diff --git a/jlens_materials/prompts/README.md b/jlens_materials/prompts/README.md new file mode 100644 index 0000000000000000000000000000000000000000..e6fc265ad8d6378e8bdd9c3e3dfac355a0da56f5 --- /dev/null +++ b/jlens_materials/prompts/README.md @@ -0,0 +1,53 @@ +# Prompt and experiment data + +`run_lens.py --prompts PATH` accepts either this toolkit's `{"prompts": [...]}` +schema or the released Jacobian Lens `{"items": [...]}` evaluation/probe-swap +schema. A directory loads every JSON file in lexical order. + +For a paper-protocol lens-quality item, provide: + +```json +{ + "slug": "stable-id", + "shape": "MULTIHOP", + "protocol": "lens_eval", + "domain": "mechanics", + "text": "The question ending immediately before its answer is", + "readout_selector": "before_answer", + "tracked": ["latent-intermediate"], + "synonyms": {"latent-intermediate": ["alternative"]}, + "answer": "answer", + "must_be_absent_from_input": true, + "must_be_absent_from_output": true +} +``` + +The available fixed selectors are `before_answer`, `final_prompt_token`, +`last_newline`, `assistant_response`, and `explicit`. `all_prompt` exists only +for exploratory visualization and is rejected by strict protocol mode. + +For `probe_swap`, also provide `swap_from`, `swap_to`, and `swap_answer`. +For `verbal_report`, omit `swap_from`: the clean greedy answer is selected +dynamically. Directed-modulation trials use the identical teacher-forced +carrier/targets across `focus`, `suppress`, and `control`, connected by a shared +`control_group`. +For paper-scale modulation, also provide stable `phrasing_id`, `carrier_id`, +and `target_family` values; strict paper runs require 24 distinct phrasings. + +[`mechanics-paper-example.json`](mechanics-paper-example.json) demonstrates all +three families. It is deliberately too small for quantitative reporting; +create at least 50 independent items per evaluation distribution and multiple +instruction phrasings/trials for modulation. + +[`materials-qualitative-pack.json`](materials-qualitative-pack.json) provides +14 materials-science case studies covering Hall--Petch strengthening, Orowan +bypass, martensitic transformation, Schmid slip, diffusional creep, dislocation +character, diffraction recognition, unnamed associations, matched modulation, +and causal swaps. Run it with `--recipe demo --min-items-per-shape 50` for close +inspection without accidentally labeling the small sample quantitative. Use +`--generation-max-new-tokens 1` when auditing the input/output-absence controls. + +`fracture.json` and `protein.json` are retained as legacy visualization sets. +The authoritative built-ins in `domain_prompts.py` include stricter scoring and +matched modulation controls; use the mechanics example as the starting point +for new paper-protocol datasets. diff --git a/jlens_materials/prompts/fracture.json b/jlens_materials/prompts/fracture.json new file mode 100644 index 0000000000000000000000000000000000000000..1a46c3d248669ae401ff6eca769f363585ac3043 --- /dev/null +++ b/jlens_materials/prompts/fracture.json @@ -0,0 +1,146 @@ +{ + "prompts": [ + { + "slug": "fracture-griffith", + "shape": "MULTIHOP", + "domain": "fracture", + "title": "Griffith criterion (multi-hop)", + "description": "Two-hop: the failure law for a brittle solid with a sharp flaw. Watch 'energy' / 'surface' / 'Griffith' surface before 'crack'.", + "text": "In linear elastic fracture mechanics, the critical stress for a brittle solid to fail is set by balancing the released strain energy against the energy needed to create new surface. The critical stress therefore scales inversely with the square root of the", + "readout_at": -1, + "tracked": [ + "energy", + "surface", + "Griffith", + "crack", + "length" + ], + "note": "Griffith's result sigma_c ~ sqrt(2 E gamma / (pi a)). The answer token is 'crack (length)'; 'energy', 'surface' and the eponym 'Griffith' are unspoken intermediates the derivation passes through -- the fracture analogue of Mars->color->red." + }, + { + "slug": "fracture-fatigue", + "shape": "MULTIHOP", + "domain": "fracture", + "title": "Cyclic loading -> failure mode (multi-hop)", + "description": "Cyclic sub-yield loading. 