normalize_token(raw) + if token is None or token in seen: + continue + seen.add(token) + scores[token][layer_index] = max( + scores[token][layer_index], + (width - rank) / width, + ) + selected, matrix, metadata = _select_stream_tokens( + depths, scores, limit=7, band_only=False, + ) + return depths, selected, matrix, { + "protocol": "seed-0 leading decoded words; no input, output, function-word, or scaffold filter", + "selected_tokens": metadata, + } + + +def filtered_prompt_stream( + records: list[dict], scaffold: set[str], +) -> tuple[np.ndarray, list[str], np.ndarray, dict]: + """Strict target-free stream: input/output removal plus three-fit consensus.""" + depths = np.asarray([row["depth"] for row in records[0]["layer_readouts"]], dtype=float) + scores: dict[str, np.ndarray] = defaultdict(lambda: np.zeros(len(depths), dtype=float)) + layer_consensus_sizes: list[int] = [] + for layer_index in range(len(depths)): + ranked = [filtered_ranked_tokens(record, layer_index, scaffold) for record in records] + consensus = set(ranked[0]) & set(ranked[1]) & set(ranked[2]) + layer_consensus_sizes.append(len(consensus)) + for token in consensus: + scores[token][layer_index] = float( + np.mean([1.0 / (values.index(token) + 1) for values in ranked]) + ) + selected, matrix, metadata = _select_stream_tokens( + depths, scores, limit=7, band_only=True, + ) + return depths, selected, matrix, { + "protocol": "input/output, frozen function-word, and global-scaffold filters; intersection across all three fits", + "selected_tokens": metadata, + "consensus_words_per_layer": layer_consensus_sizes, + } + + +def stream_panel( + ax: plt.Axes, + depths: np.ndarray, + tokens: list[str], + matrix: np.ndarray, + title: str = "", + letter: str = "", + *, + show_xlabel: bool = True, + enhanced_contrast: bool = False, +) -> None: + if letter or title: + panel_label(ax, letter, title) + ax.axvspan( + 38, 92, + color="#f2f4f5" if enhanced_contrast else "#eceff3", + alpha=0.46 if enhanced_contrast else 0.6, + zorder=0, + ) + if not tokens or matrix.size == 0 or not np.any(matrix): + ax.text(0.5, 0.5, "No stable target-free words", transform=ax.transAxes, ha="center", va="center") + else: + # Choose label depths independently of ribbon order. The token set and + # ribbon values are unchanged; this only prevents two long labels from + # being printed at the same horizontal position in the paper rendering. + label_indices: dict[int, int] = {} + occupied: list[tuple[float, str]] = [] + strength_order = sorted( + range(len(tokens)), key=lambda index: float(np.max(matrix[index])), reverse=True, + ) + for index in strength_order: + candidates = [ + int(candidate) + for candidate in np.argsort(matrix[index])[::-1] + if matrix[index, candidate] > 0 and 6 <= depths[candidate] <= 94 + ] + if not candidates: + continue + + def clears_existing(candidate: int) -> bool: + x_value = float(depths[candidate]) + return all( + abs(x_value - used_x) >= 4.5 + 0.48 * (len(tokens[index]) + len(used_token)) + for used_x, used_token in occupied + ) + + chosen = next((candidate for candidate in candidates if clears_existing(candidate)), None) + if chosen is None: + # A dense panel may make complete separation impossible. Use + # the strongest position with the largest clearance instead of + # silently dropping or manually selecting a scientific term. + chosen = max( + candidates, + key=lambda candidate: ( + min( + (abs(float(depths[candidate]) - used_x) for used_x, _ in occupied), + default=100.0, + ), + float(matrix[index, candidate]), + ), + ) + label_indices[index] = chosen + occupied.append((float(depths[chosen]), tokens[index])) + + total = matrix.sum(axis=0) + lower = -total / 2.0 + palette = HIGH_CONTRAST_TOKEN_COLORS if enhanced_contrast else TOKEN_COLORS + for index, token in enumerate(tokens): + upper = lower + matrix[index] + ax.fill_between( + depths, lower, upper, + color=palette[index % len(palette)], + alpha=0.97 if enhanced_contrast else 0.93, + linewidth=0.95 if enhanced_contrast else 0.7, + edgecolor="white", + ) + label_index = label_indices.get(index) + if label_index is not None: + label_x = float(depths[label_index]) + ax.text( + label_x, (lower[label_index] + upper[label_index]) / 2.0, token, + ha="center", va="center", fontsize=7.6, + color="#101418" if enhanced_contrast else "#17222A", + path_effects=[path_effects.withStroke( + linewidth=1.8 if enhanced_contrast else 1.35, + foreground="white", + )], + ) + lower = upper + ax.set_xlim(0, 100) + ax.set_xlabel("normalized layer depth" if show_xlabel else "") + ax.set_yticks([]) + ax.grid(axis="x", alpha=0.2) + ax.grid(axis="y", alpha=0.12) + ax.margins(y=0.08) + + +def build_stream_figure( + *, + output_stem: Path | None = None, + enhanced_contrast: bool = False, + write_metadata: bool = True, +) -> None: + runs = [json.loads(path.read_text()) for path in RUN_PATHS] + indexes = [{record["slug"]: record for record in run["prompts"]} for run in runs] + stats = json.loads(STATS_PATH.read_text()) + scaffold = { + row["token"] + for row in stats["open_vocabulary"]["methods"]["jacobian"]["global_scaffold"] + } + paired_cases = [] + fig, axes = plt.subplots( + len(STREAM_CASES), 2, figsize=(13.5, 12.8), sharex=True, + constrained_layout=True, + ) + for row_index, (letter, family_label, slug) in enumerate(STREAM_CASES): + records = [index[slug] for index in indexes] + raw = unfiltered_prompt_stream(records[0]) + filtered = filtered_prompt_stream(records, scaffold) + stream_panel( + axes[row_index, 0], raw[0], raw[1], raw[2], + show_xlabel=row_index == len(STREAM_CASES) - 1, + enhanced_contrast=enhanced_contrast, + ) + stream_panel( + axes[row_index, 1], filtered[0], filtered[1], filtered[2], + show_xlabel=row_index == len(STREAM_CASES) - 1, + enhanced_contrast=enhanced_contrast, + ) + axes[row_index, 0].text( + 0.0, 1.04, f"{letter} {family_label}", + transform=axes[row_index, 0].transAxes, + ha="left", va="bottom", fontsize=10, fontweight="bold", + ) + paired_cases.append({ + "panel": letter, + "family_label": family_label, + "slug": slug, + "prompt": records[0]["prompt_text"], + "generated_completion": records[0].get("generated_completion", ""), + "unfiltered_single_fit": raw[3], + "filtered_three_fit_consensus": filtered[3], + }) + axes[0, 0].text( + 0.5, 1.28, "Unfiltered single fit", transform=axes[0, 0].transAxes, + ha="center", va="bottom", fontsize=11, fontweight="bold", + ) + axes[0, 1].text( + 0.5, 1.28, "Filtered three-fit consensus", transform=axes[0, 1].transAxes, + ha="center", va="bottom", fontsize=11, fontweight="bold", + ) + # Keep the figure-wide ordinate clear of the row-C heading at the left edge. + # bbox_inches="tight" retains the label even with this small negative offset. + fig.supylabel("rank-derived display prominence", x=-0.012, fontsize=10) + save_all(fig, output_stem or OUT_DIR / "figure4-semantic-streams") + + payload = { + "analysis_status": "retrospective exploratory semantic-stream visualization", + "protocol": "experiments/materials-heldout-v1-semantic-stream-protocol.md", + "raw_run_sha256": {path.name: hashlib.sha256(path.read_bytes()).hexdigest() for path in RUN_PATHS}, + "function_word_count": len(FUNCTION_WORDS), + "global_scaffold": sorted(scaffold), + "paired_prompt_cases": paired_cases, + } + if write_metadata: + STREAM_STATS_PATH.write_text(json.dumps(payload, indent=2) + "\n") + + +def build_development_stream_supplement() -> None: + """Clean supplementary rendering of the original coalescence ThemeRiver.""" + run = json.loads((ROOT / "runs" / "gemma4-materials-paper-v2.json").read_text()) + record = next(row for row in run["prompts"] if row["slug"] == "paper-v2-assoc-ductile-02") + depths = np.asarray([row["depth"] for row in record["layer_readouts"]], dtype=float) + width = max(len(row["top_tokens"]) for row in record["layer_readouts"]) + series: dict[str, np.ndarray] = defaultdict(lambda: np.zeros(len(depths), dtype=float)) + for layer_index, row in enumerate(record["layer_readouts"]): + for rank, raw in enumerate(row["top_tokens"]): + token = normalize_token(raw) + if token is not None: + series[token][layer_index] = float(width - rank) + selected = [token for token, _ in sorted(series.items(), key=lambda item: float(np.sum(item[1])), reverse=True)[:12]] + matrix = np.asarray([series[token] for token in selected], dtype=float) + first = lambda token: next((index for index, value in enumerate(series[token]) if value > 0), len(depths)) + selected.sort(key=first) + matrix = np.asarray([series[token] for token in selected], dtype=float) + fig, ax = plt.subplots(figsize=(12.8, 5.0), constrained_layout=True) + stream_panel(ax, depths, selected, matrix, "", "") + save_all(fig, SUP_STREAM_STEM) + + +def trajectory(record: dict, concept: str, method: str) -> tuple[np.ndarray, np.ndarray]: + row = next(item for item in record["concept_trajectories"][method] if item["label"] == concept) + return np.asarray(row["depths"], dtype=float), np.asarray(row["ranks"], dtype=float) + + +def case_payload( + *, + title: str, + slug: str, + run_paths: list[Path], + primary: str, + alternatives: list[str], + selection_status: str, + selection_rationale: str, +) -> tuple[dict, list[dict]]: + runs = [json.loads(path.read_text()) for path in run_paths] + records = [next(record for record in run["prompts"] if record["slug"] == slug) for run in runs] + concepts = [primary, *alternatives] + concept_rows = [] + for concept in concepts: + seeds = [] + for seed, record in enumerate(records): + depths, ranks = trajectory(record, concept, "jacobian_lens") + in_band = (depths >= 38) & (depths <= 92) + band_indexes = np.flatnonzero(in_band) + best_index = int(band_indexes[np.argmin(ranks[in_band])]) + seeds.append( + { + "seed": seed, + "best_rank": int(ranks[best_index]), + "best_depth": float(depths[best_index]), + "depths": depths.tolist(), + "ranks": ranks.astype(int).tolist(), + } + ) + direct_depths, direct_ranks = trajectory(records[0], concept, "logit_lens") + direct_in_band = (direct_depths >= 38) & (direct_depths <= 92) + direct_indexes = np.flatnonzero(direct_in_band) + direct_best_index = int(direct_indexes[np.argmin(direct_ranks[direct_in_band])]) + concept_rows.append( + { + "concept": concept, + "jacobian": seeds, + "direct": { + "best_rank": int(direct_ranks[direct_best_index]), + "best_depth": float(direct_depths[direct_best_index]), + "depths": direct_depths.tolist(), + "ranks": direct_ranks.astype(int).tolist(), + }, + } + ) + + primary_row = concept_rows[0] + peak_depth = primary_row["jacobian"][0]["best_depth"] + peak_tokens = next( + row["top_tokens"] + for row in records[0]["layer_readouts"] + if math.isclose(float(row["depth"]), peak_depth) + ) + payload = { + "title": title, + "slug": slug, + "prompt": records[0]["prompt_text"], + "generated_completion": records[0]["generated_completion"].strip(), + "primary_concept": primary, + "predeclared_concepts": concepts, + "selection_status": selection_status, + "selection_rationale": selection_rationale, + "concepts_fixed_before_execution": True, + "concepts_absent_from_input_and_one_token_continuation": True, + "leading_seed0_tokens_at_primary_peak": peak_tokens[:12], + "concept_results": concept_rows, + "raw_runs": [ + { + "path": str(path.relative_to(ROOT)), + "sha256": hashlib.sha256(path.read_bytes()).hexdigest(), + } + for path in run_paths + ], + } + return payload, records + + +def prompt_card(ax: plt.Axes, label: str, case: dict, accent: str) -> None: + ax.axis("off") + ax.add_patch( + FancyBboxPatch( + (0.005, 0.04), 0.99, 0.90, + boxstyle="round,pad=0.010,rounding_size=0.018", + transform=ax.transAxes, + facecolor="#f7f8fa", + edgecolor=accent, + linewidth=1.2, + ) + ) + ax.text(0.018, 0.82, label, transform=ax.transAxes, fontsize=12, fontweight="bold", va="top") + ax.text(0.050, 0.82, case["title"], transform=ax.transAxes, fontsize=9.8, fontweight="bold", va="top") + ax.text( + 0.985, 0.82, case["selection_status"], transform=ax.transAxes, + fontsize=8.1, fontweight="bold", color=accent, ha="right", va="top", + ) + ax.text( + 0.018, 0.57, textwrap.fill(case["prompt"], width=132), + transform=ax.transAxes, fontsize=8.8, va="top", linespacing=1.08, + ) + declared = " | ".join(case["predeclared_concepts"]) + ax.text( + 0.018, 0.13, + f"Predeclared and absent: {declared} one-token continuation: {case['generated_completion']!r}", + transform=ax.transAxes, fontsize=8.0, color=GRAY, va="bottom", + ) + + +def rank_panels( + ax_traj: plt.Axes, + ax_spec: plt.Axes, + case: dict, + records: list[dict], + trajectory_label: str, + specificity_label: str, + *, + show_legend: bool, +) -> None: + primary = case["primary_concept"] + seed_colors = ["#c4553d", "#2c7f91", "#6f58a6"] + + panel_label(ax_traj, trajectory_label, "") + ax_traj.axvspan(38, 92, color="#eceff3", alpha=0.65, label="registered band") + for seed, record in enumerate(records): + x, y = trajectory(record, primary, "jacobian_lens") + ax_traj.plot( + x, y, marker="o", markersize=3.2, linewidth=1.65, + color=seed_colors[seed], label=f"Jacobian fit {seed}", + ) + x_logit, y_logit = trajectory(records[0], primary, "logit_lens") + ax_traj.plot( + x_logit, y_logit, marker="s", markersize=3.1, linewidth=1.5, + linestyle="--", color="#777777", label="direct unembedding", + ) + peak_depth = case["concept_results"][0]["jacobian"][0]["best_depth"] + ax_traj.axvline(peak_depth, color=J_COLOR, linewidth=0.9, linestyle=":", alpha=0.8) + ax_traj.set_yscale("log") + ax_traj.invert_yaxis() + ax_traj.set_xlim(0, 100) + # Rank 1 is the physical ceiling, but placing it exactly on the axes limit + # clips the stroke. A small amount of logarithmic headroom keeps sustained + # rank-1 traces fully visible without changing the rank scale. + ax_traj.set_ylim(3e5, 0.7) + ax_traj.set_xlabel("normalized layer depth") + ax_traj.set_ylabel(f"rank of {primary!r} (lower is stronger)") + best_ranks = [row["best_rank"] for row in case["concept_results"][0]["jacobian"]] + rank_summary = best_ranks[0] if len(set(best_ranks)) == 1 else f"{min(best_ranks)}-{max(best_ranks)}" + ax_traj.text( + 0.985, 0.975, f"best Jacobian rank: {rank_summary} in all 3 fits", + transform=ax_traj.transAxes, ha="right", va="top", fontsize=8.4, + color=J_COLOR, fontweight="bold", + bbox={"facecolor": "white", "edgecolor": "none", "alpha": 0.86, "pad": 2.2}, + ) + if show_legend: + ax_traj.legend(loc="lower left", frameon=True, framealpha=0.92, ncol=2, fontsize=8.0) + + panel_label(ax_spec, specificity_label, "") + concepts = case["predeclared_concepts"] + y_positions = np.arange(len(concepts)) + for seed, color in enumerate(seed_colors): + ranks = [row["jacobian"][seed]["best_rank"] for row in case["concept_results"]] + ax_spec.scatter( + ranks, y_positions + (seed - 1) * 0.08, s=50, + color=color, edgecolor="white", linewidth=0.5, + ) + direct = [row["direct"]["best_rank"] for row in case["concept_results"]] + ax_spec.scatter( + direct, y_positions, s=60, facecolor="white", edgecolor="#777777", + marker="s", linewidth=1.5, + ) + ax_spec.set_xscale("log") + ax_spec.invert_xaxis() + ax_spec.set_yticks(y_positions, concepts) + ax_spec.invert_yaxis() + ax_spec.set_xlabel("best rank in registered band (right is stronger)") + ax_spec.grid(axis="x", alpha=0.22) + + +def build_layer_resolved_cases_figure() -> None: + dive = json.loads(DEEP_DIVE_PATH.read_text()) + toughness, toughness_records = case_payload( + title="Notch resistance development example", + slug=dive["slug"], + run_paths=DEV_RUN_PATHS, + primary="toughness", + alternatives=["fracture", "crack"], + selection_status="development only - excluded from population inference", + selection_rationale=("Earlier development case retained to make the controlled-rank " + "measurement tangible; it is not one of the 50 held-out prompts."), + ) + corrosion, corrosion_records = case_payload( + title="Boundary attack held-out example", + slug="heldout-v1-assoc-boundary-attack-05", + run_paths=RUN_PATHS, + primary="corrosion", + alternatives=["sensitization", "boundary"], + selection_status="held-out - already counted in Figure 2", + selection_rationale=("Post hoc magnification of the strongest held-out boundary-attack " + "controlled-rank event; it adds no new endpoint or observation."), + ) + + artifact = { + "analysis_status": "post hoc explanatory display; no new inferential endpoint", + "relationship_to_population_analysis": ( + "Figure 2 aggregates all held-out prompt-concept pairs. Figure 7 preserves the " + "layer and lens-fit dimensions for two examples; the held-out example was already " + "counted in Figure 2 and the development example is excluded from its statistics." + ), + "cases": [toughness, corrosion], + } + FIG7_STATS_PATH.write_text(json.dumps(artifact, indent=2) + "\n") + + fig = plt.figure(figsize=(13.7, 10.1)) + gs = fig.add_gridspec( + 4, 2, + width_ratios=[1.65, 1.0], + height_ratios=[0.46, 1.0, 0.46, 1.0], + hspace=0.36, + wspace=0.27, + ) + prompt_card(fig.add_subplot(gs[0, :]), "A", toughness, PURPLE) + rank_panels( + fig.add_subplot(gs[1, 0]), fig.add_subplot(gs[1, 1]), + toughness, toughness_records, "B", "C", show_legend=True, + ) + prompt_card(fig.add_subplot(gs[2, :]), "D", corrosion, J_COLOR) + rank_panels( + fig.add_subplot(gs[3, 0]), fig.add_subplot(gs[3, 1]), + corrosion, corrosion_records, "E", "F", show_legend=False, + ) + save_all(fig, FIG7_STEM) + + +def main() -> None: + configure_style() + build_stream_figure() + build_development_stream_supplement() + build_layer_resolved_cases_figure() + print("wrote Figures 4, 7, and Supplementary Figure S2 to", OUT_DIR) + + +if __name__ == "__main__": + main() diff --git a/jlens_materials/scripts/build_fresh_law_abstraction_confirmation.py b/jlens_materials/scripts/build_fresh_law_abstraction_confirmation.py new file mode 100644 index 0000000000000000000000000000000000000000..f2cfab0717479e958e563ae1b65684651a65eba2 --- /dev/null +++ b/jlens_materials/scripts/build_fresh_law_abstraction_confirmation.py @@ -0,0 +1,431 @@ +#!/usr/bin/env python3 +"""Freeze a disjoint-law confirmation of the scaffolded physics direction. + +No law below occurs in the development benchmark. The primary layer, +position, decoder, and success rule are fixed from the completed development +run before this manifest is executed. +""" + +from __future__ import annotations + +import hashlib +import json +from datetime import datetime, timezone +from pathlib import Path + + +ROOT = Path(__file__).resolve().parents[1] +OUT = ROOT / "experiments" / "fresh-law-abstraction-confirmation-2026-07-18" +MANIFEST = OUT / "prompt_manifest.json" +PROTOCOL = OUT / "protocol.json" +RUNNER = ROOT / "scripts" / "run_elicited_physics_abstraction.py" +DEVELOPMENT = ( + ROOT + / "experiments" + / "elicited-physics-abstraction-2026-07-18" +) + + +def sha256(path: Path) -> str: + return hashlib.sha256(path.read_bytes()).hexdigest() + + +def write_json(path: Path, payload: object) -> None: + path.parent.mkdir(parents=True, exist_ok=True) + tmp = path.with_suffix(path.suffix + ".tmp") + tmp.write_text(json.dumps(payload, indent=2, sort_keys=True) + "\n") + tmp.replace(path) + + +LAWS = [ + { + "law_id": "linear-thermal-expansion", + "law_sign": 1, + "law_name": "linear thermal expansion", + "response": "specimen length L", + "control": "temperature T", + "formula_a": "L = L_ref [1 + alpha (T - T_ref)], with alpha > 0", + "formula_b": "(L - L_ref)/(T - T_ref) = alpha L_ref, with alpha > 0", + "cases": [ + ("an aluminum coupon above its reference temperature", "300 K", "500 K"), + ("a steel gauge above its reference temperature", "350 K", "650 K"), + ], + }, + { + "law_id": "hertz-contact-radius", + "law_sign": 1, + "law_name": "Hertzian contact-radius scaling", + "response": "contact radius a", + "control": "normal load F", + "formula_a": "a = (C F)^(1/3)", + "formula_b": "a^3/F = C", + "cases": [ + ("an elastic ceramic sphere on a flat", "10 N", "80 N"), + ("a steel ball on an elastic half-space", "20 N", "160 N"), + ], + }, + { + "law_id": "buckling-moment", + "law_sign": 1, + "law_name": "Euler buckling at fixed length", + "response": "critical buckling load P_cr", + "control": "second moment of area I", + "formula_a": "P_cr = C I", + "formula_b": "P_cr/I = C", + "cases": [ + ("a fixed-length steel column", "2e-8 m^4", "8e-8 m^4"), + ("a fixed-length composite strut", "1e-8 m^4", "9e-8 m^4"), + ], + }, + { + "law_id": "elastic-wave-modulus", + "law_sign": 1, + "law_name": "longitudinal elastic-wave speed at fixed density", + "response": "wave speed c", + "control": "elastic modulus E", + "formula_a": "c = sqrt(E/rho), with rho fixed", + "formula_b": "c^2/E = 1/rho, with rho fixed", + "cases": [ + ("a family of equal-density cellular solids", "5 GPa", "45 GPa"), + ("a fixed-density model composite", "10 GPa", "90 GPa"), + ], + }, + { + "law_id": "carrier-conductivity", + "law_sign": 1, + "law_name": "Drude conductivity at fixed mobility", + "response": "electrical conductivity sigma_e", + "control": "carrier density n", + "formula_a": "sigma_e = q mu n, with q and mu fixed and positive", + "formula_b": "sigma_e/n = q mu, with q and mu fixed and positive", + "cases": [ + ("a doped semiconductor at fixed mobility", "1e21 m^-3", "1e23 m^-3"), + ("a conducting oxide at fixed mobility", "2e20 m^-3", "2e22 m^-3"), + ], + }, + { + "law_id": "thermal-diffusivity-conductivity", + "law_sign": 1, + "law_name": "thermal diffusivity at fixed heat capacity and density", + "response": "thermal diffusivity alpha_th", + "control": "thermal conductivity k", + "formula_a": "alpha_th = k/(rho c_p), with rho and c_p fixed", + "formula_b": "alpha_th/k = 1/(rho c_p), with rho and c_p fixed", + "cases": [ + ("a fixed-density ceramic family", "2 W m^-1 K^-1", "18 W m^-1 K^-1"), + ("a fixed-density polymer composite", "0.2 W m^-1 K^-1", "1.8 W m^-1 K^-1"), + ], + }, + { + "law_id": "energy-release-stress", + "law_sign": 1, + "law_name": "elastic energy-release rate at fixed crack length", + "response": "energy-release rate G", + "control": "remote stress sigma", + "formula_a": "G = C sigma^2", + "formula_b": "sqrt(G)/sigma = sqrt(C)", + "cases": [ + ("a cracked glass plate at fixed geometry", "10 MPa", "40 MPa"), + ("a cracked polymer sheet at fixed geometry", "5 MPa", "25 MPa"), + ], + }, + { + "law_id": "archard-sliding-distance", + "law_sign": 1, + "law_name": "Archard wear at fixed load and hardness", + "response": "wear volume V", + "control": "sliding distance s", + "formula_a": "V = K W s/H, with K, W, and H fixed and positive", + "formula_b": "V/s = K W/H, with K, W, and H fixed and positive", + "cases": [ + ("a pin-on-disk steel contact", "10 m", "100 m"), + ("a ceramic sliding contact", "20 m", "200 m"), + ], + }, + { + "law_id": "thermal-resistance-conductivity", + "law_sign": -1, + "law_name": "steady one-dimensional thermal resistance", + "response": "thermal resistance R_th", + "control": "thermal conductivity k", + "formula_a": "R_th = L/(k A), with L and A fixed", + "formula_b": "R_th k = L/A, with L and A fixed", + "cases": [ + ("a fixed-thickness insulation panel", "0.02 W m^-1 K^-1", "0.2 W m^-1 K^-1"), + ("a fixed ceramic layer", "1 W m^-1 K^-1", "10 W m^-1 K^-1"), + ], + }, + { + "law_id": "buckling-length", + "law_sign": -1, + "law_name": "Euler buckling versus member length", + "response": "critical buckling load P_cr", + "control": "column length L", + "formula_a": "P_cr = C/L^2", + "formula_b": "P_cr L^2 = C", + "cases": [ + ("a steel column of fixed cross-section", "0.5 m", "2 m"), + ("a composite strut of fixed cross-section", "0.4 m", "1.6 m"), + ], + }, + { + "law_id": "stokes-einstein-radius", + "law_sign": -1, + "law_name": "Stokes-Einstein particle diffusion", + "response": "particle diffusivity D_p", + "control": "particle radius r", + "formula_a": "D_p = C/r", + "formula_b": "D_p r = C", + "cases": [ + ("spherical nanoparticles in a fixed solvent", "2 nm", "20 nm"), + ("colloidal spheres at fixed temperature and viscosity", "50 nm", "500 nm"), + ], + }, + { + "law_id": "resistivity-conductivity", + "law_sign": -1, + "law_name": "electrical resistivity-conductivity reciprocity", + "response": "electrical resistivity rho_e", + "control": "electrical conductivity sigma_e", + "formula_a": "rho_e = 1/sigma_e", + "formula_b": "rho_e sigma_e = 1", + "cases": [ + ("an isotropic conductor", "1e5 S m^-1", "1e7 S m^-1"), + ("a conducting composite", "1e2 S m^-1", "1e4 S m^-1"), + ], + }, + { + "law_id": "membrane-flux-thickness", + "law_sign": -1, + "law_name": "steady diffusion through a membrane", + "response": "diffusive flux magnitude J", + "control": "membrane thickness L", + "formula_a": "J = D Delta_c/L, with D and Delta_c fixed and positive", + "formula_b": "J L = D Delta_c, with D and Delta_c fixed and positive", + "cases": [ + ("hydrogen transport through a metal membrane", "0.1 mm", "1 mm"), + ("solute transport through a polymer film", "10 micrometers", "100 micrometers"), + ], + }, + { + "law_id": "acoustic-density", + "law_sign": -1, + "law_name": "acoustic speed at fixed bulk modulus", + "response": "acoustic wave speed c", + "control": "mass density rho", + "formula_a": "c = sqrt(K/rho), with K fixed", + "formula_b": "c^2 rho = K, with K fixed", + "cases": [ + ("a model medium at fixed bulk modulus", "500 kg m^-3", "2000 kg m^-3"), + ("a porous-medium series at fixed effective bulk modulus", "300 kg m^-3", "1200 kg m^-3"), + ], + }, + { + "law_id": "beam-deflection-moment", + "law_sign": -1, + "law_name": "elastic beam deflection versus section moment", + "response": "tip deflection delta", + "control": "second moment of area I", + "formula_a": "delta = C/I", + "formula_b": "delta I = C", + "cases": [ + ("a cantilever at fixed load, length, and modulus", "1e-9 m^4", "1e-7 m^4"), + ("a fixed-span beam at fixed load and modulus", "2e-8 m^4", "2e-6 m^4"), + ], + }, + { + "law_id": "indentation-diagonal", + "law_sign": -1, + "law_name": "Vickers hardness relation at fixed load", + "response": "measured hardness H_V", + "control": "indent diagonal d", + "formula_a": "H_V = C/d^2", + "formula_b": "H_V d^2 = C", + "cases": [ + ("a Vickers indentation at fixed applied load", "20 micrometers", "80 micrometers"), + ("a microhardness test at fixed applied load", "10 micrometers", "50 micrometers"), + ], + }, +] + + +def make_prompt( + law: dict, + surface: str, + case_index: int, + numerical_sign: int, + answer_order: str, +) -> dict: + system, low, high = law["cases"][case_index] + start, end = (low, high) if numerical_sign == 1 else (high, low) + physical_sign = int(law["law_sign"]) * numerical_sign + expected = "higher" if physical_sign == 1 else "lower" + ordered_words = ( + ("higher", "lower") + if answer_order == "higher-first" + else ("lower", "higher") + ) + prompt_id = ( + f"{law['law_id']}--{surface}--case{case_index + 1}--" + f"{'up' if numerical_sign == 1 else 'down'}--{answer_order}" + ) + user = ( + "Apply the same two-stage physical test to every case. " + "Stage 1: use only the constitutive equation, not associations with " + "material names, to determine the monotonic sign of the response with " + "respect to the changed control while all other quantities are fixed. " + f"For {law['law_name']}, the response is {law['response']}, the " + f"control is {law['control']}, and the relation is " + f"{law[f'formula_{surface}']}. All stated coefficients are positive. " + "Complete this relation-sign step silently. § " + f"Stage 2: in {system}, {law['control']} changes from {start} to {end}. " + "Determine the sign of this numerical change silently. ¶ " + "Now compose the relation sign with the numerical-change sign to " + f"determine the direction of {law['response']}. † " + "Reply with exactly one lowercase word from this ordered pair: " + f"{ordered_words[0]}, {ordered_words[1]}." + ) + return { + "prompt_id": prompt_id, + "law_id": law["law_id"], + "law_name": law["law_name"], + "split": "fresh-confirmation", + "law_sign": int(law["law_sign"]), + "surface": surface, + "case_index": case_index, + "system": system, + "control": law["control"], + "response": law["response"], + "numeric_start": start, + "numeric_end": end, + "numerical_sign": numerical_sign, + "physical_sign": physical_sign, + "answer_order": answer_order, + "presented_words": list(ordered_words), + "expected_answer": expected, + "user": user, + } + + +def main() -> None: + if (OUT / "raw.json").exists(): + raise RuntimeError("refusing to rebuild after fresh outputs exist") + prompts = [ + make_prompt(law, surface, case_index, numerical_sign, answer_order) + for law in LAWS + for surface in ("a", "b") + for case_index in range(2) + for numerical_sign in (-1, 1) + for answer_order in ("higher-first", "lower-first") + ] + manifest = { + "study_id": "fresh-law-abstraction-confirmation-2026-07-18", + "status": "fresh-law-confirmation", + "markers": {"law": "§", "comparison": "¶", "decision": "†"}, + "laws": LAWS, + "prompts": prompts, + "dimensions": { + "n_fresh_laws": len(LAWS), + "n_direct_laws": sum(x["law_sign"] == 1 for x in LAWS), + "n_inverse_laws": sum(x["law_sign"] == -1 for x in LAWS), + "n_prompts": len(prompts), + }, + } + write_json(MANIFEST, manifest) + protocol = { + "study_id": manifest["study_id"], + "status": "frozen-before-any-fresh-model-output", + "frozen_at": datetime.now(timezone.utc).isoformat(), + "model": "google/gemma-4-E4B-it", + "model_revision": "a4c2d58be94dda072b918d9db64ee85c8ed34e3f", + "freshness": ( + "All 16 constitutive relations are absent from the preceding " + "abstract-physics composition and elicitation cohorts. No fresh " + "model output was inspected before this protocol was written." + ), + "inputs": { + "prompt_manifest": { + "path": str(MANIFEST.relative_to(ROOT)), + "sha256": sha256(MANIFEST), + }, + "runner": { + "path": str(RUNNER.relative_to(ROOT)), + "sha256": sha256(RUNNER), + }, + "development_manifest": { + "path": str( + (DEVELOPMENT / "prompt_manifest.json").relative_to(ROOT) + ), + "sha256": sha256(DEVELOPMENT / "prompt_manifest.json"), + }, + "development_states": { + "path": str((DEVELOPMENT / "representations.npz").relative_to(ROOT)), + "sha256": sha256(DEVELOPMENT / "representations.npz"), + }, + }, + "positions": ["law", "comparison", "decision", "final_prompt"], + "primary_decoder": { + "position": "final_prompt", + "layer": 34, + "normalization": "unit L2 norm per state", + "fit_data": "all 16 completed development laws", + "fit_method": ( + "parameter-free physical-sign centroid difference; midpoint is " + "the mean of the positive and negative centroids" + ), + "fresh_refit": "none", + }, + "primary_endpoint": ( + "Mean of 16 fresh-law ROC AUC values for physical outcome. Each " + "law AUC pools two algebraic surfaces, two material cases, two " + "numerical directions, and two answer-word orders." + ), + "success_rule": { + "mean_fresh_law_auc_minimum": 0.75, + "bootstrap_95_lower_above": 0.50, + "positive_laws_minimum": 13, + "laws_total": 16, + "behavior_accuracy_minimum": 0.80, + "both_surface_mean_auc_minimum": 0.70, + "both_answer_order_mean_auc_minimum": 0.70, + }, + "secondary_frozen_endpoints": [ + "Per-law AUC separately for each algebraic surface.", + "Per-law AUC separately for each answer-word order.", + "Direct-law and inverse-law means.", + "Behavioral accuracy by law, surface, and answer order.", + "Word- and character-TF-IDF controls trained only on development text.", + "Numerical-direction AUC of the frozen physical direction.", + ], + "inference": { + "bootstrap_seed": 20260721, + "bootstrap_resamples": 50000, + "exact_test": "two-sided sign-flip test of per-law AUC minus 0.5", + }, + "guardrails": [ + "No layer, position, decoder, or prompt suffix is selected on fresh laws.", + "The physical label is the XOR/product of law sign and numerical sign.", + "Direct and inverse laws are balanced.", + "Answer-word order is counterbalanced within every law and case.", + "Two algebraically equivalent formula surfaces are tested per law.", + "The decoder is fitted only on completed old-law data.", + "All prompts and outputs are retained, including failures.", + ], + } + write_json(PROTOCOL, protocol) + (OUT / "PROTOCOL.md").write_text( + "# Fresh-law confirmation of an elicited physical-outcome direction\n\n" + "This is the first confirmatory test of the scaffolded method. A " + "parameter-free direction and layer 34 were fixed using the prior 16 " + "laws. The direction is applied without refitting to 16 new laws. " + "Within every new law, numerical increases and decreases reverse the " + "physical outcome; across direct and inverse laws, the same numerical " + "change also reverses its physical meaning. Answer order and algebraic " + "surface are crossed as nuisance variables.\n" + ) + print(f"frozen {len(prompts)} prompts over {len(LAWS)} fresh laws") + print(f"protocol sha256: {sha256(PROTOCOL)}") + + +if __name__ == "__main__": + main() diff --git a/jlens_materials/scripts/build_gemma31b_query_gated_physics.py b/jlens_materials/scripts/build_gemma31b_query_gated_physics.py new file mode 100644 index 0000000000000000000000000000000000000000..ce1bdfe5f83417cf04b428517c0515db9785be3e --- /dev/null +++ b/jlens_materials/scripts/build_gemma31b_query_gated_physics.py @@ -0,0 +1,158 @@ +#!/usr/bin/env python3 +"""Build a frozen query-gating experiment from the two-link chain inventory.""" + +from __future__ import annotations + +import hashlib +import json +from pathlib import Path + +from build_gemma31b_compositional_physics_pilot import CHAINS, MODEL, MODEL_REVISION + + +ROOT = Path(__file__).resolve().parents[1] +OUT = ROOT / "experiments" / "gemma4-31b-query-gated-physics-2026-08-25" +LAYERS = [32, 40, 45, 49, 53, 57, 59] +MARKERS = {"context": "§", "perturbation": "¶", "question": "†"} + +INTERMEDIATES = { + "underaged-precipitation-strength": "coherent precipitate fraction", + "strain-hardening": "dislocation density", + "carburization-hardness": "near-surface carbon content", + "oxidation-resistance": "oxide-scale thickness", + "sinter-neck-stiffness": "interparticle neck size", + "polymer-cure-modulus": "crosslink density", + "grain-growth-hall-petch": "grain size", + "fatigue-crack-load": "dominant crack length", + "creep-void-life": "grain-boundary void population", + "irradiation-thermal-conductivity": "retained defect density", + "corrosion-pit-fatigue": "depth of the dominant pit", + "hydrogen-cohesion": "grain-boundary hydrogen occupancy", + "recovery-dislocation-strength": "stored dislocation density", + "viscosity-pressure-drop": "melt viscosity", + "stress-relief-distortion": "residual stress", + "particle-size-dissolution": "specific surface area", + "grain-size-boundary-diffusion": "grain-boundary area density", + "pressure-free-volume-diffusion": "free volume", + "cooling-grain-strength": "final grain size", + "compaction-porosity-modulus": "residual porosity", + "grain-refiner-fatigue": "final grain size", + "temperature-viscosity-settling": "liquid viscosity", + "anneal-defects-conductivity": "retained defect density", + "crosslink-mobility-modulus": "segmental mobility", +} + + +def sha256(path: Path) -> str: + return hashlib.sha256(path.read_bytes()).hexdigest() + + +def main() -> None: + OUT.mkdir(parents=True, exist_ok=True) + prompts = [] + for chain_index, item in enumerate(CHAINS): + for numerical_sign in (-1, 1): + start, end = (item["low"], item["high"]) if numerical_sign > 0 else (item["high"], item["low"]) + intermediate_sign = item["link1"] * numerical_sign + target_sign = item["link1"] * item["link2"] * numerical_sign + prefix = ( + f"{item['context_a']} All unstated conditions remain fixed. {MARKERS['context']} " + f"The {item['control']} changes from {start} to {end}. {MARKERS['perturbation']} " + ) + questions = { + "target": (f"What happens to the {item['response']}?", target_sign), + "intermediate": (f"What happens to the {INTERMEDIATES[item['chain_id']]}?", intermediate_sign), + "invariant": ("What happens to the total specimen mass?", 0), + } + for question_type, (question, query_sign) in questions.items(): + order_flip = (chain_index + (numerical_sign > 0)) % 2 == 1 + if query_sign == 0: + suffix = " Reply with exactly one lowercase word from this ordered set: higher, lower, unchanged." + expected = "unchanged" + else: + order = ["lower", "higher"] if order_flip else ["higher", "lower"] + suffix = f" Reply with exactly one lowercase word from this ordered pair: {order[0]}, {order[1]}." + expected = "higher" if query_sign > 0 else "lower" + prompt_id = f"querygate--{item['chain_id']}--n{numerical_sign:+d}--{question_type}" + prompts.append( + { + "prompt_id": prompt_id, + "chain_id": item["chain_id"], + "split": item["split"], + "domain": item["domain"], + "question_type": question_type, + "link1_sign": item["link1"], + "link2_sign": item["link2"], + "net_law_sign": item["link1"] * item["link2"], + "numerical_sign": numerical_sign, + "intermediate_sign": intermediate_sign, + "target_sign": target_sign, + "query_sign": query_sign, + "expected_answer": expected, + "user": prefix + question + f" {MARKERS['question']}" + suffix, + } + ) + manifest = { + "study_id": "gemma4-31b-query-gated-physics-2026-08-25", + "markers": MARKERS, + "dimensions": { + "chains": len(CHAINS), + "development_chains": 12, + "test_chains": 12, + "numerical_directions": 2, + "question_types": 3, + "prompts": len(prompts), + }, + "prompts": prompts, + } + manifest_path = OUT / "prompt_manifest.json" + manifest_path.write_text(json.dumps(manifest, indent=2) + "\n") + runner = ROOT / "scripts" / "run_gemma31b_query_gated_physics.py" + analyzer = ROOT / "scripts" / "analyze_gemma31b_query_gated_physics.py" + protocol = { + "study_id": manifest["study_id"], + "status": "frozen-before-model-output", + "model": MODEL, + "model_revision": MODEL_REVISION, + "dtype": "bfloat16", + "layers_zero_indexed": LAYERS, + "positions": ["perturbation", "question", "final_prompt"], + "primary_endpoint": ( + "held-out AUC for the sign of the variable actually queried, pooling target and " + "intermediate questions at the answer-free question marker and layer 49" + ), + "secondary_endpoints": [ + "target-only and intermediate-only held-out AUC", + "same-versus-opposite query-switch consistency as determined by link-2 sign", + "failure of target-outcome decoding at the causally earlier perturbation marker", + "neutrality of invariant-mass questions under the query-outcome direction", + "layerwise emergence and pairwise answer-logit accuracy", + ], + "success_criteria": { + "pooled_query_outcome_auc_minimum": 0.80, + "target_and_intermediate_auc_minimum": 0.70, + "query_switch_consistency_minimum": 0.75, + }, + "guardrails": [ + "Every target, intermediate, and invariant triplet has an identical prefix through the perturbation marker.", + "The question marker precedes the answer vocabulary.", + "Development and test chain identities are disjoint.", + "The experiment changes only which node in the same physical chain is queried.", + ], + "inputs": { + "manifest": {"path": str(manifest_path.relative_to(ROOT)), "sha256": sha256(manifest_path)}, + "builder": {"path": str(Path(__file__).resolve().relative_to(ROOT)), "sha256": sha256(Path(__file__).resolve())}, + "runner": {"path": str(runner.relative_to(ROOT)), "sha256": sha256(runner)}, + "analyzer": {"path": str(analyzer.relative_to(ROOT)), "sha256": sha256(analyzer)}, + }, + } + (OUT / "protocol.json").write_text(json.dumps(protocol, indent=2) + "\n") + lines = ["# Exact query-gating prompts", ""] + for row in prompts: + lines.extend([f"## `{row['prompt_id']}`", "", row["user"], ""]) + (OUT / "PROMPTS.md").write_text("\n".join(lines)) + print(json.dumps(manifest["dimensions"], indent=2)) + + +if __name__ == "__main__": + main() diff --git a/jlens_materials/scripts/build_heldout_prompt_inventory_tex.py b/jlens_materials/scripts/build_heldout_prompt_inventory_tex.py new file mode 100644 index 0000000000000000000000000000000000000000..6422672fab39ffa5bbaae58ac655a447ed27998a --- /dev/null +++ b/jlens_materials/scripts/build_heldout_prompt_inventory_tex.py @@ -0,0 +1,142 @@ +#!/usr/bin/env python3 +"""Build the complete LaTeX prompt-and-result inventory for the paper SI.""" + +from __future__ import annotations + +import csv +import json +from collections import defaultdict +from pathlib import Path + + +ROOT = Path(__file__).resolve().parents[1] +MANIFEST = ROOT / "prompts" / "materials-heldout-v1-preregistered.json" +PROMPT_CSV = ROOT / "experiments" / "materials-heldout-v1_prompt_results.csv" +CONCEPT_CSV = ROOT / "experiments" / "materials-heldout-v1_concept_results.csv" +CANDIDATE_CSV = ROOT / "experiments" / "materials-heldout-v1_open_vocabulary_candidates.csv" +OUTPUT = ROOT / "paper" / "heldout_prompt_inventory.tex" + +FAMILY_TITLES = { + "ductile": "Energy-absorbing dimpled separation", + "boundary-attack": "Chromium-depleted interfacial attack", + "cyclic": "Progressive damage under repeated loading", + "cleavage": "Low-temperature cleavage", + "high-temperature-deformation": "Slow deformation at elevated temperature", + "particle-strengthening": "Hard-particle strengthening", + "rapid-transformation": "Rapid diffusionless transformation", + "line-defect-motion": "Line-defect motion", + "notch-resistance": "Resistance to unstable notch growth", + "hot-air-surface-layer": "High-temperature surface oxidation", +} + + +def rows(path: Path) -> list[dict[str, str]]: + with path.open(newline="") as handle: + return list(csv.DictReader(handle)) + + +def tex(value: object) -> str: + text = str(value) + replacements = { + "\\": r"\textbackslash{}", + "&": r"\&", + "%": r"\%", + "$": r"\$", + "#": r"\#", + "_": r"\_", + "{": r"\{", + "}": r"\}", + "~": r"\textasciitilde{}", + "^": r"\textasciicircum{}", + } + return "".join(replacements.get(char, char) for char in text) + + +def code(value: object) -> str: + return r"\texttt{" + tex(value) + "}" + + +def format_candidates(items: list[dict[str, str]]) -> str: + top = sorted(items, key=lambda row: int(row["candidate_rank"]))[:5] + return ", ".join( + f"{code(row['candidate'])} ({float(row['consensus_score']):.1f})" + for row in top + ) + + +def main() -> None: + manifest = json.loads(MANIFEST.read_text()) + prompt_results = {row["slug"]: row for row in rows(PROMPT_CSV)} + concept_results: dict[str, list[dict[str, str]]] = defaultdict(list) + for row in rows(CONCEPT_CSV): + concept_results[row["slug"]].append(row) + candidates: dict[tuple[str, str], list[dict[str, str]]] = defaultdict(list) + for row in rows(CANDIDATE_CSV): + candidates[(row["slug"], row["method"])].append(row) + + prompts_by_family: dict[str, list[dict]] = defaultdict(list) + family_order: list[str] = [] + for prompt in manifest["prompts"]: + family = prompt["target_family"] + if family not in prompts_by_family: + family_order.append(family) + prompts_by_family[family].append(prompt) + + out: list[str] = [] + running = 1 + for family in family_order: + out.append(r"\section{" + tex(FAMILY_TITLES[family]) + "}") + out.append("") + for prompt in prompts_by_family[family]: + slug = prompt["slug"] + result = prompt_results[slug] + concepts = sorted(concept_results[slug], key=lambda row: prompt["tracked"].index(row["concept"])) + dropped = result["dropped_concepts"] or "none" + continuation = result["generated_completion"].strip() or "" + out.extend( + [ + rf"\subsection*{{S{running}. {code(slug)}}}", + r"\noindent\textbf{Exact prompt.} \emph{" + tex(prompt["text"]) + "}", + "", + r"\noindent\textbf{Declared before execution:} " + + ", ".join(code(term) for term in prompt["tracked"]) + + r". \textbf{Tokenizer-dropped:} " + tex(dropped) + ".", + "", + r"\noindent\textbf{Leakage check and prompt endpoint:} one-token continuation " + + code(continuation) + + rf"; Jacobian AUC {float(result['j_auc_mean_seed']):.4f}; direct AUC {float(result['logit_auc']):.4f}; difference {float(result['delta_auc_mean_seed']):+.4f}.", + "", + r"\begin{center}", + r"\small", + r"\begin{tabular}{lrrrr}", + r"\toprule", + r"Concept & J seed 0 & J seed 1 & J seed 2 & Direct \\", + r"\midrule", + ] + ) + for row in concepts: + out.append( + f"{code(row['concept'])} & {int(row['j_rank_seed0']):,} & {int(row['j_rank_seed1']):,} & {int(row['j_rank_seed2']):,} & {int(row['logit_rank']):,} \\\\" + ) + out.extend( + [ + r"\bottomrule", + r"\end{tabular}", + r"\end{center}", + r"\noindent\textbf{Leading target-free Jacobian candidates (consensus score):} " + + format_candidates(candidates[(slug, "jacobian")]) + + ".", + "", + r"\noindent\textbf{Leading target-free direct candidates (consensus score):} " + + format_candidates(candidates[(slug, "logit")]) + + ".", + "", + ] + ) + running += 1 + OUTPUT.write_text("\n".join(out) + "\n") + print(f"wrote {OUTPUT} with {running - 1} prompts") + + +if __name__ == "__main__": + main() diff --git a/jlens_materials/scripts/build_primary_prompt_appendix.py b/jlens_materials/scripts/build_primary_prompt_appendix.py new file mode 100644 index 0000000000000000000000000000000000000000..0e562940d58f05f7d43f68ade89990033725c6fa --- /dev/null +++ b/jlens_materials/scripts/build_primary_prompt_appendix.py @@ -0,0 +1,97 @@ +#!/usr/bin/env python3 +"""Build the paper's complete 50-prompt association inventory from the frozen manifest.""" + +from __future__ import annotations + +import json +from collections import Counter +from pathlib import Path + + +ROOT = Path(__file__).resolve().parents[1] +MANIFEST = ROOT / "prompts" / "materials-paper-v2-preregistered.json" +OUTPUT = ROOT / "paper" / "primary_prompt_inventory.tex" + +FAMILY_LABELS = { + "ductile": ("D", "Ductile dimpled failure"), + "boundary-attack": ("BA", "Boundary-localized stainless attack"), + "cyclic": ("CY", "Cyclic loading"), + "cleavage": ("CL", "Low-temperature cleavage"), + "high-temperature-deformation": ("HT", "High-temperature time-dependent deformation"), + "particle-strengthening": ("PS", "Hard-particle strengthening"), + "rapid-transformation": ("RT", "Rapid diffusionless transformation"), + "line-defect-motion": ("LD", "Line-defect motion"), + "notch-resistance": ("NR", "Notch resistance"), + "hot-air-surface-layer": ("HA", "High-temperature surface reaction"), +} + +TEX_ESCAPES = { + "&": r"\&", + "%": r"\%", + "$": r"\$", + "#": r"\#", + "_": r"\_", + "{": r"\{", + "}": r"\}", + "~": r"\textasciitilde{}", + "^": r"\textasciicircum{}", + "\\": r"\textbackslash{}", +} + + +def tex_escape(value: str) -> str: + return "".join(TEX_ESCAPES.get(character, character) for character in value) + + +def main() -> None: + manifest = json.loads(MANIFEST.read_text()) + prompts = [prompt for prompt in manifest["prompts"] if prompt.get("shape") == "ASSOCIATION"] + counts = Counter(prompt["category"] for prompt in prompts) + if len(prompts) != 50 or set(counts) != set(FAMILY_LABELS) or any(count != 5 for count in counts.values()): + raise ValueError(f"Expected ten five-item families and 50 prompts; found {dict(counts)}") + + lines = [ + "% Generated from prompts/materials-paper-v2-preregistered.json.", + "% Regenerate with: python scripts/build_primary_prompt_appendix.py", + r"\setlength{\tabcolsep}{4pt}", + r"\begin{longtable}{>{\raggedright\arraybackslash}p{0.055\textwidth}>{\raggedright\arraybackslash}p{0.67\textwidth}>{\raggedright\arraybackslash}p{0.205\textwidth}}", + r"\caption{Complete primary association dataset: all 50 exact prompts and their predeclared candidate concepts. Each family contains five independent phrasings of the same physical mechanism. Asterisks mark declared multi-token labels retained in the manifest but dropped from direct single-token ranking.}\label{tab:all-primary-prompts}\\", + r"\toprule", + r"ID & Exact model input & Concepts searched for in the lens readout \\", + r"\midrule", + r"\endfirsthead", + r"\multicolumn{3}{l}{\small\itshape Table \thetable\ continued from the previous page}\\", + r"\toprule", + r"ID & Exact model input & Concepts searched for in the lens readout \\", + r"\midrule", + r"\endhead", + r"\midrule", + r"\multicolumn{3}{r}{\small\itshape Continued on the next page}\\", + r"\endfoot", + r"\bottomrule", + r"\endlastfoot", + ] + + grouped = {family: [] for family in FAMILY_LABELS} + for prompt in prompts: + grouped[prompt["category"]].append(prompt) + + for family, (prefix, label) in FAMILY_LABELS.items(): + lines.append(rf"\multicolumn{{3}}{{l}}{{\textbf{{{tex_escape(label)}}} (five independent phrasings)}}\\*") + for index, prompt in enumerate(grouped[family], start=1): + concepts = [] + for concept in prompt["tracked"]: + marker = r"$^{*}$" if concept in {"martensite", "transgranular"} else "" + concepts.append(rf"\texttt{{{tex_escape(concept)}}}{marker}") + lines.append( + rf"{prefix}{index} & {tex_escape(prompt['text'])} & {', '.join(concepts)} \\" + ) + lines.append(r"\addlinespace[0.45em]") + + lines.extend([r"\end{longtable}", ""]) + OUTPUT.write_text("\n".join(lines)) + print(f"wrote {OUTPUT}") + + +if __name__ == "__main__": + main() diff --git a/jlens_materials/scripts/build_relational_contrast_confirmation.py b/jlens_materials/scripts/build_relational_contrast_confirmation.py new file mode 100644 index 0000000000000000000000000000000000000000..c5bff729e326de58850afbd152344048f4dcf44c --- /dev/null +++ b/jlens_materials/scripts/build_relational_contrast_confirmation.py @@ -0,0 +1,314 @@ +#!/usr/bin/env python3 +"""Freeze a second fresh-law test of relational physical contrasts.""" + +from __future__ import annotations + +import hashlib +import json +from datetime import datetime, timezone +from pathlib import Path + + +ROOT = Path(__file__).resolve().parents[1] +OUT = ROOT / "experiments" / "relational-contrast-confirmation-2026-07-18" +MANIFEST = OUT / "prompt_manifest.json" +PROTOCOL = OUT / "protocol.json" +RUNNER = ROOT / "scripts" / "run_elicited_physics_abstraction.py" +DEVELOPMENT = ROOT / "experiments" / "elicited-physics-abstraction-2026-07-18" + + +def sha256(path: Path) -> str: + return hashlib.sha256(path.read_bytes()).hexdigest() + + +def write_json(path: Path, payload: object) -> None: + path.parent.mkdir(parents=True, exist_ok=True) + tmp = path.with_suffix(path.suffix + ".tmp") + tmp.write_text(json.dumps(payload, indent=2, sort_keys=True) + "\n") + tmp.replace(path) + + +LAWS = [ + { + "law_id": "hooke-strain-stress", + "law_sign": 1, + "law_name": "uniaxial Hooke response at fixed modulus", + "response": "axial strain epsilon", + "control": "axial stress sigma", + "formula_a": "epsilon = sigma/E, with E fixed and positive", + "formula_b": "epsilon E = sigma, with E fixed and positive", + "cases": [ + ("a steel coupon in its elastic regime", "20 MPa", "200 MPa"), + ("an elastic polymer specimen", "1 MPa", "10 MPa"), + ], + }, + { + "law_id": "torsion-angle-torque", + "law_sign": 1, + "law_name": "elastic torsion at fixed geometry and modulus", + "response": "twist angle theta", + "control": "applied torque T_q", + "formula_a": "theta = T_q L/(G J), with L, G, and J fixed", + "formula_b": "theta/T_q = L/(G J), with L, G, and J fixed", + "cases": [ + ("a circular steel shaft", "10 N m", "100 N m"), + ("a composite tube in elastic torsion", "5 N m", "50 N m"), + ], + }, + { + "law_id": "arrhenius-diffusivity-temperature", + "law_sign": 1, + "law_name": "Arrhenius diffusivity versus temperature", + "response": "diffusivity D", + "control": "absolute temperature T", + "formula_a": "D = D_0 exp[-Q/(R T)], with Q and R positive", + "formula_b": "ln(D/D_0) = -Q/(R T), with Q and R positive", + "cases": [ + ("carbon diffusion in austenite", "700 K", "1100 K"), + ("vacancy diffusion in a crystal", "500 K", "900 K"), + ], + }, + { + "law_id": "radiative-power-temperature", + "law_sign": 1, + "law_name": "Stefan-Boltzmann emission at fixed area and emissivity", + "response": "radiated power P_rad", + "control": "absolute temperature T", + "formula_a": "P_rad = C T^4", + "formula_b": "P_rad/T^4 = C", + "cases": [ + ("a ceramic surface at fixed emissivity", "400 K", "800 K"), + ("an oxidized metal surface at fixed area", "500 K", "1000 K"), + ], + }, + { + "law_id": "capacitance-permittivity", + "law_sign": 1, + "law_name": "parallel-plate capacitance at fixed geometry", + "response": "capacitance C_p", + "control": "dielectric permittivity epsilon_d", + "formula_a": "C_p = epsilon_d A/d, with A and d fixed", + "formula_b": "C_p/epsilon_d = A/d, with A and d fixed", + "cases": [ + ("a ceramic dielectric capacitor", "2e-11 F m^-1", "2e-10 F m^-1"), + ("a polymer dielectric stack", "3e-11 F m^-1", "9e-11 F m^-1"), + ], + }, + { + "law_id": "magnetic-induction-field", + "law_sign": 1, + "law_name": "linear magnetic induction at fixed permeability", + "response": "magnetic induction B", + "control": "magnetic field H", + "formula_a": "B = mu H, with mu fixed and positive", + "formula_b": "B/H = mu, with mu fixed and positive", + "cases": [ + ("a linear magnetic alloy below saturation", "100 A m^-1", "1000 A m^-1"), + ("a paramagnetic solid in its linear regime", "50 A m^-1", "500 A m^-1"), + ], + }, + { + "law_id": "axial-stiffness-length", + "law_sign": -1, + "law_name": "axial spring stiffness versus member length", + "response": "axial stiffness k_ax", + "control": "member length L", + "formula_a": "k_ax = E A/L, with E and A fixed", + "formula_b": "k_ax L = E A, with E and A fixed", + "cases": [ + ("a steel tie of fixed cross-section", "0.5 m", "2 m"), + ("a polymer fiber of fixed cross-section", "10 mm", "100 mm"), + ], + }, + { + "law_id": "torsion-angle-modulus", + "law_sign": -1, + "law_name": "elastic twist versus shear modulus", + "response": "twist angle theta", + "control": "shear modulus G", + "formula_a": "theta = C/G", + "formula_b": "theta G = C", + "cases": [ + ("a shaft at fixed torque and geometry", "2 GPa", "80 GPa"), + ("a composite tube at fixed torque and geometry", "5 GPa", "50 GPa"), + ], + }, + { + "law_id": "arrhenius-time-temperature", + "law_sign": -1, + "law_name": "Arrhenius process time at fixed transformation extent", + "response": "required process time t_p", + "control": "absolute temperature T", + "formula_a": "t_p = C exp[Q/(R T)], with Q and R positive", + "formula_b": "ln(t_p/C) = Q/(R T), with Q and R positive", + "cases": [ + ("a thermally activated annealing step", "600 K", "1000 K"), + ("a diffusion-limited treatment", "500 K", "900 K"), + ], + }, + { + "law_id": "grain-growth-time-mobility", + "law_sign": -1, + "law_name": "grain-growth time at fixed target size", + "response": "growth time t_g", + "control": "grain-boundary mobility M", + "formula_a": "t_g = C/M", + "formula_b": "t_g M = C", + "cases": [ + ("a polycrystal growing to a fixed grain size", "1e-15 m^4 J^-1 s^-1", "1e-13 m^4 J^-1 s^-1"), + ("a ceramic reaching a fixed coarsening extent", "2e-16 m^4 J^-1 s^-1", "2e-14 m^4 J^-1 s^-1"), + ], + }, + { + "law_id": "resistance-area", + "law_sign": -1, + "law_name": "electrical resistance versus conductor area", + "response": "electrical resistance R_e", + "control": "cross-sectional area A", + "formula_a": "R_e = rho L/A, with rho and L fixed", + "formula_b": "R_e A = rho L, with rho and L fixed", + "cases": [ + ("a metal wire of fixed length", "0.1 mm^2", "1 mm^2"), + ("a printed conductor of fixed length", "0.02 mm^2", "0.2 mm^2"), + ], + }, + { + "law_id": "penetration-depth-absorption", + "law_sign": -1, + "law_name": "optical penetration depth versus absorption coefficient", + "response": "penetration depth delta_p", + "control": "absorption coefficient alpha_a", + "formula_a": "delta_p = 1/alpha_a", + "formula_b": "delta_p alpha_a = 1", + "cases": [ + ("an absorbing semiconductor", "1e4 m^-1", "1e6 m^-1"), + ("a pigmented polymer", "2e3 m^-1", "2e5 m^-1"), + ], + }, +] + + +def make_prompt(law: dict, surface: str, case_index: int, numerical_sign: int, answer_order: str) -> dict: + system, low, high = law["cases"][case_index] + start, end = (low, high) if numerical_sign == 1 else (high, low) + physical_sign = int(law["law_sign"]) * numerical_sign + words = ("higher", "lower") if answer_order == "higher-first" else ("lower", "higher") + prompt_id = ( + f"{law['law_id']}--{surface}--case{case_index + 1}--" + f"{'up' if numerical_sign == 1 else 'down'}--{answer_order}" + ) + user = ( + "Apply the same two-stage physical test to every case. Stage 1: use " + "only the constitutive equation, not associations with material names, " + "to determine the monotonic sign of the response with respect to the " + "changed control while all other quantities are fixed. " + f"For {law['law_name']}, the response is {law['response']}, the control " + f"is {law['control']}, and the relation is {law[f'formula_{surface}']}. " + "All stated coefficients are positive. Complete this relation-sign " + f"step silently. § Stage 2: in {system}, {law['control']} changes from " + f"{start} to {end}. Determine the sign of this numerical change " + "silently. ¶ Now compose the relation sign with the numerical-change " + f"sign to determine the direction of {law['response']}. † Reply with " + "exactly one lowercase word from this ordered pair: " + f"{words[0]}, {words[1]}." + ) + return { + "prompt_id": prompt_id, + "law_id": law["law_id"], + "law_name": law["law_name"], + "split": "second-fresh-confirmation", + "law_sign": int(law["law_sign"]), + "surface": surface, + "case_index": case_index, + "system": system, + "control": law["control"], + "response": law["response"], + "numeric_start": start, + "numeric_end": end, + "numerical_sign": numerical_sign, + "physical_sign": physical_sign, + "answer_order": answer_order, + "expected_answer": "higher" if physical_sign == 1 else "lower", + "user": user, + } + + +def main() -> None: + if (OUT / "raw.json").exists(): + raise RuntimeError("refusing to rebuild after outputs exist") + prompts = [ + make_prompt(law, surface, case, numerical, order) + for law in LAWS + for surface in ("a", "b") + for case in range(2) + for numerical in (-1, 1) + for order in ("higher-first", "lower-first") + ] + manifest = { + "study_id": "relational-contrast-confirmation-2026-07-18", + "markers": {"law": "§", "comparison": "¶", "decision": "†"}, + "laws": LAWS, + "prompts": prompts, + "dimensions": {"n_laws": len(LAWS), "n_prompts": len(prompts)}, + } + write_json(MANIFEST, manifest) + protocol = { + "study_id": manifest["study_id"], + "status": "frozen-before-second-fresh-output", + "frozen_at": datetime.now(timezone.utc).isoformat(), + "model": "google/gemma-4-E4B-it", + "model_revision": "a4c2d58be94dda072b918d9db64ee85c8ed34e3f", + "positions": ["law", "comparison", "decision", "final_prompt"], + "inputs": { + "prompt_manifest": {"path": str(MANIFEST.relative_to(ROOT)), "sha256": sha256(MANIFEST)}, + "runner": {"path": str(RUNNER.relative_to(ROOT)), "sha256": sha256(RUNNER)}, + "development_manifest": {"path": str((DEVELOPMENT / "prompt_manifest.json").relative_to(ROOT)), "sha256": sha256(DEVELOPMENT / "prompt_manifest.json")}, + "development_states": {"path": str((DEVELOPMENT / "representations.npz").relative_to(ROOT)), "sha256": sha256(DEVELOPMENT / "representations.npz")}, + }, + "frozen_lens": { + "position": "final_prompt", + "layer": 34, + "state_normalization": "unit L2", + "direction": "positive-minus-negative physical-outcome centroids over all 16 old development laws", + "fresh_refit": "none", + }, + "primary_relational_endpoint": ( + "For every matched surface/case/answer-order cell, subtract the " + "frozen physical score for numerical-down from numerical-up. " + "Average contrasts within law. Positive predicts a direct law and " + "negative predicts an inverse law." + ), + "success_rule": { + "law_orientation_auc_minimum": 0.90, + "sign_accuracy_minimum": 0.80, + "both_surface_auc_minimum": 0.80, + "laws_total": len(LAWS), + }, + "inference": { + "exact_label_permutations": 924, + "bootstrap_seed": 20260722, + "bootstrap_resamples": 50000, + }, + "guardrails": [ + "All 12 relations are disjoint from both earlier abstraction cohorts.", + "No second-fresh label selects a layer, position, direction, or threshold.", + "Matched subtraction cancels law wording, formula wording, material case, and answer order within each pair.", + "Numerical direction is identical across direct and inverse laws; only its physical interpretation changes.", + "Two algebraic surfaces, two cases, and two answer orders are retained per law.", + "All outputs, including failures, are retained.", + ], + } + write_json(PROTOCOL, protocol) + (OUT / "PROTOCOL.md").write_text( + "# Confirmatory relational physical contrast\n\n" + "The frozen lens assigns a physical-outcome score to each prompt. For " + "each law, matched prompts reverse only the numerical change. The sign " + "of the resulting score difference estimates whether the constitutive " + "law is direct or inverse. No new-law label is used to fit the lens.\n" + ) + print(f"frozen {len(prompts)} prompts over {len(LAWS)} second-fresh laws") + print(f"protocol sha256: {sha256(PROTOCOL)}") + + +if __name__ == "__main__": + main() diff --git a/jlens_materials/scripts/build_relational_physics_si.py b/jlens_materials/scripts/build_relational_physics_si.py new file mode 100644 index 0000000000000000000000000000000000000000..ee48c79f0234eee517e52ce9fe276f07423827a2 --- /dev/null +++ b/jlens_materials/scripts/build_relational_physics_si.py @@ -0,0 +1,383 @@ +#!/usr/bin/env python3 +"""Build the portable SI inventory for the relational-physics benchmark.""" + +from __future__ import annotations + +import hashlib +import json +import shutil +from pathlib import Path + +import pandas as pd + + +ROOT = Path(__file__).resolve().parents[1] +SOURCE = ROOT / "experiments" / "neutral-anchored-relational-physics-2026-07-18" +PAPER = ROOT / "paper" +DATA = PAPER / "relational_physics_data" +INVENTORY = PAPER / "relational_physics_inventory.tex" + +DATA_FILES = [ + "PROTOCOL.md", + "REPORT.md", + "protocol.json", + "prompt_manifest.json", + "raw.json", + "representations.npz", + "statistics.json", + "law_level_neutral_normalized.csv", + "matched_pair_contrasts.csv", + "output_head_law_level.csv", + "word_tfidf_law_level.csv", + "character_tfidf_law_level.csv", +] + +FIGURE_FILES = [ + "neutral-anchored-relational-physics.pdf", + "neutral-anchored-relational-physics.png", +] + + +def sha256(path: Path) -> str: + return hashlib.sha256(path.read_bytes()).hexdigest() + + +def escape(value: object) -> str: + text = str(value) + replacements = { + "\\": r"\textbackslash{}", + "&": r"\&", + "%": r"\%", + "$": r"\$", + "#": r"\#", + "_": r"\_", + "{": r"\{", + "}": r"\}", + "~": r"\textasciitilde{}", + "^": r"\textasciicircum{}", + } + return "".join(replacements.get(character, character) for character in text) + + +def fmt(value: float, digits: int = 3) -> str: + return f"{float(value):+.{digits}f}" + + +def exact_prompt_examples(manifest: dict) -> list[dict]: + wanted = [ + "current-voltage--a--case1--up--higher-first", + "resistivity-wire-length-neutral--a--case1--up--higher-first", + "capacitance-separation--b--case2--up--unchanged-first", + ] + lookup = {item["prompt_id"]: item for item in manifest["prompts"]} + return [lookup[prompt_id] for prompt_id in wanted] + + +def build_inventory(manifest: dict, statistics: dict) -> str: + laws = pd.read_csv(SOURCE / "law_level_neutral_normalized.csv") + pairs = pd.read_csv(SOURCE / "matched_pair_contrasts.csv") + law_meta = {item["law_id"]: item for item in manifest["laws"]} + lines: list[str] = [] + add = lines.append + + add(r"\subsection*{S4E.1 Exact design, chronology, and prompt template}") + add("") + add( + "The task was deliberately scaffolded, but the model emitted no " + "intermediate rationale. The identical instruction asked it to infer " + "the monotonic sign of the stated equation, infer whether the numerical " + "control rose or fell, silently compose those signs, and output one " + "allowed word. The layer-34 direction and state position were selected " + "only on the 16-law development set. No state from this 60-law cohort " + "was used to fit or rotate that direction. The large-cohort protocol, " + "law list, prompt expansion, endpoints, success gates, bootstrap seed, " + "and permutation seed were fingerprinted before any corresponding " + "model output." + ) + add("") + add( + "The exact prompt generator crossed every law with two algebraically " + "equivalent equation surfaces, two material cases, numerical increase " + "and decrease, and two answer orders. The following is the literal " + "template; braces identify fields replaced by the frozen manifest." + ) + add("") + add(r"\begin{quote}\small") + add( + r"Apply the same two-stage physical test to every case. Stage 1: use " + r"only the constitutive equation, not associations with material names, " + r"to determine whether the response increases, decreases, or remains " + r"unchanged as the control increases while all other quantities are " + r"fixed. For \texttt{\{law name\}}, the response is " + r"\texttt{\{response\}}, the control is \texttt{\{control\}}, and the " + r"relation is \texttt{\{equation surface\}}. All stated coefficients " + r"are positive. Complete this relation step silently. In " + r"\texttt{\{material case\}}, \texttt{\{control\}} changes from " + r"\texttt{\{start\}} to \texttt{\{end\}}. Determine the numerical-change " + r"direction silently. Now compose the relation with the numerical " + r"change to determine the direction of \texttt{\{response\}}. Reply " + r"with exactly one lowercase word from this ordered set: " + r"\texttt{\{answer order\}}." + ) + add(r"\end{quote}") + add("") + add( + "The SI bundle includes " + r"\path{relational_physics_data/prompt_manifest.json}, a 960-entry " + "ledger containing every fully expanded prompt exactly as sent to " + "Gemma, not merely the template. It also retains all raw output rows " + "and the captured 960-by-2,560 layer-34 state array." + ) + add("") + + add(r"\begin{table}[H]") + add(r"\centering\small") + add( + r"\caption{\textbf{Frozen neutral-anchored relational design.} " + r"The ten calibration-neutral laws alone define the empirical center " + r"and scale; the ten validation-neutral laws are untouched test cases.}" + ) + add(r"\begin{tabular}{lr}") + add(r"\toprule") + add(r"Quantity & Frozen value \\") + add(r"\midrule") + dims = manifest["dimensions"] + add(rf"Direct / inverse / neutral laws & {dims['n_direct']} / {dims['n_inverse']} / {dims['n_neutral']} \\") + add(rf"Calibration / validation neutral laws & {dims['n_neutral_calibration']} / {dims['n_neutral_validation']} \\") + add(rf"Exact prompts / matched comparisons & {dims['n_prompts']} / {dims['n_matched_pairs']} \\") + add(r"Equation surfaces / cases / answer orders & 2 / 2 / 2 \\") + add(r"Frozen state & final prompt token, layer 34 \\") + add(r"Direction fitting & centroid difference on 16 earlier laws; no cohort refit \\") + add(r"Neutral center & median of 10 calibration-neutral law contrasts \\") + add(r"Robust scale & $1.4826\times$ median absolute deviation \\") + add(r"Bootstrap / ordinal permutations & 50,000 / 100,000 \\") + add(r"\bottomrule") + add(r"\end{tabular}") + add(r"\end{table}") + add("") + + add(r"\subsection*{S4E.2 Three exact prompt examples}") + add("") + for index, prompt in enumerate(exact_prompt_examples(manifest), start=1): + add(rf"\paragraph{{Example {index}: {escape(prompt['category'])} law.}}") + add(r"\begin{quote}\small") + add(escape(prompt["user"])) + add(r"\end{quote}") + add( + rf"\noindent Prompt ID: \path{{{escape(prompt['prompt_id'])}}}; " + rf"expected response: \texttt{{{escape(prompt['expected_answer'])}}}." + ) + add("") + + add(r"\clearpage") + add(r"\subsection*{S4E.3 Complete 60-law inventory}") + add("") + add( + "Every row below contributes 16 exact prompts and eight matched " + "numerical-reversal comparisons. Surface A states the relation explicitly; " + "surface B is algebraically rearranged. The two cases supply the " + "material wording and numerical endpoints. Calibration and validation " + "refer only to the neutral-law role." + ) + add("") + add(r"\begingroup\tiny") + add(r"\setlength{\tabcolsep}{2pt}") + add( + r"\begin{longtable}{>{\raggedright\arraybackslash}p{0.10\linewidth}" + r">{\raggedright\arraybackslash}p{0.065\linewidth}" + r">{\raggedright\arraybackslash}p{0.085\linewidth}" + r">{\raggedright\arraybackslash}p{0.14\linewidth}" + r">{\raggedright\arraybackslash}p{0.17\linewidth}" + r">{\raggedright\arraybackslash}p{0.17\linewidth}" + r">{\raggedright\arraybackslash}p{0.12\linewidth}}" + ) + add(r"\toprule") + add(r"Law ID & Class & Domain & Response $\leftarrow$ control & Surface A & Surface B & Cases / neutral role \\") + add(r"\midrule") + add(r"\endhead") + for law_id in laws.sort_values(["category", "domain", "law_id"])["law_id"]: + item = law_meta[law_id] + cases = "; ".join( + f"{case[0]}: {case[1]} / {case[2]}" for case in item["cases"] + ) + role = item.get("neutral_role") or "--" + add( + f"{escape(law_id)} & {escape(item['category'])} & " + f"{escape(item['domain'])} & " + f"{escape(item['response'])} $\\leftarrow$ {escape(item['control'])} & " + f"{escape(item['formula_a'])} & {escape(item['formula_b'])} & " + f"{escape(cases)}; role: {escape(role)} \\\\" + ) + add(r"\bottomrule") + add(r"\end{longtable}") + add(r"\endgroup") + add("") + + add(r"\clearpage") + add(r"\subsection*{S4E.4 Complete law-level results}") + add("") + add( + "The score is the mean of eight matched up-minus-down hidden-state " + "contrasts, centered on the calibration-neutral median and divided by " + "the calibration-neutral robust scale. A positive value is direct-like; " + "a negative value is inverse-like. Neutral-law values are not expected " + "to equal zero individually." + ) + add("") + add(r"\begingroup\scriptsize") + add( + r"\begin{longtable}{>{\raggedright\arraybackslash}p{0.27\linewidth}" + r">{\raggedright\arraybackslash}p{0.10\linewidth}" + r">{\raggedright\arraybackslash}p{0.18\linewidth}rrrr}" + ) + add(r"\toprule") + add(r"Law ID & Class & Domain & Mean $\Delta r$ & Surface A & Surface B & Robust $z$ \\") + add(r"\midrule") + add(r"\endhead") + for _, row in laws.sort_values("neutral_z").iterrows(): + add( + f"{escape(row['law_id'])} & {escape(row['category'])} & " + f"{escape(row['domain'])} & {fmt(row['mean_contrast'], 5)} & " + f"{fmt(row['surface_a_contrast'], 5)} & " + f"{fmt(row['surface_b_contrast'], 5)} & " + f"{fmt(row['neutral_z'], 3)} \\\\" + ) + add(r"\bottomrule") + add(r"\end{longtable}") + add(r"\endgroup") + add("") + + add(r"\clearpage") + add(r"\subsection*{S4E.5 Complete 480-comparison result inventory}") + add("") + add( + "Each row is one matched comparison: the two prompts have the " + "same law, equation surface, material case, and answer order, and differ " + "only in whether the numerical control decreases or increases. " + r"\(\Delta r=r(h_{\mathrm{up}})-r(h_{\mathrm{down}})\)." + ) + add("") + add(r"\begingroup\tiny") + add( + r"\begin{longtable}{>{\raggedright\arraybackslash}p{0.34\linewidth}" + r">{\raggedright\arraybackslash}p{0.09\linewidth}" + r">{\raggedright\arraybackslash}p{0.08\linewidth}" + r">{\raggedright\arraybackslash}p{0.16\linewidth}rr}" + ) + add(r"\toprule") + add(r"Law ID & Surface & Case & Answer order & $\Delta r$ & Robust $z$ \\") + add(r"\midrule") + add(r"\endhead") + for _, row in pairs.sort_values( + ["law_id", "surface", "case_index", "answer_order"] + ).iterrows(): + add( + f"{escape(row['law_id'])} & {escape(row['surface'])} & " + f"{int(row['case_index']) + 1} & {escape(row['answer_order'])} & " + f"{fmt(row['contrast'], 5)} & {fmt(row['neutral_z'], 3)} \\\\" + ) + add(r"\bottomrule") + add(r"\end{longtable}") + add(r"\endgroup") + add("") + + add(r"\subsection*{S4E.6 Frozen endpoints and artifact integrity}") + add("") + primary = statistics["primary"] + add(r"\begin{table}[H]") + add(r"\centering\small") + add(r"\caption{\textbf{Complete frozen endpoint verdict.}}") + add(r"\begin{tabular}{lr}") + add(r"\toprule") + add(r"Endpoint & Result \\") + add(r"\midrule") + add(rf"Direct versus inverse AUC & {primary['direct_vs_inverse_auc']:.3f} \\") + add(rf"Direct versus validation-neutral AUC & {primary['direct_vs_validation_neutral_auc']:.3f} \\") + add(rf"Validation-neutral versus inverse AUC & {primary['validation_neutral_vs_inverse_auc']:.3f} \\") + add(rf"Neutral-cut directional-law accuracy & {primary['correct_directional_laws']}/{primary['directional_laws_total']} ({100*primary['calibrated_direct_inverse_accuracy']:.1f}\\%) \\") + add(rf"Explicit / rearranged direct--inverse AUC & {primary['surface_direct_inverse_auc']['a']:.3f} / {primary['surface_direct_inverse_auc']['b']:.3f} \\") + add(rf"Ordinal Spearman $\rho$ / permutation $p$ & {primary['ordinal_spearman']:.3f} / {primary['ordinal_permutation_p']:.6f} \\") + add(rf"Direct / neutral / inverse median robust $z$ & {primary['category_median_z']['direct']:.3f} / {primary['category_median_z']['neutral']:.3f} / {primary['category_median_z']['inverse']:.3f} \\") + add(rf"All frozen criteria & {'PASS' if statistics['passed_all_frozen_criteria'] else 'FAIL'} \\") + add(r"\bottomrule") + add(r"\end{tabular}") + add(r"\end{table}") + add("") + add(r"\begin{table}[H]") + add(r"\centering\tiny") + add( + r"\caption{\textbf{Portable relational-physics SI files.} " + r"Hashes refer to the publication copies bundled with this " + r"Supplementary Information.}" + ) + add(r"\begin{tabular}{>{\raggedright\arraybackslash}p{0.40\linewidth}>{\raggedright\arraybackslash}p{0.52\linewidth}}") + add(r"\toprule") + add(r"File & SHA-256 \\") + add(r"\midrule") + for name in DATA_FILES: + copied = DATA / name + add(f"{escape(name)} & \\texttt{{{sha256(copied)}}} \\\\") + add(r"\bottomrule") + add(r"\end{tabular}") + add(r"\end{table}") + + return "\n".join(lines) + "\n" + + +def write_readme() -> None: + rows = [] + for name in DATA_FILES: + path = DATA / name + rows.append(f"- `{name}` — SHA-256 `{sha256(path)}`") + text = f"""# Neutral-anchored relational physics SI data + +This directory is the portable data companion for SI Section S4E. It retains +all 960 exact prompts, all raw model-output rows, the complete 960-by-2,560 +layer-34 state array, all 480 matched reversal comparisons, all 60 law aggregates, +the frozen protocol, and the final statistics. + +The prompts are not reconstructed from prose: `prompt_manifest.json` contains +the exact full user string for every prompt. `raw.json` records the clean output +diagnostics in the same prompt order, and `representations.npz` contains +`prompt_ids`, `layer`, and `raw_states`. + +Regenerate the analysis and publication figures from the repository root: + +```bash +python scripts/analyze_neutral_anchored_relational_benchmark.py +python scripts/build_relational_physics_si.py +``` + +The first command refuses silently changed frozen inputs by checking the hashes +recorded in `protocol.json`. Rerunning Gemma itself requires the original +experiment directory and: + +```bash +conda activate PyTorch +python scripts/run_neutral_anchored_relational_benchmark.py +``` + +## Integrity manifest + +{chr(10).join(rows)} +""" + (DATA / "README.md").write_text(text) + + +def main() -> None: + DATA.mkdir(parents=True, exist_ok=True) + (PAPER / "figures").mkdir(parents=True, exist_ok=True) + for name in DATA_FILES: + shutil.copy2(SOURCE / name, DATA / name) + for name in FIGURE_FILES: + shutil.copy2(SOURCE / "figures" / name, PAPER / "figures" / name) + manifest = json.loads((SOURCE / "prompt_manifest.json").read_text()) + statistics = json.loads((SOURCE / "statistics.json").read_text()) + write_readme() + INVENTORY.write_text(build_inventory(manifest, statistics)) + print(f"wrote {INVENTORY}") + print(f"copied {len(DATA_FILES)} data files to {DATA}") + + +if __name__ == "__main__": + main() diff --git a/jlens_materials/scripts/build_review_robustness_si_inventory.py b/jlens_materials/scripts/build_review_robustness_si_inventory.py new file mode 100644 index 0000000000000000000000000000000000000000..556614da62423f191aa5bf4d722baa9bfb6d85e8 --- /dev/null +++ b/jlens_materials/scripts/build_review_robustness_si_inventory.py @@ -0,0 +1,327 @@ +#!/usr/bin/env python3 +"""Build SI-ready exact prompt and robustness-result inventories.""" + +from __future__ import annotations + +import json +from pathlib import Path + +import pandas as pd + + +ROOT = Path(__file__).resolve().parents[1] +PAPER = ROOT / "paper" +OUTPUT = PAPER / "review_robustness_prompt_inventory.tex" +GRAPH_MANIFEST = ( + ROOT / "experiments" / "answer-code-binding-2026-07-17" + / "prompt_manifest.json" +) +HELDOUT_MANIFEST = ROOT / "prompts" / "materials-heldout-v1-preregistered.json" +MULTITOKEN = ( + ROOT / "experiments" / "multitoken-sequence-robustness-2026-07-18" +) + + +def tex(value: object) -> str: + text = str(value) + replacements = { + "\\": r"\textbackslash{}", + "&": r"\&", + "%": r"\%", + "$": r"\$", + "#": r"\#", + "_": r"\_", + "{": r"\{", + "}": r"\}", + "~": r"\textasciitilde{}", + "^": r"\textasciicircum{}", + "–": "--", + "—": "---", + "×": r"$\times$", + "≤": r"$\leq$", + "≥": r"$\geq$", + } + return "".join(replacements.get(character, character) for character in text) + + +def tex_id(value: object) -> str: + """Escape an identifier and permit line breaks after hyphens.""" + + return ( + tex(value) + .replace("-", r"-\allowbreak{}") + .replace(r"\_", r"\_\allowbreak{}") + ) + + +def option_free_rows() -> list[str]: + manifest = json.loads(GRAPH_MANIFEST.read_text()) + lines = [ + r"\subsection*{Exact natural question-end prompt inventory}", + ( + "The following are the complete 72 user questions used for the " + "natural question-end state capture. The runner used only the " + r"\texttt{stem} field printed here. It added no answer choices, " + "answer words, arbitrary code, response instruction, or " + "checkpoint marker. Prompt IDs retain the source manifest's " + r"\texttt{answer-code} suffix for provenance even though that " + "suffix content was not used." + ), + "", + r"\begingroup", + r"\scriptsize", + r"\setlength{\tabcolsep}{2pt}", + r"\setlength{\LTpre}{0.4em}", + r"\setlength{\LTpost}{0.4em}", + r"\begin{longtable}{>{\raggedright\arraybackslash}p{0.15\linewidth}>{\raggedright\arraybackslash}p{0.14\linewidth}>{\raggedright\arraybackslash}p{0.10\linewidth}>{\raggedright\arraybackslash}p{0.08\linewidth}>{\raggedright\arraybackslash}p{0.46\linewidth}}", + r"\caption{Complete option-free natural question-end prompt inventory.}\label{tab:s-option-free-prompts}\\", + r"\toprule", + r"Prompt ID & Mechanism & Variant & Input / outcome & Exact user question \\", + r"\midrule", + r"\endfirsthead", + r"\toprule", + r"Prompt ID & Mechanism & Variant & Input / outcome & Exact user question \\", + r"\midrule", + r"\endhead", + r"\bottomrule", + r"\endfoot", + ] + for row in manifest["prompts"]: + lines.append( + " & ".join( + [ + r"\texttt{" + tex_id(row["prompt_id"]) + "}", + tex_id(row["family_id"]), + tex_id(row["variant"]), + ( + tex(row["numeric_direction"]) + + " / " + + tex(row["expected_outcome"]) + ), + tex(row["stem"]), + ] + ) + + r" \\" + ) + lines.extend([r"\end{longtable}", r"\endgroup", ""]) + return lines + + +def multitoken_rows() -> list[str]: + manifest = json.loads(HELDOUT_MANIFEST.read_text()) + prompt_map = {row["slug"]: row for row in manifest["prompts"]} + prompt_scores = pd.read_csv( + MULTITOKEN / "prompt_jacobian_direct_contrasts.csv" + ).sort_values(["family", "slug"]) + stats = json.loads((MULTITOKEN / "statistics.json").read_text()) + tokenization = stats["tokenization"] + lines = [ + r"\subsection*{Exact multi-token robustness inventory}", + ( + "All ten originally excluded multi-token cases are printed below. " + "The target and scientific contrast were fixed before sequence " + "scoring. The Jacobian and direct columns are target-minus-contrast " + "restricted sequence log-odds averaged over the registered " + r"38--92\% depth band." + ), + "", + r"\begingroup", + r"\scriptsize", + r"\setlength{\tabcolsep}{2pt}", + r"\setlength{\LTpre}{0.4em}", + r"\setlength{\LTpost}{0.4em}", + r"\begin{longtable}{>{\raggedright\arraybackslash}p{0.12\linewidth}>{\raggedright\arraybackslash}p{0.34\linewidth}>{\raggedright\arraybackslash}p{0.18\linewidth}rrr}", + r"\caption{Complete multi-token prompt and sequence-score inventory.}\label{tab:s-multitoken-prompts}\\", + r"\toprule", + r"ID & Exact input description & Target / contrast and pieces & Jacobian & Direct & Difference \\", + r"\midrule", + r"\endfirsthead", + r"\toprule", + r"ID & Exact input description & Target / contrast and pieces & Jacobian & Direct & Difference \\", + r"\midrule", + r"\endhead", + r"\bottomrule", + r"\endfoot", + ] + for row in prompt_scores.itertuples(index=False): + prompt = prompt_map[row.slug] + tokens = tokenization[row.family] + target = " + ".join(tokens["target_pieces"]) + contrast = " + ".join(tokens["contrast_pieces"]) + pair_text = ( + f"{row.family}: {prompt['tracked'][0] if row.family == 'cleavage' else 'martensite'}" + ) + if row.family == "cleavage": + pair_text = ( + f"transgranular ({target}) / intergranular ({contrast})" + ) + else: + pair_text = f"martensite ({target}) / bainite ({contrast})" + lines.append( + " & ".join( + [ + r"\texttt{" + tex_id(row.slug) + "}", + tex(prompt["text"]), + tex(pair_text), + f"{row.jacobian_band_sequence_margin:+.3f}", + f"{row.direct_band_sequence_margin:+.3f}", + f"{row.jacobian_minus_direct:+.3f}", + ] + ) + + r" \\" + ) + lines.extend([r"\end{longtable}", r"\endgroup", ""]) + return lines + + +def cross_mechanism_rows() -> list[str]: + table = pd.read_csv( + ROOT + / "experiments" + / "cross-mechanism-outcome-2026-07-18" + / "mechanism_pair_metrics.csv" + ) + jacobian = table[ + (table["method"] == "jacobian") + & (table["opposite_response_orientation"].astype(bool)) + ].sort_values("family_pair") + lines = [ + r"\subsection*{Complete counter-numeric mechanism-pair results}", + ( + "These are all opposite-response mechanism pairs in the frozen " + "cross-mechanism analysis. In this subset, the same physical " + "outcome requires the opposite numerical direction." + ), + "", + r"\begin{table}[ht!]", + r"\centering", + r"\scriptsize", + r"\caption{All Jacobian counter-numeric mechanism-pair results.}", + r"\label{tab:s-cross-mechanism-pairs}", + r"\begin{tabular}{lrrr}", + r"\toprule", + r"Mechanism pair & AUC & Top-1 accuracy & Rankings \\", + r"\midrule", + ] + for row in jacobian.itertuples(index=False): + lines.append( + f"{tex(row.family_pair)} & {row.pairwise_auc:.3f} & " + f"{row.top1_accuracy:.3f} & {int(row.n_rankings)}" + + r" \\" + ) + lines.extend( + [r"\bottomrule", r"\end{tabular}", r"\end{table}", ""] + ) + return lines + + +def patching_rows() -> list[str]: + directory = ( + ROOT + / "experiments" + / "cross-mechanism-activation-patching-2026-07-18" + ) + subset = pd.read_csv(directory / "subset_statistics.csv") + family = pd.read_csv(directory / "donor_family_effects.csv").sort_values( + "donor_family" + ) + names = { + "all": "All mechanism pairs", + "cross_vocabulary": "Different answer vocabularies", + "opposite_orientation": "Opposite response orientations", + "both": "Both controls", + } + lines = [ + r"\subsection*{Option-free cross-mechanism activation patching}", + ( + "The 24 anchor rows in Table~" + r"\ref{tab:s-option-free-prompts} served as both receivers and " + "donors. Every receiver was patched with all four anchor donors " + "from each of the other five mechanisms at layers 16, 24, 32, " + "and 37, giving 1,920 retained interventions. Donor and receiver " + "contained no answer list. The complete intervention table is " + r"\texttt{experiments/cross-mechanism-activation-patching-" + r"2026-07-18/all\_patch\_rows.csv}." + ), + "", + r"\begin{table}[ht!]", + r"\centering", + r"\scriptsize", + ( + r"\caption{Frozen option-free activation-patching endpoints. " + "Intervals resample unordered mechanism pairs. The pair-sign and " + "structured columns are two-sided exact probabilities.}" + ), + r"\label{tab:s-option-free-patching-subsets}", + r"\begin{tabular}{lrrrrr}", + r"\toprule", + r"Subset & Transfer & 95\% interval & Pair-sign $p$ & Structured $p$ & Pairs \\", + r"\midrule", + ] + for row in subset.itertuples(index=False): + lines.append( + f"{tex(names[row.subset])} & {row.mean:+.3f} & " + f"[{row.ci_low:+.3f}, {row.ci_high:+.3f}] & " + f"{row.exact_two_sided_p:.5f} & " + f"{row.structured_exact_two_sided_p:.5f} & " + f"{int(row.n_pairs)}" + + r" \\" + ) + lines.extend( + [ + r"\bottomrule", + r"\end{tabular}", + r"\end{table}", + "", + r"\begin{table}[ht!]", + r"\centering", + r"\scriptsize", + ( + r"\caption{Activation-patching results by donor mechanism. " + "Positive physical transfer means that positive-outcome " + "donors increased the receiver's positive-minus-negative " + "answer margin relative to negative-outcome donors.}" + ), + r"\label{tab:s-option-free-patching-families}", + r"\begin{tabular}{lrrr}", + r"\toprule", + r"Donor mechanism & Physical outcome & Numerical direction & Positive ordered pairs \\", + r"\midrule", + ] + ) + for row in family.itertuples(index=False): + lines.append( + f"{tex(row.donor_family)} & " + f"{row.physical_outcome_contrast:+.3f} & " + f"{row.numeric_direction_contrast:+.3f} & " + f"{int(row.positive_ordered_pairs)}/{int(row.n_ordered_pairs)}" + + r" \\" + ) + lines.extend( + [r"\bottomrule", r"\end{tabular}", r"\end{table}", ""] + ) + return lines + + +def main() -> None: + lines = [ + "% Generated by scripts/build_review_robustness_si_inventory.py.", + "% Do not edit by hand; regenerate from frozen manifests and CSVs.", + "", + ] + lines.extend(option_free_rows()) + lines.extend(multitoken_rows()) + lines.extend(cross_mechanism_rows()) + patch_directory = ( + ROOT + / "experiments" + / "cross-mechanism-activation-patching-2026-07-18" + ) + if (patch_directory / "statistics.json").exists(): + lines.extend(patching_rows()) + OUTPUT.write_text("\n".join(lines) + "\n") + print(OUTPUT.relative_to(ROOT)) + + +if __name__ == "__main__": + main() diff --git a/jlens_materials/scripts/build_revision_figure_candidates.py b/jlens_materials/scripts/build_revision_figure_candidates.py new file mode 100644 index 0000000000000000000000000000000000000000..48269ef457f121b6e7db5d0966f6a113a85512ea --- /dev/null +++ b/jlens_materials/scripts/build_revision_figure_candidates.py @@ -0,0 +1,116 @@ +#!/usr/bin/env python3 +"""Render cosmetic revision candidates without replacing manuscript figures.""" + +from __future__ import annotations + +import json +import sys +from pathlib import Path + +import pandas as pd + + +ROOT = Path(__file__).resolve().parents[1] +SCRIPTS = ROOT / "scripts" +sys.path.insert(0, str(SCRIPTS)) + +import analyze_causal_relational_knockout as causal # noqa: E402 +import analyze_counterfactual_activation_patching as patching # noqa: E402 +import analyze_materials_heldout_v1 as heldout # noqa: E402 +import analyze_materials_latent_geometry as geometry # noqa: E402 +import build_final_paper_figures as paper_figures # noqa: E402 +import rate_materials_heldout_v1_openai as automated # noqa: E402 + + +OUT = ROOT / "experiments" / "figure-cosmetic-candidates-2026-09-02" / "figures" + + +def main() -> None: + OUT.mkdir(parents=True, exist_ok=True) + + heldout_stats = json.loads(heldout.STATS_PATH.read_text()) + heldout.plot_discovery( + heldout_stats["open_vocabulary"], + output_stem=OUT / "figure3-larger-type-candidate", + font_scale=1.10, + ) + + paper_figures.configure_style() + paper_figures.build_stream_figure( + output_stem=OUT / "figure5-higher-contrast-candidate", + enhanced_contrast=True, + write_metadata=False, + ) + + geometry.configure_style(font_scale=1.08) + geometry.plot_archived_geometry( + output_stem=OUT / "figure6-larger-type-candidate", + pca_stem=None, + font_scale=1.08, + ) + + automated_results = json.loads(automated.RESULTS_PATH.read_text()) + automated.plot_results( + automated_results, + output_stem=OUT / "figure11-larger-type-candidate", + font_scale=1.10, + ) + + patch_stats = json.loads((patching.OUT / "statistics.json").read_text()) + patching.make_figure( + pd.read_csv(patching.OUT / "layer_curves.csv"), + pd.read_csv(patching.OUT / "layer_pair_effects.csv"), + pd.read_csv(patching.OUT / "readout_relation_separation_curves.csv"), + patch_stats["readout_causality_correlations"], + output_dir=OUT, + font_scale=1.08, + legend_y=-0.070, + ) + for suffix in ("pdf", "png"): + source = OUT / f"counterfactual-activation-patching.{suffix}" + source.rename(OUT / f"figure15-larger-type-candidate.{suffix}") + + causal_stats = json.loads((causal.EXP / "statistics.json").read_text()) + causal_laws = pd.read_csv(causal.EXP / "law_level_effects.csv") + causal.plot( + causal_stats["scales"], + causal_laws, + figures_dir=OUT, + font_scale=1.08, + include_supplementary=False, + ) + generated = OUT / "causal-relational-knockout.pdf" + generated.rename(OUT / "figure16-larger-type-candidate.pdf") + for suffix in ("png", "svg"): + source = OUT / f"causal-relational-knockout.{suffix}" + source.rename(OUT / f"figure16-larger-type-candidate.{suffix}") + + readme = OUT.parent / "README.md" + readme.write_text( + "\n".join( + [ + "# Cosmetic figure candidates", + "", + "These files are trial renders and are not referenced by the manuscript.", + "", + "- Figure 3: all typography scaled by 1.10.", + "- Figure 5: colorblind-safe higher-contrast ribbons, lighter analysis band, stronger white separators, and stronger label halos.", + "- Figure 6: all typography scaled by 1.08.", + "- Figure 11: all typography scaled by 1.10.", + "- Figure 15: all typography scaled by 1.08.", + "- Figure 16 (optional): all typography scaled by 1.08; legends moved outside data and bottom-right labels rotated.", + "", + "Reproduce with:", + "", + "```bash", + "conda run -n PyTorch python scripts/build_revision_figure_candidates.py", + "```", + "", + ] + ), + encoding="utf-8", + ) + + +if __name__ == "__main__": + main() diff --git a/jlens_materials/scripts/build_social_media_animations.py b/jlens_materials/scripts/build_social_media_animations.py new file mode 100644 index 0000000000000000000000000000000000000000..466194c9480491551dafa493c81e066fcf1cd63a --- /dev/null +++ b/jlens_materials/scripts/build_social_media_animations.py @@ -0,0 +1,563 @@ +#!/usr/bin/env python3 +"""Build short, data-faithful social-media animations from paper results. + +The clips use only archived experiment outputs. They deliberately distinguish +open-vocabulary readout from chain of thought, relational measurement from +absolute-state geometry, and constrained causal steering from universal +control. +""" + +from __future__ import annotations + +import argparse +import json +import math +import sys +import textwrap +from pathlib import Path + +import matplotlib as mpl +import matplotlib.animation as animation +import matplotlib.colors as mcolors +import matplotlib.pyplot as plt +import networkx as nx +import numpy as np +import pandas as pd +from matplotlib.collections import LineCollection +from matplotlib.patches import FancyBboxPatch + + +ROOT = Path(__file__).resolve().parents[1] +OUT = ROOT / "social_media" / "animations" + +BG = "#fbfcfd" +INK = "#17324d" +MUTED = "#617384" +GRID = "#dce4e9" +TEAL = "#1b9e91" +TEAL_DARK = "#087f8c" +BLUE = "#356cb6" +PURPLE = "#7157a8" +GOLD = "#d6a234" +CORAL = "#d56a50" +GRAY = "#919ca5" +LIGHT = "#eef3f5" +TOKEN_COLORS = ["#176B87", "#2A9D8F", "#6C8EAD", "#7A6AAE", "#79A15A", "#4F86A6", "#D59C32"] + + +def configure_style() -> None: + mpl.rcParams.update( + { + "font.family": "DejaVu Sans", + "font.size": 13, + "axes.labelsize": 13, + "xtick.labelsize": 12, + "ytick.labelsize": 11, + "axes.spines.top": False, + "axes.spines.right": False, + "axes.edgecolor": "#9aa9b4", + "axes.linewidth": 1.0, + "figure.facecolor": BG, + "savefig.facecolor": BG, + "text.color": INK, + "axes.labelcolor": INK, + "xtick.color": MUTED, + "ytick.color": MUTED, + } + ) + + +def smoothstep(value: float) -> float: + value = float(np.clip(value, 0.0, 1.0)) + return value * value * (3.0 - 2.0 * value) + + +def fade(frame: int, start: int, stop: int) -> float: + if stop <= start: + return float(frame >= stop) + return smoothstep((frame - start) / (stop - start)) + + +def add_header(fig: plt.Figure, title: str, subtitle: str) -> tuple[mpl.text.Text, mpl.text.Text]: + title_artist = fig.text(0.055, 0.94, title, fontsize=25, fontweight="bold", ha="left", va="top", color=INK) + subtitle_artist = fig.text(0.055, 0.885, subtitle, fontsize=13.5, ha="left", va="top", color=MUTED) + fig.text( + 0.945, + 0.045, + "Markus J. Buehler · MIT", + fontsize=9.5, + ha="right", + va="bottom", + color=MUTED, + ) + return title_artist, subtitle_artist + + +def writer(fps: int) -> animation.FFMpegWriter: + return animation.FFMpegWriter( + fps=fps, + codec="libx264", + bitrate=5000, + extra_args=["-pix_fmt", "yuv420p", "-movflags", "+faststart"], + metadata={"artist": "Markus J. Buehler", "copyright": "CC-BY-4.0"}, + ) + + +def save_clip( + fig: plt.Figure, + update, + frames: int, + fps: int, + path: Path, + poster_path: Path, +) -> None: + path.parent.mkdir(parents=True, exist_ok=True) + movie = animation.FuncAnimation(fig, update, frames=frames, interval=1000 / fps, blit=False) + movie.save(path, writer=writer(fps), dpi=100) + update(frames - 1) + fig.savefig(poster_path, dpi=160, facecolor=BG) + plt.close(fig) + + +def stream_data() -> tuple[str, np.ndarray, list[str], np.ndarray]: + sys.path.insert(0, str(ROOT / "scripts")) + import build_final_paper_figures as paper_figures + + runs = [json.loads(path.read_text()) for path in paper_figures.RUN_PATHS] + indexes = [{row["slug"]: row for row in run["prompts"]} for run in runs] + slug = "heldout-v1-assoc-boundary-attack-05" + records = [index[slug] for index in indexes] + stats = json.loads(paper_figures.STATS_PATH.read_text()) + scaffold = { + row["token"] + for row in stats["open_vocabulary"]["methods"]["jacobian"]["global_scaffold"] + } + depths, tokens, matrix, _ = paper_figures.filtered_prompt_stream(records, scaffold) + return records[0]["prompt_text"], depths, tokens, matrix + + +def interpolate_stream( + depths: np.ndarray, matrix: np.ndarray, cutoff: float +) -> tuple[np.ndarray, np.ndarray]: + if cutoff <= depths[0]: + return np.asarray([depths[0]]), matrix[:, :1] * 0.0 + keep = depths < cutoff + x_values = list(depths[keep]) + values = matrix[:, keep] + if cutoff < depths[-1]: + x_values.append(cutoff) + interp = np.asarray([np.interp(cutoff, depths, row) for row in matrix])[:, None] + values = np.concatenate([values, interp], axis=1) + else: + x_values = list(depths) + values = matrix.copy() + return np.asarray(x_values), values + + +def build_readable_clip(fps: int, seconds: int) -> None: + prompt, depths, tokens, matrix = stream_data() + frames = fps * seconds + fig = plt.figure(figsize=(16, 9), facecolor=BG) + add_header( + fig, + "Vocabulary readout across model depth", + "Target-free candidate words for one materials description", + ) + prompt_ax = fig.add_axes([0.055, 0.67, 0.89, 0.17]) + prompt_ax.axis("off") + prompt_box = FancyBboxPatch( + (0, 0.04), + 1, + 0.88, + boxstyle="round,pad=0.018,rounding_size=0.025", + facecolor="#f1f7f7", + edgecolor="#9fc9c5", + linewidth=1.5, + transform=prompt_ax.transAxes, + ) + prompt_ax.add_patch(prompt_box) + prompt_artist = prompt_ax.text( + 0.025, + 0.5, + "Prompt · " + "\n".join(textwrap.wrap(prompt, width=112)), + transform=prompt_ax.transAxes, + ha="left", + va="center", + fontsize=13.5, + color=INK, + linespacing=1.35, + ) + + ax = fig.add_axes([0.075, 0.16, 0.85, 0.43]) + ax.set_xlim(0, 100) + ax.set_ylim(-1.45, 1.45) + ax.set_xlabel("normalized layer depth →") + ax.set_yticks([]) + ax.set_xticks([0, 20, 40, 60, 80, 100]) + ax.grid(axis="x", color=GRID, linewidth=0.9) + ax.axvspan(38, 92, color="#e9eef2", alpha=0.65, zorder=0) + ax.text(65, 1.32, "registered analysis band", ha="center", va="top", color=MUTED, fontsize=10.5) + + depth_line = ax.axvline(0, color=INK, linewidth=1.5, alpha=0.0, zorder=8) + depth_text = ax.text(0, -1.34, "", ha="center", va="bottom", fontsize=10, color=INK) + label_depths = { + "destructive": 39.0, + "damage": 43.9, + "corrosion": 48.8, + "likewise": 56.1, + "similarly": 63.4, + "conversely": 70.7, + "ditto": 87.8, + } + labels = {} + fills: list[mpl.artist.Artist] = [] + + def update(frame: int): + nonlocal fills + for artist in fills: + artist.remove() + fills = [] + reveal = fade(frame, int(0.16 * frames), int(0.78 * frames)) + cutoff = 100.0 * reveal + x_values, visible = interpolate_stream(depths, matrix, cutoff) + total = visible.sum(axis=0) + lower = -total / 2.0 + for index, token in enumerate(tokens): + upper = lower + visible[index] + fill_artist = ax.fill_between( + x_values, + lower, + upper, + color=TOKEN_COLORS[index % len(TOKEN_COLORS)], + alpha=0.92, + linewidth=0.8, + edgecolor=BG, + zorder=3, + ) + fills.append(fill_artist) + label_depth = label_depths.get(token, float(depths[int(np.argmax(matrix[index]))])) + label_index = int(np.argmin(np.abs(depths - label_depth))) + full_total = matrix.sum(axis=0) + full_lower = -full_total / 2.0 + matrix[:index].sum(axis=0) + y_value = full_lower[label_index] + matrix[index, label_index] / 2.0 + if token not in labels: + labels[token] = ax.text( + label_depth, + y_value, + token, + ha="center", + va="center", + fontsize=10.5, + color=INK, + bbox={"boxstyle": "round,pad=0.13", "facecolor": BG, "edgecolor": "none", "alpha": 0.82}, + zorder=9, + ) + label_alpha = fade(int(cutoff), int(label_depth), int(label_depth + 7)) + labels[token].set_alpha(label_alpha) + labels[token].get_bbox_patch().set_alpha(0.82 * label_alpha) + depth_line.set_xdata([cutoff, cutoff]) + depth_line.set_alpha(0.55 if reveal < 0.995 else 0.0) + depth_text.set_position((cutoff, -1.34)) + depth_text.set_text(f"{cutoff:0.0f}%" if 0 < cutoff < 99.5 else "") + prompt_artist.set_alpha(fade(frame, 0, int(0.12 * frames))) + prompt_box.set_alpha(fade(frame, 0, int(0.12 * frames))) + return fills + list(labels.values()) + [depth_line, depth_text] + + save_clip( + fig, + update, + frames, + fps, + OUT / "01-readable-concepts-landscape.mp4", + OUT / "01-readable-concepts-poster.png", + ) + + +def build_relational_clip(fps: int, seconds: int) -> None: + data = pd.read_csv( + ROOT + / "experiments" + / "neutral-anchored-relational-physics-2026-07-18" + / "law_level_neutral_normalized.csv" + ) + order = {"inverse": -1.0, "neutral": 0.0, "direct": 1.0} + palette = {"inverse": PURPLE, "neutral": GRAY, "direct": TEAL} + rng = np.random.default_rng(20260722) + x = np.asarray([order[value] for value in data["category"]]) + x = x + rng.uniform(-0.11, 0.11, len(data)) + target_y = data["neutral_z"].to_numpy(float) + colors = [palette[value] for value in data["category"]] + delays = rng.uniform(0.0, 0.16, len(data)) + + frames = fps * seconds + fig = plt.figure(figsize=(16, 9), facecolor=BG) + add_header( + fig, + "Matched hidden-state transformations", + "Direct, neutral, and inverse constitutive relations", + ) + ax = fig.add_axes([0.10, 0.16, 0.82, 0.67]) + ax.set_xlim(-1.5, 1.5) + ax.set_ylim(-7.1, 14.0) + ax.set_xticks([-1, 0, 1], ["Inverse law", "Neutral relation", "Direct law"]) + ax.set_ylabel("hidden-state transformation\n(neutral-normalized score)") + ax.axhline(0, color=MUTED, linewidth=1.2) + ax.grid(axis="y", color=GRID, linewidth=0.8) + scatter = ax.scatter(x, np.zeros_like(target_y), s=56, c=colors, alpha=0.0, edgecolor=BG, linewidth=0.7, zorder=4) + + median_lines = {} + for category in ("inverse", "neutral", "direct"): + center = order[category] + median = float(data.loc[data["category"] == category, "neutral_z"].median()) + line = ax.plot([center - 0.24, center + 0.24], [median, median], color=INK, linewidth=4.0, alpha=0.0, zorder=6)[0] + median_lines[category] = line + + def update(frame: int): + global_progress = fade(frame, int(0.12 * frames), int(0.72 * frames)) + local = np.clip((global_progress - delays) / (1.0 - delays), 0.0, 1.0) + local = local * local * (3.0 - 2.0 * local) + current_y = target_y * local + scatter.set_offsets(np.column_stack([x, current_y])) + scatter.set_alpha(min(0.88, fade(frame, int(0.08 * frames), int(0.22 * frames)))) + median_alpha = fade(frame, int(0.65 * frames), int(0.78 * frames)) + for line in median_lines.values(): + line.set_alpha(median_alpha) + return [scatter, *median_lines.values()] + + save_clip( + fig, + update, + frames, + fps, + OUT / "02-relational-physics-landscape.mp4", + OUT / "02-relational-physics-poster.png", + ) + + +def pretty_material(pair_id: str) -> str: + replacements = { + "bronze-36-9": "Bronze", + "cobalt-alloy-56-14": "Cobalt alloy", + "low-carbon-steel-72-18": "Low-carbon steel", + "magnesium-48-12": "Magnesium", + "silver-40-10": "Silver", + "titanium-64-8": "Titanium", + } + return replacements[pair_id] + + +def build_steering_clip(fps: int, seconds: int) -> None: + data = pd.read_csv(ROOT / "experiments" / "relational-grain-steering-v4_condition_effects.csv") + pivot = data.pivot(index="pair_id", columns="relation", values="own_jacobian").sort_index() + pairs = list(pivot.index) + refinements = pivot["refinement"].to_numpy(float) + coarsenings = pivot["coarsening"].to_numpy(float) + + frames = fps * seconds + fig = plt.figure(figsize=(16, 9), facecolor=BG) + add_header( + fig, + "Internal intervention across grain-size conditions", + "The same intervention under refinement and coarsening", + ) + ax = fig.add_axes([0.10, 0.17, 0.82, 0.64]) + ax.set_xlim(-1.45, 1.45) + ax.set_ylim(-1.58, 1.42) + ax.set_xticks([-1, 1], ["Refinement\nsmaller grains", "Coarsening\nlarger grains"]) + ax.set_ylabel("change in higher − lower answer log odds") + ax.axhline(0, color=MUTED, linewidth=1.2) + ax.grid(axis="y", color=GRID, linewidth=0.8) + ax.text(-1.40, 1.32, "toward higher strength", color=TEAL_DARK, fontsize=11, ha="left", va="top") + ax.text(-1.40, -1.48, "toward lower strength", color=PURPLE, fontsize=11, ha="left", va="bottom") + + line_colors = ["#0e6f81", "#198a93", "#2a9d8f", "#4b83a6", "#6c72a5", "#735c9f"] + lines = [] + left_points = [] + right_points = [] + end_labels = [] + for index, pair in enumerate(pairs): + line = ax.plot([-1, 1], [0, 0], color=line_colors[index], linewidth=2.1, alpha=0.0, zorder=3)[0] + left = ax.scatter([-1], [0], s=86, marker="o", color=line_colors[index], edgecolor=BG, linewidth=0.9, alpha=0.0, zorder=5) + right = ax.scatter([1], [0], s=86, marker="D", color=line_colors[index], edgecolor=BG, linewidth=0.9, alpha=0.0, zorder=5) + label = ax.text(1.09, coarsenings[index], pretty_material(pair), fontsize=9.6, color=line_colors[index], va="center", alpha=0.0) + lines.append(line) + left_points.append(left) + right_points.append(right) + end_labels.append(label) + + def update(frame: int): + progress = fade(frame, int(0.12 * frames), int(0.70 * frames)) + base_alpha = fade(frame, int(0.05 * frames), int(0.18 * frames)) + for index in range(len(pairs)): + y_left = refinements[index] * progress + y_right = coarsenings[index] * progress + lines[index].set_data([-1, 1], [y_left, y_right]) + lines[index].set_alpha(0.78 * base_alpha) + left_points[index].set_offsets(np.asarray([[-1, y_left]])) + right_points[index].set_offsets(np.asarray([[1, y_right]])) + left_points[index].set_alpha(base_alpha) + right_points[index].set_alpha(base_alpha) + end_labels[index].set_position((1.09, y_right)) + end_labels[index].set_alpha(fade(frame, int(0.63 * frames), int(0.76 * frames))) + return [*lines, *left_points, *right_points, *end_labels] + + save_clip( + fig, + update, + frames, + fps, + OUT / "03-causal-steering-landscape.mp4", + OUT / "03-causal-steering-poster.png", + ) + + +def family_layout(inventory: pd.DataFrame, edges: pd.DataFrame) -> dict[int, np.ndarray]: + centers = [(-3.7, 1.7), (0.0, 1.7), (3.7, 1.7), (-3.7, -1.7), (0.0, -1.7), (3.7, -1.7)] + positions: dict[int, np.ndarray] = {} + for family_index, family in enumerate(inventory["family_id"].drop_duplicates()): + nodes = inventory.loc[inventory["family_id"] == family, "array_index"].astype(int).tolist() + graph = nx.Graph() + graph.add_nodes_from(nodes) + family_edges = edges[ + edges["source_index"].isin(nodes) & edges["target_index"].isin(nodes) + ] + graph.add_edges_from( + zip(family_edges["source_index"].astype(int), family_edges["target_index"].astype(int)) + ) + local = nx.spring_layout(graph, seed=100 + family_index, k=0.62, iterations=250) + center = np.asarray(centers[family_index], dtype=float) + for node, value in local.items(): + positions[int(node)] = center + 1.12 * np.asarray(value) + return positions + + +def build_graph_clip(fps: int, seconds: int) -> None: + inventory = pd.read_csv( + ROOT / "experiments" / "option-free-relation-graph-2026-07-17" / "prompt_inventory.csv" + ) + edges = pd.read_csv( + ROOT / "experiments" / "relation-graph-visualization-2026-07-18" / "primary_graph_edges.csv" + ) + positions = family_layout(inventory, edges) + node_order = inventory["array_index"].astype(int).tolist() + xy = np.asarray([positions[index] for index in node_order]) + edge_segments = [ + [positions[int(row.source_index)], positions[int(row.target_index)]] + for row in edges.itertuples() + ] + edge_colors = [TEAL if bool(row.same_outcome) else PURPLE for row in edges.itertuples()] + + positive_outcomes = {"higher", "greater"} + physical_positive = inventory["expected_outcome"].isin(positive_outcomes).to_numpy() + numerical_positive = inventory["numeric_direction"].eq("increase").to_numpy() + positive_rgba = np.asarray(mcolors.to_rgba(TEAL)) + negative_rgba = np.asarray(mcolors.to_rgba(PURPLE)) + physical_colors = np.asarray([positive_rgba if value else negative_rgba for value in physical_positive]) + numerical_colors = np.asarray([positive_rgba if value else negative_rgba for value in numerical_positive]) + + frames = fps * seconds + fig = plt.figure(figsize=(16, 9), facecolor=BG) + add_header( + fig, + "Option-free relation graph", + "Materials questions grouped into six mechanism families", + ) + ax = fig.add_axes([0.055, 0.13, 0.84, 0.72]) + ax.set_xlim(-5.45, 5.45) + ax.set_ylim(-3.45, 3.45) + ax.axis("off") + edge_collection = LineCollection(edge_segments, colors=edge_colors, linewidths=0.9, alpha=0.0, zorder=1) + ax.add_collection(edge_collection) + node_scatter = ax.scatter(xy[:, 0], xy[:, 1], s=68, c=physical_colors, edgecolor=BG, linewidth=0.7, alpha=0.0, zorder=3) + + short_names = { + "obstacle-spacing-orowan": "Orowan stress", + "porosity-modulus": "Porosity / modulus", + "pearlite-spacing-strength": "Pearlite / strength", + "dislocation-density-strength": "Dislocations / strength", + "particle-fraction-modulus": "Particles / modulus", + "crosslink-density-modulus": "Crosslinks / modulus", + } + family_labels = [] + for family in inventory["family_id"].drop_duplicates(): + nodes = inventory.loc[inventory["family_id"] == family, "array_index"].astype(int) + center = np.mean([positions[index] for index in nodes], axis=0) + label = ax.text(center[0], center[1] + 1.24, short_names[family], ha="center", va="center", fontsize=10.5, color=INK, fontweight="bold", alpha=0.0) + family_labels.append(label) + + mode = fig.text( + 0.765, + 0.105, + "Color: physical outcome", + ha="center", + va="bottom", + fontsize=12.5, + color=INK, + bbox={"boxstyle": "round,pad=0.48", "facecolor": LIGHT, "edgecolor": "#cbd7de"}, + alpha=0.0, + ) + legend_positive = fig.text(0.865, 0.129, "● positive", fontsize=11.5, color=TEAL, ha="left", alpha=0.0) + legend_negative = fig.text(0.865, 0.094, "● negative", fontsize=11.5, color=PURPLE, ha="left", alpha=0.0) + + def update(frame: int): + build_alpha = fade(frame, int(0.04 * frames), int(0.28 * frames)) + edge_collection.set_alpha(0.24 * build_alpha) + node_scatter.set_alpha(0.94 * build_alpha) + for label in family_labels: + label.set_alpha(build_alpha) + mode_alpha = fade(frame, int(0.18 * frames), int(0.32 * frames)) + mode.set_alpha(mode_alpha) + mode.get_bbox_patch().set_alpha(mode_alpha) + legend_positive.set_alpha(mode_alpha) + legend_negative.set_alpha(mode_alpha) + + transition = fade(frame, int(0.47 * frames), int(0.66 * frames)) + colors = physical_colors * (1.0 - transition) + numerical_colors * transition + node_scatter.set_facecolors(colors) + if transition < 0.5: + mode.set_text("Color: physical outcome") + legend_positive.set_text("● positive") + legend_negative.set_text("● negative") + else: + mode.set_text("Color: numerical direction") + legend_positive.set_text("● increase") + legend_negative.set_text("● decrease") + return [edge_collection, node_scatter, mode, legend_positive, legend_negative, *family_labels] + + save_clip( + fig, + update, + frames, + fps, + OUT / "04-graph-identifiability-landscape.mp4", + OUT / "04-graph-identifiability-poster.png", + ) + + +def main() -> None: + parser = argparse.ArgumentParser() + parser.add_argument( + "--only", + choices=["all", "readable", "relational", "steering", "graph"], + default="all", + ) + parser.add_argument("--fps", type=int, default=24) + parser.add_argument("--seconds", type=int, default=10) + args = parser.parse_args() + configure_style() + OUT.mkdir(parents=True, exist_ok=True) + builders = { + "readable": build_readable_clip, + "relational": build_relational_clip, + "steering": build_steering_clip, + "graph": build_graph_clip, + } + selected = builders if args.only == "all" else {args.only: builders[args.only]} + for name, build in selected.items(): + print(f"rendering {name} animation") + build(args.fps, args.seconds) + print(f"wrote {len(selected)} animation(s) to {OUT}") + + +if __name__ == "__main__": + main() diff --git a/jlens_materials/scripts/build_social_media_hidden_state_gifs.py b/jlens_materials/scripts/build_social_media_hidden_state_gifs.py new file mode 100644 index 0000000000000000000000000000000000000000..196e8e8ff46a70f331d64d1575ff039af662003b --- /dev/null +++ b/jlens_materials/scripts/build_social_media_hidden_state_gifs.py @@ -0,0 +1,97 @@ +#!/usr/bin/env python3 +"""Create palette-optimized GIF variants from the hidden-state rotation MP4s.""" + +from __future__ import annotations + +import argparse +import shutil +import subprocess +from dataclasses import dataclass +from pathlib import Path + + +ROOT = Path(__file__).resolve().parents[1] +HERO_DIR = ROOT / "social_media" / "hero" + + +@dataclass(frozen=True) +class Preset: + name: str + width: int + fps: int + speed: float + + +PRESETS = ( + Preset("quality", width=1280, fps=18, speed=1.0), + Preset("social", width=960, fps=12, speed=1.0), + Preset("teaser", width=1280, fps=18, speed=2.0), +) + + +def render_gif(source: Path, destination: Path, preset: Preset) -> None: + filter_graph = ( + f"[0:v]setpts=PTS/{preset.speed:g}," + f"fps={preset.fps},scale={preset.width}:-2:flags=lanczos," + "split[palette_source][gif_source];" + "[palette_source]palettegen=max_colors=256:stats_mode=diff[palette];" + "[gif_source][palette]paletteuse=" + "dither=sierra2_4a:diff_mode=rectangle" + ) + subprocess.run( + [ + "ffmpeg", + "-hide_banner", + "-loglevel", + "error", + "-y", + "-i", + str(source), + "-filter_complex", + filter_graph, + "-loop", + "0", + str(destination), + ], + check=True, + ) + + +def main() -> None: + parser = argparse.ArgumentParser( + description="Build white- and black-background GIFs from the 3D MP4 loops." + ) + parser.add_argument( + "--background", + choices=("white", "black", "both"), + default="both", + ) + parser.add_argument( + "--preset", + choices=tuple(preset.name for preset in PRESETS) + ("all",), + default="all", + ) + args = parser.parse_args() + + if shutil.which("ffmpeg") is None: + raise SystemExit("ffmpeg is required but was not found on PATH") + + backgrounds = ("white", "black") if args.background == "both" else (args.background,) + presets = PRESETS if args.preset == "all" else tuple( + preset for preset in PRESETS if preset.name == args.preset + ) + + for background in backgrounds: + source = HERO_DIR / f"movements-3d-rotation-{background}.mp4" + if not source.is_file(): + raise SystemExit(f"missing source animation: {source}") + for preset in presets: + destination = HERO_DIR / ( + f"movements-3d-rotation-{background}-{preset.name}.gif" + ) + print(f"rendering {destination.name}", flush=True) + render_gif(source, destination, preset) + + +if __name__ == "__main__": + main() diff --git a/jlens_materials/scripts/build_steering_prompt_inventory_tex.py b/jlens_materials/scripts/build_steering_prompt_inventory_tex.py new file mode 100644 index 0000000000000000000000000000000000000000..0b60925259fb78215d60ebea248376464df3bd49 --- /dev/null +++ b/jlens_materials/scripts/build_steering_prompt_inventory_tex.py @@ -0,0 +1,220 @@ +#!/usr/bin/env python3 +"""Build the manuscript's broad and prospective steering-prompt inventory.""" + +from __future__ import annotations + +import json +import csv +from collections import defaultdict +from pathlib import Path + + +ROOT = Path(__file__).resolve().parents[1] +OUT = ROOT / "paper/steering_prompt_inventory.tex" +V3_RESULTS_OUT = ROOT / "paper/steering_v3_result_table.tex" +V4_RESULTS_OUT = ROOT / "paper/steering_v4_result_table.tex" + +FAMILY_LABELS = { + "intergranular-corrosion": "Intergranular corrosion", + "martensitic-transformation": "Martensitic transformation", + "grain-size-strengthening": "Grain-size strengthening", +} + + +def tex(value: object) -> str: + text = str(value) + replacements = { + "\\": r"\textbackslash{}", + "&": r"\&", + "%": r"\%", + "$": r"\$", + "#": r"\#", + "_": r"\_", + "{": r"\{", + "}": r"\}", + "~": r"\textasciitilde{}", + "^": r"\textasciicircum{}", + } + return "".join(replacements.get(char, char) for char in text) + + +def load(name: str) -> dict: + return json.loads((ROOT / name).read_text()) + + +def prompt_table(rows: list[dict]) -> list[str]: + grouped: dict[tuple[str, str], list[dict]] = defaultdict(list) + order: list[tuple[str, str]] = [] + for row in rows: + key = (row["family_id"], row["condition_id"]) + if key not in grouped: + order.append(key) + grouped[key].append(row) + + lines = [ + r"\begin{longtable}{>{\raggedright\arraybackslash}p{0.20\linewidth}>{\raggedright\arraybackslash}p{0.74\linewidth}}", + r"\toprule", + r"Condition and frozen answer & Exact rendered prompts and clean choices \\", + r"\midrule", + r"\endhead", + ] + current_family = None + for family_id, condition_id in order: + family_rows = grouped[(family_id, condition_id)] + if family_id != current_family: + lines.extend([ + rf"\multicolumn{{2}}{{l}}{{\textbf{{{tex(FAMILY_LABELS[family_id])}}}}} \\", + r"\addlinespace[2pt]", + ]) + current_family = family_id + expected = family_rows[0].get("expected_outcome") + expected_text = tex(expected) if expected is not None else "none (near-threshold)" + regime = tex(family_rows[0].get("regime", "not applicable")) + breakable_condition_id = tex(condition_id).replace("-", r"-\allowbreak{}") + left = ( + rf"\texttt{{{breakable_condition_id}}}\newline " + rf"Regime: {regime}\newline Frozen answer: \texttt{{{expected_text}}}" + ) + rendered = [] + for row in family_rows: + clean = row["clean"] + clean_choice = tex(clean["choice"]) + correctness = row.get("clean_expected_correct") + if correctness is None: + status = "not scored" + else: + status = "correct" if correctness else "incorrect" + rendered.append( + rf"\textbf{{{tex(row['presentation_order'])}.}} ``{tex(row['user'])}'' " + rf"Clean choice: \texttt{{{clean_choice}}} ({status})." + ) + right = r"\newline\smallskip ".join(rendered) + lines.extend([rf"{left} & {right} \\", r"\addlinespace[4pt]"]) + lines.extend([r"\bottomrule", r"\end{longtable}"]) + return lines + + +def main() -> None: + v3_manifest = load("experiments/semantic-steering-v3-preregistration.json") + v3_raw = load("experiments/semantic-steering-v3_raw.json") + v4_raw = load("experiments/relational-grain-steering-v4_raw.json") + + lines = [ + "% Generated by scripts/build_steering_prompt_inventory_tex.py.", + "% Do not edit manually; regenerate from the frozen manifests and raw preflight rows.", + r"\begingroup", + r"\small", + r"\setlength{\LTpre}{0.25em}", + r"\setlength{\LTpost}{0.45em}", + r"\sloppy", + r"\subsection*{S7.1 Frozen direction-fitting prompts}", + "Each sentence below was run independently. Its final-prompt state contributed one unit gradient; the three gradients within a family were averaged and renormalized. The outcome answer words were excluded.", + ] + for family in v3_manifest["families"]: + positive = ", ".join(rf"\texttt{{{tex(word)}}}" for word in family["concept_positive"]) + negative = ", ".join(rf"\texttt{{{tex(word)}}}" for word in family["concept_negative"]) + lines.append(rf"\paragraph{{{tex(FAMILY_LABELS[family['family_id']])}.}} Positive ensemble: {positive}. Negative ensemble: {negative}.") + lines.append(r"\begin{enumerate}") + for prompt in family["direction_fit_prompts"]: + lines.append(rf"\item ``{tex(prompt)}''") + lines.append(r"\end{enumerate}") + + lines.extend([ + r"\clearpage", + r"\subsection*{S7.2 Broad mechanism-steering prompts}", + "These are all 60 exact model inputs: 30 physical conditions, each shown in both ordered-answer forms. The table also records the clean choice before intervention. Near-threshold cases deliberately have no frozen correct answer.", + *prompt_table(v3_raw["preflight"]), + r"\clearpage", + r"\subsection*{S7.3 Prospective relational matched-pair prompts}", + "These are all 24 exact model inputs frozen for the prospective relation-reversal test. The confirmation reused the broad screen's direction-fitting prompts without alteration.", + *prompt_table(v4_raw["preflight"]), + r"\endgroup", + "", + ]) + OUT.write_text("\n".join(lines)) + print(OUT.relative_to(ROOT)) + + with (ROOT / "experiments/semantic-steering-v3_condition_effects.csv").open() as handle: + v3_conditions = list(csv.DictReader(handle)) + with (ROOT / "experiments/relational-grain-steering-v4_condition_effects.csv").open() as handle: + v4_conditions = list(csv.DictReader(handle)) + + v3_by_key = { + (row["target_family_id"], row["condition_id"]): row + for row in v3_conditions + } + v3_result_lines = [ + "% Generated by scripts/build_steering_prompt_inventory_tex.py.", + "% Do not edit manually; regenerate from the frozen result CSV files.", + r"\subsection*{S5.1 Every broad-screen physical-condition endpoint}", + "The matched, wrong-mechanism, direct, and random columns are changes in positive-answer minus negative-answer log odds from the negative to positive dose endpoint. Values are averaged over answer orders and over the registered replicates for each control class.", + r"\begingroup\scriptsize", + r"\begin{longtable}{>{\raggedright\arraybackslash}p{0.34\linewidth}lrrrr}", + r"\toprule", + r"Physical condition & Regime & Matched J & Wrong & Direct & Random \\", + r"\midrule", + r"\endhead", + ] + for family in v3_manifest["families"]: + family_id = family["family_id"] + v3_result_lines.extend([ + rf"\multicolumn{{6}}{{l}}{{\textbf{{{tex(FAMILY_LABELS[family_id])}}}}} \\", + r"\addlinespace[2pt]", + ]) + for prompt in family["confirmation_prompts"]: + row = v3_by_key[(family_id, prompt["condition_id"])] + v3_result_lines.extend([ + ( + rf"\texttt{{{tex(prompt['condition_id'])}}} & {tex(row['regime'])} & " + rf"${float(row['own_jacobian']):+.3f}$ & " + rf"${float(row['mean_wrong_mechanisms']):+.3f}$ & " + rf"${float(row['direct_decoder']):+.3f}$ & " + rf"${float(row['random_directions']):+.3f}$ \\" + ), + r"\addlinespace[2pt]", + ]) + v3_result_lines.extend([ + r"\bottomrule", + r"\end{longtable}", + r"\endgroup", + "", + ]) + V3_RESULTS_OUT.write_text("\n".join(v3_result_lines)) + print(V3_RESULTS_OUT.relative_to(ROOT)) + + v4_result_lines = [ + "% Generated by scripts/build_steering_prompt_inventory_tex.py.", + "% Do not edit manually; regenerate from the frozen result CSV files.", + r"\subsection*{S6.1 Every prospective matched condition}", + "Raw matched is the higher-minus-lower endpoint. Expected-aligned reverses the sign for coarsening, so a positive value means movement toward the frozen correct answer. Control columns use the same alignment.", + r"\begingroup\scriptsize", + r"\begin{longtable}{>{\raggedright\arraybackslash}p{0.25\linewidth}lrrrrr}", + r"\toprule", + r"Material pair & Relation & Raw J & Aligned J & Wrong & Direct & Random \\", + r"\midrule", + r"\endhead", + ] + for row in v4_conditions: + v4_result_lines.extend([ + ( + rf"\texttt{{{tex(row['pair_id'])}}} & {tex(row['relation'])} & " + rf"${float(row['own_jacobian']):+.3f}$ & " + rf"${float(row['own_expected_aligned']):+.3f}$ & " + rf"${float(row['wrong_expected_aligned']):+.3f}$ & " + rf"${float(row['direct_expected_aligned']):+.3f}$ & " + rf"${float(row['random_expected_aligned']):+.3f}$ \\" + ), + r"\addlinespace[2pt]", + ]) + v4_result_lines.extend([ + r"\bottomrule", + r"\end{longtable}", + r"\endgroup", + "", + ]) + V4_RESULTS_OUT.write_text("\n".join(v4_result_lines)) + print(V4_RESULTS_OUT.relative_to(ROOT)) + + +if __name__ == "__main__": + main() diff --git a/jlens_materials/scripts/build_symbolic_monotonicity_lens.py b/jlens_materials/scripts/build_symbolic_monotonicity_lens.py new file mode 100644 index 0000000000000000000000000000000000000000..af588e5ebd2ae4512a60e4242301bd1e748c1c9f --- /dev/null +++ b/jlens_materials/scripts/build_symbolic_monotonicity_lens.py @@ -0,0 +1,144 @@ +#!/usr/bin/env python3 +"""Build anonymous symbolic equations for a transferable monotonicity lens.""" + +from __future__ import annotations + +import hashlib +import json +from datetime import datetime, timezone +from pathlib import Path + + +ROOT = Path(__file__).resolve().parents[1] +OUT = ROOT / "experiments" / "symbolic-monotonicity-lens-2026-07-18" +MANIFEST = OUT / "prompt_manifest.json" +PROTOCOL = OUT / "protocol.json" +RUNNER = ROOT / "scripts" / "run_symbolic_monotonicity_lens.py" + + +VARIABLES = [ + ("u", "x"), + ("v", "z"), + ("q", "r"), + ("m", "s"), + ("a_1", "b_1"), + ("a_2", "b_2"), + ("eta", "xi"), + ("phi", "chi"), + ("omega", "lambda"), + ("Q_1", "X_1"), + ("Q_2", "X_2"), + ("response_1", "control_1"), + ("response_2", "control_2"), + ("output_alpha", "input_alpha"), + ("output_beta", "input_beta"), + ("dependent_z", "independent_z"), +] + + +def formulas(y: str, x: str) -> list[tuple[str, int, str]]: + return [ + (f"{y} = C {x}", 1, "direct-linear"), + (f"{y} = C {x}^2", 1, "direct-power"), + (f"{y} = C sqrt({x})", 1, "direct-root"), + (f"{y} = y_0 + C {x}^3", 1, "direct-offset"), + (f"{y} = C/{x}", -1, "inverse-linear"), + (f"{y} = C/{x}^2", -1, "inverse-power"), + (f"{y} = C {x}^(-1)", -1, "inverse-negative-power"), + (f"{y} = y_0 + C/sqrt({x})", -1, "inverse-offset"), + ] + + +def sha256(path: Path) -> str: + return hashlib.sha256(path.read_bytes()).hexdigest() + + +def write_json(path: Path, payload: object) -> None: + path.parent.mkdir(parents=True, exist_ok=True) + tmp = path.with_suffix(path.suffix + ".tmp") + tmp.write_text(json.dumps(payload, indent=2, sort_keys=True) + "\n") + tmp.replace(path) + + +def main() -> None: + prompts = [] + for variable_index, (response, control) in enumerate(VARIABLES): + for formula, sign, family in formulas(response, control): + prompt_id = f"symbolic-{variable_index:02d}-{family}" + user = ( + "Apply the same two-stage physical test to every case. " + "Stage 1: use only the constitutive equation, not associations " + "with material names, to determine the monotonic sign of the " + "response with respect to the changed control while all other " + "quantities are fixed. For an anonymous constitutive relation, " + f"the response is {response}, the control is {control}, and " + f"the relation is {formula}. C is positive; y_0, when present, " + "is fixed. Complete this relation-sign step silently. §" + ) + prompts.append( + { + "prompt_id": prompt_id, + "variable_index": variable_index, + "response": response, + "control": control, + "formula": formula, + "family": family, + "law_sign": sign, + "user": user, + } + ) + manifest = { + "study_id": "symbolic-monotonicity-lens-2026-07-18", + "markers": {"law": "§"}, + "prompts": prompts, + "dimensions": { + "n_prompts": len(prompts), + "n_variable_pairs": len(VARIABLES), + "n_formula_families": 8, + }, + } + write_json(MANIFEST, manifest) + protocol = { + "study_id": manifest["study_id"], + "status": "developmental-symbolic-transfer", + "frozen_at": datetime.now(timezone.utc).isoformat(), + "model": "google/gemma-4-E4B-it", + "model_revision": "a4c2d58be94dda072b918d9db64ee85c8ed34e3f", + "purpose": ( + "Fit a low-capacity monotonicity readout on anonymous explicit " + "equations and test transfer to named materials laws and implicit " + "algebraic rearrangements." + ), + "inputs": { + "prompt_manifest": { + "path": str(MANIFEST.relative_to(ROOT)), + "sha256": sha256(MANIFEST), + }, + "runner": { + "path": str(RUNNER.relative_to(ROOT)), + "sha256": sha256(RUNNER), + }, + }, + "guardrails": [ + "No materials name appears in symbolic training prompts.", + "Only explicit response-equals-control formulas appear in training.", + "Material formula surface B includes unseen implicit rearrangements.", + "Raw word and character TF-IDF use the identical train/test split.", + "All layers are retained; development transfer selects a candidate layer.", + ], + } + write_json(PROTOCOL, protocol) + (OUT / "PROTOCOL.md").write_text( + "# Anonymous symbolic monotonicity lens\n\n" + "A direction is learned from hidden states after anonymous direct and " + "inverse equations. It is then tested without refitting on named " + "materials relations. Training equations always isolate the response; " + "many material test equations do not, so successful surface-B transfer " + "requires more than matching the explicit training syntax.\n" + ) + print(f"frozen {len(prompts)} symbolic prompts") + print(f"protocol sha256: {sha256(PROTOCOL)}") + + +if __name__ == "__main__": + main() diff --git a/jlens_materials/scripts/freeze_final_state_physics_abstraction.py b/jlens_materials/scripts/freeze_final_state_physics_abstraction.py new file mode 100644 index 0000000000000000000000000000000000000000..70f513fd4bc23c00c1f93cb6fe613f901a285f3b --- /dev/null +++ b/jlens_materials/scripts/freeze_final_state_physics_abstraction.py @@ -0,0 +1,99 @@ +#!/usr/bin/env python3 +"""Freeze the analysis-only final-state physics-abstraction audit.""" + +from __future__ import annotations + +import hashlib +import json +from datetime import datetime, timezone +from pathlib import Path + + +ROOT = Path(__file__).resolve().parents[1] +OUT = ROOT / "experiments" / "final-state-physics-abstraction-2026-07-18" +PROTOCOL = OUT / "protocol.json" +ANALYZER = ROOT / "scripts" / "analyze_final_state_physics_abstraction.py" +COHORTS = { + "arbitrary_code": ROOT + / "experiments" + / "abstract-physics-composition-2026-07-18", + "semantic_answer": ROOT + / "experiments" + / "abstract-physics-semantic-2026-07-18", +} + + +def sha256(path: Path) -> str: + return hashlib.sha256(path.read_bytes()).hexdigest() + + +def main() -> None: + if (OUT / "statistics.json").exists(): + raise RuntimeError("refusing to refreeze after statistics exist") + inputs = { + "analyzer": { + "path": str(ANALYZER.relative_to(ROOT)), + "sha256": sha256(ANALYZER), + } + } + for name, path in COHORTS.items(): + inputs[name] = { + "manifest": { + "path": str((path / "prompt_manifest.json").relative_to(ROOT)), + "sha256": sha256(path / "prompt_manifest.json"), + }, + "raw": { + "path": str((path / "raw.json").relative_to(ROOT)), + "sha256": sha256(path / "raw.json"), + }, + "states": { + "path": str((path / "representations.npz").relative_to(ROOT)), + "sha256": sha256(path / "representations.npz"), + }, + } + payload = { + "study_id": "final-state-physics-abstraction-2026-07-18", + "status": "frozen-before-replication-final-state-inspection", + "frozen_at": datetime.now(timezone.utc).isoformat(), + "inputs": inputs, + "layer_selection": ( + "Within each cohort, discovery laws only; maximize mean of four " + "physical-outcome decodings that reverse law orientation and answer " + "order; earliest layer breaks ties." + ), + "primary_endpoint": ( + "At the discovery-selected final-prompt layer, train in one " + "discovery orientation/order cell and test each unseen replication " + "law in the opposite orientation/order cell; average the two answer " + "order directions within law." + ), + "independent_unit": "held-out constitutive law (n=8 per cohort)", + "cross_vocabulary_endpoint": ( + "Use the discovery-selected layer of the training cohort and " + "transfer its centroid directions to replication laws in the other " + "answer vocabulary, again reversing orientation and order." + ), + "success_rule": { + "mean_law_auc_minimum": 0.70, + "bootstrap_95_lower_above": 0.50, + "positive_laws_minimum": 6, + "laws_total": 8, + }, + "bootstrap": {"seed": 20260720, "resamples": 30000}, + "guardrails": [ + "No replication final-position AUC was inspected before freezing.", + "No layer or regularization parameter is selected on replication laws.", + "The decoder is a parameter-free centroid direction.", + "Both numerical-to-physical polarity and answer order reverse at test.", + "Every result, including failures, is retained.", + ], + } + OUT.mkdir(parents=True, exist_ok=True) + temporary = PROTOCOL.with_suffix(".json.tmp") + temporary.write_text(json.dumps(payload, indent=2, sort_keys=True) + "\n") + temporary.replace(PROTOCOL) + print(f"protocol sha256: {sha256(PROTOCOL)}") + + +if __name__ == "__main__": + main() diff --git a/jlens_materials/scripts/freeze_late_physics_replication.py b/jlens_materials/scripts/freeze_late_physics_replication.py new file mode 100644 index 0000000000000000000000000000000000000000..16481d7badc4e4d76071547b1845bf1eac821b32 --- /dev/null +++ b/jlens_materials/scripts/freeze_late_physics_replication.py @@ -0,0 +1,415 @@ +#!/usr/bin/env python3 +"""Freeze a disjoint replication of the late physical-equivalence transition.""" + +from __future__ import annotations + +import json +from datetime import datetime, timezone +from pathlib import Path + +import numpy as np +from transformers import AutoTokenizer + +import freeze_lexical_adversarial_representation as base + +ROOT = Path(__file__).resolve().parents[1] +OUT = ROOT / "experiments" / "late-physics-representation-replication-2026-07-17" +MANIFEST = OUT / "prompt_manifest.json" +PROTOCOL = OUT / "protocol.json" +PROTOCOL_MD = OUT / "PROTOCOL.md" +RUNNER = ROOT / "scripts" / "run_lexical_adversarial_representation.py" +DISCOVERY_STATS = ( + ROOT / "experiments" / "lexical-adversarial-representation-2026-07-17" + / "statistics.json" +) + + +def compact(value: float) -> str: + if value == 0: + return "0" + if abs(value) >= 10000 or abs(value) < 0.01: + exponent = int(np.floor(np.log10(abs(value)))) + coefficient = value / (10 ** exponent) + return f"{coefficient:g}e{exponent}" + return base.fmt(value, 4) + + +def orowan_stem(case: dict, start: float, end: float, paraphrase: bool) -> str: + material = case["material"] + if not paraphrase: + return ( + f"In a {material}, mean clear spacing between non-shearable " + f"obstacles changes from {base.fmt(start, 1)} nanometers to " + f"{base.fmt(end, 1)} nanometers while particle size, volume " + "fraction, dislocation character, shear modulus, and Burgers " + "vector remain fixed. What happens to Orowan bypass stress?" + ) + direction = "narrows" if end < start else "widens" + return ( + f"For the same {material}, every obstacle attribute is unchanged except " + f"the slip-plane gap, which {direction} from " + f"{base.fmt(start / 1000)} micrometers to " + f"{base.fmt(end / 1000)} micrometers. Select the trend in stress " + "required for a dislocation to bow between impenetrable particles." + ) + + +def porosity_stem(case: dict, start: float, end: float, paraphrase: bool) -> str: + material = case["material"] + if not paraphrase: + return ( + f"A {material} specimen changes from {base.fmt(start, 1)} percent " + f"to {base.fmt(end, 1)} percent connected porosity while solid " + "composition, grain structure, moisture, and crack density remain " + "fixed. What happens to effective elastic modulus?" + ) + direction = "falls" if end < start else "rises" + return ( + f"In the same {material}, void volume fraction {direction} from " + f"{base.fmt(start / 100)} to {base.fmt(end / 100)} while the " + "load-bearing solid skeleton and all non-porosity defects are " + "unchanged. Select the trend in macroscopic stiffness." + ) + + +def pearlite_stem(case: dict, start: float, end: float, paraphrase: bool) -> str: + material = case["material"] + if not paraphrase: + return ( + f"In fully pearlitic {material}, ferrite-cementite interlamellar " + f"spacing changes from {base.fmt(start, 1)} nanometers to " + f"{base.fmt(end, 1)} nanometers while composition, colony size, " + "texture, prior-austenite grain size, and porosity remain fixed. " + "What happens to yield strength?" + ) + direction = "contracts" if end < start else "expands" + return ( + f"The same pearlitic {material} keeps its chemistry and colony " + f"architecture, but the separation between adjacent ferrite and " + f"cementite plates {direction} from {base.fmt(start / 1000)} " + f"micrometers to {base.fmt(end / 1000)} micrometers. Select the trend " + "in flow strength." + ) + + +def dislocation_stem(case: dict, start: float, end: float, paraphrase: bool) -> str: + material = case["material"] + if not paraphrase: + return ( + f"A {material} specimen changes dislocation density from " + f"{compact(start)} per square meter to {compact(end)} per square " + "meter while grain size, solute content, precipitates, texture, " + "porosity, and temperature remain fixed. What happens to yield " + "strength under Taylor strengthening?" + ) + direction = "rises" if end > start else "falls" + return ( + f"In the same {material}, line-defect content {direction} from " + f"{compact(start / 1e12)} per square micrometer to " + f"{compact(end / 1e12)} per square micrometer. No grain, solute, " + "particle, texture, pore, or thermal variable changes. Select the " + "trend in flow stress." + ) + + +def particle_fraction_stem(case: dict, start: float, end: float, paraphrase: bool) -> str: + material = case["material"] + particles = case["particles"] + if not paraphrase: + return ( + f"A {material} composite changes from {base.fmt(start, 1)} percent " + f"to {base.fmt(end, 1)} percent {particles} particle volume " + "fraction while matrix composition, particle size, dispersion, " + "porosity, interface quality, and loading direction remain fixed. " + "What happens to effective elastic modulus?" + ) + direction = "grows" if end > start else "shrinks" + return ( + f"In the same {material}, the fraction of stiff {particles} " + f"reinforcement {direction} from {base.fmt(start / 100)} to " + f"{base.fmt(end / 100)} of total volume. Matrix, particle geometry, " + "distribution, void content, bonding, and load axis are unchanged. " + "Select the trend in composite stiffness." + ) + + +def crosslink_stem(case: dict, start: float, end: float, paraphrase: bool) -> str: + material = case["material"] + if not paraphrase: + return ( + f"A {material} network changes crosslink density from " + f"{base.fmt(start, 1)} moles per cubic meter to " + f"{base.fmt(end, 1)} moles per cubic meter while temperature, " + "chain chemistry, filler content, crystallinity, and strain rate " + "remain fixed. What happens to rubbery shear modulus?" + ) + direction = "increases" if end > start else "decreases" + return ( + f"For the same {material}, the number of elastically active network " + f"junctions {direction} from {base.fmt(start / 1000)} moles per liter " + f"to {base.fmt(end / 1000)} moles per liter. Thermal state, polymer " + "chemistry, fillers, order, and loading rate do not change. Select the " + "trend in small-strain network stiffness." + ) + + +FAMILIES = [ + { + "family_id": "obstacle-spacing-orowan", + "family_name": "Obstacle spacing and Orowan stress", + "outcome_positive": "higher", + "outcome_negative": "lower", + "positive_numeric_direction": "decrease", + "render": orowan_stem, + "cases": [ + {"case_id": "nickel-superalloy-180-60", "material": "nickel-based superalloy", "high": 180, "low": 60}, + {"case_id": "aluminum-copper-150-50", "material": "aluminum-copper alloy", "high": 150, "low": 50}, + {"case_id": "ferritic-steel-120-40", "material": "ferritic steel", "high": 120, "low": 40}, + {"case_id": "copper-alloy-90-30", "material": "copper alloy", "high": 90, "low": 30}, + ], + }, + { + "family_id": "porosity-modulus", + "family_name": "Porosity and elastic modulus", + "outcome_positive": "greater", + "outcome_negative": "smaller", + "positive_numeric_direction": "decrease", + "render": porosity_stem, + "cases": [ + {"case_id": "sintered-alumina-12-3", "material": "sintered alumina", "high": 12, "low": 3}, + {"case_id": "porous-titanium-20-5", "material": "porous titanium", "high": 20, "low": 5}, + {"case_id": "silicon-nitride-8-2", "material": "silicon nitride", "high": 8, "low": 2}, + {"case_id": "sandstone-16-4", "material": "quartz-rich sandstone", "high": 16, "low": 4}, + ], + }, + { + "family_id": "pearlite-spacing-strength", + "family_name": "Pearlite spacing and yield strength", + "outcome_positive": "higher", + "outcome_negative": "lower", + "positive_numeric_direction": "decrease", + "render": pearlite_stem, + "cases": [ + {"case_id": "eutectoid-steel-300-100", "material": "eutectoid steel", "high": 300, "low": 100}, + {"case_id": "rail-steel-240-80", "material": "rail steel", "high": 240, "low": 80}, + {"case_id": "wire-steel-180-60", "material": "high-carbon wire steel", "high": 180, "low": 60}, + {"case_id": "pearlitic-iron-360-120", "material": "pearlitic cast iron", "high": 360, "low": 120}, + ], + }, + { + "family_id": "dislocation-density-strength", + "family_name": "Dislocation density and flow strength", + "outcome_positive": "higher", + "outcome_negative": "lower", + "positive_numeric_direction": "increase", + "render": dislocation_stem, + "cases": [ + {"case_id": "copper-1e14-1e12", "material": "high-purity copper", "high": 1e14, "low": 1e12}, + {"case_id": "aluminum-8e13-8e11", "material": "high-purity aluminum", "high": 8e13, "low": 8e11}, + {"case_id": "nickel-5e14-5e12", "material": "nickel", "high": 5e14, "low": 5e12}, + {"case_id": "ferritic-steel-2e14-2e12", "material": "ferritic steel", "high": 2e14, "low": 2e12}, + ], + }, + { + "family_id": "particle-fraction-modulus", + "family_name": "Stiff-particle fraction and modulus", + "outcome_positive": "greater", + "outcome_negative": "smaller", + "positive_numeric_direction": "increase", + "render": particle_fraction_stem, + "cases": [ + {"case_id": "aluminum-sic-30-10", "material": "aluminum-matrix", "particles": "silicon-carbide", "high": 30, "low": 10}, + {"case_id": "magnesium-alumina-24-8", "material": "magnesium-matrix", "particles": "alumina", "high": 24, "low": 8}, + {"case_id": "nickel-wc-18-6", "material": "nickel-matrix", "particles": "tungsten-carbide", "high": 18, "low": 6}, + {"case_id": "epoxy-silica-36-12", "material": "epoxy-matrix", "particles": "silica", "high": 36, "low": 12}, + ], + }, + { + "family_id": "crosslink-density-modulus", + "family_name": "Crosslink density and rubbery modulus", + "outcome_positive": "greater", + "outcome_negative": "smaller", + "positive_numeric_direction": "increase", + "render": crosslink_stem, + "cases": [ + {"case_id": "natural-rubber-800-200", "material": "natural-rubber", "high": 800, "low": 200}, + {"case_id": "silicone-600-150", "material": "silicone-elastomer", "high": 600, "low": 150}, + {"case_id": "polyurethane-1000-250", "material": "polyurethane-elastomer", "high": 1000, "low": 250}, + {"case_id": "epdm-720-180", "material": "EPDM-elastomer", "high": 720, "low": 180}, + ], + }, +] + + +def build_manifest() -> dict: + previous = base.FAMILIES + try: + base.FAMILIES = FAMILIES + manifest = base.build_manifest() + finally: + base.FAMILIES = previous + manifest["study_id"] = "late-physics-representation-replication-2026-07-17" + manifest["created_at"] = datetime.now(timezone.utc).isoformat() + manifest["design"] = ( + "Disjoint prospective replication of a late-layer transition observed " + "in lexical-adversarial-representation-2026-07-17. No mechanism family " + "or material system is reused." + ) + return manifest + + +def main() -> None: + if PROTOCOL.exists() or MANIFEST.exists(): + raise FileExistsError( + "Frozen replication files already exist; do not overwrite them." + ) + if not DISCOVERY_STATS.exists(): + raise FileNotFoundError(DISCOVERY_STATS) + manifest = build_manifest() + preflight = base.lexical_preflight(manifest) + base.dump(MANIFEST, manifest) + + tokenizer = AutoTokenizer.from_pretrained( + base.MODEL, revision=base.MODEL_REVISION, local_files_only=True + ) + answer_words = sorted({ + row["outcome_positive"] for row in manifest["families"] + } | { + row["outcome_negative"] for row in manifest["families"] + }) + tokenization = { + word: [int(value) for value in tokenizer.encode( + word, add_special_tokens=False + )] + for word in answer_words + } + if any(len(ids) != 1 for ids in tokenization.values()): + raise RuntimeError(tokenization) + lenses = [ + {**row, "sha256": base.sha256(ROOT / row["path"])} + for row in base.LENS_ROWS + ] + protocol = { + "study_id": manifest["study_id"], + "status": ( + "prospectively frozen before any forward pass on this disjoint " + "replication cohort; late window was selected from the completed " + "discovery cohort and is not an independent choice" + ), + "frozen_at": datetime.now(timezone.utc).isoformat(), + "scientific_question": ( + "Does the late-layer shift from lexical similarity toward physical " + "equivalence replicate across six new materials mechanism families?" + ), + "model": base.MODEL, + "model_revision": base.MODEL_REVISION, + "lenses": lenses, + "source_layers": base.LAYERS, + "registered_band_percent": [80.0, 96.0], + "secondary_full_band_percent": [38.0, 92.0], + "target_free_layers": [34, 36, 37, 39], + "target_free_top_k_retained": 256, + "target_free_display_k": 30, + "frozen_stopwords": base.FROZEN_STOPWORDS, + "inputs": { + "prompt_manifest": str(MANIFEST.relative_to(ROOT)), + "prompt_manifest_sha256": base.sha256(MANIFEST), + "runner": str(RUNNER.relative_to(ROOT)), + "runner_sha256": base.sha256(RUNNER), + "discovery_statistics": str(DISCOVERY_STATS.relative_to(ROOT)), + "discovery_statistics_sha256": base.sha256(DISCOVERY_STATS), + }, + "tokenization_preflight": tokenization, + "lexical_adversarial_preflight": preflight, + "primary_endpoint": { + "representation": ( + "mean of three Jacobian transported states in the final " + "decoder basis, centered across all replication prompts at each layer" + ), + "triplet_margin": ( + "cosine(anchor, physics_paraphrase) minus " + "cosine(anchor, lexical_counterfactual)" + ), + "scalar": "mean triplet margin over the frozen 80-96 percent late window", + "inference": ( + "30000 two-stage bootstrap resamples over families and triplets; " + "seed 20260718" + ), + }, + "secondary_endpoints": [ + "late-window direct and raw-state margins", + "Jacobian-minus-direct late-window contrast", + "38-92 percent full-band margins retained for comparison with discovery", + "late 80-96 minus middle 38-70 percent transition contrast", + "clean scientific answer consistency", + "target-free top-word Jaccard margins at layers 34, 36, 37, and 39", + ], + "success_rule": { + "primary": "two-stage 95 percent CI for late Jacobian margin above zero", + "breadth": "at least 18 of 24 triplets and five of six family means positive", + "all_outputs_retained": True, + }, + "guardrails": [ + "The late window was motivated by a completed prior cohort and must be labeled as a prospective replication, not an original preregistered hypothesis.", + "Similarity does not establish causal use or a literal chain of thought.", + "No prompt, family, layer, clean error, or target-free word is excluded after execution.", + ], + } + base.dump(PROTOCOL, protocol) + PROTOCOL_MD.write_text( + "\n".join([ + "# Frozen disjoint replication of the late physical-equivalence transition", + "", + f"Frozen: `{protocol['frozen_at']}`", + "", + "## Motivation and evidential status", + "", + ( + "The completed discovery cohort failed its frozen 38--92% " + "band endpoint but showed an unregistered late rise that crossed " + "zero near 80% depth. This new cohort freezes 80--96% as the " + "primary window before running any of its prompts. It uses six " + "new mechanisms and 24 new material systems. The window is " + "prospective for this cohort but motivated by prior output." + ), + "", + "## Primary endpoint", + "", + ( + "Centered three-fit Jacobian ensemble cosine(anchor, physically " + "equivalent paraphrase) minus cosine(anchor, near-verbatim " + "physical counterfactual), averaged over 80--96% depth. Positive " + "means physics outranks wording. Inference uses the frozen " + "two-stage family/triplet bootstrap." + ), + "", + "## Frozen success rule", + "", + f"- {protocol['success_rule']['primary']}.", + f"- {protocol['success_rule']['breadth']}.", + "- Every output is retained.", + "", + "## Disjoint families", + "", + *[ + f"- {row['family_name']}" + for row in manifest["families"] + ], + "", + "## Fingerprints", + "", + f"- manifest: `{protocol['inputs']['prompt_manifest_sha256']}`", + f"- runner: `{protocol['inputs']['runner_sha256']}`", + f"- motivating statistics: `{protocol['inputs']['discovery_statistics_sha256']}`", + "", + ]) + "\n" + ) + print(f"wrote {MANIFEST}") + print(f"wrote {PROTOCOL}") + print(f"wrote {PROTOCOL_MD}") + print(f"manifest sha256: {base.sha256(MANIFEST)}") + print(f"protocol sha256: {base.sha256(PROTOCOL)}") + + +if __name__ == "__main__": + main() diff --git a/jlens_materials/scripts/freeze_patchscope_physics_relation.py b/jlens_materials/scripts/freeze_patchscope_physics_relation.py new file mode 100644 index 0000000000000000000000000000000000000000..0aa9bbe03404b670ad78f7c5bfd8f3d4030636f2 --- /dev/null +++ b/jlens_materials/scripts/freeze_patchscope_physics_relation.py @@ -0,0 +1,180 @@ +#!/usr/bin/env python3 +"""Freeze the Patchscope constitutive-polarity experiment.""" + +from __future__ import annotations + +import hashlib +import json +from datetime import datetime, timezone +from pathlib import Path + + +ROOT = Path(__file__).resolve().parents[1] +OUT = ROOT / "experiments" / "patchscope-physics-relation-2026-07-18" +PROTOCOL = OUT / "protocol.json" +SOURCE = ROOT / "experiments" / "abstract-physics-composition-2026-07-18" +RUNNER = ROOT / "scripts" / "run_patchscope_physics_relation.py" +ANALYZER = ROOT / "scripts" / "analyze_patchscope_physics_relation.py" + + +def sha256(path: Path) -> str: + return hashlib.sha256(path.read_bytes()).hexdigest() + + +TARGETS = [ + { + "target_id": "plain-direct-inverse", + "style": "plain", + "answer_vocabulary": "direct/inverse", + "positive_word": "direct", + "negative_word": "inverse", + "user": ( + "A hidden scientific constitutive relation is inserted at §. " + "Classify whether its response varies directly or inversely with " + "its controlled variable. Answer exactly one lowercase word from " + "this ordered pair: direct, inverse." + ), + }, + { + "target_id": "plain-same-opposite", + "style": "plain", + "answer_vocabulary": "same/opposite", + "positive_word": "same", + "negative_word": "opposite", + "user": ( + "A hidden scientific constitutive relation is inserted at §. " + "When its controlled variable increases, does its response move " + "in the same or opposite direction? Answer exactly one lowercase " + "word from this ordered pair: same, opposite." + ), + }, + { + "target_id": "plain-higher-lower", + "style": "plain", + "answer_vocabulary": "higher/lower", + "positive_word": "higher", + "negative_word": "lower", + "user": ( + "A hidden scientific constitutive relation is inserted at §. " + "If its controlled variable is increased while other quantities " + "are fixed, does its response become higher or lower? Answer " + "exactly one lowercase word from this ordered pair: higher, lower." + ), + }, + { + "target_id": "fewshot-direct-inverse", + "style": "two-example", + "answer_vocabulary": "direct/inverse", + "positive_word": "direct", + "negative_word": "inverse", + "user": ( + "Example one: R = C X with positive C is direct. Example two: " + "R X = C with positive C is inverse. A different hidden scientific " + "constitutive relation is inserted at §. Classify it. Answer " + "exactly one lowercase word from this ordered pair: direct, inverse." + ), + }, + { + "target_id": "fewshot-same-opposite", + "style": "two-example", + "answer_vocabulary": "same/opposite", + "positive_word": "same", + "negative_word": "opposite", + "user": ( + "Example one: for R = C X, R and X move in the same direction. " + "Example two: for R X = C, they move in opposite directions. A " + "different hidden scientific constitutive relation is inserted at " + "§. When its controlled variable increases, answer exactly one " + "lowercase word from this ordered pair: same, opposite." + ), + }, + { + "target_id": "fewshot-higher-lower", + "style": "two-example", + "answer_vocabulary": "higher/lower", + "positive_word": "higher", + "negative_word": "lower", + "user": ( + "Example one: if R = C X, increasing X makes R higher. Example two: " + "if R X = C, increasing X makes R lower. A different hidden " + "scientific constitutive relation is inserted at §. If its " + "controlled variable increases, answer exactly one lowercase word " + "from this ordered pair: higher, lower." + ), + }, +] + + +def main() -> None: + if (OUT / "raw.json").exists() or (OUT / "statistics.json").exists(): + raise RuntimeError("refusing to refreeze after execution") + payload = { + "study_id": "patchscope-physics-relation-2026-07-18", + "status": "frozen-before-output", + "frozen_at": datetime.now(timezone.utc).isoformat(), + "scientific_question": ( + "Can a fixed downstream model context nonlinearly decode the " + "direct-versus-inverse polarity of unseen constitutive laws from " + "the source state before any numerical comparison or answer words?" + ), + "model": "google/gemma-4-E4B-it", + "model_revision": "a4c2d58be94dda072b918d9db64ee85c8ed34e3f", + "patch_marker": "§", + "candidate_layers": [8, 12, 16, 20, 24, 28, 32, 36], + "targets": TARGETS, + "inputs": { + "source_manifest": { + "path": str((SOURCE / "prompt_manifest.json").relative_to(ROOT)), + "sha256": sha256(SOURCE / "prompt_manifest.json"), + }, + "source_states": { + "path": str((SOURCE / "representations.npz").relative_to(ROOT)), + "sha256": sha256(SOURCE / "representations.npz"), + }, + "runner": { + "path": str(RUNNER.relative_to(ROOT)), + "sha256": sha256(RUNNER), + }, + "analyzer": { + "path": str(ANALYZER.relative_to(ROOT)), + "sha256": sha256(ANALYZER), + }, + }, + "selection": ( + "Choose target and layer on the eight discovery laws by law-level " + "ROC-AUC, then zero-threshold accuracy, then earliest layer and " + "lexical target id." + ), + "primary_endpoint": ( + "Law-level direct-versus-inverse ROC-AUC on eight unseen " + "replication laws, averaging the two algebraically equivalent " + "source surfaces within law." + ), + "independent_unit": "constitutive law", + "inference": ( + "Exact enumeration of all 4-of-8 balanced direct/inverse law labels." + ), + "success_rule": { + "replication_law_auc_minimum": 0.90, + "replication_accuracy_minimum": 0.75, + "exact_p_maximum": 0.05, + "surface_agreement_minimum": 0.75, + }, + "guardrails": [ + "The source state is captured at the law marker before numbers, physical outcomes, or answer words.", + "Only two source states per law are retained, one per algebraically equivalent surface.", + "Target and layer selection use discovery laws only.", + "No Patchscope parameter is fitted to replication laws.", + "All target/layer candidates and failures are retained.", + "A passing result is nonlinear readability, not causal necessity.", + ], + } + OUT.mkdir(parents=True, exist_ok=True) + temporary = PROTOCOL.with_suffix(".json.tmp") + temporary.write_text(json.dumps(payload, indent=2, sort_keys=True) + "\n") + temporary.replace(PROTOCOL) + print(f"protocol sha256: {sha256(PROTOCOL)}") + + +if __name__ == "__main__": + main() diff --git a/jlens_materials/scripts/generate_key_trace_replication.py b/jlens_materials/scripts/generate_key_trace_replication.py new file mode 100644 index 0000000000000000000000000000000000000000..3f70cf4fd862b4a545bbdc28bde27ba5adc6444b --- /dev/null +++ b/jlens_materials/scripts/generate_key_trace_replication.py @@ -0,0 +1,43 @@ +#!/usr/bin/env python3 +"""Extract frozen positive and negative v2 cases for trajectory serialization.""" + +from __future__ import annotations + +import json +from pathlib import Path + + +ROOT = Path(__file__).resolve().parents[1] +SOURCE = ROOT / "prompts" / "materials-paper-v2-preregistered.json" +OUTPUT = ROOT / "prompts" / "materials-key-concept-traces-v1.json" + +SLUGS = [ + "paper-v2-assoc-notch-resistance-04", + "paper-v2-assoc-ductile-02", + "paper-v2-assoc-line-defect-motion-01", + "paper-v2-assoc-rapid-transformation-04", + "paper-v2-assoc-cleavage-03", + "paper-v2-assoc-hot-air-surface-layer-01", +] + + +def main() -> None: + source = json.loads(SOURCE.read_text()) + by_slug = {item["slug"]: item for item in source["prompts"]} + missing = [slug for slug in SLUGS if slug not in by_slug] + if missing: + raise ValueError(f"missing frozen prompts: {missing}") + payload = { + "description": ( + "Exact replication subset from materials-paper-v2. Four strong " + "cases and two prespecified weak/negative cases are rerun only to " + "persist per-layer J/logit trajectories for auditable figures." + ), + "prompts": [by_slug[slug] for slug in SLUGS], + } + OUTPUT.write_text(json.dumps(payload, indent=2) + "\n") + print(f"wrote {OUTPUT}") + + +if __name__ == "__main__": + main() diff --git a/jlens_materials/scripts/generate_materials_paper_v2.py b/jlens_materials/scripts/generate_materials_paper_v2.py new file mode 100644 index 0000000000000000000000000000000000000000..7a20deaf206ddcd96b6259be0de0316a3758b834 --- /dev/null +++ b/jlens_materials/scripts/generate_materials_paper_v2.py @@ -0,0 +1,293 @@ +#!/usr/bin/env python3 +"""Generate the frozen materials-paper-v2 evaluation prompt manifest. + +The suite contains 50 independent unnamed association vignettes (ten +mechanism families, five wordings each) and 24 matched directed-modulation +triplets. The generator is deterministic and intentionally stores every final +prompt as ordinary JSON for inspection and archival. +""" + +from __future__ import annotations + +import json +from pathlib import Path + + +ROOT = Path(__file__).resolve().parents[1] +OUTPUT = ROOT / "prompts" / "materials-paper-v2-preregistered.json" + + +ASSOCIATION_FAMILIES = [ + { + "key": "ductile", + "title": "Dimpled tensile failure", + "tracked": ["ductile", "nucleation", "coalescence", "void"], + "texts": [ + "A tensile coupon developed pronounced necking before separation. Its failure face contained rounded dimples centered on inclusions, with a cup-shaped center and a surrounding slanted lip.", + "After substantial plastic elongation, a metal bar separated along a surface filled with microscopic pits around second-phase particles. Neighboring pits had joined into larger depressions.", + "A pulled specimen narrowed strongly in its gauge section. Electron microscopy of the separated faces showed deep equiaxed dimples around particles and elongated dimples near the outer rim.", + "The load-displacement curve showed extensive post-yield deformation before final separation. The mating surfaces displayed many rounded depressions that had linked across the section.", + "A structural alloy absorbed considerable energy and formed a visible waist before failure. Its cup-and-cone surface was covered by particle-centered microscopic pits.", + ], + }, + { + "key": "boundary-attack", + "title": "Interface-localized stainless-steel attack", + "tracked": ["boundary", "corrosion", "sensitization"], + "texts": [ + "An austenitic stainless component remained near 650 degrees Celsius long enough for chromium-rich carbides to decorate grain interfaces. Chloride exposure then produced connected grooves along those interfaces while the interiors stayed intact.", + "A welded stainless plate experienced a prolonged intermediate-temperature dwell. Subsequent acid testing revealed narrow continuous attack paths around each grain and chromium-depleted zones beside carbide particles.", + "Metallography of heat-affected stainless steel showed carbide films at crystallite edges. After immersion in an aggressive aqueous solution, material loss followed those edges rather than crossing the grain interiors.", + "A stainless tube cooled slowly through the carbide-forming temperature range. Service in chloride brine later produced a network of fissures and grooves tracing the outlines of individual grains.", + "Following an intermediate-temperature heat treatment, austenitic steel contained chromium-rich particles at grain interfaces. Chemical exposure preferentially removed narrow regions next to those particles.", + ], + }, + { + "key": "cyclic", + "title": "Cyclic loading and beach-mark morphology", + "tracked": ["fatigue", "crack", "propagation"], + "texts": [ + "A rotating shaft survived many thousands of alternating load cycles before sudden separation. Concentric beach marks spread from a surface notch, and fine parallel ridges covered the smooth region.", + "A component failed after repeated stress reversals far below its monotonic strength. The separated surface contained a smooth semicircular region advancing from a machining mark and a rough final overload zone.", + "An aircraft bracket endured a long sequence of takeoff and landing loads. Microscopy showed evenly spaced ridges marking incremental advance from a bolt-hole corner.", + "A steel axle broke after millions of modest load cycles. Ratchet marks identified several surface origins, while curved arrest bands recorded successive stages of front advance.", + "A notched specimen was subjected to a sinusoidal load until separation. Most of the surface was smooth and striated, followed by a small rough region formed during the final overload.", + ], + }, + { + "key": "cleavage", + "title": "Faceted low-temperature failure", + "tracked": ["brittle", "cleavage", "transgranular"], + "texts": [ + "A body-centered alloy separated at low temperature with negligible necking. The failure face consisted of flat crystallographic facets connected by river-like markings.", + "During impact testing below the transition temperature, a ferritic specimen absorbed little energy. Its separated surface was shiny and faceted, with steps running through individual grains.", + "A thick steel plate failed suddenly in cold service without visible plastic distortion. Microscopy revealed flat planes and converging river patterns cutting across grain interiors.", + "A low-temperature bend test produced abrupt separation after almost no permanent curvature. The mating faces displayed large flat facets and feather-like markings.", + "A notched ferritic bar showed low impact energy and a bright granular-looking failure face. Microscopic steps passed directly through crystallites rather than around their edges.", + ], + }, + { + "key": "high-temperature-deformation", + "title": "Slow high-temperature deformation", + "tracked": ["creep", "diffusion", "cavity"], + "texts": [ + "A component held for months near half its melting temperature elongated slowly under a constant load well below its room-temperature yield strength. Small rounded holes accumulated at grain interfaces before rupture.", + "Under steady tensile load at elevated temperature, a polycrystal first deformed rapidly, then at an almost constant slow rate, and finally accelerated as microscopic holes linked along grain edges.", + "A turbine alloy exposed to sustained stress and heat developed time-dependent strain. Late in life, isolated pores at triple junctions enlarged and joined into an interfacial failure path.", + "A fine-grained metal changed shape gradually during a long low-stress hold close to its melting point. Atoms moved from compressed interfaces toward tensile ones and the specimen elongated.", + "Long-duration testing at high temperature produced a nearly linear strain-versus-time segment followed by rapid extension. Microscopy found linked pores along grain interfaces.", + ], + }, + { + "key": "particle-strengthening", + "title": "Hard-particle obstacle strengthening", + "tracked": ["precipitation", "bowing", "strengthening"], + "texts": [ + "A metal contains a dense dispersion of hard nonshearable nanoscale particles. Lattice line imperfections curve between neighboring particles and leave loops around them as load increases.", + "Heat treatment creates many fine second-phase particles that resist cutting. Moving line defects must arc between the particles, and reducing their spacing raises the required stress.", + "Electron microscopy after deformation shows loops encircling rigid particles within a metal matrix. The particles were too hard for moving lattice imperfections to pass through directly.", + "An aged alloy gains yield strength from closely spaced stable particles. Under load, flexible line imperfections bend around each obstacle rather than slicing it.", + "A fixed particle volume fraction is refined into a larger number of smaller nonshearable obstacles. The free span available to a moving line imperfection decreases markedly.", + ], + }, + { + "key": "rapid-transformation", + "title": "Rapid diffusionless steel transformation", + "tracked": ["martensite", "shear", "Bain", "tetragonal"], + "texts": [ + "Austenitic steel is cooled so quickly that long-range atomic redistribution cannot occur. A hard needle-like product forms by coordinated lattice motion, and its body-centered cell is elongated along one axis.", + "Rapid quenching produces thin laths without allowing carbon to redistribute over long distances. The product lattice is body-centered but has unequal axial dimensions.", + "A steel sample transforms almost instantaneously during severe cooling. Atoms move cooperatively over short distances, producing a hard plate-like phase with one stretched unit-cell axis.", + "Suppressing atomic redistribution during cooling converts the parent face-centered phase into a hard acicular product. The new body-centered cell has a c-to-a ratio different from one.", + "A quenched carbon steel contains packets of fine laths and high internal strain. Formation required a coordinated shape change rather than long-range solute transport.", + ], + }, + { + "key": "line-defect-motion", + "title": "Crystal line-imperfection motion", + "tracked": ["dislocation", "glide", "slip"], + "texts": [ + "Under resolved loading, a line imperfection moves across a close-packed plane in the direction of its mismatch vector, leaving a permanent offset at the free surface.", + "Plastic strain begins when mobile line imperfections sweep across selected crystallographic planes and create small surface steps.", + "A single crystal deforms by the passage of linear lattice imperfections along densely packed planes. Their motion produces an irreversible relative displacement of the crystal halves.", + "Etch pits tracing mobile line imperfections shift across a crystal during loading. Each passage leaves a step whose direction follows the mismatch vector.", + "Once the resolved driving force exceeds a critical value, a linear lattice defect travels on its favored plane and the specimen acquires permanent deformation.", + ], + }, + { + "key": "notch-resistance", + "title": "Resistance to notch-driven failure", + "tracked": ["toughness", "crack", "fracture"], + "texts": [ + "Two alloys have similar yield strength, but one notched specimen absorbs far more energy before unstable separation and tolerates a much larger flaw under the same remote load.", + "A pre-notched plate continues to carry increasing load while a local damaged zone blunts the notch tip. Considerable work is required before rapid extension begins.", + "An alloy with a sharp starter notch resists sudden extension under increasing tensile load. The critical stress-intensity value measured at instability is unusually high.", + "During a compact-tension test, substantial energy is dissipated near the notch tip before the specimen separates. The material tolerates larger defects than a comparison alloy.", + "A structural steel containing a sharp flaw remains stable under loads that cause immediate separation in a less damage-resistant steel. Its critical intensity threshold is larger.", + ], + }, + { + "key": "hot-air-surface-layer", + "title": "High-temperature environmental surface layer", + "tracked": ["oxidation", "oxide", "scale"], + "texts": [ + "A nickel alloy held in hot air develops an adherent multilayer surface film as metal atoms react with oxygen. Continued exposure thickens the film according to an approximately parabolic time law.", + "During prolonged furnace exposure, a metal component gains mass and forms a dark brittle surface layer rich in oxygen and metal cations.", + "A hot steel coupon exposed to air becomes covered by several ceramic-like surface layers. Growth slows as ions must travel through the existing film.", + "Elevated-temperature air exposure produces a compact protective film on a chromium-containing alloy. The film limits further oxygen ingress by solid-state transport.", + "A turbine component operated in oxygen-bearing gas develops a thick external reaction product that can spall during thermal cycling.", + ], + }, +] + + +CARRIERS = [ + "The amber notebook rested beside the ceramic bowl.", + "A silver spoon lay beneath the folded cloth.", + "The green folder remained inside the wooden drawer.", + "A small lantern stood near the closed window.", + "The paper ticket waited under the glass jar.", + "A blue scarf hung behind the narrow door.", + "The brass compass sat beside the blank card.", + "A white candle stood on the stone shelf.", + "The red envelope remained beneath the old clock.", + "A clay cup waited near the folded map.", + "The black pencil lay across the yellow pad.", + "A linen pouch rested beside the copper tray.", + "The round mirror leaned against the gray box.", + "A wooden token sat inside the shallow dish.", + "The violet ribbon lay below the framed sketch.", + "A clear bottle stood next to the woven basket.", + "The bronze key rested above the sealed letter.", + "A cotton glove lay beside the empty vase.", + "The narrow ruler remained under the green book.", + "A ceramic bird stood near the folded towel.", + "The orange marker lay inside the metal tin.", + "A glass bead rested beside the paper label.", + "The brown wallet waited below the desk lamp.", + "A steel clip lay across the plain postcard.", +] + + +FOCUS_FORMS = [ + "Concentrate on a dislocation pile-up and its stress concentration while you write it.", + "Keep a dislocation pile-up and the stress it concentrates in mind while copying it.", + "Maintain an internal focus on a dislocation pile-up and its local stress concentration as you write.", + "Mentally attend to a dislocation pile-up and the concentrated stress near its head while reproducing it.", + "Hold the idea of a dislocation pile-up and its stress concentration in mind during the writing task.", + "Direct your attention to a dislocation pile-up and the stress concentration it creates while writing.", + "While copying, actively think about a dislocation pile-up and its associated stress concentration.", + "Keep your thoughts centered on a dislocation pile-up and concentrated stress as you produce the sentence.", +] + + +SUPPRESS_FORMS = [ + "Try not to think about a dislocation pile-up or its stress concentration while you write it.", + "Keep your attention away from a dislocation pile-up and the stress it concentrates while copying it.", + "Avoid internally focusing on a dislocation pile-up or its local stress concentration as you write.", + "Mentally suppress thoughts of a dislocation pile-up and the concentrated stress near its head while reproducing it.", + "Do not hold the idea of a dislocation pile-up or its stress concentration in mind during the writing task.", + "Direct your attention away from a dislocation pile-up and the stress concentration it creates while writing.", + "While copying, actively avoid thinking about a dislocation pile-up and its associated stress concentration.", + "Keep your thoughts from dwelling on a dislocation pile-up or concentrated stress as you produce the sentence.", +] + + +def association_prompts() -> list[dict]: + prompts = [] + for family in ASSOCIATION_FAMILIES: + for index, text in enumerate(family["texts"], start=1): + prompts.append({ + "slug": f"paper-v2-assoc-{family['key']}-{index:02d}", + "shape": "ASSOCIATION", + "protocol": "lens_eval", + "domain": "materials", + "category": family["key"], + "title": f"{family['title']} {index}", + "description": ( + "Preregistered unnamed materials vignette; compare fixed-band " + "J-lens ranks with the matched logit-lens baseline." + ), + "text": text, + "readout_selector": "final_prompt_token", + "tracked": family["tracked"], + "must_be_absent_from_input": True, + "must_be_absent_from_output": True, + "note": ( + "Primary association-suite item. Inclusion requires tokenizer-level " + "input/output absence. The family-level endpoint is paired pass@k/AUC " + "for J-lens versus logit lens over the fixed 38--92% band." + ), + }) + return prompts + + +def modulation_prompts() -> list[dict]: + prompts = [] + group = "paper-v2-pileup-directed-modulation" + for index, carrier in enumerate(CARRIERS, start=1): + form_index = (index - 1) % len(FOCUS_FORMS) + carrier_id = f"carrier-{index:02d}" + triplet_id = f"triplet-{index:02d}" + common = { + "shape": "MODULATION", + "protocol": "directed_modulation", + "domain": "materials", + "description": "Preregistered matched directed-modulation triplet.", + "assistant_prefill": carrier, + "readout_selector": "assistant_response", + "tracked": ["dislocation", "pile", "stress"], + "must_be_absent_from_output": True, + "control_group": group, + "phrasing_id": triplet_id, + "carrier_id": carrier_id, + "target_family": "dislocation-pileup", + "note": ( + "Primary paired endpoint: mean log10 best-rank contrast for focus " + "versus suppress and neutral, computed within carrier and compared " + "with the same contrast under the logit lens." + ), + } + prompts.extend([ + { + **common, + "slug": f"paper-v2-mod-focus-{index:02d}", + "title": f"Directed focus {index:02d}", + "user": f"Write exactly \"{carrier}\" {FOCUS_FORMS[form_index]} Do not write anything else.", + "condition": "focus", + }, + { + **common, + "slug": f"paper-v2-mod-suppress-{index:02d}", + "title": f"Directed suppression {index:02d}", + "user": f"Write exactly \"{carrier}\" {SUPPRESS_FORMS[form_index]} Do not write anything else.", + "condition": "suppress", + }, + { + **common, + "slug": f"paper-v2-mod-control-{index:02d}", + "title": f"Neutral carrier control {index:02d}", + "user": f"Write exactly \"{carrier}\" Do not write anything else.", + "condition": "control", + }, + ]) + return prompts + + +def main() -> None: + prompts = association_prompts() + modulation_prompts() + payload = { + "description": ( + "Frozen preregistered materials-paper-v2 suite: 50 independent " + "association vignettes and 24 matched focus/suppress/neutral " + "directed-modulation triplets. Generated deterministically by " + "scripts/generate_materials_paper_v2.py." + ), + "prompts": prompts, + } + OUTPUT.write_text(json.dumps(payload, indent=2) + "\n") + print(f"wrote {OUTPUT} ({len(prompts)} prompts)") + + +if __name__ == "__main__": + main() diff --git a/jlens_materials/scripts/generate_materials_reasoning_pilot.py b/jlens_materials/scripts/generate_materials_reasoning_pilot.py new file mode 100644 index 0000000000000000000000000000000000000000..236c3f004de7d39c5052c968084087b74f430ccf --- /dev/null +++ b/jlens_materials/scripts/generate_materials_reasoning_pilot.py @@ -0,0 +1,73 @@ +#!/usr/bin/env python3 +"""Generate a frozen 20-item chat-formatted materials reasoning pilot.""" + +from __future__ import annotations + +import json +from pathlib import Path + + +ROOT = Path(__file__).resolve().parents[1] +OUT = ROOT / "prompts" / "materials-reasoning-pilot-v1.json" + + +def record(slug, title, user, tracked, answer, category): + return { + "slug": slug, + "shape": "MULTIHOP", + "protocol": "lens_eval", + "domain": "materials", + "category": category, + "title": title, + "description": "Chat-formatted one-word reasoning pilot with a preregistered latent mechanism.", + "user": user, + "readout_selector": "before_answer", + "tracked": tracked, + "answer": answer, + "must_be_absent_from_input": True, + "must_be_absent_from_output": True, + "note": "Pilot inclusion requires the clean next token to equal the declared one-word answer.", + } + + +def main(): + prompts = [] + for i, (coarse, fine) in enumerate([(50, 5), (100, 10), (40, 4), (80, 8)], 1): + prompts.append(record( + f"reason-hall-{i:02d}", "Grain-size comparison", + f"Two otherwise identical polycrystals have mean grain sizes {coarse} and {fine} micrometers. Grain interfaces impede carrier motion. Which specimen has the larger yield stress? Reply with exactly one word: fine or coarse.", + ["Hall", "Petch", "strengthening", "dislocation"], "fine", "grain-size", + )) + for i, ratio in enumerate([2, 4, 9, 16], 1): + prompts.append(record( + f"reason-griffith-{i:02d}", "Flaw-size scaling", + f"Two elastic plates have the same modulus and surface energy. One contains an internal elliptical flaw {ratio} times as long as the other. Is the critical remote tensile stress of the longer-flaw plate higher or lower? Reply with exactly one word.", + ["Griffith", "crack", "fracture"], "lower", "flaw-scaling", + )) + schmid = [(120, 80, "slip"), (60, 80, "elastic"), (95, 70, "slip"), (45, 65, "elastic")] + for i, (resolved, critical, answer) in enumerate(schmid, 1): + prompts.append(record( + f"reason-schmid-{i:02d}", "Resolved-force threshold", + f"A single crystal has a resolved driving value of {resolved} MPa on its most favorably oriented system; the critical value is {critical} MPa. Does microscopic plastic motion begin, or does the crystal remain elastic? Reply with exactly one word: slip or elastic.", + ["Schmid", "dislocation"], answer, "resolved-threshold", + )) + spacing = [("decreases", "higher"), ("increases", "lower"), ("halves", "higher"), ("doubles", "lower")] + for i, (change, answer) in enumerate(spacing, 1): + prompts.append(record( + f"reason-particle-{i:02d}", "Obstacle-spacing comparison", + f"A metal contains a fixed volume fraction of hard nonshearable particles. The center-to-center particle spacing {change}, so moving lattice line defects must curve between the obstacles. Is the required applied stress higher or lower? Reply with exactly one word.", + ["Orowan", "bowing", "precipitation", "strengthening"], answer, "particle-spacing", + )) + intensity = [(20, 40, "stable"), (50, 40, "unstable"), (15, 30, "stable"), (45, 35, "unstable")] + for i, (applied, critical, answer) in enumerate(intensity, 1): + prompts.append(record( + f"reason-intensity-{i:02d}", "Stress-intensity threshold", + f"A notched plate has applied mode-I intensity {applied} MPa square-root-meter and a critical intensity {critical} MPa square-root-meter. Is rapid extension stable or unstable? Reply with exactly one word.", + ["toughness", "crack", "fracture"], answer, "intensity-threshold", + )) + OUT.write_text(json.dumps({"description": "Frozen chat-formatted clean-answer pilot.", "prompts": prompts}, indent=2) + "\n") + print(f"wrote {OUT} ({len(prompts)} prompts)") + + +if __name__ == "__main__": + main() diff --git a/jlens_materials/scripts/generate_mechanism_name_v1.py b/jlens_materials/scripts/generate_mechanism_name_v1.py new file mode 100644 index 0000000000000000000000000000000000000000..a13e97626b29718523d9b48da3988f251447979a --- /dev/null +++ b/jlens_materials/scripts/generate_mechanism_name_v1.py @@ -0,0 +1,156 @@ +#!/usr/bin/env python3 +"""Generate the frozen mechanism-versus-eponym evaluation. + +The experiment asks whether Gemma's readable internal state preferentially +contains a physical carrier (for example, ``dislocation``) rather than the +surname attached to the law (for example, ``Schmid``). Every prompt omits +both tracked words and is stored verbatim for later audit. +""" + +from __future__ import annotations + +import json +from pathlib import Path + + +ROOT = Path(__file__).resolve().parents[1] +OUTPUT = ROOT / "prompts" / "materials-mechanism-name-v1.json" + + +FAMILIES = [ + { + "key": "grain-size-strength", + "title": "Grain-size strengthening", + "mechanism": "boundary", + "name": "Hall", + "texts": [ + "Two specimens of the same alloy receive different heat treatments. The one with much smaller crystallites requires a larger applied stress before permanent deformation begins.", + "A polycrystalline metal is processed until its average crystallite diameter is reduced by half. Its resistance to the onset of plastic flow increases.", + "An ultrafine-grained steel yields at a higher stress than a coarse-grained specimen with the same composition and phase fractions.", + "Repeated rolling and recrystallization create many small crystallites. Moving line defects encounter interfaces more frequently, and the measured yield stress rises.", + "A metal with coarse crystallites is compared with an otherwise identical fine-crystallite version. The refined material better resists the start of irreversible strain.", + "Thermomechanical processing reduces the characteristic crystal-region size without changing chemistry. The resulting sheet shows higher yield strength.", + "A line defect moving through one crystallite is blocked at the interface with a differently oriented neighbor. Making the crystallites smaller increases the number of such obstacles.", + "Two copper samples differ mainly in crystallite size. The sample containing more interfaces per unit volume begins plastic flow at the larger stress.", + "Recrystallized grains are refined from tens of micrometers to only a few micrometers. The stress needed to initiate slip increases approximately with the inverse square root of size.", + "A structural alloy is strengthened by subdividing it into smaller misoriented crystal regions, creating more barriers to the passage of moving line imperfections.", + ], + }, + { + "key": "flaw-controlled-failure", + "title": "Flaw-controlled brittle failure", + "mechanism": "crack", + "name": "Griffith", + "texts": [ + "Two brittle plates carry the same remote tensile load, but one contains a surface flaw four times longer. The plate with the larger flaw becomes unstable at a lower applied stress.", + "A glass specimen with a sharp internal slit fails well below its theoretical bond strength because the local intensity at the slit tip is strongly amplified.", + "Doubling the length of a sharp flaw in an elastic ceramic reduces the remote stress required for unstable extension by roughly the square root of two.", + "A brittle solid separates when the elastic energy released by extending a sharp defect exceeds the energy needed to create two new surfaces.", + "A polished glass fiber is much stronger than a scratched one of the same diameter because the scratch concentrates tensile loading at its tip.", + "An elastic plate containing a central slit is loaded in tension. Instability begins when the energy-release rate reaches the material's surface-formation resistance.", + "A ceramic component survives a given tensile stress when its largest flaw is short, but fails after processing introduces a much longer sharp flaw.", + "The nominal strength of a brittle specimen scales approximately with the inverse square root of the size of its dominant sharp defect.", + "A sharp notch in a glass sheet locally magnifies stress, allowing separation to start even though the average applied stress is modest.", + "A brittle plate with a pre-existing slit becomes unstable when additional extension lowers the total elastic-plus-surface energy of the system.", + ], + }, + { + "key": "resolved-slip-threshold", + "title": "Resolved slip threshold", + "mechanism": "dislocation", + "name": "Schmid", + "texts": [ + "A single crystal is pulled along an axis that places 120 MPa of resolved shear on its favored slip system. The critical value is 80 MPa, so microscopic plastic motion begins.", + "An applied uniaxial stress is projected onto a crystal's slip-plane normal and slip direction. Plastic flow starts when that resolved component exceeds a critical threshold.", + "Two identically loaded crystals have different orientations. The crystal with the larger product of the two direction cosines activates its favored slip system first.", + "A crystal remains elastic until the shear component acting along a permitted direction on a permitted plane reaches a material-specific critical value.", + "Under tension, only part of the applied load drives shear on a close-packed system. Once this projected driving force is large enough, line imperfections begin to glide.", + "Rotating a single crystal changes the axial stress required to initiate slip even though the critical resolved shear resistance is unchanged.", + "A favorably oriented slip system reaches its critical shear value before any other system, producing the first permanent surface steps.", + "The onset of plasticity in a single crystal is predicted by multiplying the applied tensile stress by geometric factors for the plane normal and slip direction.", + "One crystal orientation converts a larger fraction of the applied normal stress into shear on an allowed system, so it yields before a less favorable orientation.", + "When the resolved driving force on the most favorably oriented plane-direction pair exceeds its critical resistance, mobile line defects sweep across the crystal.", + ], + }, + { + "key": "lattice-diffusion-creep", + "title": "Lattice-diffusion creep", + "mechanism": "vacancy", + "name": "Herring", + "texts": [ + "A fine polycrystal held at high temperature and low stress lengthens as atoms diffuse through crystal interiors from compressed faces toward tensile faces.", + "At elevated temperature, atoms move through the lattice between oppositely loaded grain faces, producing slow elongation without extensive line-defect glide.", + "A polycrystalline solid changes shape under a small sustained load because empty lattice sites migrate through grain interiors in the direction opposite atomic transport.", + "Slow deformation at high homologous temperature is controlled by bulk atomic transport, and its rate increases strongly as crystallite size decreases.", + "Material is transported through crystal interiors from interfaces under compression to interfaces under tension, gradually elongating the specimen.", + "A fine-grained ceramic deforms under low stress near its melting temperature through diffusion across the volume of each crystallite rather than along interfaces.", + "Atoms leave compressed grain faces, traverse the crystal lattice, and attach at tensile faces, leading to time-dependent shape change.", + "The steady deformation rate varies inversely with the square of crystallite size and is controlled by bulk self-diffusion.", + "A high-temperature specimen elongates as missing-site defects carry mass transport through the interiors of its constituent crystals.", + "Under a small tensile load, bulk diffusion moves atoms toward faces normal to the loading direction, producing gradual axial extension.", + ], + }, + { + "key": "diffusionless-lattice-change", + "title": "Diffusionless lattice change", + "mechanism": "tetragonal", + "name": "Bain", + "texts": [ + "Rapid cooling converts a face-centered parent lattice into a body-centered product whose vertical cell dimension differs from the two horizontal dimensions.", + "A quenched carbon steel forms a hard phase by coordinated atomic motion. The product unit cell is stretched along one axis relative to the other two.", + "Long-range solute redistribution is suppressed during cooling, leaving a body-centered cell with an axial ratio different from unity.", + "The parent face-centered cell transforms through compression along one crystallographic axis and expansion along the other two, creating a distorted body-centered product.", + "A rapid diffusionless transformation produces an acicular phase whose unit cell has two equal short axes and one longer axis.", + "Carbon trapped during quenching distorts the body-centered product so that its c dimension is not equal to a and b.", + "A coordinated lattice deformation maps the parent cubic arrangement into a body-centered cell elongated in one direction.", + "The hard lath-shaped product of severe quenching has a c-to-a lattice ratio measurably greater than one.", + "Atoms move cooperatively over short distances during a rapid transformation, generating a body-centered structure with unequal axial dimensions.", + "X-ray peaks from the quenched product split because one unit-cell axis differs from the two equivalent perpendicular axes.", + ], + }, +] + + +def build() -> dict: + prompts = [] + for family in FAMILIES: + for index, text in enumerate(family["texts"], start=1): + prompts.append({ + "slug": f"mechanism-name-{family['key']}-{index:02d}", + "shape": "ASSOCIATION", + "protocol": "lens_eval", + "domain": "materials", + "category": family["key"], + "title": f"{family['title']} {index}", + "description": ( + "Frozen mechanism-versus-eponym vignette; both tracked " + "words are absent from the input and generated output." + ), + "text": text, + "readout_selector": "final_prompt_token", + "tracked": [family["mechanism"], family["name"]], + "must_be_absent_from_input": True, + "must_be_absent_from_output": True, + "note": ( + f"Paired comparison of physical carrier '{family['mechanism']}' " + f"against eponym token '{family['name']}'. Primary endpoint is " + "the within-item log10 rank difference, summarized separately " + "for J-lens and logit lens." + ), + }) + return { + "description": ( + "Frozen 50-item mechanism-versus-eponym materials evaluation: five " + "principles, ten independent wordings each." + ), + "prompts": prompts, + } + + +def main() -> None: + OUTPUT.write_text(json.dumps(build(), indent=2) + "\n") + print(f"wrote {OUTPUT}") + + +if __name__ == "__main__": + main() diff --git a/jlens_materials/scripts/pilot_algebra_scaffold.py b/jlens_materials/scripts/pilot_algebra_scaffold.py new file mode 100644 index 0000000000000000000000000000000000000000..86c24dc18c8e8f4dd03fb5fc7e175db38148ae79 --- /dev/null +++ b/jlens_materials/scripts/pilot_algebra_scaffold.py @@ -0,0 +1,135 @@ +#!/usr/bin/env python3 +"""Prompt-development audit for algebraically rearranged constitutive laws. + +Uses only the already unblinded first fresh-law cohort, restricted to formula +surface B and one material case. Results may tune a later, disjoint-law study +but are not themselves confirmatory. +""" + +from __future__ import annotations + +import json +import sys +from datetime import datetime, timezone +from pathlib import Path + +import torch + +ROOT = Path(__file__).resolve().parents[1] +sys.path[:0] = [str(ROOT), str(ROOT / "_vendor_jlens"), str(ROOT / "scripts")] + +from jlens.hooks import ActivationRecorder # noqa: E402 +from run_jacobian_steering_pilot import atomic_json, format_chat # noqa: E402 +from run_lens import load_model # noqa: E402 +from run_lexical_adversarial_representation import pair_logits # noqa: E402 +from run_semantic_steering_v3 import continuation_tokens # noqa: E402 + + +OUT = ( + ROOT + / "experiments" + / "fresh-law-abstraction-confirmation-2026-07-18" + / "algebra_scaffold_pilot.json" +) + + +def variants(text: str) -> dict[str, str]: + needle = "Complete this relation-sign step silently." + return { + "original": text, + "isolate": text.replace( + needle, + "First algebraically isolate the response as a function of the " + "control. Then determine its monotonic sign. " + needle, + ), + "derivative": text.replace( + needle, + "Algebraically isolate the response and determine the sign of its " + "derivative with respect to the control. " + needle, + ), + "generic_examples": text.replace( + "Apply the same two-stage physical test to every case. ", + "Apply the same two-stage physical test to every case. As generic " + "algebra only: if u v = C > 0, then u = C/v is inverse; if u/v = " + "C > 0, then u = C v is direct. ", + ), + "counterfactual_check": text.replace( + needle, + "Mentally compare the isolated response at a larger and a smaller " + "control value as a consistency check. " + needle, + ), + } + + +@torch.inference_mode() +def main() -> None: + manifest = json.loads( + ( + ROOT + / "experiments" + / "fresh-law-abstraction-confirmation-2026-07-18" + / "prompt_manifest.json" + ).read_text() + ) + prompts = [ + x + for x in manifest["prompts"] + if x["surface"] == "b" + and x["case_index"] == 0 + and x["answer_order"] == "higher-first" + ] + model = load_model( + "google/gemma-4-E4B-it", + dtype=torch.bfloat16, + device="mps", + revision="a4c2d58be94dda072b918d9db64ee85c8ed34e3f", + ) + answer_ids = ( + int(continuation_tokens(model.tokenizer, "higher")[0]), + int(continuation_tokens(model.tokenizer, "lower")[0]), + ) + rows = [] + final_layer = model.n_layers - 1 + for prompt in prompts: + for variant, user in variants(prompt["user"]).items(): + ids = model.encode(format_chat(model.tokenizer, user), max_length=512) + with ActivationRecorder(model.layers, at=[final_layer]) as recorder: + model.forward(ids) + final = recorder.activations[final_layer][0, -1:] + logits = pair_logits( + model, + model._final_norm( + final.to( + model._lm_head.weight.device, + model._lm_head.weight.dtype, + ) + ).float(), + answer_ids, + )[0].float().cpu() + prediction = "higher" if logits[0] > logits[1] else "lower" + rows.append( + { + "prompt_id": prompt["prompt_id"], + "law_id": prompt["law_id"], + "law_sign": prompt["law_sign"], + "physical_sign": prompt["physical_sign"], + "expected_answer": prompt["expected_answer"], + "variant": variant, + "prediction": prediction, + "correct": prediction == prompt["expected_answer"], + "higher_minus_lower_log_odds": float(logits[0] - logits[1]), + "user": user, + } + ) + atomic_json( + OUT, + { + "status": "development-only-unblinded-cohort", + "created_at": datetime.now(timezone.utc).isoformat(), + "rows": rows, + }, + ) + + +if __name__ == "__main__": + main() diff --git a/jlens_materials/scripts/pilot_clean_answers.py b/jlens_materials/scripts/pilot_clean_answers.py new file mode 100644 index 0000000000000000000000000000000000000000..d3f1c523e4d707fa558d0814f9b333b03dedb1bb --- /dev/null +++ b/jlens_materials/scripts/pilot_clean_answers.py @@ -0,0 +1,40 @@ +#!/usr/bin/env python3 +"""Run only clean next-token controls before expensive lens evaluation.""" + +from __future__ import annotations + +import json +import sys +from pathlib import Path + +ROOT = Path(__file__).resolve().parents[1] +sys.path.insert(0, str(ROOT)) + +import domain_prompts as dp # noqa: E402 +import run_lens # noqa: E402 + + +def main(): + source = ROOT / "prompts" / "materials-reasoning-pilot-v1.json" + model = run_lens.load_model("google/gemma-4-E4B-it") + rows = [] + for prompt in dp.load_prompts(str(source)): + text = dp.resolve_text(prompt, model.tokenizer) + baseline = run_lens.baseline_answer_record(model, text, prompt) + completion, _ = run_lens.generate_completion(model, text, max_new_tokens=4) + rows.append({ + "slug": prompt.slug, + "category": prompt.category, + "expected": prompt.answer, + "greedy_token": baseline.get("greedy_token"), + "correct": baseline.get("correct"), + "completion": completion, + }) + print(f"{prompt.slug}: expected={prompt.answer!r} top={baseline.get('greedy_token')!r} correct={baseline.get('correct')}") + out = ROOT / "experiments" / "materials-reasoning-pilot-v1_clean-baselines.json" + out.write_text(json.dumps(rows, indent=2) + "\n") + print(f"passed {sum(row['correct'] for row in rows)}/{len(rows)}; wrote {out}") + + +if __name__ == "__main__": + main() diff --git a/jlens_materials/scripts/plot_cross_mechanism_activation_patching.py b/jlens_materials/scripts/plot_cross_mechanism_activation_patching.py new file mode 100644 index 0000000000000000000000000000000000000000..4bae8d27010f2d4b989d0bf694b3504efe8ffdf6 --- /dev/null +++ b/jlens_materials/scripts/plot_cross_mechanism_activation_patching.py @@ -0,0 +1,251 @@ +#!