'fatigue' should lead 'crack', with 'Paris'/'striations' as deeper-layer associates.", + "text": "A steel component is loaded repeatedly far below its yield stress for millions of cycles. Tiny cracks nucleate at the surface and grow a little on every cycle until the part suddenly breaks. This progressive mode of failure is called", + "readout_at": -1, + "tracked": [ + "fatigue", + "crack", + "cyclic", + "Paris", + "striations" + ], + "note": "The answer is 'fatigue'. Whether 'Paris' (the Paris crack-growth law) or 'striations' (the fractographic fingerprint) appear in deeper layers probes how much associated fracture knowledge the workspace pulls in beyond the one-word answer." + }, + { + "slug": "fracture-toughness-chain", + "shape": "MULTIHOP", + "domain": "fracture", + "title": "Stress-intensity factor chain (multi-hop)", + "description": "Chain: geometry+load -> stress intensity K -> compare to K_Ic -> propagate. Track K / toughness / propagate in order.", + "text": "An engineer has a plate with an edge crack under tension. She first computes the stress intensity factor at the crack tip, then compares it against the material's fracture toughness. If the first exceeds the second, the crack will", + "readout_at": -1, + "tracked": [ + "stress", + "intensity", + "toughness", + "propagate", + "grow", + "unstable" + ], + "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." + }, + { + "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_at": -1, + "tracked": [ + "brittle", + "fracture", + "glass", + "shatter", + "crack" + ], + "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." + }, + { + "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_at": -1, + "tracked": [ + "ductile", + "plastic", + "necking", + "yield", + "deformation" + ], + "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." + }, + { + "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]]\nThe dominant loading state this represents is", + "readout_at": -1, + "tracked": [ + "tension", + "tensile", + "uniaxial", + "crack", + "opening", + "mode" + ], + "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." + }, + { + "slug": "fracture-modulation", + "shape": "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_at": -1, + "tracked": [ + "crack", + "plastic", + "plasticity", + "tip", + "yield", + "zone" + ], + "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." + }, + { + "slug": "fracture-report-swap", + "shape": "REPORT_SWAP", + "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.", + "readout_at": -1, + "tracked": [ + "fatigue", + "creep", + "corrosion", + "fracture", + "buckling" + ], + "swap_from": "fatigue", + "swap_to": "creep", + "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." + } + ] +} \ No newline at end of file diff --git a/jlens_materials/prompts/materials-heldout-v1-preregistered.json b/jlens_materials/prompts/materials-heldout-v1-preregistered.json new file mode 100644 index 0000000000000000000000000000000000000000..499a7a833f5afd3a72e641b6da30e373762be0f8 --- /dev/null +++ b/jlens_materials/prompts/materials-heldout-v1-preregistered.json @@ -0,0 +1,1068 @@ +{ + "format_version": 1, + "study": "materials-heldout-v1", + "frozen_before_execution": "2026-07-14", + "description": "Fifty held-out association descriptions: ten materials mechanism families with five new phrasings each. Generated and frozen before any held-out model or lens output was inspected.", + "prompts": [ + { + "slug": "heldout-v1-assoc-ductile-01", + "shape": "ASSOCIATION", + "protocol": "lens_eval", + "domain": "materials", + "category": "ductile", + "target_family": "ductile", + "phrasing_id": "heldout-ductile-01", + "title": "Energy-absorbing dimpled separation 1", + "description": "Held-out unnamed materials description; compare predetermined full-vocabulary ranks and target-free discovery under matched lenses.", + "text": "A round aluminum bar stretched into a pronounced waist before separating. The mating faces contained hemispherical pits centered on inclusions and a slanted tearing rim.", + "readout_selector": "final_prompt_token", + "tracked": [ + "ductile", + "nucleation", + "coalescence", + "void" + ], + "must_be_absent_from_input": true, + "must_be_absent_from_output": true, + "note": "Frozen held-out item. Predetermined terms are used only for the rank endpoint; open-vocabulary generation and ranking cannot use them." + }, + { + "slug": "heldout-v1-assoc-ductile-02", + "shape": "ASSOCIATION", + "protocol": "lens_eval", + "domain": "materials", + "category": "ductile", + "target_family": "ductile", + "phrasing_id": "heldout-ductile-02", + "title": "Energy-absorbing dimpled separation 2", + "description": "Held-out