/usr/bin/env python3 +"""Rebuild the cross-mechanism patching figure from archived CSV outputs. + +This plotting-only script is separate from the protocol-bound analysis. +""" + +from __future__ import annotations + +from pathlib import Path + +import matplotlib.pyplot as plt +import numpy as np +import pandas as pd + + +ROOT = Path(__file__).resolve().parents[1] +OUT = ( + ROOT + / "experiments" + / "cross-mechanism-activation-patching-2026-07-18" +) +FIG = OUT / "figures" +FAMILY_SHORT = { + "obstacle-spacing-orowan": "Orowan", + "porosity-modulus": "Porosity", + "pearlite-spacing-strength": "Pearlite", + "dislocation-density-strength": "Dislocation", + "particle-fraction-modulus": "Particles", + "crosslink-density-modulus": "Crosslinks", +} + + +def main() -> None: + unordered = pd.read_csv( + OUT / "unordered_mechanism_pair_effects.csv" + ) + ordered = pd.read_csv(OUT / "ordered_mechanism_pair_effects.csv") + receiver_layer = pd.read_csv(OUT / "receiver_layer_contrasts.csv") + subset = pd.read_csv(OUT / "subset_statistics.csv") + + plt.rcParams.update( + { + "font.family": "DejaVu Sans", + "font.size": 8.1, + "axes.labelsize": 8.4, + "xtick.labelsize": 7.0, + "ytick.labelsize": 7.0, + "legend.fontsize": 6.8, + "axes.spines.top": False, + "axes.spines.right": False, + "pdf.fonttype": 42, + "svg.fonttype": "none", + } + ) + teal = "#176B7A" + orange = "#D17C38" + purple = "#7B6BA8" + green = "#5B8C5A" + gray = "#8B8E91" + fig, axes = plt.subplots(2, 2, figsize=(7.15, 5.6)) + + pair_table = unordered.sort_values( + "physical_outcome_contrast", ascending=True + ) + y = np.arange(len(pair_table)) + axes[0, 0].hlines( + y, + pair_table["numeric_direction_contrast"], + pair_table["physical_outcome_contrast"], + color="#C9CBCC", + linewidth=0.9, + ) + axes[0, 0].scatter( + pair_table["physical_outcome_contrast"], + y, + color=teal, + s=23, + label="physical outcome", + zorder=3, + ) + axes[0, 0].scatter( + pair_table["numeric_direction_contrast"], + y, + color=orange, + s=23, + label="numerical direction", + zorder=3, + ) + labels = [] + for pair in pair_table["family_pair"]: + left, right = pair.split(" | ") + labels.append(f"{FAMILY_SHORT[left]}–{FAMILY_SHORT[right]}") + axes[0, 0].set_yticks(y, labels) + axes[0, 0].axvline(0, color=gray, linestyle="--", linewidth=0.8) + axes[0, 0].set_xlabel("patched answer-margin contrast") + axes[0, 0].legend( + frameon=False, + loc="upper left", + bbox_to_anchor=(0.01, 0.99), + ) + + names = { + "all": "All pairs", + "cross_vocabulary": "Different answer words", + "opposite_orientation": "Reversed numeric trend", + "both": "Both controls", + } + order = [ + "all", + "cross_vocabulary", + "opposite_orientation", + "both", + ] + summary = subset.set_index("subset").loc[order] + x = np.arange(len(summary)) + means = summary["mean"].to_numpy() + axes[0, 1].errorbar( + x, + means, + yerr=np.vstack( + [ + means - summary["ci_low"].to_numpy(), + summary["ci_high"].to_numpy() - means, + ] + ), + fmt="o", + color=teal, + ecolor=teal, + capsize=3, + markersize=5, + ) + axes[0, 1].axhline(0, color=gray, linestyle="--", linewidth=0.8) + axes[0, 1].set_xticks( + x, + [names[name] for name in order], + rotation=18, + ha="right", + ) + axes[0, 1].set_ylabel("physical-outcome transfer") + + curve = ( + receiver_layer.groupby(["layer", "depth_percent"], as_index=False) + .agg( + physical=("physical_outcome_contrast", "mean"), + numeric=("numeric_direction_contrast", "mean"), + ) + .sort_values("depth_percent") + ) + axes[1, 0].plot( + curve["depth_percent"], + curve["physical"], + color=teal, + marker="o", + linewidth=1.5, + label="physical outcome", + ) + axes[1, 0].plot( + curve["depth_percent"], + curve["numeric"], + color=orange, + marker="s", + linewidth=1.5, + label="numerical direction", + ) + axes[1, 0].axhline(0, color=gray, linestyle="--", linewidth=0.8) + axes[1, 0].set_xlabel("layer depth (%)") + axes[1, 0].set_ylabel("patched answer-margin contrast") + axes[1, 0].legend(frameon=False, loc="upper left") + + family = ( + ordered.groupby("donor_family", as_index=False) + .agg(physical=("physical_outcome_contrast", "mean")) + .sort_values("physical") + ) + orientation = { + "obstacle-spacing-orowan": "inverse", + "porosity-modulus": "inverse", + "pearlite-spacing-strength": "inverse", + "dislocation-density-strength": "direct", + "particle-fraction-modulus": "direct", + "crosslink-density-modulus": "direct", + } + colors = [ + purple if orientation[row] == "direct" else green + for row in family["donor_family"] + ] + y_family = np.arange(len(family)) + axes[1, 1].barh( + y_family, + family["physical"], + color=colors, + alpha=0.9, + ) + axes[1, 1].axvline(0, color=gray, linestyle="--", linewidth=0.8) + axes[1, 1].set_yticks( + y_family, + [FAMILY_SHORT[row] for row in family["donor_family"]], + ) + axes[1, 1].set_xlabel("physical-outcome transfer") + axes[1, 1].legend( + handles=[ + plt.Line2D( + [0], + [0], + marker="s", + linestyle="none", + color=purple, + label="property rises with input", + ), + plt.Line2D( + [0], + [0], + marker="s", + linestyle="none", + color=green, + label="property falls with input", + ), + ], + frameon=False, + loc="lower right", + ) + + for label, axis in zip("ABCD", axes.flat): + axis.text( + -0.17, + 1.04, + label, + transform=axis.transAxes, + fontweight="bold", + fontsize=10, + ) + fig.subplots_adjust( + left=0.19, + right=0.985, + bottom=0.12, + top=0.97, + wspace=0.37, + hspace=0.43, + ) + FIG.mkdir(parents=True, exist_ok=True) + for suffix in ("pdf", "png", "svg"): + fig.savefig( + FIG / f"cross-mechanism-activation-patching.{suffix}", + dpi=300, + bbox_inches="tight", + ) + plt.close(fig) + + +if __name__ == "__main__": + main() diff --git a/jlens_materials/scripts/plot_gemma31b_equation_free_direction_rotation.py b/jlens_materials/scripts/plot_gemma31b_equation_free_direction_rotation.py new file mode 100644 index 0000000000000000000000000000000000000000..b1decb3e956cc0b0ba7ffbeadbf95f675a991f89 --- /dev/null +++ b/jlens_materials/scripts/plot_gemma31b_equation_free_direction_rotation.py @@ -0,0 +1,243 @@ +#!/usr/bin/env python3 +"""Visualize the Gemma 31B equation-free readout geometry at layers 40 and 49.""" + +from __future__ import annotations + +import json +import math +from pathlib import Path + +import matplotlib + +matplotlib.use("Agg") +import matplotlib.pyplot as plt # noqa: E402 +from matplotlib.lines import Line2D # noqa: E402 +from matplotlib.patches import Arc # noqa: E402 +import numpy as np # noqa: E402 +import pandas as pd # noqa: E402 + + +ROOT = Path(__file__).resolve().parents[1] +ANALYSIS = ( + ROOT + / "experiments" + / "gemma4-31b-equation-free-layer-comparison-2026-08-23" +) +RUN40 = ROOT / "experiments" / "gemma4-31b-equation-free-relational-layer40-v1" +RUN49 = ROOT / "experiments" / "gemma4-31b-equation-free-relational-v1" +FIGURES = ANALYSIS / "figures" + +COLORS = { + "direct": "#168F88", + "neutral": "#8A8F94", + "inverse": "#6D58A5", + "layer40": "#2474A6", + "layer49": "#7B4AA3", + "reference": "#6F7478", +} + + +def direction(path: Path) -> np.ndarray: + with np.load(path / "frozen_direction.npz") as archive: + vector = archive["direction"].astype(float) + return vector / np.linalg.norm(vector) + + +def style_axis(axis: plt.Axes) -> None: + axis.spines[["top", "right"]].set_visible(False) + axis.tick_params(labelsize=8.5, width=0.8) + + +def main() -> None: + FIGURES.mkdir(parents=True, exist_ok=True) + statistics = json.loads((ANALYSIS / "statistics.json").read_text()) + laws = pd.read_csv(ANALYSIS / "law_level_cross_layer.csv") + + direction40 = direction(RUN40) + direction49 = direction(RUN49) + cosine = float(np.dot(direction40, direction49)) + angle = float(math.degrees(math.acos(np.clip(cosine, -1.0, 1.0)))) + law_corr = statistics["cross_layer"]["law_mean_contrast"] + + plt.rcParams.update( + { + "font.family": "DejaVu Sans", + "font.size": 9, + "axes.linewidth": 0.8, + "pdf.fonttype": 42, + "ps.fonttype": 42, + } + ) + figure = plt.figure(figsize=(12.2, 4.0), constrained_layout=True) + grid = figure.add_gridspec(1, 3, width_ratios=[0.82, 1.2, 0.9]) + + # Panel A: the exact two-vector angle represented in their spanning plane. + axis = figure.add_subplot(grid[0, 0]) + endpoint40 = np.array([1.0, 0.0]) + endpoint49 = np.array([cosine, math.sqrt(max(0.0, 1.0 - cosine**2))]) + axis.annotate( + "", + xy=endpoint40, + xytext=(0, 0), + arrowprops={"arrowstyle": "-|>", "lw": 2.6, "color": COLORS["layer40"]}, + ) + axis.annotate( + "", + xy=endpoint49, + xytext=(0, 0), + arrowprops={"arrowstyle": "-|>", "lw": 2.6, "color": COLORS["layer49"]}, + ) + axis.add_patch( + Arc( + (0, 0), + 0.9, + 0.9, + theta1=0, + theta2=angle, + color=COLORS["reference"], + lw=1.0, + ) + ) + label_angle = math.radians(angle / 2) + axis.text( + 0.57 * math.cos(label_angle), + 0.57 * math.sin(label_angle), + f"{angle:.1f}°", + ha="center", + va="center", + color=COLORS["reference"], + fontsize=9, + ) + axis.text(1.02, -0.02, "layer 40", color=COLORS["layer40"], va="center") + axis.text( + endpoint49[0] + 0.02, + endpoint49[1] + 0.03, + "layer 49", + color=COLORS["layer49"], + va="bottom", + ) + axis.text( + 0.02, + -0.16, + f"direction cosine = {cosine:.3f}", + ha="left", + va="top", + color=COLORS["reference"], + fontsize=8.5, + ) + axis.set_aspect("equal") + axis.set_xlim(-0.08, 1.25) + axis.set_ylim(-0.22, 1.18) + axis.axis("off") + axis.text(-0.08, 1.05, "A", transform=axis.transAxes, fontweight="bold", fontsize=11) + + # Panel B: law-level physical coordinate is stable across the two depths. + axis = figure.add_subplot(grid[0, 1]) + x = laws["mean_contrast_layer40"].to_numpy() + y = laws["mean_contrast_layer49"].to_numpy() + lower = float(min(x.min(), y.min())) + upper = float(max(x.max(), y.max())) + pad = 0.08 * (upper - lower) + limits = (lower - pad, upper + pad) + axis.plot(limits, limits, linestyle="--", color=COLORS["reference"], lw=1.0) + for category in ("direct", "neutral", "inverse"): + subset = laws[laws["category"] == category] + axis.scatter( + subset["mean_contrast_layer40"], + subset["mean_contrast_layer49"], + s=30, + color=COLORS[category], + edgecolor="white", + linewidth=0.45, + alpha=0.9, + zorder=3, + ) + axis.axhline(0, color=COLORS["reference"], lw=0.6, alpha=0.45) + axis.axvline(0, color=COLORS["reference"], lw=0.6, alpha=0.45) + axis.set_xlim(limits) + axis.set_ylim(limits) + axis.set_aspect("equal", adjustable="box") + axis.set_xlabel("law contrast at layer 40", fontsize=9) + axis.set_ylabel("law contrast at layer 49", fontsize=9) + axis.text( + 0.04, + 0.96, + f"Pearson r = {law_corr['pearson_r']:.3f}\nSpearman ρ = {law_corr['spearman_rho']:.3f}", + transform=axis.transAxes, + ha="left", + va="top", + fontsize=8.5, + ) + style_axis(axis) + axis.text(-0.16, 1.04, "B", transform=axis.transAxes, fontweight="bold", fontsize=11) + + # Panel C: every law's raw coordinate at the two measured depths. + axis = figure.add_subplot(grid[0, 2]) + for category in ("direct", "neutral", "inverse"): + subset = laws[laws["category"] == category] + for row in subset.itertuples(index=False): + axis.plot( + [0, 1], + [row.mean_contrast_layer40, row.mean_contrast_layer49], + color=COLORS[category], + alpha=0.34, + lw=0.85, + zorder=1, + ) + axis.scatter( + np.zeros(len(subset)), + subset["mean_contrast_layer40"], + color=COLORS[category], + s=15, + alpha=0.8, + zorder=2, + ) + axis.scatter( + np.ones(len(subset)), + subset["mean_contrast_layer49"], + color=COLORS[category], + s=15, + alpha=0.8, + zorder=2, + ) + axis.axhline(0, color=COLORS["reference"], lw=0.8) + axis.set_xlim(-0.28, 1.28) + axis.set_xticks([0, 1], ["layer 40\n67.8% depth", "layer 49\n83.1% depth"]) + axis.set_ylabel("law-level matched contrast", fontsize=9) + style_axis(axis) + axis.text(-0.19, 1.04, "C", transform=axis.transAxes, fontweight="bold", fontsize=11) + + handles = [ + Line2D([0], [0], marker="o", linestyle="", color=COLORS[name], label=name) + for name in ("direct", "neutral", "inverse") + ] + figure.legend( + handles=handles, + loc="outside lower center", + ncol=3, + frameon=False, + fontsize=8.5, + ) + + stem = FIGURES / "equation-free-cross-layer-geometry" + figure.savefig(stem.with_suffix(".png"), dpi=300, bbox_inches="tight") + figure.savefig(stem.with_suffix(".pdf"), bbox_inches="tight") + figure.savefig(stem.with_suffix(".svg"), bbox_inches="tight") + plt.close(figure) + + caption = ( + "**Equation-free physical ordering is preserved while the fitted readout axis changes across depth.** " + "(A) The independently fitted layer-40 and layer-49 physical-outcome directions are shown exactly in " + "their two-vector spanning plane; their cosine is 0.352 (69.4 degrees). This is a two-depth comparison, " + "not an observed continuous rotation trajectory. (B) Despite the change in axis, raw matched contrasts " + "for the same 60 held-out laws are nearly identical across layers (Pearson r=0.996; Spearman rho=0.949). " + "(C) Each line follows one law between the two measured depths. Direct, neutral, and inverse relations " + "retain their ordering. A separate direction was fitted at each layer using the unchanged supplied-equation " + "development cohort and then evaluated on the identical equation-free test prompts." + ) + (FIGURES / "equation-free-cross-layer-geometry-caption.md").write_text(caption + "\n") + print(stem.with_suffix(".png")) + + +if __name__ == "__main__": + main() diff --git a/jlens_materials/scripts/plot_multitoken_sequence_robustness.py b/jlens_materials/scripts/plot_multitoken_sequence_robustness.py new file mode 100644 index 0000000000000000000000000000000000000000..631bcd9e278aa8f7c6b22201e2fe28386889f006 --- /dev/null +++ b/jlens_materials/scripts/plot_multitoken_sequence_robustness.py @@ -0,0 +1,266 @@ +#!/usr/bin/env python3 +"""Rebuild the multi-token robustness figure from archived CSV outputs. + +This plotting-only script is deliberately separate from the protocol-bound +model runner. It changes no scores, endpoints, or verdicts. +""" + +from __future__ import annotations + +from pathlib import Path + +import matplotlib.pyplot as plt +import numpy as np +import pandas as pd +from matplotlib.lines import Line2D + + +ROOT = Path(__file__).resolve().parents[1] +OUT = ROOT / "experiments" / "multitoken-sequence-robustness-2026-07-18" +FIG = OUT / "figures" + + +def main() -> None: + rows = pd.read_csv(OUT / "layer_sequence_scores.csv") + summary = pd.read_csv(OUT / "prompt_band_summary.csv") + + plt.rcParams.update( + { + "font.family": "DejaVu Sans", + "font.size": 8.2, + "axes.labelsize": 8.5, + "xtick.labelsize": 7.2, + "ytick.labelsize": 7.2, + "legend.fontsize": 6.8, + "axes.spines.top": False, + "axes.spines.right": False, + "pdf.fonttype": 42, + "svg.fonttype": "none", + } + ) + colors = {"jacobian_ensemble": "#16697A", "direct": "#B56576"} + family_colors = { + "cleavage": "#D17C38", + "rapid-transformation": "#5B8C5A", + } + fig, axes = plt.subplots(2, 2, figsize=(7.15, 5.55)) + + primary = summary[ + summary["method"].isin(["jacobian_ensemble", "direct"]) + ].copy() + family_order = ["cleavage", "rapid-transformation"] + slug_order: list[str] = [] + tick_labels: list[str] = [] + for family, prefix in (("cleavage", "C"), ("rapid-transformation", "M")): + family_slugs = sorted( + primary.loc[primary["family"] == family, "slug"].unique() + ) + slug_order.extend(family_slugs) + tick_labels.extend( + [f"{prefix}{index}" for index in range(1, len(family_slugs) + 1)] + ) + x = np.arange(len(slug_order)) + for offset, method, label in ( + (-0.13, "jacobian_ensemble", "Jacobian"), + (0.13, "direct", "Direct"), + ): + values = ( + primary[primary["method"] == method] + .set_index("slug") + .loc[slug_order, "band_sequence_margin"] + ) + axes[0, 0].scatter( + x + offset, + values, + color=colors[method], + s=25, + label=label, + zorder=3, + ) + axes[0, 0].axhline(0, color="#8B8E91", linestyle="--", linewidth=0.9) + axes[0, 0].axvline(4.5, color="#D5D7D9", linewidth=0.8) + axes[0, 0].set_ylabel("target minus contrast\nsequence score") + axes[0, 0].set_xticks(x, tick_labels) + axes[0, 0].legend(frameon=False, ncol=2, loc="upper left") + axes[0, 0].text( + -0.15, + 1.04, + "A", + transform=axes[0, 0].transAxes, + fontweight="bold", + fontsize=10, + ) + + layers = np.sort(rows["layer"].unique()) + depths = ( + rows[["layer", "depth_percent"]] + .drop_duplicates() + .set_index("layer") + .loc[layers, "depth_percent"] + .to_numpy() + ) + for family, linestyle in ( + ("cleavage", "-"), + ("rapid-transformation", "--"), + ): + for method, label in ( + ("jacobian_ensemble", "Jacobian"), + ("direct", "Direct"), + ): + curve = ( + rows[ + (rows["family"] == family) + & (rows["method"] == method) + ] + .groupby("layer", as_index=False)["sequence_margin"] + .mean() + .set_index("layer") + .loc[layers] + ) + axes[0, 1].plot( + depths, + curve["sequence_margin"], + color=colors[method], + linestyle=linestyle, + linewidth=1.5, + label=( + f"{label}, " + f"{'transgranular' if family == 'cleavage' else 'martensite'}" + ), + ) + axes[0, 1].axvspan(38, 92, color="#D9E7EA", alpha=0.45) + axes[0, 1].axhline(0, color="#8B8E91", linestyle=":", linewidth=0.9) + axes[0, 1].set_xlabel("layer depth (%)") + axes[0, 1].set_ylabel("target minus contrast\nsequence score") + axes[0, 1].legend(frameon=False, fontsize=6.3, loc="best") + axes[0, 1].text( + -0.15, + 1.04, + "B", + transform=axes[0, 1].transAxes, + fontweight="bold", + fontsize=10, + ) + + jacobian = summary[summary["method"] == "jacobian_ensemble"] + for family in family_order: + subset = jacobian[jacobian["family"] == family] + axes[1, 0].scatter( + subset["band_first_piece_margin"], + subset["band_sequence_margin"], + color=family_colors[family], + s=34, + edgecolor="white", + linewidth=0.5, + zorder=3, + ) + low = min( + float(jacobian["band_first_piece_margin"].min()), + float(jacobian["band_sequence_margin"].min()), + ) + high = max( + float(jacobian["band_first_piece_margin"].max()), + float(jacobian["band_sequence_margin"].max()), + ) + axes[1, 0].plot( + [low, high], + [low, high], + color="#8B8E91", + linestyle="--", + linewidth=0.9, + ) + axes[1, 0].set_xlabel("first-piece margin") + axes[1, 0].set_ylabel("full-sequence margin") + axes[1, 0].legend( + handles=[ + Line2D( + [0], + [0], + marker="o", + linestyle="none", + markerfacecolor=family_colors["cleavage"], + markeredgecolor="white", + label="transgranular vs intergranular", + ), + Line2D( + [0], + [0], + marker="o", + linestyle="none", + markerfacecolor=family_colors["rapid-transformation"], + markeredgecolor="white", + label="martensite vs bainite", + ), + ], + frameon=False, + loc="upper left", + fontsize=6.3, + ) + axes[1, 0].text( + -0.15, + 1.04, + "C", + transform=axes[1, 0].transAxes, + fontweight="bold", + fontsize=10, + ) + + seeds = summary[summary["method"].str.startswith("jacobian_seed")] + for family, marker in ( + ("cleavage", "o"), + ("rapid-transformation", "s"), + ): + subset = ( + seeds[seeds["family"] == family] + .groupby("method", as_index=False)["band_sequence_margin"] + .mean() + .set_index("method") + ) + axes[1, 1].scatter( + [0, 1, 2], + subset.loc[ + ["jacobian_seed0", "jacobian_seed1", "jacobian_seed2"], + "band_sequence_margin", + ], + color=family_colors[family], + marker=marker, + s=34, + label=( + "transgranular vs intergranular" + if family == "cleavage" + else "martensite vs bainite" + ), + ) + axes[1, 1].axhline(0, color="#8B8E91", linestyle="--", linewidth=0.9) + axes[1, 1].set_xticks([0, 1, 2], ["fit 0", "fit 1", "fit 2"]) + axes[1, 1].set_ylabel("mean band sequence margin") + axes[1, 1].legend(frameon=False, fontsize=6.3, loc="best") + axes[1, 1].text( + -0.15, + 1.04, + "D", + transform=axes[1, 1].transAxes, + fontweight="bold", + fontsize=10, + ) + + fig.subplots_adjust( + left=0.11, + right=0.985, + bottom=0.11, + top=0.97, + wspace=0.34, + hspace=0.38, + ) + FIG.mkdir(parents=True, exist_ok=True) + for suffix in ("pdf", "png", "svg"): + fig.savefig( + FIG / f"multitoken-sequence-robustness.{suffix}", + dpi=300, + bbox_inches="tight", + ) + plt.close(fig) + + +if __name__ == "__main__": + main() diff --git a/jlens_materials/scripts/plot_paper_v1_summary.py b/jlens_materials/scripts/plot_paper_v1_summary.py new file mode 100644 index 0000000000000000000000000000000000000000..173e217b7ee8c7a80d6628df36237e17cabec25b --- /dev/null +++ b/jlens_materials/scripts/plot_paper_v1_summary.py @@ -0,0 +1,216 @@ +#!/usr/bin/env python3 +"""Create the compact, descriptive summary figure for materials paper v1. + +The figure deliberately shows item-level ranks and every modulation condition. +It does not turn the exploratory, sample-insufficient runs into an aggregate +statistical claim. +""" + +from __future__ import annotations + +import json +from pathlib import Path + +import matplotlib.pyplot as plt + + +ROOT = Path(__file__).resolve().parents[1] +OLD_QUAL = ROOT / "runs" / "gemma4-materials-qual.json" +OLD_CAUSAL = ROOT / "runs" / "gemma4-materials-causal.json" +NEW_RUN = ROOT / "runs" / "gemma4-materials-paper-v1.json" +OUT_DIR = ROOT / "figures" / "gemma4-materials-paper-v1" + + +def _load(path: Path) -> dict: + return json.loads(path.read_text()) + + +def _prompt(run: dict, title: str) -> dict: + return next(record for record in run["prompts"] if record["title"] == title) + + +def _ranks(record: dict) -> dict[str, tuple[int, int]]: + return { + item["label"]: (item["best_rank"] + 1, item["logit_lens_best_rank"] + 1) + for item in record["emergence"] + } + + +def _association_panel(ax, original: dict, replication: dict, labels: list[str], title: str) -> None: + colors = {"original": "#2a788e", "replication": "#d1495b"} + markers = {"original": "o", "replication": "s"} + all_values = [] + for version, record in (("original", original), ("replication", replication)): + rank_map = _ranks(record) + for label in labels: + if label not in rank_map: + continue + j_rank, logit_rank = rank_map[label] + all_values.extend((j_rank, logit_rank)) + ax.scatter( + logit_rank, + j_rank, + s=72, + color=colors[version], + marker=markers[version], + edgecolor="white", + linewidth=0.8, + zorder=3, + label=version if label == labels[0] else None, + ) + offsets = { + ("original", "nucleation"): (5, 9), + ("original", "coalescence"): (5, -11), + ("replication", "boundary"): (5, -12), + ("replication", "sensitization"): (5, 7), + } + ax.annotate( + f"{label} ({'O' if version == 'original' else 'R'})", + (logit_rank, j_rank), + xytext=offsets.get((version, label), (5, 5)), + textcoords="offset points", + fontsize=8, + ) + lo = max(1, min(all_values) / 2) + hi = max(all_values) * 2 + ax.plot([lo, hi], [lo, hi], "--", color="#777777", linewidth=1) + ax.set_xscale("log") + ax.set_yscale("log") + ax.set_xlim(lo, hi) + ax.set_ylim(lo, hi) + ax.set_xlabel("matched logit-lens best rank") + ax.set_ylabel("J-lens best rank") + ax.set_title(title, loc="left", fontweight="bold") + ax.text( + 0.02, + 0.02, + "Below dashed line = stronger J-lens rank", + transform=ax.transAxes, + fontsize=8, + color="#555555", + ) + ax.grid(True, which="both", alpha=0.18) + + +def _modulation_panel(ax, old_run: dict, new_run: dict) -> None: + records = { + "notebook": { + record["condition"]: record + for record in old_run["prompts"] + if record.get("shape") == "MODULATION" + }, + "brass key": { + record["condition"]: record + for record in new_run["prompts"] + if record.get("control_group") == "paper-v1-pileup-brass-key" + }, + "white cup": { + record["condition"]: record + for record in new_run["prompts"] + if record.get("control_group") == "paper-v1-pileup-white-cup" + }, + } + conditions = ("focus", "suppress", "control") + concepts = ("dislocation", "pile", "stress") + colors = {"focus": "#d1495b", "suppress": "#2a788e", "control": "#777777"} + markers = {"focus": "o", "suppress": "s", "control": "^"} + xlabels = [] + values = {condition: [] for condition in conditions} + for carrier, condition_records in records.items(): + for concept in concepts: + xlabels.append(f"{carrier}\n{concept}") + for condition in conditions: + rank_map = _ranks(condition_records[condition]) + values[condition].append(rank_map[concept][0]) + + x = list(range(len(xlabels))) + for condition in conditions: + ax.plot( + x, + values[condition], + color=colors[condition], + marker=markers[condition], + linewidth=1.5, + markersize=6, + label=condition, + ) + for boundary in (2.5, 5.5): + ax.axvline(boundary, color="#bbbbbb", linewidth=0.8) + ax.set_yscale("log") + ax.invert_yaxis() + ax.set_xticks(x, xlabels, rotation=35, ha="right", fontsize=8) + ax.set_ylabel("J-lens best rank (rank 1 at top)") + ax.set_title( + "C Directed-attention trace replicated across carriers", + loc="left", + fontweight="bold", + ) + ax.grid(True, axis="y", which="both", alpha=0.18) + ax.legend(frameon=False, ncol=3, loc="lower left") + ax.text( + 0.01, + 0.98, + "Every focus point is stronger than its matched suppress and neutral point.", + transform=ax.transAxes, + va="top", + fontsize=8, + color="#555555", + ) + + +def main() -> None: + old_qual = _load(OLD_QUAL) + old_causal = _load(OLD_CAUSAL) + new_run = _load(NEW_RUN) + + plt.rcParams.update({ + "font.family": "DejaVu Sans", + "axes.spines.top": False, + "axes.spines.right": False, + }) + fig = plt.figure(figsize=(17.5, 6.2), constrained_layout=True) + grid = fig.add_gridspec(1, 3, width_ratios=(1, 1, 1.75)) + + _association_panel( + fig.add_subplot(grid[0, 0]), + _prompt(old_qual, "Unnamed ductile-fracture vignette"), + _prompt(new_run, "Ductile-fracture replication vignette"), + ["ductile", "nucleation", "coalescence"], + "A Ductile-fracture association", + ) + _association_panel( + fig.add_subplot(grid[0, 1]), + _prompt(old_qual, "Unnamed boundary-localized degradation"), + _prompt(new_run, "Boundary-attack replication vignette"), + ["boundary", "corrosion", "sensitization"], + "B Boundary-attack association", + ) + _modulation_panel(fig.add_subplot(grid[0, 2]), old_causal, new_run) + + fig.suptitle( + "Gemma-4 materials case studies: association replication and matched modulation", + fontsize=15, + fontweight="bold", + ) + fig.text( + 0.5, + -0.015, + "Exploratory item-level results using the legacy 1,000-prompt lens; not a population-level quantitative claim.", + ha="center", + fontsize=9, + color="#555555", + ) + + OUT_DIR.mkdir(parents=True, exist_ok=True) + for suffix in ("png", "pdf"): + fig.savefig( + OUT_DIR / f"paper-v1-key-results.{suffix}", + dpi=220, + bbox_inches="tight", + facecolor="white", + ) + plt.close(fig) + + +if __name__ == "__main__": + main() diff --git a/jlens_materials/scripts/rate_materials_heldout_v1_openai.py b/jlens_materials/scripts/rate_materials_heldout_v1_openai.py new file mode 100644 index 0000000000000000000000000000000000000000..4dc32e49aea1a74650fbe46fb56a38cadbadd6ee --- /dev/null +++ b/jlens_materials/scripts/rate_materials_heldout_v1_openai.py @@ -0,0 +1,801 @@ +#!/usr/bin/env python3 +"""Run a reproducible, secondary OpenAI rater on frozen blinded word sets. + +This does not replace the preregistered materials-expert ratings. The model sees +only shuffled candidate words, support counts, and the ten allowed labels. The +answer key is loaded only after all raw blinded responses have been saved. +""" + +from __future__ import annotations + +import argparse +import csv +import hashlib +import itertools +import json +import math +import random +import sys +from collections import Counter, defaultdict +from datetime import datetime, timezone +from pathlib import Path + +import matplotlib.pyplot as plt +import numpy as np +from matplotlib.colors import ListedColormap +from matplotlib.patches import FancyArrowPatch, FancyBboxPatch +from scipy.stats import spearmanr + + +ROOT = Path(__file__).resolve().parents[1] +sys.path.insert(0, str(ROOT)) + +import llm # noqa: E402 + + +BLIND_CSV = ROOT / "experiments" / "MATERIALS_HELDOUT_V1_BLINDED_SETS.csv" +KEY_CSV = ROOT / "experiments" / "MATERIALS_HELDOUT_V1_BLINDED_KEY.csv" +STATS_PATH = ROOT / "experiments" / "materials-heldout-v1_statistics.json" +RAW_PATH = ROOT / "experiments" / "materials-heldout-v1_automated_blinded_raw.json" +RESULTS_PATH = ROOT / "experiments" / "materials-heldout-v1_automated_blinded_results.json" +RATINGS_CSV = ROOT / "experiments" / "materials-heldout-v1_automated_blinded_ratings.csv" +REPORT_PATH = ROOT / "experiments" / "MATERIALS_HELDOUT_V1_AUTOMATED_RATER.md" +INTEGRATED_REPORT_PATH = ROOT / "experiments" / "MATERIALS_HELDOUT_V1_ANALYSIS.md" +FIGURE_PATH = ROOT / "figures" / "materials-heldout-v1" / "automated-family-identification" +DISPLAY_FAMILY = { + "boundary-attack": "boundary attack", + "cleavage": "cleavage", + "cyclic": "cyclic damage", + "ductile": "ductile failure", + "high-temperature-deformation": "high-temperature deformation", + "hot-air-surface-layer": "surface oxidation", + "line-defect-motion": "line-defect motion", + "notch-resistance": "notch resistance", + "particle-strengthening": "particle strengthening", + "rapid-transformation": "rapid transformation", +} +ANALYSIS_SEED = 20260714 +N_NULL = 100_000 +N_BOOT = 20_000 + + +LABELS = [ + "boundary-attack", + "cleavage", + "cyclic", + "ductile", + "high-temperature-deformation", + "hot-air-surface-layer", + "line-defect-motion", + "notch-resistance", + "particle-strengthening", + "rapid-transformation", +] + +SYSTEM = """\ +You are serving as a blinded materials-science classifier. You will receive +sets of vocabulary words decoded from an unknown representation. Infer which +one of ten physical mechanism families best matches each set. Use only the +candidate words, their support counts, and the supplied label names. You do not +know the decoding method, the original prompt, or the answer key. + +Classify every set independently. Labels may be reused any number of times; do +not impose a one-to-one assignment across sets. Weak or noisy sets still require +the single best label. Return only the requested JSON array, with no Markdown. +""" + + +def sha256(path: Path) -> str: + return hashlib.sha256(path.read_bytes()).hexdigest() + + +def read_csv(path: Path) -> list[dict[str, str]]: + with path.open(newline="") as handle: + return list(csv.DictReader(handle)) + + +def format_user_prompt( + rows: list[dict[str, str]], labels: list[str] +) -> str: + lines = [ + "Allowed labels (choose exactly one for each set):", + *[f"- {label}" for label in labels], + "", + "Blinded candidate sets:", + ] + for row in rows: + candidates = [] + for index in range(1, 9): + token = row.get(f"candidate_{index}", "").strip() + support = row.get(f"support_{index}", "").strip() + if token: + candidates.append(f"{token} [{support} prompts]") + lines.append(f"{row['set_id']}: " + "; ".join(candidates)) + lines.extend([ + "", + "Return a JSON array with exactly one object per set, using this schema:", + '[{"set_id":"SET-01","predicted_family":"one-allowed-label",' + '"confidence":0.0,"rationale":"at most 20 words"}]', + "Confidence must be between 0 and 1. Keep each rationale under 20 words.", + ]) + return "\n".join(lines) + + +def parse_response(text: str, expected_set_ids: set[str]) -> list[dict]: + stripped = text.strip() + start = stripped.find("[") + end = stripped.rfind("]") + if start < 0 or end < start: + raise ValueError("response does not contain a JSON array") + data = json.loads(stripped[start:end + 1]) + if not isinstance(data, list): + raise ValueError("response JSON is not a list") + parsed = [] + seen = set() + for row in data: + set_id = str(row.get("set_id", "")) + label = str(row.get("predicted_family", "")) + if set_id not in expected_set_ids or set_id in seen: + raise ValueError(f"invalid or duplicate set id {set_id!r}") + if label not in LABELS: + raise ValueError(f"invalid family label {label!r}") + confidence = float(row.get("confidence", 0.0)) + if not 0 <= confidence <= 1: + raise ValueError(f"invalid confidence for {set_id}") + parsed.append({ + "set_id": set_id, + "predicted_family": label, + "confidence": confidence, + "rationale": str(row.get("rationale", "")), + }) + seen.add(set_id) + if seen != expected_set_ids: + missing = sorted(expected_set_ids - seen) + raise ValueError(f"response omitted set ids: {missing}") + return parsed + + +def run_blinded_passes(model: str, passes: int, *, force: bool) -> dict: + blind_hash = sha256(BLIND_CSV) + if RAW_PATH.is_file() and not force: + existing = json.loads(RAW_PATH.read_text()) + if ( + existing.get("blinded_sets_sha256") == blind_hash + and existing.get("model") == model + and len(existing.get("passes", [])) == passes + and all(item.get("parsed_ratings") for item in existing["passes"]) + ): + print(f"reusing {passes} saved blinded API passes from {RAW_PATH}") + return existing + + blind_rows = read_csv(BLIND_CSV) + expected_set_ids = {row["set_id"] for row in blind_rows} + raw = { + "analysis_status": "secondary automated blinded rater; not human expert data", + "created_at": datetime.now(timezone.utc).isoformat(), + "provider": "openai", + "model": model, + "system_prompt": SYSTEM, + "blinded_sets_path": str(BLIND_CSV.relative_to(ROOT)), + "blinded_sets_sha256": blind_hash, + "answer_key_not_loaded_during_calls": True, + "passes": [], + "failed_attempts": [], + } + for pass_index in range(passes): + rng = random.Random(ANALYSIS_SEED + pass_index) + row_order = list(blind_rows) + label_order = list(LABELS) + rng.shuffle(row_order) + rng.shuffle(label_order) + user_prompt = format_user_prompt(row_order, label_order) + parsed = None + response = "" + for attempt_index, effort in enumerate(("low", "minimal"), start=1): + response = llm.complete( + "openai", + model, + SYSTEM, + [{"type": "text", "text": user_prompt}], + max_tokens=8000, + effort=effort, + ) + try: + parsed = parse_response(response, expected_set_ids) + except (ValueError, json.JSONDecodeError) as exc: + raw["failed_attempts"].append({ + "pass_index": pass_index, + "attempt_index": attempt_index, + "reasoning_effort": effort, + "user_prompt": user_prompt, + "raw_response": response, + "parse_error": f"{type(exc).__name__}: {exc}", + }) + RAW_PATH.write_text(json.dumps(raw, indent=2) + "\n") + if attempt_index == 2: + raise + continue + break + if parsed is None: + raise RuntimeError(f"pass {pass_index} produced no valid rating") + raw["passes"].append({ + "pass_index": pass_index, + "randomization_seed": ANALYSIS_SEED + pass_index, + "set_order": [row["set_id"] for row in row_order], + "label_order": label_order, + "successful_reasoning_effort": effort, + "user_prompt": user_prompt, + "raw_response": response, + "parsed_ratings": parsed, + }) + RAW_PATH.write_text(json.dumps(raw, indent=2) + "\n") + print(f"saved blinded pass {pass_index + 1}/{passes}") + return raw + + +def majority_prediction(ratings: list[dict]) -> tuple[str, dict, str]: + counts = Counter(row["predicted_family"] for row in ratings) + maximum = max(counts.values()) + tied = [label for label, count in counts.items() if count == maximum] + tie_break = "vote count" + if len(tied) > 1: + mean_confidence = { + label: float(np.mean([ + row["confidence"] for row in ratings + if row["predicted_family"] == label + ])) + for label in tied + } + best_confidence = max(mean_confidence.values()) + tied = [ + label for label in tied + if abs(mean_confidence[label] - best_confidence) <= 1e-12 + ] + tie_break = "mean confidence among vote-count ties" + if len(tied) > 1: + tied.sort() + tie_break = "lexicographic after vote-count and confidence tie" + return tied[0], dict(sorted(counts.items())), tie_break + + +def fleiss_kappa(rows: list[dict], passes: int) -> float: + by_set: dict[str, Counter] = defaultdict(Counter) + for row in rows: + by_set[row["set_id"]][row["predicted_family"]] += 1 + matrix = np.asarray([ + [by_set[set_id][label] for label in LABELS] + for set_id in sorted(by_set) + ], dtype=float) + item_agreement = ( + np.sum(matrix ** 2, axis=1) - passes + ) / (passes * (passes - 1)) + observed = float(np.mean(item_agreement)) + category_frequency = np.sum(matrix, axis=0) / (matrix.shape[0] * passes) + expected = float(np.sum(category_frequency ** 2)) + return float((observed - expected) / (1 - expected)) if expected < 1 else float("nan") + + +def shuffled_null( + predictions: list[str], truths: list[str], *, seed: int +) -> dict: + rng = np.random.default_rng(seed) + predictions_array = np.asarray(predictions) + truths_array = np.asarray(truths) + observed = float(np.mean(predictions_array == truths_array)) + estimates = np.empty(N_NULL, dtype=float) + for index in range(N_NULL): + estimates[index] = float(np.mean( + predictions_array == truths_array[rng.permutation(len(truths_array))] + )) + return { + "observed_accuracy": observed, + "null_mean": float(np.mean(estimates)), + "null_95_low": float(np.quantile(estimates, 0.025)), + "null_95_high": float(np.quantile(estimates, 0.975)), + "p_greater_or_equal": float( + (np.sum(estimates >= observed - 1e-15) + 1) / (N_NULL + 1) + ), + "n_shuffles": N_NULL, + } + + +def exact_paired_sign_flip(family_differences: list[float]) -> dict: + values = np.asarray(family_differences, dtype=float) + observed = float(np.mean(values)) + null = np.asarray([ + float(np.mean(values * np.asarray(signs))) + for signs in itertools.product((-1.0, 1.0), repeat=len(values)) + ]) + return { + "observed_jacobian_minus_logit_accuracy": observed, + "p_one_sided_j_greater": float(np.mean(null >= observed - 1e-15)), + "p_two_sided": float(np.mean(np.abs(null) >= abs(observed) - 1e-15)), + "n_permutations": int(len(null)), + } + + +def bootstrap_spearman(x_values: list[float], y_values: list[float], *, seed: int) -> dict: + x = np.asarray(x_values, dtype=float) + y = np.asarray(y_values, dtype=float) + observed = float(spearmanr(x, y).statistic) if len(np.unique(y)) > 1 else float("nan") + rng = np.random.default_rng(seed) + estimates = [] + for _ in range(N_BOOT): + selected = rng.integers(0, len(x), size=len(x)) + sample_x = x[selected] + sample_y = y[selected] + if len(np.unique(sample_x)) < 2 or len(np.unique(sample_y)) < 2: + continue + estimate = float(spearmanr(sample_x, sample_y).statistic) + if math.isfinite(estimate): + estimates.append(estimate) + return { + "rho": observed, + "bootstrap_low": float(np.quantile(estimates, 0.025)) if estimates else None, + "bootstrap_high": float(np.quantile(estimates, 0.975)) if estimates else None, + "n_families": len(x), + "n_valid_resamples": len(estimates), + } + + +def analyze(raw: dict) -> tuple[dict, list[dict]]: + # The answer key is intentionally opened only after all API responses exist. + key_rows = read_csv(KEY_CSV) + key = {row["set_id"]: row for row in key_rows} + ratings = [] + for pass_result in raw["passes"]: + for rating in pass_result["parsed_ratings"]: + truth = key[rating["set_id"]] + ratings.append({ + "pass_index": pass_result["pass_index"], + **rating, + "method": truth["method"], + "true_family": truth["mechanism_family"], + "correct": rating["predicted_family"] == truth["mechanism_family"], + }) + + by_set: dict[str, list[dict]] = defaultdict(list) + for row in ratings: + by_set[row["set_id"]].append(row) + majority_rows = [] + for set_id in sorted(by_set): + prediction, vote_counts, tie_break = majority_prediction(by_set[set_id]) + truth = key[set_id] + correct_votes = sum(row["correct"] for row in by_set[set_id]) + majority_rows.append({ + "set_id": set_id, + "method": truth["method"], + "true_family": truth["mechanism_family"], + "majority_prediction": prediction, + "majority_correct": prediction == truth["mechanism_family"], + "vote_fraction_correct": correct_votes / len(by_set[set_id]), + "vote_counts": vote_counts, + "tie_break": tie_break, + }) + + per_pass = [] + for pass_index in range(len(raw["passes"])): + pass_rows = [row for row in ratings if row["pass_index"] == pass_index] + per_pass.append({ + "pass_index": pass_index, + "overall_accuracy": float(np.mean([row["correct"] for row in pass_rows])), + "jacobian_accuracy": float(np.mean([ + row["correct"] for row in pass_rows if row["method"] == "jacobian" + ])), + "logit_accuracy": float(np.mean([ + row["correct"] for row in pass_rows if row["method"] == "logit" + ])), + }) + + method_metrics = {} + for method in ("jacobian", "logit"): + method_majority = [row for row in majority_rows if row["method"] == method] + predictions = [row["majority_prediction"] for row in method_majority] + truths = [row["true_family"] for row in method_majority] + method_ratings = [row for row in ratings if row["method"] == method] + method_metrics[method] = { + "majority_accuracy": float(np.mean([ + row["majority_correct"] for row in method_majority + ])), + "correct_families": int(sum(row["majority_correct"] for row in method_majority)), + "n_families": len(method_majority), + "mean_individual_pass_accuracy": float(np.mean([ + result[f"{method}_accuracy"] for result in per_pass + ])), + "fleiss_kappa": fleiss_kappa(method_ratings, len(raw["passes"])), + "shuffled_label_null": shuffled_null( + predictions, truths, seed=ANALYSIS_SEED + (1 if method == "jacobian" else 2) + ), + } + + family_rows = {} + for family in LABELS: + family_rows[family] = {} + for method in ("jacobian", "logit"): + family_rows[family][method] = next( + row for row in majority_rows + if row["method"] == method and row["true_family"] == family + ) + family_differences = [ + float(family_rows[family]["jacobian"]["majority_correct"]) + - float(family_rows[family]["logit"]["majority_correct"]) + for family in LABELS + ] + + study_stats = json.loads(STATS_PATH.read_text()) + controlled_families = study_stats["controlled"]["families"] + h4 = {} + for method in ("jacobian", "logit"): + recovery_key = "j_auc_mean" if method == "jacobian" else "logit_auc_mean" + recovery = [controlled_families[family][recovery_key] for family in LABELS] + majority_success = [ + float(family_rows[family][method]["majority_correct"]) + for family in LABELS + ] + vote_fraction = [ + float(family_rows[family][method]["vote_fraction_correct"]) + for family in LABELS + ] + h4[method] = { + "majority_success": bootstrap_spearman( + recovery, majority_success, + seed=ANALYSIS_SEED + (10 if method == "jacobian" else 11), + ), + "vote_fraction_sensitivity": bootstrap_spearman( + recovery, vote_fraction, + seed=ANALYSIS_SEED + (20 if method == "jacobian" else 21), + ), + "family_values": [ + { + "family": family, + "controlled_recovery_auc": recovery[index], + "majority_success": majority_success[index], + "vote_fraction_correct": vote_fraction[index], + } + for index, family in enumerate(LABELS) + ], + } + + results = { + "analysis_status": ( + "secondary automated blinded analysis; does not satisfy the " + "preregistered human-rater endpoint" + ), + "created_at": datetime.now(timezone.utc).isoformat(), + "provider": raw["provider"], + "model": raw["model"], + "n_order_randomized_passes": len(raw["passes"]), + "blinded_sets_sha256": raw["blinded_sets_sha256"], + "answer_key_sha256": sha256(KEY_CSV), + "per_pass": per_pass, + "overall_fleiss_kappa": fleiss_kappa(ratings, len(raw["passes"])), + "methods": method_metrics, + "paired_family_comparison": exact_paired_sign_flip(family_differences), + "majority_by_set": majority_rows, + "controlled_discovery_relationship_h4_secondary": h4, + "limitations": [ + "five passes of one model are not five independent human raters", + "automated semantic judgments are a secondary analysis", + "human materials-science ratings remain required for the primary endpoint", + ], + } + return results, ratings + + +def write_ratings_csv(rows: list[dict]) -> None: + with RATINGS_CSV.open("w", newline="") as handle: + writer = csv.DictWriter(handle, fieldnames=list(rows[0])) + writer.writeheader() + writer.writerows(rows) + + +def plot_results( + results: dict, + *, + output_stem: Path = FIGURE_PATH, + font_scale: float = 1.0, +) -> None: + output_stem.parent.mkdir(parents=True, exist_ok=True) + plt.rcParams.update({ + "font.family": "DejaVu Sans", + "font.size": 10 * font_scale, + "axes.spines.top": False, + "axes.spines.right": False, + }) + jacobian_color = "#087F8C" + logit_color = "#66727A" + neutral = "#70757A" + figure = plt.figure(figsize=(15.2, 8.0), constrained_layout=True) + grid = figure.add_gridspec(2, 3, height_ratios=[0.43, 1.0]) + protocol_axis = figure.add_subplot(grid[0, :]) + axes = [figure.add_subplot(grid[1, index]) for index in range(3)] + + protocol_axis.set_xlim(0, 1) + protocol_axis.set_ylim(0, 1) + protocol_axis.axis("off") + protocol_boxes = [ + (0.035, "20 candidate-word sets\n10 families x 2 readouts"), + (0.285, "Shuffle sets and hide\nprompt, method, and answer key"), + (0.535, "Choose 1 of 10 family labels\nfrom the candidate words alone"), + (0.785, "Repeat 5 orderings\nthen take majority vote"), + ] + box_width = 0.18 + box_y = 0.36 + box_height = 0.43 + for x_value, label in protocol_boxes: + protocol_axis.add_patch( + FancyBboxPatch( + (x_value, box_y), box_width, box_height, + boxstyle="round,pad=0.012,rounding_size=0.015", + facecolor="#f4f7f8", edgecolor="#7c8b92", linewidth=1.1, + ) + ) + protocol_axis.text( + x_value + box_width / 2, box_y + box_height / 2, label, + ha="center", va="center", fontsize=9.2 * font_scale, + ) + for first, second in zip(protocol_boxes[:-1], protocol_boxes[1:], strict=True): + protocol_axis.add_patch( + FancyArrowPatch( + (first[0] + box_width + 0.008, box_y + box_height / 2), + (second[0] - 0.008, box_y + box_height / 2), + arrowstyle="-|>", mutation_scale=12, linewidth=1.1, color="#626c72", + ) + ) + protocol_axis.text( + 0.5, 0.12, + "This tests whether a word set carries recognizable materials meaning; it does not ask the rater to solve the original engineering prompt.", + ha="center", va="center", fontsize=8.8 * font_scale, color="#596168", + ) + + axis = axes[0] + pass_indices = np.arange(len(results["per_pass"])) + width = 0.34 + axis.bar(pass_indices - width / 2, + [row["jacobian_accuracy"] for row in results["per_pass"]], + width=width, color=jacobian_color, label="Jacobian") + axis.bar(pass_indices + width / 2, + [row["logit_accuracy"] for row in results["per_pass"]], + width=width, color=logit_color, label="Direct") + axis.axhline(0.1, color=neutral, linestyle="--", linewidth=1, label="10-label chance") + axis.set_xticks(pass_indices, [str(index + 1) for index in pass_indices]) + axis.set_ylim(0, 1.03) + axis.set_xlabel("Order-randomized automated pass") + axis.set_ylabel("Classification accuracy") + axis.legend( + frameon=False, loc="lower center", bbox_to_anchor=(0.64, 1.015), + ncol=3, fontsize=8 * font_scale, + ) + axis.grid(True, axis="y", alpha=0.18) + + axis = axes[1] + rows = {row["true_family"]: {} for row in results["majority_by_set"]} + for row in results["majority_by_set"]: + rows[row["true_family"]][row["method"]] = row + correctness = np.asarray([ + [ + float(rows[family]["jacobian"]["majority_correct"]), + float(rows[family]["logit"]["majority_correct"]), + ] + for family in LABELS + ]) + axis.imshow( + correctness, + vmin=0, + vmax=1, + cmap=ListedColormap(["#e5e8ea", "#4a9096"]), + aspect="auto", + ) + axis.set_yticks(np.arange(len(LABELS)), [DISPLAY_FAMILY[label] for label in LABELS]) + axis.set_xticks([0, 1], ["Jacobian", "Direct"]) + for row_index in range(len(LABELS)): + for col_index in range(2): + correct = bool(correctness[row_index, col_index]) + axis.text( + col_index, row_index, "correct" if correct else "miss", + ha="center", va="center", fontsize=8.2 * font_scale, + color="white" if correct else "#30383d", + ) + axis.tick_params(axis="x", top=True, labeltop=True, bottom=False, labelbottom=False) + axis.grid(False) + for spine in axis.spines.values(): + spine.set_visible(False) + + axis = axes[2] + h4 = results["controlled_discovery_relationship_h4_secondary"]["jacobian"] + for row in h4["family_values"]: + axis.scatter(row["controlled_recovery_auc"], row["vote_fraction_correct"], + color=jacobian_color, s=45, alpha=0.82) + if row["vote_fraction_correct"] < 1 or row["family"] == "boundary-attack": + offset = (5, -10) if row["family"] == "boundary-attack" else (5, 3) + axis.annotate(DISPLAY_FAMILY[row["family"]], + (row["controlled_recovery_auc"], row["vote_fraction_correct"]), + xytext=offset, textcoords="offset points", fontsize=8 * font_scale) + sensitivity = h4["vote_fraction_sensitivity"] + axis.set_xlabel("Controlled Jacobian recovery AUC") + axis.set_ylabel("Automated vote fraction correct") + axis.set_ylim(-0.04, 1.04) + axis.text(0.98, 0.06, f"Spearman rho = {sensitivity['rho']:.2f}", + transform=axis.transAxes, ha="right", va="bottom", color=neutral) + axis.grid(True, alpha=0.18) + + protocol_axis.text( + 0.0, + 1.02, + "A", + transform=protocol_axis.transAxes, + fontsize=10 * font_scale, + fontweight="bold", + ha="left", + va="bottom", + clip_on=False, + ) + for panel_axis, label in zip(axes, "BCD", strict=True): + panel_axis.text( + 0.0, + 1.06, + label, + transform=panel_axis.transAxes, + fontsize=10 * font_scale, + fontweight="bold", + ha="left", + va="bottom", + clip_on=False, + ) + + for suffix in ("png", "pdf", "svg"): + figure.savefig(output_stem.with_suffix(f".{suffix}"), dpi=240, + bbox_inches="tight", facecolor="white") + plt.close(figure) + + +def write_report(results: dict) -> None: + jacobian = results["methods"]["jacobian"] + logit = results["methods"]["logit"] + paired = results["paired_family_comparison"] + h4 = results["controlled_discovery_relationship_h4_secondary"]["jacobian"] + lines = [ + "# Secondary automated blinded family identification", + "", + "## Status", + "", + "This is a reproducible automated secondary analysis. It does **not** satisfy the preregistered requirement for at least three materials-science raters. Five order-randomized passes of one model are repeated judgments from one automated system, not five independent experts.", + "", + "## What the model saw", + "", + f"The rater was `{results['model']}` through the OpenAI Responses API. It saw only the 20 shuffled sets of eight candidate words, each word's support across five phrasings, and the ten allowed mechanism-family names. It did not receive the original prompts, the decoding method, or the answer key. The exact system prompt, five exact user prompts, raw responses, and parsed responses are retained in `materials-heldout-v1_automated_blinded_raw.json`.", + "", + "Each set was classified independently; labels could be reused. Majority vote was computed across the five order-randomized passes. Vote-count ties were resolved first by mean confidence and then lexicographically, as encoded before scoring.", + "", + "![Automated blinded rating](../figures/materials-heldout-v1/automated-family-identification.png)", + "", + "## Results", + "", + f"Jacobian candidate sets were identified correctly for **{jacobian['correct_families']}/{jacobian['n_families']} families ({jacobian['majority_accuracy']:.0%})**. Direct-unembedding sets were correct for **{logit['correct_families']}/{logit['n_families']} ({logit['majority_accuracy']:.0%})**.", + "", + f"Across paired physical families, the Jacobian-minus-direct accuracy difference was {paired['observed_jacobian_minus_logit_accuracy']:+.2f}; the exact sign-flip p-values were {paired['p_one_sided_j_greater']:.4f} one-sided and {paired['p_two_sided']:.4f} two-sided.", + "", + f"Agreement across the five automated passes was Fleiss kappa = {results['overall_fleiss_kappa']:.3f}. The shuffled-label p-values were {jacobian['shuffled_label_null']['p_greater_or_equal']:.4f} for Jacobian sets and {logit['shuffled_label_null']['p_greater_or_equal']:.4f} for direct sets.", + "", + "| pass | Jacobian accuracy | direct accuracy | overall |", + "|---:|---:|---:|---:|", + ] + for row in results["per_pass"]: + lines.append( + f"| {row['pass_index'] + 1} | {row['jacobian_accuracy']:.0%} | " + f"{row['logit_accuracy']:.0%} | {row['overall_accuracy']:.0%} |" + ) + lines.extend([ + "", + "## Controlled-versus-discovered relationship", + "", + f"For the Jacobian method, family-level controlled recovery and automated majority identification had Spearman rho = {h4['majority_success']['rho']:.3f}. Using the fraction of the five automated passes that were correct gave rho = {h4['vote_fraction_sensitivity']['rho']:.3f}, with a family-bootstrap interval of {h4['vote_fraction_sensitivity']['bootstrap_low']} to {h4['vote_fraction_sensitivity']['bootstrap_high']}.", + "", + "This small, ten-family secondary correlation is not a substitute for the preregistered human-rater H4 analysis. A weak relationship would mean that finding a preselected word and assembling a semantically identifiable word neighborhood capture different aspects of the representation.", + "", + "## Per-set majority outcomes", + "", + "| set | hidden method | true family | majority prediction | correct | correct votes |", + "|---|---|---|---|---:|---:|", + ]) + for row in results["majority_by_set"]: + lines.append( + f"| {row['set_id']} | {row['method']} | {row['true_family']} | " + f"{row['majority_prediction']} | {'yes' if row['majority_correct'] else 'no'} | " + f"{row['vote_fraction_correct']:.0%} |" + ) + lines.extend([ + "", + "## Interpretation", + "", + "The automated comparison tests whether target-free decoded word neighborhoods are recognizable as materials mechanisms. It does not test whether the model internally wrote a hidden explanation, and it cannot establish causal reasoning. The human expert rating sheet remains the primary next step.", + "", + "## Artifacts", + "", + "- Raw blinded prompts and responses: `materials-heldout-v1_automated_blinded_raw.json`", + "- Unblinded machine-readable results: `materials-heldout-v1_automated_blinded_results.json`", + "- Every individual rating: `materials-heldout-v1_automated_blinded_ratings.csv`", + "- Reproduce or reuse saved calls: `conda run -n PyTorch python scripts/rate_materials_heldout_v1_openai.py`", + "", + ]) + REPORT_PATH.write_text("\n".join(lines)) + + +def update_integrated_report(results: dict) -> None: + begin = "" + end = "" + jacobian = results["methods"]["jacobian"] + logit = results["methods"]["logit"] + paired = results["paired_family_comparison"] + h4 = results["controlled_discovery_relationship_h4_secondary"]["jacobian"] + section = "\n".join([ + begin, + "", + "## Completed automated blinded secondary analysis", + "", + f"Five order-randomized blinded passes of `{results['model']}` identified " + f"{jacobian['correct_families']}/{jacobian['n_families']} Jacobian family sets " + f"and {logit['correct_families']}/{logit['n_families']} direct-unembedding sets " + "by majority vote. The paired family accuracy difference was " + f"{paired['observed_jacobian_minus_logit_accuracy']:+.2f} " + f"(exact p={paired['p_two_sided']:.3f}); repeat-pass Fleiss kappa was " + f"{results['overall_fleiss_kappa']:.3f}. Both methods failed on cleavage.", + "", + "The candidate neighborhoods are therefore strongly materials-informative under this " + "automated test, but not uniquely Jacobian. Controlled Jacobian recovery and automated " + f"vote fraction were weakly related (rho={h4['vote_fraction_sensitivity']['rho']:.3f}, " + f"family-bootstrap interval {h4['vote_fraction_sensitivity']['bootstrap_low']:.3f} to " + f"{h4['vote_fraction_sensitivity']['bootstrap_high']:.3f}).", + "", + "This does not complete the primary human-rater endpoint: five passes of one automated " + "model are not independent materials experts. See " + "[`MATERIALS_HELDOUT_V1_AUTOMATED_RATER.md`](MATERIALS_HELDOUT_V1_AUTOMATED_RATER.md) " + "for every result and the raw-response audit trail.", + "", + end, + "", + ]) + text = INTEGRATED_REPORT_PATH.read_text() + if begin in text and end in text: + prefix, remainder = text.split(begin, 1) + _, suffix = remainder.split(end, 1) + text = prefix.rstrip() + "\n\n" + section + suffix.lstrip("\n") + else: + text = text.rstrip() + "\n\n" + section + INTEGRATED_REPORT_PATH.write_text(text) + + +def main() -> None: + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument("--model", default=llm.default_model("openai")) + parser.add_argument("--passes", type=int, default=5) + parser.add_argument("--force", action="store_true") + args = parser.parse_args() + if args.passes < 3: + raise SystemExit("use at least three automated passes") + raw = run_blinded_passes(args.model, args.passes, force=args.force) + results, ratings = analyze(raw) + RESULTS_PATH.write_text(json.dumps(results, indent=2) + "\n") + write_ratings_csv(ratings) + plot_results(results) + write_report(results) + update_integrated_report(results) + + study_stats = json.loads(STATS_PATH.read_text()) + study_stats["automated_blinded_secondary"] = results + STATS_PATH.write_text(json.dumps(study_stats, indent=2) + "\n") + print(json.dumps({ + "model": results["model"], + "overall_fleiss_kappa": results["overall_fleiss_kappa"], + "methods": results["methods"], + "paired_family_comparison": results["paired_family_comparison"], + "h4": results["controlled_discovery_relationship_h4_secondary"], + }, indent=2)) + for path in (RAW_PATH, RESULTS_PATH, RATINGS_CSV, REPORT_PATH, + INTEGRATED_REPORT_PATH, + FIGURE_PATH.with_suffix(".pdf"), STATS_PATH): + print(f"wrote {path}") + + +if __name__ == "__main__": + main() diff --git a/jlens_materials/scripts/run_activation_patching_distance_controls.py b/jlens_materials/scripts/run_activation_patching_distance_controls.py new file mode 100644 index 0000000000000000000000000000000000000000..bf7bcaf9ee4ec778eb9c6997adcd6b3a3b16a6bc --- /dev/null +++ b/jlens_materials/scripts/run_activation_patching_distance_controls.py @@ -0,0 +1,201 @@ +#!/usr/bin/env python3 +"""Run frozen state-distance-matched falsification controls for activation patching.""" + +from __future__ import annotations + +import argparse +import json +import platform +import sys +from datetime import datetime, timezone +from pathlib import Path + +import torch + +ROOT = Path(__file__).resolve().parents[1] +sys.path.insert(0, str(ROOT)) +sys.path.insert(0, str(ROOT / "_vendor_jlens")) +sys.path.insert(0, str(ROOT / "scripts")) + +from run_lens import _DTYPES, load_model # noqa: E402 +from run_jacobian_steering_pilot import atomic_json, format_chat, sha256 # noqa: E402 +from run_semantic_steering_v3 import continuation_tokens # noqa: E402 +from run_counterfactual_activation_patching import ( # noqa: E402 + capture_prompt, + donor_ids, + expanded_prompts, + patched_log_odds, +) + + +DEFAULT_PROTOCOL = ( + "experiments/candidate-activation-patching-2026-07-16/falsification_protocol.json" +) +DEFAULT_OUTPUT = ( + "experiments/candidate-activation-patching-2026-07-16/distance_controls_raw.json" +) + + +def validate(path: Path, expected: str) -> None: + if sha256(path) != expected: + raise RuntimeError(f"fingerprint mismatch: {path}") + + +def main() -> None: + parser = argparse.ArgumentParser() + parser.add_argument("--protocol", default=DEFAULT_PROTOCOL) + parser.add_argument("--output", default=DEFAULT_OUTPUT) + parser.add_argument("--dtype", choices=sorted(_DTYPES), default="bfloat16") + parser.add_argument("--device", default=None) + parser.add_argument("--resume", action="store_true") + args = parser.parse_args() + + false_path = (ROOT / args.protocol).resolve() + false = json.loads(false_path.read_text()) + primary_path = ROOT / false["primary_protocol"] + raw_path = ROOT / false["primary_raw"] + validate(primary_path, false["primary_protocol_sha256"]) + validate(raw_path, false["primary_raw_sha256"]) + primary = json.loads(primary_path.read_text()) + source_path = ROOT / primary["inputs"]["prompt_manifest"] + validate(source_path, primary["inputs"]["prompt_manifest_sha256"]) + source = json.loads(source_path.read_text()) + grain = next( + row for row in source["families"] if row["family_id"] == "grain-size-strengthening" + ) + prompts = expanded_prompts(grain) + prompt_by_id = {row["prompt_id"]: row for row in prompts} + index = { + (row["pair_id"], row["relation"], row["presentation_order"]): row + for row in prompts + } + pair_order = primary["cyclic_pair_order"] + next_pair = { + pair_order[position]: pair_order[(position + 1) % len(pair_order)] + for position in range(len(pair_order)) + } + layers = [int(layer) for layer in false["layers"]] + + model = load_model( + primary["model"], dtype=_DTYPES[args.dtype], device=args.device, + revision=primary["model_revision"], + ) + higher = continuation_tokens(model.tokenizer, "higher") + lower = continuation_tokens(model.tokenizer, "lower") + if len(higher) != 1 or len(lower) != 1: + raise RuntimeError("higher/lower must remain single tokens") + answer_ids = (higher[0], lower[0]) + + output_path = (ROOT / args.output).resolve() + if args.resume and output_path.exists(): + payload = json.loads(output_path.read_text()) + if payload["provenance"]["protocol_sha256"] != sha256(false_path): + raise RuntimeError("resume protocol mismatch") + else: + payload = { + "study_id": false["study_id"], + "created_at": datetime.now(timezone.utc).isoformat(), + "provenance": { + "protocol": str(false_path.relative_to(ROOT)), + "protocol_sha256": sha256(false_path), + "runner": str(Path(__file__).resolve().relative_to(ROOT)), + "runner_sha256": sha256(Path(__file__).resolve()), + "primary_raw_sha256": sha256(raw_path), + "model": primary["model"], + "model_revision": primary["model_revision"], + "device": str(model.input_device), + "dtype": args.dtype, + "torch": torch.__version__, + "python": sys.version, + "platform": platform.platform(), + }, + "rows": [], + } + atomic_json(output_path, payload) + + print(f"capturing {len(prompts)} prompts", flush=True) + captured = {} + for number, prompt in enumerate(prompts, start=1): + text = format_chat(model.tokenizer, prompt["user"]) + captured[prompt["prompt_id"]] = capture_prompt( + model, text, layers, answer_ids + ) + print(f" clean {number:02d}/{len(prompts):02d}", flush=True) + + control_sources = { + "distance_matched_cross_material_same": "cross_material_same", + "distance_matched_order_only": "order_only", + } + complete = { + (row["receiver_prompt_id"], row["control"], int(row["layer"])) + for row in payload["rows"] + } + total = len(prompts) * len(control_sources) * len(layers) + done = len(complete) + print(f"running {total} distance-matched patches", flush=True) + for receiver_number, receiver in enumerate(prompts, start=1): + receiver_id = receiver["prompt_id"] + receiver_data = captured[receiver_id] + choices = donor_ids(receiver, index, next_pair) + reverse_id = choices["matched_reverse"] + reverse_data = captured[reverse_id] + receiver_sign = 1.0 if receiver["relation"] == "refinement" else -1.0 + for control, source_control in control_sources.items(): + donor_id = choices[source_control] + donor = prompt_by_id[donor_id] + donor_data = captured[donor_id] + for layer in layers: + key = (receiver_id, control, layer) + if key in complete: + continue + receiver_state = receiver_data["states"][layer] + reverse_difference = reverse_data["states"][layer] - receiver_state + control_difference = donor_data["states"][layer] - receiver_state + target_norm = reverse_difference.norm() + source_norm = control_difference.norm() + if float(source_norm) <= 1e-12: + raise RuntimeError(f"zero control difference: {key}") + scale = target_norm / source_norm + replacement = receiver_state + scale * control_difference + achieved = (replacement - receiver_state).norm() + patched = patched_log_odds( + model, receiver_data["input_ids"], layer, replacement, answer_ids + ) + clean = float(receiver_data["clean_log_odds"]) + shift = patched - clean + payload["rows"].append({ + "receiver_prompt_id": receiver_id, + "receiver_condition_id": receiver["condition_id"], + "receiver_pair_id": receiver["pair_id"], + "receiver_relation": receiver["relation"], + "receiver_presentation_order": receiver["presentation_order"], + "source_donor_prompt_id": donor_id, + "source_donor_pair_id": donor["pair_id"], + "source_donor_relation": donor["relation"], + "matched_reverse_donor_prompt_id": reverse_id, + "control": control, + "layer": layer, + "depth_percent": 100.0 * layer / (model.n_layers - 1), + "clean_higher_minus_lower": clean, + "patched_higher_minus_lower": patched, + "raw_shift": shift, + "counterfactual_aligned_shift": -receiver_sign * shift, + "source_difference_norm": float(source_norm), + "target_reverse_difference_norm": float(target_norm), + "achieved_difference_norm": float(achieved), + "scale": float(scale), + }) + complete.add(key) + done += 1 + if done % 25 == 0: + atomic_json(output_path, payload) + atomic_json(output_path, payload) + print(f" receiver {receiver_number:02d}/{len(prompts):02d}", flush=True) + + payload["completed_at"] = datetime.now(timezone.utc).isoformat() + atomic_json(output_path, payload) + print(f"complete: {len(payload['rows'])} rows", flush=True) + + +if __name__ == "__main__": + main() diff --git a/jlens_materials/scripts/run_counterfactual_activation_patching.py b/jlens_materials/scripts/run_counterfactual_activation_patching.py new file mode 100644 index 0000000000000000000000000000000000000000..46da392582c5bd7ac92ecf070dbe302cdceef839 --- /dev/null +++ b/jlens_materials/scripts/run_counterfactual_activation_patching.py @@ -0,0 +1,397 @@ +#!/usr/bin/env python3 +"""Run the frozen materials counterfactual activation-patching candidate study.""" + +from __future__ import annotations + +import argparse +import json +import platform +import sys +from contextlib import contextmanager +from datetime import datetime, timezone +from pathlib import Path + +import torch + +ROOT = Path(__file__).resolve().parents[1] +sys.path.insert(0, str(ROOT)) +sys.path.insert(0, str(ROOT / "_vendor_jlens")) +sys.path.insert(0, str(ROOT / "scripts")) + +import jlens # noqa: E402 +from jlens.hooks import ActivationRecorder # noqa: E402 +from run_lens import _DTYPES, load_model # noqa: E402 +from run_jacobian_steering_pilot import atomic_json, format_chat, sha256 # noqa: E402 +from run_semantic_steering_v3 import continuation_tokens, prompt_rows # noqa: E402 + + +DEFAULT_PROTOCOL = ( + "experiments/candidate-activation-patching-2026-07-16/protocol.json" +) +DEFAULT_OUTPUT = ( + "experiments/candidate-activation-patching-2026-07-16/raw.json" +) + + +def validate_hash(path: Path, expected: str) -> None: + actual = sha256(path) + if actual != expected: + raise RuntimeError(f"fingerprint mismatch for {path}: {actual} != {expected}") + + +def expanded_prompts(family: dict) -> list[dict]: + base = {row["condition_id"]: row for row in family["confirmation_prompts"]} + rows = [] + for row in prompt_rows(family): + source = base[row["condition_id"]] + rows.append({ + **row, + "pair_id": source["pair_id"], + "relation": source["relation"], + }) + return rows + + +@contextmanager +def replace_residual(model, layer: int, position: int, replacement: torch.Tensor): + """Replace one post-block residual with an exact donor residual.""" + + def hook(_module, _inputs, output): + tensor = output if torch.is_tensor(output) else output[0] + patched = tensor.clone() + patched[:, position, :] = replacement.to(patched.device, patched.dtype) + if torch.is_tensor(output): + return patched + return (patched, *output[1:]) + + handle = model.layers[layer].register_forward_hook(hook) + try: + yield + finally: + handle.remove() + + +def two_token_logits( + model, states: torch.Tensor, token_ids: tuple[int, int] +) -> torch.Tensor: + """Exact LM-head logits for two vocabulary entries without a full-vocab GEMM.""" + device = model._lm_head.weight.device + dtype = model._lm_head.weight.dtype + normalized = model._final_norm(states.to(device=device, dtype=dtype)) + ids = torch.tensor(token_ids, device=device, dtype=torch.long) + weight = model._lm_head.weight.index_select(0, ids) + logits = normalized @ weight.T + bias = getattr(model._lm_head, "bias", None) + if bias is not None: + logits = logits + bias.index_select(0, ids) + softcap = getattr(model, "_logit_softcap", None) + if softcap is not None: + logits = softcap * torch.tanh(logits / softcap) + return logits.float() + + +@torch.inference_mode() +def capture_prompt(model, text: str, layers: list[int], token_ids: tuple[int, int]) -> dict: + input_ids = model.encode(text, max_length=512) + final = model.n_layers - 1 + capture = sorted(set(layers + [final])) + with ActivationRecorder(model.layers, at=capture) as recorder: + model.forward(input_ids) + states = { + layer: recorder.activations[layer][0, -1].detach().float().cpu() + for layer in layers + } + final_state = recorder.activations[final][0, -1:].detach() + pair_logits = two_token_logits(model, final_state, token_ids)[0].cpu() + full_logits = model.unembed(final_state).float()[0] + logp = torch.log_softmax(full_logits, dim=-1) + top_id = int(full_logits.argmax()) + return { + "input_ids": input_ids.detach(), + "states": states, + "clean_log_odds": float(pair_logits[0] - pair_logits[1]), + "clean_pair_logits": [float(value) for value in pair_logits], + "clean_pair_probability": float( + logp[token_ids[0]].exp() + logp[token_ids[1]].exp() + ), + "clean_top_token_id": top_id, + "clean_top_token": model.tokenizer.decode( + [top_id], clean_up_tokenization_spaces=False + ).strip(), + "clean_top_is_registered_answer": top_id in token_ids, + } + + +@torch.inference_mode() +def patched_log_odds( + model, + input_ids: torch.Tensor, + layer: int, + donor_state: torch.Tensor, + token_ids: tuple[int, int], +) -> float: + final = model.n_layers - 1 + with replace_residual(model, layer, -1, donor_state): + with ActivationRecorder(model.layers, at=[final]) as recorder: + model.forward(input_ids) + final_state = recorder.activations[final][0, -1:].detach() + logits = two_token_logits(model, final_state, token_ids)[0].cpu() + return float(logits[0] - logits[1]) + + +@torch.inference_mode() +def readout_curves( + model, + lenses: list, + prompts: list[dict], + captured: dict[str, dict], + layers: list[int], + token_ids: tuple[int, int], +) -> list[dict]: + output = [] + hidden_by_layer = { + layer: torch.stack([captured[row["prompt_id"]]["states"][layer] for row in prompts]) + for layer in layers + } + for layer in layers: + hidden = hidden_by_layer[layer] + direct = two_token_logits(model, hidden, token_ids).cpu() + for prompt, logits in zip(prompts, direct): + output.append({ + "prompt_id": prompt["prompt_id"], + "pair_id": prompt["pair_id"], + "condition_id": prompt["condition_id"], + "relation": prompt["relation"], + "presentation_order": prompt["presentation_order"], + "method": "direct", + "lens_seed": None, + "layer": layer, + "depth_percent": 100.0 * layer / (model.n_layers - 1), + "higher_minus_lower": float(logits[0] - logits[1]), + }) + for lens_seed, lens in enumerate(lenses): + transported = lens.transport(hidden, layer) + logits_batch = two_token_logits(model, transported, token_ids).cpu() + for prompt, logits in zip(prompts, logits_batch): + output.append({ + "prompt_id": prompt["prompt_id"], + "pair_id": prompt["pair_id"], + "condition_id": prompt["condition_id"], + "relation": prompt["relation"], + "presentation_order": prompt["presentation_order"], + "method": "jacobian", + "lens_seed": lens_seed, + "layer": layer, + "depth_percent": 100.0 * layer / (model.n_layers - 1), + "higher_minus_lower": float(logits[0] - logits[1]), + }) + return output + + +def donor_ids(prompt: dict, index: dict[tuple[str, str, str], dict], next_pair: dict[str, str]) -> dict[str, str]: + relation = prompt["relation"] + reverse = "coarsening" if relation == "refinement" else "refinement" + order = prompt["presentation_order"] + opposite_order = "negative-first" if order == "positive-first" else "positive-first" + pair_id = prompt["pair_id"] + cross = next_pair[pair_id] + return { + "matched_reverse": index[(pair_id, reverse, order)]["prompt_id"], + "cross_material_reverse": index[(cross, reverse, order)]["prompt_id"], + "cross_material_same": index[(cross, relation, order)]["prompt_id"], + "order_only": index[(pair_id, relation, opposite_order)]["prompt_id"], + } + + +def main() -> None: + parser = argparse.ArgumentParser() + parser.add_argument("--protocol", default=DEFAULT_PROTOCOL) + parser.add_argument("--output", default=DEFAULT_OUTPUT) + parser.add_argument("--dtype", choices=sorted(_DTYPES), default="bfloat16") + parser.add_argument("--device", default=None) + parser.add_argument("--resume", action="store_true") + args = parser.parse_args() + + protocol_path = (ROOT / args.protocol).resolve() + output_path = (ROOT / args.output).resolve() + protocol = json.loads(protocol_path.read_text()) + inputs = protocol["inputs"] + prompt_path = ROOT / inputs["prompt_manifest"] + validate_hash(prompt_path, inputs["prompt_manifest_sha256"]) + validate_hash( + ROOT / inputs["previously_inspected_raw"], + inputs["previously_inspected_raw_sha256"], + ) + validate_hash(ROOT / inputs["latent_metadata"], inputs["latent_metadata_sha256"]) + for lens_row in protocol["lenses"]: + validate_hash(ROOT / lens_row["path"], lens_row["sha256"]) + + source = json.loads(prompt_path.read_text()) + grain = next( + row for row in source["families"] if row["family_id"] == "grain-size-strengthening" + ) + prompts = expanded_prompts(grain) + prompt_by_id = {row["prompt_id"]: row for row in prompts} + index = { + (row["pair_id"], row["relation"], row["presentation_order"]): row + for row in prompts + } + pair_order = protocol["cyclic_pair_order"] + next_pair = { + pair_order[index]: pair_order[(index + 1) % len(pair_order)] + for index in range(len(pair_order)) + } + layers = [int(layer) for layer in protocol["source_layers"]] + + model = load_model( + protocol["model"], + dtype=_DTYPES[args.dtype], + device=args.device, + revision=protocol["model_revision"], + ) + higher = continuation_tokens(model.tokenizer, "higher") + lower = continuation_tokens(model.tokenizer, "lower") + if len(higher) != 1 or len(lower) != 1: + raise RuntimeError(f"registered answers are not single tokens: {higher=} {lower=}") + token_ids = (higher[0], lower[0]) + lenses = [jlens.JacobianLens.load(ROOT / row["path"]) for row in protocol["lenses"]] + if any(lens.source_layers != layers for lens in lenses): + raise RuntimeError("lens source layers do not match frozen patching layers") + + if args.resume and output_path.exists(): + payload = json.loads(output_path.read_text()) + if payload["provenance"]["protocol_sha256"] != sha256(protocol_path): + raise RuntimeError("resume output has a different frozen protocol") + else: + payload = { + "study_id": protocol["study_id"], + "created_at": datetime.now(timezone.utc).isoformat(), + "provenance": { + "protocol": str(protocol_path.relative_to(ROOT)), + "protocol_sha256": sha256(protocol_path), + "runner": str(Path(__file__).resolve().relative_to(ROOT)), + "runner_sha256": sha256(Path(__file__).resolve()), + "model": protocol["model"], + "model_revision": protocol["model_revision"], + "dtype": args.dtype, + "device": str(model.input_device), + "torch": torch.__version__, + "python": sys.version, + "platform": platform.platform(), + "lenses": protocol["lenses"], + }, + "answer_tokens": { + "higher": higher, + "lower": lower, + "decoded_higher": model.tokenizer.decode(higher), + "decoded_lower": model.tokenizer.decode(lower), + }, + "clean_prompts": [], + "readout_rows": [], + "patch_rows": [], + } + atomic_json(output_path, payload) + + print(f"capturing {len(prompts)} clean prompts at {len(layers)} layers", flush=True) + captured = {} + clean_done = {row["prompt_id"] for row in payload["clean_prompts"]} + for prompt_index, prompt in enumerate(prompts, start=1): + text = format_chat(model.tokenizer, prompt["user"]) + result = capture_prompt(model, text, layers, token_ids) + captured[prompt["prompt_id"]] = result + if prompt["prompt_id"] not in clean_done: + payload["clean_prompts"].append({ + **{key: prompt[key] for key in [ + "prompt_id", "condition_id", "pair_id", "relation", + "presentation_order", "presented_words", "expected_outcome", "user", + ]}, + "n_tokens": int(result["input_ids"].shape[1]), + **{key: value for key, value in result.items() if key not in {"input_ids", "states"}}, + }) + clean_done.add(prompt["prompt_id"]) + atomic_json(output_path, payload) + print(f" clean {prompt_index:02d}/{len(prompts):02d}: {prompt['prompt_id']}", flush=True) + + if not payload["readout_rows"]: + print("computing Jacobian and direct readout curves", flush=True) + payload["readout_rows"] = readout_curves( + model, lenses, prompts, captured, layers, token_ids + ) + atomic_json(output_path, payload) + + completed = { + (row["receiver_prompt_id"], row["control"], int(row["layer"])) + for row in payload["patch_rows"] + } + total = len(prompts) * len(protocol["donor_controls"]) * len(layers) + done = len(completed) + print(f"patching {total} receiver/control/layer combinations", flush=True) + for receiver_index, receiver in enumerate(prompts, start=1): + receiver_id = receiver["prompt_id"] + receiver_data = captured[receiver_id] + receiver_sign = 1.0 if receiver["relation"] == "refinement" else -1.0 + donors = donor_ids(receiver, index, next_pair) + for control in protocol["donor_controls"]: + donor_id = donors[control] + donor = prompt_by_id[donor_id] + donor_data = captured[donor_id] + for layer in layers: + key = (receiver_id, control, layer) + if key in completed: + continue + patched = patched_log_odds( + model, + receiver_data["input_ids"], + layer, + donor_data["states"][layer], + token_ids, + ) + clean = float(receiver_data["clean_log_odds"]) + shift = patched - clean + receiver_state = receiver_data["states"][layer] + donor_state = donor_data["states"][layer] + cosine = float(torch.nn.functional.cosine_similarity( + receiver_state.unsqueeze(0), donor_state.unsqueeze(0) + )[0]) + payload["patch_rows"].append({ + "receiver_prompt_id": receiver_id, + "receiver_condition_id": receiver["condition_id"], + "receiver_pair_id": receiver["pair_id"], + "receiver_relation": receiver["relation"], + "receiver_presentation_order": receiver["presentation_order"], + "donor_prompt_id": donor_id, + "donor_condition_id": donor["condition_id"], + "donor_pair_id": donor["pair_id"], + "donor_relation": donor["relation"], + "donor_presentation_order": donor["presentation_order"], + "control": control, + "layer": layer, + "depth_percent": 100.0 * layer / (model.n_layers - 1), + "clean_higher_minus_lower": clean, + "donor_clean_higher_minus_lower": float(donor_data["clean_log_odds"]), + "patched_higher_minus_lower": patched, + "raw_shift": shift, + "counterfactual_aligned_shift": -receiver_sign * shift, + "receiver_donor_state_cosine": cosine, + "receiver_state_norm": float(receiver_state.norm()), + "donor_state_norm": float(donor_state.norm()), + "state_difference_norm": float((donor_state - receiver_state).norm()), + }) + completed.add(key) + done += 1 + if done % 25 == 0: + atomic_json(output_path, payload) + atomic_json(output_path, payload) + print(f" patched receiver {receiver_index:02d}/{len(prompts):02d}", flush=True) + + payload["completed_at"] = datetime.now(timezone.utc).isoformat() + atomic_json(output_path, payload) + print( + f"complete: {len(payload['clean_prompts'])} clean prompts, " + f"{len(payload['readout_rows'])} readouts, {len(payload['patch_rows'])} patches", + flush=True, + ) + + +if __name__ == "__main__": + main() diff --git a/jlens_materials/scripts/run_cross_mechanism_activation_patching.py b/jlens_materials/scripts/run_cross_mechanism_activation_patching.py new file mode 100644 index 0000000000000000000000000000000000000000..7cb91e1495145e471a6d82151b16d245cc836fdd --- /dev/null +++ b/jlens_materials/scripts/run_cross_mechanism_activation_patching.py @@ -0,0 +1,432 @@ +#!/usr/bin/env python3 +"""Run frozen option-free cross-mechanism activation patching. + +Donor states come from the archived natural question-end representation +array. The receiver is the corresponding option-free scientific question. +Only the final prompt-position residual is replaced, and receiver-specific +answer logits are measured after the remaining frozen Gemma layers. +""" + +from __future__ import annotations + +import argparse +import json +import platform +import sys +from contextlib import contextmanager +from datetime import datetime, timezone +from pathlib import Path + +import numpy as np +import torch + + +ROOT = Path(__file__).resolve().parents[1] +sys.path.insert(0, str(ROOT)) +sys.path.insert(0, str(ROOT / "_vendor_jlens")) +sys.path.insert(0, str(ROOT / "scripts")) + +from jlens.hooks import ActivationRecorder # noqa: E402 +from run_jacobian_steering_pilot import atomic_json, format_chat, sha256 # noqa: E402 +from run_lens import _DTYPES, load_model # noqa: E402 +from run_semantic_steering_v3 import continuation_tokens # noqa: E402 + + +DEFAULT_PROTOCOL = ( + "experiments/cross-mechanism-activation-patching-2026-07-18/" + "protocol.json" +) +DEFAULT_OUTPUT = ( + "experiments/cross-mechanism-activation-patching-2026-07-18/raw.json" +) + + +def validate_hash(path: Path, expected: str) -> None: + actual = sha256(path) + if actual != expected: + raise RuntimeError( + f"fingerprint mismatch for {path}: {actual} != {expected}" + ) + + +@contextmanager +def replace_residual( + model, + layer: int, + position: int, + replacement: torch.Tensor, +): + """Replace one post-block residual with one archived donor residual.""" + + def hook(_module, _inputs, output): + tensor = output if torch.is_tensor(output) else output[0] + patched = tensor.clone() + patched[:, position, :] = replacement.to( + patched.device, patched.dtype + ) + if torch.is_tensor(output): + return patched + return (patched, *output[1:]) + + handle = model.layers[layer].register_forward_hook(hook) + try: + yield + finally: + handle.remove() + + +def two_token_logits( + model, + states: torch.Tensor, + token_ids: tuple[int, int], +) -> torch.Tensor: + """Calculate exact LM-head logits for two vocabulary entries.""" + + device = model._lm_head.weight.device + dtype = model._lm_head.weight.dtype + normalized = model._final_norm(states.to(device=device, dtype=dtype)) + ids = torch.tensor(token_ids, device=device, dtype=torch.long) + weight = model._lm_head.weight.index_select(0, ids) + logits = normalized @ weight.T + bias = getattr(model._lm_head, "bias", None) + if bias is not None: + logits = logits + bias.index_select(0, ids) + softcap = getattr(model, "_logit_softcap", None) + if softcap is not None: + logits = softcap * torch.tanh(logits / softcap) + return logits.float() + + +@torch.inference_mode() +def clean_receiver( + model, + input_ids: torch.Tensor, + token_ids: tuple[int, int], +) -> dict: + final_layer = model.n_layers - 1 + with ActivationRecorder(model.layers, at=[final_layer]) as recorder: + model.forward(input_ids) + final_state = recorder.activations[final_layer][0, -1:].detach() + logits = two_token_logits(model, final_state, token_ids)[0].cpu() + return { + "positive_logit": float(logits[0]), + "negative_logit": float(logits[1]), + "positive_minus_negative": float(logits[0] - logits[1]), + } + + +@torch.inference_mode() +def patched_receiver_margin( + model, + input_ids: torch.Tensor, + layer: int, + donor_state: torch.Tensor, + token_ids: tuple[int, int], +) -> float: + final_layer = model.n_layers - 1 + with replace_residual(model, layer, -1, donor_state): + with ActivationRecorder(model.layers, at=[final_layer]) as recorder: + model.forward(input_ids) + final_state = recorder.activations[final_layer][0, -1:].detach() + logits = two_token_logits(model, final_state, token_ids)[0].cpu() + return float(logits[0] - logits[1]) + + +def main() -> None: + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument("--protocol", default=DEFAULT_PROTOCOL) + parser.add_argument("--output", default=DEFAULT_OUTPUT) + parser.add_argument("--dtype", choices=sorted(_DTYPES), default="bfloat16") + parser.add_argument("--device", default=None) + parser.add_argument("--resume", action="store_true") + parser.add_argument( + "--local-model-snapshot", + type=Path, + default=None, + help=( + "optional immutable local Hugging Face snapshot; scientific " + "revision remains fixed by the protocol" + ), + ) + args = parser.parse_args() + + protocol_path = (ROOT / args.protocol).resolve() + output_path = (ROOT / args.output).resolve() + protocol = json.loads(protocol_path.read_text()) + validate_hash( + Path(__file__).resolve(), + protocol["inputs"]["runner"]["sha256"], + ) + manifest_path = ROOT / protocol["inputs"]["prompt_manifest"]["path"] + states_path = ROOT / protocol["inputs"]["representations"]["path"] + validate_hash( + manifest_path, + protocol["inputs"]["prompt_manifest"]["sha256"], + ) + validate_hash( + states_path, + protocol["inputs"]["representations"]["sha256"], + ) + + manifest = json.loads(manifest_path.read_text()) + family_rows = {row["family_id"]: row for row in manifest["families"]} + prompts = [ + row for row in manifest["prompts"] if row["variant"] == "anchor" + ] + if len(prompts) != 24: + raise RuntimeError(f"expected 24 anchor prompts, found {len(prompts)}") + prompt_ids = [str(row["prompt_id"]) for row in prompts] + + with np.load(states_path) as arrays: + archived_prompt_ids = arrays["prompt_ids"].astype(str).tolist() + archived_layers = arrays["layers"].astype(int).tolist() + positions = arrays["positions"].astype(str).tolist() + if positions != ["question_end"]: + raise RuntimeError(f"unexpected archived positions: {positions}") + selected_layers = [int(layer) for layer in protocol["source_layers"]] + if not set(selected_layers).issubset(archived_layers): + raise RuntimeError("protocol layer absent from archived states") + prompt_index = { + prompt_id: index + for index, prompt_id in enumerate(archived_prompt_ids) + } + layer_index = { + layer: index for index, layer in enumerate(archived_layers) + } + donor_states = { + (prompt_id, layer): torch.from_numpy( + arrays["raw_states"][ + 0, + prompt_index[prompt_id], + layer_index[layer], + ].astype(np.float32) + ) + for prompt_id in prompt_ids + for layer in selected_layers + } + + model_source = ( + str(args.local_model_snapshot.expanduser().resolve()) + if args.local_model_snapshot is not None + else protocol["model"] + ) + revision = None if args.local_model_snapshot is not None else protocol[ + "model_revision" + ] + model = load_model( + model_source, + dtype=_DTYPES[args.dtype], + device=args.device, + revision=revision, + ) + + answer_tokens: dict[str, tuple[int, int]] = {} + for family_id, family in family_rows.items(): + positive = continuation_tokens( + model.tokenizer, family["outcome_positive"] + ) + negative = continuation_tokens( + model.tokenizer, family["outcome_negative"] + ) + if len(positive) != 1 or len(negative) != 1: + raise RuntimeError( + f"answer pair is not single-token for {family_id}: " + f"{positive=} {negative=}" + ) + answer_tokens[family_id] = (positive[0], negative[0]) + + encoded: dict[str, torch.Tensor] = {} + clean: dict[str, dict] = {} + for index, prompt in enumerate(prompts, start=1): + prompt_id = str(prompt["prompt_id"]) + text = format_chat(model.tokenizer, str(prompt["stem"])) + input_ids = model.encode(text, max_length=512) + encoded[prompt_id] = input_ids.detach() + clean[prompt_id] = clean_receiver( + model, + input_ids, + answer_tokens[str(prompt["family_id"])], + ) + clean[prompt_id].update( + { + "prompt_id": prompt_id, + "family_id": str(prompt["family_id"]), + "triplet_id": str(prompt["triplet_id"]), + "variant": str(prompt["variant"]), + "expected_outcome": str(prompt["expected_outcome"]), + "numeric_direction": str(prompt["numeric_direction"]), + "n_tokens": int(input_ids.shape[1]), + } + ) + print(f" clean receiver {index:02d}/{len(prompts):02d}", flush=True) + + if args.resume and output_path.exists(): + payload = json.loads(output_path.read_text()) + if payload["protocol_sha256"] != sha256(protocol_path): + raise RuntimeError("resume output uses another protocol") + else: + payload = { + "study_id": protocol["study_id"], + "created_at": datetime.now(timezone.utc).isoformat(), + "protocol": str(protocol_path.relative_to(ROOT)), + "protocol_sha256": sha256(protocol_path), + "runner_sha256": sha256(Path(__file__).resolve()), + "model": protocol["model"], + "model_revision": protocol["model_revision"], + "execution_model_source": model_source, + "device": str(model.input_device), + "dtype": args.dtype, + "torch": torch.__version__, + "python": sys.version, + "platform": platform.platform(), + "answer_token_ids": { + family_id: list(token_ids) + for family_id, token_ids in answer_tokens.items() + }, + "clean_receivers": list(clean.values()), + "patch_rows": [], + } + + completed = { + ( + row["receiver_prompt_id"], + row["donor_prompt_id"], + int(row["layer"]), + ) + for row in payload["patch_rows"] + } + total = ( + len(prompts) + * (len(family_rows) - 1) + * 4 + * len(protocol["source_layers"]) + ) + print(f"running {total} frozen cross-mechanism patches", flush=True) + for receiver_number, receiver in enumerate(prompts, start=1): + receiver_id = str(receiver["prompt_id"]) + receiver_family = str(receiver["family_id"]) + receiver_family_row = family_rows[receiver_family] + receiver_vocab = ( + receiver_family_row["outcome_positive"], + receiver_family_row["outcome_negative"], + ) + eligible_donors = [ + donor + for donor in prompts + if donor["family_id"] != receiver_family + ] + if len(eligible_donors) != 20: + raise RuntimeError("unexpected cross-mechanism donor count") + for donor in eligible_donors: + donor_id = str(donor["prompt_id"]) + donor_family = str(donor["family_id"]) + donor_family_row = family_rows[donor_family] + donor_vocab = ( + donor_family_row["outcome_positive"], + donor_family_row["outcome_negative"], + ) + donor_outcome_sign = ( + 1.0 + if donor["expected_outcome"] + == donor_family_row["outcome_positive"] + else -1.0 + ) + donor_numeric_sign = ( + 1.0 if donor["numeric_direction"] == "increase" else -1.0 + ) + receiver_orientation = ( + 1 + if receiver_family_row["positive_numeric_direction"] + == "increase" + else -1 + ) + donor_orientation = ( + 1 + if donor_family_row["positive_numeric_direction"] + == "increase" + else -1 + ) + for layer in protocol["source_layers"]: + key = (receiver_id, donor_id, int(layer)) + if key in completed: + continue + patched_margin = patched_receiver_margin( + model, + encoded[receiver_id], + int(layer), + donor_states[(donor_id, int(layer))], + answer_tokens[receiver_family], + ) + clean_margin = float( + clean[receiver_id]["positive_minus_negative"] + ) + shift = patched_margin - clean_margin + receiver_state = donor_states[(receiver_id, int(layer))] + donor_state = donor_states[(donor_id, int(layer))] + payload["patch_rows"].append( + { + "receiver_prompt_id": receiver_id, + "receiver_family": receiver_family, + "receiver_case": str(receiver["triplet_id"]), + "receiver_expected_outcome": str( + receiver["expected_outcome"] + ), + "receiver_numeric_direction": str( + receiver["numeric_direction"] + ), + "receiver_answer_vocabulary": list(receiver_vocab), + "donor_prompt_id": donor_id, + "donor_family": donor_family, + "donor_case": str(donor["triplet_id"]), + "donor_expected_outcome": str( + donor["expected_outcome"] + ), + "donor_numeric_direction": str( + donor["numeric_direction"] + ), + "donor_answer_vocabulary": list(donor_vocab), + "cross_vocabulary": donor_vocab != receiver_vocab, + "opposite_response_orientation": ( + donor_orientation != receiver_orientation + ), + "layer": int(layer), + "depth_percent": ( + 100.0 + * int(layer) + / (model.n_layers - 1) + ), + "receiver_clean_positive_minus_negative": clean_margin, + "patched_positive_minus_negative": patched_margin, + "patch_shift": shift, + "donor_outcome_sign": donor_outcome_sign, + "donor_numeric_sign": donor_numeric_sign, + "donor_outcome_aligned_shift": ( + donor_outcome_sign * shift + ), + "donor_numeric_aligned_shift": ( + donor_numeric_sign * shift + ), + "receiver_donor_state_distance": float( + (donor_state - receiver_state).norm() + ), + } + ) + payload["completed_at"] = datetime.now(timezone.utc).isoformat() + atomic_json(output_path, payload) + print( + f" patched receiver {receiver_number:02d}/{len(prompts):02d}; " + f"{len(payload['patch_rows'])}/{total}", + flush=True, + ) + + if len(payload["patch_rows"]) != total: + raise RuntimeError( + f"incomplete patch output: {len(payload['patch_rows'])}/{total}" + ) + payload["completed_at"] = datetime.now(timezone.utc).isoformat() + atomic_json(output_path, payload) + print(f"wrote {output_path.relative_to(ROOT)}", flush=True) + + +if __name__ == "__main__": + main() diff --git a/jlens_materials/scripts/run_equation_free_relational_pilot.py b/jlens_materials/scripts/run_equation_free_relational_pilot.py new file mode 100644 index 0000000000000000000000000000000000000000..fe517d9d352560b3497b0283c546adc223aabf4d --- /dev/null +++ b/jlens_materials/scripts/run_equation_free_relational_pilot.py @@ -0,0 +1,140 @@ +#!/usr/bin/env python3 +"""Capture raw layer-34 states for the equation-free relational pilot.""" + +from __future__ import annotations + +import json +import platform +import sys +from datetime import datetime, timezone +from pathlib import Path + +import numpy as np +import torch + + +ROOT = Path(__file__).resolve().parents[1] +sys.path[:0] = [str(ROOT), str(ROOT / "_vendor_jlens"), str(ROOT / "scripts")] + +from jlens.hooks import ActivationRecorder # noqa: E402 +from run_jacobian_steering_pilot import atomic_json, format_chat, sha256 # noqa: E402 +from run_lens import generate_completion, load_model # noqa: E402 +from run_semantic_steering_v3 import continuation_tokens # noqa: E402 + + +OUT = ROOT / "experiments" / "equation-free-relational-pilot-2026-08-21" + + +@torch.inference_mode() +def main() -> None: + protocol_path = OUT / "protocol.json" + manifest_path = OUT / "prompt_manifest.json" + protocol = json.loads(protocol_path.read_text()) + manifest = json.loads(manifest_path.read_text()) + if sha256(manifest_path) != protocol["inputs"]["prompt_manifest"]["sha256"]: + raise RuntimeError("prompt manifest fingerprint mismatch") + if sha256(Path(__file__).resolve()) != protocol["inputs"]["runner"]["sha256"]: + raise RuntimeError("runner fingerprint mismatch") + model = load_model( + protocol["model"], + dtype=torch.bfloat16, + device="mps", + revision=protocol["model_revision"], + ) + probe_layer = int(protocol["frozen_readout"]["layer"]) + final_layer = model.n_layers - 1 + answer_tokens = {} + # The matched output-head control uses the same two one-token answers as + # the original benchmark. ``unchanged`` is a two-token Gemma continuation, + # so three-way behavior is measured separately by ordinary greedy + # generation rather than by an invalid single-token comparison. + for word in ("higher", "lower"): + tokens = continuation_tokens(model.tokenizer, word) + if len(tokens) != 1: + raise RuntimeError(f"{word!r} is not a one-token continuation: {tokens}") + answer_tokens[word] = int(tokens[0]) + + prompts = manifest["prompts"] + states = np.empty((len(prompts), model.d_model), dtype=np.float16) + rows = [] + for index, prompt in enumerate(prompts): + ids = model.encode(format_chat(model.tokenizer, prompt["user"]), max_length=512) + with ActivationRecorder(model.layers, at=[probe_layer, final_layer]) as recorder: + model.forward(ids) + states[index] = recorder.activations[probe_layer][0, -1].float().cpu().numpy().astype(np.float16) + final_state = recorder.activations[final_layer][0, -1:] + logits = model.unembed(final_state).float()[0].cpu() + selected = {word: float(logits[token_id]) for word, token_id in answer_tokens.items()} + completion, generated_ids = generate_completion( + model, + format_chat(model.tokenizer, prompt["user"]), + max_new_tokens=3, + ) + normalized_completion = completion.strip().lower() + predicted = next( + ( + word + for word in ("higher", "lower", "unchanged") + if normalized_completion.startswith(word) + ), + None, + ) + rows.append( + { + "prompt_id": prompt["prompt_id"], + "law_id": prompt["law_id"], + "category": prompt["category"], + "domain": prompt["domain"], + "surface": prompt["surface"], + "case_index": prompt["case_index"], + "numerical_sign": prompt["numerical_sign"], + "physical_sign": prompt["physical_sign"], + "answer_order": prompt["answer_order"], + "expected_answer": prompt["expected_answer"], + "predicted_answer": predicted, + "greedy_completion": completion, + "greedy_token_ids": generated_ids, + "correct": predicted == prompt["expected_answer"], + "answer_logits": selected, + "higher_minus_lower_logit": selected["higher"] - selected["lower"], + "n_tokens": int(ids.shape[1]), + } + ) + if (index + 1) % 12 == 0 or index == 0: + print(f"{index + 1:04d}/{len(prompts):04d} {prompt['prompt_id']}", flush=True) + + state_path = OUT / "representations.npz" + np.savez_compressed( + state_path, + prompt_ids=np.asarray([x["prompt_id"] for x in prompts]), + layer=np.asarray([probe_layer], dtype=np.int16), + raw_states=states, + ) + atomic_json( + OUT / "raw.json", + { + "study_id": protocol["study_id"], + "completed_at": datetime.now(timezone.utc).isoformat(), + "provenance": { + "protocol_sha256": sha256(protocol_path), + "manifest_sha256": sha256(manifest_path), + "runner_sha256": sha256(Path(__file__).resolve()), + "states_sha256": sha256(state_path), + "model": protocol["model"], + "model_revision": protocol["model_revision"], + "probe_layer": probe_layer, + "device": str(model.input_device), + "dtype": "bfloat16", + "torch": torch.__version__, + "python": sys.version, + "platform": platform.platform(), + }, + "answer_tokens": answer_tokens, + "clean_rows": rows, + }, + ) + print(f"saved {states.shape} layer-{probe_layer} states to {state_path}") + + +if __name__ == "__main__": + main() diff --git a/jlens_materials/scripts/run_gemma31b_compositional_physics_pilot.py b/jlens_materials/scripts/run_gemma31b_compositional_physics_pilot.py new file mode 100644 index 0000000000000000000000000000000000000000..dba0dfb0d30e9edf65e31766a9e477764ed7a0e3 --- /dev/null +++ b/jlens_materials/scripts/run_gemma31b_compositional_physics_pilot.py @@ -0,0 +1,238 @@ +#!/usr/bin/env python3 +"""Capture selected-layer 31B states for the frozen composition pilot.""" + +from __future__ import annotations + +import argparse +import hashlib +import json +import os +import platform +import sys +import time +from datetime import datetime, timezone +from pathlib import Path + +import numpy as np +import torch + + +ROOT = Path(__file__).resolve().parents[1] +sys.path[:0] = [str(ROOT), str(ROOT / "_vendor_jlens"), str(ROOT / "scripts")] + +from jlens.hooks import ActivationRecorder # noqa: E402 +from run_jacobian_steering_pilot import format_chat # noqa: E402 +from run_lens import _DTYPES, load_model # noqa: E402 +from run_semantic_steering_v3 import continuation_tokens # noqa: E402 + + +DEFAULT_OUT = "experiments/gemma4-31b-compositional-physics-2026-08-25" + + +def sha256(path: Path) -> str: + digest = hashlib.sha256() + with path.open("rb") as handle: + for chunk in iter(lambda: handle.read(1024 * 1024), b""): + digest.update(chunk) + return digest.hexdigest() + + +def atomic_json(path: Path, payload: dict) -> None: + temporary = path.with_suffix(path.suffix + ".tmp") + temporary.write_text(json.dumps(payload, indent=2) + "\n") + temporary.replace(path) + + +def read_jsonl(path: Path) -> list[dict]: + if not path.exists(): + return [] + return [json.loads(line) for line in path.read_text().splitlines() if line.strip()] + + +def append_jsonl(path: Path, row: dict) -> None: + with path.open("a", encoding="utf-8") as handle: + handle.write(json.dumps(row, sort_keys=True) + "\n") + handle.flush() + os.fsync(handle.fileno()) + + +def marker_positions(input_ids: torch.Tensor, marker_ids: dict[str, int], prompt_id: str) -> dict[str, int]: + positions = {} + for name, token_id in marker_ids.items(): + matches = (input_ids[0] == token_id).nonzero(as_tuple=False).flatten().tolist() + if len(matches) != 1: + raise RuntimeError(f"{prompt_id}: expected one {name} marker, found {matches}") + positions[name] = int(matches[0]) + if not positions["context"] < positions["perturbation"] < positions["question"]: + raise RuntimeError(f"{prompt_id}: marker order is invalid: {positions}") + positions["final_prompt"] = int(input_ids.shape[1] - 1) + return positions + + +def validate_protocol(out: Path) -> tuple[dict, dict]: + protocol_path = out / "protocol.json" + protocol = json.loads(protocol_path.read_text()) + for name in ("manifest", "builder", "runner", "analyzer"): + entry = protocol["inputs"][name] + path = ROOT / entry["path"] + if sha256(path) != entry["sha256"]: + raise RuntimeError(f"frozen {name} fingerprint differs: {path}") + manifest_path = ROOT / protocol["inputs"]["manifest"]["path"] + manifest = json.loads(manifest_path.read_text()) + if len(manifest["prompts"]) != 192: + raise RuntimeError("expected 192 frozen prompts") + return protocol, manifest + + +@torch.inference_mode() +def capture(model, protocol: dict, manifest: dict, out: Path, max_seq_len: int) -> None: + layers = [int(value) for value in protocol["layers_zero_indexed"]] + positions = list(protocol["positions"]) + prompts = manifest["prompts"] + if layers[-1] != model.n_layers - 1: + raise RuntimeError("the final model layer must be captured for answer logits") + + marker_ids = {} + for name, marker in manifest["markers"].items(): + ids = model.tokenizer.encode(" " + marker, add_special_tokens=False) + if len(ids) != 1: + raise RuntimeError(f"marker {marker!r} is not one token: {ids}") + marker_ids[name] = int(ids[0]) + + token_ids = {} + for word in ("higher", "lower", "A", "B"): + ids = continuation_tokens(model.tokenizer, word) + if len(ids) != 1: + raise RuntimeError(f"answer {word!r} is not one token: {ids}") + token_ids[word] = int(ids[0]) + + rows_path = out / "rows.jsonl" + states_path = out / "states.npy" + existing = read_jsonl(rows_path) + for index, row in enumerate(existing): + if row["prompt_id"] != prompts[index]["prompt_id"]: + raise RuntimeError(f"resume mismatch at prompt {index}") + if states_path.exists(): + states = np.lib.format.open_memmap(states_path, mode="r+") + expected_shape = (len(positions), len(prompts), len(layers), model.d_model) + if states.shape != expected_shape: + raise RuntimeError(f"state shape {states.shape} != {expected_shape}") + else: + states = np.lib.format.open_memmap( + states_path, + mode="w+", + dtype=np.float16, + shape=(len(positions), len(prompts), len(layers), model.d_model), + ) + + started = time.perf_counter() + for prompt_index in range(len(existing), len(prompts)): + prompt = prompts[prompt_index] + text = format_chat(model.tokenizer, prompt["user"]) + input_ids = model.encode(text, max_length=max_seq_len) + locations = marker_positions(input_ids, marker_ids, prompt["prompt_id"]) + with ActivationRecorder(model.layers, at=layers) as recorder: + model.forward(input_ids) + for layer_index, layer in enumerate(layers): + activation = recorder.activations[layer][0] + for position_index, position in enumerate(positions): + states[position_index, prompt_index, layer_index] = ( + activation[locations[position]] + .detach() + .float() + .cpu() + .numpy() + .astype(np.float16) + ) + + final_state = recorder.activations[layers[-1]][0, locations["final_prompt"] : locations["final_prompt"] + 1] + logits = model.unembed(final_state).float()[0].cpu() + selected_logits = {word: float(logits[token_id]) for word, token_id in token_ids.items()} + pair = ("higher", "lower") if prompt["answer_format"] == "semantic" else ("A", "B") + predicted = pair[0] if selected_logits[pair[0]] > selected_logits[pair[1]] else pair[1] + top_id = int(logits.argmax()) + row = { + **{key: prompt[key] for key in prompt if key != "user"}, + "predicted_answer": predicted, + "pairwise_correct": predicted == prompt["expected_answer"], + "answer_logits": selected_logits, + "pairwise_logit_margin_for_expected": float( + selected_logits[prompt["expected_answer"]] + - selected_logits[pair[1] if prompt["expected_answer"] == pair[0] else pair[0]] + ), + "global_top_token_id": top_id, + "global_top_token": model.tokenizer.decode([top_id], clean_up_tokenization_spaces=False).strip(), + "global_top_is_pair_member": top_id in (token_ids[pair[0]], token_ids[pair[1]]), + "n_tokens": int(input_ids.shape[1]), + "positions": locations, + } + states.flush() + append_jsonl(rows_path, row) + elapsed = time.perf_counter() - started + rate = elapsed / max(prompt_index + 1 - len(existing), 1) + print( + f"composition: {prompt_index + 1:03d}/{len(prompts):03d} " + f"{prompt['prompt_id']} ({rate:.2f} s/prompt)", + flush=True, + ) + if torch.backends.mps.is_available() and (prompt_index + 1) % 8 == 0: + torch.mps.empty_cache() + + payload = { + "study_id": protocol["study_id"], + "completed_at": datetime.now(timezone.utc).isoformat(), + "provenance": { + "protocol_sha256": sha256(out / "protocol.json"), + "manifest_sha256": sha256(out / "prompt_manifest.json"), + "runner_sha256": sha256(Path(__file__).resolve()), + "states_sha256": sha256(states_path), + "rows_sha256": sha256(rows_path), + "model": protocol["model"], + "model_revision": protocol["model_revision"], + "device": str(model.input_device), + "dtype": protocol["dtype"], + "torch": torch.__version__, + "python": sys.version, + "platform": platform.platform(), + }, + "dimensions": { + **manifest["dimensions"], + "positions": len(positions), + "layers": len(layers), + "d_model": model.d_model, + }, + "markers": {name: {"text": manifest["markers"][name], "token_id": marker_ids[name]} for name in marker_ids}, + "answer_token_ids": token_ids, + "rows": read_jsonl(rows_path), + } + atomic_json(out / "raw.json", payload) + + +def main() -> None: + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument("--output-dir", default=DEFAULT_OUT) + parser.add_argument("--device", default=None) + parser.add_argument("--dtype", choices=sorted(_DTYPES), default="bfloat16") + parser.add_argument("--max-seq-len", type=int, default=384) + parser.add_argument("--validate-only", action="store_true") + args = parser.parse_args() + out = (ROOT / args.output_dir).resolve() + protocol, manifest = validate_protocol(out) + if args.validate_only: + print(json.dumps({"protocol": protocol, "dimensions": manifest["dimensions"]}, indent=2)) + return + if (out / "raw.json").exists(): + print(f"completed result already exists: {out / 'raw.json'}") + return + model = load_model( + protocol["model"], + dtype=_DTYPES[args.dtype], + device=args.device, + revision=protocol["model_revision"], + ) + capture(model, protocol, manifest, out, args.max_seq_len) + print(f"complete: {out / 'raw.json'}") + + +if __name__ == "__main__": + main() diff --git a/jlens_materials/scripts/run_gemma_scale_equation_free_relational.py b/jlens_materials/scripts/run_gemma_scale_equation_free_relational.py new file mode 100644 index 0000000000000000000000000000000000000000..d3bc4e68eec3eb5c889bb8720580bfa052561890 --- /dev/null +++ b/jlens_materials/scripts/run_gemma_scale_equation_free_relational.py @@ -0,0 +1,672 @@ +#!/usr/bin/env python3 +"""Replicate the frozen equation-free 60-law relation test at a Gemma scale. + +The experiment keeps the original 16-law development manifest and the frozen +960-prompt equation-free test manifest unchanged. A model-specific centroid +direction is fitted from development states at a prespecified layer and applied +without refitting to the 60-law cohort. State capture is checkpointed so a +long accelerator run can be resumed with the identical command. +""" + +from __future__ import annotations + +import argparse +import hashlib +import json +import os +import platform +import shutil +import sys +from datetime import datetime, timezone +from pathlib import Path + +import numpy as np +import torch + + +ROOT = Path(__file__).resolve().parents[1] +sys.path[:0] = [str(ROOT), str(ROOT / "_vendor_jlens"), str(ROOT / "scripts")] + +from jlens.hooks import ActivationRecorder # noqa: E402 +from analyze_neutral_anchored_relational_benchmark import ( # noqa: E402 + aggregate_laws, + calibrate, + endpoint_metrics, + fit_centroid, + matched_pairs, + normalize, + ordinal_permutation, + pair_auc, + project, + prompt_arrays, + stratified_auc_bootstrap, + tfidf_scores, +) +from run_jacobian_steering_pilot import ( # noqa: E402 + atomic_json, + format_chat, +) +from run_lens import _DTYPES, generate_completion, load_model # noqa: E402 +from run_semantic_steering_v3 import continuation_tokens # noqa: E402 + + +DEFAULT_DEVELOPMENT = ( + "experiments/elicited-physics-abstraction-2026-07-18/prompt_manifest.json" +) +DEFAULT_TEST = ( + "experiments/equation-free-relational-physics-2026-08-21/" + "prompt_manifest.json" +) +DEFAULT_OUT = "experiments/gemma-scale-equation-free-relational-v1" +PROTOCOL_VERSION = 1 + + +def sha256(path: Path) -> str: + digest = hashlib.sha256() + with path.open("rb") as handle: + for chunk in iter(lambda: handle.read(1024 * 1024), b""): + digest.update(chunk) + return digest.hexdigest() + + +def utc_now() -> str: + return datetime.now(timezone.utc).isoformat() + + +def read_json(path: Path) -> dict: + return json.loads(path.read_text(encoding="utf-8")) + + +def append_jsonl(path: Path, row: dict) -> None: + with path.open("a", encoding="utf-8") as handle: + handle.write(json.dumps(row, sort_keys=True) + "\n") + handle.flush() + os.fsync(handle.fileno()) + + +def read_jsonl(path: Path) -> list[dict]: + if not path.exists(): + return [] + rows = [] + for line_number, line in enumerate(path.read_text(encoding="utf-8").splitlines(), 1): + if not line.strip(): + continue + try: + rows.append(json.loads(line)) + except json.JSONDecodeError as exc: + raise RuntimeError(f"invalid JSONL at {path}:{line_number}") from exc + return rows + + +def resolve(path: str) -> Path: + candidate = Path(path) + return candidate.resolve() if candidate.is_absolute() else (ROOT / candidate).resolve() + + +def validate_manifests(development: dict, test: dict) -> dict: + dev_prompts = development.get("prompts", []) + test_prompts = test.get("prompts", []) + if len(dev_prompts) != 128: + raise RuntimeError(f"expected 128 development prompts, found {len(dev_prompts)}") + if len(test_prompts) != 960: + raise RuntimeError(f"expected 960 equation-free prompts, found {len(test_prompts)}") + if len({row["prompt_id"] for row in dev_prompts}) != len(dev_prompts): + raise RuntimeError("development prompt IDs are not unique") + if len({row["prompt_id"] for row in test_prompts}) != len(test_prompts): + raise RuntimeError("test prompt IDs are not unique") + dev_laws = {row["law_id"] for row in dev_prompts} + test_laws = {row["law_id"] for row in test_prompts} + categories = { + name: sum(row["category"] == name for row in test_prompts) + for name in ("direct", "neutral", "inverse") + } + if len(dev_laws) != 16 or len(test_laws) != 60: + raise RuntimeError( + f"expected 16 development and 60 test laws, found {len(dev_laws)} and {len(test_laws)}" + ) + if categories != {"direct": 320, "neutral": 320, "inverse": 320}: + raise RuntimeError(f"unexpected equation-free category counts: {categories}") + forbidden = ( + "constitutive equation", + "stage 1", + "stage 2", + "compose the relation", + ) + contaminated = [ + row["prompt_id"] + for row in test_prompts + if any(term in row["user"].lower() for term in forbidden) + ] + if contaminated: + raise RuntimeError(f"equation-free prompts contain scaffold text: {contaminated[:3]}") + return { + "development_prompts": len(dev_prompts), + "development_laws": len(dev_laws), + "test_prompts": len(test_prompts), + "test_laws": len(test_laws), + "test_category_prompts": categories, + "equation_scaffold_hits": len(contaminated), + } + + +def frozen_spec(args: argparse.Namespace, development_path: Path, test_path: Path) -> dict: + return { + "protocol_version": PROTOCOL_VERSION, + "study_id": "gemma-scale-equation-free-relational-v1", + "model": args.model, + "model_revision": args.model_revision, + "probe_layer_zero_indexed": args.probe_layer, + "development_position": "final_prompt", + "state_normalization": "unit L2", + "direction_fit": ( + "positive-minus-negative physical-sign centroids over the unchanged " + "16-law development manifest only" + ), + "test_refit": "none", + "dtype": args.dtype, + "max_seq_len": args.max_seq_len, + "generation_max_new_tokens": args.generation_max_new_tokens, + "inputs": { + "development_manifest": { + "path": str(development_path.relative_to(ROOT)), + "sha256": sha256(development_path), + }, + "equation_free_manifest": { + "path": str(test_path.relative_to(ROOT)), + "sha256": sha256(test_path), + }, + "runner": { + "path": str(Path(__file__).resolve().relative_to(ROOT)), + "sha256": sha256(Path(__file__).resolve()), + }, + }, + "inference": { + "bootstrap_resamples": 50000, + "bootstrap_seed": 20260822, + "permutations": 100000, + "permutation_seed": 20260823, + }, + "success_rule": { + "direct_inverse_auc_minimum": 0.90, + "direct_neutral_auc_minimum": 0.80, + "neutral_inverse_auc_minimum": 0.80, + "calibrated_directional_accuracy_minimum": 0.80, + "both_surface_direct_inverse_auc_minimum": 0.85, + "ordinal_spearman_minimum": 0.60, + "ordinal_permutation_p_maximum": 0.01, + }, + } + + +def freeze_or_validate_protocol(out: Path, spec: dict) -> dict: + protocol_path = out / "protocol.json" + if protocol_path.exists(): + protocol = read_json(protocol_path) + observed = {key: protocol[key] for key in spec} + if observed != spec: + raise RuntimeError( + "existing frozen protocol differs from this command; use a new --output-dir" + ) + return protocol + protocol = {**spec, "frozen_at": utc_now(), "status": "frozen-before-model-output"} + atomic_json(protocol_path, protocol) + return protocol + + +def single_answer_tokens(model) -> dict[str, int]: + output = {} + for word in ("higher", "lower"): + ids = continuation_tokens(model.tokenizer, word) + if len(ids) != 1: + raise RuntimeError(f"{word!r} is not a one-token continuation: {ids}") + output[word] = int(ids[0]) + return output + + +@torch.inference_mode() +def capture_cohort( + *, + model, + prompts: list[dict], + probe_layer: int, + max_seq_len: int, + answer_tokens: dict[str, int], + states_path: Path, + rows_path: Path, + generate_tokens: int, + label: str, +) -> tuple[np.ndarray, list[dict]]: + existing = read_jsonl(rows_path) + if len(existing) > len(prompts): + raise RuntimeError(f"{rows_path} has more rows than its manifest") + for index, row in enumerate(existing): + if row["prompt_id"] != prompts[index]["prompt_id"]: + raise RuntimeError(f"resume order mismatch at {label} row {index}") + + if states_path.exists(): + states = np.lib.format.open_memmap(states_path, mode="r+") + if states.shape != (len(prompts), model.d_model): + raise RuntimeError(f"state shape mismatch for {states_path}: {states.shape}") + else: + states = np.lib.format.open_memmap( + states_path, + mode="w+", + dtype=np.float16, + shape=(len(prompts), model.d_model), + ) + + final_layer = model.n_layers - 1 + for index in range(len(existing), len(prompts)): + prompt = prompts[index] + text = format_chat(model.tokenizer, prompt["user"]) + input_ids = model.encode(text, max_length=max_seq_len) + with ActivationRecorder(model.layers, at=[probe_layer, final_layer]) as recorder: + model.forward(input_ids) + states[index] = ( + recorder.activations[probe_layer][0, -1] + .detach() + .float() + .cpu() + .numpy() + .astype(np.float16) + ) + final_state = recorder.activations[final_layer][0, -1:] + logits = model.unembed(final_state).float()[0].cpu() + selected = { + word: float(logits[token_id]) for word, token_id in answer_tokens.items() + } + completion = "" + generated_ids: list[int] = [] + predicted = None + if generate_tokens > 0: + completion, generated_ids = generate_completion( + model, text, max_new_tokens=generate_tokens + ) + normalized_completion = completion.strip().lower() + predicted = next( + ( + word + for word in ("higher", "lower", "unchanged") + if normalized_completion.startswith(word) + ), + None, + ) + states.flush() + row = { + "prompt_id": prompt["prompt_id"], + "law_id": prompt["law_id"], + "split": prompt.get("split"), + "category": prompt.get("category"), + "domain": prompt.get("domain"), + "neutral_role": prompt.get("neutral_role"), + "surface": prompt["surface"], + "case_index": prompt["case_index"], + "law_sign": prompt["law_sign"], + "numerical_sign": prompt["numerical_sign"], + "physical_sign": prompt["physical_sign"], + "answer_order": prompt.get("answer_order"), + "expected_answer": prompt["expected_answer"], + "predicted_answer": predicted, + "correct": predicted == prompt["expected_answer"] if predicted else None, + "greedy_completion": completion, + "greedy_token_ids": generated_ids, + "answer_logits": selected, + "higher_minus_lower_logit": selected["higher"] - selected["lower"], + "n_tokens": int(input_ids.shape[1]), + } + append_jsonl(rows_path, row) + if (index + 1) % 20 == 0 or index == len(existing): + print(f"{label}: {index + 1:04d}/{len(prompts):04d} {prompt['prompt_id']}", flush=True) + return np.asarray(states), read_jsonl(rows_path) + + +def control_table(scores: np.ndarray, meta: dict) -> tuple[object, dict]: + table = aggregate_laws(matched_pairs(scores, meta)) + calibration = table.loc[ + (table["category"] == "neutral") + & (table["neutral_role"] == "calibration"), + "mean_contrast", + ].to_numpy() + center = float(np.median(calibration)) + mad = float(np.median(np.abs(calibration - center))) + scale = 1.4826 * mad + if scale <= 1e-12: + scale = 1.0 + table["natural_centered_contrast"] = table["mean_contrast"] - center + table["natural_z"] = table["natural_centered_contrast"] / scale + return table, { + **endpoint_metrics(table, "natural_centered_contrast"), + "natural_neutral_center": center, + "natural_neutral_scale": scale, + } + + +def analyze( + *, + out: Path, + protocol: dict, + development: dict, + test: dict, + dev_states: np.ndarray, + test_states: np.ndarray, + test_rows: list[dict], + answer_tokens: dict[str, int], + model, +) -> None: + dev_values = normalize(dev_states) + dev_labels = np.asarray( + [row["physical_sign"] > 0 for row in development["prompts"]] + ) + direction, midpoint = fit_centroid(dev_values, dev_labels) + hidden_scores = project(normalize(test_states), direction, midpoint) + meta = prompt_arrays(test["prompts"]) + pairs = matched_pairs(hidden_scores, meta) + laws = aggregate_laws(pairs) + center, scale = calibrate(laws) + laws["natural_centered_contrast"] = laws["mean_contrast"] - center + laws["natural_z"] = laws["natural_centered_contrast"] / scale + pairs["natural_z"] = (pairs["contrast"] - center) / scale + + endpoints = endpoint_metrics(laws, "natural_centered_contrast") + directional = laws[laws["category"].isin(["direct", "inverse"])].copy() + correct = ( + (directional["natural_centered_contrast"] > 0) + == (directional["category"] == "direct") + ) + accuracy = float(correct.mean()) + laws = laws.merge( + directional.assign(sign_correct=correct.to_numpy())[["law_id", "sign_correct"]], + on="law_id", + how="left", + ) + + surface_auc = {} + for surface in ("a", "b"): + column = f"surface_{surface}_contrast" + surface_auc[surface] = pair_auc( + laws.loc[laws["category"] == "direct", column].to_numpy(), + laws.loc[laws["category"] == "inverse", column].to_numpy(), + ) + + ordinal = laws[ + (laws["category"] != "neutral") | (laws["neutral_role"] == "validation") + ] + inference = protocol["inference"] + rho, ordinal_p = ordinal_permutation( + ordinal["law_sign"].to_numpy(), + ordinal["natural_z"].to_numpy(), + inference["permutation_seed"], + inference["permutations"], + ) + direct = laws.loc[ + laws["category"] == "direct", "natural_centered_contrast" + ].to_numpy() + inverse = laws.loc[ + laws["category"] == "inverse", "natural_centered_contrast" + ].to_numpy() + neutral = laws.loc[ + (laws["category"] == "neutral") + & (laws["neutral_role"] == "validation"), + "natural_centered_contrast", + ].to_numpy() + bootstrap = { + "direct_vs_inverse": stratified_auc_bootstrap( + direct, + inverse, + inference["bootstrap_seed"], + inference["bootstrap_resamples"], + ), + "direct_vs_neutral": stratified_auc_bootstrap( + direct, + neutral, + inference["bootstrap_seed"] + 1, + inference["bootstrap_resamples"], + ), + "neutral_vs_inverse": stratified_auc_bootstrap( + neutral, + inverse, + inference["bootstrap_seed"] + 2, + inference["bootstrap_resamples"], + ), + } + + dev_for_text = {"prompts": development["prompts"]} + test_for_text = {"prompts": test["prompts"]} + output_scores = np.asarray( + [row["higher_minus_lower_logit"] for row in test_rows], dtype=float + ) + controls = {} + control_tables = {} + for name, scores in { + "output_head": output_scores, + "word_tfidf": tfidf_scores(dev_for_text, test_for_text, "word"), + "character_tfidf": tfidf_scores(dev_for_text, test_for_text, "character"), + }.items(): + table, metrics = control_table(scores, meta) + controls[name] = metrics + control_tables[name] = table + + behavior_evaluable = protocol["generation_max_new_tokens"] > 0 + behavior = { + "evaluated": behavior_evaluable, + "unparsed_completions": ( + sum(row["predicted_answer"] is None for row in test_rows) + if behavior_evaluable + else None + ), + "overall_accuracy": ( + float(np.mean([bool(row["correct"]) for row in test_rows])) + if behavior_evaluable + else None + ), + "accuracy_by_category": {}, + } + if behavior_evaluable: + for category in ("direct", "neutral", "inverse"): + selected = [row for row in test_rows if row["category"] == category] + behavior["accuracy_by_category"][category] = float( + np.mean([bool(row["correct"]) for row in selected]) + ) + + rule = protocol["success_rule"] + passed = bool( + endpoints["direct_vs_inverse_auc"] >= rule["direct_inverse_auc_minimum"] + and endpoints["direct_vs_validation_neutral_auc"] + >= rule["direct_neutral_auc_minimum"] + and endpoints["validation_neutral_vs_inverse_auc"] + >= rule["neutral_inverse_auc_minimum"] + and accuracy >= rule["calibrated_directional_accuracy_minimum"] + and min(surface_auc.values()) + >= rule["both_surface_direct_inverse_auc_minimum"] + and rho >= rule["ordinal_spearman_minimum"] + and ordinal_p <= rule["ordinal_permutation_p_maximum"] + ) + + np.savez_compressed( + out / "frozen_direction.npz", + direction=direction.astype(np.float32), + midpoint=midpoint.astype(np.float32), + probe_layer=np.asarray([protocol["probe_layer_zero_indexed"]], dtype=np.int16), + ) + np.savez_compressed( + out / "representations.npz", + development_prompt_ids=np.asarray( + [row["prompt_id"] for row in development["prompts"]] + ), + development_states=np.asarray(dev_states, dtype=np.float16), + equation_free_prompt_ids=np.asarray( + [row["prompt_id"] for row in test["prompts"]] + ), + equation_free_states=np.asarray(test_states, dtype=np.float16), + probe_layer=np.asarray([protocol["probe_layer_zero_indexed"]], dtype=np.int16), + ) + pairs.to_csv(out / "matched_pair_contrasts.csv", index=False) + laws.to_csv(out / "law_level_results.csv", index=False) + for name, table in control_tables.items(): + table.to_csv(out / f"{name}_law_level.csv", index=False) + + raw = { + "study_id": protocol["study_id"], + "completed_at": utc_now(), + "provenance": { + "protocol_sha256": sha256(out / "protocol.json"), + "runner_sha256": sha256(Path(__file__).resolve()), + "development_manifest_sha256": protocol["inputs"]["development_manifest"]["sha256"], + "equation_free_manifest_sha256": protocol["inputs"]["equation_free_manifest"]["sha256"], + "model": protocol["model"], + "model_revision": protocol["model_revision"], + "probe_layer_zero_indexed": protocol["probe_layer_zero_indexed"], + "n_layers": model.n_layers, + "d_model": model.d_model, + "device": str(model.input_device), + "dtype": protocol["dtype"], + "torch": torch.__version__, + "python": sys.version, + "platform": platform.platform(), + }, + "answer_tokens": answer_tokens, + "clean_rows": test_rows, + } + atomic_json(out / "raw.json", raw) + statistics = { + "study_id": protocol["study_id"], + "analysis_status": "prespecified cross-scale replication", + "model": protocol["model"], + "model_revision": protocol["model_revision"], + "probe_layer_zero_indexed": protocol["probe_layer_zero_indexed"], + "normalized_depth_percent": 100.0 + * protocol["probe_layer_zero_indexed"] + / max(model.n_layers - 1, 1), + "neutral_calibration": { + "center": center, + "robust_scale_1.4826_mad": scale, + "n_calibration_laws": 10, + "n_validation_laws": 10, + }, + "primary": { + **endpoints, + "bootstrap_95": bootstrap, + "calibrated_directional_accuracy": accuracy, + "correct_directional_laws": int(correct.sum()), + "directional_laws_total": int(len(correct)), + "surface_direct_inverse_auc": surface_auc, + "ordinal_spearman": rho, + "ordinal_permutation_p": ordinal_p, + "category_median_z": { + category: float(group["natural_z"].median()) + for category, group in laws.groupby("category") + }, + }, + "controls": controls, + "behavior": behavior, + "passed_all_prespecified_criteria": passed, + "limitations": [ + "The same 60 law identities and exact equation-free prompts are reused from the 4B revision experiment.", + "The model-specific direction is fitted on the unchanged 16-law development cohort at a prespecified normalized depth.", + "This tests cross-scale transfer of the measurement, not spontaneous discovery of unfamiliar physical laws.", + ], + } + atomic_json(out / "statistics.json", statistics) + model_label = protocol["model"].split("/")[-1] + report = [ + f"# {model_label} equation-free relational replication", + "", + f"- Model: `{protocol['model']}` at `{protocol['model_revision']}`", + f"- Probe layer: {protocol['probe_layer_zero_indexed']} of {model.n_layers} (0-indexed; {statistics['normalized_depth_percent']:.1f}% depth)", + "- Development: unchanged 16-law, 128-prompt supplied-equation cohort", + "- Test: unchanged 60-law, 960-prompt equation-free cohort", + f"- Direct versus inverse AUC: **{endpoints['direct_vs_inverse_auc']:.3f}**", + f"- Ordinal Spearman rho: **{rho:.3f}** (permutation p={ordinal_p:.6g})", + f"- Directional laws correctly oriented: **{int(correct.sum())}/{len(correct)}**", + f"- Passed all prespecified criteria: **{passed}**", + "", + "See `protocol.json`, `raw.json`, `representations.npz`, `matched_pair_contrasts.csv`, `law_level_results.csv`, and `statistics.json`.", + "", + ] + (out / "REPORT.md").write_text("\n".join(report), encoding="utf-8") + + +def main() -> None: + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument("--model", required=True) + parser.add_argument("--model-revision", required=True) + parser.add_argument("--probe-layer", type=int, required=True) + parser.add_argument("--output-dir", default=DEFAULT_OUT) + parser.add_argument("--development-manifest", default=DEFAULT_DEVELOPMENT) + parser.add_argument("--test-manifest", default=DEFAULT_TEST) + parser.add_argument("--device", default=None) + parser.add_argument("--dtype", choices=sorted(_DTYPES), default="bfloat16") + parser.add_argument("--max-seq-len", type=int, default=512) + parser.add_argument("--generation-max-new-tokens", type=int, default=3) + parser.add_argument("--validate-only", action="store_true") + args = parser.parse_args() + + out = resolve(args.output_dir) + development_path = resolve(args.development_manifest) + test_path = resolve(args.test_manifest) + out.mkdir(parents=True, exist_ok=True) + development = read_json(development_path) + test = read_json(test_path) + preflight = validate_manifests(development, test) + spec = frozen_spec(args, development_path, test_path) + protocol = freeze_or_validate_protocol(out, spec) + shutil.copyfile(development_path, out / "development_prompt_manifest.json") + shutil.copyfile(test_path, out / "equation_free_prompt_manifest.json") + atomic_json(out / "preflight.json", {**preflight, "validated_at": utc_now()}) + if args.validate_only: + print(json.dumps(preflight, indent=2)) + print(f"frozen protocol: {out / 'protocol.json'}") + return + if (out / "statistics.json").exists(): + print(f"completed result already exists: {out / 'statistics.json'}") + return + + model = load_model( + args.model, + dtype=_DTYPES[args.dtype], + device=args.device, + revision=args.model_revision, + ) + if not 0 <= args.probe_layer < model.n_layers: + raise RuntimeError( + f"probe layer {args.probe_layer} is invalid for {model.n_layers} layers" + ) + answer_tokens = single_answer_tokens(model) + dev_states, _ = capture_cohort( + model=model, + prompts=development["prompts"], + probe_layer=args.probe_layer, + max_seq_len=args.max_seq_len, + answer_tokens=answer_tokens, + states_path=out / "development_states.npy", + rows_path=out / "development_rows.jsonl", + generate_tokens=0, + label="development", + ) + test_states, test_rows = capture_cohort( + model=model, + prompts=test["prompts"], + probe_layer=args.probe_layer, + max_seq_len=args.max_seq_len, + answer_tokens=answer_tokens, + states_path=out / "equation_free_states.npy", + rows_path=out / "equation_free_rows.jsonl", + generate_tokens=args.generation_max_new_tokens, + label="equation-free", + ) + analyze( + out=out, + protocol=protocol, + development=development, + test=test, + dev_states=dev_states, + test_states=test_states, + test_rows=test_rows, + answer_tokens=answer_tokens, + model=model, + ) + print(f"complete: {out / 'REPORT.md'}") + + +if __name__ == "__main__": + main() diff --git a/jlens_materials/scripts/run_lexical_adversarial_representation.py b/jlens_materials/scripts/run_lexical_adversarial_representation.py new file mode 100644 index 0000000000000000000000000000000000000000..b950c68c2df849f0bdb2932235f8e47231b442e1 --- /dev/null +++ b/jlens_materials/scripts/run_lexical_adversarial_representation.py @@ -0,0 +1,366 @@ +#!/usr/bin/env python3 +"""Execute the frozen lexical-adversarial materials representation study.""" + +from __future__ import annotations + +import argparse +import json +import platform +import sys +from datetime import datetime, timezone +from pathlib import Path + +import numpy as np +import torch + +ROOT = Path(__file__).resolve().parents[1] +sys.path.insert(0, str(ROOT)) +sys.path.insert(0, str(ROOT / "_vendor_jlens")) +sys.path.insert(0, str(ROOT / "scripts")) + +import jlens # noqa: E402 +from jlens.hooks import ActivationRecorder # noqa: E402 +from run_jacobian_steering_pilot import atomic_json, format_chat, sha256 # noqa: E402 +from run_lens import _DTYPES, load_model # noqa: E402 +from run_semantic_steering_v3 import continuation_tokens # noqa: E402 + + +DEFAULT_PROTOCOL = ( + "experiments/lexical-adversarial-representation-2026-07-17/protocol.json" +) +DEFAULT_OUTPUT = ( + "experiments/lexical-adversarial-representation-2026-07-17/raw.json" +) +DEFAULT_STATES = ( + "experiments/lexical-adversarial-representation-2026-07-17/" + "representations.npz" +) + + +def validate_hash(path: Path, expected: str) -> None: + actual = sha256(path) + if actual != expected: + raise RuntimeError(f"fingerprint mismatch for {path}: {actual} != {expected}") + + +def normalized_decoder_basis(model, states: torch.Tensor) -> torch.Tensor: + device = model._lm_head.weight.device + dtype = model._lm_head.weight.dtype + return model._final_norm(states.to(device=device, dtype=dtype)).float() + + +def pair_logits( + model, normalized: torch.Tensor, token_ids: tuple[int, int] +) -> torch.Tensor: + device = model._lm_head.weight.device + weight = model._lm_head.weight.index_select( + 0, torch.tensor(token_ids, device=device, dtype=torch.long) + ).float() + logits = normalized.float() @ weight.T + bias = getattr(model._lm_head, "bias", None) + if bias is not None: + logits = logits + bias.index_select( + 0, torch.tensor(token_ids, device=device, dtype=torch.long) + ).float() + softcap = getattr(model, "_logit_softcap", None) + if softcap is not None: + logits = softcap * torch.tanh(logits / softcap) + return logits + + +@torch.inference_mode() +def capture_states( + model, + prompts: list[dict], + layers: list[int], + answer_ids: dict[str, tuple[int, int]], +) -> tuple[np.ndarray, list[dict]]: + states = np.empty( + (len(prompts), len(layers), model.d_model), + dtype=np.float16, + ) + clean_rows: list[dict] = [] + for prompt_index, prompt in enumerate(prompts): + text = format_chat(model.tokenizer, prompt["user"]) + input_ids = model.encode(text, max_length=512) + final = model.n_layers - 1 + capture = sorted(set(layers + [final])) + with ActivationRecorder(model.layers, at=capture) as recorder: + model.forward(input_ids) + for layer_index, layer in enumerate(layers): + states[prompt_index, layer_index] = ( + recorder.activations[layer][0, -1] + .detach() + .float() + .cpu() + .numpy() + .astype(np.float16) + ) + final_state = recorder.activations[final][0, -1:].detach() + normalized = normalized_decoder_basis(model, final_state) + ids = answer_ids[prompt["family_id"]] + logits = pair_logits(model, normalized, ids)[0].detach().cpu() + full_logits = model.unembed(final_state).float()[0] + top_id = int(full_logits.argmax()) + predicted = ( + prompt["outcome_positive"] if float(logits[0]) > float(logits[1]) + else prompt["outcome_negative"] + ) + clean_rows.append({ + "prompt_id": prompt["prompt_id"], + "triplet_id": prompt["triplet_id"], + "family_id": prompt["family_id"], + "variant": prompt["variant"], + "expected_outcome": prompt["expected_outcome"], + "predicted_registered_outcome": predicted, + "registered_pair_correct": predicted == prompt["expected_outcome"], + "positive_minus_negative_log_odds": float(logits[0] - logits[1]), + "global_top_token_id": top_id, + "global_top_token": model.tokenizer.decode( + [top_id], clean_up_tokenization_spaces=False + ).strip(), + "global_top_is_registered_answer": top_id in ids, + "n_tokens": int(input_ids.shape[1]), + }) + print( + f" captured {prompt_index + 1:02d}/{len(prompts):02d}: " + f"{prompt['prompt_id']}", + flush=True, + ) + return states, clean_rows + + +@torch.inference_mode() +def decoder_basis_representations( + model, + lenses: list[jlens.JacobianLens], + raw_states: np.ndarray, + layers: list[int], + chunk_size: int, +) -> tuple[np.ndarray, np.ndarray]: + n_prompts, n_layers, d_model = raw_states.shape + direct = np.empty((n_prompts, n_layers, d_model), dtype=np.float16) + transported = np.empty( + (len(lenses), n_prompts, n_layers, d_model), dtype=np.float16 + ) + device = model.input_device + + for layer_index, layer in enumerate(layers): + source_cpu = torch.from_numpy(raw_states[:, layer_index].astype(np.float32)) + for start in range(0, n_prompts, chunk_size): + stop = min(start + chunk_size, n_prompts) + source = source_cpu[start:stop].to(device=device) + direct[start:stop, layer_index] = ( + normalized_decoder_basis(model, source) + .detach() + .cpu() + .numpy() + .astype(np.float16) + ) + for lens_index, lens in enumerate(lenses): + matrix = lens.jacobians[layer].to(device=device) + for start in range(0, n_prompts, chunk_size): + stop = min(start + chunk_size, n_prompts) + source = source_cpu[start:stop].to(device=device) + target = source @ matrix.T + transported[lens_index, start:stop, layer_index] = ( + normalized_decoder_basis(model, target) + .detach() + .cpu() + .numpy() + .astype(np.float16) + ) + del matrix + if device.type == "mps": + torch.mps.empty_cache() + print( + f" transported layer {layer_index + 1:02d}/{len(layers):02d}: {layer}", + flush=True, + ) + return direct, transported + + +@torch.inference_mode() +def top_token_rows( + model, + direct: np.ndarray, + jacobian: np.ndarray, + prompts: list[dict], + layers: list[int], + selected_layers: list[int], + top_k: int, + chunk_size: int, +) -> list[dict]: + rows: list[dict] = [] + device = model.input_device + layer_to_index = {layer: index for index, layer in enumerate(layers)} + jacobian_mean = jacobian.astype(np.float32).mean(axis=0) + decoded_cache: dict[int, str] = {} + for method, array in [ + ("direct", direct), + ("jacobian_ensemble", jacobian_mean), + ]: + for layer in selected_layers: + layer_index = layer_to_index[layer] + for start in range(0, len(prompts), chunk_size): + stop = min(start + chunk_size, len(prompts)) + states = torch.from_numpy( + array[start:stop, layer_index].astype(np.float32) + ).to(device=device, dtype=model._lm_head.weight.dtype) + logits = model.unembed(states) + values, indices = torch.topk(logits, k=top_k, dim=-1) + values = values.float().detach().cpu().numpy() + indices = indices.detach().cpu().numpy() + for local_index, prompt_index in enumerate(range(start, stop)): + token_ids = [int(value) for value in indices[local_index]] + for token_id in token_ids: + if token_id not in decoded_cache: + decoded_cache[token_id] = model.tokenizer.decode( + [token_id], clean_up_tokenization_spaces=False + ).strip() + rows.append({ + "prompt_id": prompts[prompt_index]["prompt_id"], + "triplet_id": prompts[prompt_index]["triplet_id"], + "family_id": prompts[prompt_index]["family_id"], + "variant": prompts[prompt_index]["variant"], + "method": method, + "layer": layer, + "depth_percent": 100.0 * layer / (model.n_layers - 1), + "token_ids": token_ids, + "tokens": [decoded_cache[token_id] for token_id in token_ids], + "scores": [float(value) for value in values[local_index]], + }) + del logits, values, indices + if device.type == "mps": + torch.mps.empty_cache() + print(f" decoded {method} layer {layer}", flush=True) + return rows + + +def main() -> None: + parser = argparse.ArgumentParser() + parser.add_argument("--protocol", default=DEFAULT_PROTOCOL) + parser.add_argument("--output", default=DEFAULT_OUTPUT) + parser.add_argument("--states-output", default=DEFAULT_STATES) + parser.add_argument("--dtype", choices=sorted(_DTYPES), default="bfloat16") + parser.add_argument("--device", default=None) + parser.add_argument("--chunk-size", type=int, default=12) + args = parser.parse_args() + + protocol_path = (ROOT / args.protocol).resolve() + output_path = (ROOT / args.output).resolve() + states_path = (ROOT / args.states_output).resolve() + protocol = json.loads(protocol_path.read_text()) + manifest_path = ROOT / protocol["inputs"]["prompt_manifest"] + validate_hash(manifest_path, protocol["inputs"]["prompt_manifest_sha256"]) + validate_hash( + ROOT / protocol["inputs"]["runner"], + protocol["inputs"]["runner_sha256"], + ) + for lens_row in protocol["lenses"]: + validate_hash(ROOT / lens_row["path"], lens_row["sha256"]) + manifest = json.loads(manifest_path.read_text()) + prompts = manifest["prompts"] + layers = [int(layer) for layer in protocol["source_layers"]] + + model = load_model( + protocol["model"], + dtype=_DTYPES[args.dtype], + device=args.device, + revision=protocol["model_revision"], + ) + answer_ids: dict[str, tuple[int, int]] = {} + answer_token_record: dict[str, dict] = {} + for family in manifest["families"]: + positive = continuation_tokens(model.tokenizer, family["outcome_positive"]) + negative = continuation_tokens(model.tokenizer, family["outcome_negative"]) + if len(positive) != 1 or len(negative) != 1: + raise RuntimeError( + f"registered answers are not single tokens for {family['family_id']}: " + f"{positive=} {negative=}" + ) + answer_ids[family["family_id"]] = (positive[0], negative[0]) + answer_token_record[family["family_id"]] = { + family["outcome_positive"]: positive, + family["outcome_negative"]: negative, + } + + lenses = [ + jlens.JacobianLens.load(ROOT / row["path"]) for row in protocol["lenses"] + ] + if any(lens.source_layers != layers for lens in lenses): + raise RuntimeError("lens source layers do not match frozen layers") + + print( + f"capturing {len(prompts)} prompts at {len(layers)} layers on " + f"{model.input_device}", + flush=True, + ) + raw_states, clean_rows = capture_states(model, prompts, layers, answer_ids) + print("transporting representations", flush=True) + direct, jacobian = decoder_basis_representations( + model, lenses, raw_states, layers, args.chunk_size + ) + print("decoding target-free top-token neighborhoods", flush=True) + top_rows = top_token_rows( + model, + direct, + jacobian, + prompts, + layers, + [int(layer) for layer in protocol["target_free_layers"]], + int(protocol["target_free_top_k_retained"]), + args.chunk_size, + ) + + states_path.parent.mkdir(parents=True, exist_ok=True) + np.savez_compressed( + states_path, + prompt_ids=np.asarray([row["prompt_id"] for row in prompts]), + layers=np.asarray(layers, dtype=np.int16), + raw_states=raw_states, + direct_decoder_basis=direct, + jacobian_decoder_basis=jacobian, + ) + payload = { + "study_id": protocol["study_id"], + "created_at": datetime.now(timezone.utc).isoformat(), + "completed_at": datetime.now(timezone.utc).isoformat(), + "provenance": { + "protocol": str(protocol_path.relative_to(ROOT)), + "protocol_sha256": sha256(protocol_path), + "prompt_manifest": str(manifest_path.relative_to(ROOT)), + "prompt_manifest_sha256": sha256(manifest_path), + "runner": str(Path(__file__).resolve().relative_to(ROOT)), + "runner_sha256": sha256(Path(__file__).resolve()), + "states": str(states_path.relative_to(ROOT)), + "model": protocol["model"], + "model_revision": protocol["model_revision"], + "dtype": args.dtype, + "device": str(model.input_device), + "torch": torch.__version__, + "python": sys.version, + "platform": platform.platform(), + "lenses": protocol["lenses"], + }, + "dimensions": { + "n_prompts": len(prompts), + "n_triplets": len(manifest["triplets"]), + "n_families": len(manifest["families"]), + "n_layers": len(layers), + "d_model": model.d_model, + }, + "answer_tokens": answer_token_record, + "clean_rows": clean_rows, + "target_free_top_tokens": top_rows, + } + atomic_json(output_path, payload) + print( + f"complete: {len(clean_rows)} clean rows, " + f"{len(top_rows)} target-free rows, states={states_path}", + flush=True, + ) + + +if __name__ == "__main__": + main() diff --git a/jlens_materials/scripts/run_option_free_question_end_states.py b/jlens_materials/scripts/run_option_free_question_end_states.py new file mode 100644 index 0000000000000000000000000000000000000000..99f44255b65ea70dba6e060ad1949b7127c885d4 --- /dev/null +++ b/jlens_materials/scripts/run_option_free_question_end_states.py @@ -0,0 +1,243 @@ +#!/usr/bin/env python3 +"""Capture Gemma states after scientific questions with no answer scaffold. + +The runner is protocol-bound. It formats each archived scientific stem as a +complete chat user turn, records the final prompt-position residual at all 25 +registered layers, applies the same three frozen Jacobian lenses, and retains +the model's clean next-token behavior. It does not append answer choices, +answer words, an arbitrary code, or the earlier checkpoint marker. +""" + +from __future__ import annotations + +import argparse +import json +import platform +import sys +from datetime import datetime, timezone +from pathlib import Path + +import numpy as np +import torch + +ROOT = Path(__file__).resolve().parents[1] +sys.path.insert(0, str(ROOT)) +sys.path.insert(0, str(ROOT / "_vendor_jlens")) +sys.path.insert(0, str(ROOT / "scripts")) + +import jlens # noqa: E402 +from jlens.hooks import ActivationRecorder # noqa: E402 +from run_jacobian_steering_pilot import atomic_json, format_chat, sha256 # noqa: E402 +from run_lens import _DTYPES, load_model # noqa: E402 +from run_lexical_adversarial_representation import ( # noqa: E402 + decoder_basis_representations, +) + + +DEFAULT_PROTOCOL = ( + "experiments/option-free-question-end-2026-07-18/protocol.json" +) +DEFAULT_OUTPUT = ( + "experiments/option-free-question-end-2026-07-18/raw.json" +) +DEFAULT_STATES = ( + "experiments/option-free-question-end-2026-07-18/representations.npz" +) + + +def validate_hash(path: Path, expected: str) -> None: + actual = sha256(path) + if actual != expected: + raise RuntimeError(f"fingerprint mismatch for {path}: {actual} != {expected}") + + +@torch.inference_mode() +def capture_states( + model, + prompts: list[dict], + layers: list[int], +) -> tuple[np.ndarray, list[dict]]: + states = np.empty( + (len(prompts), len(layers), model.d_model), dtype=np.float16 + ) + clean_rows = [] + final_layer = model.n_layers - 1 + capture = sorted(set(layers + [final_layer])) + for prompt_index, prompt in enumerate(prompts): + user = str(prompt["stem"]) + if any( + marker in user.lower() + for marker in ( + "answer exactly", + "output a", + "output b", + "internal checkpoint", + "if the scientific answer", + ) + ): + raise RuntimeError( + f"answer scaffold leaked into option-free stem: {prompt['prompt_id']}" + ) + text = format_chat(model.tokenizer, user) + input_ids = model.encode(text, max_length=512) + final_position = int(input_ids.shape[1] - 1) + with ActivationRecorder(model.layers, at=capture) as recorder: + model.forward(input_ids) + for layer_index, layer in enumerate(layers): + states[prompt_index, layer_index] = ( + recorder.activations[layer][0, final_position] + .detach() + .float() + .cpu() + .numpy() + .astype(np.float16) + ) + final_state = recorder.activations[final_layer][ + 0, final_position : final_position + 1 + ].detach() + full_logits = model.unembed(final_state).float()[0] + top_values, top_ids = torch.topk(full_logits, k=10) + clean_rows.append( + { + "prompt_id": prompt["prompt_id"], + "family_id": prompt["family_id"], + "triplet_id": prompt["triplet_id"], + "variant": prompt["variant"], + "expected_outcome": prompt["expected_outcome"], + "n_tokens": int(input_ids.shape[1]), + "final_position": final_position, + "top_tokens": [ + { + "rank": rank + 1, + "token_id": int(token_id), + "token": model.tokenizer.decode( + [int(token_id)], + clean_up_tokenization_spaces=False, + ), + "logit": float(value), + } + for rank, (value, token_id) in enumerate( + zip( + top_values.detach().cpu().tolist(), + top_ids.detach().cpu().tolist(), + ) + ) + ], + } + ) + print( + f" captured {prompt_index + 1:02d}/{len(prompts):02d}: " + f"{prompt['prompt_id']}", + flush=True, + ) + return states, clean_rows + + +def main() -> None: + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument("--protocol", default=DEFAULT_PROTOCOL) + parser.add_argument("--output", default=DEFAULT_OUTPUT) + parser.add_argument("--states-output", default=DEFAULT_STATES) + parser.add_argument("--dtype", choices=sorted(_DTYPES), default="bfloat16") + parser.add_argument("--device", default=None) + parser.add_argument("--chunk-size", type=int, default=12) + args = parser.parse_args() + + protocol_path = (ROOT / args.protocol).resolve() + output_path = (ROOT / args.output).resolve() + states_path = (ROOT / args.states_output).resolve() + protocol = json.loads(protocol_path.read_text()) + + manifest_path = ROOT / protocol["inputs"]["prompt_manifest"]["path"] + runner_path = Path(__file__).resolve() + validate_hash( + manifest_path, protocol["inputs"]["prompt_manifest"]["sha256"] + ) + validate_hash(runner_path, protocol["inputs"]["runner"]["sha256"]) + for lens_row in protocol["lenses"]: + validate_hash(ROOT / lens_row["path"], lens_row["sha256"]) + + manifest = json.loads(manifest_path.read_text()) + prompts = manifest["prompts"] + if len(prompts) != 72: + raise RuntimeError(f"expected 72 prompts, found {len(prompts)}") + layers = [int(layer) for layer in protocol["source_layers"]] + + model = load_model( + protocol["model"], + dtype=_DTYPES[args.dtype], + device=args.device, + revision=protocol["model_revision"], + ) + lenses = [ + jlens.JacobianLens.load(ROOT / row["path"]) + for row in protocol["lenses"] + ] + if any(lens.source_layers != layers for lens in lenses): + raise RuntimeError("lens source layers do not match protocol") + + raw, clean_rows = capture_states(model, prompts, layers) + print("transporting option-free question-end states", flush=True) + direct, jacobian = decoder_basis_representations( + model, lenses, raw, layers, args.chunk_size + ) + + states_path.parent.mkdir(parents=True, exist_ok=True) + np.savez_compressed( + states_path, + prompt_ids=np.asarray([row["prompt_id"] for row in prompts]), + positions=np.asarray(["question_end"]), + layers=np.asarray(layers, dtype=np.int16), + raw_states=raw[None, ...], + direct_decoder_basis=direct[None, ...], + jacobian_decoder_basis=jacobian[:, None, ...], + ) + payload = { + "study_id": protocol["study_id"], + "created_at": datetime.now(timezone.utc).isoformat(), + "completed_at": datetime.now(timezone.utc).isoformat(), + "provenance": { + "protocol": str(protocol_path.relative_to(ROOT)), + "protocol_sha256": sha256(protocol_path), + "prompt_manifest": str(manifest_path.relative_to(ROOT)), + "prompt_manifest_sha256": sha256(manifest_path), + "runner": str(runner_path.relative_to(ROOT)), + "runner_sha256": sha256(runner_path), + "states": str(states_path.relative_to(ROOT)), + "model": protocol["model"], + "model_revision": protocol["model_revision"], + "dtype": args.dtype, + "device": str(model.input_device), + "torch": torch.__version__, + "python": sys.version, + "platform": platform.platform(), + "lenses": protocol["lenses"], + }, + "dimensions": { + "n_prompts": len(prompts), + "n_triplets": len(manifest["triplets"]), + "n_families": len(manifest["families"]), + "n_layers": len(layers), + "n_positions": 1, + "d_model": model.d_model, + }, + "position": { + "name": "question_end", + "definition": ( + "final chat-template prompt token after the complete scientific " + "stem, with no answer choices, answer words, arbitrary code, " + "response-format instruction, or checkpoint marker" + ), + }, + "clean_rows": clean_rows, + } + atomic_json(output_path, payload) + print( + f"complete: {len(clean_rows)} prompts, " + f"raw={raw.shape}, direct={direct.shape}, jacobian={jacobian.shape}", + flush=True, + ) + + +if __name__ == "__main__": + main() diff --git a/jlens_materials/scripts/run_semantic_steering_v3.py b/jlens_materials/scripts/run_semantic_steering_v3.py new file mode 100644 index 0000000000000000000000000000000000000000..2dbd4d18c0e1c9dd8212d6f93d7e788d9cf11c46 --- /dev/null +++ b/jlens_materials/scripts/run_semantic_steering_v3.py @@ -0,0 +1,501 @@ +#!/usr/bin/env python3 +"""Run the frozen semantic-answer and cross-mechanism steering confirmation. + +Unlike v2, this study never maps answers onto A/B proxy tokens. It measures +the actual next-token contrast between the two scientific outcome words and +applies all three mechanism directions at each target family's fixed layer. +""" + +from __future__ import annotations + +import argparse +import json +import math +import platform +import sys +from datetime import datetime, timezone +from pathlib import Path + +import numpy as np +import torch + +ROOT = Path(__file__).resolve().parents[1] +sys.path.insert(0, str(ROOT)) +sys.path.insert(0, str(ROOT / "_vendor_jlens")) +sys.path.insert(0, str(ROOT / "scripts")) + +import jlens # noqa: E402 +from jlens.hooks import ActivationRecorder # noqa: E402 +from run_lens import _DTYPES, load_model # noqa: E402 +from run_jacobian_steering_pilot import ( # noqa: E402 + add_residual, + atomic_json, + capture_clean, + format_chat, + patched_logits, + sha256, + single_token, +) +from run_ensemble_steering_v2 import ( # noqa: E402 + direction_fit_hidden, + family_token_ids, + orthogonal_random, + semantic_direct_direction, + semantic_jacobian_direction, +) + + +def prompt_rows(family: dict) -> list[dict]: + """Expand each physical condition into two answer-word presentation orders.""" + output = [] + positive = family["outcome_positive"] + negative = family["outcome_negative"] + for prompt in family["confirmation_prompts"]: + for order, words in [ + ("positive-first", [positive, negative]), + ("negative-first", [negative, positive]), + ]: + output.append({ + "family_id": family["family_id"], + "condition_id": prompt["condition_id"], + "prompt_id": ( + f"{family['family_id']}--confirm--{prompt['condition_id']}--{order}" + ), + "presentation_order": order, + "presented_words": words, + "expected_outcome": prompt["expected_outcome"], + "regime": prompt["regime"], + "user": ( + f"{prompt['stem']} Answer exactly one lowercase word from this " + f"ordered pair: {words[0]}, {words[1]}." + ), + }) + return output + + +def continuation_tokens(tokenizer, word: str) -> list[int]: + """Tokenize the exact lowercase continuation without a leading space.""" + token_ids = tokenizer.encode(word, add_special_tokens=False) + if not token_ids: + raise ValueError(f"empty continuation tokenization for {word!r}") + return [int(token_id) for token_id in token_ids] + + +@torch.no_grad() +def answer_sequence_log_probability( + model, + input_ids: torch.Tensor, + next_logits: torch.Tensor, + layer: int, + delta: torch.Tensor | None, + answer_tokens: list[int], +) -> float: + """Teacher-force one exact answer while patching only the final prompt token.""" + if len(answer_tokens) == 1: + return float(torch.log_softmax(next_logits, dim=-1)[answer_tokens[0]]) + prefix = torch.tensor( + answer_tokens[:-1], device=input_ids.device, dtype=input_ids.dtype + ).unsqueeze(0) + extended = torch.cat([input_ids, prefix], dim=1) + prompt_final_position = int(input_ids.shape[1] - 1) + final_layer = model.n_layers - 1 + if delta is None: + with ActivationRecorder(model.layers, at=[final_layer]) as recorder: + model.forward(extended) + else: + with add_residual(model, layer, prompt_final_position, delta): + with ActivationRecorder(model.layers, at=[final_layer]) as recorder: + model.forward(extended) + start = prompt_final_position + stop = start + len(answer_tokens) + residual = recorder.activations[final_layer][0, start:stop].detach() + logits = model.unembed(residual).float().cpu() + logp = torch.log_softmax(logits, dim=-1) + token_index = torch.tensor(answer_tokens, dtype=torch.long) + positions = torch.arange(len(answer_tokens), dtype=torch.long) + return float(logp[positions, token_index].sum()) + + +def semantic_distribution_summary( + logits: torch.Tensor, + clean_logits: torch.Tensor, + positive_tokens: list[int], + negative_tokens: list[int], + positive_sequence_logp: float, + negative_sequence_logp: float, + positive_word: str, + negative_word: str, + tokenizer, +) -> dict: + """Summarize the exact scientific answer-token contrast and output integrity.""" + logp = torch.log_softmax(logits, dim=-1) + clean_logp = torch.log_softmax(clean_logits, dim=-1) + clean_p = clean_logp.exp() + kl = float((clean_p * (clean_logp - logp)).sum()) + top_id = int(logits.argmax()) + first_ids = {positive_tokens[0], negative_tokens[0]} + choice = ( + positive_word + if positive_sequence_logp >= negative_sequence_logp + else negative_word + ) + return { + "positive_log_odds": float( + positive_sequence_logp - negative_sequence_logp + ), + "positive_sequence_log_probability": positive_sequence_logp, + "negative_sequence_log_probability": negative_sequence_logp, + "sequence_probability_positive": float(math.exp(positive_sequence_logp)), + "sequence_probability_negative": float(math.exp(negative_sequence_logp)), + "choice": choice, + "global_top_token": tokenizer.decode( + [top_id], clean_up_tokenization_spaces=False + ).strip(), + "global_top_token_id": top_id, + "global_top_is_valid_choice": top_id in first_ids, + "probability_positive": float(logp[positive_tokens[0]].exp()), + "probability_negative": float(logp[negative_tokens[0]].exp()), + "valid_choice_probability": float( + sum(logp[token_id].exp() for token_id in first_ids) + ), + "kl_clean_to_intervened": kl, + } + + +def initialize_payload( + manifest: dict, + manifest_path: Path, + output_path: Path, + model, + args, +) -> dict: + if args.resume and output_path.exists(): + payload = json.loads(output_path.read_text()) + if payload["provenance"]["manifest_sha256"] != sha256(manifest_path): + raise SystemExit("resume manifest hash does not match existing output") + return payload + runner_path = Path(__file__).resolve() + helper_paths = [ + ROOT / "scripts/run_ensemble_steering_v2.py", + ROOT / "scripts/run_jacobian_steering_pilot.py", + ] + return { + "study_id": manifest["study_id"], + "created_at": datetime.now(timezone.utc).isoformat(), + "provenance": { + "manifest": str(manifest_path.relative_to(ROOT)), + "manifest_sha256": sha256(manifest_path), + "runner": str(runner_path.relative_to(ROOT)), + "runner_sha256": sha256(runner_path), + "helper_sha256": { + str(path.relative_to(ROOT)): sha256(path) for path in helper_paths + }, + "model": manifest["model"], + "model_revision": manifest["model_revision"], + "dtype": args.dtype, + "device": str(model.input_device), + "torch": torch.__version__, + "python": sys.version, + "platform": platform.platform(), + "lenses": [ + {"seed": seed, "path": path, "sha256": sha256(ROOT / path)} + for seed, path in enumerate(manifest["lens_paths"]) + ], + }, + "token_inventory": {}, + "direction_diagnostics": {}, + "preflight": [], + "intervention_rows": [], + } + + +def cosine_matrix(vectors: list[torch.Tensor]) -> list[list[float]]: + matrix = np.eye(len(vectors), dtype=float) + for i in range(len(vectors)): + for j in range(i + 1, len(vectors)): + value = float(torch.dot(vectors[i], vectors[j])) + matrix[i, j] = value + matrix[j, i] = value + return matrix.tolist() + + +def main() -> None: + parser = argparse.ArgumentParser() + parser.add_argument( + "--manifest", + default="experiments/semantic-steering-v3-preregistration.json", + ) + parser.add_argument( + "--output", default="experiments/semantic-steering-v3_raw.json" + ) + parser.add_argument("--dtype", choices=sorted(_DTYPES), default="bfloat16") + parser.add_argument("--device", default=None) + parser.add_argument("--family", action="append", default=[]) + parser.add_argument("--prompt-id", action="append", default=[]) + parser.add_argument("--resume", action="store_true") + args = parser.parse_args() + + manifest_path = (ROOT / args.manifest).resolve() + output_path = (ROOT / args.output).resolve() + manifest = json.loads(manifest_path.read_text()) + families = manifest["families"] + family_by_id = {family["family_id"]: family for family in families} + if args.family: + unknown = set(args.family) - set(family_by_id) + if unknown: + raise SystemExit(f"unknown families: {sorted(unknown)}") + target_families = [family for family in families if family["family_id"] in args.family] + else: + target_families = families + + model = load_model( + manifest["model"], + dtype=_DTYPES[args.dtype], + device=args.device, + revision=manifest["model_revision"], + ) + lenses = [jlens.JacobianLens.load(ROOT / path) for path in manifest["lens_paths"]] + payload = initialize_payload(manifest, manifest_path, output_path, model, args) + + # Validate every answer and direction token before constructing any direction. + token_inventory = {} + answer_ids = {} + concept_ids = {} + for family in families: + family_id = family["family_id"] + positive_concepts, negative_concepts = family_token_ids(model.tokenizer, family) + positive_answer = continuation_tokens( + model.tokenizer, family["outcome_positive"] + ) + negative_answer = continuation_tokens( + model.tokenizer, family["outcome_negative"] + ) + concept_ids[family_id] = (positive_concepts, negative_concepts) + answer_ids[family_id] = (positive_answer, negative_answer) + token_inventory[family_id] = { + "concept_positive": dict(zip(family["concept_positive"], positive_concepts)), + "concept_negative": dict(zip(family["concept_negative"], negative_concepts)), + "outcome_positive": { + "word": family["outcome_positive"], "token_ids": positive_answer, + "decoded": model.tokenizer.decode( + positive_answer, clean_up_tokenization_spaces=False + ), + }, + "outcome_negative": { + "word": family["outcome_negative"], "token_ids": negative_answer, + "decoded": model.tokenizer.decode( + negative_answer, clean_up_tokenization_spaces=False + ), + }, + } + payload["token_inventory"] = token_inventory + atomic_json(output_path, payload) + + # Build every source mechanism at each target layer. This is the frozen, + # layer-matched cross-mechanism design; no result-dependent selection occurs. + target_layers = sorted({int(family["fixed_layer"]) for family in target_families}) + jacobian_directions: dict[tuple[int, str, int], torch.Tensor] = {} + direct_directions: dict[tuple[int, str], torch.Tensor] = {} + for layer in target_layers: + layer_diag = payload["direction_diagnostics"].setdefault(str(layer), {}) + seed0_sources = [] + for source_family in families: + source_id = source_family["family_id"] + fit_hidden = direction_fit_hidden(model, source_family, [layer])[layer] + positive_ids, negative_ids = concept_ids[source_id] + source_vectors = [] + fit_cosines = {} + for lens_seed, lens in enumerate(lenses): + direction, cosines = semantic_jacobian_direction( + model, lens, layer, fit_hidden, positive_ids, negative_ids + ) + jacobian_directions[(layer, source_id, lens_seed)] = direction + source_vectors.append(direction) + fit_cosines[str(lens_seed)] = cosines + direct, direct_cosines = semantic_direct_direction( + model, fit_hidden, positive_ids, negative_ids + ) + direct_directions[(layer, source_id)] = direct + layer_diag[source_id] = { + "fit_prompt_cosines_to_mean_by_lens_seed": fit_cosines, + "lens_seed_direction_cosine_matrix": cosine_matrix(source_vectors), + "direct_fit_prompt_cosines_to_mean": direct_cosines, + "seed0_jacobian_cosine_with_direct": float( + torch.dot(source_vectors[0], direct) + ), + } + seed0_sources.append(source_vectors[0]) + layer_diag["source_family_order"] = [family["family_id"] for family in families] + layer_diag["seed0_cross_mechanism_cosine_matrix"] = cosine_matrix(seed0_sources) + atomic_json(output_path, payload) + + completed = { + ( + row["prompt_id"], row["method"], row.get("source_family_id"), + row.get("lens_seed"), row.get("random_seed"), + float(row["dose_percent"]), + ) + for row in payload["intervention_rows"] + } + preflight_done = {row["prompt_id"] for row in payload["preflight"]} + doses = [float(value) for value in manifest["design"]["doses_percent_residual_norm"]] + random_seeds = [int(value) for value in manifest["design"]["random_seeds"]] + requested_prompt_ids = set(args.prompt_id) + known_prompt_ids = { + row["prompt_id"] for family in target_families for row in prompt_rows(family) + } + if requested_prompt_ids: + unknown = requested_prompt_ids - known_prompt_ids + if unknown: + raise SystemExit(f"unknown prompt ids: {sorted(unknown)}") + + checkpoint_counter = 0 + for target_index, family in enumerate(target_families): + target_id = family["family_id"] + layer = int(family["fixed_layer"]) + positive_tokens, negative_tokens = answer_ids[target_id] + own_seed0 = jacobian_directions[(layer, target_id, 0)] + own_direct = direct_directions[(layer, target_id)] + randoms = { + random_seed: orthogonal_random( + own_seed0, + own_direct, + 20260716 + 10000 * list(family_by_id).index(target_id) + random_seed, + ) + for random_seed in random_seeds + } + random_diag = { + str(seed): { + "cosine_with_own_seed0": float(torch.dot(direction, own_seed0)), + "cosine_with_own_direct": float(torch.dot(direction, own_direct)), + } + for seed, direction in randoms.items() + } + payload["direction_diagnostics"][str(layer)].setdefault( + "random_controls_by_target", {} + )[target_id] = random_diag + atomic_json(output_path, payload) + + directions: list[tuple[str, str | None, int | None, int | None, str, torch.Tensor]] = [] + for source_family in families: + source_id = source_family["family_id"] + control_type = "own" if source_id == target_id else "wrong-mechanism" + for lens_seed in range(len(lenses)): + directions.append(( + "semantic_jacobian", source_id, lens_seed, None, control_type, + jacobian_directions[(layer, source_id, lens_seed)], + )) + directions.append(( + "semantic_direct", target_id, None, None, "direct", + direct_directions[(layer, target_id)], + )) + directions.extend( + ("random", None, None, seed, "random", direction) + for seed, direction in randoms.items() + ) + + print(f"[target={target_id}] layer={layer} directions={len(directions)}", flush=True) + rows = prompt_rows(family) + if requested_prompt_ids: + rows = [row for row in rows if row["prompt_id"] in requested_prompt_ids] + for prompt_index, prompt in enumerate(rows, start=1): + text = format_chat(model.tokenizer, prompt["user"]) + input_ids = model.encode(text, max_length=512) + hidden_by_layer, clean_logits = capture_clean(model, text, [layer]) + hidden = hidden_by_layer[layer] + residual_norm = float(hidden.norm()) + clean_positive_logp = answer_sequence_log_probability( + model, input_ids, clean_logits, layer, None, positive_tokens + ) + clean_negative_logp = answer_sequence_log_probability( + model, input_ids, clean_logits, layer, None, negative_tokens + ) + clean_summary = semantic_distribution_summary( + clean_logits, clean_logits, positive_tokens, negative_tokens, + clean_positive_logp, clean_negative_logp, + family["outcome_positive"], family["outcome_negative"], model.tokenizer, + ) + if prompt["prompt_id"] not in preflight_done: + payload["preflight"].append({ + **prompt, + "fixed_layer": layer, + "outcome_positive": family["outcome_positive"], + "outcome_negative": family["outcome_negative"], + "clean_expected_correct": ( + None if prompt["expected_outcome"] is None + else clean_summary["choice"] == prompt["expected_outcome"] + ), + "clean": clean_summary, + }) + preflight_done.add(prompt["prompt_id"]) + atomic_json(output_path, payload) + + for method, source_id, lens_seed, random_seed, control_type, direction in directions: + for dose in doses: + key = ( + prompt["prompt_id"], method, source_id, lens_seed, + random_seed, dose, + ) + if key in completed: + continue + delta = direction * dose / 100.0 * residual_norm + logits = clean_logits if dose == 0.0 else patched_logits( + model, text, layer, delta + ) + if dose == 0.0: + positive_logp = clean_positive_logp + negative_logp = clean_negative_logp + else: + positive_logp = answer_sequence_log_probability( + model, input_ids, logits, layer, delta, positive_tokens + ) + negative_logp = answer_sequence_log_probability( + model, input_ids, logits, layer, delta, negative_tokens + ) + summary = semantic_distribution_summary( + logits, clean_logits, positive_tokens, negative_tokens, + positive_logp, negative_logp, + family["outcome_positive"], family["outcome_negative"], + model.tokenizer, + ) + payload["intervention_rows"].append({ + **{field: prompt[field] for field in [ + "family_id", "condition_id", "prompt_id", + "presentation_order", "presented_words", + "expected_outcome", "regime", + ]}, + "target_family_id": target_id, + "source_family_id": source_id, + "control_type": control_type, + "method": method, + "lens_seed": lens_seed, + "random_seed": random_seed, + "layer": layer, + "dose_percent": dose, + "residual_norm": residual_norm, + "delta_norm": float(delta.norm()), + **summary, + }) + completed.add(key) + checkpoint_counter += 1 + if checkpoint_counter % 25 == 0: + atomic_json(output_path, payload) + atomic_json(output_path, payload) + print( + f" {prompt_index:02d}/{len(rows):02d} {prompt['condition_id']} " + f"{prompt['presentation_order']}: clean={clean_summary['choice']} " + f"pair_mass={clean_summary['valid_choice_probability']:.3f}", + flush=True, + ) + + payload["completed_at"] = datetime.now(timezone.utc).isoformat() + atomic_json(output_path, payload) + print( + f"complete: {len(payload['preflight'])} prompts, " + f"{len(payload['intervention_rows'])} rows -> {output_path.relative_to(ROOT)}", + flush=True, + ) + + +if __name__ == "__main__": + main() diff --git a/jlens_materials/scripts/validate_equation_free_relational_benchmark.py b/jlens_materials/scripts/validate_equation_free_relational_benchmark.py new file mode 100644 index 0000000000000000000000000000000000000000..53e175832a5bd23b4abc275ab599567b03ce9332 --- /dev/null +++ b/jlens_materials/scripts/validate_equation_free_relational_benchmark.py @@ -0,0 +1,103 @@ +#!/usr/bin/env python3 +"""Validate the equation-free 60-law benchmark before and after execution.""" + +from __future__ import annotations + +import hashlib +import json +from pathlib import Path + +import numpy as np +import pandas as pd + + +ROOT = Path(__file__).resolve().parents[1] +OUT = ROOT / "experiments" / "equation-free-relational-physics-2026-08-21" + + +def sha256(path: Path) -> str: + return hashlib.sha256(path.read_bytes()).hexdigest() + + +def require(condition: bool, message: str) -> None: + if not condition: + raise RuntimeError(message) + + +def main() -> None: + protocol_path = OUT / "protocol.json" + manifest_path = OUT / "prompt_manifest.json" + protocol = json.loads(protocol_path.read_text()) + manifest = json.loads(manifest_path.read_text()) + for name, record in protocol["inputs"].items(): + path = ROOT / record["path"] + require(path.exists(), f"missing frozen input: {name}") + require(sha256(path) == record["sha256"], f"frozen input hash mismatch: {name}") + + laws = manifest["laws"] + prompts = manifest["prompts"] + require(len(laws) == 60, "law count is not 60") + require(len({row["law_id"] for row in laws}) == 60, "law ids are not unique") + require(len(prompts) == 960, "prompt count is not 960") + require(len({row["prompt_id"] for row in prompts}) == 960, "prompt ids are not unique") + categories = {category: sum(row["category"] == category for row in prompts) for category in ("direct", "inverse", "neutral")} + require(categories == {"direct": 320, "inverse": 320, "neutral": 320}, f"unbalanced prompt categories: {categories}") + roles = {role: sum(row.get("neutral_role") == role for row in laws) for role in ("calibration", "validation")} + require(roles == {"calibration": 10, "validation": 10}, f"bad neutral split: {roles}") + forbidden = ("constitutive equation", "stage 1", "stage 2", "silently compose", " = ") + require(not any(term in row["user"].lower() for row in prompts for term in forbidden), "equation/decomposition scaffold appears in prompt text") + require(all(row["physical_sign"] == row["law_sign"] * row["numerical_sign"] for row in prompts), "bad physical signs") + cells = {} + for row in prompts: + key = (row["law_id"], row["surface"], row["case_index"], row["answer_order"]) + cells.setdefault(key, []).append(row["numerical_sign"]) + require(len(cells) == 480, "matched-cell count is not 480") + require(all(sorted(values) == [-1, 1] for values in cells.values()), "a matched cell lacks both numerical directions") + + checks = { + "frozen_input_hashes": "pass", + "laws": len(laws), + "prompts": len(prompts), + "prompt_categories": categories, + "neutral_roles": roles, + "matched_cells": len(cells), + "no_equation_or_decomposition_scaffold": "pass", + } + raw_path = OUT / "raw.json" + status = "preflight-pass" + if raw_path.exists(): + raw = json.loads(raw_path.read_text()) + state_path = OUT / "representations.npz" + require(raw["provenance"]["protocol_sha256"] == sha256(protocol_path), "raw protocol hash mismatch") + require(raw["provenance"]["manifest_sha256"] == sha256(manifest_path), "raw manifest hash mismatch") + require(raw["provenance"]["states_sha256"] == sha256(state_path), "raw state hash mismatch") + prompt_ids = [row["prompt_id"] for row in prompts] + require([row["prompt_id"] for row in raw["clean_rows"]] == prompt_ids, "raw rows do not preserve manifest order") + require(len(raw["clean_rows"]) == 960, "raw row count is not 960") + require(all(row["predicted_answer"] in {"higher", "lower", "unchanged"} for row in raw["clean_rows"]), "unparsed greedy completion") + with np.load(state_path, allow_pickle=False) as arrays: + require(arrays["raw_states"].shape == (960, 2560), "bad state-array shape") + require(arrays["prompt_ids"].astype(str).tolist() == prompt_ids, "state ids do not preserve manifest order") + require(int(arrays["layer"][0]) == 34, "captured layer is not 34") + checks.update({"raw_rows": 960, "state_shape": [960, 2560], "captured_layer": 34, "unparsed_completions": 0}) + statistics_path = OUT / "statistics.json" + if statistics_path.exists(): + statistics = json.loads(statistics_path.read_text()) + require(statistics["analysis_sha256"] == protocol["inputs"]["analyzer"]["sha256"], "statistics analyzer hash mismatch") + pairs = pd.read_csv(OUT / "matched_pair_contrasts.csv") + law_rows = pd.read_csv(OUT / "law_level_results.csv") + behavior = pd.read_csv(OUT / "behavior_rows.csv") + require(len(pairs) == 480, "matched-pair output count is not 480") + require(len(law_rows) == 60, "law output count is not 60") + require(len(behavior) == 960, "behavior output count is not 960") + checks.update({"matched_pair_rows": 480, "law_result_rows": 60, "behavior_rows": 960}) + status = "complete-pass" + + output = {"study_id": protocol["study_id"], "status": status, "checks": checks} + target = OUT / ("validation.json" if status == "complete-pass" else "validation_preflight.json") + target.write_text(json.dumps(output, indent=2, sort_keys=True) + "\n") + print(json.dumps(output, indent=2, sort_keys=True)) + + +if __name__ == "__main__": + main() diff --git a/jlens_materials/scripts/validate_equation_free_relational_pilot.py b/jlens_materials/scripts/validate_equation_free_relational_pilot.py new file mode 100644 index 0000000000000000000000000000000000000000..18cbd0cb02bba667639207d35603b4223e80b65c --- /dev/null +++ b/jlens_materials/scripts/validate_equation_free_relational_pilot.py @@ -0,0 +1,95 @@ +#!/usr/bin/env python3 +"""Validate the complete equation-free relational pilot artifact bundle.""" + +from __future__ import annotations + +import hashlib +import json +from pathlib import Path + +import numpy as np +import pandas as pd + + +ROOT = Path(__file__).resolve().parents[1] +OUT = ROOT / "experiments" / "equation-free-relational-pilot-2026-08-21" + + +def sha256(path: Path) -> str: + return hashlib.sha256(path.read_bytes()).hexdigest() + + +def require(condition: bool, message: str) -> None: + if not condition: + raise RuntimeError(message) + + +def main() -> None: + protocol_path = OUT / "protocol.json" + manifest_path = OUT / "prompt_manifest.json" + raw_path = OUT / "raw.json" + protocol = json.loads(protocol_path.read_text()) + manifest = json.loads(manifest_path.read_text()) + raw = json.loads(raw_path.read_text()) + statistics = json.loads((OUT / "statistics.json").read_text()) + + for name, record in protocol["inputs"].items(): + path = ROOT / record["path"] + require(path.exists(), f"missing frozen input {name}: {path}") + require(sha256(path) == record["sha256"], f"frozen input hash mismatch: {name}") + require(raw["provenance"]["protocol_sha256"] == sha256(protocol_path), "raw protocol hash mismatch") + require(raw["provenance"]["manifest_sha256"] == sha256(manifest_path), "raw manifest hash mismatch") + state_path = OUT / "representations.npz" + require(raw["provenance"]["states_sha256"] == sha256(state_path), "raw state hash mismatch") + require(statistics["analysis_sha256"] == protocol["inputs"]["analyzer"]["sha256"], "statistics analyzer hash mismatch") + + prompts = manifest["prompts"] + require(len(prompts) == 192, "prompt count is not 192") + require(len({row["prompt_id"] for row in prompts}) == 192, "prompt ids are not unique") + counts = {category: sum(row["category"] == category for row in prompts) for category in ("direct", "inverse", "neutral")} + require(counts == {"direct": 64, "inverse": 64, "neutral": 64}, f"unbalanced prompt categories: {counts}") + forbidden = ("constitutive", "equation", "stage 1", "stage 2", "silently compose", " = ") + require(not any(term in row["user"].lower() for row in prompts for term in forbidden), "forbidden equation/scaffold text appears in a prompt") + require(all(row["physical_sign"] == row["law_sign"] * row["numerical_sign"] for row in prompts), "physical signs do not equal law sign times numerical sign") + + manifest_ids = [row["prompt_id"] for row in prompts] + raw_ids = [row["prompt_id"] for row in raw["clean_rows"]] + require(raw_ids == manifest_ids, "raw rows do not preserve manifest order") + require(len(raw["clean_rows"]) == 192, "raw row count is not 192") + require(all(row["predicted_answer"] in {"higher", "lower", "unchanged"} for row in raw["clean_rows"]), "one or more greedy completions are unparsed") + with np.load(state_path, allow_pickle=False) as arrays: + require(arrays["raw_states"].shape == (192, 2560), "unexpected state-array shape") + require(arrays["prompt_ids"].astype(str).tolist() == manifest_ids, "state ids do not preserve manifest order") + require(int(arrays["layer"][0]) == 34, "captured layer is not 34") + + pairs = pd.read_csv(OUT / "matched_pair_contrasts.csv") + laws = pd.read_csv(OUT / "law_level_results.csv") + behavior = pd.read_csv(OUT / "behavior_rows.csv") + require(len(pairs) == 96, "matched-pair count is not 96") + require(len(laws) == 12, "law-level row count is not 12") + require(len(behavior) == 192, "behavior row count is not 192") + require((pairs.groupby("law_id").size() == 8).all(), "a law does not contain eight matched pairs") + require((behavior.groupby("law_id").size() == 16).all(), "a law does not contain 16 behavior rows") + + validation = { + "study_id": protocol["study_id"], + "status": "pass", + "checks": { + "frozen_input_hashes": "pass", + "raw_provenance_hashes": "pass", + "prompt_count_and_balance": counts, + "no_equation_or_decomposition_scaffold": "pass", + "raw_rows": len(raw["clean_rows"]), + "state_shape": [192, 2560], + "captured_layer": 34, + "matched_pairs": len(pairs), + "law_rows": len(laws), + "unparsed_completions": 0, + }, + } + (OUT / "validation.json").write_text(json.dumps(validation, indent=2, sort_keys=True) + "\n") + print(json.dumps(validation, indent=2, sort_keys=True)) + + +if __name__ == "__main__": + main() diff --git a/jlens_materials/scripts/validate_gemma31b_goal_specific_causal_roles.py b/jlens_materials/scripts/validate_gemma31b_goal_specific_causal_roles.py new file mode 100644 index 0000000000000000000000000000000000000000..9f241c939670f68b5d7f71dbb2d096f2a1fbf109 --- /dev/null +++ b/jlens_materials/scripts/validate_gemma31b_goal_specific_causal_roles.py @@ -0,0 +1,151 @@ +#!/usr/bin/env python3 +"""Validate the prospective 31B goal-specific causal-role experiment bundle.""" + +from __future__ import annotations + +import json +from pathlib import Path + +import numpy as np + + +ROOT = Path(__file__).resolve().parents[1] +OUT = ROOT / "experiments" / "gemma4-31b-goal-specific-causal-roles-2026-09-02" +DISCOVERY = ROOT / "experiments" / "gemma4-31b-query-gated-physics-2026-08-25" + + +def require(condition: bool, message: str) -> None: + if not condition: + raise RuntimeError(message) + + +def read_jsonl(path: Path) -> list[dict]: + return [json.loads(line) for line in path.read_text().splitlines() if line.strip()] + + +def main() -> None: + protocol = json.loads((OUT / "protocol.json").read_text()) + manifest = json.loads((OUT / "prompt_manifest.json").read_text()) + discovery = json.loads((DISCOVERY / "prompt_manifest.json").read_text()) + prompts = manifest["prompts"] + require(protocol["status"] == "frozen-before-new-target-output", "protocol status changed") + require(manifest["status"] == "frozen-before-new-target-output", "manifest status changed") + require(len(prompts) == 288, "expected 288 exact prompts") + require(len({row["prompt_id"] for row in prompts}) == 288, "duplicate prompt ids") + chains = {row["chain_id"] for row in prompts} + development_chains = {row["chain_id"] for row in discovery["prompts"]} + require(len(chains) == 24, "expected 24 prospective chains") + require(not chains.intersection(development_chains), "target/development chain leakage") + cells = {} + for chain_id in chains: + row = next(item for item in prompts if item["chain_id"] == chain_id) + cells[(row["link1_sign"], row["link2_sign"])] = cells.get((row["link1_sign"], row["link2_sign"]), 0) + 1 + require(set(cells.values()) == {6}, f"unbalanced link-sign cells: {cells}") + by_id = {row["prompt_id"]: row for row in prompts} + for row in prompts: + counterpart = by_id[row["counterpart_prompt_id"]] + require(counterpart["chain_id"] == row["chain_id"], "counterpart chain mismatch") + require(counterpart["surface"] == row["surface"], "counterpart surface mismatch") + require(counterpart["question_type"] == row["question_type"], "counterpart role mismatch") + require(counterpart["numerical_sign"] == -row["numerical_sign"], "counterpart sign mismatch") + require(counterpart["answer_words"] == row["answer_words"], "counterpart answer order mismatch") + require("=" not in row["user"], "equation-like symbol found in prompt") + require("multiply" not in row["user"].lower(), "sign-composition instruction found") + + payload = { + "study_id": protocol["study_id"], + "manifest": { + "valid": True, + "prompts": len(prompts), + "target_chains": len(chains), + "development_chains": len(development_chains), + "target_development_overlap": 0, + "link_sign_counts": {str(key): value for key, value in sorted(cells.items())}, + }, + "outputs_present": False, + } + required_outputs = [ + OUT / "run_record.json", + OUT / "clean_rows.jsonl", + OUT / "intervention_rows.jsonl", + OUT / "clean_states.npy", + OUT / "frozen_role_directions.npz", + ] + if all(path.exists() for path in required_outputs): + record = json.loads((OUT / "run_record.json").read_text()) + clean = read_jsonl(OUT / "clean_rows.jsonl") + interventions = read_jsonl(OUT / "intervention_rows.jsonl") + require(record["complete"], "run record is incomplete") + require(len(clean) == 288, "clean row count mismatch") + require(len(interventions) == 288 * 46, "intervention row count mismatch") + require(len({(row["prompt_id"], row["intervention"]) for row in interventions}) == len(interventions), "duplicate intervention rows") + states = np.load(OUT / "clean_states.npy", mmap_mode="r") + require(states.shape == (288, 5376), f"state shape mismatch: {states.shape}") + directions = np.load(OUT / "frozen_role_directions.npz", allow_pickle=False) + axes = directions["axes"].astype(np.float64) + controls = directions["control_axes"].astype(np.float64) + require(axes.shape == (2, 5376), "role-axis shape mismatch") + require(controls.shape == (4, 5376), "control-axis shape mismatch") + control_role = np.einsum("id,jd->ij", controls, axes, optimize=True) + control_gram = np.einsum("id,jd->ij", controls, controls, optimize=True) + require(np.max(np.abs(control_role)) < 3e-6, "controls overlap role span") + require(np.max(np.abs(control_gram - np.eye(4))) < 3e-6, "controls not orthonormal") + by_intervention = {(row["prompt_id"], row["intervention"]): row for row in interventions} + max_distance_error = 0.0 + max_other_role_distance_error = 0.0 + for prompt in prompts: + for role in ("target", "intermediate"): + for operation in ("neutralize", "counterfactual"): + physical = by_intervention[(prompt["prompt_id"], f"{role}_{operation}")] + target_distance = float(physical["replacement_distance"]) + for control_index in range(4): + for orientation in ("plus", "minus"): + control = by_intervention[( + prompt["prompt_id"], + f"{role}_{operation}_control_{control_index}_{orientation}", + )] + max_distance_error = max( + max_distance_error, + abs(float(control["replacement_distance"]) - target_distance), + ) + if prompt["question_type"] in {"target", "intermediate"}: + own_role = prompt["question_type"] + for operation in ("neutralize", "counterfactual"): + own_distance = float(by_intervention[( + prompt["prompt_id"], f"{own_role}_{operation}" + )]["replacement_distance"]) + other_distance = float(by_intervention[( + prompt["prompt_id"], f"matched_other_{operation}" + )]["replacement_distance"]) + max_other_role_distance_error = max( + max_other_role_distance_error, abs(own_distance - other_distance) + ) + own_restore = float(by_intervention[( + prompt["prompt_id"], f"full_counterfactual_{own_role}_restored" + )]["replacement_distance"]) + other_restore = float(by_intervention[( + prompt["prompt_id"], "full_counterfactual_matched_other_restored" + )]["replacement_distance"]) + max_other_role_distance_error = max( + max_other_role_distance_error, abs(own_restore - other_restore) + ) + require(max_distance_error < 1e-9, f"control distance mismatch: {max_distance_error}") + require(max_other_role_distance_error < 1e-9, f"other-role dose mismatch: {max_other_role_distance_error}") + payload.update({ + "outputs_present": True, + "run_complete": True, + "clean_rows": len(clean), + "intervention_rows": len(interventions), + "state_shape": list(states.shape), + "direction_cosine": float(np.einsum("i,i->", axes[0], axes[1])), + "max_control_role_dot": float(np.max(np.abs(control_role))), + "max_control_gram_error": float(np.max(np.abs(control_gram - np.eye(4)))), + "max_distance_match_error": max_distance_error, + "max_other_role_distance_match_error": max_other_role_distance_error, + }) + (OUT / "validation.json").write_text(json.dumps(payload, indent=2) + "\n") + print(json.dumps(payload, indent=2)) + + +if __name__ == "__main__": + main() diff --git a/jlens_materials/swap.py b/jlens_materials/swap.py new file mode 100644 index 0000000000000000000000000000000000000000..7ab06b337b49445f6453ac2b0cdfa3a65eea31d6 --- /dev/null +++ b/jlens_materials/swap.py @@ -0,0 +1,330 @@ +# Copyright 2026. Apache-2.0. +"""Causal J-lens coordinate swaps (paper Figure 4C / verbal-report swap). + +The read-out figures show what an activation is *disposed* to say. This module +does the causal half: it edits the residual stream along J-lens directions and +checks whether the model's *output* changes accordingly. + +J-lens direction for token ``t`` at layer ``l`` +----------------------------------------------- +A layer-``l`` residual ``h`` produces the pre-softmax lens logit for token ``t`` +as `` = ``. So the residual-space direction +that carries token ``t``'s J-lens coordinate is:: + + v_{l,t} = normalize(J_l^T @ W_U[t]) + +(the final-norm Jacobian is folded in only approximately — the same +approximation the paper's steering uses: "the unit-normalized transpose row for +that token"). + +Two interventions +----------------- +* ``projection`` : the original approximation in this repo, + ``h += (alpha * v_t - v_s)``. This is exact only when the source and + target vectors are effectively orthogonal. +* ``pinv`` : the paper-style coordinate swap. Form + ``V = [v_s, v_t]``, solve ``c = V† h`` for the source/target coordinates, set + ``c' = [c_t, alpha * c_s]``, and patch ``h' = h + V(c' - c)``. The component + of ``h`` orthogonal to ``span(v_s, v_t)`` is unchanged. + +``run_swap`` reports the model's next-token distribution at the final position +with and without the swap. Rank movement is retained as a diagnostic, but a +paper-style causal success requires a correct clean answer and the declared +counterfactual answer after intervention. + + python swap.py --model Qwen/Qwen2.5-0.5B-Instruct --lens lens_qwen2forcausallm.pt \ + --prompt "Think of a metal failure mode. Answer in one word." \ + --source fatigue --target creep +""" + +from __future__ import annotations + +import argparse +import sys +from pathlib import Path + +import torch + +# use the vendored jlens (committed under _vendor_jlens/) without installing +_REPO = Path(__file__).resolve().parent / "_vendor_jlens" +if (_REPO / "jlens").is_dir(): + sys.path.insert(0, str(_REPO)) +import jlens # noqa: E402 +from jlens.hooks import ActivationRecorder # noqa: E402 + + +def band_layers(lens, n_layers: int, lo: float = 0.38, hi: float = 0.92) -> list[int]: + """Fitted layers in a preregistered band (fractions or depth percentages).""" + if lo <= 1 and hi <= 1: + lo, hi = lo * 100.0, hi * 100.0 + if not 0 <= lo <= hi <= 100: + raise ValueError(f"invalid workspace band {(lo, hi)}") + denom = max(n_layers - 1, 1) + return [layer for layer in lens.source_layers + if lo <= 100.0 * layer / denom <= hi] + + +def jlens_direction(lens, model, layer: int, token_id: int) -> torch.Tensor: + """Unit residual-space direction carrying token ``token_id``'s J-lens + coordinate at ``layer``: ``normalize(J_l^T @ W_U[token_id])``.""" + W_U = model._lm_head.weight # [vocab, d_model] + w_t = W_U[token_id].float().to("cpu") # [d_model] + J = lens.jacobians[layer].float() # [d_model, d_model] + v = J.T @ w_t # J_l^T W_U[t] + return (v / (v.norm() + 1e-8)) + + +def _single_token(tokenizer, word: str) -> int: + for f in (" " + word, word, " " + word.capitalize(), word.capitalize()): + ids = tokenizer.encode(f, add_special_tokens=False) + if len(ids) == 1: + return ids[0] + raise ValueError(f"{word!r} is not a single token for this model (paper §9.1)") + + +def _answer_token_ids(tokenizer, answer: str | None) -> set[int]: + if not answer: + return set() + ids = set() + for form in (" " + answer, answer, " " + answer.capitalize(), answer.capitalize(), + " " + answer.lower(), answer.lower()): + encoded = tokenizer.encode(form, add_special_tokens=False) + if len(encoded) == 1: + ids.add(int(encoded[0])) + return ids + + +class SwapHook: + """Forward hooks on band layers that apply a J-lens coordinate swap to the + residual at the given positions on the next forward pass.""" + + def __init__(self, model, lens, layers, positions, v_by_layer, alpha, + method: str): + self._blocks = model.layers + self._layers = layers + self._positions = positions + self._v = v_by_layer # {layer: (v_source, v_target)} on the block's device + self._alpha = alpha + self._method = method + self._handles = [] + + def _make(self, layer): + v_s, v_t, V_cpu, V_pinv_cpu = self._v[layer] + + def hook(module, inputs, output): + tensor = output if torch.is_tensor(output) else output[0] + dev, dt = tensor.device, tensor.dtype + h = tensor[:, self._positions, :] # [b, P, d] + if self._method == "projection": + vs = v_s.to(dev, dt) + vt = v_t.to(dev, dt) + coord = (h * vs).sum(-1, keepdim=True) # + delta = coord * (self._alpha * vt - vs) # swap s -> t + else: + V = V_cpu.to(dev, torch.float32) # [d,2] + V_pinv = V_pinv_cpu.to(dev, torch.float32) # [2,d] + h_f = h.float() + coords = h_f @ V_pinv.T # [b,P,2] + swapped = torch.stack( + (coords[..., 1], self._alpha * coords[..., 0]), dim=-1 + ) + delta = (swapped - coords) @ V.T # [b,P,d] + delta = delta.to(dt) + tensor[:, self._positions, :] = h + delta + return output + + return hook + + def __enter__(self): + for layer in self._layers: + self._handles.append( + self._blocks[layer].register_forward_hook(self._make(layer))) + return self + + def __exit__(self, *exc): + for h in self._handles: + h.remove() + self._handles = [] + + +@torch.no_grad() +def run_swap( + model, + lens, + prompt: str, + source: str, + target: str, + *, + alpha: float = 1.0, + top: int = 8, + band=(0.38, 0.92), + method: str = "pinv", + positions: list[int] | None = None, + expected_answer: str | None = None, + acceptable_answers: tuple[str, ...] = (), + swap_answer: str | None = None, + source_token_id: int | None = None, +) -> dict: + """Apply a coordinate swap and grade its declared causal outcome. + + Omitting ``source``/``source_token_id`` implements the verbal-report + protocol: the model's clean greedy next token becomes the swap source. + Probe-swap items should provide both ``expected_answer`` and + ``swap_answer``; ``causal_success`` is true only when both outcomes match. + """ + if method not in {"pinv", "projection"}: + raise ValueError("method must be 'pinv' or 'projection'") + tok = model.tokenizer + t_id = _single_token(tok, target) + layers = band_layers(lens, model.n_layers, *band) + if not layers: + raise ValueError("no fitted layers in the requested band") + + input_ids = model.encode(prompt, max_length=256) + seq_len = input_ids.shape[1] + positions = list(range(seq_len)) if positions is None else list(positions) + if not positions or any(pos < 0 or pos >= seq_len for pos in positions): + raise ValueError(f"swap positions must lie in [0, {seq_len}); got {positions}") + + def final_probs() -> torch.Tensor: + with ActivationRecorder(model.layers, at=[model.n_layers - 1]) as rec: + model.forward(input_ids) + resid = rec.activations[model.n_layers - 1][0, -1:].float() + return model.unembed(resid).float()[0].softmax(-1) + + clean = final_probs() + dynamic_source = source_token_id is None and not source + if source_token_id is None and source: + s_id = _single_token(tok, source) + elif source_token_id is not None: + s_id = int(source_token_id) + else: + s_id = int(clean.argmax()) + source = tok.decode([s_id], clean_up_tokenization_spaces=False).strip() + if s_id == t_id: + raise ValueError("source and target resolve to the same token") + + def mats_for(layer): + v_s = jlens_direction(lens, model, layer, s_id) + v_t = jlens_direction(lens, model, layer, t_id) + V = torch.stack([v_s, v_t], dim=1).float() + return