unnamed materials description; compare predetermined full-vocabulary ranks and target-free discovery under matched lenses.", + "text": "A nickel coupon accumulated large permanent elongation before rupture. Microscopy showed a honeycomb of deep depressions surrounding second-phase particles.", + "readout_selector": "final_prompt_token", + "tracked": [ + "ductile", + "nucleation", + "coalescence", + "void" + ], + "must_be_absent_from_input": true, + "must_be_absent_from_output": true, + "note": "Frozen held-out item. Predetermined terms are used only for the rank endpoint; open-vocabulary generation and ranking cannot use them." + }, + { + "slug": "heldout-v1-assoc-ductile-03", + "shape": "ASSOCIATION", + "protocol": "lens_eval", + "domain": "materials", + "category": "ductile", + "target_family": "ductile", + "phrasing_id": "heldout-ductile-03", + "title": "Energy-absorbing dimpled separation 3", + "description": "Held-out unnamed materials description; compare predetermined full-vocabulary ranks and target-free discovery under matched lenses.", + "text": "A copper tensile specimen lost much of its cross-sectional area before failure. Small particle-centered holes had enlarged and joined across the final ligament.", + "readout_selector": "final_prompt_token", + "tracked": [ + "ductile", + "nucleation", + "coalescence", + "void" + ], + "must_be_absent_from_input": true, + "must_be_absent_from_output": true, + "note": "Frozen held-out item. Predetermined terms are used only for the rank endpoint; open-vocabulary generation and ranking cannot use them." + }, + { + "slug": "heldout-v1-assoc-ductile-04", + "shape": "ASSOCIATION", + "protocol": "lens_eval", + "domain": "materials", + "category": "ductile", + "target_family": "ductile", + "phrasing_id": "heldout-ductile-04", + "title": "Energy-absorbing dimpled separation 4", + "description": "Held-out unnamed materials description; compare predetermined full-vocabulary ranks and target-free discovery under matched lenses.", + "text": "An impact-tested structural metal absorbed substantial energy and developed broad lateral lips. Its failure surface was covered by rounded microscopic depressions.", + "readout_selector": "final_prompt_token", + "tracked": [ + "ductile", + "nucleation", + "coalescence", + "void" + ], + "must_be_absent_from_input": true, + "must_be_absent_from_output": true, + "note": "Frozen held-out item. Predetermined terms are used only for the rank endpoint; open-vocabulary generation and ranking cannot use them." + }, + { + "slug": "heldout-v1-assoc-ductile-05", + "shape": "ASSOCIATION", + "protocol": "lens_eval", + "domain": "materials", + "category": "ductile", + "target_family": "ductile", + "phrasing_id": "heldout-ductile-05", + "title": "Energy-absorbing dimpled separation 5", + "description": "Held-out unnamed materials description; compare predetermined full-vocabulary ranks and target-free discovery under matched lenses.", + "text": "A pressurized metal tube bulged strongly before opening. The torn wall displayed elongated pits near the surface and deeper equiaxed pits toward the center.", + "readout_selector": "final_prompt_token", + "tracked": [ + "ductile", + "nucleation", + "coalescence", + "void" + ], + "must_be_absent_from_input": true, + "must_be_absent_from_output": true, + "note": "Frozen held-out item. Predetermined terms are used only for the rank endpoint; open-vocabulary generation and ranking cannot use them." + }, + { + "slug": "heldout-v1-assoc-boundary-attack-01", + "shape": "ASSOCIATION", + "protocol": "lens_eval", + "domain": "materials", + "category": "boundary-attack", + "target_family": "boundary-attack", + "phrasing_id": "heldout-boundary-attack-01", + "title": "Chromium-depleted interfacial attack 1", + "description": "Held-out unnamed materials description; compare predetermined full-vocabulary ranks and target-free discovery under matched lenses.", + "text": "A stainless weld spent several hours near 675 degrees Celsius. After nitric-acid exposure, narrow trenches followed the grain edges beside chromium-rich carbide rows.", + "readout_selector": "final_prompt_token", + "tracked": [ + "boundary", + "corrosion", + "sensitization" + ], + "must_be_absent_from_input": true, + "must_be_absent_from_output": true, + "note": "Frozen held-out item. Predetermined terms are used only for the rank endpoint; open-vocabulary generation and ranking cannot use