v_s, v_t, V, torch.linalg.pinv(V) + + v_by_layer = {layer: mats_for(layer) for layer in layers} + + def topk(probs): + idx = probs.topk(top).indices + return [(tok.decode([int(i)]).strip(), float(probs[int(i)])) for i in idx] + + def rank_of(probs, token_id): + return int((probs > probs[token_id]).sum()) + + with SwapHook(model, lens, layers, positions, v_by_layer, alpha, method): + swapped = final_probs() + + clean_answer_ids = set() + for answer in (expected_answer, *acceptable_answers): + clean_answer_ids.update(_answer_token_ids(tok, answer)) + swap_answer_ids = _answer_token_ids(tok, swap_answer) + clean_top_id, swapped_top_id = int(clean.argmax()), int(swapped.argmax()) + baseline_correct = (clean_top_id in clean_answer_ids + if clean_answer_ids else None) + counterfactual_correct = (swapped_top_id in swap_answer_ids + if swap_answer_ids else None) + causal_success = ( + bool(baseline_correct and counterfactual_correct) + if baseline_correct is not None and counterfactual_correct is not None + else None + ) + target_top1 = swapped_top_id == t_id + protocol_success = causal_success if causal_success is not None else ( + target_top1 if dynamic_source else None + ) + + return { + "prompt": prompt, + "source": source, + "target": target, + "alpha": alpha, + "method": method, + "band_layers": layers, + "swap_positions": positions, + "clean_top": topk(clean), + "swapped_top": topk(swapped), + "source_rank_clean": rank_of(clean, s_id), + "source_rank_swapped": rank_of(swapped, s_id), + "target_rank_clean": rank_of(clean, t_id), + "target_rank_swapped": rank_of(swapped, t_id), + "expected_answer": expected_answer, + "swap_answer": swap_answer, + "baseline_correct": baseline_correct, + "counterfactual_correct": counterfactual_correct, + "causal_success": causal_success, + "target_top1": target_top1, + "protocol_success": protocol_success, + "dynamic_source": dynamic_source, + } + + +_DTYPES = {"float32": torch.float32, + "bfloat16": torch.bfloat16, + "float16": torch.float16} + + +def _load(model_name, lens_path, dtype=torch.float32): + import transformers + dev = ("cuda" if torch.cuda.is_available() + else "mps" if torch.backends.mps.is_available() else "cpu") + hf = transformers.AutoModelForCausalLM.from_pretrained( + model_name, dtype=dtype).to(dev) + tok = transformers.AutoTokenizer.from_pretrained(model_name) + model = jlens.from_hf(hf, tok) + lens = jlens.JacobianLens.load(lens_path) + return model, lens + + +def main() -> None: + ap = argparse.ArgumentParser() + ap.add_argument("--model", required=True) + ap.add_argument("--lens", required=True) + ap.add_argument("--prompt", required=True) + ap.add_argument("--source", default=None, + help="swap-out token; omit to use the clean greedy answer") + ap.add_argument("--target", required=True) + ap.add_argument("--alpha", type=float, default=1.0) + ap.add_argument("--answer", default=None, help="expected clean answer") + ap.add_argument("--swap-answer", default=None, + help="expected counterfactual answer after the swap") + ap.add_argument("--workspace-band", default="38,92", metavar="LO,HI") + ap.add_argument("--method", choices=["pinv", "projection"], default="pinv") + ap.add_argument("--dtype", choices=list(_DTYPES), default="float32", + help="model weight dtype (default float32; bfloat16 ~halves " + "memory on GPU). The lens .pt is loaded in float32 " + "regardless.") + args = ap.parse_args() + model, lens = _load(args.model, args.lens, dtype=_DTYPES[args.dtype]) + band = tuple(float(x) for x in args.workspace_band.split(",")) + if len(band) != 2: + raise SystemExit("--workspace-band must be LO,HI") + out = run_swap(model, lens, args.prompt, args.source or "", args.target, + alpha=args.alpha, method=args.method, band=band, + expected_answer=args.answer, swap_answer=args.swap_answer) + print(f"\nPrompt: {out['prompt']!r}") + print(f"Swap J-lens coordinate: {out['source']} -> {out['target']} " + f"(alpha={out['alpha']}, method={out['method']}) across band layers " + f"{out['band_layers']}\n") + print(f"{'CLEAN next-token':<34}{'AFTER SWAP'}") + for (cw, cp), (sw, sp) in zip(out["clean_top"], out["swapped_top"]): + print(f" {cw:<20}{cp:>6.3f} {sw:<20}{sp:>6.3f}") + print(f"\n source '{out['source']}': rank {out['source_rank_clean']+1} " + f"-> {out['source_rank_swapped']+1} (higher = more suppressed)") + print(f" target '{out['target']}': rank {out['target_rank_clean']+1} " + f"-> {out['target_rank_swapped']+1} (lower = more installed)") + if out["causal_success"] is not None: + print(f" clean answer correct: {out['baseline_correct']}; " + f"counterfactual correct: {out['counterfactual_correct']}; " + f"causal success: {out['causal_success']}") + + +if __name__ == "__main__": + main() diff --git a/paper/causal_relational_knockout_data/CONTROL_AMENDMENT.md b/paper/causal_relational_knockout_data/CONTROL_AMENDMENT.md new file mode 100644 index 0000000000000000000000000000000000000000..423513cb4d875f7b5538a17b4860ee2166e7067e --- /dev/null +++ b/paper/causal_relational_knockout_data/CONTROL_AMENDMENT.md @@ -0,0 +1,9 @@ +# Frozen amendment to the matched-direction control + +This amendment was written at 2026-09-02 11:49:56 UTC, before any output from the amended controls. It changes only the specificity control; the frozen prompts, model revisions, layers, physical directions, primary interventions, endpoints, and inferential unit remain unchanged. + +The original three isotropic Gaussian axes were a poor control at 12B: equal-length perturbations along those axes caused severe nonspecific changes to the complete next-token distribution. A control that leaves the model's activation manifold is not a fair comparator for a direction estimated from real hidden-state transformations. + +The amended control therefore uses real development-set transformations. Within every matched development pair, we subtract the unit-normalized negative-outcome state from the positive-outcome state. For each of the three prespecified seeds, balanced random signs are assigned to these paired differences before averaging. The common physical direction and any earlier control axes are removed, and the result is normalized. This produces three mutually orthogonal, activation-derived null axes that retain the scale and covariance structure of observed state changes but remove their shared physical orientation. + +For every target prompt, both the positive and negative orientation of every null axis receive exactly the L2 length of the corresponding physical intervention. The six oriented controls are averaged within prompt before law-level analysis. Pairing the two orientations cancels first-order arbitrary-direction effects while retaining nonlinear or generic disruption. Superseded control rows remain archived in labeled debug directories and are excluded from the final summaries. diff --git a/paper/causal_relational_knockout_data/PROMPTS.md b/paper/causal_relational_knockout_data/PROMPTS.md new file mode 100644 index 0000000000000000000000000000000000000000..dd7dcea4139c100807238622ddc3c1e1ff9ec331 --- /dev/null +++ b/paper/causal_relational_knockout_data/PROMPTS.md @@ -0,0 +1,1540 @@ +# Exact prompts for the causal relational knockout study + +All prompts below were written before any intervention output was examined. +No equation, law name, direct/inverse label, or sign-composition instruction is shown to the model. + +## `crk24--elastic-strain-tensile-stress--n-1--higher-lower--standard` + +- Law identity: `elastic-strain-tensile-stress` +- Registered category: `direct` +- Expected answer: `lower` +- Matched counterfactual: `crk24--elastic-strain-tensile-stress--n+1--higher-lower--standard` +- Prompt: For a uniaxially loaded titanium coupon in its linear-elastic regime, all other relevant physical conditions are unchanged. The tensile stress changes from 240 MPa to 40 MPa. What happens to elastic axial strain? Reply with exactly one lowercase word from this ordered set: higher, lower. + +## `crk24--elastic-strain-tensile-stress--n-1--higher-lower--reversed` + +- Law identity: `elastic-strain-tensile-stress` +- Registered category: `direct` +- Expected answer: `lower` +- Matched counterfactual: `crk24--elastic-strain-tensile-stress--n+1--higher-lower--reversed` +- Prompt: For a uniaxially loaded titanium coupon in its linear-elastic regime, all other relevant physical conditions are unchanged. The tensile stress changes from 240 MPa to 40 MPa. What happens to elastic axial strain? Reply with exactly one lowercase word from this ordered set: lower, higher. + +## `crk24--elastic-strain-tensile-stress--n-1--greater-smaller--standard` + +- Law identity: `elastic-strain-tensile-stress` +- Registered category: `direct` +- Expected answer: `smaller` +- Matched counterfactual: `crk24--elastic-strain-tensile-stress--n+1--greater-smaller--standard` +- Prompt: For a uniaxially loaded titanium coupon in its linear-elastic regime, all other relevant physical conditions are unchanged. The tensile stress changes from 240 MPa to 40 MPa. What happens to elastic axial strain? Reply with exactly one lowercase word from this ordered set: greater, smaller. + +## `crk24--elastic-strain-tensile-stress--n-1--greater-smaller--reversed` + +- Law identity: `elastic-strain-tensile-stress` +- Registered category: `direct` +- Expected answer: `smaller` +- Matched counterfactual: `crk24--elastic-strain-tensile-stress--n+1--greater-smaller--reversed` +- Prompt: For a uniaxially loaded titanium coupon in its linear-elastic regime, all other relevant physical conditions are unchanged. The tensile stress changes from 240 MPa to 40 MPa. What happens to elastic axial strain? Reply with exactly one lowercase word from this ordered set: smaller, greater. + +## `crk24--elastic-strain-tensile-stress--n+1--higher-lower--standard` + +- Law identity: `elastic-strain-tensile-stress` +- Registered category: `direct` +- Expected answer: `higher` +- Matched counterfactual: `crk24--elastic-strain-tensile-stress--n-1--higher-lower--standard` +- Prompt: For a uniaxially loaded titanium coupon in its linear-elastic regime, all other relevant physical conditions are unchanged. The tensile stress changes from 40 MPa to 240 MPa. What happens to elastic axial strain? Reply with exactly one lowercase word from this ordered set: higher, lower. + +## `crk24--elastic-strain-tensile-stress--n+1--higher-lower--reversed` + +- Law identity: `elastic-strain-tensile-stress` +- Registered category: `direct` +- Expected answer: `higher` +- Matched counterfactual: `crk24--elastic-strain-tensile-stress--n-1--higher-lower--reversed` +- Prompt: For a uniaxially loaded titanium coupon in its linear-elastic regime, all other relevant physical conditions are unchanged. The tensile stress changes from 40 MPa to 240 MPa. What happens to elastic axial strain? Reply with exactly one lowercase word from this ordered set: lower, higher. + +## `crk24--elastic-strain-tensile-stress--n+1--greater-smaller--standard` + +- Law identity: `elastic-strain-tensile-stress` +- Registered category: `direct` +- Expected answer: `greater` +- Matched counterfactual: `crk24--elastic-strain-tensile-stress--n-1--greater-smaller--standard` +- Prompt: For a uniaxially loaded titanium coupon in its linear-elastic regime, all other relevant physical conditions are unchanged. The tensile stress changes from 40 MPa to 240 MPa. What happens to elastic axial strain? Reply with exactly one lowercase word from this ordered set: greater, smaller. + +## `crk24--elastic-strain-tensile-stress--n+1--greater-smaller--reversed` + +- Law identity: `elastic-strain-tensile-stress` +- Registered category: `direct` +- Expected answer: `greater` +- Matched counterfactual: `crk24--elastic-strain-tensile-stress--n-1--greater-smaller--reversed` +- Prompt: For a uniaxially loaded titanium coupon in its linear-elastic regime, all other relevant physical conditions are unchanged. The tensile stress changes from 40 MPa to 240 MPa. What happens to elastic axial strain? Reply with exactly one lowercase word from this ordered set: smaller, greater. + +## `crk24--thermal-strain-temperature-rise--n-1--higher-lower--standard` + +- Law identity: `thermal-strain-temperature-rise` +- Registered category: `direct` +- Expected answer: `lower` +- Matched counterfactual: `crk24--thermal-strain-temperature-rise--n+1--higher-lower--standard` +- Prompt: For a freely expanding nickel strip with fixed thermal-expansion coefficient, all other relevant physical conditions are unchanged. The temperature rise above its reference state changes from 180 kelvin to 20 kelvin. What happens to thermal strain? Reply with exactly one lowercase word from this ordered set: higher, lower. + +## `crk24--thermal-strain-temperature-rise--n-1--higher-lower--reversed` + +- Law identity: `thermal-strain-temperature-rise` +- Registered category: `direct` +- Expected answer: `lower` +- Matched counterfactual: `crk24--thermal-strain-temperature-rise--n+1--higher-lower--reversed` +- Prompt: For a freely expanding nickel strip with fixed thermal-expansion coefficient, all other relevant physical conditions are unchanged. The temperature rise above its reference state changes from 180 kelvin to 20 kelvin. What happens to thermal strain? Reply with exactly one lowercase word from this ordered set: lower, higher. + +## `crk24--thermal-strain-temperature-rise--n-1--greater-smaller--standard` + +- Law identity: `thermal-strain-temperature-rise` +- Registered category: `direct` +- Expected answer: `smaller` +- Matched counterfactual: `crk24--thermal-strain-temperature-rise--n+1--greater-smaller--standard` +- Prompt: For a freely expanding nickel strip with fixed thermal-expansion coefficient, all other relevant physical conditions are unchanged. The temperature rise above its reference state changes from 180 kelvin to 20 kelvin. What happens to thermal strain? Reply with exactly one lowercase word from this ordered set: greater, smaller. + +## `crk24--thermal-strain-temperature-rise--n-1--greater-smaller--reversed` + +- Law identity: `thermal-strain-temperature-rise` +- Registered category: `direct` +- Expected answer: `smaller` +- Matched counterfactual: `crk24--thermal-strain-temperature-rise--n+1--greater-smaller--reversed` +- Prompt: For a freely expanding nickel strip with fixed thermal-expansion coefficient, all other relevant physical conditions are unchanged. The temperature rise above its reference state changes from 180 kelvin to 20 kelvin. What happens to thermal strain? Reply with exactly one lowercase word from this ordered set: smaller, greater. + +## `crk24--thermal-strain-temperature-rise--n+1--higher-lower--standard` + +- Law identity: `thermal-strain-temperature-rise` +- Registered category: `direct` +- Expected answer: `higher` +- Matched counterfactual: `crk24--thermal-strain-temperature-rise--n-1--higher-lower--standard` +- Prompt: For a freely expanding nickel strip with fixed thermal-expansion coefficient, all other relevant physical conditions are unchanged. The temperature rise above its reference state changes from 20 kelvin to 180 kelvin. What happens to thermal strain? Reply with exactly one lowercase word from this ordered set: higher, lower. + +## `crk24--thermal-strain-temperature-rise--n+1--higher-lower--reversed` + +- Law identity: `thermal-strain-temperature-rise` +- Registered category: `direct` +- Expected answer: `higher` +- Matched counterfactual: `crk24--thermal-strain-temperature-rise--n-1--higher-lower--reversed` +- Prompt: For a freely expanding nickel strip with fixed thermal-expansion coefficient, all other relevant physical conditions are unchanged. The temperature rise above its reference state changes from 20 kelvin to 180 kelvin. What happens to thermal strain? Reply with exactly one lowercase word from this ordered set: lower, higher. + +## `crk24--thermal-strain-temperature-rise--n+1--greater-smaller--standard` + +- Law identity: `thermal-strain-temperature-rise` +- Registered category: `direct` +- Expected answer: `greater` +- Matched counterfactual: `crk24--thermal-strain-temperature-rise--n-1--greater-smaller--standard` +- Prompt: For a freely expanding nickel strip with fixed thermal-expansion coefficient, all other relevant physical conditions are unchanged. The temperature rise above its reference state changes from 20 kelvin to 180 kelvin. What happens to thermal strain? Reply with exactly one lowercase word from this ordered set: greater, smaller. + +## `crk24--thermal-strain-temperature-rise--n+1--greater-smaller--reversed` + +- Law identity: `thermal-strain-temperature-rise` +- Registered category: `direct` +- Expected answer: `greater` +- Matched counterfactual: `crk24--thermal-strain-temperature-rise--n-1--greater-smaller--reversed` +- Prompt: For a freely expanding nickel strip with fixed thermal-expansion coefficient, all other relevant physical conditions are unchanged. The temperature rise above its reference state changes from 20 kelvin to 180 kelvin. What happens to thermal strain? Reply with exactly one lowercase word from this ordered set: smaller, greater. + +## `crk24--heat-flux-temperature-gradient--n-1--higher-lower--standard` + +- Law identity: `heat-flux-temperature-gradient` +- Registered category: `direct` +- Expected answer: `lower` +- Matched counterfactual: `crk24--heat-flux-temperature-gradient--n+1--higher-lower--standard` +- Prompt: For a homogeneous ceramic slab with constant thermal conductivity, all other relevant physical conditions are unchanged. The temperature-gradient magnitude changes from 45 kelvin per meter to 5 kelvin per meter. What happens to conductive heat-flux magnitude? Reply with exactly one lowercase word from this ordered set: higher, lower. + +## `crk24--heat-flux-temperature-gradient--n-1--higher-lower--reversed` + +- Law identity: `heat-flux-temperature-gradient` +- Registered category: `direct` +- Expected answer: `lower` +- Matched counterfactual: `crk24--heat-flux-temperature-gradient--n+1--higher-lower--reversed` +- Prompt: For a homogeneous ceramic slab with constant thermal conductivity, all other relevant physical conditions are unchanged. The temperature-gradient magnitude changes from 45 kelvin per meter to 5 kelvin per meter. What happens to conductive heat-flux magnitude? Reply with exactly one lowercase word from this ordered set: lower, higher. + +## `crk24--heat-flux-temperature-gradient--n-1--greater-smaller--standard` + +- Law identity: `heat-flux-temperature-gradient` +- Registered category: `direct` +- Expected answer: `smaller` +- Matched counterfactual: `crk24--heat-flux-temperature-gradient--n+1--greater-smaller--standard` +- Prompt: For a homogeneous ceramic slab with constant thermal conductivity, all other relevant physical conditions are unchanged. The temperature-gradient magnitude changes from 45 kelvin per meter to 5 kelvin per meter. What happens to conductive heat-flux magnitude? Reply with exactly one lowercase word from this ordered set: greater, smaller. + +## `crk24--heat-flux-temperature-gradient--n-1--greater-smaller--reversed` + +- Law identity: `heat-flux-temperature-gradient` +- Registered category: `direct` +- Expected answer: `smaller` +- Matched counterfactual: `crk24--heat-flux-temperature-gradient--n+1--greater-smaller--reversed` +- Prompt: For a homogeneous ceramic slab with constant thermal conductivity, all other relevant physical conditions are unchanged. The temperature-gradient magnitude changes from 45 kelvin per meter to 5 kelvin per meter. What happens to conductive heat-flux magnitude? Reply with exactly one lowercase word from this ordered set: smaller, greater. + +## `crk24--heat-flux-temperature-gradient--n+1--higher-lower--standard` + +- Law identity: `heat-flux-temperature-gradient` +- Registered category: `direct` +- Expected answer: `higher` +- Matched counterfactual: `crk24--heat-flux-temperature-gradient--n-1--higher-lower--standard` +- Prompt: For a homogeneous ceramic slab with constant thermal conductivity, all other relevant physical conditions are unchanged. The temperature-gradient magnitude changes from 5 kelvin per meter to 45 kelvin per meter. What happens to conductive heat-flux magnitude? Reply with exactly one lowercase word from this ordered set: higher, lower. + +## `crk24--heat-flux-temperature-gradient--n+1--higher-lower--reversed` + +- Law identity: `heat-flux-temperature-gradient` +- Registered category: `direct` +- Expected answer: `higher` +- Matched counterfactual: `crk24--heat-flux-temperature-gradient--n-1--higher-lower--reversed` +- Prompt: For a homogeneous ceramic slab with constant thermal conductivity, all other relevant physical conditions are unchanged. The temperature-gradient magnitude changes from 5 kelvin per meter to 45 kelvin per meter. What happens to conductive heat-flux magnitude? Reply with exactly one lowercase word from this ordered set: lower, higher. + +## `crk24--heat-flux-temperature-gradient--n+1--greater-smaller--standard` + +- Law identity: `heat-flux-temperature-gradient` +- Registered category: `direct` +- Expected answer: `greater` +- Matched counterfactual: `crk24--heat-flux-temperature-gradient--n-1--greater-smaller--standard` +- Prompt: For a homogeneous ceramic slab with constant thermal conductivity, all other relevant physical conditions are unchanged. The temperature-gradient magnitude changes from 5 kelvin per meter to 45 kelvin per meter. What happens to conductive heat-flux magnitude? Reply with exactly one lowercase word from this ordered set: greater, smaller. + +## `crk24--heat-flux-temperature-gradient--n+1--greater-smaller--reversed` + +- Law identity: `heat-flux-temperature-gradient` +- Registered category: `direct` +- Expected answer: `greater` +- Matched counterfactual: `crk24--heat-flux-temperature-gradient--n-1--greater-smaller--reversed` +- Prompt: For a homogeneous ceramic slab with constant thermal conductivity, all other relevant physical conditions are unchanged. The temperature-gradient magnitude changes from 5 kelvin per meter to 45 kelvin per meter. What happens to conductive heat-flux magnitude? Reply with exactly one lowercase word from this ordered set: smaller, greater. + +## `crk24--diffusion-flux-concentration-gradient--n-1--higher-lower--standard` + +- Law identity: `diffusion-flux-concentration-gradient` +- Registered category: `direct` +- Expected answer: `lower` +- Matched counterfactual: `crk24--diffusion-flux-concentration-gradient--n+1--higher-lower--standard` +- Prompt: For a single-phase alloy at fixed temperature with constant diffusivity, all other relevant physical conditions are unchanged. The concentration-gradient magnitude changes from 1.8 mole fraction per meter to 0.2 mole fraction per meter. What happens to diffusive solute-flux magnitude? Reply with exactly one lowercase word from this ordered set: higher, lower. + +## `crk24--diffusion-flux-concentration-gradient--n-1--higher-lower--reversed` + +- Law identity: `diffusion-flux-concentration-gradient` +- Registered category: `direct` +- Expected answer: `lower` +- Matched counterfactual: `crk24--diffusion-flux-concentration-gradient--n+1--higher-lower--reversed` +- Prompt: For a single-phase alloy at fixed temperature with constant diffusivity, all other relevant physical conditions are unchanged. The concentration-gradient magnitude changes from 1.8 mole fraction per meter to 0.2 mole fraction per meter. What happens to diffusive solute-flux magnitude? Reply with exactly one lowercase word from this ordered set: lower, higher. + +## `crk24--diffusion-flux-concentration-gradient--n-1--greater-smaller--standard` + +- Law identity: `diffusion-flux-concentration-gradient` +- Registered category: `direct` +- Expected answer: `smaller` +- Matched counterfactual: `crk24--diffusion-flux-concentration-gradient--n+1--greater-smaller--standard` +- Prompt: For a single-phase alloy at fixed temperature with constant diffusivity, all other relevant physical conditions are unchanged. The concentration-gradient magnitude changes from 1.8 mole fraction per meter to 0.2 mole fraction per meter. What happens to diffusive solute-flux magnitude? Reply with exactly one lowercase word from this ordered set: greater, smaller. + +## `crk24--diffusion-flux-concentration-gradient--n-1--greater-smaller--reversed` + +- Law identity: `diffusion-flux-concentration-gradient` +- Registered category: `direct` +- Expected answer: `smaller` +- Matched counterfactual: `crk24--diffusion-flux-concentration-gradient--n+1--greater-smaller--reversed` +- Prompt: For a single-phase alloy at fixed temperature with constant diffusivity, all other relevant physical conditions are unchanged. The concentration-gradient magnitude changes from 1.8 mole fraction per meter to 0.2 mole fraction per meter. What happens to diffusive solute-flux magnitude? Reply with exactly one lowercase word from this ordered set: smaller, greater. + +## `crk24--diffusion-flux-concentration-gradient--n+1--higher-lower--standard` + +- Law identity: `diffusion-flux-concentration-gradient` +- Registered category: `direct` +- Expected answer: `higher` +- Matched counterfactual: `crk24--diffusion-flux-concentration-gradient--n-1--higher-lower--standard` +- Prompt: For a single-phase alloy at fixed temperature with constant diffusivity, all other relevant physical conditions are unchanged. The concentration-gradient magnitude changes from 0.2 mole fraction per meter to 1.8 mole fraction per meter. What happens to diffusive solute-flux magnitude? Reply with exactly one lowercase word from this ordered set: higher, lower. + +## `crk24--diffusion-flux-concentration-gradient--n+1--higher-lower--reversed` + +- Law identity: `diffusion-flux-concentration-gradient` +- Registered category: `direct` +- Expected answer: `higher` +- Matched counterfactual: `crk24--diffusion-flux-concentration-gradient--n-1--higher-lower--reversed` +- Prompt: For a single-phase alloy at fixed temperature with constant diffusivity, all other relevant physical conditions are unchanged. The concentration-gradient magnitude changes from 0.2 mole fraction per meter to 1.8 mole fraction per meter. What happens to diffusive solute-flux magnitude? Reply with exactly one lowercase word from this ordered set: lower, higher. + +## `crk24--diffusion-flux-concentration-gradient--n+1--greater-smaller--standard` + +- Law identity: `diffusion-flux-concentration-gradient` +- Registered category: `direct` +- Expected answer: `greater` +- Matched counterfactual: `crk24--diffusion-flux-concentration-gradient--n-1--greater-smaller--standard` +- Prompt: For a single-phase alloy at fixed temperature with constant diffusivity, all other relevant physical conditions are unchanged. The concentration-gradient magnitude changes from 0.2 mole fraction per meter to 1.8 mole fraction per meter. What happens to diffusive solute-flux magnitude? Reply with exactly one lowercase word from this ordered set: greater, smaller. + +## `crk24--diffusion-flux-concentration-gradient--n+1--greater-smaller--reversed` + +- Law identity: `diffusion-flux-concentration-gradient` +- Registered category: `direct` +- Expected answer: `greater` +- Matched counterfactual: `crk24--diffusion-flux-concentration-gradient--n-1--greater-smaller--reversed` +- Prompt: For a single-phase alloy at fixed temperature with constant diffusivity, all other relevant physical conditions are unchanged. The concentration-gradient magnitude changes from 0.2 mole fraction per meter to 1.8 mole fraction per meter. What happens to diffusive solute-flux magnitude? Reply with exactly one lowercase word from this ordered set: smaller, greater. + +## `crk24--resistance-conductor-length--n-1--higher-lower--standard` + +- Law identity: `resistance-conductor-length` +- Registered category: `direct` +- Expected answer: `lower` +- Matched counterfactual: `crk24--resistance-conductor-length--n+1--higher-lower--standard` +- Prompt: For a uniform copper trace with fixed cross-section and resistivity, all other relevant physical conditions are unchanged. The conductor length changes from 18 centimeters to 2 centimeters. What happens to electrical resistance? Reply with exactly one lowercase word from this ordered set: higher, lower. + +## `crk24--resistance-conductor-length--n-1--higher-lower--reversed` + +- Law identity: `resistance-conductor-length` +- Registered category: `direct` +- Expected answer: `lower` +- Matched counterfactual: `crk24--resistance-conductor-length--n+1--higher-lower--reversed` +- Prompt: For a uniform copper trace with fixed cross-section and resistivity, all other relevant physical conditions are unchanged. The conductor length changes from 18 centimeters to 2 centimeters. What happens to electrical resistance? Reply with exactly one lowercase word from this ordered set: lower, higher. + +## `crk24--resistance-conductor-length--n-1--greater-smaller--standard` + +- Law identity: `resistance-conductor-length` +- Registered category: `direct` +- Expected answer: `smaller` +- Matched counterfactual: `crk24--resistance-conductor-length--n+1--greater-smaller--standard` +- Prompt: For a uniform copper trace with fixed cross-section and resistivity, all other relevant physical conditions are unchanged. The conductor length changes from 18 centimeters to 2 centimeters. What happens to electrical resistance? Reply with exactly one lowercase word from this ordered set: greater, smaller. + +## `crk24--resistance-conductor-length--n-1--greater-smaller--reversed` + +- Law identity: `resistance-conductor-length` +- Registered category: `direct` +- Expected answer: `smaller` +- Matched counterfactual: `crk24--resistance-conductor-length--n+1--greater-smaller--reversed` +- Prompt: For a uniform copper trace with fixed cross-section and resistivity, all other relevant physical conditions are unchanged. The conductor length changes from 18 centimeters to 2 centimeters. What happens to electrical resistance? Reply with exactly one lowercase word from this ordered set: smaller, greater. + +## `crk24--resistance-conductor-length--n+1--higher-lower--standard` + +- Law identity: `resistance-conductor-length` +- Registered category: `direct` +- Expected answer: `higher` +- Matched counterfactual: `crk24--resistance-conductor-length--n-1--higher-lower--standard` +- Prompt: For a uniform copper trace with fixed cross-section and resistivity, all other relevant physical conditions are unchanged. The conductor length changes from 2 centimeters to 18 centimeters. What happens to electrical resistance? Reply with exactly one lowercase word from this ordered set: higher, lower. + +## `crk24--resistance-conductor-length--n+1--higher-lower--reversed` + +- Law identity: `resistance-conductor-length` +- Registered category: `direct` +- Expected answer: `higher` +- Matched counterfactual: `crk24--resistance-conductor-length--n-1--higher-lower--reversed` +- Prompt: For a uniform copper trace with fixed cross-section and resistivity, all other relevant physical conditions are unchanged. The conductor length changes from 2 centimeters to 18 centimeters. What happens to electrical resistance? Reply with exactly one lowercase word from this ordered set: lower, higher. + +## `crk24--resistance-conductor-length--n+1--greater-smaller--standard` + +- Law identity: `resistance-conductor-length` +- Registered category: `direct` +- Expected answer: `greater` +- Matched counterfactual: `crk24--resistance-conductor-length--n-1--greater-smaller--standard` +- Prompt: For a uniform copper trace with fixed cross-section and resistivity, all other relevant physical conditions are unchanged. The conductor length changes from 2 centimeters to 18 centimeters. What happens to electrical resistance? Reply with exactly one lowercase word from this ordered set: greater, smaller. + +## `crk24--resistance-conductor-length--n+1--greater-smaller--reversed` + +- Law identity: `resistance-conductor-length` +- Registered category: `direct` +- Expected answer: `greater` +- Matched counterfactual: `crk24--resistance-conductor-length--n-1--greater-smaller--reversed` +- Prompt: For a uniform copper trace with fixed cross-section and resistivity, all other relevant physical conditions are unchanged. The conductor length changes from 2 centimeters to 18 centimeters. What happens to electrical resistance? Reply with exactly one lowercase word from this ordered set: smaller, greater. + +## `crk24--capacitance-permittivity--n-1--higher-lower--standard` + +- Law identity: `capacitance-permittivity` +- Registered category: `direct` +- Expected answer: `lower` +- Matched counterfactual: `crk24--capacitance-permittivity--n+1--higher-lower--standard` +- Prompt: For a parallel-plate capacitor with fixed area and plate spacing, all other relevant physical conditions are unchanged. The dielectric relative permittivity changes from 12 to 2. What happens to capacitance? Reply with exactly one lowercase word from this ordered set: higher, lower. + +## `crk24--capacitance-permittivity--n-1--higher-lower--reversed` + +- Law identity: `capacitance-permittivity` +- Registered category: `direct` +- Expected answer: `lower` +- Matched counterfactual: `crk24--capacitance-permittivity--n+1--higher-lower--reversed` +- Prompt: For a parallel-plate capacitor with fixed area and plate spacing, all other relevant physical conditions are unchanged. The dielectric relative permittivity changes from 12 to 2. What happens to capacitance? Reply with exactly one lowercase word from this ordered set: lower, higher. + +## `crk24--capacitance-permittivity--n-1--greater-smaller--standard` + +- Law identity: `capacitance-permittivity` +- Registered category: `direct` +- Expected answer: `smaller` +- Matched counterfactual: `crk24--capacitance-permittivity--n+1--greater-smaller--standard` +- Prompt: For a parallel-plate capacitor with fixed area and plate spacing, all other relevant physical conditions are unchanged. The dielectric relative permittivity changes from 12 to 2. What happens to capacitance? Reply with exactly one lowercase word from this ordered set: greater, smaller. + +## `crk24--capacitance-permittivity--n-1--greater-smaller--reversed` + +- Law identity: `capacitance-permittivity` +- Registered category: `direct` +- Expected answer: `smaller` +- Matched counterfactual: `crk24--capacitance-permittivity--n+1--greater-smaller--reversed` +- Prompt: For a parallel-plate capacitor with fixed area and plate spacing, all other relevant physical conditions are unchanged. The dielectric relative permittivity changes from 12 to 2. What happens to capacitance? Reply with exactly one lowercase word from this ordered set: smaller, greater. + +## `crk24--capacitance-permittivity--n+1--higher-lower--standard` + +- Law identity: `capacitance-permittivity` +- Registered category: `direct` +- Expected answer: `higher` +- Matched counterfactual: `crk24--capacitance-permittivity--n-1--higher-lower--standard` +- Prompt: For a parallel-plate capacitor with fixed area and plate spacing, all other relevant physical conditions are unchanged. The dielectric relative permittivity changes from 2 to 12. What happens to capacitance? Reply with exactly one lowercase word from this ordered set: higher, lower. + +## `crk24--capacitance-permittivity--n+1--higher-lower--reversed` + +- Law identity: `capacitance-permittivity` +- Registered category: `direct` +- Expected answer: `higher` +- Matched counterfactual: `crk24--capacitance-permittivity--n-1--higher-lower--reversed` +- Prompt: For a parallel-plate capacitor with fixed area and plate spacing, all other relevant physical conditions are unchanged. The dielectric relative permittivity changes from 2 to 12. What happens to capacitance? Reply with exactly one lowercase word from this ordered set: lower, higher. + +## `crk24--capacitance-permittivity--n+1--greater-smaller--standard` + +- Law identity: `capacitance-permittivity` +- Registered category: `direct` +- Expected answer: `greater` +- Matched counterfactual: `crk24--capacitance-permittivity--n-1--greater-smaller--standard` +- Prompt: For a parallel-plate capacitor with fixed area and plate spacing, all other relevant physical conditions are unchanged. The dielectric relative permittivity changes from 2 to 12. What happens to capacitance? Reply with exactly one lowercase word from this ordered set: greater, smaller. + +## `crk24--capacitance-permittivity--n+1--greater-smaller--reversed` + +- Law identity: `capacitance-permittivity` +- Registered category: `direct` +- Expected answer: `greater` +- Matched counterfactual: `crk24--capacitance-permittivity--n-1--greater-smaller--reversed` +- Prompt: For a parallel-plate capacitor with fixed area and plate spacing, all other relevant physical conditions are unchanged. The dielectric relative permittivity changes from 2 to 12. What happens to capacitance? Reply with exactly one lowercase word from this ordered set: smaller, greater. + +## `crk24--oxide-thickness-exposure-time--n-1--higher-lower--standard` + +- Law identity: `oxide-thickness-exposure-time` +- Registered category: `direct` +- Expected answer: `lower` +- Matched counterfactual: `crk24--oxide-thickness-exposure-time--n+1--higher-lower--standard` +- Prompt: For an alloy oxidizing isothermally within one parabolic-growth regime, all other relevant physical conditions are unchanged. The oxidation exposure time changes from 25 hours to 1 hour. What happens to oxide-scale thickness? Reply with exactly one lowercase word from this ordered set: higher, lower. + +## `crk24--oxide-thickness-exposure-time--n-1--higher-lower--reversed` + +- Law identity: `oxide-thickness-exposure-time` +- Registered category: `direct` +- Expected answer: `lower` +- Matched counterfactual: `crk24--oxide-thickness-exposure-time--n+1--higher-lower--reversed` +- Prompt: For an alloy oxidizing isothermally within one parabolic-growth regime, all other relevant physical conditions are unchanged. The oxidation exposure time changes from 25 hours to 1 hour. What happens to oxide-scale thickness? Reply with exactly one lowercase word from this ordered set: lower, higher. + +## `crk24--oxide-thickness-exposure-time--n-1--greater-smaller--standard` + +- Law identity: `oxide-thickness-exposure-time` +- Registered category: `direct` +- Expected answer: `smaller` +- Matched counterfactual: `crk24--oxide-thickness-exposure-time--n+1--greater-smaller--standard` +- Prompt: For an alloy oxidizing isothermally within one parabolic-growth regime, all other relevant physical conditions are unchanged. The oxidation exposure time changes from 25 hours to 1 hour. What happens to oxide-scale thickness? Reply with exactly one lowercase word from this ordered set: greater, smaller. + +## `crk24--oxide-thickness-exposure-time--n-1--greater-smaller--reversed` + +- Law identity: `oxide-thickness-exposure-time` +- Registered category: `direct` +- Expected answer: `smaller` +- Matched counterfactual: `crk24--oxide-thickness-exposure-time--n+1--greater-smaller--reversed` +- Prompt: For an alloy oxidizing isothermally within one parabolic-growth regime, all other relevant physical conditions are unchanged. The oxidation exposure time changes from 25 hours to 1 hour. What happens to oxide-scale thickness? Reply with exactly one lowercase word from this ordered set: smaller, greater. + +## `crk24--oxide-thickness-exposure-time--n+1--higher-lower--standard` + +- Law identity: `oxide-thickness-exposure-time` +- Registered category: `direct` +- Expected answer: `higher` +- Matched counterfactual: `crk24--oxide-thickness-exposure-time--n-1--higher-lower--standard` +- Prompt: For an alloy oxidizing isothermally within one parabolic-growth regime, all other relevant physical conditions are unchanged. The oxidation exposure time changes from 1 hour to 25 hours. What happens to oxide-scale thickness? Reply with exactly one lowercase word from this ordered set: higher, lower. + +## `crk24--oxide-thickness-exposure-time--n+1--higher-lower--reversed` + +- Law identity: `oxide-thickness-exposure-time` +- Registered category: `direct` +- Expected answer: `higher` +- Matched counterfactual: `crk24--oxide-thickness-exposure-time--n-1--higher-lower--reversed` +- Prompt: For an alloy oxidizing isothermally within one parabolic-growth regime, all other relevant physical conditions are unchanged. The oxidation exposure time changes from 1 hour to 25 hours. What happens to oxide-scale thickness? Reply with exactly one lowercase word from this ordered set: lower, higher. + +## `crk24--oxide-thickness-exposure-time--n+1--greater-smaller--standard` + +- Law identity: `oxide-thickness-exposure-time` +- Registered category: `direct` +- Expected answer: `greater` +- Matched counterfactual: `crk24--oxide-thickness-exposure-time--n-1--greater-smaller--standard` +- Prompt: For an alloy oxidizing isothermally within one parabolic-growth regime, all other relevant physical conditions are unchanged. The oxidation exposure time changes from 1 hour to 25 hours. What happens to oxide-scale thickness? Reply with exactly one lowercase word from this ordered set: greater, smaller. + +## `crk24--oxide-thickness-exposure-time--n+1--greater-smaller--reversed` + +- Law identity: `oxide-thickness-exposure-time` +- Registered category: `direct` +- Expected answer: `greater` +- Matched counterfactual: `crk24--oxide-thickness-exposure-time--n-1--greater-smaller--reversed` +- Prompt: For an alloy oxidizing isothermally within one parabolic-growth regime, all other relevant physical conditions are unchanged. The oxidation exposure time changes from 1 hour to 25 hours. What happens to oxide-scale thickness? Reply with exactly one lowercase word from this ordered set: smaller, greater. + +## `crk24--creep-rate-applied-stress--n-1--higher-lower--standard` + +- Law identity: `creep-rate-applied-stress` +- Registered category: `direct` +- Expected answer: `lower` +- Matched counterfactual: `crk24--creep-rate-applied-stress--n+1--higher-lower--standard` +- Prompt: For a superalloy deforming in one steady-state creep regime at fixed temperature, all other relevant physical conditions are unchanged. The applied uniaxial stress changes from 180 MPa to 60 MPa. What happens to steady creep rate? Reply with exactly one lowercase word from this ordered set: higher, lower. + +## `crk24--creep-rate-applied-stress--n-1--higher-lower--reversed` + +- Law identity: `creep-rate-applied-stress` +- Registered category: `direct` +- Expected answer: `lower` +- Matched counterfactual: `crk24--creep-rate-applied-stress--n+1--higher-lower--reversed` +- Prompt: For a superalloy deforming in one steady-state creep regime at fixed temperature, all other relevant physical conditions are unchanged. The applied uniaxial stress changes from 180 MPa to 60 MPa. What happens to steady creep rate? Reply with exactly one lowercase word from this ordered set: lower, higher. + +## `crk24--creep-rate-applied-stress--n-1--greater-smaller--standard` + +- Law identity: `creep-rate-applied-stress` +- Registered category: `direct` +- Expected answer: `smaller` +- Matched counterfactual: `crk24--creep-rate-applied-stress--n+1--greater-smaller--standard` +- Prompt: For a superalloy deforming in one steady-state creep regime at fixed temperature, all other relevant physical conditions are unchanged. The applied uniaxial stress changes from 180 MPa to 60 MPa. What happens to steady creep rate? Reply with exactly one lowercase word from this ordered set: greater, smaller. + +## `crk24--creep-rate-applied-stress--n-1--greater-smaller--reversed` + +- Law identity: `creep-rate-applied-stress` +- Registered category: `direct` +- Expected answer: `smaller` +- Matched counterfactual: `crk24--creep-rate-applied-stress--n+1--greater-smaller--reversed` +- Prompt: For a superalloy deforming in one steady-state creep regime at fixed temperature, all other relevant physical conditions are unchanged. The applied uniaxial stress changes from 180 MPa to 60 MPa. What happens to steady creep rate? Reply with exactly one lowercase word from this ordered set: smaller, greater. + +## `crk24--creep-rate-applied-stress--n+1--higher-lower--standard` + +- Law identity: `creep-rate-applied-stress` +- Registered category: `direct` +- Expected answer: `higher` +- Matched counterfactual: `crk24--creep-rate-applied-stress--n-1--higher-lower--standard` +- Prompt: For a superalloy deforming in one steady-state creep regime at fixed temperature, all other relevant physical conditions are unchanged. The applied uniaxial stress changes from 60 MPa to 180 MPa. What happens to steady creep rate? Reply with exactly one lowercase word from this ordered set: higher, lower. + +## `crk24--creep-rate-applied-stress--n+1--higher-lower--reversed` + +- Law identity: `creep-rate-applied-stress` +- Registered category: `direct` +- Expected answer: `higher` +- Matched counterfactual: `crk24--creep-rate-applied-stress--n-1--higher-lower--reversed` +- Prompt: For a superalloy deforming in one steady-state creep regime at fixed temperature, all other relevant physical conditions are unchanged. The applied uniaxial stress changes from 60 MPa to 180 MPa. What happens to steady creep rate? Reply with exactly one lowercase word from this ordered set: lower, higher. + +## `crk24--creep-rate-applied-stress--n+1--greater-smaller--standard` + +- Law identity: `creep-rate-applied-stress` +- Registered category: `direct` +- Expected answer: `greater` +- Matched counterfactual: `crk24--creep-rate-applied-stress--n-1--greater-smaller--standard` +- Prompt: For a superalloy deforming in one steady-state creep regime at fixed temperature, all other relevant physical conditions are unchanged. The applied uniaxial stress changes from 60 MPa to 180 MPa. What happens to steady creep rate? Reply with exactly one lowercase word from this ordered set: greater, smaller. + +## `crk24--creep-rate-applied-stress--n+1--greater-smaller--reversed` + +- Law identity: `creep-rate-applied-stress` +- Registered category: `direct` +- Expected answer: `greater` +- Matched counterfactual: `crk24--creep-rate-applied-stress--n-1--greater-smaller--reversed` +- Prompt: For a superalloy deforming in one steady-state creep regime at fixed temperature, all other relevant physical conditions are unchanged. The applied uniaxial stress changes from 60 MPa to 180 MPa. What happens to steady creep rate? Reply with exactly one lowercase word from this ordered set: smaller, greater. + +## `crk24--dislocation-speed-shear-stress--n-1--higher-lower--standard` + +- Law identity: `dislocation-speed-shear-stress` +- Registered category: `direct` +- Expected answer: `lower` +- Matched counterfactual: `crk24--dislocation-speed-shear-stress--n+1--higher-lower--standard` +- Prompt: For a crystal in a drag-controlled glide regime at fixed temperature, all other relevant physical conditions are unchanged. The resolved shear stress changes from 48 MPa to 8 MPa. What happens to mean dislocation glide speed? Reply with exactly one lowercase word from this ordered set: higher, lower. + +## `crk24--dislocation-speed-shear-stress--n-1--higher-lower--reversed` + +- Law identity: `dislocation-speed-shear-stress` +- Registered category: `direct` +- Expected answer: `lower` +- Matched counterfactual: `crk24--dislocation-speed-shear-stress--n+1--higher-lower--reversed` +- Prompt: For a crystal in a drag-controlled glide regime at fixed temperature, all other relevant physical conditions are unchanged. The resolved shear stress changes from 48 MPa to 8 MPa. What happens to mean dislocation glide speed? Reply with exactly one lowercase word from this ordered set: lower, higher. + +## `crk24--dislocation-speed-shear-stress--n-1--greater-smaller--standard` + +- Law identity: `dislocation-speed-shear-stress` +- Registered category: `direct` +- Expected answer: `smaller` +- Matched counterfactual: `crk24--dislocation-speed-shear-stress--n+1--greater-smaller--standard` +- Prompt: For a crystal in a drag-controlled glide regime at fixed temperature, all other relevant physical conditions are unchanged. The resolved shear stress changes from 48 MPa to 8 MPa. What happens to mean dislocation glide speed? Reply with exactly one lowercase word from this ordered set: greater, smaller. + +## `crk24--dislocation-speed-shear-stress--n-1--greater-smaller--reversed` + +- Law identity: `dislocation-speed-shear-stress` +- Registered category: `direct` +- Expected answer: `smaller` +- Matched counterfactual: `crk24--dislocation-speed-shear-stress--n+1--greater-smaller--reversed` +- Prompt: For a crystal in a drag-controlled glide regime at fixed temperature, all other relevant physical conditions are unchanged. The resolved shear stress changes from 48 MPa to 8 MPa. What happens to mean dislocation glide speed? Reply with exactly one lowercase word from this ordered set: smaller, greater. + +## `crk24--dislocation-speed-shear-stress--n+1--higher-lower--standard` + +- Law identity: `dislocation-speed-shear-stress` +- Registered category: `direct` +- Expected answer: `higher` +- Matched counterfactual: `crk24--dislocation-speed-shear-stress--n-1--higher-lower--standard` +- Prompt: For a crystal in a drag-controlled glide regime at fixed temperature, all other relevant physical conditions are unchanged. The resolved shear stress changes from 8 MPa to 48 MPa. What happens to mean dislocation glide speed? Reply with exactly one lowercase word from this ordered set: higher, lower. + +## `crk24--dislocation-speed-shear-stress--n+1--higher-lower--reversed` + +- Law identity: `dislocation-speed-shear-stress` +- Registered category: `direct` +- Expected answer: `higher` +- Matched counterfactual: `crk24--dislocation-speed-shear-stress--n-1--higher-lower--reversed` +- Prompt: For a crystal in a drag-controlled glide regime at fixed temperature, all other relevant physical conditions are unchanged. The resolved shear stress changes from 8 MPa to 48 MPa. What happens to mean dislocation glide speed? Reply with exactly one lowercase word from this ordered set: lower, higher. + +## `crk24--dislocation-speed-shear-stress--n+1--greater-smaller--standard` + +- Law identity: `dislocation-speed-shear-stress` +- Registered category: `direct` +- Expected answer: `greater` +- Matched counterfactual: `crk24--dislocation-speed-shear-stress--n-1--greater-smaller--standard` +- Prompt: For a crystal in a drag-controlled glide regime at fixed temperature, all other relevant physical conditions are unchanged. The resolved shear stress changes from 8 MPa to 48 MPa. What happens to mean dislocation glide speed? Reply with exactly one lowercase word from this ordered set: greater, smaller. + +## `crk24--dislocation-speed-shear-stress--n+1--greater-smaller--reversed` + +- Law identity: `dislocation-speed-shear-stress` +- Registered category: `direct` +- Expected answer: `greater` +- Matched counterfactual: `crk24--dislocation-speed-shear-stress--n-1--greater-smaller--reversed` +- Prompt: For a crystal in a drag-controlled glide regime at fixed temperature, all other relevant physical conditions are unchanged. The resolved shear stress changes from 8 MPa to 48 MPa. What happens to mean dislocation glide speed? Reply with exactly one lowercase word from this ordered set: smaller, greater. + +## `crk24--vacancy-concentration-temperature--n-1--higher-lower--standard` + +- Law identity: `vacancy-concentration-temperature` +- Registered category: `direct` +- Expected answer: `lower` +- Matched counterfactual: `crk24--vacancy-concentration-temperature--n+1--higher-lower--standard` +- Prompt: For a pure crystal below melting with fixed vacancy-formation energy, all other relevant physical conditions are unchanged. The absolute temperature changes from 1000 kelvin to 500 kelvin. What happens to equilibrium vacancy concentration? Reply with exactly one lowercase word from this ordered set: higher, lower. + +## `crk24--vacancy-concentration-temperature--n-1--higher-lower--reversed` + +- Law identity: `vacancy-concentration-temperature` +- Registered category: `direct` +- Expected answer: `lower` +- Matched counterfactual: `crk24--vacancy-concentration-temperature--n+1--higher-lower--reversed` +- Prompt: For a pure crystal below melting with fixed vacancy-formation energy, all other relevant physical conditions are unchanged. The absolute temperature changes from 1000 kelvin to 500 kelvin. What happens to equilibrium vacancy concentration? Reply with exactly one lowercase word from this ordered set: lower, higher. + +## `crk24--vacancy-concentration-temperature--n-1--greater-smaller--standard` + +- Law identity: `vacancy-concentration-temperature` +- Registered category: `direct` +- Expected answer: `smaller` +- Matched counterfactual: `crk24--vacancy-concentration-temperature--n+1--greater-smaller--standard` +- Prompt: For a pure crystal below melting with fixed vacancy-formation energy, all other relevant physical conditions are unchanged. The absolute temperature changes from 1000 kelvin to 500 kelvin. What happens to equilibrium vacancy concentration? Reply with exactly one lowercase word from this ordered set: greater, smaller. + +## `crk24--vacancy-concentration-temperature--n-1--greater-smaller--reversed` + +- Law identity: `vacancy-concentration-temperature` +- Registered category: `direct` +- Expected answer: `smaller` +- Matched counterfactual: `crk24--vacancy-concentration-temperature--n+1--greater-smaller--reversed` +- Prompt: For a pure crystal below melting with fixed vacancy-formation energy, all other relevant physical conditions are unchanged. The absolute temperature changes from 1000 kelvin to 500 kelvin. What happens to equilibrium vacancy concentration? Reply with exactly one lowercase word from this ordered set: smaller, greater. + +## `crk24--vacancy-concentration-temperature--n+1--higher-lower--standard` + +- Law identity: `vacancy-concentration-temperature` +- Registered category: `direct` +- Expected answer: `higher` +- Matched counterfactual: `crk24--vacancy-concentration-temperature--n-1--higher-lower--standard` +- Prompt: For a pure crystal below melting with fixed vacancy-formation energy, all other relevant physical conditions are unchanged. The absolute temperature changes from 500 kelvin to 1000 kelvin. What happens to equilibrium vacancy concentration? Reply with exactly one lowercase word from this ordered set: higher, lower. + +## `crk24--vacancy-concentration-temperature--n+1--higher-lower--reversed` + +- Law identity: `vacancy-concentration-temperature` +- Registered category: `direct` +- Expected answer: `higher` +- Matched counterfactual: `crk24--vacancy-concentration-temperature--n-1--higher-lower--reversed` +- Prompt: For a pure crystal below melting with fixed vacancy-formation energy, all other relevant physical conditions are unchanged. The absolute temperature changes from 500 kelvin to 1000 kelvin. What happens to equilibrium vacancy concentration? Reply with exactly one lowercase word from this ordered set: lower, higher. + +## `crk24--vacancy-concentration-temperature--n+1--greater-smaller--standard` + +- Law identity: `vacancy-concentration-temperature` +- Registered category: `direct` +- Expected answer: `greater` +- Matched counterfactual: `crk24--vacancy-concentration-temperature--n-1--greater-smaller--standard` +- Prompt: For a pure crystal below melting with fixed vacancy-formation energy, all other relevant physical conditions are unchanged. The absolute temperature changes from 500 kelvin to 1000 kelvin. What happens to equilibrium vacancy concentration? Reply with exactly one lowercase word from this ordered set: greater, smaller. + +## `crk24--vacancy-concentration-temperature--n+1--greater-smaller--reversed` + +- Law identity: `vacancy-concentration-temperature` +- Registered category: `direct` +- Expected answer: `greater` +- Matched counterfactual: `crk24--vacancy-concentration-temperature--n-1--greater-smaller--reversed` +- Prompt: For a pure crystal below melting with fixed vacancy-formation energy, all other relevant physical conditions are unchanged. The absolute temperature changes from 500 kelvin to 1000 kelvin. What happens to equilibrium vacancy concentration? Reply with exactly one lowercase word from this ordered set: smaller, greater. + +## `crk24--sintering-neck-hold-time--n-1--higher-lower--standard` + +- Law identity: `sintering-neck-hold-time` +- Registered category: `direct` +- Expected answer: `lower` +- Matched counterfactual: `crk24--sintering-neck-hold-time--n+1--higher-lower--standard` +- Prompt: For two identical powder particles in one early-stage isothermal sintering regime, all other relevant physical conditions are unchanged. The sintering hold time changes from 50 minutes to 2 minutes. What happens to interparticle neck radius? Reply with exactly one lowercase word from this ordered set: higher, lower. + +## `crk24--sintering-neck-hold-time--n-1--higher-lower--reversed` + +- Law identity: `sintering-neck-hold-time` +- Registered category: `direct` +- Expected answer: `lower` +- Matched counterfactual: `crk24--sintering-neck-hold-time--n+1--higher-lower--reversed` +- Prompt: For two identical powder particles in one early-stage isothermal sintering regime, all other relevant physical conditions are unchanged. The sintering hold time changes from 50 minutes to 2 minutes. What happens to interparticle neck radius? Reply with exactly one lowercase word from this ordered set: lower, higher. + +## `crk24--sintering-neck-hold-time--n-1--greater-smaller--standard` + +- Law identity: `sintering-neck-hold-time` +- Registered category: `direct` +- Expected answer: `smaller` +- Matched counterfactual: `crk24--sintering-neck-hold-time--n+1--greater-smaller--standard` +- Prompt: For two identical powder particles in one early-stage isothermal sintering regime, all other relevant physical conditions are unchanged. The sintering hold time changes from 50 minutes to 2 minutes. What happens to interparticle neck radius? Reply with exactly one lowercase word from this ordered set: greater, smaller. + +## `crk24--sintering-neck-hold-time--n-1--greater-smaller--reversed` + +- Law identity: `sintering-neck-hold-time` +- Registered category: `direct` +- Expected answer: `smaller` +- Matched counterfactual: `crk24--sintering-neck-hold-time--n+1--greater-smaller--reversed` +- Prompt: For two identical powder particles in one early-stage isothermal sintering regime, all other relevant physical conditions are unchanged. The sintering hold time changes from 50 minutes to 2 minutes. What happens to interparticle neck radius? Reply with exactly one lowercase word from this ordered set: smaller, greater. + +## `crk24--sintering-neck-hold-time--n+1--higher-lower--standard` + +- Law identity: `sintering-neck-hold-time` +- Registered category: `direct` +- Expected answer: `higher` +- Matched counterfactual: `crk24--sintering-neck-hold-time--n-1--higher-lower--standard` +- Prompt: For two identical powder particles in one early-stage isothermal sintering regime, all other relevant physical conditions are unchanged. The sintering hold time changes from 2 minutes to 50 minutes. What happens to interparticle neck radius? Reply with exactly one lowercase word from this ordered set: higher, lower. + +## `crk24--sintering-neck-hold-time--n+1--higher-lower--reversed` + +- Law identity: `sintering-neck-hold-time` +- Registered category: `direct` +- Expected answer: `higher` +- Matched counterfactual: `crk24--sintering-neck-hold-time--n-1--higher-lower--reversed` +- Prompt: For two identical powder particles in one early-stage isothermal sintering regime, all other relevant physical conditions are unchanged. The sintering hold time changes from 2 minutes to 50 minutes. What happens to interparticle neck radius? Reply with exactly one lowercase word from this ordered set: lower, higher. + +## `crk24--sintering-neck-hold-time--n+1--greater-smaller--standard` + +- Law identity: `sintering-neck-hold-time` +- Registered category: `direct` +- Expected answer: `greater` +- Matched counterfactual: `crk24--sintering-neck-hold-time--n-1--greater-smaller--standard` +- Prompt: For two identical powder particles in one early-stage isothermal sintering regime, all other relevant physical conditions are unchanged. The sintering hold time changes from 2 minutes to 50 minutes. What happens to interparticle neck radius? Reply with exactly one lowercase word from this ordered set: greater, smaller. + +## `crk24--sintering-neck-hold-time--n+1--greater-smaller--reversed` + +- Law identity: `sintering-neck-hold-time` +- Registered category: `direct` +- Expected answer: `greater` +- Matched counterfactual: `crk24--sintering-neck-hold-time--n-1--greater-smaller--reversed` +- Prompt: For two identical powder particles in one early-stage isothermal sintering regime, all other relevant physical conditions are unchanged. The sintering hold time changes from 2 minutes to 50 minutes. What happens to interparticle neck radius? Reply with exactly one lowercase word from this ordered set: smaller, greater. + +## `crk24--crack-opening-tensile-load--n-1--higher-lower--standard` + +- Law identity: `crack-opening-tensile-load` +- Registered category: `direct` +- Expected answer: `lower` +- Matched counterfactual: `crk24--crack-opening-tensile-load--n+1--higher-lower--standard` +- Prompt: For a cracked plate under small-scale yielding with fixed crack geometry, all other relevant physical conditions are unchanged. The remote tensile load changes from 28 kilonewtons to 4 kilonewtons. What happens to crack-opening displacement? Reply with exactly one lowercase word from this ordered set: higher, lower. + +## `crk24--crack-opening-tensile-load--n-1--higher-lower--reversed` + +- Law identity: `crack-opening-tensile-load` +- Registered category: `direct` +- Expected answer: `lower` +- Matched counterfactual: `crk24--crack-opening-tensile-load--n+1--higher-lower--reversed` +- Prompt: For a cracked plate under small-scale yielding with fixed crack geometry, all other relevant physical conditions are unchanged. The remote tensile load changes from 28 kilonewtons to 4 kilonewtons. What happens to crack-opening displacement? Reply with exactly one lowercase word from this ordered set: lower, higher. + +## `crk24--crack-opening-tensile-load--n-1--greater-smaller--standard` + +- Law identity: `crack-opening-tensile-load` +- Registered category: `direct` +- Expected answer: `smaller` +- Matched counterfactual: `crk24--crack-opening-tensile-load--n+1--greater-smaller--standard` +- Prompt: For a cracked plate under small-scale yielding with fixed crack geometry, all other relevant physical conditions are unchanged. The remote tensile load changes from 28 kilonewtons to 4 kilonewtons. What happens to crack-opening displacement? Reply with exactly one lowercase word from this ordered set: greater, smaller. + +## `crk24--crack-opening-tensile-load--n-1--greater-smaller--reversed` + +- Law identity: `crack-opening-tensile-load` +- Registered category: `direct` +- Expected answer: `smaller` +- Matched counterfactual: `crk24--crack-opening-tensile-load--n+1--greater-smaller--reversed` +- Prompt: For a cracked plate under small-scale yielding with fixed crack geometry, all other relevant physical conditions are unchanged. The remote tensile load changes from 28 kilonewtons to 4 kilonewtons. What happens to crack-opening displacement? Reply with exactly one lowercase word from this ordered set: smaller, greater. + +## `crk24--crack-opening-tensile-load--n+1--higher-lower--standard` + +- Law identity: `crack-opening-tensile-load` +- Registered category: `direct` +- Expected answer: `higher` +- Matched counterfactual: `crk24--crack-opening-tensile-load--n-1--higher-lower--standard` +- Prompt: For a cracked plate under small-scale yielding with fixed crack geometry, all other relevant physical conditions are unchanged. The remote tensile load changes from 4 kilonewtons to 28 kilonewtons. What happens to crack-opening displacement? Reply with exactly one lowercase word from this ordered set: higher, lower. + +## `crk24--crack-opening-tensile-load--n+1--higher-lower--reversed` + +- Law identity: `crack-opening-tensile-load` +- Registered category: `direct` +- Expected answer: `higher` +- Matched counterfactual: `crk24--crack-opening-tensile-load--n-1--higher-lower--reversed` +- Prompt: For a cracked plate under small-scale yielding with fixed crack geometry, all other relevant physical conditions are unchanged. The remote tensile load changes from 4 kilonewtons to 28 kilonewtons. What happens to crack-opening displacement? Reply with exactly one lowercase word from this ordered set: lower, higher. + +## `crk24--crack-opening-tensile-load--n+1--greater-smaller--standard` + +- Law identity: `crack-opening-tensile-load` +- Registered category: `direct` +- Expected answer: `greater` +- Matched counterfactual: `crk24--crack-opening-tensile-load--n-1--greater-smaller--standard` +- Prompt: For a cracked plate under small-scale yielding with fixed crack geometry, all other relevant physical conditions are unchanged. The remote tensile load changes from 4 kilonewtons to 28 kilonewtons. What happens to crack-opening displacement? Reply with exactly one lowercase word from this ordered set: greater, smaller. + +## `crk24--crack-opening-tensile-load--n+1--greater-smaller--reversed` + +- Law identity: `crack-opening-tensile-load` +- Registered category: `direct` +- Expected answer: `greater` +- Matched counterfactual: `crk24--crack-opening-tensile-load--n-1--greater-smaller--reversed` +- Prompt: For a cracked plate under small-scale yielding with fixed crack geometry, all other relevant physical conditions are unchanged. The remote tensile load changes from 4 kilonewtons to 28 kilonewtons. What happens to crack-opening displacement? Reply with exactly one lowercase word from this ordered set: smaller, greater. + +## `crk24--fatigue-life-stress-amplitude--n-1--higher-lower--standard` + +- Law identity: `fatigue-life-stress-amplitude` +- Registered category: `inverse` +- Expected answer: `higher` +- Matched counterfactual: `crk24--fatigue-life-stress-amplitude--n+1--higher-lower--standard` +- Prompt: For a polished steel specimen in one high-cycle fatigue regime, all other relevant physical conditions are unchanged. The cyclic stress amplitude changes from 300 MPa to 120 MPa. What happens to fatigue life? Reply with exactly one lowercase word from this ordered set: higher, lower. + +## `crk24--fatigue-life-stress-amplitude--n-1--higher-lower--reversed` + +- Law identity: `fatigue-life-stress-amplitude` +- Registered category: `inverse` +- Expected answer: `higher` +- Matched counterfactual: `crk24--fatigue-life-stress-amplitude--n+1--higher-lower--reversed` +- Prompt: For a polished steel specimen in one high-cycle fatigue regime, all other relevant physical conditions are unchanged. The cyclic stress amplitude changes from 300 MPa to 120 MPa. What happens to fatigue life? Reply with exactly one lowercase word from this ordered set: lower, higher. + +## `crk24--fatigue-life-stress-amplitude--n-1--greater-smaller--standard` + +- Law identity: `fatigue-life-stress-amplitude` +- Registered category: `inverse` +- Expected answer: `greater` +- Matched counterfactual: `crk24--fatigue-life-stress-amplitude--n+1--greater-smaller--standard` +- Prompt: For a polished steel specimen in one high-cycle fatigue regime, all other relevant physical conditions are unchanged. The cyclic stress amplitude changes from 300 MPa to 120 MPa. What happens to fatigue life? Reply with exactly one lowercase word from this ordered set: greater, smaller. + +## `crk24--fatigue-life-stress-amplitude--n-1--greater-smaller--reversed` + +- Law identity: `fatigue-life-stress-amplitude` +- Registered category: `inverse` +- Expected answer: `greater` +- Matched counterfactual: `crk24--fatigue-life-stress-amplitude--n+1--greater-smaller--reversed` +- Prompt: For a polished steel specimen in one high-cycle fatigue regime, all other relevant physical conditions are unchanged. The cyclic stress amplitude changes from 300 MPa to 120 MPa. What happens to fatigue life? Reply with exactly one lowercase word from this ordered set: smaller, greater. + +## `crk24--fatigue-life-stress-amplitude--n+1--higher-lower--standard` + +- Law identity: `fatigue-life-stress-amplitude` +- Registered category: `inverse` +- Expected answer: `lower` +- Matched counterfactual: `crk24--fatigue-life-stress-amplitude--n-1--higher-lower--standard` +- Prompt: For a polished steel specimen in one high-cycle fatigue regime, all other relevant physical conditions are unchanged. The cyclic stress amplitude changes from 120 MPa to 300 MPa. What happens to fatigue life? Reply with exactly one lowercase word from this ordered set: higher, lower. + +## `crk24--fatigue-life-stress-amplitude--n+1--higher-lower--reversed` + +- Law identity: `fatigue-life-stress-amplitude` +- Registered category: `inverse` +- Expected answer: `lower` +- Matched counterfactual: `crk24--fatigue-life-stress-amplitude--n-1--higher-lower--reversed` +- Prompt: For a polished steel specimen in one high-cycle fatigue regime, all other relevant physical conditions are unchanged. The cyclic stress amplitude changes from 120 MPa to 300 MPa. What happens to fatigue life? Reply with exactly one lowercase word from this ordered set: lower, higher. + +## `crk24--fatigue-life-stress-amplitude--n+1--greater-smaller--standard` + +- Law identity: `fatigue-life-stress-amplitude` +- Registered category: `inverse` +- Expected answer: `smaller` +- Matched counterfactual: `crk24--fatigue-life-stress-amplitude--n-1--greater-smaller--standard` +- Prompt: For a polished steel specimen in one high-cycle fatigue regime, all other relevant physical conditions are unchanged. The cyclic stress amplitude changes from 120 MPa to 300 MPa. What happens to fatigue life? Reply with exactly one lowercase word from this ordered set: greater, smaller. + +## `crk24--fatigue-life-stress-amplitude--n+1--greater-smaller--reversed` + +- Law identity: `fatigue-life-stress-amplitude` +- Registered category: `inverse` +- Expected answer: `smaller` +- Matched counterfactual: `crk24--fatigue-life-stress-amplitude--n-1--greater-smaller--reversed` +- Prompt: For a polished steel specimen in one high-cycle fatigue regime, all other relevant physical conditions are unchanged. The cyclic stress amplitude changes from 120 MPa to 300 MPa. What happens to fatigue life? Reply with exactly one lowercase word from this ordered set: smaller, greater. + +## `crk24--temperature-gradient-conductivity--n-1--higher-lower--standard` + +- Law identity: `temperature-gradient-conductivity` +- Registered category: `inverse` +- Expected answer: `higher` +- Matched counterfactual: `crk24--temperature-gradient-conductivity--n+1--higher-lower--standard` +- Prompt: For a homogeneous slab carrying a fixed steady heat flux, all other relevant physical conditions are unchanged. The thermal conductivity changes from 20 watts per meter-kelvin to 2 watts per meter-kelvin. What happens to required temperature-gradient magnitude? Reply with exactly one lowercase word from this ordered set: higher, lower. + +## `crk24--temperature-gradient-conductivity--n-1--higher-lower--reversed` + +- Law identity: `temperature-gradient-conductivity` +- Registered category: `inverse` +- Expected answer: `higher` +- Matched counterfactual: `crk24--temperature-gradient-conductivity--n+1--higher-lower--reversed` +- Prompt: For a homogeneous slab carrying a fixed steady heat flux, all other relevant physical conditions are unchanged. The thermal conductivity changes from 20 watts per meter-kelvin to 2 watts per meter-kelvin. What happens to required temperature-gradient magnitude? Reply with exactly one lowercase word from this ordered set: lower, higher. + +## `crk24--temperature-gradient-conductivity--n-1--greater-smaller--standard` + +- Law identity: `temperature-gradient-conductivity` +- Registered category: `inverse` +- Expected answer: `greater` +- Matched counterfactual: `crk24--temperature-gradient-conductivity--n+1--greater-smaller--standard` +- Prompt: For a homogeneous slab carrying a fixed steady heat flux, all other relevant physical conditions are unchanged. The thermal conductivity changes from 20 watts per meter-kelvin to 2 watts per meter-kelvin. What happens to required temperature-gradient magnitude? Reply with exactly one lowercase word from this ordered set: greater, smaller. + +## `crk24--temperature-gradient-conductivity--n-1--greater-smaller--reversed` + +- Law identity: `temperature-gradient-conductivity` +- Registered category: `inverse` +- Expected answer: `greater` +- Matched counterfactual: `crk24--temperature-gradient-conductivity--n+1--greater-smaller--reversed` +- Prompt: For a homogeneous slab carrying a fixed steady heat flux, all other relevant physical conditions are unchanged. The thermal conductivity changes from 20 watts per meter-kelvin to 2 watts per meter-kelvin. What happens to required temperature-gradient magnitude? Reply with exactly one lowercase word from this ordered set: smaller, greater. + +## `crk24--temperature-gradient-conductivity--n+1--higher-lower--standard` + +- Law identity: `temperature-gradient-conductivity` +- Registered category: `inverse` +- Expected answer: `lower` +- Matched counterfactual: `crk24--temperature-gradient-conductivity--n-1--higher-lower--standard` +- Prompt: For a homogeneous slab carrying a fixed steady heat flux, all other relevant physical conditions are unchanged. The thermal conductivity changes from 2 watts per meter-kelvin to 20 watts per meter-kelvin. What happens to required temperature-gradient magnitude? Reply with exactly one lowercase word from this ordered set: higher, lower. + +## `crk24--temperature-gradient-conductivity--n+1--higher-lower--reversed` + +- Law identity: `temperature-gradient-conductivity` +- Registered category: `inverse` +- Expected answer: `lower` +- Matched counterfactual: `crk24--temperature-gradient-conductivity--n-1--higher-lower--reversed` +- Prompt: For a homogeneous slab carrying a fixed steady heat flux, all other relevant physical conditions are unchanged. The thermal conductivity changes from 2 watts per meter-kelvin to 20 watts per meter-kelvin. What happens to required temperature-gradient magnitude? Reply with exactly one lowercase word from this ordered set: lower, higher. + +## `crk24--temperature-gradient-conductivity--n+1--greater-smaller--standard` + +- Law identity: `temperature-gradient-conductivity` +- Registered category: `inverse` +- Expected answer: `smaller` +- Matched counterfactual: `crk24--temperature-gradient-conductivity--n-1--greater-smaller--standard` +- Prompt: For a homogeneous slab carrying a fixed steady heat flux, all other relevant physical conditions are unchanged. The thermal conductivity changes from 2 watts per meter-kelvin to 20 watts per meter-kelvin. What happens to required temperature-gradient magnitude? Reply with exactly one lowercase word from this ordered set: greater, smaller. + +## `crk24--temperature-gradient-conductivity--n+1--greater-smaller--reversed` + +- Law identity: `temperature-gradient-conductivity` +- Registered category: `inverse` +- Expected answer: `smaller` +- Matched counterfactual: `crk24--temperature-gradient-conductivity--n-1--greater-smaller--reversed` +- Prompt: For a homogeneous slab carrying a fixed steady heat flux, all other relevant physical conditions are unchanged. The thermal conductivity changes from 2 watts per meter-kelvin to 20 watts per meter-kelvin. What happens to required temperature-gradient magnitude? Reply with exactly one lowercase word from this ordered set: smaller, greater. + +## `crk24--homogenization-time-diffusivity--n-1--higher-lower--standard` + +- Law identity: `homogenization-time-diffusivity` +- Registered category: `inverse` +- Expected answer: `higher` +- Matched counterfactual: `crk24--homogenization-time-diffusivity--n+1--higher-lower--standard` +- Prompt: For an alloy diffusion couple with fixed homogenization distance, all other relevant physical conditions are unchanged. The solute diffusivity changes from 1e-13 square meters per second to 1e-15 square meters per second. What happens to characteristic homogenization time? Reply with exactly one lowercase word from this ordered set: higher, lower. + +## `crk24--homogenization-time-diffusivity--n-1--higher-lower--reversed` + +- Law identity: `homogenization-time-diffusivity` +- Registered category: `inverse` +- Expected answer: `higher` +- Matched counterfactual: `crk24--homogenization-time-diffusivity--n+1--higher-lower--reversed` +- Prompt: For an alloy diffusion couple with fixed homogenization distance, all other relevant physical conditions are unchanged. The solute diffusivity changes from 1e-13 square meters per second to 1e-15 square meters per second. What happens to characteristic homogenization time? Reply with exactly one lowercase word from this ordered set: lower, higher. + +## `crk24--homogenization-time-diffusivity--n-1--greater-smaller--standard` + +- Law identity: `homogenization-time-diffusivity` +- Registered category: `inverse` +- Expected answer: `greater` +- Matched counterfactual: `crk24--homogenization-time-diffusivity--n+1--greater-smaller--standard` +- Prompt: For an alloy diffusion couple with fixed homogenization distance, all other relevant physical conditions are unchanged. The solute diffusivity changes from 1e-13 square meters per second to 1e-15 square meters per second. What happens to characteristic homogenization time? Reply with exactly one lowercase word from this ordered set: greater, smaller. + +## `crk24--homogenization-time-diffusivity--n-1--greater-smaller--reversed` + +- Law identity: `homogenization-time-diffusivity` +- Registered category: `inverse` +- Expected answer: `greater` +- Matched counterfactual: `crk24--homogenization-time-diffusivity--n+1--greater-smaller--reversed` +- Prompt: For an alloy diffusion couple with fixed homogenization distance, all other relevant physical conditions are unchanged. The solute diffusivity changes from 1e-13 square meters per second to 1e-15 square meters per second. What happens to characteristic homogenization time? Reply with exactly one lowercase word from this ordered set: smaller, greater. + +## `crk24--homogenization-time-diffusivity--n+1--higher-lower--standard` + +- Law identity: `homogenization-time-diffusivity` +- Registered category: `inverse` +- Expected answer: `lower` +- Matched counterfactual: `crk24--homogenization-time-diffusivity--n-1--higher-lower--standard` +- Prompt: For an alloy diffusion couple with fixed homogenization distance, all other relevant physical conditions are unchanged. The solute diffusivity changes from 1e-15 square meters per second to 1e-13 square meters per second. What happens to characteristic homogenization time? Reply with exactly one lowercase word from this ordered set: higher, lower. + +## `crk24--homogenization-time-diffusivity--n+1--higher-lower--reversed` + +- Law identity: `homogenization-time-diffusivity` +- Registered category: `inverse` +- Expected answer: `lower` +- Matched counterfactual: `crk24--homogenization-time-diffusivity--n-1--higher-lower--reversed` +- Prompt: For an alloy diffusion couple with fixed homogenization distance, all other relevant physical conditions are unchanged. The solute diffusivity changes from 1e-15 square meters per second to 1e-13 square meters per second. What happens to characteristic homogenization time? Reply with exactly one lowercase word from this ordered set: lower, higher. + +## `crk24--homogenization-time-diffusivity--n+1--greater-smaller--standard` + +- Law identity: `homogenization-time-diffusivity` +- Registered category: `inverse` +- Expected answer: `smaller` +- Matched counterfactual: `crk24--homogenization-time-diffusivity--n-1--greater-smaller--standard` +- Prompt: For an alloy diffusion couple with fixed homogenization distance, all other relevant physical conditions are unchanged. The solute diffusivity changes from 1e-15 square meters per second to 1e-13 square meters per second. What happens to characteristic homogenization time? Reply with exactly one lowercase word from this ordered set: greater, smaller. + +## `crk24--homogenization-time-diffusivity--n+1--greater-smaller--reversed` + +- Law identity: `homogenization-time-diffusivity` +- Registered category: `inverse` +- Expected answer: `smaller` +- Matched counterfactual: `crk24--homogenization-time-diffusivity--n-1--greater-smaller--reversed` +- Prompt: For an alloy diffusion couple with fixed homogenization distance, all other relevant physical conditions are unchanged. The solute diffusivity changes from 1e-15 square meters per second to 1e-13 square meters per second. What happens to characteristic homogenization time? Reply with exactly one lowercase word from this ordered set: smaller, greater. + +## `crk24--resistivity-carrier-mobility--n-1--higher-lower--standard` + +- Law identity: `resistivity-carrier-mobility` +- Registered category: `inverse` +- Expected answer: `higher` +- Matched counterfactual: `crk24--resistivity-carrier-mobility--n+1--higher-lower--standard` +- Prompt: For a semiconductor with fixed carrier concentration and charge, all other relevant physical conditions are unchanged. The carrier mobility changes from 900 square centimeters per volt-second to 100 square centimeters per volt-second. What happens to electrical resistivity? Reply with exactly one lowercase word from this ordered set: higher, lower. + +## `crk24--resistivity-carrier-mobility--n-1--higher-lower--reversed` + +- Law identity: `resistivity-carrier-mobility` +- Registered category: `inverse` +- Expected answer: `higher` +- Matched counterfactual: `crk24--resistivity-carrier-mobility--n+1--higher-lower--reversed` +- Prompt: For a semiconductor with fixed carrier concentration and charge, all other relevant physical conditions are unchanged. The carrier mobility changes from 900 square centimeters per volt-second to 100 square centimeters per volt-second. What happens to electrical resistivity? Reply with exactly one lowercase word from this ordered set: lower, higher. + +## `crk24--resistivity-carrier-mobility--n-1--greater-smaller--standard` + +- Law identity: `resistivity-carrier-mobility` +- Registered category: `inverse` +- Expected answer: `greater` +- Matched counterfactual: `crk24--resistivity-carrier-mobility--n+1--greater-smaller--standard` +- Prompt: For a semiconductor with fixed carrier concentration and charge, all other relevant physical conditions are unchanged. The carrier mobility changes from 900 square centimeters per volt-second to 100 square centimeters per volt-second. What happens to electrical resistivity? Reply with exactly one lowercase word from this ordered set: greater, smaller. + +## `crk24--resistivity-carrier-mobility--n-1--greater-smaller--reversed` + +- Law identity: `resistivity-carrier-mobility` +- Registered category: `inverse` +- Expected answer: `greater` +- Matched counterfactual: `crk24--resistivity-carrier-mobility--n+1--greater-smaller--reversed` +- Prompt: For a semiconductor with fixed carrier concentration and charge, all other relevant physical conditions are unchanged. The carrier mobility changes from 900 square centimeters per volt-second to 100 square centimeters per volt-second. What happens to electrical resistivity? Reply with exactly one lowercase word from this ordered set: smaller, greater. + +## `crk24--resistivity-carrier-mobility--n+1--higher-lower--standard` + +- Law identity: `resistivity-carrier-mobility` +- Registered category: `inverse` +- Expected answer: `lower` +- Matched counterfactual: `crk24--resistivity-carrier-mobility--n-1--higher-lower--standard` +- Prompt: For a semiconductor with fixed carrier concentration and charge, all other relevant physical conditions are unchanged. The carrier mobility changes from 100 square centimeters per volt-second to 900 square centimeters per volt-second. What happens to electrical resistivity? Reply with exactly one lowercase word from this ordered set: higher, lower. + +## `crk24--resistivity-carrier-mobility--n+1--higher-lower--reversed` + +- Law identity: `resistivity-carrier-mobility` +- Registered category: `inverse` +- Expected answer: `lower` +- Matched counterfactual: `crk24--resistivity-carrier-mobility--n-1--higher-lower--reversed` +- Prompt: For a semiconductor with fixed carrier concentration and charge, all other relevant physical conditions are unchanged. The carrier mobility changes from 100 square centimeters per volt-second to 900 square centimeters per volt-second. What happens to electrical resistivity? Reply with exactly one lowercase word from this ordered set: lower, higher. + +## `crk24--resistivity-carrier-mobility--n+1--greater-smaller--standard` + +- Law identity: `resistivity-carrier-mobility` +- Registered category: `inverse` +- Expected answer: `smaller` +- Matched counterfactual: `crk24--resistivity-carrier-mobility--n-1--greater-smaller--standard` +- Prompt: For a semiconductor with fixed carrier concentration and charge, all other relevant physical conditions are unchanged. The carrier mobility changes from 100 square centimeters per volt-second to 900 square centimeters per volt-second. What happens to electrical resistivity? Reply with exactly one lowercase word from this ordered set: greater, smaller. + +## `crk24--resistivity-carrier-mobility--n+1--greater-smaller--reversed` + +- Law identity: `resistivity-carrier-mobility` +- Registered category: `inverse` +- Expected answer: `smaller` +- Matched counterfactual: `crk24--resistivity-carrier-mobility--n-1--greater-smaller--reversed` +- Prompt: For a semiconductor with fixed carrier concentration and charge, all other relevant physical conditions are unchanged. The carrier mobility changes from 100 square centimeters per volt-second to 900 square centimeters per volt-second. What happens to electrical resistivity? Reply with exactly one lowercase word from this ordered set: smaller, greater. + +## `crk24--cantilever-frequency-length--n-1--higher-lower--standard` + +- Law identity: `cantilever-frequency-length` +- Registered category: `inverse` +- Expected answer: `higher` +- Matched counterfactual: `crk24--cantilever-frequency-length--n+1--higher-lower--standard` +- Prompt: For a uniform cantilever with fixed material, cross-section, and end condition, all other relevant physical conditions are unchanged. The cantilever length changes from 80 millimeters to 20 millimeters. What happens to fundamental resonance frequency? Reply with exactly one lowercase word from this ordered set: higher, lower. + +## `crk24--cantilever-frequency-length--n-1--higher-lower--reversed` + +- Law identity: `cantilever-frequency-length` +- Registered category: `inverse` +- Expected answer: `higher` +- Matched counterfactual: `crk24--cantilever-frequency-length--n+1--higher-lower--reversed` +- Prompt: For a uniform cantilever with fixed material, cross-section, and end condition, all other relevant physical conditions are unchanged. The cantilever length changes from 80 millimeters to 20 millimeters. What happens to fundamental resonance frequency? Reply with exactly one lowercase word from this ordered set: lower, higher. + +## `crk24--cantilever-frequency-length--n-1--greater-smaller--standard` + +- Law identity: `cantilever-frequency-length` +- Registered category: `inverse` +- Expected answer: `greater` +- Matched counterfactual: `crk24--cantilever-frequency-length--n+1--greater-smaller--standard` +- Prompt: For a uniform cantilever with fixed material, cross-section, and end condition, all other relevant physical conditions are unchanged. The cantilever length changes from 80 millimeters to 20 millimeters. What happens to fundamental resonance frequency? Reply with exactly one lowercase word from this ordered set: greater, smaller. + +## `crk24--cantilever-frequency-length--n-1--greater-smaller--reversed` + +- Law identity: `cantilever-frequency-length` +- Registered category: `inverse` +- Expected answer: `greater` +- Matched counterfactual: `crk24--cantilever-frequency-length--n+1--greater-smaller--reversed` +- Prompt: For a uniform cantilever with fixed material, cross-section, and end condition, all other relevant physical conditions are unchanged. The cantilever length changes from 80 millimeters to 20 millimeters. What happens to fundamental resonance frequency? Reply with exactly one lowercase word from this ordered set: smaller, greater. + +## `crk24--cantilever-frequency-length--n+1--higher-lower--standard` + +- Law identity: `cantilever-frequency-length` +- Registered category: `inverse` +- Expected answer: `lower` +- Matched counterfactual: `crk24--cantilever-frequency-length--n-1--higher-lower--standard` +- Prompt: For a uniform cantilever with fixed material, cross-section, and end condition, all other relevant physical conditions are unchanged. The cantilever length changes from 20 millimeters to 80 millimeters. What happens to fundamental resonance frequency? Reply with exactly one lowercase word from this ordered set: higher, lower. + +## `crk24--cantilever-frequency-length--n+1--higher-lower--reversed` + +- Law identity: `cantilever-frequency-length` +- Registered category: `inverse` +- Expected answer: `lower` +- Matched counterfactual: `crk24--cantilever-frequency-length--n-1--higher-lower--reversed` +- Prompt: For a uniform cantilever with fixed material, cross-section, and end condition, all other relevant physical conditions are unchanged. The cantilever length changes from 20 millimeters to 80 millimeters. What happens to fundamental resonance frequency? Reply with exactly one lowercase word from this ordered set: lower, higher. + +## `crk24--cantilever-frequency-length--n+1--greater-smaller--standard` + +- Law identity: `cantilever-frequency-length` +- Registered category: `inverse` +- Expected answer: `smaller` +- Matched counterfactual: `crk24--cantilever-frequency-length--n-1--greater-smaller--standard` +- Prompt: For a uniform cantilever with fixed material, cross-section, and end condition, all other relevant physical conditions are unchanged. The cantilever length changes from 20 millimeters to 80 millimeters. What happens to fundamental resonance frequency? Reply with exactly one lowercase word from this ordered set: greater, smaller. + +## `crk24--cantilever-frequency-length--n+1--greater-smaller--reversed` + +- Law identity: `cantilever-frequency-length` +- Registered category: `inverse` +- Expected answer: `smaller` +- Matched counterfactual: `crk24--cantilever-frequency-length--n-1--greater-smaller--reversed` +- Prompt: For a uniform cantilever with fixed material, cross-section, and end condition, all other relevant physical conditions are unchanged. The cantilever length changes from 20 millimeters to 80 millimeters. What happens to fundamental resonance frequency? Reply with exactly one lowercase word from this ordered set: smaller, greater. + +## `crk24--strength-porosity--n-1--higher-lower--standard` + +- Law identity: `strength-porosity` +- Registered category: `inverse` +- Expected answer: `higher` +- Matched counterfactual: `crk24--strength-porosity--n+1--higher-lower--standard` +- Prompt: For a brittle ceramic with fixed composition and pore morphology, all other relevant physical conditions are unchanged. The porosity changes from 18 percent to 2 percent. What happens to tensile strength? Reply with exactly one lowercase word from this ordered set: higher, lower. + +## `crk24--strength-porosity--n-1--higher-lower--reversed` + +- Law identity: `strength-porosity` +- Registered category: `inverse` +- Expected answer: `higher` +- Matched counterfactual: `crk24--strength-porosity--n+1--higher-lower--reversed` +- Prompt: For a brittle ceramic with fixed composition and pore morphology, all other relevant physical conditions are unchanged. The porosity changes from 18 percent to 2 percent. What happens to tensile strength? Reply with exactly one lowercase word from this ordered set: lower, higher. + +## `crk24--strength-porosity--n-1--greater-smaller--standard` + +- Law identity: `strength-porosity` +- Registered category: `inverse` +- Expected answer: `greater` +- Matched counterfactual: `crk24--strength-porosity--n+1--greater-smaller--standard` +- Prompt: For a brittle ceramic with fixed composition and pore morphology, all other relevant physical conditions are unchanged. The porosity changes from 18 percent to 2 percent. What happens to tensile strength? Reply with exactly one lowercase word from this ordered set: greater, smaller. + +## `crk24--strength-porosity--n-1--greater-smaller--reversed` + +- Law identity: `strength-porosity` +- Registered category: `inverse` +- Expected answer: `greater` +- Matched counterfactual: `crk24--strength-porosity--n+1--greater-smaller--reversed` +- Prompt: For a brittle ceramic with fixed composition and pore morphology, all other relevant physical conditions are unchanged. The porosity changes from 18 percent to 2 percent. What happens to tensile strength? Reply with exactly one lowercase word from this ordered set: smaller, greater. + +## `crk24--strength-porosity--n+1--higher-lower--standard` + +- Law identity: `strength-porosity` +- Registered category: `inverse` +- Expected answer: `lower` +- Matched counterfactual: `crk24--strength-porosity--n-1--higher-lower--standard` +- Prompt: For a brittle ceramic with fixed composition and pore morphology, all other relevant physical conditions are unchanged. The porosity changes from 2 percent to 18 percent. What happens to tensile strength? Reply with exactly one lowercase word from this ordered set: higher, lower. + +## `crk24--strength-porosity--n+1--higher-lower--reversed` + +- Law identity: `strength-porosity` +- Registered category: `inverse` +- Expected answer: `lower` +- Matched counterfactual: `crk24--strength-porosity--n-1--higher-lower--reversed` +- Prompt: For a brittle ceramic with fixed composition and pore morphology, all other relevant physical conditions are unchanged. The porosity changes from 2 percent to 18 percent. What happens to tensile strength? Reply with exactly one lowercase word from this ordered set: lower, higher. + +## `crk24--strength-porosity--n+1--greater-smaller--standard` + +- Law identity: `strength-porosity` +- Registered category: `inverse` +- Expected answer: `smaller` +- Matched counterfactual: `crk24--strength-porosity--n-1--greater-smaller--standard` +- Prompt: For a brittle ceramic with fixed composition and pore morphology, all other relevant physical conditions are unchanged. The porosity changes from 2 percent to 18 percent. What happens to tensile strength? Reply with exactly one lowercase word from this ordered set: greater, smaller. + +## `crk24--strength-porosity--n+1--greater-smaller--reversed` + +- Law identity: `strength-porosity` +- Registered category: `inverse` +- Expected answer: `smaller` +- Matched counterfactual: `crk24--strength-porosity--n-1--greater-smaller--reversed` +- Prompt: For a brittle ceramic with fixed composition and pore morphology, all other relevant physical conditions are unchanged. The porosity changes from 2 percent to 18 percent. What happens to tensile strength? Reply with exactly one lowercase word from this ordered set: smaller, greater. + +## `crk24--corrosion-current-polarization-resistance--n-1--higher-lower--standard` + +- Law identity: `corrosion-current-polarization-resistance` +- Registered category: `inverse` +- Expected answer: `higher` +- Matched counterfactual: `crk24--corrosion-current-polarization-resistance--n+1--higher-lower--standard` +- Prompt: For a metal-electrolyte interface within the linear-polarization regime, all other relevant physical conditions are unchanged. The polarization resistance changes from 20 kilohm square-centimeters to 2 kilohm square-centimeters. What happens to corrosion-current density? Reply with exactly one lowercase word from this ordered set: higher, lower. + +## `crk24--corrosion-current-polarization-resistance--n-1--higher-lower--reversed` + +- Law identity: `corrosion-current-polarization-resistance` +- Registered category: `inverse` +- Expected answer: `higher` +- Matched counterfactual: `crk24--corrosion-current-polarization-resistance--n+1--higher-lower--reversed` +- Prompt: For a metal-electrolyte interface within the linear-polarization regime, all other relevant physical conditions are unchanged. The polarization resistance changes from 20 kilohm square-centimeters to 2 kilohm square-centimeters. What happens to corrosion-current density? Reply with exactly one lowercase word from this ordered set: lower, higher. + +## `crk24--corrosion-current-polarization-resistance--n-1--greater-smaller--standard` + +- Law identity: `corrosion-current-polarization-resistance` +- Registered category: `inverse` +- Expected answer: `greater` +- Matched counterfactual: `crk24--corrosion-current-polarization-resistance--n+1--greater-smaller--standard` +- Prompt: For a metal-electrolyte interface within the linear-polarization regime, all other relevant physical conditions are unchanged. The polarization resistance changes from 20 kilohm square-centimeters to 2 kilohm square-centimeters. What happens to corrosion-current density? Reply with exactly one lowercase word from this ordered set: greater, smaller. + +## `crk24--corrosion-current-polarization-resistance--n-1--greater-smaller--reversed` + +- Law identity: `corrosion-current-polarization-resistance` +- Registered category: `inverse` +- Expected answer: `greater` +- Matched counterfactual: `crk24--corrosion-current-polarization-resistance--n+1--greater-smaller--reversed` +- Prompt: For a metal-electrolyte interface within the linear-polarization regime, all other relevant physical conditions are unchanged. The polarization resistance changes from 20 kilohm square-centimeters to 2 kilohm square-centimeters. What happens to corrosion-current density? Reply with exactly one lowercase word from this ordered set: smaller, greater. + +## `crk24--corrosion-current-polarization-resistance--n+1--higher-lower--standard` + +- Law identity: `corrosion-current-polarization-resistance` +- Registered category: `inverse` +- Expected answer: `lower` +- Matched counterfactual: `crk24--corrosion-current-polarization-resistance--n-1--higher-lower--standard` +- Prompt: For a metal-electrolyte interface within the linear-polarization regime, all other relevant physical conditions are unchanged. The polarization resistance changes from 2 kilohm square-centimeters to 20 kilohm square-centimeters. What happens to corrosion-current density? Reply with exactly one lowercase word from this ordered set: higher, lower. + +## `crk24--corrosion-current-polarization-resistance--n+1--higher-lower--reversed` + +- Law identity: `corrosion-current-polarization-resistance` +- Registered category: `inverse` +- Expected answer: `lower` +- Matched counterfactual: `crk24--corrosion-current-polarization-resistance--n-1--higher-lower--reversed` +- Prompt: For a metal-electrolyte interface within the linear-polarization regime, all other relevant physical conditions are unchanged. The polarization resistance changes from 2 kilohm square-centimeters to 20 kilohm square-centimeters. What happens to corrosion-current density? Reply with exactly one lowercase word from this ordered set: lower, higher. + +## `crk24--corrosion-current-polarization-resistance--n+1--greater-smaller--standard` + +- Law identity: `corrosion-current-polarization-resistance` +- Registered category: `inverse` +- Expected answer: `smaller` +- Matched counterfactual: `crk24--corrosion-current-polarization-resistance--n-1--greater-smaller--standard` +- Prompt: For a metal-electrolyte interface within the linear-polarization regime, all other relevant physical conditions are unchanged. The polarization resistance changes from 2 kilohm square-centimeters to 20 kilohm square-centimeters. What happens to corrosion-current density? Reply with exactly one lowercase word from this ordered set: greater, smaller. + +## `crk24--corrosion-current-polarization-resistance--n+1--greater-smaller--reversed` + +- Law identity: `corrosion-current-polarization-resistance` +- Registered category: `inverse` +- Expected answer: `smaller` +- Matched counterfactual: `crk24--corrosion-current-polarization-resistance--n-1--greater-smaller--reversed` +- Prompt: For a metal-electrolyte interface within the linear-polarization regime, all other relevant physical conditions are unchanged. The polarization resistance changes from 2 kilohm square-centimeters to 20 kilohm square-centimeters. What happens to corrosion-current density? Reply with exactly one lowercase word from this ordered set: smaller, greater. + +## `crk24--cooling-rate-section-thickness--n-1--higher-lower--standard` + +- Law identity: `cooling-rate-section-thickness` +- Registered category: `inverse` +- Expected answer: `higher` +- Matched counterfactual: `crk24--cooling-rate-section-thickness--n+1--higher-lower--standard` +- Prompt: For a metal plate quenched with fixed alloy chemistry and surface conditions, all other relevant physical conditions are unchanged. The section thickness changes from 20 millimeters to 2 millimeters. What happens to cooling-rate magnitude at the center? Reply with exactly one lowercase word from this ordered set: higher, lower. + +## `crk24--cooling-rate-section-thickness--n-1--higher-lower--reversed` + +- Law identity: `cooling-rate-section-thickness` +- Registered category: `inverse` +- Expected answer: `higher` +- Matched counterfactual: `crk24--cooling-rate-section-thickness--n+1--higher-lower--reversed` +- Prompt: For a metal plate quenched with fixed alloy chemistry and surface conditions, all other relevant physical conditions are unchanged. The section thickness changes from 20 millimeters to 2 millimeters. What happens to cooling-rate magnitude at the center? Reply with exactly one lowercase word from this ordered set: lower, higher. + +## `crk24--cooling-rate-section-thickness--n-1--greater-smaller--standard` + +- Law identity: `cooling-rate-section-thickness` +- Registered category: `inverse` +- Expected answer: `greater` +- Matched counterfactual: `crk24--cooling-rate-section-thickness--n+1--greater-smaller--standard` +- Prompt: For a metal plate quenched with fixed alloy chemistry and surface conditions, all other relevant physical conditions are unchanged. The section thickness changes from 20 millimeters to 2 millimeters. What happens to cooling-rate magnitude at the center? Reply with exactly one lowercase word from this ordered set: greater, smaller. + +## `crk24--cooling-rate-section-thickness--n-1--greater-smaller--reversed` + +- Law identity: `cooling-rate-section-thickness` +- Registered category: `inverse` +- Expected answer: `greater` +- Matched counterfactual: `crk24--cooling-rate-section-thickness--n+1--greater-smaller--reversed` +- Prompt: For a metal plate quenched with fixed alloy chemistry and surface conditions, all other relevant physical conditions are unchanged. The section thickness changes from 20 millimeters to 2 millimeters. What happens to cooling-rate magnitude at the center? Reply with exactly one lowercase word from this ordered set: smaller, greater. + +## `crk24--cooling-rate-section-thickness--n+1--higher-lower--standard` + +- Law identity: `cooling-rate-section-thickness` +- Registered category: `inverse` +- Expected answer: `lower` +- Matched counterfactual: `crk24--cooling-rate-section-thickness--n-1--higher-lower--standard` +- Prompt: For a metal plate quenched with fixed alloy chemistry and surface conditions, all other relevant physical conditions are unchanged. The section thickness changes from 2 millimeters to 20 millimeters. What happens to cooling-rate magnitude at the center? Reply with exactly one lowercase word from this ordered set: higher, lower. + +## `crk24--cooling-rate-section-thickness--n+1--higher-lower--reversed` + +- Law identity: `cooling-rate-section-thickness` +- Registered category: `inverse` +- Expected answer: `lower` +- Matched counterfactual: `crk24--cooling-rate-section-thickness--n-1--higher-lower--reversed` +- Prompt: For a metal plate quenched with fixed alloy chemistry and surface conditions, all other relevant physical conditions are unchanged. The section thickness changes from 2 millimeters to 20 millimeters. What happens to cooling-rate magnitude at the center? Reply with exactly one lowercase word from this ordered set: lower, higher. + +## `crk24--cooling-rate-section-thickness--n+1--greater-smaller--standard` + +- Law identity: `cooling-rate-section-thickness` +- Registered category: `inverse` +- Expected answer: `smaller` +- Matched counterfactual: `crk24--cooling-rate-section-thickness--n-1--greater-smaller--standard` +- Prompt: For a metal plate quenched with fixed alloy chemistry and surface conditions, all other relevant physical conditions are unchanged. The section thickness changes from 2 millimeters to 20 millimeters. What happens to cooling-rate magnitude at the center? Reply with exactly one lowercase word from this ordered set: greater, smaller. + +## `crk24--cooling-rate-section-thickness--n+1--greater-smaller--reversed` + +- Law identity: `cooling-rate-section-thickness` +- Registered category: `inverse` +- Expected answer: `smaller` +- Matched counterfactual: `crk24--cooling-rate-section-thickness--n-1--greater-smaller--reversed` +- Prompt: For a metal plate quenched with fixed alloy chemistry and surface conditions, all other relevant physical conditions are unchanged. The section thickness changes from 2 millimeters to 20 millimeters. What happens to cooling-rate magnitude at the center? Reply with exactly one lowercase word from this ordered set: smaller, greater. + +## `crk24--rupture-life-temperature--n-1--higher-lower--standard` + +- Law identity: `rupture-life-temperature` +- Registered category: `inverse` +- Expected answer: `higher` +- Matched counterfactual: `crk24--rupture-life-temperature--n+1--higher-lower--standard` +- Prompt: For a superalloy held at fixed tensile stress in one creep-rupture regime, all other relevant physical conditions are unchanged. The absolute temperature changes from 1150 kelvin to 850 kelvin. What happens to creep-rupture life? Reply with exactly one lowercase word from this ordered set: higher, lower. + +## `crk24--rupture-life-temperature--n-1--higher-lower--reversed` + +- Law identity: `rupture-life-temperature` +- Registered category: `inverse` +- Expected answer: `higher` +- Matched counterfactual: `crk24--rupture-life-temperature--n+1--higher-lower--reversed` +- Prompt: For a superalloy held at fixed tensile stress in one creep-rupture regime, all other relevant physical conditions are unchanged. The absolute temperature changes from 1150 kelvin to 850 kelvin. What happens to creep-rupture life? Reply with exactly one lowercase word from this ordered set: lower, higher. + +## `crk24--rupture-life-temperature--n-1--greater-smaller--standard` + +- Law identity: `rupture-life-temperature` +- Registered category: `inverse` +- Expected answer: `greater` +- Matched counterfactual: `crk24--rupture-life-temperature--n+1--greater-smaller--standard` +- Prompt: For a superalloy held at fixed tensile stress in one creep-rupture regime, all other relevant physical conditions are unchanged. The absolute temperature changes from 1150 kelvin to 850 kelvin. What happens to creep-rupture life? Reply with exactly one lowercase word from this ordered set: greater, smaller. + +## `crk24--rupture-life-temperature--n-1--greater-smaller--reversed` + +- Law identity: `rupture-life-temperature` +- Registered category: `inverse` +- Expected answer: `greater` +- Matched counterfactual: `crk24--rupture-life-temperature--n+1--greater-smaller--reversed` +- Prompt: For a superalloy held at fixed tensile stress in one creep-rupture regime, all other relevant physical conditions are unchanged. The absolute temperature changes from 1150 kelvin to 850 kelvin. What happens to creep-rupture life? Reply with exactly one lowercase word from this ordered set: smaller, greater. + +## `crk24--rupture-life-temperature--n+1--higher-lower--standard` + +- Law identity: `rupture-life-temperature` +- Registered category: `inverse` +- Expected answer: `lower` +- Matched counterfactual: `crk24--rupture-life-temperature--n-1--higher-lower--standard` +- Prompt: For a superalloy held at fixed tensile stress in one creep-rupture regime, all other relevant physical conditions are unchanged. The absolute temperature changes from 850 kelvin to 1150 kelvin. What happens to creep-rupture life? Reply with exactly one lowercase word from this ordered set: higher, lower. + +## `crk24--rupture-life-temperature--n+1--higher-lower--reversed` + +- Law identity: `rupture-life-temperature` +- Registered category: `inverse` +- Expected answer: `lower` +- Matched counterfactual: `crk24--rupture-life-temperature--n-1--higher-lower--reversed` +- Prompt: For a superalloy held at fixed tensile stress in one creep-rupture regime, all other relevant physical conditions are unchanged. The absolute temperature changes from 850 kelvin to 1150 kelvin. What happens to creep-rupture life? Reply with exactly one lowercase word from this ordered set: lower, higher. + +## `crk24--rupture-life-temperature--n+1--greater-smaller--standard` + +- Law identity: `rupture-life-temperature` +- Registered category: `inverse` +- Expected answer: `smaller` +- Matched counterfactual: `crk24--rupture-life-temperature--n-1--greater-smaller--standard` +- Prompt: For a superalloy held at fixed tensile stress in one creep-rupture regime, all other relevant physical conditions are unchanged. The absolute temperature changes from 850 kelvin to 1150 kelvin. What happens to creep-rupture life? Reply with exactly one lowercase word from this ordered set: greater, smaller. + +## `crk24--rupture-life-temperature--n+1--greater-smaller--reversed` + +- Law identity: `rupture-life-temperature` +- Registered category: `inverse` +- Expected answer: `smaller` +- Matched counterfactual: `crk24--rupture-life-temperature--n-1--greater-smaller--reversed` +- Prompt: For a superalloy held at fixed tensile stress in one creep-rupture regime, all other relevant physical conditions are unchanged. The absolute temperature changes from 850 kelvin to 1150 kelvin. What happens to creep-rupture life? Reply with exactly one lowercase word from this ordered set: smaller, greater. + +## `crk24--nucleation-rate-interfacial-energy--n-1--higher-lower--standard` + +- Law identity: `nucleation-rate-interfacial-energy` +- Registered category: `inverse` +- Expected answer: `higher` +- Matched counterfactual: `crk24--nucleation-rate-interfacial-energy--n+1--higher-lower--standard` +- Prompt: For a homogeneous nucleation process at fixed undercooling and temperature, all other relevant physical conditions are unchanged. The nucleus-matrix interfacial energy changes from 0.25 joules per square meter to 0.05 joules per square meter. What happens to nucleation rate? Reply with exactly one lowercase word from this ordered set: higher, lower. + +## `crk24--nucleation-rate-interfacial-energy--n-1--higher-lower--reversed` + +- Law identity: `nucleation-rate-interfacial-energy` +- Registered category: `inverse` +- Expected answer: `higher` +- Matched counterfactual: `crk24--nucleation-rate-interfacial-energy--n+1--higher-lower--reversed` +- Prompt: For a homogeneous nucleation process at fixed undercooling and temperature, all other relevant physical conditions are unchanged. The nucleus-matrix interfacial energy changes from 0.25 joules per square meter to 0.05 joules per square meter. What happens to nucleation rate? Reply with exactly one lowercase word from this ordered set: lower, higher. + +## `crk24--nucleation-rate-interfacial-energy--n-1--greater-smaller--standard` + +- Law identity: `nucleation-rate-interfacial-energy` +- Registered category: `inverse` +- Expected answer: `greater` +- Matched counterfactual: `crk24--nucleation-rate-interfacial-energy--n+1--greater-smaller--standard` +- Prompt: For a homogeneous nucleation process at fixed undercooling and temperature, all other relevant physical conditions are unchanged. The nucleus-matrix interfacial energy changes from 0.25 joules per square meter to 0.05 joules per square meter. What happens to nucleation rate? Reply with exactly one lowercase word from this ordered set: greater, smaller. + +## `crk24--nucleation-rate-interfacial-energy--n-1--greater-smaller--reversed` + +- Law identity: `nucleation-rate-interfacial-energy` +- Registered category: `inverse` +- Expected answer: `greater` +- Matched counterfactual: `crk24--nucleation-rate-interfacial-energy--n+1--greater-smaller--reversed` +- Prompt: For a homogeneous nucleation process at fixed undercooling and temperature, all other relevant physical conditions are unchanged. The nucleus-matrix interfacial energy changes from 0.25 joules per square meter to 0.05 joules per square meter. What happens to nucleation rate? Reply with exactly one lowercase word from this ordered set: smaller, greater. + +## `crk24--nucleation-rate-interfacial-energy--n+1--higher-lower--standard` + +- Law identity: `nucleation-rate-interfacial-energy` +- Registered category: `inverse` +- Expected answer: `lower` +- Matched counterfactual: `crk24--nucleation-rate-interfacial-energy--n-1--higher-lower--standard` +- Prompt: For a homogeneous nucleation process at fixed undercooling and temperature, all other relevant physical conditions are unchanged. The nucleus-matrix interfacial energy changes from 0.05 joules per square meter to 0.25 joules per square meter. What happens to nucleation rate? Reply with exactly one lowercase word from this ordered set: higher, lower. + +## `crk24--nucleation-rate-interfacial-energy--n+1--higher-lower--reversed` + +- Law identity: `nucleation-rate-interfacial-energy` +- Registered category: `inverse` +- Expected answer: `lower` +- Matched counterfactual: `crk24--nucleation-rate-interfacial-energy--n-1--higher-lower--reversed` +- Prompt: For a homogeneous nucleation process at fixed undercooling and temperature, all other relevant physical conditions are unchanged. The nucleus-matrix interfacial energy changes from 0.05 joules per square meter to 0.25 joules per square meter. What happens to nucleation rate? Reply with exactly one lowercase word from this ordered set: lower, higher. + +## `crk24--nucleation-rate-interfacial-energy--n+1--greater-smaller--standard` + +- Law identity: `nucleation-rate-interfacial-energy` +- Registered category: `inverse` +- Expected answer: `smaller` +- Matched counterfactual: `crk24--nucleation-rate-interfacial-energy--n-1--greater-smaller--standard` +- Prompt: For a homogeneous nucleation process at fixed undercooling and temperature, all other relevant physical conditions are unchanged. The nucleus-matrix interfacial energy changes from 0.05 joules per square meter to 0.25 joules per square meter. What happens to nucleation rate? Reply with exactly one lowercase word from this ordered set: greater, smaller. + +## `crk24--nucleation-rate-interfacial-energy--n+1--greater-smaller--reversed` + +- Law identity: `nucleation-rate-interfacial-energy` +- Registered category: `inverse` +- Expected answer: `smaller` +- Matched counterfactual: `crk24--nucleation-rate-interfacial-energy--n-1--greater-smaller--reversed` +- Prompt: For a homogeneous nucleation process at fixed undercooling and temperature, all other relevant physical conditions are unchanged. The nucleus-matrix interfacial energy changes from 0.05 joules per square meter to 0.25 joules per square meter. What happens to nucleation rate? Reply with exactly one lowercase word from this ordered set: smaller, greater. + +## `crk24--surface-volume-ratio-particle-diameter--n-1--higher-lower--standard` + +- Law identity: `surface-volume-ratio-particle-diameter` +- Registered category: `inverse` +- Expected answer: `higher` +- Matched counterfactual: `crk24--surface-volume-ratio-particle-diameter--n+1--higher-lower--standard` +- Prompt: For a population of geometrically similar spherical particles, all other relevant physical conditions are unchanged. The particle diameter changes from 100 nanometers to 10 nanometers. What happens to surface-area-to-volume ratio? Reply with exactly one lowercase word from this ordered set: higher, lower. + +## `crk24--surface-volume-ratio-particle-diameter--n-1--higher-lower--reversed` + +- Law identity: `surface-volume-ratio-particle-diameter` +- Registered category: `inverse` +- Expected answer: `higher` +- Matched counterfactual: `crk24--surface-volume-ratio-particle-diameter--n+1--higher-lower--reversed` +- Prompt: For a population of geometrically similar spherical particles, all other relevant physical conditions are unchanged. The particle diameter changes from 100 nanometers to 10 nanometers. What happens to surface-area-to-volume ratio? Reply with exactly one lowercase word from this ordered set: lower, higher. + +## `crk24--surface-volume-ratio-particle-diameter--n-1--greater-smaller--standard` + +- Law identity: `surface-volume-ratio-particle-diameter` +- Registered category: `inverse` +- Expected answer: `greater` +- Matched counterfactual: `crk24--surface-volume-ratio-particle-diameter--n+1--greater-smaller--standard` +- Prompt: For a population of geometrically similar spherical particles, all other relevant physical conditions are unchanged. The particle diameter changes from 100 nanometers to 10 nanometers. What happens to surface-area-to-volume ratio? Reply with exactly one lowercase word from this ordered set: greater, smaller. + +## `crk24--surface-volume-ratio-particle-diameter--n-1--greater-smaller--reversed` + +- Law identity: `surface-volume-ratio-particle-diameter` +- Registered category: `inverse` +- Expected answer: `greater` +- Matched counterfactual: `crk24--surface-volume-ratio-particle-diameter--n+1--greater-smaller--reversed` +- Prompt: For a population of geometrically similar spherical particles, all other relevant physical conditions are unchanged. The particle diameter changes from 100 nanometers to 10 nanometers. What happens to surface-area-to-volume ratio? Reply with exactly one lowercase word from this ordered set: smaller, greater. + +## `crk24--surface-volume-ratio-particle-diameter--n+1--higher-lower--standard` + +- Law identity: `surface-volume-ratio-particle-diameter` +- Registered category: `inverse` +- Expected answer: `lower` +- Matched counterfactual: `crk24--surface-volume-ratio-particle-diameter--n-1--higher-lower--standard` +- Prompt: For a population of geometrically similar spherical particles, all other relevant physical conditions are unchanged. The particle diameter changes from 10 nanometers to 100 nanometers. What happens to surface-area-to-volume ratio? Reply with exactly one lowercase word from this ordered set: higher, lower. + +## `crk24--surface-volume-ratio-particle-diameter--n+1--higher-lower--reversed` + +- Law identity: `surface-volume-ratio-particle-diameter` +- Registered category: `inverse` +- Expected answer: `lower` +- Matched counterfactual: `crk24--surface-volume-ratio-particle-diameter--n-1--higher-lower--reversed` +- Prompt: For a population of geometrically similar spherical particles, all other relevant physical conditions are unchanged. The particle diameter changes from 10 nanometers to 100 nanometers. What happens to surface-area-to-volume ratio? Reply with exactly one lowercase word from this ordered set: lower, higher. + +## `crk24--surface-volume-ratio-particle-diameter--n+1--greater-smaller--standard` + +- Law identity: `surface-volume-ratio-particle-diameter` +- Registered category: `inverse` +- Expected answer: `smaller` +- Matched counterfactual: `crk24--surface-volume-ratio-particle-diameter--n-1--greater-smaller--standard` +- Prompt: For a population of geometrically similar spherical particles, all other relevant physical conditions are unchanged. The particle diameter changes from 10 nanometers to 100 nanometers. What happens to surface-area-to-volume ratio? Reply with exactly one lowercase word from this ordered set: greater, smaller. + +## `crk24--surface-volume-ratio-particle-diameter--n+1--greater-smaller--reversed` + +- Law identity: `surface-volume-ratio-particle-diameter` +- Registered category: `inverse` +- Expected answer: `smaller` +- Matched counterfactual: `crk24--surface-volume-ratio-particle-diameter--n-1--greater-smaller--reversed` +- Prompt: For a population of geometrically similar spherical particles, all other relevant physical conditions are unchanged. The particle diameter changes from 10 nanometers to 100 nanometers. What happens to surface-area-to-volume ratio? Reply with exactly one lowercase word from this ordered set: smaller, greater. + +## `crk24--buckling-load-column-length--n-1--higher-lower--standard` + +- Law identity: `buckling-load-column-length` +- Registered category: `inverse` +- Expected answer: `higher` +- Matched counterfactual: `crk24--buckling-load-column-length--n+1--higher-lower--standard` +- Prompt: For a slender elastic column with fixed flexural rigidity and end conditions, all other relevant physical conditions are unchanged. The column length changes from 2 meters to 0.5 meters. What happens to critical buckling load? Reply with exactly one lowercase word from this ordered set: higher, lower. + +## `crk24--buckling-load-column-length--n-1--higher-lower--reversed` + +- Law identity: `buckling-load-column-length` +- Registered category: `inverse` +- Expected answer: `higher` +- Matched counterfactual: `crk24--buckling-load-column-length--n+1--higher-lower--reversed` +- Prompt: For a slender elastic column with fixed flexural rigidity and end conditions, all other relevant physical conditions are unchanged. The column length changes from 2 meters to 0.5 meters. What happens to critical buckling load? Reply with exactly one lowercase word from this ordered set: lower, higher. + +## `crk24--buckling-load-column-length--n-1--greater-smaller--standard` + +- Law identity: `buckling-load-column-length` +- Registered category: `inverse` +- Expected answer: `greater` +- Matched counterfactual: `crk24--buckling-load-column-length--n+1--greater-smaller--standard` +- Prompt: For a slender elastic column with fixed flexural rigidity and end conditions, all other relevant physical conditions are unchanged. The column length changes from 2 meters to 0.5 meters. What happens to critical buckling load? Reply with exactly one lowercase word from this ordered set: greater, smaller. + +## `crk24--buckling-load-column-length--n-1--greater-smaller--reversed` + +- Law identity: `buckling-load-column-length` +- Registered category: `inverse` +- Expected answer: `greater` +- Matched counterfactual: `crk24--buckling-load-column-length--n+1--greater-smaller--reversed` +- Prompt: For a slender elastic column with fixed flexural rigidity and end conditions, all other relevant physical conditions are unchanged. The column length changes from 2 meters to 0.5 meters. What happens to critical buckling load? Reply with exactly one lowercase word from this ordered set: smaller, greater. + +## `crk24--buckling-load-column-length--n+1--higher-lower--standard` + +- Law identity: `buckling-load-column-length` +- Registered category: `inverse` +- Expected answer: `lower` +- Matched counterfactual: `crk24--buckling-load-column-length--n-1--higher-lower--standard` +- Prompt: For a slender elastic column with fixed flexural rigidity and end conditions, all other relevant physical conditions are unchanged. The column length changes from 0.5 meters to 2 meters. What happens to critical buckling load? Reply with exactly one lowercase word from this ordered set: higher, lower. + +## `crk24--buckling-load-column-length--n+1--higher-lower--reversed` + +- Law identity: `buckling-load-column-length` +- Registered category: `inverse` +- Expected answer: `lower` +- Matched counterfactual: `crk24--buckling-load-column-length--n-1--higher-lower--reversed` +- Prompt: For a slender elastic column with fixed flexural rigidity and end conditions, all other relevant physical conditions are unchanged. The column length changes from 0.5 meters to 2 meters. What happens to critical buckling load? Reply with exactly one lowercase word from this ordered set: lower, higher. + +## `crk24--buckling-load-column-length--n+1--greater-smaller--standard` + +- Law identity: `buckling-load-column-length` +- Registered category: `inverse` +- Expected answer: `smaller` +- Matched counterfactual: `crk24--buckling-load-column-length--n-1--greater-smaller--standard` +- Prompt: For a slender elastic column with fixed flexural rigidity and end conditions, all other relevant physical conditions are unchanged. The column length changes from 0.5 meters to 2 meters. What happens to critical buckling load? Reply with exactly one lowercase word from this ordered set: greater, smaller. + +## `crk24--buckling-load-column-length--n+1--greater-smaller--reversed` + +- Law identity: `buckling-load-column-length` +- Registered category: `inverse` +- Expected answer: `smaller` +- Matched counterfactual: `crk24--buckling-load-column-length--n-1--greater-smaller--reversed` +- Prompt: For a slender elastic column with fixed flexural rigidity and end conditions, all other relevant physical conditions are unchanged. The column length changes from 0.5 meters to 2 meters. What happens to critical buckling load? Reply with exactly one lowercase word from this ordered set: smaller, greater. diff --git a/paper/causal_relational_knockout_data/PROTOCOL.md b/paper/causal_relational_knockout_data/PROTOCOL.md new file mode 100644 index 0000000000000000000000000000000000000000..825742783488fb3af1a7e5249755ad875ef9ad26 --- /dev/null +++ b/paper/causal_relational_knockout_data/PROTOCOL.md @@ -0,0 +1,3 @@ +# Frozen causal relational knockout protocol + +This protocol was written before any model output for the 24-law cohort. The machine-readable specification is `protocol.json`; all 192 exact prompts are in `prompt_manifest.json` and `PROMPTS.md`. The experiment tests a selective linear coordinate, not unique necessity of a complete physical concept. diff --git a/paper/causal_relational_knockout_data/README.md b/paper/causal_relational_knockout_data/README.md new file mode 100644 index 0000000000000000000000000000000000000000..6e1a8102e73e736fe8982fbc1cd9ead8f58888ac --- /dev/null +++ b/paper/causal_relational_knockout_data/README.md @@ -0,0 +1,45 @@ +# Causal relational knockout supplementary data + +This portable directory contains the prompt-complete and analysis-complete record +for the 24-law causal relational-coordinate experiment reported in the revised +manuscript and Supplementary Section S7A. + +## Complete design and prompt record + +- `PROMPTS.md`: all 192 exact equation-free user prompts. +- `prompt_manifest.json`: the same prompts and all law, answer, direction, and + pairing metadata in machine-readable form. +- `protocol.json` and `PROTOCOL.md`: the frozen target-cohort protocol. +- `control_amendment.json` and `CONTROL_AMENDMENT.md`: the activation-matched + control amendment frozen before any amended-control output. + +## Complete analyzed record + +- `statistics.json`, `summary_table.csv`, `stratum_effects.csv`, + `law_level_effects.csv`, and `prompt_level_effects.csv`: every reported + endpoint and its prompt-, law-, stratum-, and model-scale decomposition. +- `validation.json`: completeness, identifier-overlap, distance-matching, basis, array, + and model-revision checks. Identifier checks did not establish physical-law + disjointness; see the retrospective correction in SI S7A and the sibling + `comment3_causal_audit_data/` directory. +- `RESULTS.md`: plain-language design, numerical results, interpretation, figure + captions, reproduction commands, and complete artifact map. + +## Raw scale-specific record + +The `4b/`, `12b/`, and `31b/` directories contain all 192 clean rows, all 3,456 +intervention rows, all clean target-layer states, the fitted physical and control +directions, run metadata, and scale-specific statistics. The 31B directory also +contains the 108-comparison batched-versus-unbatched audit. + +The three 96 MB arrays of full-vocabulary clean logits are omitted from this +portable journal bundle because every reported answer logit and probability is +already present in the clean and intervention rows. They are included in the complete +Hugging Face dataset `lamm-mit/gemma4-interpretability`, under +`jlens_materials/experiments/causal-relational-knockout-2026-09-02/`, and can be regenerated from the fixed model +revisions, prompts, layers, and scripts without refitting a lens. + +No target prompt, law, natural error, intervention row, or failed case was +removed. Superseded diagnostic control rows are retained in the complete +Hugging Face research archive but are excluded from inference as documented in +`CONTROL_AMENDMENT.md`. diff --git a/paper/causal_relational_knockout_data/RESULTS.md b/paper/causal_relational_knockout_data/RESULTS.md new file mode 100644 index 0000000000000000000000000000000000000000..890a7c76dfb753718e27878f4f3e092a2bac2e71 --- /dev/null +++ b/paper/causal_relational_knockout_data/RESULTS.md @@ -0,0 +1,131 @@ +# Causal relational knockout, reversal, and repair study + +## Status + +The experiment is complete for Gemma 4B, 12B, and 31B. No manuscript file was edited. The exact 192 prompts, frozen protocol, control amendment, raw outputs, validation report, statistics, tables, and publication-ready figures are retained in this directory. + +The central question is deliberately narrower than “is this the model's unique physics representation?” It asks whether a physical-outcome coordinate fitted on an independent 16-law development cohort makes a selective causal contribution to decisions on 24 new, equation-free physical relations. + +## Plain-language design + +Each target prompt changes one physical input and asks whether a response becomes higher/lower or greater/smaller. The 24 target relations never appear in the data used to define the intervention direction. Every relation is tested with both numerical directions, both answer vocabularies, and both answer orders, giving 192 prompts per model. + +At one previously selected late layer (4B layer 34, 12B layer 39, and 31B layer 49), the experiment performs five complementary tests: + +1. **Neutralization:** remove only the coordinate's signed evidence by setting it to the development-set midpoint. If the coordinate contributes to the ordinary decision, the correct-answer logit margin should fall. +2. **Coordinate counterfactual:** replace only that coordinate with its value from the matched prompt whose numerical change is reversed. This tests whether the coordinate is sufficient to move the decision toward the counterfactual answer. +3. **Full-state counterfactual:** replace the complete final-prompt-token vector at the tested layer with its matched counterfactual vector; all other token positions remain unchanged. This establishes the total effect transferable through that final-token state. +4. **Restoration:** start from the counterfactual final-token vector but restore the tested coordinate to its original value. This asks how much counterfactual transfer depends on that coordinate in its changed background. +5. **Error repair and signed falsification:** move naturally wrong cases toward the correct development centroid, and move all cases toward the opposite centroid. + +Specificity is assessed using three activation-derived null axes. Each null axis is formed from real matched development-set transformations after balanced sign shuffling and removal of the shared physical direction. Both orientations of each axis are applied at exactly the same L2 distance as the physical intervention. The six antithetic controls are averaged within prompt before inference. + +All uncertainty intervals resample the 24 law identities, not the 192 correlated prompt variants. Exact sign-flip tests also use law as the independent unit. + +## Main numerical results + +Values are means with 95% law-bootstrap intervals where shown. “Margin” is the registered correct-answer logit minus the registered alternative-answer logit. + +| Measurement | 4B | 12B | 31B | +|---|---:|---:|---:| +| Clean answer-margin accuracy | 95.3% | 95.8% | 100% | +| Margin lost after coordinate neutralization | 4.33 [3.67, 4.89] | 3.05 [2.58, 3.49] | 2.73 [2.64, 2.82] | +| Matched-control margin loss | −0.09 [−0.11, −0.07] | −0.05 [−0.08, −0.03] | 0.02 [0.00, 0.04] | +| Physical-minus-control neutralization effect | 4.42 [3.77, 5.00] | 3.10 [2.64, 3.53] | 2.71 [2.63, 2.79] | +| Movement under one-coordinate counterfactual | 9.50 [8.01, 10.83] | 7.02 [5.93, 8.02] | 6.47 [6.25, 6.70] | +| Matched-control counterfactual movement | −0.25 [−0.31, −0.20] | −0.06 [−0.10, −0.02] | 0.11 [0.09, 0.13] | +| Movement under full-state counterfactual | 17.96 [14.92, 20.52] | 21.94 [19.16, 24.42] | 34.80 [33.90, 35.63] | +| Coordinate effect / full-state effect | 52.9% [50.1, 55.9] | 32.0% [29.1, 34.8] | 18.6% [17.9, 19.3] | +| Full-state effect removed by restoring coordinate | 54.4% [51.6, 57.3] | 53.3% [50.8, 55.6] | 49.6% [48.4, 50.7] | +| Laws with positive physical-minus-control neutralization | 23/24 | 24/24 | 24/24 | +| Laws with positive physical-minus-control counterfactual effect | 23/24 | 24/24 | 24/24 | +| Initially negative margins repaired past zero | 4/8 | 2/8 | not applicable (0 errors) | + +For both specificity endpoints at every scale, the law-level paired sign-permutation test reached the prespecified Monte Carlo floor, (p=1.0\times10^{-5}). The effects remained positive in direct and inverse laws, in both answer vocabularies, and in both answer orders; all stratum estimates and intervals are in `statistics.json`. + +## What the result establishes + +**The coordinate is selectively causally involved across all three scales.** Neutralizing it lowers the correct-answer margin, and replacing it with the matched counterfactual value moves the margin toward the opposite answer. Equal-length transformations along real activation-derived null axes remain near zero. The effect is broad: 23/24 laws at 4B and 24/24 at both larger scales have the expected sign. + +**The result goes beyond sufficiency, but does not establish unique global necessity.** At 4B, neutralization converts 15 correct decisions to errors and repairs 2 existing errors (exact McNemar (p=0.00235)); answer-margin accuracy falls from 95.3% to 88.5%. At 12B and 31B, neutralization still causes a reproducible margin loss but does not lower discrete accuracy. The larger models have enough remaining margin or redundant evidence to retain the correct answer. The defensible claim is therefore that this coordinate makes a selective contribution to ordinary decisions, with direct behavioral necessity demonstrated at 4B—not that it is the only representation required for every answer. + +**The coordinate has a context-dependent mediating role.** A coordinate-only counterfactual explains a decreasing fraction of the complete final-token-vector transfer as scale grows (52.9%, 32.0%, and 18.6%). Yet restoring the original coordinate inside the counterfactual final-token vector removes about half of the full margin effect at every scale. It also restores the original answer in 114/184, 109/182, and 152/192 cases in which the full-vector swap had selected the counterfactual answer. Thus the coordinate is not an isolated scalar explanation of the full decision; its causal effect depends on the rest of the hidden state. + +**Error repair is real but partial.** Moving only the coordinate to the correct development centroid repairs 4/8 strictly negative 4B margins and 2/8 strictly negative 12B margins. Conversely, moving it to the wrong centroid converts 60, 43, and 6 initially correct decisions into errors at 4B, 12B, and 31B. The 31B cohort contains no natural errors to repair. These results support targeted error repair as an existence proof, not a universal correction method. + +**The one 4B law-level exception is scientifically informative.** Rupture-life versus temperature is negative on all three causal endpoints at 4B, and the unmodified 4B model answers all eight variants of that law incorrectly. The same law is correct and has positive causal effects at 12B and 31B. This shows that the intervention direction tracks the model's decision process rather than mechanically imposing the registered ground-truth label. + +## Important control audit + +The target cohort and primary interventions were frozen before any target-model output. During implementation audit, an initial raw-versus-normalized coordinate mismatch was identified and corrected; superseded rows are retained under `debug-raw-coordinate/`. + +The original isotropic Gaussian controls were also found to cause severe nonspecific output-distribution disruption at 12B. They were therefore not used as evidence. Before generating any replacement-control output, `control_amendment.json` and `CONTROL_AMENDMENT.md` froze the activation-matched, antithetic control described above. The superseded Gaussian rows remain under `debug-label-signed-random/` and `debug-gaussian-own-axis-sign/`. This amendment changes no prompt, layer, physical direction, primary intervention, endpoint, or inference rule. + +## Numerical and completeness audits + +`validation.json` confirms: + +- 192 unique prompts and 24 law identities; +- no overlap between the 24 target laws and 16 development laws; +- 3,456 intervention rows per scale (18 conditions for each prompt), with no missing or duplicate keys; +- zero control-distance mismatch at stored precision; +- maximum direction-basis Gram error below (4.4\times10^{-9}); +- expected hidden-state and full-vocabulary-logit array shapes; and +- exact model revisions. + +Because the 31B interventions were batched for tractable execution, six fixed prompts spanning the cohort were recomputed unbatched for all 18 interventions. Across 108 comparisons, the answer-margin sign agreed in 100% of cases; the median absolute numerical difference was 0.086 logits and the maximum was 0.344 logits. Full records are in `31b/batch_equivalence_audit.json`. + +## Candidate figure captions + +### `figures/causal-relational-knockout.*` + +**A development-fitted relational coordinate makes a selective, context-dependent causal contribution to equation-free physical decisions.** (A) Setting only the frozen physical-outcome coordinate to its development midpoint reduces the correct-answer margin at all three Gemma scales, whereas equal-length activation-matched controls remain near zero. Pale points are the 24 law identities; large markers and bars are law means and 95% law-bootstrap intervals. (B) Replacing only the coordinate with its value from the matched reversed-input prompt moves the decision toward the counterfactual answer, while full-state replacement gives the total transferable effect and matched controls remain near zero. (C) The coordinate-only share of full-state transfer decreases with scale, but restoring the original coordinate inside the full counterfactual state removes approximately one-half of the total effect at every scale. (D) Neutralization changes discrete accuracy chiefly at 4B; coordinate-only counterfactuals flip many 4B and 12B decisions but not the higher-margin 31B decisions; full-state counterfactuals reverse almost all decisions, and coordinate restoration recovers a large fraction. The coordinate is causally involved but is neither an isolated nor uniquely necessary representation. + +### `figures/causal-relational-knockout-law-audit.*` + +**The neutralization effect is broad across unseen physical relations.** Each line is one of 24 law identities and connects the mean effect of six equal-length activation-matched controls to the effect of neutralizing the frozen relational coordinate. The effect has the expected sign for 23/24 laws at 4B and 24/24 at 12B and 31B. The sole 4B exception is rupture life versus temperature, for which the unmodified 4B model answers all eight prompt variants incorrectly. + +### `figures/causal-relational-knockout-error-repair.*` + +**A correct-centroid intervention repairs a subset of natural errors.** Every line is a prompt with a strictly negative clean correct-answer margin; no case was selected by post-intervention behavior. Moving only the frozen coordinate to the registered correct development centroid repairs 4/8 errors at 4B and 2/8 at 12B. Teal lines cross the decision boundary; gray lines improve or retain the margin without crossing zero. The 31B model made no baseline error on this cohort and is therefore omitted. + +## Reproduction + +From the repository root in the `PyTorch` environment: + +```bash +python scripts/build_causal_relational_knockout.py + +PYTORCH_ENABLE_MPS_FALLBACK=1 MPLCONFIGDIR=/tmp/matplotlib-cache \ + python -u scripts/run_causal_relational_knockout.py \ + --scale 4b --dtype bfloat16 --device mps --intervention-batch-size 6 + +PYTORCH_ENABLE_MPS_FALLBACK=1 MPLCONFIGDIR=/tmp/matplotlib-cache \ + python -u scripts/run_causal_relational_knockout.py \ + --scale 12b --dtype bfloat16 --device mps --intervention-batch-size 12 + +PYTORCH_ENABLE_MPS_FALLBACK=1 MPLCONFIGDIR=/tmp/matplotlib-cache \ + python -u scripts/run_causal_relational_knockout.py \ + --scale 31b --dtype bfloat16 --device mps --intervention-batch-size 18 + +MPLCONFIGDIR=/tmp/matplotlib-cache \ + python scripts/validate_causal_relational_knockout.py + +MPLCONFIGDIR=/tmp/matplotlib-cache \ + python scripts/analyze_causal_relational_knockout.py +``` + +Use `--resume` after an interrupted run. The analysis deliberately does not require a Jacobian-lens checkpoint: the tested coordinate is defined directly in the raw hidden-state space from independent development data. + +## File map + +- `protocol.json`, `PROTOCOL.md`: original frozen design. +- `control_amendment.json`, `CONTROL_AMENDMENT.md`: frozen specificity-control amendment. +- `prompt_manifest.json`, `PROMPTS.md`: all exact prompts and metadata. +- `4b/`, `12b/`, `31b/`: clean rows, hidden states, clean logits, directions, intervention rows, preflight metadata, run records, and scale-specific statistics. +- `statistics.json`: complete cross-scale numerical summary. +- `law_level_effects.csv`: independent-unit effect table. +- `prompt_level_effects.csv`: complete prompt-level analysis table. +- `validation.json`: structural and numerical invariant audit. +- `figures/`: PDF, SVG, and high-resolution PNG exports. +- `debug-*`: explicitly excluded superseded implementation/control outputs. diff --git a/paper/causal_relational_knockout_data/control_amendment.json b/paper/causal_relational_knockout_data/control_amendment.json new file mode 100644 index 0000000000000000000000000000000000000000..55aae568c7dd36aacee516ae5519a2f054b6279a --- /dev/null +++ b/paper/causal_relational_knockout_data/control_amendment.json @@ -0,0 +1,18 @@ +{ + "amendment_id": "causal-relational-knockout-control-v2", + "frozen_at": "2026-09-02T11:49:56Z", + "status": "frozen-before-any-amended-control-output", + "scope": "Random-direction controls only; the cohort, layers, fitted physical directions, primary interventions, endpoints, and inference procedures are unchanged.", + "reason": "Isotropic Gaussian directions produced severe nonspecific output-distribution disruption in the 12B audit and therefore did not provide activation-manifold-matched controls.", + "construction": { + "source": "The same independent development states used to fit the frozen physical direction.", + "paired_differences": "For each matched development pair, subtract the unit-normalized negative-outcome state from the unit-normalized positive-outcome state.", + "null_axes": "For each prespecified seed, apply a balanced random sign permutation to the paired differences, average them, remove the physical-direction component and components along earlier control axes, and unit-normalize.", + "seeds": [1701, 1702, 1703], + "orientations": ["plus", "minus"], + "distance_matching": "For every target prompt and operation, both orientations receive exactly the L2 length of the corresponding physical-coordinate perturbation.", + "summary": "Average the six oriented controls (three axes times two orientations) within prompt before law-level inference. Antithetic orientations cancel first-order arbitrary-axis effects while retaining nonlinear or generic disruption." + }, + "audit_policy": "The superseded Gaussian-control rows are retained in explicitly labeled debug directories and excluded from confirmatory summaries.", + "interpretation_guardrail": "A positive result supports a selective contribution of the tested coordinate, not unique necessity of a complete physical representation." +} diff --git a/paper/causal_relational_knockout_data/law_level_effects.csv b/paper/causal_relational_knockout_data/law_level_effects.csv new file mode 100644 index 0000000000000000000000000000000000000000..17a1672011c15094086dd75ca7ac142d210a6705 --- /dev/null +++ b/paper/causal_relational_knockout_data/law_level_effects.csv @@ -0,0 +1,1441 @@ +law_id,intervention,movement,margin_change,kl,answer_mass,scale +buckling-load-column-length,correct_centroid,1.515625,-1.515625,0.010294147215989202,0.9996441230177879,4b 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Reply with exactly one lowercase word from this ordered set: greater, smaller." + }, + { + "prompt_id": "crk24--thermal-strain-temperature-rise--n+1--greater-smaller--reversed", + "counterpart_prompt_id": "crk24--thermal-strain-temperature-rise--n-1--greater-smaller--reversed", + "law_id": "thermal-strain-temperature-rise", + "domain": "thermal mechanics", + "category": "direct", + "law_sign": 1, + "numerical_sign": 1, + "physical_sign": 1, + "answer_vocabulary": "greater-smaller", + "answer_order": "reversed", + "answer_words": [ + "greater", + "smaller" + ], + "presented_words": [ + "smaller", + "greater" + ], + "expected_answer": "greater", + "incorrect_answer": "smaller", + "user": "For a freely expanding nickel strip with fixed thermal-expansion coefficient, all other relevant physical conditions are unchanged. The temperature rise above its reference state changes from 20 kelvin to 180 kelvin. What happens to thermal strain? Reply with exactly one lowercase word from this ordered set: smaller, greater." + }, + { + "prompt_id": "crk24--heat-flux-temperature-gradient--n-1--higher-lower--standard", + "counterpart_prompt_id": "crk24--heat-flux-temperature-gradient--n+1--higher-lower--standard", + "law_id": "heat-flux-temperature-gradient", + "domain": "thermal transport", + "category": "direct", + "law_sign": 1, + "numerical_sign": -1, + "physical_sign": -1, + "answer_vocabulary": "higher-lower", + "answer_order": "standard", + "answer_words": [ + "higher", + "lower" + ], + "presented_words": [ + "higher", + "lower" + ], + "expected_answer": "lower", + "incorrect_answer": "higher", + "user": "For a homogeneous ceramic slab with constant thermal conductivity, all other relevant physical conditions are unchanged. The temperature-gradient magnitude changes from 45 kelvin per meter to 5 kelvin per meter. What happens to conductive heat-flux magnitude? Reply with exactly one lowercase word from this ordered set: higher, lower." + }, + { + "prompt_id": "crk24--heat-flux-temperature-gradient--n-1--higher-lower--reversed", + "counterpart_prompt_id": "crk24--heat-flux-temperature-gradient--n+1--higher-lower--reversed", + "law_id": "heat-flux-temperature-gradient", + "domain": "thermal transport", + "category": "direct", + "law_sign": 1, + "numerical_sign": -1, + "physical_sign": -1, + "answer_vocabulary": "higher-lower", + "answer_order": "reversed", + "answer_words": [ + "higher", + "lower" + ], + "presented_words": [ + "lower", + "higher" + ], + "expected_answer": "lower", + "incorrect_answer": "higher", + "user": "For a homogeneous ceramic slab with constant thermal conductivity, all other relevant physical conditions are unchanged. The temperature-gradient magnitude changes from 45 kelvin per meter to 5 kelvin per meter. What happens to conductive heat-flux magnitude? Reply with exactly one lowercase word from this ordered set: lower, higher." + }, + { + "prompt_id": "crk24--heat-flux-temperature-gradient--n-1--greater-smaller--standard", + "counterpart_prompt_id": "crk24--heat-flux-temperature-gradient--n+1--greater-smaller--standard", + "law_id": "heat-flux-temperature-gradient", + "domain": "thermal transport", + "category": "direct", + "law_sign": 1, + "numerical_sign": -1, + "physical_sign": -1, + "answer_vocabulary": "greater-smaller", + "answer_order": "standard", + "answer_words": [ + "greater", + "smaller" + ], + "presented_words": [ + "greater", + "smaller" + ], + "expected_answer": "smaller", + "incorrect_answer": "greater", + "user": "For a homogeneous ceramic slab with constant thermal conductivity, all other relevant physical conditions are unchanged. The temperature-gradient magnitude changes from 45 kelvin per meter to 5 kelvin per meter. What happens to conductive heat-flux magnitude? Reply with exactly one lowercase word from this ordered set: greater, smaller." + }, + { + "prompt_id": "crk24--heat-flux-temperature-gradient--n-1--greater-smaller--reversed", + "counterpart_prompt_id": "crk24--heat-flux-temperature-gradient--n+1--greater-smaller--reversed", + "law_id": "heat-flux-temperature-gradient", + "domain": "thermal transport", + "category": "direct", + "law_sign": 1, + "numerical_sign": -1, + "physical_sign": -1, + "answer_vocabulary": "greater-smaller", + "answer_order": "reversed", + "answer_words": [ + "greater", + "smaller" + ], + "presented_words": [ + "smaller", + "greater" + ], + "expected_answer": "smaller", + "incorrect_answer": "greater", + "user": "For a homogeneous ceramic slab with constant thermal conductivity, all other relevant physical conditions are unchanged. The temperature-gradient magnitude changes from 45 kelvin per meter to 5 kelvin per meter. What happens to conductive heat-flux magnitude? Reply with exactly one lowercase word from this ordered set: smaller, greater." + }, + { + "prompt_id": "crk24--heat-flux-temperature-gradient--n+1--higher-lower--standard", + "counterpart_prompt_id": "crk24--heat-flux-temperature-gradient--n-1--higher-lower--standard", + "law_id": "heat-flux-temperature-gradient", + "domain": "thermal transport", + "category": "direct", + "law_sign": 1, + "numerical_sign": 1, + "physical_sign": 1, + "answer_vocabulary": "higher-lower", + "answer_order": "standard", + "answer_words": [ + "higher", + "lower" + ], + "presented_words": [ + "higher", + "lower" + ], + "expected_answer": "higher", + "incorrect_answer": "lower", + "user": "For a homogeneous ceramic slab with constant thermal conductivity, all other relevant physical conditions are unchanged. The temperature-gradient magnitude changes from 5 kelvin per meter to 45 kelvin per meter. What happens to conductive heat-flux magnitude? Reply with exactly one lowercase word from this ordered set: higher, lower." + }, + { + "prompt_id": "crk24--heat-flux-temperature-gradient--n+1--higher-lower--reversed", + "counterpart_prompt_id": "crk24--heat-flux-temperature-gradient--n-1--higher-lower--reversed", + "law_id": "heat-flux-temperature-gradient", + "domain": "thermal transport", + "category": "direct", + "law_sign": 1, + "numerical_sign": 1, + "physical_sign": 1, + "answer_vocabulary": "higher-lower", + "answer_order": "reversed", + "answer_words": [ + "higher", + "lower" + ], + "presented_words": [ + "lower", + "higher" + ], + "expected_answer": "higher", + "incorrect_answer": "lower", + "user": "For a homogeneous ceramic slab with constant thermal conductivity, all other relevant physical conditions are unchanged. The temperature-gradient magnitude changes from 5 kelvin per meter to 45 kelvin per meter. What happens to conductive heat-flux magnitude? Reply with exactly one lowercase word from this ordered set: lower, higher." + }, + { + "prompt_id": "crk24--heat-flux-temperature-gradient--n+1--greater-smaller--standard", + "counterpart_prompt_id": "crk24--heat-flux-temperature-gradient--n-1--greater-smaller--standard", + "law_id": "heat-flux-temperature-gradient", + "domain": "thermal transport", + "category": "direct", + "law_sign": 1, + "numerical_sign": 1, + "physical_sign": 1, + "answer_vocabulary": "greater-smaller", + "answer_order": "standard", + "answer_words": [ + "greater", + "smaller" + ], + "presented_words": [ + "greater", + "smaller" + ], + "expected_answer": "greater", + "incorrect_answer": "smaller", + "user": "For a homogeneous ceramic slab with constant thermal conductivity, all other relevant physical conditions are unchanged. The temperature-gradient magnitude changes from 5 kelvin per meter to 45 kelvin per meter. What happens to conductive heat-flux magnitude? Reply with exactly one lowercase word from this ordered set: greater, smaller." + }, + { + "prompt_id": "crk24--heat-flux-temperature-gradient--n+1--greater-smaller--reversed", + "counterpart_prompt_id": "crk24--heat-flux-temperature-gradient--n-1--greater-smaller--reversed", + "law_id": "heat-flux-temperature-gradient", + "domain": "thermal transport", + "category": "direct", + "law_sign": 1, + "numerical_sign": 1, + "physical_sign": 1, + "answer_vocabulary": "greater-smaller", + "answer_order": "reversed", + "answer_words": [ + "greater", + "smaller" + ], + "presented_words": [ + "smaller", + "greater" + ], + "expected_answer": "greater", + "incorrect_answer": "smaller", + "user": "For a homogeneous ceramic slab with constant thermal conductivity, all other relevant physical conditions are unchanged. The temperature-gradient magnitude changes from 5 kelvin per meter to 45 kelvin per meter. What happens to conductive heat-flux magnitude? Reply with exactly one lowercase word from this ordered set: smaller, greater." + }, + { + "prompt_id": "crk24--diffusion-flux-concentration-gradient--n-1--higher-lower--standard", + "counterpart_prompt_id": "crk24--diffusion-flux-concentration-gradient--n+1--higher-lower--standard", + "law_id": "diffusion-flux-concentration-gradient", + "domain": "mass transport", + "category": "direct", + "law_sign": 1, + "numerical_sign": -1, + "physical_sign": -1, + "answer_vocabulary": "higher-lower", + "answer_order": "standard", + "answer_words": [ + "higher", + "lower" + ], + "presented_words": [ + "higher", + "lower" + ], + "expected_answer": "lower", + "incorrect_answer": "higher", + "user": "For a single-phase alloy at fixed temperature with constant diffusivity, all other relevant physical conditions are unchanged. The concentration-gradient magnitude changes from 1.8 mole fraction per meter to 0.2 mole fraction per meter. What happens to diffusive solute-flux magnitude? Reply with exactly one lowercase word from this ordered set: higher, lower." + }, + { + "prompt_id": "crk24--diffusion-flux-concentration-gradient--n-1--higher-lower--reversed", + "counterpart_prompt_id": "crk24--diffusion-flux-concentration-gradient--n+1--higher-lower--reversed", + "law_id": "diffusion-flux-concentration-gradient", + "domain": "mass transport", + "category": "direct", + "law_sign": 1, + "numerical_sign": -1, + "physical_sign": -1, + "answer_vocabulary": "higher-lower", + "answer_order": "reversed", + "answer_words": [ + "higher", + "lower" + ], + "presented_words": [ + "lower", + "higher" + ], + "expected_answer": "lower", + "incorrect_answer": "higher", + "user": "For a single-phase alloy at fixed temperature with constant diffusivity, all other relevant physical conditions are unchanged. The concentration-gradient magnitude changes from 1.8 mole fraction per meter to 0.2 mole fraction per meter. What happens to diffusive solute-flux magnitude? Reply with exactly one lowercase word from this ordered set: lower, higher." + }, + { + "prompt_id": "crk24--diffusion-flux-concentration-gradient--n-1--greater-smaller--standard", + "counterpart_prompt_id": "crk24--diffusion-flux-concentration-gradient--n+1--greater-smaller--standard", + "law_id": "diffusion-flux-concentration-gradient", + "domain": "mass transport", + "category": "direct", + "law_sign": 1, + "numerical_sign": -1, + "physical_sign": -1, + "answer_vocabulary": "greater-smaller", + "answer_order": "standard", + "answer_words": [ + "greater", + "smaller" + ], + "presented_words": [ + "greater", + "smaller" + ], + "expected_answer": "smaller", + "incorrect_answer": "greater", + "user": "For a single-phase alloy at fixed temperature with constant diffusivity, all other relevant physical conditions are unchanged. The concentration-gradient magnitude changes from 1.8 mole fraction per meter to 0.2 mole fraction per meter. What happens to diffusive solute-flux magnitude? Reply with exactly one lowercase word from this ordered set: greater, smaller." + }, + { + "prompt_id": "crk24--diffusion-flux-concentration-gradient--n-1--greater-smaller--reversed", + "counterpart_prompt_id": "crk24--diffusion-flux-concentration-gradient--n+1--greater-smaller--reversed", + "law_id": "diffusion-flux-concentration-gradient", + "domain": "mass transport", + "category": "direct", + "law_sign": 1, + "numerical_sign": -1, + "physical_sign": -1, + "answer_vocabulary": "greater-smaller", + "answer_order": "reversed", + "answer_words": [ + "greater", + "smaller" + ], + "presented_words": [ + "smaller", + "greater" + ], + "expected_answer": "smaller", + "incorrect_answer": "greater", + "user": "For a single-phase alloy at fixed temperature with constant diffusivity, all other relevant physical conditions are unchanged. The concentration-gradient magnitude changes from 1.8 mole fraction per meter to 0.2 mole fraction per meter. What happens to diffusive solute-flux magnitude? Reply with exactly one lowercase word from this ordered set: smaller, greater." + }, + { + "prompt_id": "crk24--diffusion-flux-concentration-gradient--n+1--higher-lower--standard", + "counterpart_prompt_id": "crk24--diffusion-flux-concentration-gradient--n-1--higher-lower--standard", + "law_id": "diffusion-flux-concentration-gradient", + "domain": "mass transport", + "category": "direct", + "law_sign": 1, + "numerical_sign": 1, + "physical_sign": 1, + "answer_vocabulary": "higher-lower", + "answer_order": "standard", + "answer_words": [ + "higher", + "lower" + ], + "presented_words": [ + "higher", + "lower" + ], + "expected_answer": "higher", + "incorrect_answer": "lower", + "user": "For a single-phase alloy at fixed temperature with constant diffusivity, all other relevant physical conditions are unchanged. The concentration-gradient magnitude changes from 0.2 mole fraction per meter to 1.8 mole fraction per meter. What happens to diffusive solute-flux magnitude? Reply with exactly one lowercase word from this ordered set: higher, lower." + }, + { + "prompt_id": "crk24--diffusion-flux-concentration-gradient--n+1--higher-lower--reversed", + "counterpart_prompt_id": "crk24--diffusion-flux-concentration-gradient--n-1--higher-lower--reversed", + "law_id": "diffusion-flux-concentration-gradient", + "domain": "mass transport", + "category": "direct", + "law_sign": 1, + "numerical_sign": 1, + "physical_sign": 1, + "answer_vocabulary": "higher-lower", + "answer_order": "reversed", + "answer_words": [ + "higher", + "lower" + ], + "presented_words": [ + "lower", + "higher" + ], + "expected_answer": "higher", + "incorrect_answer": "lower", + "user": "For a single-phase alloy at fixed temperature with constant diffusivity, all other relevant physical conditions are unchanged. The concentration-gradient magnitude changes from 0.2 mole fraction per meter to 1.8 mole fraction per meter. What happens to diffusive solute-flux magnitude? Reply with exactly one lowercase word from this ordered set: lower, higher." + }, + { + "prompt_id": "crk24--diffusion-flux-concentration-gradient--n+1--greater-smaller--standard", + "counterpart_prompt_id": "crk24--diffusion-flux-concentration-gradient--n-1--greater-smaller--standard", + "law_id": "diffusion-flux-concentration-gradient", + "domain": "mass transport", + "category": "direct", + "law_sign": 1, + "numerical_sign": 1, + "physical_sign": 1, + "answer_vocabulary": "greater-smaller", + "answer_order": "standard", + "answer_words": [ + "greater", + "smaller" + ], + "presented_words": [ + "greater", + "smaller" + ], + "expected_answer": "greater", + "incorrect_answer": "smaller", + "user": "For a single-phase alloy at fixed temperature with constant diffusivity, all other relevant physical conditions are unchanged. The concentration-gradient magnitude changes from 0.2 mole fraction per meter to 1.8 mole fraction per meter. What happens to diffusive solute-flux magnitude? Reply with exactly one lowercase word from this ordered set: greater, smaller." + }, + { + "prompt_id": "crk24--diffusion-flux-concentration-gradient--n+1--greater-smaller--reversed", + "counterpart_prompt_id": "crk24--diffusion-flux-concentration-gradient--n-1--greater-smaller--reversed", + "law_id": "diffusion-flux-concentration-gradient", + "domain": "mass transport", + "category": "direct", + "law_sign": 1, + "numerical_sign": 1, + "physical_sign": 1, + "answer_vocabulary": "greater-smaller", + "answer_order": "reversed", + "answer_words": [ + "greater", + "smaller" + ], + "presented_words": [ + "smaller", + "greater" + ], + "expected_answer": "greater", + "incorrect_answer": "smaller", + "user": "For a single-phase alloy at fixed temperature with constant diffusivity, all other relevant physical conditions are unchanged. The concentration-gradient magnitude changes from 0.2 mole fraction per meter to 1.8 mole fraction per meter. What happens to diffusive solute-flux magnitude? Reply with exactly one lowercase word from this ordered set: smaller, greater." + }, + { + "prompt_id": "crk24--resistance-conductor-length--n-1--higher-lower--standard", + "counterpart_prompt_id": "crk24--resistance-conductor-length--n+1--higher-lower--standard", + "law_id": "resistance-conductor-length", + "domain": "electrical materials", + "category": "direct", + "law_sign": 1, + "numerical_sign": -1, + "physical_sign": -1, + "answer_vocabulary": "higher-lower", + "answer_order": "standard", + "answer_words": [ + "higher", + "lower" + ], + "presented_words": [ + "higher", + "lower" + ], + "expected_answer": "lower", + "incorrect_answer": "higher", + "user": "For a uniform copper trace with fixed cross-section and resistivity, all other relevant physical conditions are unchanged. The conductor length changes from 18 centimeters to 2 centimeters. What happens to electrical resistance? Reply with exactly one lowercase word from this ordered set: higher, lower." + }, + { + "prompt_id": "crk24--resistance-conductor-length--n-1--higher-lower--reversed", + "counterpart_prompt_id": "crk24--resistance-conductor-length--n+1--higher-lower--reversed", + "law_id": "resistance-conductor-length", + "domain": "electrical materials", + "category": "direct", + "law_sign": 1, + "numerical_sign": -1, + "physical_sign": -1, + "answer_vocabulary": "higher-lower", + "answer_order": "reversed", + "answer_words": [ + "higher", + "lower" + ], + "presented_words": [ + "lower", + "higher" + ], + "expected_answer": "lower", + "incorrect_answer": "higher", + "user": "For a uniform copper trace with fixed cross-section and resistivity, all other relevant physical conditions are unchanged. The conductor length changes from 18 centimeters to 2 centimeters. What happens to electrical resistance? Reply with exactly one lowercase word from this ordered set: lower, higher." + }, + { + "prompt_id": "crk24--resistance-conductor-length--n-1--greater-smaller--standard", + "counterpart_prompt_id": "crk24--resistance-conductor-length--n+1--greater-smaller--standard", + "law_id": "resistance-conductor-length", + "domain": "electrical materials", + "category": "direct", + "law_sign": 1, + "numerical_sign": -1, + "physical_sign": -1, + "answer_vocabulary": "greater-smaller", + "answer_order": "standard", + "answer_words": [ + "greater", + "smaller" + ], + "presented_words": [ + "greater", + "smaller" + ], + "expected_answer": "smaller", + "incorrect_answer": "greater", + "user": "For a uniform copper trace with fixed cross-section and resistivity, all other relevant physical conditions are unchanged. The conductor length changes from 18 centimeters to 2 centimeters. What happens to electrical resistance? Reply with exactly one lowercase word from this ordered set: greater, smaller." + }, + { + "prompt_id": "crk24--resistance-conductor-length--n-1--greater-smaller--reversed", + "counterpart_prompt_id": "crk24--resistance-conductor-length--n+1--greater-smaller--reversed", + "law_id": "resistance-conductor-length", + "domain": "electrical materials", + "category": "direct", + "law_sign": 1, + "numerical_sign": -1, + "physical_sign": -1, + "answer_vocabulary": "greater-smaller", + "answer_order": "reversed", + "answer_words": [ + "greater", + "smaller" + ], + "presented_words": [ + "smaller", + "greater" + ], + "expected_answer": "smaller", + "incorrect_answer": "greater", + "user": "For a uniform copper trace with fixed cross-section and resistivity, all other relevant physical conditions are unchanged. The conductor length changes from 18 centimeters to 2 centimeters. What happens to electrical resistance? Reply with exactly one lowercase word from this ordered set: smaller, greater." + }, + { + "prompt_id": "crk24--resistance-conductor-length--n+1--higher-lower--standard", + "counterpart_prompt_id": "crk24--resistance-conductor-length--n-1--higher-lower--standard", + "law_id": "resistance-conductor-length", + "domain": "electrical materials", + "category": "direct", + "law_sign": 1, + "numerical_sign": 1, + "physical_sign": 1, + "answer_vocabulary": "higher-lower", + "answer_order": "standard", + "answer_words": [ + "higher", + "lower" + ], + "presented_words": [ + "higher", + "lower" + ], + "expected_answer": "higher", + "incorrect_answer": "lower", + "user": "For a uniform copper trace with fixed cross-section and resistivity, all other relevant physical conditions are unchanged. The conductor length changes from 2 centimeters to 18 centimeters. What happens to electrical resistance? Reply with exactly one lowercase word from this ordered set: higher, lower." + }, + { + "prompt_id": "crk24--resistance-conductor-length--n+1--higher-lower--reversed", + "counterpart_prompt_id": "crk24--resistance-conductor-length--n-1--higher-lower--reversed", + "law_id": "resistance-conductor-length", + "domain": "electrical materials", + "category": "direct", + "law_sign": 1, + "numerical_sign": 1, + "physical_sign": 1, + "answer_vocabulary": "higher-lower", + "answer_order": "reversed", + "answer_words": [ + "higher", + "lower" + ], + "presented_words": [ + "lower", + "higher" + ], + "expected_answer": "higher", + "incorrect_answer": "lower", + "user": "For a uniform copper trace with fixed cross-section and resistivity, all other relevant physical conditions are unchanged. The conductor length changes from 2 centimeters to 18 centimeters. What happens to electrical resistance? Reply with exactly one lowercase word from this ordered set: lower, higher." + }, + { + "prompt_id": "crk24--resistance-conductor-length--n+1--greater-smaller--standard", + "counterpart_prompt_id": "crk24--resistance-conductor-length--n-1--greater-smaller--standard", + "law_id": "resistance-conductor-length", + "domain": "electrical materials", + "category": "direct", + "law_sign": 1, + "numerical_sign": 1, + "physical_sign": 1, + "answer_vocabulary": "greater-smaller", + "answer_order": "standard", + "answer_words": [ + "greater", + "smaller" + ], + "presented_words": [ + "greater", + "smaller" + ], + "expected_answer": "greater", + "incorrect_answer": "smaller", + "user": "For a uniform copper trace with fixed cross-section and resistivity, all other relevant physical conditions are unchanged. The conductor length changes from 2 centimeters to 18 centimeters. What happens to electrical resistance? Reply with exactly one lowercase word from this ordered set: greater, smaller." + }, + { + "prompt_id": "crk24--resistance-conductor-length--n+1--greater-smaller--reversed", + "counterpart_prompt_id": "crk24--resistance-conductor-length--n-1--greater-smaller--reversed", + "law_id": "resistance-conductor-length", + "domain": "electrical materials", + "category": "direct", + "law_sign": 1, + "numerical_sign": 1, + "physical_sign": 1, + "answer_vocabulary": "greater-smaller", + "answer_order": "reversed", + "answer_words": [ + "greater", + "smaller" + ], + "presented_words": [ + "smaller", + "greater" + ], + "expected_answer": "greater", + "incorrect_answer": "smaller", + "user": "For a uniform copper trace with fixed cross-section and resistivity, all other relevant physical conditions are unchanged. The conductor length changes from 2 centimeters to 18 centimeters. What happens to electrical resistance? Reply with exactly one lowercase word from this ordered set: smaller, greater." + }, + { + "prompt_id": "crk24--capacitance-permittivity--n-1--higher-lower--standard", + "counterpart_prompt_id": "crk24--capacitance-permittivity--n+1--higher-lower--standard", + "law_id": "capacitance-permittivity", + "domain": "dielectrics", + "category": "direct", + "law_sign": 1, + "numerical_sign": -1, + "physical_sign": -1, + "answer_vocabulary": "higher-lower", + "answer_order": "standard", + "answer_words": [ + "higher", + "lower" + ], + "presented_words": [ + "higher", + "lower" + ], + "expected_answer": "lower", + "incorrect_answer": "higher", + "user": "For a parallel-plate capacitor with fixed area and plate spacing, all other relevant physical conditions are unchanged. The dielectric relative permittivity changes from 12 to 2. What happens to capacitance? Reply with exactly one lowercase word from this ordered set: higher, lower." + }, + { + "prompt_id": "crk24--capacitance-permittivity--n-1--higher-lower--reversed", + "counterpart_prompt_id": "crk24--capacitance-permittivity--n+1--higher-lower--reversed", + "law_id": "capacitance-permittivity", + "domain": "dielectrics", + "category": "direct", + "law_sign": 1, + "numerical_sign": -1, + "physical_sign": -1, + "answer_vocabulary": "higher-lower", + "answer_order": "reversed", + "answer_words": [ + "higher", + "lower" + ], + "presented_words": [ + "lower", + "higher" + ], + "expected_answer": "lower", + "incorrect_answer": "higher", + "user": "For a parallel-plate capacitor with fixed area and plate spacing, all other relevant physical conditions are unchanged. The dielectric relative permittivity changes from 12 to 2. What happens to capacitance? Reply with exactly one lowercase word from this ordered set: lower, higher." + }, + { + "prompt_id": "crk24--capacitance-permittivity--n-1--greater-smaller--standard", + "counterpart_prompt_id": "crk24--capacitance-permittivity--n+1--greater-smaller--standard", + "law_id": "capacitance-permittivity", + "domain": "dielectrics", + "category": "direct", + "law_sign": 1, + "numerical_sign": -1, + "physical_sign": -1, + "answer_vocabulary": "greater-smaller", + "answer_order": "standard", + "answer_words": [ + "greater", + "smaller" + ], + "presented_words": [ + "greater", + "smaller" + ], + "expected_answer": "smaller", + "incorrect_answer": "greater", + "user": "For a parallel-plate capacitor with fixed area and plate spacing, all other relevant physical conditions are unchanged. The dielectric relative permittivity changes from 12 to 2. What happens to capacitance? Reply with exactly one lowercase word from this ordered set: greater, smaller." + }, + { + "prompt_id": "crk24--capacitance-permittivity--n-1--greater-smaller--reversed", + "counterpart_prompt_id": "crk24--capacitance-permittivity--n+1--greater-smaller--reversed", + "law_id": "capacitance-permittivity", + "domain": "dielectrics", + "category": "direct", + "law_sign": 1, + "numerical_sign": -1, + "physical_sign": -1, + "answer_vocabulary": "greater-smaller", + "answer_order": "reversed", + "answer_words": [ + "greater", + "smaller" + ], + "presented_words": [ + "smaller", + "greater" + ], + "expected_answer": "smaller", + "incorrect_answer": "greater", + "user": "For a parallel-plate capacitor with fixed area and plate spacing, all other relevant physical conditions are unchanged. The dielectric relative permittivity changes from 12 to 2. What happens to capacitance? Reply with exactly one lowercase word from this ordered set: smaller, greater." + }, + { + "prompt_id": "crk24--capacitance-permittivity--n+1--higher-lower--standard", + "counterpart_prompt_id": "crk24--capacitance-permittivity--n-1--higher-lower--standard", + "law_id": "capacitance-permittivity", + "domain": "dielectrics", + "category": "direct", + "law_sign": 1, + "numerical_sign": 1, + "physical_sign": 1, + "answer_vocabulary": "higher-lower", + "answer_order": "standard", + "answer_words": [ + "higher", + "lower" + ], + "presented_words": [ + "higher", + "lower" + ], + "expected_answer": "higher", + "incorrect_answer": "lower", + "user": "For a parallel-plate capacitor with fixed area and plate spacing, all other relevant physical conditions are unchanged. The dielectric relative permittivity changes from 2 to 12. What happens to capacitance? Reply with exactly one lowercase word from this ordered set: higher, lower." + }, + { + "prompt_id": "crk24--capacitance-permittivity--n+1--higher-lower--reversed", + "counterpart_prompt_id": "crk24--capacitance-permittivity--n-1--higher-lower--reversed", + "law_id": "capacitance-permittivity", + "domain": "dielectrics", + "category": "direct", + "law_sign": 1, + "numerical_sign": 1, + "physical_sign": 1, + "answer_vocabulary": "higher-lower", + "answer_order": "reversed", + "answer_words": [ + "higher", + "lower" + ], + "presented_words": [ + "lower", + "higher" + ], + "expected_answer": "higher", + "incorrect_answer": "lower", + "user": "For a parallel-plate capacitor with fixed area and plate spacing, all other relevant physical conditions are unchanged. The dielectric relative permittivity changes from 2 to 12. What happens to capacitance? Reply with exactly one lowercase word from this ordered set: lower, higher." + }, + { + "prompt_id": "crk24--capacitance-permittivity--n+1--greater-smaller--standard", + "counterpart_prompt_id": "crk24--capacitance-permittivity--n-1--greater-smaller--standard", + "law_id": "capacitance-permittivity", + "domain": "dielectrics", + "category": "direct", + "law_sign": 1, + "numerical_sign": 1, + "physical_sign": 1, + "answer_vocabulary": "greater-smaller", + "answer_order": "standard", + "answer_words": [ + "greater", + "smaller" + ], + "presented_words": [ + "greater", + "smaller" + ], + "expected_answer": "greater", + "incorrect_answer": "smaller", + "user": "For a parallel-plate capacitor with fixed area and plate spacing, all other relevant physical conditions are unchanged. The dielectric relative permittivity changes from 2 to 12. What happens to capacitance? Reply with exactly one lowercase word from this ordered set: greater, smaller." + }, + { + "prompt_id": "crk24--capacitance-permittivity--n+1--greater-smaller--reversed", + "counterpart_prompt_id": "crk24--capacitance-permittivity--n-1--greater-smaller--reversed", + "law_id": "capacitance-permittivity", + "domain": "dielectrics", + "category": "direct", + "law_sign": 1, + "numerical_sign": 1, + "physical_sign": 1, + "answer_vocabulary": "greater-smaller", + "answer_order": "reversed", + "answer_words": [ + "greater", + "smaller" + ], + "presented_words": [ + "smaller", + "greater" + ], + "expected_answer": "greater", + "incorrect_answer": "smaller", + "user": "For a parallel-plate capacitor with fixed area and plate spacing, all other relevant physical conditions are unchanged. The dielectric relative permittivity changes from 2 to 12. What happens to capacitance? Reply with exactly one lowercase word from this ordered set: smaller, greater." + }, + { + "prompt_id": "crk24--oxide-thickness-exposure-time--n-1--higher-lower--standard", + "counterpart_prompt_id": "crk24--oxide-thickness-exposure-time--n+1--higher-lower--standard", + "law_id": "oxide-thickness-exposure-time", + "domain": "oxidation", + "category": "direct", + "law_sign": 1, + "numerical_sign": -1, + "physical_sign": -1, + "answer_vocabulary": "higher-lower", + "answer_order": "standard", + "answer_words": [ + "higher", + "lower" + ], + "presented_words": [ + "higher", + "lower" + ], + "expected_answer": "lower", + "incorrect_answer": "higher", + "user": "For an alloy oxidizing isothermally within one parabolic-growth regime, all other relevant physical conditions are unchanged. The oxidation exposure time changes from 25 hours to 1 hour. What happens to oxide-scale thickness? Reply with exactly one lowercase word from this ordered set: higher, lower." + }, + { + "prompt_id": "crk24--oxide-thickness-exposure-time--n-1--higher-lower--reversed", + "counterpart_prompt_id": "crk24--oxide-thickness-exposure-time--n+1--higher-lower--reversed", + "law_id": "oxide-thickness-exposure-time", + "domain": "oxidation", + "category": "direct", + "law_sign": 1, + "numerical_sign": -1, + "physical_sign": -1, + "answer_vocabulary": "higher-lower", + "answer_order": "reversed", + "answer_words": [ + "higher", + "lower" + ], + "presented_words": [ + "lower", + "higher" + ], + "expected_answer": "lower", + "incorrect_answer": "higher", + "user": "For an alloy oxidizing isothermally within one parabolic-growth regime, all other relevant physical conditions are unchanged. The oxidation exposure time changes from 25 hours to 1 hour. What happens to oxide-scale thickness? Reply with exactly one lowercase word from this ordered set: lower, higher." + }, + { + "prompt_id": "crk24--oxide-thickness-exposure-time--n-1--greater-smaller--standard", + "counterpart_prompt_id": "crk24--oxide-thickness-exposure-time--n+1--greater-smaller--standard", + "law_id": "oxide-thickness-exposure-time", + "domain": "oxidation", + "category": "direct", + "law_sign": 1, + "numerical_sign": -1, + "physical_sign": -1, + "answer_vocabulary": "greater-smaller", + "answer_order": "standard", + "answer_words": [ + "greater", + "smaller" + ], + "presented_words": [ + "greater", + "smaller" + ], + "expected_answer": "smaller", + "incorrect_answer": "greater", + "user": "For an alloy oxidizing isothermally within one parabolic-growth regime, all other relevant physical conditions are unchanged. The oxidation exposure time changes from 25 hours to 1 hour. What happens to oxide-scale thickness? Reply with exactly one lowercase word from this ordered set: greater, smaller." + }, + { + "prompt_id": "crk24--oxide-thickness-exposure-time--n-1--greater-smaller--reversed", + "counterpart_prompt_id": "crk24--oxide-thickness-exposure-time--n+1--greater-smaller--reversed", + "law_id": "oxide-thickness-exposure-time", + "domain": "oxidation", + "category": "direct", + "law_sign": 1, + "numerical_sign": -1, + "physical_sign": -1, + "answer_vocabulary": "greater-smaller", + "answer_order": "reversed", + "answer_words": [ + "greater", + "smaller" + ], + "presented_words": [ + "smaller", + "greater" + ], + "expected_answer": "smaller", + "incorrect_answer": "greater", + "user": "For an alloy oxidizing isothermally within one parabolic-growth regime, all other relevant physical conditions are unchanged. The oxidation exposure time changes from 25 hours to 1 hour. What happens to oxide-scale thickness? Reply with exactly one lowercase word from this ordered set: smaller, greater." + }, + { + "prompt_id": "crk24--oxide-thickness-exposure-time--n+1--higher-lower--standard", + "counterpart_prompt_id": "crk24--oxide-thickness-exposure-time--n-1--higher-lower--standard", + "law_id": "oxide-thickness-exposure-time", + "domain": "oxidation", + "category": "direct", + "law_sign": 1, + "numerical_sign": 1, + "physical_sign": 1, + "answer_vocabulary": "higher-lower", + "answer_order": "standard", + "answer_words": [ + "higher", + "lower" + ], + "presented_words": [ + "higher", + "lower" + ], + "expected_answer": "higher", + "incorrect_answer": "lower", + "user": "For an alloy oxidizing isothermally within one parabolic-growth regime, all other relevant physical conditions are unchanged. The oxidation exposure time changes from 1 hour to 25 hours. What happens to oxide-scale thickness? Reply with exactly one lowercase word from this ordered set: higher, lower." + }, + { + "prompt_id": "crk24--oxide-thickness-exposure-time--n+1--higher-lower--reversed", + "counterpart_prompt_id": "crk24--oxide-thickness-exposure-time--n-1--higher-lower--reversed", + "law_id": "oxide-thickness-exposure-time", + "domain": "oxidation", + "category": "direct", + "law_sign": 1, + "numerical_sign": 1, + "physical_sign": 1, + "answer_vocabulary": "higher-lower", + "answer_order": "reversed", + "answer_words": [ + "higher", + "lower" + ], + "presented_words": [ + "lower", + "higher" + ], + "expected_answer": "higher", + "incorrect_answer": "lower", + "user": "For an alloy oxidizing isothermally within one parabolic-growth regime, all other relevant physical conditions are unchanged. The oxidation exposure time changes from 1 hour to 25 hours. What happens to oxide-scale thickness? Reply with exactly one lowercase word from this ordered set: lower, higher." + }, + { + "prompt_id": "crk24--oxide-thickness-exposure-time--n+1--greater-smaller--standard", + "counterpart_prompt_id": "crk24--oxide-thickness-exposure-time--n-1--greater-smaller--standard", + "law_id": "oxide-thickness-exposure-time", + "domain": "oxidation", + "category": "direct", + "law_sign": 1, + "numerical_sign": 1, + "physical_sign": 1, + "answer_vocabulary": "greater-smaller", + "answer_order": "standard", + "answer_words": [ + "greater", + "smaller" + ], + "presented_words": [ + "greater", + "smaller" + ], + "expected_answer": "greater", + "incorrect_answer": "smaller", + "user": "For an alloy oxidizing isothermally within one parabolic-growth regime, all other relevant physical conditions are unchanged. The oxidation exposure time changes from 1 hour to 25 hours. What happens to oxide-scale thickness? Reply with exactly one lowercase word from this ordered set: greater, smaller." + }, + { + "prompt_id": "crk24--oxide-thickness-exposure-time--n+1--greater-smaller--reversed", + "counterpart_prompt_id": "crk24--oxide-thickness-exposure-time--n-1--greater-smaller--reversed", + "law_id": "oxide-thickness-exposure-time", + "domain": "oxidation", + "category": "direct", + "law_sign": 1, + "numerical_sign": 1, + "physical_sign": 1, + "answer_vocabulary": "greater-smaller", + "answer_order": "reversed", + "answer_words": [ + "greater", + "smaller" + ], + "presented_words": [ + "smaller", + "greater" + ], + "expected_answer": "greater", + "incorrect_answer": "smaller", + "user": "For an alloy oxidizing isothermally within one parabolic-growth regime, all other relevant physical conditions are unchanged. The oxidation exposure time changes from 1 hour to 25 hours. What happens to oxide-scale thickness? Reply with exactly one lowercase word from this ordered set: smaller, greater." + }, + { + "prompt_id": "crk24--creep-rate-applied-stress--n-1--higher-lower--standard", + "counterpart_prompt_id": "crk24--creep-rate-applied-stress--n+1--higher-lower--standard", + "law_id": "creep-rate-applied-stress", + "domain": "high-temperature deformation", + "category": "direct", + "law_sign": 1, + "numerical_sign": -1, + "physical_sign": -1, + "answer_vocabulary": "higher-lower", + "answer_order": "standard", + "answer_words": [ + "higher", + "lower" + ], + "presented_words": [ + "higher", + "lower" + ], + "expected_answer": "lower", + "incorrect_answer": "higher", + "user": "For a superalloy deforming in one steady-state creep regime at fixed temperature, all other relevant physical conditions are unchanged. The applied uniaxial stress changes from 180 MPa to 60 MPa. What happens to steady creep rate? Reply with exactly one lowercase word from this ordered set: higher, lower." + }, + { + "prompt_id": "crk24--creep-rate-applied-stress--n-1--higher-lower--reversed", + "counterpart_prompt_id": "crk24--creep-rate-applied-stress--n+1--higher-lower--reversed", + "law_id": "creep-rate-applied-stress", + "domain": "high-temperature deformation", + "category": "direct", + "law_sign": 1, + "numerical_sign": -1, + "physical_sign": -1, + "answer_vocabulary": "higher-lower", + "answer_order": "reversed", + "answer_words": [ + "higher", + "lower" + ], + "presented_words": [ + "lower", + "higher" + ], + "expected_answer": "lower", + "incorrect_answer": "higher", + "user": "For a superalloy deforming in one steady-state creep regime at fixed temperature, all other relevant physical conditions are unchanged. The applied uniaxial stress changes from 180 MPa to 60 MPa. What happens to steady creep rate? Reply with exactly one lowercase word from this ordered set: lower, higher." + }, + { + "prompt_id": "crk24--creep-rate-applied-stress--n-1--greater-smaller--standard", + "counterpart_prompt_id": "crk24--creep-rate-applied-stress--n+1--greater-smaller--standard", + "law_id": "creep-rate-applied-stress", + "domain": "high-temperature deformation", + "category": "direct", + "law_sign": 1, + "numerical_sign": -1, + "physical_sign": -1, + "answer_vocabulary": "greater-smaller", + "answer_order": "standard", + "answer_words": [ + "greater", + "smaller" + ], + "presented_words": [ + "greater", + "smaller" + ], + "expected_answer": "smaller", + "incorrect_answer": "greater", + "user": "For a superalloy deforming in one steady-state creep regime at fixed temperature, all other relevant physical conditions are unchanged. The applied uniaxial stress changes from 180 MPa to 60 MPa. What happens to steady creep rate? Reply with exactly one lowercase word from this ordered set: greater, smaller." + }, + { + "prompt_id": "crk24--creep-rate-applied-stress--n-1--greater-smaller--reversed", + "counterpart_prompt_id": "crk24--creep-rate-applied-stress--n+1--greater-smaller--reversed", + "law_id": "creep-rate-applied-stress", + "domain": "high-temperature deformation", + "category": "direct", + "law_sign": 1, + "numerical_sign": -1, + "physical_sign": -1, + "answer_vocabulary": "greater-smaller", + "answer_order": "reversed", + "answer_words": [ + "greater", + "smaller" + ], + "presented_words": [ + "smaller", + "greater" + ], + "expected_answer": "smaller", + "incorrect_answer": "greater", + "user": "For a superalloy deforming in one steady-state creep regime at fixed temperature, all other relevant physical conditions are unchanged. The applied uniaxial stress changes from 180 MPa to 60 MPa. What happens to steady creep rate? Reply with exactly one lowercase word from this ordered set: smaller, greater." + }, + { + "prompt_id": "crk24--creep-rate-applied-stress--n+1--higher-lower--standard", + "counterpart_prompt_id": "crk24--creep-rate-applied-stress--n-1--higher-lower--standard", + "law_id": "creep-rate-applied-stress", + "domain": "high-temperature deformation", + "category": "direct", + "law_sign": 1, + "numerical_sign": 1, + "physical_sign": 1, + "answer_vocabulary": "higher-lower", + "answer_order": "standard", + "answer_words": [ + "higher", + "lower" + ], + "presented_words": [ + "higher", + "lower" + ], + "expected_answer": "higher", + "incorrect_answer": "lower", + "user": "For a superalloy deforming in one steady-state creep regime at fixed temperature, all other relevant physical conditions are unchanged. The applied uniaxial stress changes from 60 MPa to 180 MPa. What happens to steady creep rate? Reply with exactly one lowercase word from this ordered set: higher, lower." + }, + { + "prompt_id": "crk24--creep-rate-applied-stress--n+1--higher-lower--reversed", + "counterpart_prompt_id": "crk24--creep-rate-applied-stress--n-1--higher-lower--reversed", + "law_id": "creep-rate-applied-stress", + "domain": "high-temperature deformation", + "category": "direct", + "law_sign": 1, + "numerical_sign": 1, + "physical_sign": 1, + "answer_vocabulary": "higher-lower", + "answer_order": "reversed", + "answer_words": [ + "higher", + "lower" + ], + "presented_words": [ + "lower", + "higher" + ], + "expected_answer": "higher", + "incorrect_answer": "lower", + "user": "For a superalloy deforming in one steady-state creep regime at fixed temperature, all other relevant physical conditions are unchanged. The applied uniaxial stress changes from 60 MPa to 180 MPa. What happens to steady creep rate? Reply with exactly one lowercase word from this ordered set: lower, higher." + }, + { + "prompt_id": "crk24--creep-rate-applied-stress--n+1--greater-smaller--standard", + "counterpart_prompt_id": "crk24--creep-rate-applied-stress--n-1--greater-smaller--standard", + "law_id": "creep-rate-applied-stress", + "domain": "high-temperature deformation", + "category": "direct", + "law_sign": 1, + "numerical_sign": 1, + "physical_sign": 1, + "answer_vocabulary": "greater-smaller", + "answer_order": "standard", + "answer_words": [ + "greater", + "smaller" + ], + "presented_words": [ + "greater", + "smaller" + ], + "expected_answer": "greater", + "incorrect_answer": "smaller", + "user": "For a superalloy deforming in one steady-state creep regime at fixed temperature, all other relevant physical conditions are unchanged. The applied uniaxial stress changes from 60 MPa to 180 MPa. What happens to steady creep rate? Reply with exactly one lowercase word from this ordered set: greater, smaller." + }, + { + "prompt_id": "crk24--creep-rate-applied-stress--n+1--greater-smaller--reversed", + "counterpart_prompt_id": "crk24--creep-rate-applied-stress--n-1--greater-smaller--reversed", + "law_id": "creep-rate-applied-stress", + "domain": "high-temperature deformation", + "category": "direct", + "law_sign": 1, + "numerical_sign": 1, + "physical_sign": 1, + "answer_vocabulary": "greater-smaller", + "answer_order": "reversed", + "answer_words": [ + "greater", + "smaller" + ], + "presented_words": [ + "smaller", + "greater" + ], + "expected_answer": "greater", + "incorrect_answer": "smaller", + "user": "For a superalloy deforming in one steady-state creep regime at fixed temperature, all other relevant physical conditions are unchanged. The applied uniaxial stress changes from 60 MPa to 180 MPa. What happens to steady creep rate? Reply with exactly one lowercase word from this ordered set: smaller, greater." + }, + { + "prompt_id": "crk24--dislocation-speed-shear-stress--n-1--higher-lower--standard", + "counterpart_prompt_id": "crk24--dislocation-speed-shear-stress--n+1--higher-lower--standard", + "law_id": "dislocation-speed-shear-stress", + "domain": "crystal plasticity", + "category": "direct", + "law_sign": 1, + "numerical_sign": -1, + "physical_sign": -1, + "answer_vocabulary": "higher-lower", + "answer_order": "standard", + "answer_words": [ + "higher", + "lower" + ], + "presented_words": [ + "higher", + "lower" + ], + "expected_answer": "lower", + "incorrect_answer": "higher", + "user": "For a crystal in a drag-controlled glide regime at fixed temperature, all other relevant physical conditions are unchanged. The resolved shear stress changes from 48 MPa to 8 MPa. What happens to mean dislocation glide speed? Reply with exactly one lowercase word from this ordered set: higher, lower." + }, + { + "prompt_id": "crk24--dislocation-speed-shear-stress--n-1--higher-lower--reversed", + "counterpart_prompt_id": "crk24--dislocation-speed-shear-stress--n+1--higher-lower--reversed", + "law_id": "dislocation-speed-shear-stress", + "domain": "crystal plasticity", + "category": "direct", + "law_sign": 1, + "numerical_sign": -1, + "physical_sign": -1, + "answer_vocabulary": "higher-lower", + "answer_order": "reversed", + "answer_words": [ + "higher", + "lower" + ], + "presented_words": [ + "lower", + "higher" + ], + "expected_answer": "lower", + "incorrect_answer": "higher", + "user": "For a crystal in a drag-controlled glide regime at fixed temperature, all other relevant physical conditions are unchanged. The resolved shear stress changes from 48 MPa to 8 MPa. What happens to mean dislocation glide speed? Reply with exactly one lowercase word from this ordered set: lower, higher." + }, + { + "prompt_id": "crk24--dislocation-speed-shear-stress--n-1--greater-smaller--standard", + "counterpart_prompt_id": "crk24--dislocation-speed-shear-stress--n+1--greater-smaller--standard", + "law_id": "dislocation-speed-shear-stress", + "domain": "crystal plasticity", + "category": "direct", + "law_sign": 1, + "numerical_sign": -1, + "physical_sign": -1, + "answer_vocabulary": "greater-smaller", + "answer_order": "standard", + "answer_words": [ + "greater", + "smaller" + ], + "presented_words": [ + "greater", + "smaller" + ], + "expected_answer": "smaller", + "incorrect_answer": "greater", + "user": "For a crystal in a drag-controlled glide regime at fixed temperature, all other relevant physical conditions are unchanged. The resolved shear stress changes from 48 MPa to 8 MPa. What happens to mean dislocation glide speed? Reply with exactly one lowercase word from this ordered set: greater, smaller." + }, + { + "prompt_id": "crk24--dislocation-speed-shear-stress--n-1--greater-smaller--reversed", + "counterpart_prompt_id": "crk24--dislocation-speed-shear-stress--n+1--greater-smaller--reversed", + "law_id": "dislocation-speed-shear-stress", + "domain": "crystal plasticity", + "category": "direct", + "law_sign": 1, + "numerical_sign": -1, + "physical_sign": -1, + "answer_vocabulary": "greater-smaller", + "answer_order": "reversed", + "answer_words": [ + "greater", + "smaller" + ], + "presented_words": [ + "smaller", + "greater" + ], + "expected_answer": "smaller", + "incorrect_answer": "greater", + "user": "For a crystal in a drag-controlled glide regime at fixed temperature, all other relevant physical conditions are unchanged. The resolved shear stress changes from 48 MPa to 8 MPa. What happens to mean dislocation glide speed? Reply with exactly one lowercase word from this ordered set: smaller, greater." + }, + { + "prompt_id": "crk24--dislocation-speed-shear-stress--n+1--higher-lower--standard", + "counterpart_prompt_id": "crk24--dislocation-speed-shear-stress--n-1--higher-lower--standard", + "law_id": "dislocation-speed-shear-stress", + "domain": "crystal plasticity", + "category": "direct", + "law_sign": 1, + "numerical_sign": 1, + "physical_sign": 1, + "answer_vocabulary": "higher-lower", + "answer_order": "standard", + "answer_words": [ + "higher", + "lower" + ], + "presented_words": [ + "higher", + "lower" + ], + "expected_answer": "higher", + "incorrect_answer": "lower", + "user": "For a crystal in a drag-controlled glide regime at fixed temperature, all other relevant physical conditions are unchanged. The resolved shear stress changes from 8 MPa to 48 MPa. What happens to mean dislocation glide speed? Reply with exactly one lowercase word from this ordered set: higher, lower." + }, + { + "prompt_id": "crk24--dislocation-speed-shear-stress--n+1--higher-lower--reversed", + "counterpart_prompt_id": "crk24--dislocation-speed-shear-stress--n-1--higher-lower--reversed", + "law_id": "dislocation-speed-shear-stress", + "domain": "crystal plasticity", + "category": "direct", + "law_sign": 1, + "numerical_sign": 1, + "physical_sign": 1, + "answer_vocabulary": "higher-lower", + "answer_order": "reversed", + "answer_words": [ + "higher", + "lower" + ], + "presented_words": [ + "lower", + "higher" + ], + "expected_answer": "higher", + "incorrect_answer": "lower", + "user": "For a crystal in a drag-controlled glide regime at fixed temperature, all other relevant physical conditions are unchanged. The resolved shear stress changes from 8 MPa to 48 MPa. What happens to mean dislocation glide speed? Reply with exactly one lowercase word from this ordered set: lower, higher." + }, + { + "prompt_id": "crk24--dislocation-speed-shear-stress--n+1--greater-smaller--standard", + "counterpart_prompt_id": "crk24--dislocation-speed-shear-stress--n-1--greater-smaller--standard", + "law_id": "dislocation-speed-shear-stress", + "domain": "crystal plasticity", + "category": "direct", + "law_sign": 1, + "numerical_sign": 1, + "physical_sign": 1, + "answer_vocabulary": "greater-smaller", + "answer_order": "standard", + "answer_words": [ + "greater", + "smaller" + ], + "presented_words": [ + "greater", + "smaller" + ], + "expected_answer": "greater", + "incorrect_answer": "smaller", + "user": "For a crystal in a drag-controlled glide regime at fixed temperature, all other relevant physical conditions are unchanged. The resolved shear stress changes from 8 MPa to 48 MPa. What happens to mean dislocation glide speed? Reply with exactly one lowercase word from this ordered set: greater, smaller." + }, + { + "prompt_id": "crk24--dislocation-speed-shear-stress--n+1--greater-smaller--reversed", + "counterpart_prompt_id": "crk24--dislocation-speed-shear-stress--n-1--greater-smaller--reversed", + "law_id": "dislocation-speed-shear-stress", + "domain": "crystal plasticity", + "category": "direct", + "law_sign": 1, + "numerical_sign": 1, + "physical_sign": 1, + "answer_vocabulary": "greater-smaller", + "answer_order": "reversed", + "answer_words": [ + "greater", + "smaller" + ], + "presented_words": [ + "smaller", + "greater" + ], + "expected_answer": "greater", + "incorrect_answer": "smaller", + "user": "For a crystal in a drag-controlled glide regime at fixed temperature, all other relevant physical conditions are unchanged. The resolved shear stress changes from 8 MPa to 48 MPa. What happens to mean dislocation glide speed? Reply with exactly one lowercase word from this ordered set: smaller, greater." + }, + { + "prompt_id": "crk24--vacancy-concentration-temperature--n-1--higher-lower--standard", + "counterpart_prompt_id": "crk24--vacancy-concentration-temperature--n+1--higher-lower--standard", + "law_id": "vacancy-concentration-temperature", + "domain": "point defects", + "category": "direct", + "law_sign": 1, + "numerical_sign": -1, + "physical_sign": -1, + "answer_vocabulary": "higher-lower", + "answer_order": "standard", + "answer_words": [ + "higher", + "lower" + ], + "presented_words": [ + "higher", + "lower" + ], + "expected_answer": "lower", + "incorrect_answer": "higher", + "user": "For a pure crystal below melting with fixed vacancy-formation energy, all other relevant physical conditions are unchanged. The absolute temperature changes from 1000 kelvin to 500 kelvin. What happens to equilibrium vacancy concentration? Reply with exactly one lowercase word from this ordered set: higher, lower." + }, + { + "prompt_id": "crk24--vacancy-concentration-temperature--n-1--higher-lower--reversed", + "counterpart_prompt_id": "crk24--vacancy-concentration-temperature--n+1--higher-lower--reversed", + "law_id": "vacancy-concentration-temperature", + "domain": "point defects", + "category": "direct", + "law_sign": 1, + "numerical_sign": -1, + "physical_sign": -1, + "answer_vocabulary": "higher-lower", + "answer_order": "reversed", + "answer_words": [ + "higher", + "lower" + ], + "presented_words": [ + "lower", + "higher" + ], + "expected_answer": "lower", + "incorrect_answer": "higher", + "user": "For a pure crystal below melting with fixed vacancy-formation energy, all other relevant physical conditions are unchanged. The absolute temperature changes from 1000 kelvin to 500 kelvin. What happens to equilibrium vacancy concentration? Reply with exactly one lowercase word from this ordered set: lower, higher." + }, + { + "prompt_id": "crk24--vacancy-concentration-temperature--n-1--greater-smaller--standard", + "counterpart_prompt_id": "crk24--vacancy-concentration-temperature--n+1--greater-smaller--standard", + "law_id": "vacancy-concentration-temperature", + "domain": "point defects", + "category": "direct", + "law_sign": 1, + "numerical_sign": -1, + "physical_sign": -1, + "answer_vocabulary": "greater-smaller", + "answer_order": "standard", + "answer_words": [ + "greater", + "smaller" + ], + "presented_words": [ + "greater", + "smaller" + ], + "expected_answer": "smaller", + "incorrect_answer": "greater", + "user": "For a pure crystal below melting with fixed vacancy-formation energy, all other relevant physical conditions are unchanged. The absolute temperature changes from 1000 kelvin to 500 kelvin. What happens to equilibrium vacancy concentration? Reply with exactly one lowercase word from this ordered set: greater, smaller." + }, + { + "prompt_id": "crk24--vacancy-concentration-temperature--n-1--greater-smaller--reversed", + "counterpart_prompt_id": "crk24--vacancy-concentration-temperature--n+1--greater-smaller--reversed", + "law_id": "vacancy-concentration-temperature", + "domain": "point defects", + "category": "direct", + "law_sign": 1, + "numerical_sign": -1, + "physical_sign": -1, + "answer_vocabulary": "greater-smaller", + "answer_order": "reversed", + "answer_words": [ + "greater", + "smaller" + ], + "presented_words": [ + "smaller", + "greater" + ], + "expected_answer": "smaller", + "incorrect_answer": "greater", + "user": "For a pure crystal below melting with fixed vacancy-formation energy, all other relevant physical conditions are unchanged. The absolute temperature changes from 1000 kelvin to 500 kelvin. What happens to equilibrium vacancy concentration? Reply with exactly one lowercase word from this ordered set: smaller, greater." + }, + { + "prompt_id": "crk24--vacancy-concentration-temperature--n+1--higher-lower--standard", + "counterpart_prompt_id": "crk24--vacancy-concentration-temperature--n-1--higher-lower--standard", + "law_id": "vacancy-concentration-temperature", + "domain": "point defects", + "category": "direct", + "law_sign": 1, + "numerical_sign": 1, + "physical_sign": 1, + "answer_vocabulary": "higher-lower", + "answer_order": "standard", + "answer_words": [ + "higher", + "lower" + ], + "presented_words": [ + "higher", + "lower" + ], + "expected_answer": "higher", + "incorrect_answer": "lower", + "user": "For a pure crystal below melting with fixed vacancy-formation energy, all other relevant physical conditions are unchanged. The absolute temperature changes from 500 kelvin to 1000 kelvin. What happens to equilibrium vacancy concentration? Reply with exactly one lowercase word from this ordered set: higher, lower." + }, + { + "prompt_id": "crk24--vacancy-concentration-temperature--n+1--higher-lower--reversed", + "counterpart_prompt_id": "crk24--vacancy-concentration-temperature--n-1--higher-lower--reversed", + "law_id": "vacancy-concentration-temperature", + "domain": "point defects", + "category": "direct", + "law_sign": 1, + "numerical_sign": 1, + "physical_sign": 1, + "answer_vocabulary": "higher-lower", + "answer_order": "reversed", + "answer_words": [ + "higher", + "lower" + ], + "presented_words": [ + "lower", + "higher" + ], + "expected_answer": "higher", + "incorrect_answer": "lower", + "user": "For a pure crystal below melting with fixed vacancy-formation energy, all other relevant physical conditions are unchanged. The absolute temperature changes from 500 kelvin to 1000 kelvin. What happens to equilibrium vacancy concentration? Reply with exactly one lowercase word from this ordered set: lower, higher." + }, + { + "prompt_id": "crk24--vacancy-concentration-temperature--n+1--greater-smaller--standard", + "counterpart_prompt_id": "crk24--vacancy-concentration-temperature--n-1--greater-smaller--standard", + "law_id": "vacancy-concentration-temperature", + "domain": "point defects", + "category": "direct", + "law_sign": 1, + "numerical_sign": 1, + "physical_sign": 1, + "answer_vocabulary": "greater-smaller", + "answer_order": "standard", + "answer_words": [ + "greater", + "smaller" + ], + "presented_words": [ + "greater", + "smaller" + ], + "expected_answer": "greater", + "incorrect_answer": "smaller", + "user": "For a pure crystal below melting with fixed vacancy-formation energy, all other relevant physical conditions are unchanged. The absolute temperature changes from 500 kelvin to 1000 kelvin. What happens to equilibrium vacancy concentration? Reply with exactly one lowercase word from this ordered set: greater, smaller." + }, + { + "prompt_id": "crk24--vacancy-concentration-temperature--n+1--greater-smaller--reversed", + "counterpart_prompt_id": "crk24--vacancy-concentration-temperature--n-1--greater-smaller--reversed", + "law_id": "vacancy-concentration-temperature", + "domain": "point defects", + "category": "direct", + "law_sign": 1, + "numerical_sign": 1, + "physical_sign": 1, + "answer_vocabulary": "greater-smaller", + "answer_order": "reversed", + "answer_words": [ + "greater", + "smaller" + ], + "presented_words": [ + "smaller", + "greater" + ], + "expected_answer": "greater", + "incorrect_answer": "smaller", + "user": "For a pure crystal below melting with fixed vacancy-formation energy, all other relevant physical conditions are unchanged. The absolute temperature changes from 500 kelvin to 1000 kelvin. What happens to equilibrium vacancy concentration? Reply with exactly one lowercase word from this ordered set: smaller, greater." + }, + { + "prompt_id": "crk24--sintering-neck-hold-time--n-1--higher-lower--standard", + "counterpart_prompt_id": "crk24--sintering-neck-hold-time--n+1--higher-lower--standard", + "law_id": "sintering-neck-hold-time", + "domain": "powder processing", + "category": "direct", + "law_sign": 1, + "numerical_sign": -1, + "physical_sign": -1, + "answer_vocabulary": "higher-lower", + "answer_order": "standard", + "answer_words": [ + "higher", + "lower" + ], + "presented_words": [ + "higher", + "lower" + ], + "expected_answer": "lower", + "incorrect_answer": "higher", + "user": "For two identical powder particles in one early-stage isothermal sintering regime, all other relevant physical conditions are unchanged. The sintering hold time changes from 50 minutes to 2 minutes. What happens to interparticle neck radius? Reply with exactly one lowercase word from this ordered set: higher, lower." + }, + { + "prompt_id": "crk24--sintering-neck-hold-time--n-1--higher-lower--reversed", + "counterpart_prompt_id": "crk24--sintering-neck-hold-time--n+1--higher-lower--reversed", + "law_id": "sintering-neck-hold-time", + "domain": "powder processing", + "category": "direct", + "law_sign": 1, + "numerical_sign": -1, + "physical_sign": -1, + "answer_vocabulary": "higher-lower", + "answer_order": "reversed", + "answer_words": [ + "higher", + "lower" + ], + "presented_words": [ + "lower", + "higher" + ], + "expected_answer": "lower", + "incorrect_answer": "higher", + "user": "For two identical powder particles in one early-stage isothermal sintering regime, all other relevant physical conditions are unchanged. The sintering hold time changes from 50 minutes to 2 minutes. What happens to interparticle neck radius? Reply with exactly one lowercase word from this ordered set: lower, higher." + }, + { + "prompt_id": "crk24--sintering-neck-hold-time--n-1--greater-smaller--standard", + "counterpart_prompt_id": "crk24--sintering-neck-hold-time--n+1--greater-smaller--standard", + "law_id": "sintering-neck-hold-time", + "domain": "powder processing", + "category": "direct", + "law_sign": 1, + "numerical_sign": -1, + "physical_sign": -1, + "answer_vocabulary": "greater-smaller", + "answer_order": "standard", + "answer_words": [ + "greater", + "smaller" + ], + "presented_words": [ + "greater", + "smaller" + ], + "expected_answer": "smaller", + "incorrect_answer": "greater", + "user": "For two identical powder particles in one early-stage isothermal sintering regime, all other relevant physical conditions are unchanged. The sintering hold time changes from 50 minutes to 2 minutes. What happens to interparticle neck radius? Reply with exactly one lowercase word from this ordered set: greater, smaller." + }, + { + "prompt_id": "crk24--sintering-neck-hold-time--n-1--greater-smaller--reversed", + "counterpart_prompt_id": "crk24--sintering-neck-hold-time--n+1--greater-smaller--reversed", + "law_id": "sintering-neck-hold-time", + "domain": "powder processing", + "category": "direct", + "law_sign": 1, + "numerical_sign": -1, + "physical_sign": -1, + "answer_vocabulary": "greater-smaller", + "answer_order": "reversed", + "answer_words": [ + "greater", + "smaller" + ], + "presented_words": [ + "smaller", + "greater" + ], + "expected_answer": "smaller", + "incorrect_answer": "greater", + "user": "For two identical powder particles in one early-stage isothermal sintering regime, all other relevant physical conditions are unchanged. The sintering hold time changes from 50 minutes to 2 minutes. What happens to interparticle neck radius? Reply with exactly one lowercase word from this ordered set: smaller, greater." + }, + { + "prompt_id": "crk24--sintering-neck-hold-time--n+1--higher-lower--standard", + "counterpart_prompt_id": "crk24--sintering-neck-hold-time--n-1--higher-lower--standard", + "law_id": "sintering-neck-hold-time", + "domain": "powder processing", + "category": "direct", + "law_sign": 1, + "numerical_sign": 1, + "physical_sign": 1, + "answer_vocabulary": "higher-lower", + "answer_order": "standard", + "answer_words": [ + "higher", + "lower" + ], + "presented_words": [ + "higher", + "lower" + ], + "expected_answer": "higher", + "incorrect_answer": "lower", + "user": "For two identical powder particles in one early-stage isothermal sintering regime, all other relevant physical conditions are unchanged. The sintering hold time changes from 2 minutes to 50 minutes. What happens to interparticle neck radius? Reply with exactly one lowercase word from this ordered set: higher, lower." + }, + { + "prompt_id": "crk24--sintering-neck-hold-time--n+1--higher-lower--reversed", + "counterpart_prompt_id": "crk24--sintering-neck-hold-time--n-1--higher-lower--reversed", + "law_id": "sintering-neck-hold-time", + "domain": "powder processing", + "category": "direct", + "law_sign": 1, + "numerical_sign": 1, + "physical_sign": 1, + "answer_vocabulary": "higher-lower", + "answer_order": "reversed", + "answer_words": [ + "higher", + "lower" + ], + "presented_words": [ + "lower", + "higher" + ], + "expected_answer": "higher", + "incorrect_answer": "lower", + "user": "For two identical powder particles in one early-stage isothermal sintering regime, all other relevant physical conditions are unchanged. The sintering hold time changes from 2 minutes to 50 minutes. What happens to interparticle neck radius? Reply with exactly one lowercase word from this ordered set: lower, higher." + }, + { + "prompt_id": "crk24--sintering-neck-hold-time--n+1--greater-smaller--standard", + "counterpart_prompt_id": "crk24--sintering-neck-hold-time--n-1--greater-smaller--standard", + "law_id": "sintering-neck-hold-time", + "domain": "powder processing", + "category": "direct", + "law_sign": 1, + "numerical_sign": 1, + "physical_sign": 1, + "answer_vocabulary": "greater-smaller", + "answer_order": "standard", + "answer_words": [ + "greater", + "smaller" + ], + "presented_words": [ + "greater", + "smaller" + ], + "expected_answer": "greater", + "incorrect_answer": "smaller", + "user": "For two identical powder particles in one early-stage isothermal sintering regime, all other relevant physical conditions are unchanged. The sintering hold time changes from 2 minutes to 50 minutes. What happens to interparticle neck radius? Reply with exactly one lowercase word from this ordered set: greater, smaller." + }, + { + "prompt_id": "crk24--sintering-neck-hold-time--n+1--greater-smaller--reversed", + "counterpart_prompt_id": "crk24--sintering-neck-hold-time--n-1--greater-smaller--reversed", + "law_id": "sintering-neck-hold-time", + "domain": "powder processing", + "category": "direct", + "law_sign": 1, + "numerical_sign": 1, + "physical_sign": 1, + "answer_vocabulary": "greater-smaller", + "answer_order": "reversed", + "answer_words": [ + "greater", + "smaller" + ], + "presented_words": [ + "smaller", + "greater" + ], + "expected_answer": "greater", + "incorrect_answer": "smaller", + "user": "For two identical powder particles in one early-stage isothermal sintering regime, all other relevant physical conditions are unchanged. The sintering hold time changes from 2 minutes to 50 minutes. What happens to interparticle neck radius? Reply with exactly one lowercase word from this ordered set: smaller, greater." + }, + { + "prompt_id": "crk24--crack-opening-tensile-load--n-1--higher-lower--standard", + "counterpart_prompt_id": "crk24--crack-opening-tensile-load--n+1--higher-lower--standard", + "law_id": "crack-opening-tensile-load", + "domain": "fracture mechanics", + "category": "direct", + "law_sign": 1, + "numerical_sign": -1, + "physical_sign": -1, + "answer_vocabulary": "higher-lower", + "answer_order": "standard", + "answer_words": [ + "higher", + "lower" + ], + "presented_words": [ + "higher", + "lower" + ], + "expected_answer": "lower", + "incorrect_answer": "higher", + "user": "For a cracked plate under small-scale yielding with fixed crack geometry, all other relevant physical conditions are unchanged. The remote tensile load changes from 28 kilonewtons to 4 kilonewtons. What happens to crack-opening displacement? Reply with exactly one lowercase word from this ordered set: higher, lower." + }, + { + "prompt_id": "crk24--crack-opening-tensile-load--n-1--higher-lower--reversed", + "counterpart_prompt_id": "crk24--crack-opening-tensile-load--n+1--higher-lower--reversed", + "law_id": "crack-opening-tensile-load", + "domain": "fracture mechanics", + "category": "direct", + "law_sign": 1, + "numerical_sign": -1, + "physical_sign": -1, + "answer_vocabulary": "higher-lower", + "answer_order": "reversed", + "answer_words": [ + "higher", + "lower" + ], + "presented_words": [ + "lower", + "higher" + ], + "expected_answer": "lower", + "incorrect_answer": "higher", + "user": "For a cracked plate under small-scale yielding with fixed crack geometry, all other relevant physical conditions are unchanged. The remote tensile load changes from 28 kilonewtons to 4 kilonewtons. What happens to crack-opening displacement? Reply with exactly one lowercase word from this ordered set: lower, higher." + }, + { + "prompt_id": "crk24--crack-opening-tensile-load--n-1--greater-smaller--standard", + "counterpart_prompt_id": "crk24--crack-opening-tensile-load--n+1--greater-smaller--standard", + "law_id": "crack-opening-tensile-load", + "domain": "fracture mechanics", + "category": "direct", + "law_sign": 1, + "numerical_sign": -1, + "physical_sign": -1, + "answer_vocabulary": "greater-smaller", + "answer_order": "standard", + "answer_words": [ + "greater", + "smaller" + ], + "presented_words": [ + "greater", + "smaller" + ], + "expected_answer": "smaller", + "incorrect_answer": "greater", + "user": "For a cracked plate under small-scale yielding with fixed crack geometry, all other relevant physical conditions are unchanged. The remote tensile load changes from 28 kilonewtons to 4 kilonewtons. What happens to crack-opening displacement? Reply with exactly one lowercase word from this ordered set: greater, smaller." + }, + { + "prompt_id": "crk24--crack-opening-tensile-load--n-1--greater-smaller--reversed", + "counterpart_prompt_id": "crk24--crack-opening-tensile-load--n+1--greater-smaller--reversed", + "law_id": "crack-opening-tensile-load", + "domain": "fracture mechanics", + "category": "direct", + "law_sign": 1, + "numerical_sign": -1, + "physical_sign": -1, + "answer_vocabulary": "greater-smaller", + "answer_order": "reversed", + "answer_words": [ + "greater", + "smaller" + ], + "presented_words": [ + "smaller", + "greater" + ], + "expected_answer": "smaller", + "incorrect_answer": "greater", + "user": "For a cracked plate under small-scale yielding with fixed crack geometry, all other relevant physical conditions are unchanged. The remote tensile load changes from 28 kilonewtons to 4 kilonewtons. What happens to crack-opening displacement? Reply with exactly one lowercase word from this ordered set: smaller, greater." + }, + { + "prompt_id": "crk24--crack-opening-tensile-load--n+1--higher-lower--standard", + "counterpart_prompt_id": "crk24--crack-opening-tensile-load--n-1--higher-lower--standard", + "law_id": "crack-opening-tensile-load", + "domain": "fracture mechanics", + "category": "direct", + "law_sign": 1, + "numerical_sign": 1, + "physical_sign": 1, + "answer_vocabulary": "higher-lower", + "answer_order": "standard", + "answer_words": [ + "higher", + "lower" + ], + "presented_words": [ + "higher", + "lower" + ], + "expected_answer": "higher", + "incorrect_answer": "lower", + "user": "For a cracked plate under small-scale yielding with fixed crack geometry, all other relevant physical conditions are unchanged. The remote tensile load changes from 4 kilonewtons to 28 kilonewtons. What happens to crack-opening displacement? Reply with exactly one lowercase word from this ordered set: higher, lower." + }, + { + "prompt_id": "crk24--crack-opening-tensile-load--n+1--higher-lower--reversed", + "counterpart_prompt_id": "crk24--crack-opening-tensile-load--n-1--higher-lower--reversed", + "law_id": "crack-opening-tensile-load", + "domain": "fracture mechanics", + "category": "direct", + "law_sign": 1, + "numerical_sign": 1, + "physical_sign": 1, + "answer_vocabulary": "higher-lower", + "answer_order": "reversed", + "answer_words": [ + "higher", + "lower" + ], + "presented_words": [ + "lower", + "higher" + ], + "expected_answer": "higher", + "incorrect_answer": "lower", + "user": "For a cracked plate under small-scale yielding with fixed crack geometry, all other relevant physical conditions are unchanged. The remote tensile load changes from 4 kilonewtons to 28 kilonewtons. What happens to crack-opening displacement? Reply with exactly one lowercase word from this ordered set: lower, higher." + }, + { + "prompt_id": "crk24--crack-opening-tensile-load--n+1--greater-smaller--standard", + "counterpart_prompt_id": "crk24--crack-opening-tensile-load--n-1--greater-smaller--standard", + "law_id": "crack-opening-tensile-load", + "domain": "fracture mechanics", + "category": "direct", + "law_sign": 1, + "numerical_sign": 1, + "physical_sign": 1, + "answer_vocabulary": "greater-smaller", + "answer_order": "standard", + "answer_words": [ + "greater", + "smaller" + ], + "presented_words": [ + "greater", + "smaller" + ], + "expected_answer": "greater", + "incorrect_answer": "smaller", + "user": "For a cracked plate under small-scale yielding with fixed crack geometry, all other relevant physical conditions are unchanged. The remote tensile load changes from 4 kilonewtons to 28 kilonewtons. What happens to crack-opening displacement? Reply with exactly one lowercase word from this ordered set: greater, smaller." + }, + { + "prompt_id": "crk24--crack-opening-tensile-load--n+1--greater-smaller--reversed", + "counterpart_prompt_id": "crk24--crack-opening-tensile-load--n-1--greater-smaller--reversed", + "law_id": "crack-opening-tensile-load", + "domain": "fracture mechanics", + "category": "direct", + "law_sign": 1, + "numerical_sign": 1, + "physical_sign": 1, + "answer_vocabulary": "greater-smaller", + "answer_order": "reversed", + "answer_words": [ + "greater", + "smaller" + ], + "presented_words": [ + "smaller", + "greater" + ], + "expected_answer": "greater", + "incorrect_answer": "smaller", + "user": "For a cracked plate under small-scale yielding with fixed crack geometry, all other relevant physical conditions are unchanged. The remote tensile load changes from 4 kilonewtons to 28 kilonewtons. What happens to crack-opening displacement? Reply with exactly one lowercase word from this ordered set: smaller, greater." + }, + { + "prompt_id": "crk24--fatigue-life-stress-amplitude--n-1--higher-lower--standard", + "counterpart_prompt_id": "crk24--fatigue-life-stress-amplitude--n+1--higher-lower--standard", + "law_id": "fatigue-life-stress-amplitude", + "domain": "fatigue", + "category": "inverse", + "law_sign": -1, + "numerical_sign": -1, + "physical_sign": 1, + "answer_vocabulary": "higher-lower", + "answer_order": "standard", + "answer_words": [ + "higher", + "lower" + ], + "presented_words": [ + "higher", + "lower" + ], + "expected_answer": "higher", + "incorrect_answer": "lower", + "user": "For a polished steel specimen in one high-cycle fatigue regime, all other relevant physical conditions are unchanged. The cyclic stress amplitude changes from 300 MPa to 120 MPa. What happens to fatigue life? Reply with exactly one lowercase word from this ordered set: higher, lower." + }, + { + "prompt_id": "crk24--fatigue-life-stress-amplitude--n-1--higher-lower--reversed", + "counterpart_prompt_id": "crk24--fatigue-life-stress-amplitude--n+1--higher-lower--reversed", + "law_id": "fatigue-life-stress-amplitude", + "domain": "fatigue", + "category": "inverse", + "law_sign": -1, + "numerical_sign": -1, + "physical_sign": 1, + "answer_vocabulary": "higher-lower", + "answer_order": "reversed", + "answer_words": [ + "higher", + "lower" + ], + "presented_words": [ + "lower", + "higher" + ], + "expected_answer": "higher", + "incorrect_answer": "lower", + "user": "For a polished steel specimen in one high-cycle fatigue regime, all other relevant physical conditions are unchanged. The cyclic stress amplitude changes from 300 MPa to 120 MPa. What happens to fatigue life? Reply with exactly one lowercase word from this ordered set: lower, higher." + }, + { + "prompt_id": "crk24--fatigue-life-stress-amplitude--n-1--greater-smaller--standard", + "counterpart_prompt_id": "crk24--fatigue-life-stress-amplitude--n+1--greater-smaller--standard", + "law_id": "fatigue-life-stress-amplitude", + "domain": "fatigue", + "category": "inverse", + "law_sign": -1, + "numerical_sign": -1, + "physical_sign": 1, + "answer_vocabulary": "greater-smaller", + "answer_order": "standard", + "answer_words": [ + "greater", + "smaller" + ], + "presented_words": [ + "greater", + "smaller" + ], + "expected_answer": "greater", + "incorrect_answer": "smaller", + "user": "For a polished steel specimen in one high-cycle fatigue regime, all other relevant physical conditions are unchanged. The cyclic stress amplitude changes from 300 MPa to 120 MPa. What happens to fatigue life? Reply with exactly one lowercase word from this ordered set: greater, smaller." + }, + { + "prompt_id": "crk24--fatigue-life-stress-amplitude--n-1--greater-smaller--reversed", + "counterpart_prompt_id": "crk24--fatigue-life-stress-amplitude--n+1--greater-smaller--reversed", + "law_id": "fatigue-life-stress-amplitude", + "domain": "fatigue", + "category": "inverse", + "law_sign": -1, + "numerical_sign": -1, + "physical_sign": 1, + "answer_vocabulary": "greater-smaller", + "answer_order": "reversed", + "answer_words": [ + "greater", + "smaller" + ], + "presented_words": [ + "smaller", + "greater" + ], + "expected_answer": "greater", + "incorrect_answer": "smaller", + "user": "For a polished steel specimen in one high-cycle fatigue regime, all other relevant physical conditions are unchanged. The cyclic stress amplitude changes from 300 MPa to 120 MPa. What happens to fatigue life? Reply with exactly one lowercase word from this ordered set: smaller, greater." + }, + { + "prompt_id": "crk24--fatigue-life-stress-amplitude--n+1--higher-lower--standard", + "counterpart_prompt_id": "crk24--fatigue-life-stress-amplitude--n-1--higher-lower--standard", + "law_id": "fatigue-life-stress-amplitude", + "domain": "fatigue", + "category": "inverse", + "law_sign": -1, + "numerical_sign": 1, + "physical_sign": -1, + "answer_vocabulary": "higher-lower", + "answer_order": "standard", + "answer_words": [ + "higher", + "lower" + ], + "presented_words": [ + "higher", + "lower" + ], + "expected_answer": "lower", + "incorrect_answer": "higher", + "user": "For a polished steel specimen in one high-cycle fatigue regime, all other relevant physical conditions are unchanged. The cyclic stress amplitude changes from 120 MPa to 300 MPa. What happens to fatigue life? Reply with exactly one lowercase word from this ordered set: higher, lower." + }, + { + "prompt_id": "crk24--fatigue-life-stress-amplitude--n+1--higher-lower--reversed", + "counterpart_prompt_id": "crk24--fatigue-life-stress-amplitude--n-1--higher-lower--reversed", + "law_id": "fatigue-life-stress-amplitude", + "domain": "fatigue", + "category": "inverse", + "law_sign": -1, + "numerical_sign": 1, + "physical_sign": -1, + "answer_vocabulary": "higher-lower", + "answer_order": "reversed", + "answer_words": [ + "higher", + "lower" + ], + "presented_words": [ + "lower", + "higher" + ], + "expected_answer": "lower", + "incorrect_answer": "higher", + "user": "For a polished steel specimen in one high-cycle fatigue regime, all other relevant physical conditions are unchanged. The cyclic stress amplitude changes from 120 MPa to 300 MPa. What happens to fatigue life? Reply with exactly one lowercase word from this ordered set: lower, higher." + }, + { + "prompt_id": "crk24--fatigue-life-stress-amplitude--n+1--greater-smaller--standard", + "counterpart_prompt_id": "crk24--fatigue-life-stress-amplitude--n-1--greater-smaller--standard", + "law_id": "fatigue-life-stress-amplitude", + "domain": "fatigue", + "category": "inverse", + "law_sign": -1, + "numerical_sign": 1, + "physical_sign": -1, + "answer_vocabulary": "greater-smaller", + "answer_order": "standard", + "answer_words": [ + "greater", + "smaller" + ], + "presented_words": [ + "greater", + "smaller" + ], + "expected_answer": "smaller", + "incorrect_answer": "greater", + "user": "For a polished steel specimen in one high-cycle fatigue regime, all other relevant physical conditions are unchanged. The cyclic stress amplitude changes from 120 MPa to 300 MPa. What happens to fatigue life? Reply with exactly one lowercase word from this ordered set: greater, smaller." + }, + { + "prompt_id": "crk24--fatigue-life-stress-amplitude--n+1--greater-smaller--reversed", + "counterpart_prompt_id": "crk24--fatigue-life-stress-amplitude--n-1--greater-smaller--reversed", + "law_id": "fatigue-life-stress-amplitude", + "domain": "fatigue", + "category": "inverse", + "law_sign": -1, + "numerical_sign": 1, + "physical_sign": -1, + "answer_vocabulary": "greater-smaller", + "answer_order": "reversed", + "answer_words": [ + "greater", + "smaller" + ], + "presented_words": [ + "smaller", + "greater" + ], + "expected_answer": "smaller", + "incorrect_answer": "greater", + "user": "For a polished steel specimen in one high-cycle fatigue regime, all other relevant physical conditions are unchanged. The cyclic stress amplitude changes from 120 MPa to 300 MPa. What happens to fatigue life? Reply with exactly one lowercase word from this ordered set: smaller, greater." + }, + { + "prompt_id": "crk24--temperature-gradient-conductivity--n-1--higher-lower--standard", + "counterpart_prompt_id": "crk24--temperature-gradient-conductivity--n+1--higher-lower--standard", + "law_id": "temperature-gradient-conductivity", + "domain": "thermal transport", + "category": "inverse", + "law_sign": -1, + "numerical_sign": -1, + "physical_sign": 1, + "answer_vocabulary": "higher-lower", + "answer_order": "standard", + "answer_words": [ + "higher", + "lower" + ], + "presented_words": [ + "higher", + "lower" + ], + "expected_answer": "higher", + "incorrect_answer": "lower", + "user": "For a homogeneous slab carrying a fixed steady heat flux, all other relevant physical conditions are unchanged. The thermal conductivity changes from 20 watts per meter-kelvin to 2 watts per meter-kelvin. What happens to required temperature-gradient magnitude? Reply with exactly one lowercase word from this ordered set: higher, lower." + }, + { + "prompt_id": "crk24--temperature-gradient-conductivity--n-1--higher-lower--reversed", + "counterpart_prompt_id": "crk24--temperature-gradient-conductivity--n+1--higher-lower--reversed", + "law_id": "temperature-gradient-conductivity", + "domain": "thermal transport", + "category": "inverse", + "law_sign": -1, + "numerical_sign": -1, + "physical_sign": 1, + "answer_vocabulary": "higher-lower", + "answer_order": "reversed", + "answer_words": [ + "higher", + "lower" + ], + "presented_words": [ + "lower", + "higher" + ], + "expected_answer": "higher", + "incorrect_answer": "lower", + "user": "For a homogeneous slab carrying a fixed steady heat flux, all other relevant physical conditions are unchanged. The thermal conductivity changes from 20 watts per meter-kelvin to 2 watts per meter-kelvin. What happens to required temperature-gradient magnitude? Reply with exactly one lowercase word from this ordered set: lower, higher." + }, + { + "prompt_id": "crk24--temperature-gradient-conductivity--n-1--greater-smaller--standard", + "counterpart_prompt_id": "crk24--temperature-gradient-conductivity--n+1--greater-smaller--standard", + "law_id": "temperature-gradient-conductivity", + "domain": "thermal transport", + "category": "inverse", + "law_sign": -1, + "numerical_sign": -1, + "physical_sign": 1, + "answer_vocabulary": "greater-smaller", + "answer_order": "standard", + "answer_words": [ + "greater", + "smaller" + ], + "presented_words": [ + "greater", + "smaller" + ], + "expected_answer": "greater", + "incorrect_answer": "smaller", + "user": "For a homogeneous slab carrying a fixed steady heat flux, all other relevant physical conditions are unchanged. The thermal conductivity changes from 20 watts per meter-kelvin to 2 watts per meter-kelvin. What happens to required temperature-gradient magnitude? Reply with exactly one lowercase word from this ordered set: greater, smaller." + }, + { + "prompt_id": "crk24--temperature-gradient-conductivity--n-1--greater-smaller--reversed", + "counterpart_prompt_id": "crk24--temperature-gradient-conductivity--n+1--greater-smaller--reversed", + "law_id": "temperature-gradient-conductivity", + "domain": "thermal transport", + "category": "inverse", + "law_sign": -1, + "numerical_sign": -1, + "physical_sign": 1, + "answer_vocabulary": "greater-smaller", + "answer_order": "reversed", + "answer_words": [ + "greater", + "smaller" + ], + "presented_words": [ + "smaller", + "greater" + ], + "expected_answer": "greater", + "incorrect_answer": "smaller", + "user": "For a homogeneous slab carrying a fixed steady heat flux, all other relevant physical conditions are unchanged. The thermal conductivity changes from 20 watts per meter-kelvin to 2 watts per meter-kelvin. What happens to required temperature-gradient magnitude? Reply with exactly one lowercase word from this ordered set: smaller, greater." + }, + { + "prompt_id": "crk24--temperature-gradient-conductivity--n+1--higher-lower--standard", + "counterpart_prompt_id": "crk24--temperature-gradient-conductivity--n-1--higher-lower--standard", + "law_id": "temperature-gradient-conductivity", + "domain": "thermal transport", + "category": "inverse", + "law_sign": -1, + "numerical_sign": 1, + "physical_sign": -1, + "answer_vocabulary": "higher-lower", + "answer_order": "standard", + "answer_words": [ + "higher", + "lower" + ], + "presented_words": [ + "higher", + "lower" + ], + "expected_answer": "lower", + "incorrect_answer": "higher", + "user": "For a homogeneous slab carrying a fixed steady heat flux, all other relevant physical conditions are unchanged. The thermal conductivity changes from 2 watts per meter-kelvin to 20 watts per meter-kelvin. What happens to required temperature-gradient magnitude? Reply with exactly one lowercase word from this ordered set: higher, lower." + }, + { + "prompt_id": "crk24--temperature-gradient-conductivity--n+1--higher-lower--reversed", + "counterpart_prompt_id": "crk24--temperature-gradient-conductivity--n-1--higher-lower--reversed", + "law_id": "temperature-gradient-conductivity", + "domain": "thermal transport", + "category": "inverse", + "law_sign": -1, + "numerical_sign": 1, + "physical_sign": -1, + "answer_vocabulary": "higher-lower", + "answer_order": "reversed", + "answer_words": [ + "higher", + "lower" + ], + "presented_words": [ + "lower", + "higher" + ], + "expected_answer": "lower", + "incorrect_answer": "higher", + "user": "For a homogeneous slab carrying a fixed steady heat flux, all other relevant physical conditions are unchanged. The thermal conductivity changes from 2 watts per meter-kelvin to 20 watts per meter-kelvin. What happens to required temperature-gradient magnitude? Reply with exactly one lowercase word from this ordered set: lower, higher." + }, + { + "prompt_id": "crk24--temperature-gradient-conductivity--n+1--greater-smaller--standard", + "counterpart_prompt_id": "crk24--temperature-gradient-conductivity--n-1--greater-smaller--standard", + "law_id": "temperature-gradient-conductivity", + "domain": "thermal transport", + "category": "inverse", + "law_sign": -1, + "numerical_sign": 1, + "physical_sign": -1, + "answer_vocabulary": "greater-smaller", + "answer_order": "standard", + "answer_words": [ + "greater", + "smaller" + ], + "presented_words": [ + "greater", + "smaller" + ], + "expected_answer": "smaller", + "incorrect_answer": "greater", + "user": "For a homogeneous slab carrying a fixed steady heat flux, all other relevant physical conditions are unchanged. The thermal conductivity changes from 2 watts per meter-kelvin to 20 watts per meter-kelvin. What happens to required temperature-gradient magnitude? Reply with exactly one lowercase word from this ordered set: greater, smaller." + }, + { + "prompt_id": "crk24--temperature-gradient-conductivity--n+1--greater-smaller--reversed", + "counterpart_prompt_id": "crk24--temperature-gradient-conductivity--n-1--greater-smaller--reversed", + "law_id": "temperature-gradient-conductivity", + "domain": "thermal transport", + "category": "inverse", + "law_sign": -1, + "numerical_sign": 1, + "physical_sign": -1, + "answer_vocabulary": "greater-smaller", + "answer_order": "reversed", + "answer_words": [ + "greater", + "smaller" + ], + "presented_words": [ + "smaller", + "greater" + ], + "expected_answer": "smaller", + "incorrect_answer": "greater", + "user": "For a homogeneous slab carrying a fixed steady heat flux, all other relevant physical conditions are unchanged. The thermal conductivity changes from 2 watts per meter-kelvin to 20 watts per meter-kelvin. What happens to required temperature-gradient magnitude? Reply with exactly one lowercase word from this ordered set: smaller, greater." + }, + { + "prompt_id": "crk24--homogenization-time-diffusivity--n-1--higher-lower--standard", + "counterpart_prompt_id": "crk24--homogenization-time-diffusivity--n+1--higher-lower--standard", + "law_id": "homogenization-time-diffusivity", + "domain": "mass transport", + "category": "inverse", + "law_sign": -1, + "numerical_sign": -1, + "physical_sign": 1, + "answer_vocabulary": "higher-lower", + "answer_order": "standard", + "answer_words": [ + "higher", + "lower" + ], + "presented_words": [ + "higher", + "lower" + ], + "expected_answer": "higher", + "incorrect_answer": "lower", + "user": "For an alloy diffusion couple with fixed homogenization distance, all other relevant physical conditions are unchanged. The solute diffusivity changes from 1e-13 square meters per second to 1e-15 square meters per second. What happens to characteristic homogenization time? Reply with exactly one lowercase word from this ordered set: higher, lower." + }, + { + "prompt_id": "crk24--homogenization-time-diffusivity--n-1--higher-lower--reversed", + "counterpart_prompt_id": "crk24--homogenization-time-diffusivity--n+1--higher-lower--reversed", + "law_id": "homogenization-time-diffusivity", + "domain": "mass transport", + "category": "inverse", + "law_sign": -1, + "numerical_sign": -1, + "physical_sign": 1, + "answer_vocabulary": "higher-lower", + "answer_order": "reversed", + "answer_words": [ + "higher", + "lower" + ], + "presented_words": [ + "lower", + "higher" + ], + "expected_answer": "higher", + "incorrect_answer": "lower", + "user": "For an alloy diffusion couple with fixed homogenization distance, all other relevant physical conditions are unchanged. The solute diffusivity changes from 1e-13 square meters per second to 1e-15 square meters per second. What happens to characteristic homogenization time? Reply with exactly one lowercase word from this ordered set: lower, higher." + }, + { + "prompt_id": "crk24--homogenization-time-diffusivity--n-1--greater-smaller--standard", + "counterpart_prompt_id": "crk24--homogenization-time-diffusivity--n+1--greater-smaller--standard", + "law_id": "homogenization-time-diffusivity", + "domain": "mass transport", + "category": "inverse", + "law_sign": -1, + "numerical_sign": -1, + "physical_sign": 1, + "answer_vocabulary": "greater-smaller", + "answer_order": "standard", + "answer_words": [ + "greater", + "smaller" + ], + "presented_words": [ + "greater", + "smaller" + ], + "expected_answer": "greater", + "incorrect_answer": "smaller", + "user": "For an alloy diffusion couple with fixed homogenization distance, all other relevant physical conditions are unchanged. The solute diffusivity changes from 1e-13 square meters per second to 1e-15 square meters per second. What happens to characteristic homogenization time? Reply with exactly one lowercase word from this ordered set: greater, smaller." + }, + { + "prompt_id": "crk24--homogenization-time-diffusivity--n-1--greater-smaller--reversed", + "counterpart_prompt_id": "crk24--homogenization-time-diffusivity--n+1--greater-smaller--reversed", + "law_id": "homogenization-time-diffusivity", + "domain": "mass transport", + "category": "inverse", + "law_sign": -1, + "numerical_sign": -1, + "physical_sign": 1, + "answer_vocabulary": "greater-smaller", + "answer_order": "reversed", + "answer_words": [ + "greater", + "smaller" + ], + "presented_words": [ + "smaller", + "greater" + ], + "expected_answer": "greater", + "incorrect_answer": "smaller", + "user": "For an alloy diffusion couple with fixed homogenization distance, all other relevant physical conditions are unchanged. The solute diffusivity changes from 1e-13 square meters per second to 1e-15 square meters per second. What happens to characteristic homogenization time? Reply with exactly one lowercase word from this ordered set: smaller, greater." + }, + { + "prompt_id": "crk24--homogenization-time-diffusivity--n+1--higher-lower--standard", + "counterpart_prompt_id": "crk24--homogenization-time-diffusivity--n-1--higher-lower--standard", + "law_id": "homogenization-time-diffusivity", + "domain": "mass transport", + "category": "inverse", + "law_sign": -1, + "numerical_sign": 1, + "physical_sign": -1, + "answer_vocabulary": "higher-lower", + "answer_order": "standard", + "answer_words": [ + "higher", + "lower" + ], + "presented_words": [ + "higher", + "lower" + ], + "expected_answer": "lower", + "incorrect_answer": "higher", + "user": "For an alloy diffusion couple with fixed homogenization distance, all other relevant physical conditions are unchanged. The solute diffusivity changes from 1e-15 square meters per second to 1e-13 square meters per second. What happens to characteristic homogenization time? Reply with exactly one lowercase word from this ordered set: higher, lower." + }, + { + "prompt_id": "crk24--homogenization-time-diffusivity--n+1--higher-lower--reversed", + "counterpart_prompt_id": "crk24--homogenization-time-diffusivity--n-1--higher-lower--reversed", + "law_id": "homogenization-time-diffusivity", + "domain": "mass transport", + "category": "inverse", + "law_sign": -1, + "numerical_sign": 1, + "physical_sign": -1, + "answer_vocabulary": "higher-lower", + "answer_order": "reversed", + "answer_words": [ + "higher", + "lower" + ], + "presented_words": [ + "lower", + "higher" + ], + "expected_answer": "lower", + "incorrect_answer": "higher", + "user": "For an alloy diffusion couple with fixed homogenization distance, all other relevant physical conditions are unchanged. The solute diffusivity changes from 1e-15 square meters per second to 1e-13 square meters per second. What happens to characteristic homogenization time? Reply with exactly one lowercase word from this ordered set: lower, higher." + }, + { + "prompt_id": "crk24--homogenization-time-diffusivity--n+1--greater-smaller--standard", + "counterpart_prompt_id": "crk24--homogenization-time-diffusivity--n-1--greater-smaller--standard", + "law_id": "homogenization-time-diffusivity", + "domain": "mass transport", + "category": "inverse", + "law_sign": -1, + "numerical_sign": 1, + "physical_sign": -1, + "answer_vocabulary": "greater-smaller", + "answer_order": "standard", + "answer_words": [ + "greater", + "smaller" + ], + "presented_words": [ + "greater", + "smaller" + ], + "expected_answer": "smaller", + "incorrect_answer": "greater", + "user": "For an alloy diffusion couple with fixed homogenization distance, all other relevant physical conditions are unchanged. The solute diffusivity changes from 1e-15 square meters per second to 1e-13 square meters per second. What happens to characteristic homogenization time? Reply with exactly one lowercase word from this ordered set: greater, smaller." + }, + { + "prompt_id": "crk24--homogenization-time-diffusivity--n+1--greater-smaller--reversed", + "counterpart_prompt_id": "crk24--homogenization-time-diffusivity--n-1--greater-smaller--reversed", + "law_id": "homogenization-time-diffusivity", + "domain": "mass transport", + "category": "inverse", + "law_sign": -1, + "numerical_sign": 1, + "physical_sign": -1, + "answer_vocabulary": "greater-smaller", + "answer_order": "reversed", + "answer_words": [ + "greater", + "smaller" + ], + "presented_words": [ + "smaller", + "greater" + ], + "expected_answer": "smaller", + "incorrect_answer": "greater", + "user": "For an alloy diffusion couple with fixed homogenization distance, all other relevant physical conditions are unchanged. The solute diffusivity changes from 1e-15 square meters per second to 1e-13 square meters per second. What happens to characteristic homogenization time? Reply with exactly one lowercase word from this ordered set: smaller, greater." + }, + { + "prompt_id": "crk24--resistivity-carrier-mobility--n-1--higher-lower--standard", + "counterpart_prompt_id": "crk24--resistivity-carrier-mobility--n+1--higher-lower--standard", + "law_id": "resistivity-carrier-mobility", + "domain": "electronic materials", + "category": "inverse", + "law_sign": -1, + "numerical_sign": -1, + "physical_sign": 1, + "answer_vocabulary": "higher-lower", + "answer_order": "standard", + "answer_words": [ + "higher", + "lower" + ], + "presented_words": [ + "higher", + "lower" + ], + "expected_answer": "higher", + "incorrect_answer": "lower", + "user": "For a semiconductor with fixed carrier concentration and charge, all other relevant physical conditions are unchanged. The carrier mobility changes from 900 square centimeters per volt-second to 100 square centimeters per volt-second. What happens to electrical resistivity? Reply with exactly one lowercase word from this ordered set: higher, lower." + }, + { + "prompt_id": "crk24--resistivity-carrier-mobility--n-1--higher-lower--reversed", + "counterpart_prompt_id": "crk24--resistivity-carrier-mobility--n+1--higher-lower--reversed", + "law_id": "resistivity-carrier-mobility", + "domain": "electronic materials", + "category": "inverse", + "law_sign": -1, + "numerical_sign": -1, + "physical_sign": 1, + "answer_vocabulary": "higher-lower", + "answer_order": "reversed", + "answer_words": [ + "higher", + "lower" + ], + "presented_words": [ + "lower", + "higher" + ], + "expected_answer": "higher", + "incorrect_answer": "lower", + "user": "For a semiconductor with fixed carrier concentration and charge, all other relevant physical conditions are unchanged. The carrier mobility changes from 900 square centimeters per volt-second to 100 square centimeters per volt-second. What happens to electrical resistivity? Reply with exactly one lowercase word from this ordered set: lower, higher." + }, + { + "prompt_id": "crk24--resistivity-carrier-mobility--n-1--greater-smaller--standard", + "counterpart_prompt_id": "crk24--resistivity-carrier-mobility--n+1--greater-smaller--standard", + "law_id": "resistivity-carrier-mobility", + "domain": "electronic materials", + "category": "inverse", + "law_sign": -1, + "numerical_sign": -1, + "physical_sign": 1, + "answer_vocabulary": "greater-smaller", + "answer_order": "standard", + "answer_words": [ + "greater", + "smaller" + ], + "presented_words": [ + "greater", + "smaller" + ], + "expected_answer": "greater", + "incorrect_answer": "smaller", + "user": "For a semiconductor with fixed carrier concentration and charge, all other relevant physical conditions are unchanged. The carrier mobility changes from 900 square centimeters per volt-second to 100 square centimeters per volt-second. What happens to electrical resistivity? Reply with exactly one lowercase word from this ordered set: greater, smaller." + }, + { + "prompt_id": "crk24--resistivity-carrier-mobility--n-1--greater-smaller--reversed", + "counterpart_prompt_id": "crk24--resistivity-carrier-mobility--n+1--greater-smaller--reversed", + "law_id": "resistivity-carrier-mobility", + "domain": "electronic materials", + "category": "inverse", + "law_sign": -1, + "numerical_sign": -1, + "physical_sign": 1, + "answer_vocabulary": "greater-smaller", + "answer_order": "reversed", + "answer_words": [ + "greater", + "smaller" + ], + "presented_words": [ + "smaller", + "greater" + ], + "expected_answer": "greater", + "incorrect_answer": "smaller", + "user": "For a semiconductor with fixed carrier concentration and charge, all other relevant physical conditions are unchanged. The carrier mobility changes from 900 square centimeters per volt-second to 100 square centimeters per volt-second. What happens to electrical resistivity? Reply with exactly one lowercase word from this ordered set: smaller, greater." + }, + { + "prompt_id": "crk24--resistivity-carrier-mobility--n+1--higher-lower--standard", + "counterpart_prompt_id": "crk24--resistivity-carrier-mobility--n-1--higher-lower--standard", + "law_id": "resistivity-carrier-mobility", + "domain": "electronic materials", + "category": "inverse", + "law_sign": -1, + "numerical_sign": 1, + "physical_sign": -1, + "answer_vocabulary": "higher-lower", + "answer_order": "standard", + "answer_words": [ + "higher", + "lower" + ], + "presented_words": [ + "higher", + "lower" + ], + "expected_answer": "lower", + "incorrect_answer": "higher", + "user": "For a semiconductor with fixed carrier concentration and charge, all other relevant physical conditions are unchanged. The carrier mobility changes from 100 square centimeters per volt-second to 900 square centimeters per volt-second. What happens to electrical resistivity? Reply with exactly one lowercase word from this ordered set: higher, lower." + }, + { + "prompt_id": "crk24--resistivity-carrier-mobility--n+1--higher-lower--reversed", + "counterpart_prompt_id": "crk24--resistivity-carrier-mobility--n-1--higher-lower--reversed", + "law_id": "resistivity-carrier-mobility", + "domain": "electronic materials", + "category": "inverse", + "law_sign": -1, + "numerical_sign": 1, + "physical_sign": -1, + "answer_vocabulary": "higher-lower", + "answer_order": "reversed", + "answer_words": [ + "higher", + "lower" + ], + "presented_words": [ + "lower", + "higher" + ], + "expected_answer": "lower", + "incorrect_answer": "higher", + "user": "For a semiconductor with fixed carrier concentration and charge, all other relevant physical conditions are unchanged. The carrier mobility changes from 100 square centimeters per volt-second to 900 square centimeters per volt-second. What happens to electrical resistivity? Reply with exactly one lowercase word from this ordered set: lower, higher." + }, + { + "prompt_id": "crk24--resistivity-carrier-mobility--n+1--greater-smaller--standard", + "counterpart_prompt_id": "crk24--resistivity-carrier-mobility--n-1--greater-smaller--standard", + "law_id": "resistivity-carrier-mobility", + "domain": "electronic materials", + "category": "inverse", + "law_sign": -1, + "numerical_sign": 1, + "physical_sign": -1, + "answer_vocabulary": "greater-smaller", + "answer_order": "standard", + "answer_words": [ + "greater", + "smaller" + ], + "presented_words": [ + "greater", + "smaller" + ], + "expected_answer": "smaller", + "incorrect_answer": "greater", + "user": "For a semiconductor with fixed carrier concentration and charge, all other relevant physical conditions are unchanged. The carrier mobility changes from 100 square centimeters per volt-second to 900 square centimeters per volt-second. What happens to electrical resistivity? Reply with exactly one lowercase word from this ordered set: greater, smaller." + }, + { + "prompt_id": "crk24--resistivity-carrier-mobility--n+1--greater-smaller--reversed", + "counterpart_prompt_id": "crk24--resistivity-carrier-mobility--n-1--greater-smaller--reversed", + "law_id": "resistivity-carrier-mobility", + "domain": "electronic materials", + "category": "inverse", + "law_sign": -1, + "numerical_sign": 1, + "physical_sign": -1, + "answer_vocabulary": "greater-smaller", + "answer_order": "reversed", + "answer_words": [ + "greater", + "smaller" + ], + "presented_words": [ + "smaller", + "greater" + ], + "expected_answer": "smaller", + "incorrect_answer": "greater", + "user": "For a semiconductor with fixed carrier concentration and charge, all other relevant physical conditions are unchanged. The carrier mobility changes from 100 square centimeters per volt-second to 900 square centimeters per volt-second. What happens to electrical resistivity? Reply with exactly one lowercase word from this ordered set: smaller, greater." + }, + { + "prompt_id": "crk24--cantilever-frequency-length--n-1--higher-lower--standard", + "counterpart_prompt_id": "crk24--cantilever-frequency-length--n+1--higher-lower--standard", + "law_id": "cantilever-frequency-length", + "domain": "structural dynamics", + "category": "inverse", + "law_sign": -1, + "numerical_sign": -1, + "physical_sign": 1, + "answer_vocabulary": "higher-lower", + "answer_order": "standard", + "answer_words": [ + "higher", + "lower" + ], + "presented_words": [ + "higher", + "lower" + ], + "expected_answer": "higher", + "incorrect_answer": "lower", + "user": "For a uniform cantilever with fixed material, cross-section, and end condition, all other relevant physical conditions are unchanged. The cantilever length changes from 80 millimeters to 20 millimeters. What happens to fundamental resonance frequency? Reply with exactly one lowercase word from this ordered set: higher, lower." + }, + { + "prompt_id": "crk24--cantilever-frequency-length--n-1--higher-lower--reversed", + "counterpart_prompt_id": "crk24--cantilever-frequency-length--n+1--higher-lower--reversed", + "law_id": "cantilever-frequency-length", + "domain": "structural dynamics", + "category": "inverse", + "law_sign": -1, + "numerical_sign": -1, + "physical_sign": 1, + "answer_vocabulary": "higher-lower", + "answer_order": "reversed", + "answer_words": [ + "higher", + "lower" + ], + "presented_words": [ + "lower", + "higher" + ], + "expected_answer": "higher", + "incorrect_answer": "lower", + "user": "For a uniform cantilever with fixed material, cross-section, and end condition, all other relevant physical conditions are unchanged. The cantilever length changes from 80 millimeters to 20 millimeters. What happens to fundamental resonance frequency? Reply with exactly one lowercase word from this ordered set: lower, higher." + }, + { + "prompt_id": "crk24--cantilever-frequency-length--n-1--greater-smaller--standard", + "counterpart_prompt_id": "crk24--cantilever-frequency-length--n+1--greater-smaller--standard", + "law_id": "cantilever-frequency-length", + "domain": "structural dynamics", + "category": "inverse", + "law_sign": -1, + "numerical_sign": -1, + "physical_sign": 1, + "answer_vocabulary": "greater-smaller", + "answer_order": "standard", + "answer_words": [ + "greater", + "smaller" + ], + "presented_words": [ + "greater", + "smaller" + ], + "expected_answer": "greater", + "incorrect_answer": "smaller", + "user": "For a uniform cantilever with fixed material, cross-section, and end condition, all other relevant physical conditions are unchanged. The cantilever length changes from 80 millimeters to 20 millimeters. What happens to fundamental resonance frequency? Reply with exactly one lowercase word from this ordered set: greater, smaller." + }, + { + "prompt_id": "crk24--cantilever-frequency-length--n-1--greater-smaller--reversed", + "counterpart_prompt_id": "crk24--cantilever-frequency-length--n+1--greater-smaller--reversed", + "law_id": "cantilever-frequency-length", + "domain": "structural dynamics", + "category": "inverse", + "law_sign": -1, + "numerical_sign": -1, + "physical_sign": 1, + "answer_vocabulary": "greater-smaller", + "answer_order": "reversed", + "answer_words": [ + "greater", + "smaller" + ], + "presented_words": [ + "smaller", + "greater" + ], + "expected_answer": "greater", + "incorrect_answer": "smaller", + "user": "For a uniform cantilever with fixed material, cross-section, and end condition, all other relevant physical conditions are unchanged. The cantilever length changes from 80 millimeters to 20 millimeters. What happens to fundamental resonance frequency? Reply with exactly one lowercase word from this ordered set: smaller, greater." + }, + { + "prompt_id": "crk24--cantilever-frequency-length--n+1--higher-lower--standard", + "counterpart_prompt_id": "crk24--cantilever-frequency-length--n-1--higher-lower--standard", + "law_id": "cantilever-frequency-length", + "domain": "structural dynamics", + "category": "inverse", + "law_sign": -1, + "numerical_sign": 1, + "physical_sign": -1, + "answer_vocabulary": "higher-lower", + "answer_order": "standard", + "answer_words": [ + "higher", + "lower" + ], + "presented_words": [ + "higher", + "lower" + ], + "expected_answer": "lower", + "incorrect_answer": "higher", + "user": "For a uniform cantilever with fixed material, cross-section, and end condition, all other relevant physical conditions are unchanged. The cantilever length changes from 20 millimeters to 80 millimeters. What happens to fundamental resonance frequency? Reply with exactly one lowercase word from this ordered set: higher, lower." + }, + { + "prompt_id": "crk24--cantilever-frequency-length--n+1--higher-lower--reversed", + "counterpart_prompt_id": "crk24--cantilever-frequency-length--n-1--higher-lower--reversed", + "law_id": "cantilever-frequency-length", + "domain": "structural dynamics", + "category": "inverse", + "law_sign": -1, + "numerical_sign": 1, + "physical_sign": -1, + "answer_vocabulary": "higher-lower", + "answer_order": "reversed", + "answer_words": [ + "higher", + "lower" + ], + "presented_words": [ + "lower", + "higher" + ], + "expected_answer": "lower", + "incorrect_answer": "higher", + "user": "For a uniform cantilever with fixed material, cross-section, and end condition, all other relevant physical conditions are unchanged. The cantilever length changes from 20 millimeters to 80 millimeters. What happens to fundamental resonance frequency? Reply with exactly one lowercase word from this ordered set: lower, higher." + }, + { + "prompt_id": "crk24--cantilever-frequency-length--n+1--greater-smaller--standard", + "counterpart_prompt_id": "crk24--cantilever-frequency-length--n-1--greater-smaller--standard", + "law_id": "cantilever-frequency-length", + "domain": "structural dynamics", + "category": "inverse", + "law_sign": -1, + "numerical_sign": 1, + "physical_sign": -1, + "answer_vocabulary": "greater-smaller", + "answer_order": "standard", + "answer_words": [ + "greater", + "smaller" + ], + "presented_words": [ + "greater", + "smaller" + ], + "expected_answer": "smaller", + "incorrect_answer": "greater", + "user": "For a uniform cantilever with fixed material, cross-section, and end condition, all other relevant physical conditions are unchanged. The cantilever length changes from 20 millimeters to 80 millimeters. What happens to fundamental resonance frequency? Reply with exactly one lowercase word from this ordered set: greater, smaller." + }, + { + "prompt_id": "crk24--cantilever-frequency-length--n+1--greater-smaller--reversed", + "counterpart_prompt_id": "crk24--cantilever-frequency-length--n-1--greater-smaller--reversed", + "law_id": "cantilever-frequency-length", + "domain": "structural dynamics", + "category": "inverse", + "law_sign": -1, + "numerical_sign": 1, + "physical_sign": -1, + "answer_vocabulary": "greater-smaller", + "answer_order": "reversed", + "answer_words": [ + "greater", + "smaller" + ], + "presented_words": [ + "smaller", + "greater" + ], + "expected_answer": "smaller", + "incorrect_answer": "greater", + "user": "For a uniform cantilever with fixed material, cross-section, and end condition, all other relevant physical conditions are unchanged. The cantilever length changes from 20 millimeters to 80 millimeters. What happens to fundamental resonance frequency? Reply with exactly one lowercase word from this ordered set: smaller, greater." + }, + { + "prompt_id": "crk24--strength-porosity--n-1--higher-lower--standard", + "counterpart_prompt_id": "crk24--strength-porosity--n+1--higher-lower--standard", + "law_id": "strength-porosity", + "domain": "porous materials", + "category": "inverse", + "law_sign": -1, + "numerical_sign": -1, + "physical_sign": 1, + "answer_vocabulary": "higher-lower", + "answer_order": "standard", + "answer_words": [ + "higher", + "lower" + ], + "presented_words": [ + "higher", + "lower" + ], + "expected_answer": "higher", + "incorrect_answer": "lower", + "user": "For a brittle ceramic with fixed composition and pore morphology, all other relevant physical conditions are unchanged. The porosity changes from 18 percent to 2 percent. What happens to tensile strength? Reply with exactly one lowercase word from this ordered set: higher, lower." + }, + { + "prompt_id": "crk24--strength-porosity--n-1--higher-lower--reversed", + "counterpart_prompt_id": "crk24--strength-porosity--n+1--higher-lower--reversed", + "law_id": "strength-porosity", + "domain": "porous materials", + "category": "inverse", + "law_sign": -1, + "numerical_sign": -1, + "physical_sign": 1, + "answer_vocabulary": "higher-lower", + "answer_order": "reversed", + "answer_words": [ + "higher", + "lower" + ], + "presented_words": [ + "lower", + "higher" + ], + "expected_answer": "higher", + "incorrect_answer": "lower", + "user": "For a brittle ceramic with fixed composition and pore morphology, all other relevant physical conditions are unchanged. The porosity changes from 18 percent to 2 percent. What happens to tensile strength? Reply with exactly one lowercase word from this ordered set: lower, higher." + }, + { + "prompt_id": "crk24--strength-porosity--n-1--greater-smaller--standard", + "counterpart_prompt_id": "crk24--strength-porosity--n+1--greater-smaller--standard", + "law_id": "strength-porosity", + "domain": "porous materials", + "category": "inverse", + "law_sign": -1, + "numerical_sign": -1, + "physical_sign": 1, + "answer_vocabulary": "greater-smaller", + "answer_order": "standard", + "answer_words": [ + "greater", + "smaller" + ], + "presented_words": [ + "greater", + "smaller" + ], + "expected_answer": "greater", + "incorrect_answer": "smaller", + "user": "For a brittle ceramic with fixed composition and pore morphology, all other relevant physical conditions are unchanged. The porosity changes from 18 percent to 2 percent. What happens to tensile strength? Reply with exactly one lowercase word from this ordered set: greater, smaller." + }, + { + "prompt_id": "crk24--strength-porosity--n-1--greater-smaller--reversed", + "counterpart_prompt_id": "crk24--strength-porosity--n+1--greater-smaller--reversed", + "law_id": "strength-porosity", + "domain": "porous materials", + "category": "inverse", + "law_sign": -1, + "numerical_sign": -1, + "physical_sign": 1, + "answer_vocabulary": "greater-smaller", + "answer_order": "reversed", + "answer_words": [ + "greater", + "smaller" + ], + "presented_words": [ + "smaller", + "greater" + ], + "expected_answer": "greater", + "incorrect_answer": "smaller", + "user": "For a brittle ceramic with fixed composition and pore morphology, all other relevant physical conditions are unchanged. The porosity changes from 18 percent to 2 percent. What happens to tensile strength? Reply with exactly one lowercase word from this ordered set: smaller, greater." + }, + { + "prompt_id": "crk24--strength-porosity--n+1--higher-lower--standard", + "counterpart_prompt_id": "crk24--strength-porosity--n-1--higher-lower--standard", + "law_id": "strength-porosity", + "domain": "porous materials", + "category": "inverse", + "law_sign": -1, + "numerical_sign": 1, + "physical_sign": -1, + "answer_vocabulary": "higher-lower", + "answer_order": "standard", + "answer_words": [ + "higher", + "lower" + ], + "presented_words": [ + "higher", + "lower" + ], + "expected_answer": "lower", + "incorrect_answer": "higher", + "user": "For a brittle ceramic with fixed composition and pore morphology, all other relevant physical conditions are unchanged. The porosity changes from 2 percent to 18 percent. What happens to tensile strength? 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Reply with exactly one lowercase word from this ordered set: lower, higher." + }, + { + "prompt_id": "crk24--strength-porosity--n+1--greater-smaller--standard", + "counterpart_prompt_id": "crk24--strength-porosity--n-1--greater-smaller--standard", + "law_id": "strength-porosity", + "domain": "porous materials", + "category": "inverse", + "law_sign": -1, + "numerical_sign": 1, + "physical_sign": -1, + "answer_vocabulary": "greater-smaller", + "answer_order": "standard", + "answer_words": [ + "greater", + "smaller" + ], + "presented_words": [ + "greater", + "smaller" + ], + "expected_answer": "smaller", + "incorrect_answer": "greater", + "user": "For a brittle ceramic with fixed composition and pore morphology, all other relevant physical conditions are unchanged. The porosity changes from 2 percent to 18 percent. What happens to tensile strength? Reply with exactly one lowercase word from this ordered set: greater, smaller." + }, + { + "prompt_id": "crk24--strength-porosity--n+1--greater-smaller--reversed", + "counterpart_prompt_id": "crk24--strength-porosity--n-1--greater-smaller--reversed", + "law_id": "strength-porosity", + "domain": "porous materials", + "category": "inverse", + "law_sign": -1, + "numerical_sign": 1, + "physical_sign": -1, + "answer_vocabulary": "greater-smaller", + "answer_order": "reversed", + "answer_words": [ + "greater", + "smaller" + ], + "presented_words": [ + "smaller", + "greater" + ], + "expected_answer": "smaller", + "incorrect_answer": "greater", + "user": "For a brittle ceramic with fixed composition and pore morphology, all other relevant physical conditions are unchanged. The porosity changes from 2 percent to 18 percent. What happens to tensile strength? Reply with exactly one lowercase word from this ordered set: smaller, greater." + }, + { + "prompt_id": "crk24--corrosion-current-polarization-resistance--n-1--higher-lower--standard", + "counterpart_prompt_id": "crk24--corrosion-current-polarization-resistance--n+1--higher-lower--standard", + "law_id": "corrosion-current-polarization-resistance", + "domain": "corrosion", + "category": "inverse", + "law_sign": -1, + "numerical_sign": -1, + "physical_sign": 1, + "answer_vocabulary": "higher-lower", + "answer_order": "standard", + "answer_words": [ + "higher", + "lower" + ], + "presented_words": [ + "higher", + "lower" + ], + "expected_answer": "higher", + "incorrect_answer": "lower", + "user": "For a metal-electrolyte interface within the linear-polarization regime, all other relevant physical conditions are unchanged. The polarization resistance changes from 20 kilohm square-centimeters to 2 kilohm square-centimeters. What happens to corrosion-current density? Reply with exactly one lowercase word from this ordered set: higher, lower." + }, + { + "prompt_id": "crk24--corrosion-current-polarization-resistance--n-1--higher-lower--reversed", + "counterpart_prompt_id": "crk24--corrosion-current-polarization-resistance--n+1--higher-lower--reversed", + "law_id": "corrosion-current-polarization-resistance", + "domain": "corrosion", + "category": "inverse", + "law_sign": -1, + "numerical_sign": -1, + "physical_sign": 1, + "answer_vocabulary": "higher-lower", + "answer_order": "reversed", + "answer_words": [ + "higher", + "lower" + ], + "presented_words": [ + "lower", + "higher" + ], + "expected_answer": "higher", + "incorrect_answer": "lower", + "user": "For a metal-electrolyte interface within the linear-polarization regime, all other relevant physical conditions are unchanged. The polarization resistance changes from 20 kilohm square-centimeters to 2 kilohm square-centimeters. What happens to corrosion-current density? Reply with exactly one lowercase word from this ordered set: lower, higher." + }, + { + "prompt_id": "crk24--corrosion-current-polarization-resistance--n-1--greater-smaller--standard", + "counterpart_prompt_id": "crk24--corrosion-current-polarization-resistance--n+1--greater-smaller--standard", + "law_id": "corrosion-current-polarization-resistance", + "domain": "corrosion", + "category": "inverse", + "law_sign": -1, + "numerical_sign": -1, + "physical_sign": 1, + "answer_vocabulary": "greater-smaller", + "answer_order": "standard", + "answer_words": [ + "greater", + "smaller" + ], + "presented_words": [ + "greater", + "smaller" + ], + "expected_answer": "greater", + "incorrect_answer": "smaller", + "user": "For a metal-electrolyte interface within the linear-polarization regime, all other relevant physical conditions are unchanged. The polarization resistance changes from 20 kilohm square-centimeters to 2 kilohm square-centimeters. What happens to corrosion-current density? Reply with exactly one lowercase word from this ordered set: greater, smaller." + }, + { + "prompt_id": "crk24--corrosion-current-polarization-resistance--n-1--greater-smaller--reversed", + "counterpart_prompt_id": "crk24--corrosion-current-polarization-resistance--n+1--greater-smaller--reversed", + "law_id": "corrosion-current-polarization-resistance", + "domain": "corrosion", + "category": "inverse", + "law_sign": -1, + "numerical_sign": -1, + "physical_sign": 1, + "answer_vocabulary": "greater-smaller", + "answer_order": "reversed", + "answer_words": [ + "greater", + "smaller" + ], + "presented_words": [ + "smaller", + "greater" + ], + "expected_answer": "greater", + "incorrect_answer": "smaller", + "user": "For a metal-electrolyte interface within the linear-polarization regime, all other relevant physical conditions are unchanged. The polarization resistance changes from 20 kilohm square-centimeters to 2 kilohm square-centimeters. What happens to corrosion-current density? Reply with exactly one lowercase word from this ordered set: smaller, greater." + }, + { + "prompt_id": "crk24--corrosion-current-polarization-resistance--n+1--higher-lower--standard", + "counterpart_prompt_id": "crk24--corrosion-current-polarization-resistance--n-1--higher-lower--standard", + "law_id": "corrosion-current-polarization-resistance", + "domain": "corrosion", + "category": "inverse", + "law_sign": -1, + "numerical_sign": 1, + "physical_sign": -1, + "answer_vocabulary": "higher-lower", + "answer_order": "standard", + "answer_words": [ + "higher", + "lower" + ], + "presented_words": [ + "higher", + "lower" + ], + "expected_answer": "lower", + "incorrect_answer": "higher", + "user": "For a metal-electrolyte interface within the linear-polarization regime, all other relevant physical conditions are unchanged. The polarization resistance changes from 2 kilohm square-centimeters to 20 kilohm square-centimeters. What happens to corrosion-current density? Reply with exactly one lowercase word from this ordered set: higher, lower." + }, + { + "prompt_id": "crk24--corrosion-current-polarization-resistance--n+1--higher-lower--reversed", + "counterpart_prompt_id": "crk24--corrosion-current-polarization-resistance--n-1--higher-lower--reversed", + "law_id": "corrosion-current-polarization-resistance", + "domain": "corrosion", + "category": "inverse", + "law_sign": -1, + "numerical_sign": 1, + "physical_sign": -1, + "answer_vocabulary": "higher-lower", + "answer_order": "reversed", + "answer_words": [ + "higher", + "lower" + ], + "presented_words": [ + "lower", + "higher" + ], + "expected_answer": "lower", + "incorrect_answer": "higher", + "user": "For a metal-electrolyte interface within the linear-polarization regime, all other relevant physical conditions are unchanged. The polarization resistance changes from 2 kilohm square-centimeters to 20 kilohm square-centimeters. What happens to corrosion-current density? Reply with exactly one lowercase word from this ordered set: lower, higher." + }, + { + "prompt_id": "crk24--corrosion-current-polarization-resistance--n+1--greater-smaller--standard", + "counterpart_prompt_id": "crk24--corrosion-current-polarization-resistance--n-1--greater-smaller--standard", + "law_id": "corrosion-current-polarization-resistance", + "domain": "corrosion", + "category": "inverse", + "law_sign": -1, + "numerical_sign": 1, + "physical_sign": -1, + "answer_vocabulary": "greater-smaller", + "answer_order": "standard", + "answer_words": [ + "greater", + "smaller" + ], + "presented_words": [ + "greater", + "smaller" + ], + "expected_answer": "smaller", + "incorrect_answer": "greater", + "user": "For a metal-electrolyte interface within the linear-polarization regime, all other relevant physical conditions are unchanged. The polarization resistance changes from 2 kilohm square-centimeters to 20 kilohm square-centimeters. What happens to corrosion-current density? Reply with exactly one lowercase word from this ordered set: greater, smaller." + }, + { + "prompt_id": "crk24--corrosion-current-polarization-resistance--n+1--greater-smaller--reversed", + "counterpart_prompt_id": "crk24--corrosion-current-polarization-resistance--n-1--greater-smaller--reversed", + "law_id": "corrosion-current-polarization-resistance", + "domain": "corrosion", + "category": "inverse", + "law_sign": -1, + "numerical_sign": 1, + "physical_sign": -1, + "answer_vocabulary": "greater-smaller", + "answer_order": "reversed", + "answer_words": [ + "greater", + "smaller" + ], + "presented_words": [ + "smaller", + "greater" + ], + "expected_answer": "smaller", + "incorrect_answer": "greater", + "user": "For a metal-electrolyte interface within the linear-polarization regime, all other relevant physical conditions are unchanged. The polarization resistance changes from 2 kilohm square-centimeters to 20 kilohm square-centimeters. What happens to corrosion-current density? Reply with exactly one lowercase word from this ordered set: smaller, greater." + }, + { + "prompt_id": "crk24--cooling-rate-section-thickness--n-1--higher-lower--standard", + "counterpart_prompt_id": "crk24--cooling-rate-section-thickness--n+1--higher-lower--standard", + "law_id": "cooling-rate-section-thickness", + "domain": "heat treatment", + "category": "inverse", + "law_sign": -1, + "numerical_sign": -1, + "physical_sign": 1, + "answer_vocabulary": "higher-lower", + "answer_order": "standard", + "answer_words": [ + "higher", + "lower" + ], + "presented_words": [ + "higher", + "lower" + ], + "expected_answer": "higher", + "incorrect_answer": "lower", + "user": "For a metal plate quenched with fixed alloy chemistry and surface conditions, all other relevant physical conditions are unchanged. The section thickness changes from 20 millimeters to 2 millimeters. What happens to cooling-rate magnitude at the center? Reply with exactly one lowercase word from this ordered set: higher, lower." + }, + { + "prompt_id": "crk24--cooling-rate-section-thickness--n-1--higher-lower--reversed", + "counterpart_prompt_id": "crk24--cooling-rate-section-thickness--n+1--higher-lower--reversed", + "law_id": "cooling-rate-section-thickness", + "domain": "heat treatment", + "category": "inverse", + "law_sign": -1, + "numerical_sign": -1, + "physical_sign": 1, + "answer_vocabulary": "higher-lower", + "answer_order": "reversed", + "answer_words": [ + "higher", + "lower" + ], + "presented_words": [ + "lower", + "higher" + ], + "expected_answer": "higher", + "incorrect_answer": "lower", + "user": "For a metal plate quenched with fixed alloy chemistry and surface conditions, all other relevant physical conditions are unchanged. The section thickness changes from 20 millimeters to 2 millimeters. What happens to cooling-rate magnitude at the center? Reply with exactly one lowercase word from this ordered set: lower, higher." + }, + { + "prompt_id": "crk24--cooling-rate-section-thickness--n-1--greater-smaller--standard", + "counterpart_prompt_id": "crk24--cooling-rate-section-thickness--n+1--greater-smaller--standard", + "law_id": "cooling-rate-section-thickness", + "domain": "heat treatment", + "category": "inverse", + "law_sign": -1, + "numerical_sign": -1, + "physical_sign": 1, + "answer_vocabulary": "greater-smaller", + "answer_order": "standard", + "answer_words": [ + "greater", + "smaller" + ], + "presented_words": [ + "greater", + "smaller" + ], + "expected_answer": "greater", + "incorrect_answer": "smaller", + "user": "For a metal plate quenched with fixed alloy chemistry and surface conditions, all other relevant physical conditions are unchanged. The section thickness changes from 20 millimeters to 2 millimeters. What happens to cooling-rate magnitude at the center? Reply with exactly one lowercase word from this ordered set: greater, smaller." + }, + { + "prompt_id": "crk24--cooling-rate-section-thickness--n-1--greater-smaller--reversed", + "counterpart_prompt_id": "crk24--cooling-rate-section-thickness--n+1--greater-smaller--reversed", + "law_id": "cooling-rate-section-thickness", + "domain": "heat treatment", + "category": "inverse", + "law_sign": -1, + "numerical_sign": -1, + "physical_sign": 1, + "answer_vocabulary": "greater-smaller", + "answer_order": "reversed", + "answer_words": [ + "greater", + "smaller" + ], + "presented_words": [ + "smaller", + "greater" + ], + "expected_answer": "greater", + "incorrect_answer": "smaller", + "user": "For a metal plate quenched with fixed alloy chemistry and surface conditions, all other relevant physical conditions are unchanged. The section thickness changes from 20 millimeters to 2 millimeters. What happens to cooling-rate magnitude at the center? Reply with exactly one lowercase word from this ordered set: smaller, greater." + }, + { + "prompt_id": "crk24--cooling-rate-section-thickness--n+1--higher-lower--standard", + "counterpart_prompt_id": "crk24--cooling-rate-section-thickness--n-1--higher-lower--standard", + "law_id": "cooling-rate-section-thickness", + "domain": "heat treatment", + "category": "inverse", + "law_sign": -1, + "numerical_sign": 1, + "physical_sign": -1, + "answer_vocabulary": "higher-lower", + "answer_order": "standard", + "answer_words": [ + "higher", + "lower" + ], + "presented_words": [ + "higher", + "lower" + ], + "expected_answer": "lower", + "incorrect_answer": "higher", + "user": "For a metal plate quenched with fixed alloy chemistry and surface conditions, all other relevant physical conditions are unchanged. The section thickness changes from 2 millimeters to 20 millimeters. What happens to cooling-rate magnitude at the center? Reply with exactly one lowercase word from this ordered set: higher, lower." + }, + { + "prompt_id": "crk24--cooling-rate-section-thickness--n+1--higher-lower--reversed", + "counterpart_prompt_id": "crk24--cooling-rate-section-thickness--n-1--higher-lower--reversed", + "law_id": "cooling-rate-section-thickness", + "domain": "heat treatment", + "category": "inverse", + "law_sign": -1, + "numerical_sign": 1, + "physical_sign": -1, + "answer_vocabulary": "higher-lower", + "answer_order": "reversed", + "answer_words": [ + "higher", + "lower" + ], + "presented_words": [ + "lower", + "higher" + ], + "expected_answer": "lower", + "incorrect_answer": "higher", + "user": "For a metal plate quenched with fixed alloy chemistry and surface conditions, all other relevant physical conditions are unchanged. The section thickness changes from 2 millimeters to 20 millimeters. What happens to cooling-rate magnitude at the center? Reply with exactly one lowercase word from this ordered set: lower, higher." + }, + { + "prompt_id": "crk24--cooling-rate-section-thickness--n+1--greater-smaller--standard", + "counterpart_prompt_id": "crk24--cooling-rate-section-thickness--n-1--greater-smaller--standard", + "law_id": "cooling-rate-section-thickness", + "domain": "heat treatment", + "category": "inverse", + "law_sign": -1, + "numerical_sign": 1, + "physical_sign": -1, + "answer_vocabulary": "greater-smaller", + "answer_order": "standard", + "answer_words": [ + "greater", + "smaller" + ], + "presented_words": [ + "greater", + "smaller" + ], + "expected_answer": "smaller", + "incorrect_answer": "greater", + "user": "For a metal plate quenched with fixed alloy chemistry and surface conditions, all other relevant physical conditions are unchanged. The section thickness changes from 2 millimeters to 20 millimeters. What happens to cooling-rate magnitude at the center? Reply with exactly one lowercase word from this ordered set: greater, smaller." + }, + { + "prompt_id": "crk24--cooling-rate-section-thickness--n+1--greater-smaller--reversed", + "counterpart_prompt_id": "crk24--cooling-rate-section-thickness--n-1--greater-smaller--reversed", + "law_id": "cooling-rate-section-thickness", + "domain": "heat treatment", + "category": "inverse", + "law_sign": -1, + "numerical_sign": 1, + "physical_sign": -1, + "answer_vocabulary": "greater-smaller", + "answer_order": "reversed", + "answer_words": [ + "greater", + "smaller" + ], + "presented_words": [ + "smaller", + "greater" + ], + "expected_answer": "smaller", + "incorrect_answer": "greater", + "user": "For a metal plate quenched with fixed alloy chemistry and surface conditions, all other relevant physical conditions are unchanged. The section thickness changes from 2 millimeters to 20 millimeters. What happens to cooling-rate magnitude at the center? Reply with exactly one lowercase word from this ordered set: smaller, greater." + }, + { + "prompt_id": "crk24--rupture-life-temperature--n-1--higher-lower--standard", + "counterpart_prompt_id": "crk24--rupture-life-temperature--n+1--higher-lower--standard", + "law_id": "rupture-life-temperature", + "domain": "high-temperature deformation", + "category": "inverse", + "law_sign": -1, + "numerical_sign": -1, + "physical_sign": 1, + "answer_vocabulary": "higher-lower", + "answer_order": "standard", + "answer_words": [ + "higher", + "lower" + ], + "presented_words": [ + "higher", + "lower" + ], + "expected_answer": "higher", + "incorrect_answer": "lower", + "user": "For a superalloy held at fixed tensile stress in one creep-rupture regime, all other relevant physical conditions are unchanged. The absolute temperature changes from 1150 kelvin to 850 kelvin. What happens to creep-rupture life? Reply with exactly one lowercase word from this ordered set: higher, lower." + }, + { + "prompt_id": "crk24--rupture-life-temperature--n-1--higher-lower--reversed", + "counterpart_prompt_id": "crk24--rupture-life-temperature--n+1--higher-lower--reversed", + "law_id": "rupture-life-temperature", + "domain": "high-temperature deformation", + "category": "inverse", + "law_sign": -1, + "numerical_sign": -1, + "physical_sign": 1, + "answer_vocabulary": "higher-lower", + "answer_order": "reversed", + "answer_words": [ + "higher", + "lower" + ], + "presented_words": [ + "lower", + "higher" + ], + "expected_answer": "higher", + "incorrect_answer": "lower", + "user": "For a superalloy held at fixed tensile stress in one creep-rupture regime, all other relevant physical conditions are unchanged. The absolute temperature changes from 1150 kelvin to 850 kelvin. What happens to creep-rupture life? Reply with exactly one lowercase word from this ordered set: lower, higher." + }, + { + "prompt_id": "crk24--rupture-life-temperature--n-1--greater-smaller--standard", + "counterpart_prompt_id": "crk24--rupture-life-temperature--n+1--greater-smaller--standard", + "law_id": "rupture-life-temperature", + "domain": "high-temperature deformation", + "category": "inverse", + "law_sign": -1, + "numerical_sign": -1, + "physical_sign": 1, + "answer_vocabulary": "greater-smaller", + "answer_order": "standard", + "answer_words": [ + "greater", + "smaller" + ], + "presented_words": [ + "greater", + "smaller" + ], + "expected_answer": "greater", + "incorrect_answer": "smaller", + "user": "For a superalloy held at fixed tensile stress in one creep-rupture regime, all other relevant physical conditions are unchanged. The absolute temperature changes from 1150 kelvin to 850 kelvin. What happens to creep-rupture life? Reply with exactly one lowercase word from this ordered set: greater, smaller." + }, + { + "prompt_id": "crk24--rupture-life-temperature--n-1--greater-smaller--reversed", + "counterpart_prompt_id": "crk24--rupture-life-temperature--n+1--greater-smaller--reversed", + "law_id": "rupture-life-temperature", + "domain": "high-temperature deformation", + "category": "inverse", + "law_sign": -1, + "numerical_sign": -1, + "physical_sign": 1, + "answer_vocabulary": "greater-smaller", + "answer_order": "reversed", + "answer_words": [ + "greater", + "smaller" + ], + "presented_words": [ + "smaller", + "greater" + ], + "expected_answer": "greater", + "incorrect_answer": "smaller", + "user": "For a superalloy held at fixed tensile stress in one creep-rupture regime, all other relevant physical conditions are unchanged. The absolute temperature changes from 1150 kelvin to 850 kelvin. What happens to creep-rupture life? Reply with exactly one lowercase word from this ordered set: smaller, greater." + }, + { + "prompt_id": "crk24--rupture-life-temperature--n+1--higher-lower--standard", + "counterpart_prompt_id": "crk24--rupture-life-temperature--n-1--higher-lower--standard", + "law_id": "rupture-life-temperature", + "domain": "high-temperature deformation", + "category": "inverse", + "law_sign": -1, + "numerical_sign": 1, + "physical_sign": -1, + "answer_vocabulary": "higher-lower", + "answer_order": "standard", + "answer_words": [ + "higher", + "lower" + ], + "presented_words": [ + "higher", + "lower" + ], + "expected_answer": "lower", + "incorrect_answer": "higher", + "user": "For a superalloy held at fixed tensile stress in one creep-rupture regime, all other relevant physical conditions are unchanged. The absolute temperature changes from 850 kelvin to 1150 kelvin. What happens to creep-rupture life? Reply with exactly one lowercase word from this ordered set: higher, lower." + }, + { + "prompt_id": "crk24--rupture-life-temperature--n+1--higher-lower--reversed", + "counterpart_prompt_id": "crk24--rupture-life-temperature--n-1--higher-lower--reversed", + "law_id": "rupture-life-temperature", + "domain": "high-temperature deformation", + "category": "inverse", + "law_sign": -1, + "numerical_sign": 1, + "physical_sign": -1, + "answer_vocabulary": "higher-lower", + "answer_order": "reversed", + "answer_words": [ + "higher", + "lower" + ], + "presented_words": [ + "lower", + "higher" + ], + "expected_answer": "lower", + "incorrect_answer": "higher", + "user": "For a superalloy held at fixed tensile stress in one creep-rupture regime, all other relevant physical conditions are unchanged. The absolute temperature changes from 850 kelvin to 1150 kelvin. What happens to creep-rupture life? Reply with exactly one lowercase word from this ordered set: lower, higher." + }, + { + "prompt_id": "crk24--rupture-life-temperature--n+1--greater-smaller--standard", + "counterpart_prompt_id": "crk24--rupture-life-temperature--n-1--greater-smaller--standard", + "law_id": "rupture-life-temperature", + "domain": "high-temperature deformation", + "category": "inverse", + "law_sign": -1, + "numerical_sign": 1, + "physical_sign": -1, + "answer_vocabulary": "greater-smaller", + "answer_order": "standard", + "answer_words": [ + "greater", + "smaller" + ], + "presented_words": [ + "greater", + "smaller" + ], + "expected_answer": "smaller", + "incorrect_answer": "greater", + "user": "For a superalloy held at fixed tensile stress in one creep-rupture regime, all other relevant physical conditions are unchanged. The absolute temperature changes from 850 kelvin to 1150 kelvin. What happens to creep-rupture life? Reply with exactly one lowercase word from this ordered set: greater, smaller." + }, + { + "prompt_id": "crk24--rupture-life-temperature--n+1--greater-smaller--reversed", + "counterpart_prompt_id": "crk24--rupture-life-temperature--n-1--greater-smaller--reversed", + "law_id": "rupture-life-temperature", + "domain": "high-temperature deformation", + "category": "inverse", + "law_sign": -1, + "numerical_sign": 1, + "physical_sign": -1, + "answer_vocabulary": "greater-smaller", + "answer_order": "reversed", + "answer_words": [ + "greater", + "smaller" + ], + "presented_words": [ + "smaller", + "greater" + ], + "expected_answer": "smaller", + "incorrect_answer": "greater", + "user": "For a superalloy held at fixed tensile stress in one creep-rupture regime, all other relevant physical conditions are unchanged. The absolute temperature changes from 850 kelvin to 1150 kelvin. What happens to creep-rupture life? Reply with exactly one lowercase word from this ordered set: smaller, greater." + }, + { + "prompt_id": "crk24--nucleation-rate-interfacial-energy--n-1--higher-lower--standard", + "counterpart_prompt_id": "crk24--nucleation-rate-interfacial-energy--n+1--higher-lower--standard", + "law_id": "nucleation-rate-interfacial-energy", + "domain": "phase transformation", + "category": "inverse", + "law_sign": -1, + "numerical_sign": -1, + "physical_sign": 1, + "answer_vocabulary": "higher-lower", + "answer_order": "standard", + "answer_words": [ + "higher", + "lower" + ], + "presented_words": [ + "higher", + "lower" + ], + "expected_answer": "higher", + "incorrect_answer": "lower", + "user": "For a homogeneous nucleation process at fixed undercooling and temperature, all other relevant physical conditions are unchanged. The nucleus-matrix interfacial energy changes from 0.25 joules per square meter to 0.05 joules per square meter. What happens to nucleation rate? Reply with exactly one lowercase word from this ordered set: higher, lower." + }, + { + "prompt_id": "crk24--nucleation-rate-interfacial-energy--n-1--higher-lower--reversed", + "counterpart_prompt_id": "crk24--nucleation-rate-interfacial-energy--n+1--higher-lower--reversed", + "law_id": "nucleation-rate-interfacial-energy", + "domain": "phase transformation", + "category": "inverse", + "law_sign": -1, + "numerical_sign": -1, + "physical_sign": 1, + "answer_vocabulary": "higher-lower", + "answer_order": "reversed", + "answer_words": [ + "higher", + "lower" + ], + "presented_words": [ + "lower", + "higher" + ], + "expected_answer": "higher", + "incorrect_answer": "lower", + "user": "For a homogeneous nucleation process at fixed undercooling and temperature, all other relevant physical conditions are unchanged. The nucleus-matrix interfacial energy changes from 0.25 joules per square meter to 0.05 joules per square meter. What happens to nucleation rate? Reply with exactly one lowercase word from this ordered set: lower, higher." + }, + { + "prompt_id": "crk24--nucleation-rate-interfacial-energy--n-1--greater-smaller--standard", + "counterpart_prompt_id": "crk24--nucleation-rate-interfacial-energy--n+1--greater-smaller--standard", + "law_id": "nucleation-rate-interfacial-energy", + "domain": "phase transformation", + "category": "inverse", + "law_sign": -1, + "numerical_sign": -1, + "physical_sign": 1, + "answer_vocabulary": "greater-smaller", + "answer_order": "standard", + "answer_words": [ + "greater", + "smaller" + ], + "presented_words": [ + "greater", + "smaller" + ], + "expected_answer": "greater", + "incorrect_answer": "smaller", + "user": "For a homogeneous nucleation process at fixed undercooling and temperature, all other relevant physical conditions are unchanged. The nucleus-matrix interfacial energy changes from 0.25 joules per square meter to 0.05 joules per square meter. What happens to nucleation rate? Reply with exactly one lowercase word from this ordered set: greater, smaller." + }, + { + "prompt_id": "crk24--nucleation-rate-interfacial-energy--n-1--greater-smaller--reversed", + "counterpart_prompt_id": "crk24--nucleation-rate-interfacial-energy--n+1--greater-smaller--reversed", + "law_id": "nucleation-rate-interfacial-energy", + "domain": "phase transformation", + "category": "inverse", + "law_sign": -1, + "numerical_sign": -1, + "physical_sign": 1, + "answer_vocabulary": "greater-smaller", + "answer_order": "reversed", + "answer_words": [ + "greater", + "smaller" + ], + "presented_words": [ + "smaller", + "greater" + ], + "expected_answer": "greater", + "incorrect_answer": "smaller", + "user": "For a homogeneous nucleation process at fixed undercooling and temperature, all other relevant physical conditions are unchanged. The nucleus-matrix interfacial energy changes from 0.25 joules per square meter to 0.05 joules per square meter. What happens to nucleation rate? Reply with exactly one lowercase word from this ordered set: smaller, greater." + }, + { + "prompt_id": "crk24--nucleation-rate-interfacial-energy--n+1--higher-lower--standard", + "counterpart_prompt_id": "crk24--nucleation-rate-interfacial-energy--n-1--higher-lower--standard", + "law_id": "nucleation-rate-interfacial-energy", + "domain": "phase transformation", + "category": "inverse", + "law_sign": -1, + "numerical_sign": 1, + "physical_sign": -1, + "answer_vocabulary": "higher-lower", + "answer_order": "standard", + "answer_words": [ + "higher", + "lower" + ], + "presented_words": [ + "higher", + "lower" + ], + "expected_answer": "lower", + "incorrect_answer": "higher", + "user": "For a homogeneous nucleation process at fixed undercooling and temperature, all other relevant physical conditions are unchanged. The nucleus-matrix interfacial energy changes from 0.05 joules per square meter to 0.25 joules per square meter. What happens to nucleation rate? Reply with exactly one lowercase word from this ordered set: higher, lower." + }, + { + "prompt_id": "crk24--nucleation-rate-interfacial-energy--n+1--higher-lower--reversed", + "counterpart_prompt_id": "crk24--nucleation-rate-interfacial-energy--n-1--higher-lower--reversed", + "law_id": "nucleation-rate-interfacial-energy", + "domain": "phase transformation", + "category": "inverse", + "law_sign": -1, + "numerical_sign": 1, + "physical_sign": -1, + "answer_vocabulary": "higher-lower", + "answer_order": "reversed", + "answer_words": [ + "higher", + "lower" + ], + "presented_words": [ + "lower", + "higher" + ], + "expected_answer": "lower", + "incorrect_answer": "higher", + "user": "For a homogeneous nucleation process at fixed undercooling and temperature, all other relevant physical conditions are unchanged. The nucleus-matrix interfacial energy changes from 0.05 joules per square meter to 0.25 joules per square meter. What happens to nucleation rate? Reply with exactly one lowercase word from this ordered set: lower, higher." + }, + { + "prompt_id": "crk24--nucleation-rate-interfacial-energy--n+1--greater-smaller--standard", + "counterpart_prompt_id": "crk24--nucleation-rate-interfacial-energy--n-1--greater-smaller--standard", + "law_id": "nucleation-rate-interfacial-energy", + "domain": "phase transformation", + "category": "inverse", + "law_sign": -1, + "numerical_sign": 1, + "physical_sign": -1, + "answer_vocabulary": "greater-smaller", + "answer_order": "standard", + "answer_words": [ + "greater", + "smaller" + ], + "presented_words": [ + "greater", + "smaller" + ], + "expected_answer": "smaller", + "incorrect_answer": "greater", + "user": "For a homogeneous nucleation process at fixed undercooling and temperature, all other relevant physical conditions are unchanged. The nucleus-matrix interfacial energy changes from 0.05 joules per square meter to 0.25 joules per square meter. What happens to nucleation rate? Reply with exactly one lowercase word from this ordered set: greater, smaller." + }, + { + "prompt_id": "crk24--nucleation-rate-interfacial-energy--n+1--greater-smaller--reversed", + "counterpart_prompt_id": "crk24--nucleation-rate-interfacial-energy--n-1--greater-smaller--reversed", + "law_id": "nucleation-rate-interfacial-energy", + "domain": "phase transformation", + "category": "inverse", + "law_sign": -1, + "numerical_sign": 1, + "physical_sign": -1, + "answer_vocabulary": "greater-smaller", + "answer_order": "reversed", + "answer_words": [ + "greater", + "smaller" + ], + "presented_words": [ + "smaller", + "greater" + ], + "expected_answer": "smaller", + "incorrect_answer": "greater", + "user": "For a homogeneous nucleation process at fixed undercooling and temperature, all other relevant physical conditions are unchanged. The nucleus-matrix interfacial energy changes from 0.05 joules per square meter to 0.25 joules per square meter. What happens to nucleation rate? Reply with exactly one lowercase word from this ordered set: smaller, greater." + }, + { + "prompt_id": "crk24--surface-volume-ratio-particle-diameter--n-1--higher-lower--standard", + "counterpart_prompt_id": "crk24--surface-volume-ratio-particle-diameter--n+1--higher-lower--standard", + "law_id": "surface-volume-ratio-particle-diameter", + "domain": "nanomaterials", + "category": "inverse", + "law_sign": -1, + "numerical_sign": -1, + "physical_sign": 1, + "answer_vocabulary": "higher-lower", + "answer_order": "standard", + "answer_words": [ + "higher", + "lower" + ], + "presented_words": [ + "higher", + "lower" + ], + "expected_answer": "higher", + "incorrect_answer": "lower", + "user": "For a population of geometrically similar spherical particles, all other relevant physical conditions are unchanged. The particle diameter changes from 100 nanometers to 10 nanometers. What happens to surface-area-to-volume ratio? Reply with exactly one lowercase word from this ordered set: higher, lower." + }, + { + "prompt_id": "crk24--surface-volume-ratio-particle-diameter--n-1--higher-lower--reversed", + "counterpart_prompt_id": "crk24--surface-volume-ratio-particle-diameter--n+1--higher-lower--reversed", + "law_id": "surface-volume-ratio-particle-diameter", + "domain": "nanomaterials", + "category": "inverse", + "law_sign": -1, + "numerical_sign": -1, + "physical_sign": 1, + "answer_vocabulary": "higher-lower", + "answer_order": "reversed", + "answer_words": [ + "higher", + "lower" + ], + "presented_words": [ + "lower", + "higher" + ], + "expected_answer": "higher", + "incorrect_answer": "lower", + "user": "For a population of geometrically similar spherical particles, all other relevant physical conditions are unchanged. The particle diameter changes from 100 nanometers to 10 nanometers. What happens to surface-area-to-volume ratio? Reply with exactly one lowercase word from this ordered set: lower, higher." + }, + { + "prompt_id": "crk24--surface-volume-ratio-particle-diameter--n-1--greater-smaller--standard", + "counterpart_prompt_id": "crk24--surface-volume-ratio-particle-diameter--n+1--greater-smaller--standard", + "law_id": "surface-volume-ratio-particle-diameter", + "domain": "nanomaterials", + "category": "inverse", + "law_sign": -1, + "numerical_sign": -1, + "physical_sign": 1, + "answer_vocabulary": "greater-smaller", + "answer_order": "standard", + "answer_words": [ + "greater", + "smaller" + ], + "presented_words": [ + "greater", + "smaller" + ], + "expected_answer": "greater", + "incorrect_answer": "smaller", + "user": "For a population of geometrically similar spherical particles, all other relevant physical conditions are unchanged. The particle diameter changes from 100 nanometers to 10 nanometers. What happens to surface-area-to-volume ratio? Reply with exactly one lowercase word from this ordered set: greater, smaller." + }, + { + "prompt_id": "crk24--surface-volume-ratio-particle-diameter--n-1--greater-smaller--reversed", + "counterpart_prompt_id": "crk24--surface-volume-ratio-particle-diameter--n+1--greater-smaller--reversed", + "law_id": "surface-volume-ratio-particle-diameter", + "domain": "nanomaterials", + "category": "inverse", + "law_sign": -1, + "numerical_sign": -1, + "physical_sign": 1, + "answer_vocabulary": "greater-smaller", + "answer_order": "reversed", + "answer_words": [ + "greater", + "smaller" + ], + "presented_words": [ + "smaller", + "greater" + ], + "expected_answer": "greater", + "incorrect_answer": "smaller", + "user": "For a population of geometrically similar spherical particles, all other relevant physical conditions are unchanged. The particle diameter changes from 100 nanometers to 10 nanometers. What happens to surface-area-to-volume ratio? Reply with exactly one lowercase word from this ordered set: smaller, greater." + }, + { + "prompt_id": "crk24--surface-volume-ratio-particle-diameter--n+1--higher-lower--standard", + "counterpart_prompt_id": "crk24--surface-volume-ratio-particle-diameter--n-1--higher-lower--standard", + "law_id": "surface-volume-ratio-particle-diameter", + "domain": "nanomaterials", + "category": "inverse", + "law_sign": -1, + "numerical_sign": 1, + "physical_sign": -1, + "answer_vocabulary": "higher-lower", + "answer_order": "standard", + "answer_words": [ + "higher", + "lower" + ], + "presented_words": [ + "higher", + "lower" + ], + "expected_answer": "lower", + "incorrect_answer": "higher", + "user": "For a population of geometrically similar spherical particles, all other relevant physical conditions are unchanged. The particle diameter changes from 10 nanometers to 100 nanometers. What happens to surface-area-to-volume ratio? Reply with exactly one lowercase word from this ordered set: higher, lower." + }, + { + "prompt_id": "crk24--surface-volume-ratio-particle-diameter--n+1--higher-lower--reversed", + "counterpart_prompt_id": "crk24--surface-volume-ratio-particle-diameter--n-1--higher-lower--reversed", + "law_id": "surface-volume-ratio-particle-diameter", + "domain": "nanomaterials", + "category": "inverse", + "law_sign": -1, + "numerical_sign": 1, + "physical_sign": -1, + "answer_vocabulary": "higher-lower", + "answer_order": "reversed", + "answer_words": [ + "higher", + "lower" + ], + "presented_words": [ + "lower", + "higher" + ], + "expected_answer": "lower", + "incorrect_answer": "higher", + "user": "For a population of geometrically similar spherical particles, all other relevant physical conditions are unchanged. The particle diameter changes from 10 nanometers to 100 nanometers. What happens to surface-area-to-volume ratio? Reply with exactly one lowercase word from this ordered set: lower, higher." + }, + { + "prompt_id": "crk24--surface-volume-ratio-particle-diameter--n+1--greater-smaller--standard", + "counterpart_prompt_id": "crk24--surface-volume-ratio-particle-diameter--n-1--greater-smaller--standard", + "law_id": "surface-volume-ratio-particle-diameter", + "domain": "nanomaterials", + "category": "inverse", + "law_sign": -1, + "numerical_sign": 1, + "physical_sign": -1, + "answer_vocabulary": "greater-smaller", + "answer_order": "standard", + "answer_words": [ + "greater", + "smaller" + ], + "presented_words": [ + "greater", + "smaller" + ], + "expected_answer": "smaller", + "incorrect_answer": "greater", + "user": "For a population of geometrically similar spherical particles, all other relevant physical conditions are unchanged. The particle diameter changes from 10 nanometers to 100 nanometers. What happens to surface-area-to-volume ratio? Reply with exactly one lowercase word from this ordered set: greater, smaller." + }, + { + "prompt_id": "crk24--surface-volume-ratio-particle-diameter--n+1--greater-smaller--reversed", + "counterpart_prompt_id": "crk24--surface-volume-ratio-particle-diameter--n-1--greater-smaller--reversed", + "law_id": "surface-volume-ratio-particle-diameter", + "domain": "nanomaterials", + "category": "inverse", + "law_sign": -1, + "numerical_sign": 1, + "physical_sign": -1, + "answer_vocabulary": "greater-smaller", + "answer_order": "reversed", + "answer_words": [ + "greater", + "smaller" + ], + "presented_words": [ + "smaller", + "greater" + ], + "expected_answer": "smaller", + "incorrect_answer": "greater", + "user": "For a population of geometrically similar spherical particles, all other relevant physical conditions are unchanged. The particle diameter changes from 10 nanometers to 100 nanometers. What happens to surface-area-to-volume ratio? Reply with exactly one lowercase word from this ordered set: smaller, greater." + }, + { + "prompt_id": "crk24--buckling-load-column-length--n-1--higher-lower--standard", + "counterpart_prompt_id": "crk24--buckling-load-column-length--n+1--higher-lower--standard", + "law_id": "buckling-load-column-length", + "domain": "structural mechanics", + "category": "inverse", + "law_sign": -1, + "numerical_sign": -1, + "physical_sign": 1, + "answer_vocabulary": "higher-lower", + "answer_order": "standard", + "answer_words": [ + "higher", + "lower" + ], + "presented_words": [ + "higher", + "lower" + ], + "expected_answer": "higher", + "incorrect_answer": "lower", + "user": "For a slender elastic column with fixed flexural rigidity and end conditions, all other relevant physical conditions are unchanged. The column length changes from 2 meters to 0.5 meters. What happens to critical buckling load? Reply with exactly one lowercase word from this ordered set: higher, lower." + }, + { + "prompt_id": "crk24--buckling-load-column-length--n-1--higher-lower--reversed", + "counterpart_prompt_id": "crk24--buckling-load-column-length--n+1--higher-lower--reversed", + "law_id": "buckling-load-column-length", + "domain": "structural mechanics", + "category": "inverse", + "law_sign": -1, + "numerical_sign": -1, + "physical_sign": 1, + "answer_vocabulary": "higher-lower", + "answer_order": "reversed", + "answer_words": [ + "higher", + "lower" + ], + "presented_words": [ + "lower", + "higher" + ], + "expected_answer": "higher", + "incorrect_answer": "lower", + "user": "For a slender elastic column with fixed flexural rigidity and end conditions, all other relevant physical conditions are unchanged. The column length changes from 2 meters to 0.5 meters. What happens to critical buckling load? Reply with exactly one lowercase word from this ordered set: lower, higher." + }, + { + "prompt_id": "crk24--buckling-load-column-length--n-1--greater-smaller--standard", + "counterpart_prompt_id": "crk24--buckling-load-column-length--n+1--greater-smaller--standard", + "law_id": "buckling-load-column-length", + "domain": "structural mechanics", + "category": "inverse", + "law_sign": -1, + "numerical_sign": -1, + "physical_sign": 1, + "answer_vocabulary": "greater-smaller", + "answer_order": "standard", + "answer_words": [ + "greater", + "smaller" + ], + "presented_words": [ + "greater", + "smaller" + ], + "expected_answer": "greater", + "incorrect_answer": "smaller", + "user": "For a slender elastic column with fixed flexural rigidity and end conditions, all other relevant physical conditions are unchanged. The column length changes from 2 meters to 0.5 meters. What happens to critical buckling load? Reply with exactly one lowercase word from this ordered set: greater, smaller." + }, + { + "prompt_id": "crk24--buckling-load-column-length--n-1--greater-smaller--reversed", + "counterpart_prompt_id": "crk24--buckling-load-column-length--n+1--greater-smaller--reversed", + "law_id": "buckling-load-column-length", + "domain": "structural mechanics", + "category": "inverse", + "law_sign": -1, + "numerical_sign": -1, + "physical_sign": 1, + "answer_vocabulary": "greater-smaller", + "answer_order": "reversed", + "answer_words": [ + "greater", + "smaller" + ], + "presented_words": [ + "smaller", + "greater" + ], + "expected_answer": "greater", + "incorrect_answer": "smaller", + "user": "For a slender elastic column with fixed flexural rigidity and end conditions, all other relevant physical conditions are unchanged. The column length changes from 2 meters to 0.5 meters. What happens to critical buckling load? Reply with exactly one lowercase word from this ordered set: smaller, greater." + }, + { + "prompt_id": "crk24--buckling-load-column-length--n+1--higher-lower--standard", + "counterpart_prompt_id": "crk24--buckling-load-column-length--n-1--higher-lower--standard", + "law_id": "buckling-load-column-length", + "domain": "structural mechanics", + "category": "inverse", + "law_sign": -1, + "numerical_sign": 1, + "physical_sign": -1, + "answer_vocabulary": "higher-lower", + "answer_order": "standard", + "answer_words": [ + "higher", + "lower" + ], + "presented_words": [ + "higher", + "lower" + ], + "expected_answer": "lower", + "incorrect_answer": "higher", + "user": "For a slender elastic column with fixed flexural rigidity and end conditions, all other relevant physical conditions are unchanged. The column length changes from 0.5 meters to 2 meters. What happens to critical buckling load? Reply with exactly one lowercase word from this ordered set: higher, lower." + }, + { + "prompt_id": "crk24--buckling-load-column-length--n+1--higher-lower--reversed", + "counterpart_prompt_id": "crk24--buckling-load-column-length--n-1--higher-lower--reversed", + "law_id": "buckling-load-column-length", + "domain": "structural mechanics", + "category": "inverse", + "law_sign": -1, + "numerical_sign": 1, + "physical_sign": -1, + "answer_vocabulary": "higher-lower", + "answer_order": "reversed", + "answer_words": [ + "higher", + "lower" + ], + "presented_words": [ + "lower", + "higher" + ], + "expected_answer": "lower", + "incorrect_answer": "higher", + "user": "For a slender elastic column with fixed flexural rigidity and end conditions, all other relevant physical conditions are unchanged. The column length changes from 0.5 meters to 2 meters. What happens to critical buckling load? Reply with exactly one lowercase word from this ordered set: lower, higher." + }, + { + "prompt_id": "crk24--buckling-load-column-length--n+1--greater-smaller--standard", + "counterpart_prompt_id": "crk24--buckling-load-column-length--n-1--greater-smaller--standard", + "law_id": "buckling-load-column-length", + "domain": "structural mechanics", + "category": "inverse", + "law_sign": -1, + "numerical_sign": 1, + "physical_sign": -1, + "answer_vocabulary": "greater-smaller", + "answer_order": "standard", + "answer_words": [ + "greater", + "smaller" + ], + "presented_words": [ + "greater", + "smaller" + ], + "expected_answer": "smaller", + "incorrect_answer": "greater", + "user": "For a slender elastic column with fixed flexural rigidity and end conditions, all other relevant physical conditions are unchanged. The column length changes from 0.5 meters to 2 meters. What happens to critical buckling load? Reply with exactly one lowercase word from this ordered set: greater, smaller." + }, + { + "prompt_id": "crk24--buckling-load-column-length--n+1--greater-smaller--reversed", + "counterpart_prompt_id": "crk24--buckling-load-column-length--n-1--greater-smaller--reversed", + "law_id": "buckling-load-column-length", + "domain": "structural mechanics", + "category": "inverse", + "law_sign": -1, + "numerical_sign": 1, + "physical_sign": -1, + "answer_vocabulary": "greater-smaller", + "answer_order": "reversed", + "answer_words": [ + "greater", + "smaller" + ], + "presented_words": [ + "smaller", + "greater" + ], + "expected_answer": "smaller", + "incorrect_answer": "greater", + "user": "For a slender elastic column with fixed flexural rigidity and end conditions, all other relevant physical conditions are unchanged. The column length changes from 0.5 meters to 2 meters. What happens to critical buckling load? Reply with exactly one lowercase word from this ordered set: smaller, greater." + } + ] +} diff --git a/paper/causal_relational_knockout_data/protocol.json b/paper/causal_relational_knockout_data/protocol.json new file mode 100644 index 0000000000000000000000000000000000000000..b1f26cf25a177771764c5b218def1f9a0d32994b --- /dev/null +++ b/paper/causal_relational_knockout_data/protocol.json @@ -0,0 +1,88 @@ +{ + "study_id": "causal-relational-knockout-24law-v1", + "status": "frozen-before-any-study-model-output", + "frozen_at": "2026-09-02T11:03:03.653464+00:00", + "scientific_question": "Does a development-fitted relational coordinate make a selective causal contribution to ordinary correct decisions, mediate counterfactual answer changes, and repair initially incorrect answer margins?", + "cohort": { + "law_identities": 24, + "direct": 12, + "inverse": 12, + "prompts": 192, + "factors": [ + "two numerical directions", + "higher/lower and greater/smaller answer vocabularies", + "both presentation orders" + ], + "independent_unit": "law identity", + "equation_free": true, + "disjoint_from_direction_fit_laws": true + }, + "models": { + "4b": { + "model": "google/gemma-4-E4B-it", + "revision": "a4c2d58be94dda072b918d9db64ee85c8ed34e3f", + "probe_layer": 34, + "development_manifest": "experiments/elicited-physics-abstraction-2026-07-18/prompt_manifest.json", + "development_states": "experiments/elicited-physics-abstraction-2026-07-18/representations.npz" + }, + "12b": { + "model": "google/gemma-4-12B-it", + "revision": "707f0a3b8a3c7ad586ed01e27eafbad8a27dd0f7", + "probe_layer": 39, + "development_manifest": "experiments/gemma4-12b-equation-free-relational-v1/development_prompt_manifest.json", + "development_states": "experiments/gemma4-12b-equation-free-relational-v1/development_states.npy" + }, + "31b": { + "model": "google/gemma-4-31B-it", + "revision": "842da3794eaa0b77d5f08bae87a17459d91ff475", + "probe_layer": 49, + "development_manifest": "experiments/gemma4-31b-equation-free-relational-v1/development_prompt_manifest.json", + "development_states": "experiments/gemma4-31b-equation-free-relational-v1/development_states.npy" + } + }, + "direction": "One unit physical-outcome centroid direction per model, fitted only on the pre-existing 16-law development cohort at the prespecified layer.", + "position": "final prompt token after the registered transformer block", + "interventions": { + "clean": "Unmodified forward pass.", + "neutralize": "Set only the normalized-state coordinate along the frozen direction to the development midpoint, retaining the orthogonal residual.", + "counterfactual_coordinate": "Set only that coordinate to the value in the exact matched prompt with reversed numerical change.", + "correct_centroid": "Set only that coordinate to the development centroid for the prompt's registered physical outcome; evaluated as error repair only on all initially wrong answer-margin cases.", + "wrong_centroid": "Set the coordinate to the opposite development centroid as a signed falsification.", + "full_counterfactual": "Replace the complete state with its exact matched counterfactual state.", + "full_counterfactual_direction_restored": "Apply the full counterfactual state but restore its frozen-direction coordinate to the clean receiver value.", + "random_controls": "Three deterministic unit directions orthogonal to the frozen direction; each receives exactly the same receiver-specific L2 perturbation as neutralization or counterfactual-coordinate replacement." + }, + "primary_endpoints": { + "necessity": "Law-level clean correct-answer margin minus neutralized margin; the 95% law bootstrap interval must exceed zero and the effect must exceed the mean matched random-control effect.", + "counterfactual_sufficiency": "Law-level movement toward the matched counterfactual answer under the one-coordinate swap; interval above zero and larger than random control.", + "causal_mediation": "Ratio of one-coordinate counterfactual shift to full-state counterfactual shift, reported with an untruncated law bootstrap interval.", + "direction_restoration": "Reduction of the full-state counterfactual effect when the frozen coordinate is restored to its clean value.", + "error_repair": "On every clean case whose registered answer margin is below zero, change in correct-answer margin and fraction crossing above zero after the correct-centroid intervention. No minimum error count is guaranteed." + }, + "secondary_endpoints": [ + "answer-margin accuracy before and after each intervention", + "higher/lower versus greater/smaller strata", + "direct versus inverse laws", + "answer-order agreement", + "next-token KL divergence and registered-answer probability mass", + "4B, 12B, and 31B replication without assuming monotonic scaling" + ], + "inference": { + "law_bootstrap_resamples": 30000, + "bootstrap_seed": 20260902, + "law_sign_permutations": 100000, + "permutation_seed": 20260903, + "random_direction_seeds": [ + 1701, + 1702, + 1703 + ] + }, + "guardrails": [ + "Every frozen prompt and intervention row is retained.", + "No layer, direction, prompt, answer vocabulary, or control is selected from study output.", + "Necessity is claimed only for a selective contribution of the tested linear coordinate, never unique or global necessity.", + "A negative neutralization result leaves the prior sufficiency claim unchanged.", + "The 31B study uses the same intervention definitions despite having only one archived Jacobian lens; the primary direction is raw-state based and lens independent." + ] +} diff --git a/paper/causal_relational_knockout_data/statistics.json b/paper/causal_relational_knockout_data/statistics.json new file mode 100644 index 0000000000000000000000000000000000000000..0a0db62b57f6f5cad56741a5f507e21233ee1962 --- /dev/null +++ b/paper/causal_relational_knockout_data/statistics.json @@ -0,0 +1,833 @@ +{ + "study_id": "causal-relational-knockout-24law-v1", + "scales": [ + { + "scale": "4b", + "model": "google/gemma-4-E4B-it", + "probe_layer": 34, + "n_laws": 24, + "n_prompts": 192, + "baseline_answer_margin_accuracy": 0.953125, + "necessity_margin_drop": { + "estimate_ci95": [ + 4.326822916666667, + 3.6708984375, + 4.892903645833333 + ], + "activation_control_estimate_ci95": [ + -0.09129503038194446, + -0.11046040852864585, + -0.0726318359375 + ], + "specific_estimate_ci95": [ + 4.418117947048611, + 3.76536627875434, + 4.996524047851562 + ], + "specific_permutation_p": 9.99990000099999e-06 + }, + "counterfactual_coordinate": { + "estimate_ci95": [ + 9.4951171875, + 8.011360677083333, + 10.830086263020833 + ], + "activation_control_estimate_ci95": [ + -0.2530619303385417, + -0.3072681003146701, + -0.1994424608018663 + ], + "specific_estimate_ci95": [ + 9.748179117838541, + 8.22444101969401, + 11.11812981499566 + ], + "specific_permutation_p": 9.99990000099999e-06 + }, + "full_counterfactual": { + "estimate_ci95": [ + 17.962565104166668, + 14.91827392578125, + 20.5211669921875 + ], + "direction_restored_estimate_ci95": [ + 8.197265625, + 6.613606770833333, + 9.5514404296875 + ], + "restoration_reduction_estimate_ci95": [ + 9.765299479166666, + 8.213212076822916, + 11.159834798177082 + ], + "restoration_permutation_p": 9.99990000099999e-06 + }, + "causal_mediation_fraction": { + "coordinate_over_full_estimate_ci95": [ + 0.5286058607129265, + 0.5007903662320922, + 0.5585188215670325 + ], + "restoration_over_full_estimate_ci95": [ + 0.5436472698936228, + 0.5161442408320372, + 0.5726049762569513 + ] + }, + "law_level_breadth": { + "neutralization_positive": 23, + "neutralization_specific_positive": 23, + "counterfactual_coordinate_positive": 23, + "counterfactual_specific_positive": 23, + "restoration_reduction_positive": 23, + "denominator_laws": 24, + "neutralization_specific_one_sided_sign_p": 1.4901161193847656e-06, + "counterfactual_specific_one_sided_sign_p": 1.4901161193847656e-06, + "restoration_one_sided_sign_p": 1.4901161193847656e-06 + }, + "next_token_kl": { + "neutralize_estimate_ci95": [ + 0.12564799683460912, + 0.09612246389158789, + 0.15837211263756143 + ], + "neutralize_control_estimate_ci95": [ + 0.0008095244219635101, + 0.00039002800259177586, + 0.0013809340085534517 + ], + "counterfactual_coordinate_estimate_ci95": [ + 1.3825703688918718, + 1.1239652162278426, + 1.6453840967222386 + ], + "counterfactual_control_estimate_ci95": [ + 0.00444981699733743, + 0.002519775203181597, + 0.006847416449345659 + ], + "full_counterfactual_estimate_ci95": [ + 8.691391470842063, + 7.49562340095484, + 9.794079802829462 + ] + }, + "error_repair": { + "initially_wrong_prompts": 8, + "repaired_prompts": 4, + "repair_fraction": 0.5, + "mean_margin_before": -3.078125, + "mean_margin_after": -0.1875, + "mean_margin_change": 2.890625 + }, + "answer_margin_accuracy_by_intervention": { + "clean": 0.953125, + "correct_centroid": 0.9791666666666666, + "counterfactual_control_0_minus": 0.953125, + "counterfactual_control_0_plus": 0.9583333333333334, + "counterfactual_control_1_minus": 0.953125, + "counterfactual_control_1_plus": 0.9583333333333334, + "counterfactual_control_2_minus": 0.953125, + "counterfactual_control_2_plus": 0.9583333333333334, + "counterfactual_coordinate": 0.4739583333333333, + "full_counterfactual": 0.041666666666666664, + "full_counterfactual_direction_restored": 0.6302083333333334, + "neutralize": 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+31b,category,direct,12,2.8076536390516496,2.7016358905368376,2.9134031719631617,6.658977932400172,6.351785956488715,6.963634745279947 +31b,category,inverse,12,2.6146024068196616,2.5225807083977596,2.695723692576091,6.069081624348957,5.848576482137044,6.265432824028863 +31b,answer_vocabulary,greater-smaller,24,1.6684519449869792,1.597330305311415,1.7446322547064885,3.812260945638021,3.659687678019206,3.967014100816515 +31b,answer_vocabulary,higher-lower,24,3.753804100884332,3.653686131371392,3.8551483896043566,8.91579861111111,8.589399804009332,9.263592190212673 +31b,answer_order,reversed,24,2.1019058227539062,2.021393415662977,2.1833878835042317,4.796902126736112,4.609622149997287,4.9718435499403215 +31b,answer_order,standard,24,3.3203502231174045,3.23121985329522,3.4128231684366863,7.9311574300130205,7.649350357055665,8.232495752970376 diff --git a/paper/causal_relational_knockout_data/summary_table.csv b/paper/causal_relational_knockout_data/summary_table.csv new file mode 100644 index 0000000000000000000000000000000000000000..4b041c29bbe544f107da752d6eb7401d439e67bc --- /dev/null +++ b/paper/causal_relational_knockout_data/summary_table.csv @@ -0,0 +1,4 @@ +scale,clean_accuracy,neutralization_margin_loss,neutralization_specific,neutralization_specific_ci_low,neutralization_specific_ci_high,counterfactual_coordinate_movement,counterfactual_specific,full_counterfactual_movement,coordinate_over_full,restoration_over_full,positive_neutralization_laws,positive_counterfactual_laws,initially_wrong_prompts,repaired_prompts +4b,0.953125,4.326822916666667,4.418117947048611,3.76536627875434,4.996524047851562,9.4951171875,9.748179117838541,17.962565104166668,0.5286058607129265,0.5436472698936228,23,23,8,4 +12b,0.9583333333333334,3.049170176188151,3.1021385176314253,2.6351800388760034,3.5337335182974736,7.022085825602214,7.083322975370618,21.93874740600586,0.3200768802178686,0.5332330280763723,24,24,8,2 +31b,1.0,2.7307262420654297,2.7111280229356556,2.6331515100267198,2.790174622005886,6.472181955973308,6.364029778374565,34.79676183064779,0.1859995475289552,0.4957825583923165,24,24,0,0 diff --git a/paper/causal_relational_knockout_data/validation.json b/paper/causal_relational_knockout_data/validation.json new file mode 100644 index 0000000000000000000000000000000000000000..1985a4f33c5d77d9d9085ba613ce037883a222a0 --- /dev/null +++ b/paper/causal_relational_knockout_data/validation.json @@ -0,0 +1,61 @@ +{ + "valid": true, + "study_id": "causal-relational-knockout-24law-v1", + "amendment_id": "causal-relational-knockout-control-v2", + "prompt_count": 192, + "law_count": 24, + "expected_methods_per_prompt": 18, + "scales": { + "4b": { + "clean_rows": 192, + "intervention_rows": 3456, + "methods_per_prompt": 18, + "maximum_control_distance_error": 0.0, + "maximum_direction_gram_error": 3.5921156669831145e-09, + "clean_state_shape": [ + 192, + 2560 + ], + "clean_logit_shape": [ + 192, + 262144 + ], + "development_law_count": 16, + "target_development_law_overlap": [] + }, + "12b": { + "clean_rows": 192, + "intervention_rows": 3456, + "methods_per_prompt": 18, + "maximum_control_distance_error": 0.0, + "maximum_direction_gram_error": 4.381985780959816e-09, + "clean_state_shape": [ + 192, + 3840 + ], + "clean_logit_shape": [ + 192, + 262144 + ], + "development_law_count": 16, + "target_development_law_overlap": [] + }, + "31b": { + "clean_rows": 192, + "intervention_rows": 3456, + "methods_per_prompt": 18, + "maximum_control_distance_error": 0.0, + "maximum_direction_gram_error": 7.749871855367019e-10, + "clean_state_shape": [ + 192, + 5376 + ], + "clean_logit_shape": [ + 192, + 262144 + ], + "development_law_count": 16, + "target_development_law_overlap": [] + } + } +} diff --git a/paper/comment3_causal_audit_data/ANALYSIS_PLAN.md b/paper/comment3_causal_audit_data/ANALYSIS_PLAN.md new file mode 100644 index 0000000000000000000000000000000000000000..0bdbb6ab026d6e3335c6d2c57a638f795f915957 --- /dev/null +++ b/paper/comment3_causal_audit_data/ANALYSIS_PLAN.md @@ -0,0 +1,47 @@ +# Comment 3 retrospective causal-data audit + +Analysis rules recorded at 2026-09-06 09:46 UTC, before computing the filtered-cohort results in this audit. The original aggregate results and the three exact physical overlaps were already known. This is a retrospective sensitivity analysis, not a new prospective experiment or externally timestamped preregistration. + +The user requested data collection and analysis before deciding on manuscript changes or response-letter wording. The original experiment, manuscript, and response letter remain source inputs. This audit writes only to its own directory and a separate analysis script. + +## Questions + +1. Are the archived observations complete and consistent with the reported full-cohort results? +2. Which target relations repeat development physics despite having distinct law IDs? +3. Do neutralization and counterfactual-coordinate effects survive exclusion of exact repeats? +4. How do the effects change under increasingly conservative physical-family exclusions? +5. Which initially wrong decisions are repaired, which initially correct decisions become wrong, and how many independent laws produce those events? +6. Do effects persist separately by answer vocabulary, answer order, constitutive sign, and correct-answer polarity? + +## Cohorts fixed for this audit + +The taxonomy in `law_overlap_taxonomy.json` is based on the physical descriptions and the development equations, not a cutoff applied to causal effect sizes. Because outcomes of the original study were already available, this is not a blinded classification. In particular, the previously reported 4B rupture-life exception is known and its inclusion/exclusion must remain visible. + +| Cohort | Inclusion rule | Purpose | +|---|---|---| +| `full_24` | All target relations | Reproduce the original result and retain every observation. | +| `exclude_exact` | Remove exact physical repeats under different IDs | Main response to the specific disjoint-law defect; expected 21 relations. | +| `exclude_exact_and_related` | Also remove clear related-family cases | Broader sensitivity; expected 14 relations. Ambiguous cases remain identified. | +| `conservative_unmatched` | Also remove all broadly or ambiguously related cases | Deliberately stringent diagnostic; expected 6 relations. This small hand-classified subset is not certified universal family independence. | + +Exact repeats are stress-to-steady-creep rate, concentration-gradient-to-diffusive-flux magnitude, and diffusivity-to-homogenization time. Shared mathematical proportionality alone is not sufficient to call two relations the same physical family. Related and ambiguous categories explicitly expose the dependence on taxonomy breadth. + +## Computation and verification + +- Reconstruct intervention effects directly from the archived clean and intervened correct-answer margins. Do not select rows using their effect sign. +- Verify exact prompt IDs, all eight factor cells per relation, all 18 interventions per prompt, paired counterfactual metadata, finite margins, and equal-length control perturbations. +- Verify the raw development states reconstruct each stored direction and that the three overlaps occur in every model's actual fitting manifest. +- Compare the reconstructed law means and full-cohort summary against the original saved analyses. Check whether the manuscript's portable raw files agree byte-for-byte with the experiment records. +- Keep the original physical directions, layers, state positions, and activation-derived antithetic controls unchanged. Do not refit a direction after excluding target relations. +- Average the six signed control interventions within each prompt, then average prompt variants within each relation. +- Primary sensitivity endpoints: physical-minus-control neutralization margin loss and physical-minus-control coordinate-counterfactual movement. Also retain unadjusted physical/control means, full-state transfer, restoration, and both mediation ratios. +- Use 30,000 relation-cluster bootstrap draws for 95% intervals. Use exact two-sided sign-flip tests on relation means, rather than interpreting the original Monte Carlo floor as an exact p-value. Validate the exact algorithm against brute-force enumeration on small arrays. +- Report Holm-adjusted p-values across the six main `exclude_exact` specificity comparisons (two endpoints times three scales). Other nested subsets and strata are retrospective diagnostics, not independent confirmations. +- Accuracy refers to the stored forced binary answer margin, not free generation. Report strict-negative, tied, and positive margins separately. Reproduce original prompt-level McNemar counts for comparison, but use relation-level paired accuracy differences for the sensitivity inference because prompt variants are correlated. +- Repair includes every initially strictly negative margin and uses the correct development centroid. Report prompt counts and law counts; this is oracle-label-assisted repair, not an autonomous correction algorithm. +- For the small stringent subset, additionally report inference after averaging relations within the specified canonical families. Keep the family definition explicit and do not equate a small subset with population coverage. +- Record vocabulary/order/sign/polarity strata and paired effect differences between vocabularies. Positive effects within both vocabularies do not imply equality, or validate the old grain direction's failed `increase/decrease` and inverse-Hall–Petch transfers. + +## Deliverables + +`REPORT.md`, a complete overlap inventory, input hashes and validation, reconstructed prompt/law effect tables, all cohort summary statistics, strata, accuracy transitions, repair-case tables, leave-one-law-out diagnostics, and a standalone analysis script. A compact scientific plot may accompany the tables. No manuscript edits or response-letter draft are part of this stage. diff --git a/paper/comment3_causal_audit_data/README.md b/paper/comment3_causal_audit_data/README.md new file mode 100644 index 0000000000000000000000000000000000000000..8199e75c69e2816c5bd86cceca6f22d54b670af9 --- /dev/null +++ b/paper/comment3_causal_audit_data/README.md @@ -0,0 +1,31 @@ +# Retrospective Comment 3 causal-data audit + +This directory supplies the new overlap inventory and sensitivity results in SI S7A. It supplements the original 24-relation experiment in the sibling `causal_relational_knockout_data/` directory. No new Gemma inference was performed for this audit, and the original directions, prompts, interventions, protocols, and result files remain unchanged. + +The original validator checked identifiers. Three target relations repeat development physics: stress/creep rate, concentration-gradient/diffusive flux, and diffusivity/homogenization time. The primary sensitivity retains 21 relations and 168 prompts at each scale. The 14- and six-relation cohorts additionally exclude clearly related and ambiguously related physical families under an explicitly retrospective taxonomy. + +## Data and interpretation + +- `ANALYSIS_PLAN.md`, `law_overlap_taxonomy.json`, and `overlap_inventory.csv`: inclusion rules, all 24 classifications, development equations, and rationales. +- `development_law_inventory.csv`: all 16 relations actually used in each model's direction fit. +- `cohort_statistics.json` and `cohort_effects.csv`: all cohort estimates, intervals, exact p-values, and six-test primary Holm adjustment. +- `stratified_effects.csv`: answer vocabulary/order, constitutive sign, and physical-outcome polarity. +- `accuracy_transitions.csv`, `all_baseline_errors_and_ties.csv`, and `repair_summary.csv`: binary decisions, ties, strict errors, and the limited law coverage of known-correct-centroid repair. +- `reconstructed_prompt_effects.csv`, `reconstructed_law_effects.csv`, and `leave_one_law_out.csv`: all effects, including negative observations and excluded relations. +- `overlap_sensitivity.png` and `vocabulary_and_polarity.png`: diagnostic plots. +- `validation.json` and `input_sha256.json`: the original audit's numerical reproduction and input hashes, recorded **before the subsequent manuscript/SI edits**. Paths and manuscript hashes refer to that archived source-repository state, not the now-revised manuscript. +- `bundle_manifest.json`: byte-identical provenance for copied audit files and the original analysis script. + +These are retrospective sensitivity analyses, not externally timestamped preregistration or a prospective family-held-out experiment. Positive mean effects support selective, context-dependent contribution; they do not establish vocabulary-invariant necessity or identify a unique materials mechanism. The five-family stringent diagnostic has exact two-sided p = 0.0625. + +## Portable numerical reproduction + +With NumPy and pandas installed, run from this directory: + +```sh +python reproduce_sensitivity.py --output-dir reproduced +``` + +The runner reads the archived clean/intervention JSONL files from `../causal_relational_knockout_data/`, reconstructs all four cohorts at each of three scales, and checks every cohort-summary value against `cohort_statistics.json` with absolute numerical tolerance 1e-10. It also exports accuracy and stratum tables. It does not require model weights, network access, vocabulary-logit arrays, or development matrices, and does not overwrite the archived results. + +The preserved `analyze_causal_relational_comment3_audit.py` is the complete source-repository audit script. Running its `main()` requires the original repository layout and development-state/logit artifacts recorded in `input_sha256.json`; those additional checks reconstruct the fitted directions and verify clean margins from logits. The portable runner imports its statistical functions but does not claim to repeat those unavailable matrix-level checks from the portable files alone. diff --git a/paper/comment3_causal_audit_data/accuracy_transitions.csv b/paper/comment3_causal_audit_data/accuracy_transitions.csv new file mode 100644 index 0000000000000000000000000000000000000000..900abd7b324df3bd101d8315b85c5ccec510504e --- /dev/null +++ b/paper/comment3_causal_audit_data/accuracy_transitions.csv @@ -0,0 +1,73 @@ +scale,cohort,intervention,n_prompts,n_laws,clean_accuracy,intervention_accuracy,correct_to_wrong,correct_to_wrong_laws,positive_to_strict_negative,positive_to_strict_negative_laws,positive_to_tie,wrong_to_correct_including_ties,wrong_to_correct_laws,strict_negative_to_positive,tie_to_positive,intervention_ties,prompt_mcnemar_descriptive_only,law_accuracy_delta_estimate,law_accuracy_delta_ci95_low,law_accuracy_delta_ci95_high,law_accuracy_delta_n_clusters,law_accuracy_delta_exact_sign_flip_p,law_accuracy_delta_positive_clusters,law_accuracy_delta_zero_clusters +4b,full_24,neutralize,192,24,0.953125,0.8854166666666666,15,10,10,7,5,2,2,1,1,5,0.002349853515625,-0.06770833333333333,-0.125,-0.020833333333333332,24,0.02734375,2,12 +4b,full_24,counterfactual_coordinate,192,24,0.953125,0.4739583333333333,93,23,91,23,2,1,1,0,1,2,9.592548607487504e-27,-0.4791666666666667,-0.546875,-0.4010416666666667,24,2.384185791015625e-07,1,0 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"retrospective_definition_before_filtered_results; not blinded or preregistered", + "definition_time_utc": "2026-09-06T09:46:40Z", + "levels": { + "exact": "Same physical response/control relation in the development data under a different ID.", + "related": "Different response/control relation but clear shared constitutive family or physical mechanism.", + "ambiguous": "Broad physical connection or analogous transport/energy mechanism; deliberately excluded in the strictest diagnostic only.", + "unmatched": "No same relation or related developmental family identified under this stated taxonomy; not a claim about pretraining novelty." + }, + "laws": [ + {"law_id":"elastic-strain-tensile-stress","canonical_family":"linear_elasticity","level":"related","development_matches":["beam-deflection","voigt-reinforcement"],"reason":"Axial stress-strain and elastic beam deflection use the linear elastic constitutive family, although the varied control and measured response differ."}, + {"law_id":"thermal-strain-temperature-rise","canonical_family":"thermal_expansion","level":"ambiguous","development_matches":["beam-deflection","voigt-reinforcement"],"reason":"Thermal expansion has a distinct constitutive coefficient and is not an exact elastic-loading repeat, but a broad thermoelastic family definition can group it with elastic response."}, + {"law_id":"heat-flux-temperature-gradient","canonical_family":"thermal_transport","level":"ambiguous","development_matches":["fick-flux","diffusion-distance","diffusion-time"],"reason":"Fourier heat conduction differs physically from Fickian mass diffusion, but both are gradient-driven diffusive transport; exclude under the broadest transport-family screen."}, + {"law_id":"diffusion-flux-concentration-gradient","canonical_family":"mass_diffusion","level":"exact","development_matches":["fick-flux"],"reason":"Same Fickian flux-magnitude versus concentration-gradient relation at fixed diffusivity."}, + {"law_id":"resistance-conductor-length","canonical_family":"electrical_conduction","level":"unmatched","development_matches":[],"reason":"Ohmic resistance versus conductor length has no electrical-conduction counterpart among the 16 development relations; a linear algebraic form alone is not physical-family identity."}, + {"law_id":"capacitance-permittivity","canonical_family":"electrostatics","level":"unmatched","development_matches":[],"reason":"Capacitor permittivity dependence has no electrostatic counterpart in the development cohort."}, + {"law_id":"oxide-thickness-exposure-time","canonical_family":"oxidation_growth","level":"ambiguous","development_matches":["diffusion-distance","diffusion-time","fick-flux"],"reason":"The target explicitly specifies parabolic oxide growth, closely related to diffusion-controlled length growth, but in a distinct reactive system."}, + {"law_id":"creep-rate-applied-stress","canonical_family":"creep","level":"exact","development_matches":["norton-creep"],"reason":"Same positive stress-to-steady-creep-rate dependence at fixed temperature, including superalloy material contexts."}, + {"law_id":"dislocation-speed-shear-stress","canonical_family":"dislocation_plasticity","level":"related","development_matches":["taylor-strengthening","orowan-spacing","hall-petch"],"reason":"Drag-controlled glide is a distinct mobility relation within dislocation-mediated plasticity, already represented by strengthening and obstacle-bypass development cases."}, + {"law_id":"vacancy-concentration-temperature","canonical_family":"equilibrium_point_defects","level":"unmatched","development_matches":[],"reason":"Equilibrium vacancy concentration versus temperature is not a relation in the development cohort. A possible microscopic role of vacancies in creep or diffusion is too indirect to define an identical constitutive family here."}, + {"law_id":"sintering-neck-hold-time","canonical_family":"sintering","level":"ambiguous","development_matches":["diffusion-distance","fick-flux","capillary-pressure"],"reason":"Early-stage sintering can couple capillary driving forces and mass transport, both represented in development; the target does not specify enough to identify a unique sintering transport mechanism."}, + {"law_id":"crack-opening-tensile-load","canonical_family":"fracture_mechanics","level":"related","development_matches":["griffith-strength","griffith-energy-release","stress-intensity"],"reason":"Small-scale-yielding crack opening shares the crack-tip/fracture-mechanics family although it varies load rather than crack length."}, + {"law_id":"fatigue-life-stress-amplitude","canonical_family":"fatigue","level":"ambiguous","development_matches":["griffith-strength","griffith-energy-release","stress-intensity"],"reason":"High-cycle fatigue life differs from the static fracture relations, but a broad failure/fracture-family screen may group them; exclude conservatively."}, + {"law_id":"temperature-gradient-conductivity","canonical_family":"thermal_transport","level":"ambiguous","development_matches":["fick-flux","diffusion-time"],"reason":"A rearranged Fourier relation is not an exact Fickian repeat but belongs to a broad diffusive-transport umbrella."}, + {"law_id":"homogenization-time-diffusivity","canonical_family":"mass_diffusion","level":"exact","development_matches":["diffusion-time"],"reason":"Same inverse homogenization-time versus diffusivity relation at fixed length, with identical numerical diffusivity endpoints in one development case."}, + {"law_id":"resistivity-carrier-mobility","canonical_family":"electrical_conduction","level":"unmatched","development_matches":[],"reason":"Carrier mobility and electrical resistivity have no electrical-conduction counterpart in the development relations. This shares a canonical family with the target resistance-length relation."}, + {"law_id":"cantilever-frequency-length","canonical_family":"linear_elasticity","level":"related","development_matches":["beam-deflection"],"reason":"Elastic cantilever resonance and static beam deflection share the elastic-beam family despite different outputs."}, + {"law_id":"strength-porosity","canonical_family":"porous_solid_constitutive","level":"related","development_matches":["porosity-modulus"],"reason":"Strength and elastic modulus are different properties, but both are effective-property relations for porous solids as porosity varies."}, + {"law_id":"corrosion-current-polarization-resistance","canonical_family":"electrochemical_corrosion","level":"unmatched","development_matches":[],"reason":"Linear-polarization corrosion-current dependence is not represented by a development relation."}, + {"law_id":"cooling-rate-section-thickness","canonical_family":"thermal_transport","level":"ambiguous","development_matches":["diffusion-time","diffusion-distance"],"reason":"Cooling-rate dependence on thickness concerns thermal diffusion times and boundary conditions, distinct from but broadly related to development mass-transport scaling."}, + {"law_id":"rupture-life-temperature","canonical_family":"creep","level":"related","development_matches":["norton-creep","coble-grain-size"],"reason":"Creep-rupture lifetime at varying temperature is distinct from steady creep-rate laws but belongs to the same high-temperature creep/deformation family. Its known negative 4B result must remain visible in the full and exact-exclusion cohorts."}, + {"law_id":"nucleation-rate-interfacial-energy","canonical_family":"nucleation","level":"ambiguous","development_matches":["capillary-pressure"],"reason":"Nucleation barriers and Young-Laplace pressure differ, but both involve interfacial free energy and curvature; exclude under a broad interfacial-physics family definition."}, + {"law_id":"surface-volume-ratio-particle-diameter","canonical_family":"particle_geometry","level":"unmatched","development_matches":[],"reason":"Surface-area-to-volume ratio is geometric rather than a capillary-pressure relation. Shared inverse-size mathematics alone does not make the measured physical quantities equivalent."}, + {"law_id":"buckling-load-column-length","canonical_family":"linear_elasticity","level":"related","development_matches":["beam-deflection"],"reason":"Elastic column buckling and beam deflection share the elastic structural mechanics family, with distinct stability versus displacement endpoints."} + ] +} diff --git a/paper/comment3_causal_audit_data/overlap_inventory.csv b/paper/comment3_causal_audit_data/overlap_inventory.csv new file mode 100644 index 0000000000000000000000000000000000000000..183623ab13e98e319b39792801c00f83d4ec7264 --- /dev/null +++ b/paper/comment3_causal_audit_data/overlap_inventory.csv @@ -0,0 +1,25 @@ +law_id,domain,law_sign,system,control,response,low,high,canonical_family,level,development_matches,reason,development_evidence,included_full_24,included_exclude_exact,included_exclude_exact_and_related,included_conservative_unmatched +elastic-strain-tensile-stress,mechanics,1,a uniaxially loaded titanium coupon in its linear-elastic regime,tensile stress,elastic axial strain,40 MPa,240 MPa,linear_elasticity,related,beam-deflection;voigt-reinforcement,"Axial stress-strain and elastic beam deflection use the linear elastic constitutive family, although the varied control and measured response differ.","beam-deflection: Young modulus E -> midspan deflection delta; delta = C/E | voigt-reinforcement: stiff reinforcement fraction f -> longitudinal modulus E_L; E_L = E_m + (E_r - E_m) f, with E_r > E_m",True,True,False,False +thermal-strain-temperature-rise,thermal mechanics,1,a freely expanding nickel strip with fixed thermal-expansion coefficient,temperature rise above its reference state,thermal strain,20 kelvin,180 kelvin,thermal_expansion,ambiguous,beam-deflection;voigt-reinforcement,"Thermal expansion has a distinct constitutive coefficient and is not an exact elastic-loading repeat, but a broad thermoelastic family definition can group it with elastic response.","beam-deflection: Young modulus E -> midspan deflection delta; delta = C/E | voigt-reinforcement: stiff reinforcement fraction f -> longitudinal modulus E_L; E_L = E_m + (E_r - E_m) f, with E_r > E_m",True,True,True,False +heat-flux-temperature-gradient,thermal transport,1,a homogeneous ceramic slab with constant thermal conductivity,temperature-gradient magnitude,conductive heat-flux magnitude,5 kelvin per meter,45 kelvin per meter,thermal_transport,ambiguous,fick-flux;diffusion-distance;diffusion-time,"Fourier heat conduction differs physically from Fickian mass diffusion, but both are gradient-driven diffusive transport; exclude under the broadest transport-family screen.",fick-flux: concentration-gradient magnitude g -> diffusive flux magnitude J_mag; J_mag = D g | diffusion-distance: annealing time t -> characteristic diffusion distance ell; ell = sqrt(D t) | diffusion-time: diffusivity D -> homogenization time t; t = L^2/D,True,True,True,False +diffusion-flux-concentration-gradient,mass transport,1,a single-phase alloy at fixed temperature with constant diffusivity,concentration-gradient magnitude,diffusive solute-flux magnitude,0.2 mole fraction per meter,1.8 mole fraction per meter,mass_diffusion,exact,fick-flux,Same Fickian flux-magnitude versus concentration-gradient relation at fixed diffusivity.,fick-flux: concentration-gradient magnitude g -> diffusive flux magnitude J_mag; J_mag = D g,True,False,False,False +resistance-conductor-length,electrical materials,1,a uniform copper trace with fixed cross-section and resistivity,conductor length,electrical resistance,2 centimeters,18 centimeters,electrical_conduction,unmatched,,Ohmic resistance versus conductor length has no electrical-conduction counterpart among the 16 development relations; a linear algebraic form alone is not physical-family identity.,,True,True,True,True +capacitance-permittivity,dielectrics,1,a parallel-plate capacitor with fixed area and plate spacing,dielectric relative permittivity,capacitance,2,12,electrostatics,unmatched,,Capacitor permittivity dependence has no electrostatic counterpart in the development cohort.,,True,True,True,True +oxide-thickness-exposure-time,oxidation,1,an alloy oxidizing isothermally within one parabolic-growth regime,oxidation exposure time,oxide-scale thickness,1 hour,25 hours,oxidation_growth,ambiguous,diffusion-distance;diffusion-time;fick-flux,"The target explicitly specifies parabolic oxide growth, closely related to diffusion-controlled length growth, but in a distinct reactive system.",diffusion-distance: annealing time t -> characteristic diffusion distance ell; ell = sqrt(D t) | diffusion-time: diffusivity D -> homogenization time t; t = L^2/D | fick-flux: concentration-gradient magnitude g -> diffusive flux magnitude J_mag; J_mag = D g,True,True,True,False +creep-rate-applied-stress,high-temperature deformation,1,a superalloy deforming in one steady-state creep regime at fixed temperature,applied uniaxial stress,steady creep rate,60 MPa,180 MPa,creep,exact,norton-creep,"Same positive stress-to-steady-creep-rate dependence at fixed temperature, including superalloy material contexts.","norton-creep: applied stress sigma -> steady creep rate eps_dot; eps_dot = A sigma^n, with n > 0",True,False,False,False +dislocation-speed-shear-stress,crystal plasticity,1,a crystal in a drag-controlled glide regime at fixed temperature,resolved shear stress,mean dislocation glide speed,8 MPa,48 MPa,dislocation_plasticity,related,taylor-strengthening;orowan-spacing;hall-petch,"Drag-controlled glide is a distinct mobility relation within dislocation-mediated plasticity, already represented by strengthening and obstacle-bypass development cases.",taylor-strengthening: dislocation density rho -> flow stress sigma_y; sigma_y = sigma_0 + C sqrt(rho) | orowan-spacing: obstacle spacing lambda -> bypass stress tau; tau = C/lambda | hall-petch: grain size d -> yield stress sigma_y; sigma_y = sigma_0 + k/sqrt(d),True,True,False,False +vacancy-concentration-temperature,point defects,1,a pure crystal below melting with fixed vacancy-formation energy,absolute temperature,equilibrium vacancy concentration,500 kelvin,1000 kelvin,equilibrium_point_defects,unmatched,,Equilibrium vacancy concentration versus temperature is not a relation in the development cohort. A possible microscopic role of vacancies in creep or diffusion is too indirect to define an identical constitutive family here.,,True,True,True,True +sintering-neck-hold-time,powder processing,1,two identical powder particles in one early-stage isothermal sintering regime,sintering hold time,interparticle neck radius,2 minutes,50 minutes,sintering,ambiguous,diffusion-distance;fick-flux;capillary-pressure,"Early-stage sintering can couple capillary driving forces and mass transport, both represented in development; the target does not specify enough to identify a unique sintering transport mechanism.",diffusion-distance: annealing time t -> characteristic diffusion distance ell; ell = sqrt(D t) | fick-flux: concentration-gradient magnitude g -> diffusive flux magnitude J_mag; J_mag = D g | capillary-pressure: pore radius r -> capillary pressure magnitude p_c; p_c = C/r,True,True,True,False +crack-opening-tensile-load,fracture mechanics,1,a cracked plate under small-scale yielding with fixed crack geometry,remote tensile load,crack-opening displacement,4 kilonewtons,28 kilonewtons,fracture_mechanics,related,griffith-strength;griffith-energy-release;stress-intensity,Small-scale-yielding crack opening shares the crack-tip/fracture-mechanics family although it varies load rather than crack length.,griffith-strength: crack half-length a -> critical fracture stress sigma_c; sigma_c = C/sqrt(a) | griffith-energy-release: crack half-length a -> energy-release rate G; G = C a | stress-intensity: crack length a -> stress-intensity factor K_I; K_I = C sqrt(a),True,True,False,False +fatigue-life-stress-amplitude,fatigue,-1,a polished steel specimen in one high-cycle fatigue regime,cyclic stress amplitude,fatigue life,120 MPa,300 MPa,fatigue,ambiguous,griffith-strength;griffith-energy-release;stress-intensity,"High-cycle fatigue life differs from the static fracture relations, but a broad failure/fracture-family screen may group them; exclude conservatively.",griffith-strength: crack half-length a -> critical fracture stress sigma_c; sigma_c = C/sqrt(a) | griffith-energy-release: crack half-length a -> energy-release rate G; G = C a | stress-intensity: crack length a -> stress-intensity factor K_I; K_I = C sqrt(a),True,True,True,False +temperature-gradient-conductivity,thermal transport,-1,a homogeneous slab carrying a fixed steady heat flux,thermal conductivity,required temperature-gradient magnitude,2 watts per meter-kelvin,20 watts per meter-kelvin,thermal_transport,ambiguous,fick-flux;diffusion-time,A rearranged Fourier relation is not an exact Fickian repeat but belongs to a broad diffusive-transport umbrella.,fick-flux: concentration-gradient magnitude g -> diffusive flux magnitude J_mag; J_mag = D g | diffusion-time: diffusivity D -> homogenization time t; t = L^2/D,True,True,True,False +homogenization-time-diffusivity,mass transport,-1,an alloy diffusion couple with fixed homogenization distance,solute diffusivity,characteristic homogenization time,1e-15 square meters per second,1e-13 square meters per second,mass_diffusion,exact,diffusion-time,"Same inverse homogenization-time versus diffusivity relation at fixed length, with identical numerical diffusivity endpoints in one development case.",diffusion-time: diffusivity D -> homogenization time t; t = L^2/D,True,False,False,False +resistivity-carrier-mobility,electronic materials,-1,a semiconductor with fixed carrier concentration and charge,carrier mobility,electrical resistivity,100 square centimeters per volt-second,900 square centimeters per volt-second,electrical_conduction,unmatched,,Carrier mobility and electrical resistivity have no electrical-conduction counterpart in the development relations. This shares a canonical family with the target resistance-length relation.,,True,True,True,True +cantilever-frequency-length,structural dynamics,-1,"a uniform cantilever with fixed material, cross-section, and end condition",cantilever length,fundamental resonance frequency,20 millimeters,80 millimeters,linear_elasticity,related,beam-deflection,Elastic cantilever resonance and static beam deflection share the elastic-beam family despite different outputs.,beam-deflection: Young modulus E -> midspan deflection delta; delta = C/E,True,True,False,False +strength-porosity,porous materials,-1,a brittle ceramic with fixed composition and pore morphology,porosity,tensile strength,2 percent,18 percent,porous_solid_constitutive,related,porosity-modulus,"Strength and elastic modulus are different properties, but both are effective-property relations for porous solids as porosity varies.","porosity-modulus: porosity P -> effective elastic modulus E; E/E_0 = (1 - P)^n, with n > 0 and 0 < P < 1",True,True,False,False +corrosion-current-polarization-resistance,corrosion,-1,a metal-electrolyte interface within the linear-polarization regime,polarization resistance,corrosion-current density,2 kilohm square-centimeters,20 kilohm square-centimeters,electrochemical_corrosion,unmatched,,Linear-polarization corrosion-current dependence is not represented by a development relation.,,True,True,True,True +cooling-rate-section-thickness,heat treatment,-1,a metal plate quenched with fixed alloy chemistry and surface conditions,section thickness,cooling-rate magnitude at the center,2 millimeters,20 millimeters,thermal_transport,ambiguous,diffusion-time;diffusion-distance,"Cooling-rate dependence on thickness concerns thermal diffusion times and boundary conditions, distinct from but broadly related to development mass-transport scaling.",diffusion-time: diffusivity D -> homogenization time t; t = L^2/D | diffusion-distance: annealing time t -> characteristic diffusion distance ell; ell = sqrt(D t),True,True,True,False +rupture-life-temperature,high-temperature deformation,-1,a superalloy held at fixed tensile stress in one creep-rupture regime,absolute temperature,creep-rupture life,850 kelvin,1150 kelvin,creep,related,norton-creep;coble-grain-size,Creep-rupture lifetime at varying temperature is distinct from steady creep-rate laws but belongs to the same high-temperature creep/deformation family. Its known negative 4B result must remain visible in the full and exact-exclusion cohorts.,"norton-creep: applied stress sigma -> steady creep rate eps_dot; eps_dot = A sigma^n, with n > 0 | coble-grain-size: grain size d -> creep rate eps_dot; eps_dot = C/d^3",True,True,False,False +nucleation-rate-interfacial-energy,phase transformation,-1,a homogeneous nucleation process at fixed undercooling and temperature,nucleus-matrix interfacial energy,nucleation rate,0.05 joules per square meter,0.25 joules per square meter,nucleation,ambiguous,capillary-pressure,"Nucleation barriers and Young-Laplace pressure differ, but both involve interfacial free energy and curvature; exclude under a broad interfacial-physics family definition.",capillary-pressure: pore radius r -> capillary pressure magnitude p_c; p_c = C/r,True,True,True,False +surface-volume-ratio-particle-diameter,nanomaterials,-1,a population of geometrically similar spherical particles,particle diameter,surface-area-to-volume ratio,10 nanometers,100 nanometers,particle_geometry,unmatched,,Surface-area-to-volume ratio is geometric rather than a capillary-pressure relation. Shared inverse-size mathematics alone does not make the measured physical quantities equivalent.,,True,True,True,True +buckling-load-column-length,structural mechanics,-1,a slender elastic column with fixed flexural rigidity and end conditions,column length,critical buckling load,0.5 meters,2 meters,linear_elasticity,related,beam-deflection,"Elastic column buckling and beam deflection share the elastic structural mechanics family, with distinct stability versus displacement endpoints.",beam-deflection: Young modulus E -> midspan deflection delta; delta = C/E,True,True,False,False diff --git a/paper/comment3_causal_audit_data/reconstructed_law_effects.csv b/paper/comment3_causal_audit_data/reconstructed_law_effects.csv new file mode 100644 index 0000000000000000000000000000000000000000..17a1672011c15094086dd75ca7ac142d210a6705 --- /dev/null +++ b/paper/comment3_causal_audit_data/reconstructed_law_effects.csv @@ -0,0 +1,1441 @@ +law_id,intervention,movement,margin_change,kl,answer_mass,scale +buckling-load-column-length,correct_centroid,1.515625,-1.515625,0.010294147215989202,0.9996441230177879,4b +buckling-load-column-length,counterfactual_control_0_minus,4.0703125,-4.0703125,0.003004572838108288,0.9996230453252792,4b +buckling-load-column-length,counterfactual_control_0_plus,-5.1494140625,5.1494140625,0.0007627282707289851,0.9999528676271439,4b +buckling-load-column-length,counterfactual_control_1_minus,-1.203125,1.203125,0.0002796616473119684,0.9999632835388184,4b +buckling-load-column-length,counterfactual_control_1_plus,1.328125,-1.328125,0.000509032468926307,0.9996209591627121,4b +buckling-load-column-length,counterfactual_control_2_minus,2.2734375,-2.2734375,0.0029838456553079595,0.9996334910392761,4b +buckling-load-column-length,counterfactual_control_2_plus,-2.68359375,2.68359375,0.0009712073572245572,0.9999654591083527,4b +buckling-load-column-length,counterfactual_control_mean,-0.22737630208333331,0.22737630208333331,0.001418508039601344,0.9997931843002638,4b +buckling-load-column-length,counterfactual_coordinate,9.7734375,-9.7734375,0.9846094809472561,0.9983805492520332,4b +buckling-load-column-length,full_counterfactual,19.7265625,-19.7265625,9.817676723003387,0.9999212175607681,4b +buckling-load-column-length,full_counterfactual_direction_restored,9.7421875,-9.7421875,1.0878289630636573,0.9985598027706146,4b +buckling-load-column-length,neutralize,4.1171875,-4.1171875,0.13014493000309812,0.9989284723997116,4b +buckling-load-column-length,neutralize_control_0_minus,1.796875,-1.796875,0.0008504108628812901,0.9997624307870865,4b +buckling-load-column-length,neutralize_control_0_plus,-2.125,2.125,0.0004611298606391756,0.9999485015869141,4b +buckling-load-column-length,neutralize_control_1_minus,-0.6328125,0.6328125,0.00010825449841433965,0.9999498352408409,4b +buckling-load-column-length,neutralize_control_1_plus,0.59375,-0.59375,0.00022643146667888914,0.9997470304369926,4b +buckling-load-column-length,neutralize_control_2_minus,1.171875,-1.171875,0.0011267178055192062,0.9997633323073387,4b +buckling-load-column-length,neutralize_control_2_plus,-1.3828125,1.3828125,0.0005729409206765013,0.9999586865305901,4b +buckling-load-column-length,neutralize_control_mean,-0.09635416666666667,0.09635416666666667,0.0005576475691349003,0.9998549694816271,4b +buckling-load-column-length,wrong_centroid,6.9765625,-6.9765625,0.6645760137907928,0.998448483645916,4b +cantilever-frequency-length,correct_centroid,1.2421875,-1.2421875,0.00809704787712362,0.9988297671079636,4b +cantilever-frequency-length,counterfactual_control_0_minus,4.2109375,-4.2109375,0.0035306802528793924,0.9992084205150604,4b +cantilever-frequency-length,counterfactual_control_0_plus,-5.50634765625,5.50634765625,0.0012402134736362314,0.9999122470617294,4b 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0000000000000000000000000000000000000000..ecc8499d94a272fdec11d6d21ebd5ea11529c340 --- /dev/null +++ b/paper/comment3_causal_audit_data/reconstructed_prompt_effects.csv @@ -0,0 +1,577 @@ 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+12b,exclude_exact,21,168,8,2,2,1,"['cooling-rate-section-thickness', 'dislocation-speed-shear-stress']",['dislocation-speed-shear-stress'],-5.640625,-1.1015625,4.5390625 +12b,exclude_exact_and_related,14,112,4,1,0,0,['cooling-rate-section-thickness'],[],-5.328125,-3.046875,2.28125 +12b,conservative_unmatched,6,48,0,0,0,0,[],[],,, +31b,full_24,24,192,0,0,0,0,[],[],,, +31b,exclude_exact,21,168,0,0,0,0,[],[],,, +31b,exclude_exact_and_related,14,112,0,0,0,0,[],[],,, +31b,conservative_unmatched,6,48,0,0,0,0,[],[],,, diff --git a/paper/comment3_causal_audit_data/reproduce_sensitivity.py b/paper/comment3_causal_audit_data/reproduce_sensitivity.py new file mode 100644 index 0000000000000000000000000000000000000000..25bd24aa6ecdd167c3edb411e190c29aa2509511 --- /dev/null +++ b/paper/comment3_causal_audit_data/reproduce_sensitivity.py @@ -0,0 +1,75 @@ +#!/usr/bin/env python3 +"""Reproduce Comment 3 sensitivity results using only the portable SI bundle. + +Requires NumPy and pandas. Uses the sibling causal_relational_knockout_data +clean/intervention JSONL files, without model inference or development matrices. +The full archived script separately verifies those matrices in the source repo. +""" +import argparse +import importlib.util +import json +from pathlib import Path + +import numpy as np +import pandas as pd + + +def main(): + here = Path(__file__).resolve().parent + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument('--output-dir', type=Path, default=here / 'reproduced') + args = parser.parse_args() + spec = importlib.util.spec_from_file_location('audit', here / 'analyze_causal_relational_comment3_audit.py') + audit = importlib.util.module_from_spec(spec) + spec.loader.exec_module(audit) + audit.SOURCE = here.parent / 'causal_relational_knockout_data' + manifest = json.loads((audit.SOURCE / 'prompt_manifest.json').read_text()) + taxonomy = pd.DataFrame(json.loads((here / 'law_overlap_taxonomy.json').read_text())['laws']).set_index('law_id') + expected = json.loads((here / 'cohort_statistics.json').read_text())['cohorts'] + results, transitions, strata = [], [], [] + for si, scale in enumerate(audit.SCALES): + _, _, prompts, _, _, checks = audit.build_scale(scale, manifest) + prompts = prompts.join(taxonomy[['level', 'canonical_family']], on='law_id') + for ci, (cohort, levels) in enumerate(audit.COHORTS.items()): + frame = prompts[prompts.level.isin(levels)] + summary, trans, strat = audit.cohort_analysis(frame, scale, cohort, audit.SEED + si * 100 + ci * 10000) + results.append(summary) + transitions.extend(trans) + strata.extend(strat) + primary = [(s, e) for s in results if s['cohort'] == 'exclude_exact' + for e in ['neutralization_specific', 'counterfactual_specific']] + ordered = sorted(primary, key=lambda pair: pair[0]['endpoints'][pair[1]]['exact_sign_flip_p']) + running = 0.0 + for rank, (summary, endpoint) in enumerate(ordered): + result = summary['endpoints'][endpoint] + running = max(running, min(1., (len(ordered) - rank) * result['exact_sign_flip_p'])) + result['holm_p_six_primary_tests'] = running + + def compare(a, b, key='root'): + if isinstance(a, dict): + assert a.keys() == b.keys(), key + for k in a: + compare(a[k], b[k], key + '.' + k) + elif isinstance(a, list): + assert len(a) == len(b), key + for i, (x, y) in enumerate(zip(a, b)): + compare(x, y, key + f'[{i}]') + elif isinstance(a, (int, float)): + assert np.isclose(a, b, rtol=0, atol=1e-10), (key, a, b) + else: + assert a == b, (key, a, b) + + compare(results, expected) + checks = audit.check_sign_flip() + args.output_dir.mkdir(parents=True, exist_ok=True) + (args.output_dir / 'cohort_statistics.json').write_text(json.dumps({'cohorts': results}, indent=2, allow_nan=False) + '\n') + pd.DataFrame(transitions).to_csv(args.output_dir / 'accuracy_transitions.csv', index=False) + pd.DataFrame(strata).to_csv(args.output_dir / 'stratified_effects.csv', index=False) + status = {'status': 'passed', 'cohorts_reproduced': len(results), 'numeric_absolute_tolerance': 1e-10, + 'exact_sign_flip_checks': checks, 'model_inference': False, 'development_direction_reconstruction': False} + (args.output_dir / 'validation.json').write_text(json.dumps(status, indent=2) + '\n') + print(json.dumps(status, indent=2)) + + +if __name__ == '__main__': + main() diff --git a/paper/comment3_causal_audit_data/stratified_effects.csv b/paper/comment3_causal_audit_data/stratified_effects.csv new file mode 100644 index 0000000000000000000000000000000000000000..cd8b3614d8fa8879d189b49fdb8273b6dede25b7 --- /dev/null +++ b/paper/comment3_causal_audit_data/stratified_effects.csv @@ -0,0 +1,193 @@ +scale,cohort,factor,value,endpoint,n_prompts,estimate,ci95_low,ci95_high,n_clusters,exact_sign_flip_p,positive_clusters,zero_clusters +4b,full_24,category,direct,neutralization_specific,96,5.353407118055556,4.950900607638888,5.7353515625,12,0.00048828125,12,0 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+1,329 @@ +{ + "exact_sign_flip_checks": 17, + "scales": { + "4b": { + "clean_rows": 192, + "intervention_rows": 3456, + "law_count": 24, + "factor_cells_per_law": 8, + "methods_per_prompt": 18, + "max_reconstructed_margin_error": 0.0, + "max_control_distance_error": { + "neutralize_control_": 0.0, + "counterfactual_control_": 0.0 + }, + "clean_strict_negative": 8, + "clean_ties": 1, + "development_direction": { + "n_prompts": 128, + "n_laws": 16, + "positive_count": 64, + "max_direction_error": 0.0, + "coordinate_errors": { + "midpoint_coordinate": 0.0, + "positive_coordinate": 0.0, + "negative_coordinate": 0.0 + }, + "max_basis_orthogonality_error": 3.5921156669831145e-09, + "actual_fitted_law_ids": [ + "beam-deflection", + "capillary-pressure", + "coble-grain-size", + "diffusion-distance", + "diffusion-time", + "fick-flux", + "griffith-energy-release", + "griffith-strength", + "hall-petch", + "norton-creep", + "orowan-spacing", + "porosity-modulus", + "rubber-network", + "stress-intensity", + "taylor-strengthening", + "voigt-reinforcement" + ], + "original_split_labels_all_used": [ + "discovery", + "replication" + ] + }, + "max_clean_logit_margin_error": 0.0, + "clean_state_shape": [ + 192, + 2560 + ], + "clean_logit_shape": [ + 192, + 262144 + ], + "max_original_law_table_error": 1.7763568394002505e-15, + "max_original_estimate_and_ci_error": 8.881784197001252e-16 + }, + "12b": { + "clean_rows": 192, + "intervention_rows": 3456, + "law_count": 24, + "factor_cells_per_law": 8, + "methods_per_prompt": 18, + "max_reconstructed_margin_error": 0.0, + "max_control_distance_error": { + "neutralize_control_": 0.0, + "counterfactual_control_": 0.0 + }, + "clean_strict_negative": 8, + "clean_ties": 0, + "development_direction": { + "n_prompts": 128, + "n_laws": 16, + "positive_count": 64, + "max_direction_error": 0.0, + "coordinate_errors": { + "midpoint_coordinate": 0.0, + "positive_coordinate": 0.0, + "negative_coordinate": 0.0 + }, + "max_basis_orthogonality_error": 4.381985780959816e-09, + "actual_fitted_law_ids": [ + "beam-deflection", + "capillary-pressure", + "coble-grain-size", + "diffusion-distance", + "diffusion-time", + "fick-flux", + "griffith-energy-release", + "griffith-strength", + "hall-petch", + "norton-creep", + "orowan-spacing", + "porosity-modulus", + "rubber-network", + "stress-intensity", + "taylor-strengthening", + "voigt-reinforcement" + ], + "original_split_labels_all_used": [ + "discovery", + "replication" + ] + }, + "max_clean_logit_margin_error": 0.0, + "clean_state_shape": [ + 192, + 3840 + ], + "clean_logit_shape": [ + 192, + 262144 + ], + "max_original_law_table_error": 1.7763568394002505e-15, + "max_original_estimate_and_ci_error": 0.0 + }, + "31b": { + "clean_rows": 192, + "intervention_rows": 3456, + "law_count": 24, + "factor_cells_per_law": 8, + "methods_per_prompt": 18, + "max_reconstructed_margin_error": 0.0, + "max_control_distance_error": { + "neutralize_control_": 0.0, + "counterfactual_control_": 0.0 + }, + "clean_strict_negative": 0, + "clean_ties": 0, + "development_direction": { + "n_prompts": 128, + "n_laws": 16, + "positive_count": 64, + "max_direction_error": 0.0, + "coordinate_errors": { + "midpoint_coordinate": 0.0, + "positive_coordinate": 0.0, + "negative_coordinate": 0.0 + }, + "max_basis_orthogonality_error": 7.749871855367019e-10, + "actual_fitted_law_ids": [ + "beam-deflection", + "capillary-pressure", + "coble-grain-size", + "diffusion-distance", + "diffusion-time", + "fick-flux", + "griffith-energy-release", + "griffith-strength", + "hall-petch", + "norton-creep", + "orowan-spacing", + "porosity-modulus", + "rubber-network", + "stress-intensity", + "taylor-strengthening", + "voigt-reinforcement" + ], + "original_split_labels_all_used": [ + "discovery", + "replication" + ] + }, + "max_clean_logit_margin_error": 0.0, + "clean_state_shape": [ + 192, + 5376 + ], + "clean_logit_shape": [ + 192, + 262144 + ], + "max_original_law_table_error": 1.7763568394002505e-15, + "max_original_estimate_and_ci_error": 8.881784197001252e-16 + } + }, + "portable_matches": [ + { + "relative_path": "12b/clean_rows.jsonl", + "sha256_equal": true + }, + { + "relative_path": "12b/clean_states.npy", + "sha256_equal": true + }, + { + "relative_path": "12b/frozen_direction.npz", + "sha256_equal": true + }, + { + "relative_path": "12b/intervention_rows.jsonl", + "sha256_equal": true + }, + { + "relative_path": "12b/preflight.json", + "sha256_equal": true + }, + { + "relative_path": "12b/run_record.json", + "sha256_equal": true + }, + { + "relative_path": "12b/statistics.json", + "sha256_equal": true + }, + { + "relative_path": "31b/batch_equivalence_audit.json", + "sha256_equal": true + }, + { + "relative_path": "31b/clean_rows.jsonl", + "sha256_equal": true + }, + { + "relative_path": "31b/clean_states.npy", + "sha256_equal": true + }, + { + "relative_path": "31b/frozen_direction.npz", + "sha256_equal": true + }, + { + "relative_path": "31b/intervention_rows.jsonl", + "sha256_equal": true + }, + { + "relative_path": "31b/preflight.json", + "sha256_equal": true + }, + { + "relative_path": "31b/run_record.json", + "sha256_equal": true + }, + { + "relative_path": "31b/statistics.json", + "sha256_equal": true + }, + { + "relative_path": "4b/clean_rows.jsonl", + "sha256_equal": true + }, + { + "relative_path": "4b/clean_states.npy", + "sha256_equal": true + }, + { + "relative_path": "4b/frozen_direction.npz", + "sha256_equal": true + }, + { + "relative_path": "4b/intervention_rows.jsonl", + "sha256_equal": true + }, + { + "relative_path": "4b/preflight.json", + "sha256_equal": true + }, + { + "relative_path": "4b/run_record.json", + "sha256_equal": true + }, + { + "relative_path": "4b/statistics.json", + "sha256_equal": true + }, + { + "relative_path": "CONTROL_AMENDMENT.md", + "sha256_equal": true + }, + { + "relative_path": "PROMPTS.md", + "sha256_equal": true + }, + { + "relative_path": "PROTOCOL.md", + "sha256_equal": true + }, + { + "relative_path": "RESULTS.md", + "sha256_equal": true + }, + { + "relative_path": "control_amendment.json", + "sha256_equal": true + }, + { + "relative_path": "law_level_effects.csv", + "sha256_equal": true + }, + { + "relative_path": "prompt_level_effects.csv", + "sha256_equal": true + }, + { + "relative_path": "prompt_manifest.json", + "sha256_equal": true + }, + { + "relative_path": "protocol.json", + "sha256_equal": true + }, + { + "relative_path": "statistics.json", + "sha256_equal": true + }, + { + "relative_path": "stratum_effects.csv", + "sha256_equal": true + }, + { + "relative_path": "summary_table.csv", + "sha256_equal": true + }, + { + "relative_path": "validation.json", + "sha256_equal": true + } + ], + "status": "passed", + "source_and_manuscript_hashes_unchanged": true, + "n_hashed_inputs": 98, + "semantic_disjointness_of_original_24": "fails: three exact physical relations repeat under different IDs", + "family_classification": "retrospective expert-reviewable sensitivity taxonomy, not a prospective family holdout" +} diff --git a/pyproject.toml b/pyproject.toml new file mode 100644 index 0000000000000000000000000000000000000000..e6418a7e82d4fa4db754577bdc41c2e5b349f26e --- /dev/null +++ b/pyproject.toml @@ -0,0 +1,85 @@ +[build-system] +requires = ["setuptools>=68", "wheel"] +build-backend = "setuptools.build_meta" + +[project] +name = "jlens-materials" +version = "0.1.0" +description = "Jacobian-lens interpretability toolkit for materials science and protein prompts — fit/apply the lens, plot publication figures, run causal swaps, and write an LLM-authored LaTeX report." +readme = "README.md" +requires-python = ">=3.10" +license = { text = "Apache-2.0" } +authors = [{ name = "Markus J. Buehler", email = "mbuehler@mit.edu" }] +keywords = [ + "interpretability", "jacobian-lens", "mechanistic-interpretability", + "transformers", "materials-science", "protein", "fracture-mechanics", +] +classifiers = [ + "Programming Language :: Python :: 3", + "License :: OSI Approved :: Apache Software License", + "Intended Audience :: Science/Research", + "Topic :: Scientific/Engineering :: Artificial Intelligence", +] + +# Core runtime — everything the fit -> apply -> figures -> analysis -> report +# pipeline needs. Providers (anthropic/openai) are included so the LLM analysis +# works out of the box; the --offline path does not require them at runtime. +dependencies = [ + "torch>=2.2", + "transformers>=4.45", # Gemma-3/4 need a recent release (dev env: 5.9) + "accelerate>=0.30", # device_map + efficient model loading + "sentencepiece>=0.2", # Gemma / Llama / Qwen tokenizers + "datasets>=2.19", # wikitext prompts for lens fitting (--fit) + "huggingface_hub>=0.30", # private Hub storage for fitted lens bundles + "numpy>=1.24", + "matplotlib>=3.7", + "wordfreq==3.1.1", # frozen English-frequency screen for 31B SI streams + "pillow>=10.0", # GIF export for the "thinking" animation + "tqdm>=4.66", # progress bars for the slow lens fit + "anthropic>=0.40", # analysis provider: Claude (Messages API) + "openai>=1.66", # analysis provider: OpenAI (Responses API) +] + +[project.optional-dependencies] +# Everything above already covers a full run; `dev` adds tooling only. +dev = ["ruff>=0.6", "ipython>=8.0", "pytest>=8.0"] + +[project.urls] +Homepage = "https://github.com/lamm-mit/Substrates" +Repository = "https://github.com/lamm-mit/Substrates" +"Jacobian lens (upstream)" = "https://github.com/lamm-mit/jacobian-lens" + +# --------------------------------------------------------------------------- +# Layout: the toolkit modules live flat in jlens_materials/ and import each +# other by bare name (import matviz, ...). We expose them as top-level modules +# and ship the vendored `jlens` (with its data/ resources) as a package, so an +# editable install makes `import run_lens`, `import matviz`, `import jlens` +# resolve from anywhere — while the scripts still run in-place from the dir. +# --------------------------------------------------------------------------- +[tool.setuptools] +py-modules = [ + "analyze", "animate", "artifacts", "auto_swap", "compare", "domain_prompts", + "lens_hub", "llm", "matviz", "paper_protocol", "report", "report_latex", "run_lens", "swap", +] +packages = ["jlens", "explorer"] + +[tool.setuptools.package-dir] +"" = "jlens_materials" +"jlens" = "jlens_materials/_vendor_jlens/jlens" + +[tool.setuptools.package-data] +jlens = ["data/*"] +explorer = ["README.md", "launch.sh", "static/*", "skills/**/*"] + +# Convenience entry points — same as `python .py`, runnable anywhere. +[project.scripts] +jlens-run = "run_lens:main" +jlens-analyze = "analyze:main" +jlens-report = "report_latex:main" +jlens-report-md = "report:main" +jlens-compare = "compare:main" +jlens-animate = "animate:main" +jlens-swap = "swap:main" +jlens-auto-swap = "auto_swap:main" +jlens-artifacts = "artifacts:main" +jlens-explorer = "explorer.server:main" diff --git a/reproduction/reproduce_controlled.py b/reproduction/reproduce_controlled.py new file mode 100644 index 0000000000000000000000000000000000000000..57d183eed7c91392e206945be67c1340294c608b --- /dev/null +++ b/reproduction/reproduce_controlled.py @@ -0,0 +1,16 @@ +#!/usr/bin/env python3 +"""Recompute controlled readout and the separately appended cached rater analysis.""" +from pathlib import Path +import json,sys +root=Path.cwd();sys.path[:0]=[str(root/'scripts'),str(root)] +import analyze_materials_heldout_v1 as readout +import rate_materials_heldout_v1_openai as rater +readout.main() +# Analyze saved responses directly: deliberately never call run_blinded_passes. +raw=json.loads(rater.RAW_PATH.read_text()) +results,ratings=rater.analyze(raw) +rater.RESULTS_PATH.write_text(json.dumps(results,indent=2)+'\n') +rater.write_ratings_csv(ratings);rater.plot_results(results) +statistics=json.loads(rater.STATS_PATH.read_text());statistics['automated_blinded_secondary']=results +rater.STATS_PATH.write_text(json.dumps(statistics,indent=2)+'\n') +print(json.dumps({'cached_rater_passes':len(raw['passes']),'new_api_calls':0})) diff --git a/reproduction/reproduce_scale.py b/reproduction/reproduce_scale.py new file mode 100644 index 0000000000000000000000000000000000000000..2bff34809847589ee13a57e8d527949325fa7735 --- /dev/null +++ b/reproduction/reproduce_scale.py @@ -0,0 +1,21 @@ +#!/usr/bin/env python3 +"""Recompute a larger-scale benchmark from saved arrays; never load a model.""" +import argparse,json,sys +from pathlib import Path +from types import SimpleNamespace +import numpy as np + +p=argparse.ArgumentParser();p.add_argument('--root',type=Path,required=True);p.add_argument('--scale',choices=['12b','31b'],required=True);a=p.parse_args() +root=a.root.resolve()/'jlens_materials';sys.path[:0]=[str(root/'scripts'),str(root)] +import run_gemma_scale_equation_free_relational as analysis +out=root/'experiments'/f'gemma4-{a.scale}-equation-free-relational-v1' +load=lambda n:json.loads((out/n).read_text()) +protocol=load('protocol.json');raw=load('raw.json');meta=raw['provenance'] +rows=[json.loads(s) for s in (out/'equation_free_rows.jsonl').read_text().splitlines() if s.strip()] +# The original analyzer uses these attributes only to record metadata. This is +# a metadata container, not a model or approximation to one. +metadata=SimpleNamespace(n_layers=meta['n_layers'],d_model=meta['d_model'],input_device=meta['device']) +analysis.analyze(out=out,protocol=protocol,development=load('development_prompt_manifest.json'), + test=load('equation_free_prompt_manifest.json'),dev_states=np.load(out/'development_states.npy'), + test_states=np.load(out/'equation_free_states.npy'),test_rows=rows,answer_tokens=raw['answer_tokens'],model=metadata) +print(json.dumps({'scale':a.scale,'model_inference':False,'result':str(out/'statistics.json')})) diff --git a/reproduction/run_reproduction.py b/reproduction/run_reproduction.py new file mode 100644 index 0000000000000000000000000000000000000000..3e17f486bd1a94234a79560517b741ee7e03eef3 --- /dev/null +++ b/reproduction/run_reproduction.py @@ -0,0 +1,82 @@ +#!/usr/bin/env python3 +"""Run independent analysis checks in a disposable copy; preserve all originals.""" +import argparse,json,math,os,shutil,subprocess,sys,time +from pathlib import Path + +W=Path(__file__).resolve().parent +def job(name,script,outputs):return {'name':name,'argv':['scripts/'+script+'.py'],'outputs':outputs} +JOBS=[ + job('controlled_readout','analyze_materials_heldout_v1',['experiments/materials-heldout-v1_statistics.json']), + job('target_free_classification','analyze_candidate_nonsteering',['experiments/candidate-nonsteering-2026-07-16/semantic_classification_statistics.json','experiments/candidate-nonsteering-2026-07-16/ontology_rsa_statistics.json','experiments/candidate-nonsteering-2026-07-16/concept_role_onset_statistics.json']), + job('broad_steering','analyze_semantic_steering_v3',['experiments/semantic-steering-v3_statistics.json']), + job('grain_steering','analyze_relational_grain_steering_v4',['experiments/relational-grain-steering-v4_statistics.json']), + job('full_state_patching','analyze_counterfactual_activation_patching',['experiments/candidate-activation-patching-2026-07-16/statistics.json']), + job('cross_mechanism_patching','analyze_cross_mechanism_activation_patching',['experiments/cross-mechanism-activation-patching-2026-07-18/statistics.json']), + job('supplied_equation_60_laws','analyze_neutral_anchored_relational_benchmark',['experiments/neutral-anchored-relational-physics-2026-07-18/statistics.json']), + job('equation_free_4b','analyze_equation_free_relational_benchmark_amended',['experiments/equation-free-relational-physics-2026-08-21/statistics.json']), + job('equation_free_scale_comparison','analyze_gemma_equation_free_multiscale',['experiments/gemma-equation-free-multiscale-2026-08-25/statistics.json']), + job('readability_scale_comparison','analyze_gemma_multiscale_heldout_v1',['experiments/gemma-multiscale-heldout-v1-2026-08-25/statistics.json']), + job('coordinate_interventions','analyze_causal_relational_knockout',['experiments/causal-relational-knockout-2026-09-02/statistics.json']), + job('coordinate_integrity','validate_causal_relational_knockout',[]), + job('lexical_adversarial','analyze_lexical_adversarial_representation',['experiments/lexical-adversarial-representation-2026-07-17/statistics.json']), + job('late_replication','analyze_late_physics_replication',['experiments/late-physics-representation-replication-2026-07-17/statistics.json']), + job('answer_scaffold','analyze_answer_scaffold_audit',['experiments/answer-scaffold-audit-2026-07-17/statistics.json']), + job('answer_code','analyze_answer_code_binding',['experiments/answer-code-binding-2026-07-17/statistics.json']), + job('graph_integrity','audit_graph_topology_rigorous',[]), + job('graph_generalization_integrity','validate_graph_generalization_audit',[]), + job('graph_figures','plot_graph_generalization_audit',[]), + job('latent_geometry','analyze_materials_latent_geometry',['experiments/materials-heldout-v1_latent_geometry_statistics.json']), +] + +def numeric_leaves(o,p=''): + if isinstance(o,dict): + for k,v in o.items(): + if any(s in k.lower() for s in ['sha256','timestamp','elapsed','created_at','completed_at','provenance','inputs','file_hash','runtime','python_version']):continue + yield from numeric_leaves(v,p+'.'+k) + elif isinstance(o,list): + for i,v in enumerate(o):yield from numeric_leaves(v,p+f'[{i}]') + elif isinstance(o,(int,float,bool)):yield p,o + +def main(): + ap=argparse.ArgumentParser();ap.add_argument('--source',type=Path,default=W/'hf_dataset');ap.add_argument('--only',nargs='*');ap.add_argument('--scratch',type=Path,default=W/'reproduction_scratch');ap.add_argument('--report',type=Path,default=W/'REPRODUCTION_RESULTS.json');args=ap.parse_args() + source=args.source.resolve();scratch=args.scratch.resolve();scratch.mkdir(parents=True,exist_ok=True) + if not (scratch/'jlens_materials').exists():shutil.copytree(source/'jlens_materials',scratch/'jlens_materials') + (scratch/'jlens_materials/paper/figures').mkdir(parents=True,exist_ok=True) + logs=W/'reproduction_logs';logs.mkdir(exist_ok=True) + jobs=list(JOBS) + for scale in ['12b','31b']: + jobs.append({'name':'equation_free_'+scale,'argv':[str(W/'reproduce_scale.py'),'--root',str(scratch),'--scale',scale], + 'outputs':[f'experiments/gemma4-{scale}-equation-free-relational-v1/statistics.json']}) + if args.only:jobs=[j for j in jobs if j['name'] in args.only] + baseline={f.relative_to(source):f for f in (source/'jlens_materials').rglob('*') if f.is_file()} + report=[] + env={**os.environ,'MPLBACKEND':'Agg','MPLCONFIGDIR':str(W/'mpl_cache'),'NUMBA_CACHE_DIR':str(W/'numba_cache'),'HF_HUB_OFFLINE':'1','TRANSFORMERS_OFFLINE':'1','OMP_NUM_THREADS':'1','OPENBLAS_NUM_THREADS':'1'} + for j in jobs: + if j['name']=='controlled_readout':j['argv']=[str(W/'reproduce_controlled.py')] + # Each check starts from the same original artifacts, even when an + # earlier analyzer rewrote a derived input consumed by a later check. + for rel,f in baseline.items(): + t=scratch/rel + if not t.exists() or t.stat().st_mtime_ns!=f.stat().st_mtime_ns or t.stat().st_size!=f.stat().st_size: + t.parent.mkdir(parents=True,exist_ok=True);shutil.copy2(f,t) + before={o:json.loads((source/'jlens_materials'/o).read_text()) for o in j['outputs'] if (source/'jlens_materials'/o).exists()} + start=time.monotonic();log=logs/(j['name']+'.log') + print('START '+j['name'],flush=True) + try: + with log.open('w') as f:r=subprocess.run([sys.executable,*j['argv']],cwd=scratch/'jlens_materials',env=env,stdout=f,stderr=subprocess.STDOUT,timeout=1200) + code=r.returncode + except subprocess.TimeoutExpired:code=124 + differences=[];count=0 + if code==0: + for name,old in before.items(): + new=json.loads((scratch/'jlens_materials'/name).read_text());values=dict(numeric_leaves(new)) + for key,value in numeric_leaves(old): + count+=1 + v=values.get(key) + if v is None or not ((isinstance(value,float) and math.isnan(value) and isinstance(v,float) and math.isnan(v)) or math.isclose(value,v,rel_tol=1e-8,abs_tol=1e-10)): + differences.append({'file':name,'key':key,'expected':value,'observed':v}) + item={'name':j['name'],'command':'python '+' '.join(j['argv']),'exit_code':code,'seconds':round(time.monotonic()-start,2),'numeric_values_compared':count,'differences':differences,'status':'passed' if code==0 and not differences else 'failed','log':log.name} + report.append(item);args.report.write_text(json.dumps(report,indent=2,allow_nan=True)+'\n') + print(json.dumps({k:v for k,v in item.items() if k not in ['command','differences']})+' differences='+str(len(differences)),flush=True) + print('Complete. Results: '+str(args.report),flush=True) +if __name__=='__main__':main() diff --git a/requirements-analysis.txt b/requirements-analysis.txt new file mode 100644 index 0000000000000000000000000000000000000000..778bdf4348bb030b50470a10356c8283ac48050f --- /dev/null +++ b/requirements-analysis.txt @@ -0,0 +1,7 @@ +numpy==2.2.6 +scipy==1.13.1 +pandas==3.0.3 +matplotlib==3.11.1 +scikit-learn==1.6.1 +networkx==3.6.1 +umap-learn==0.5.9.post2