them." + }, + { + "slug": "heldout-v1-assoc-boundary-attack-02", + "shape": "ASSOCIATION", + "protocol": "lens_eval", + "domain": "materials", + "category": "boundary-attack", + "target_family": "boundary-attack", + "phrasing_id": "heldout-boundary-attack-02", + "title": "Chromium-depleted interfacial attack 2", + "description": "Held-out unnamed materials description; compare predetermined full-vocabulary ranks and target-free discovery under matched lenses.", + "text": "An austenitic pipe was slowly cooled through an intermediate temperature range. A later chemical test exposed a connected network of material loss outlining individual grains.", + "readout_selector": "final_prompt_token", + "tracked": [ + "boundary", + "corrosion", + "sensitization" + ], + "must_be_absent_from_input": true, + "must_be_absent_from_output": true, + "note": "Frozen held-out item. Predetermined terms are used only for the rank endpoint; open-vocabulary generation and ranking cannot use them." + }, + { + "slug": "heldout-v1-assoc-boundary-attack-03", + "shape": "ASSOCIATION", + "protocol": "lens_eval", + "domain": "materials", + "category": "boundary-attack", + "target_family": "boundary-attack", + "phrasing_id": "heldout-boundary-attack-03", + "title": "Chromium-depleted interfacial attack 3", + "description": "Held-out unnamed materials description; compare predetermined full-vocabulary ranks and target-free discovery under matched lenses.", + "text": "Carbide particles decorated the interfaces in heat-affected stainless steel. Immersion testing removed chromium-depleted regions next to those particles while grain interiors remained sound.", + "readout_selector": "final_prompt_token", + "tracked": [ + "boundary", + "corrosion", + "sensitization" + ], + "must_be_absent_from_input": true, + "must_be_absent_from_output": true, + "note": "Frozen held-out item. Predetermined terms are used only for the rank endpoint; open-vocabulary generation and ranking cannot use them." + }, + { + "slug": "heldout-v1-assoc-boundary-attack-04", + "shape": "ASSOCIATION", + "protocol": "lens_eval", + "domain": "materials", + "category": "boundary-attack", + "target_family": "boundary-attack", + "phrasing_id": "heldout-boundary-attack-04", + "title": "Chromium-depleted interfacial attack 4", + "description": "Held-out unnamed materials description; compare predetermined full-vocabulary ranks and target-free discovery under matched lenses.", + "text": "A stainless vessel received an improper post-weld thermal cycle. In service, fissures traced the polygonal grain-edge network instead of cutting through the crystallites.", + "readout_selector": "final_prompt_token", + "tracked": [ + "boundary", + "corrosion", + "sensitization" + ], + "must_be_absent_from_input": true, + "must_be_absent_from_output": true, + "note": "Frozen held-out item. Predetermined terms are used only for the rank endpoint; open-vocabulary generation and ranking cannot use them." + }, + { + "slug": "heldout-v1-assoc-boundary-attack-05", + "shape": "ASSOCIATION", + "protocol": "lens_eval", + "domain": "materials", + "category": "boundary-attack", + "target_family": "boundary-attack", + "phrasing_id": "heldout-boundary-attack-05", + "title": "Chromium-depleted interfacial attack 5", + "description": "Held-out unnamed materials description; compare predetermined full-vocabulary ranks and target-free discovery under matched lenses.", + "text": "Austenitic sheet aged near 650 degrees Celsius showed continuous carbide films around grains. An aggressive liquid subsequently produced narrow grooves along the same paths.", + "readout_selector": "final_prompt_token", + "tracked": [ + "boundary", + "corrosion", + "sensitization" + ], + "must_be_absent_from_input": true, + "must_be_absent_from_output": true, + "note": "Frozen held-out item. Predetermined terms are used only for the rank endpoint; open-vocabulary generation and ranking cannot use them." + }, + { + "slug": "heldout-v1-assoc-cyclic-01", + "shape": "ASSOCIATION", + "protocol": "lens_eval", + "domain": "materials", + "category": "cyclic", + "target_family": "cyclic", + "phrasing_id": "heldout-cyclic-01", + "title": "Progressive damage under repeated loading 1", + "description": "Held-out unnamed materials description; compare predetermined full-vocabulary ranks and target-free discovery under matched lenses.", + "text": "A compressor blade endured millions of small vibration cycles before failing. Curved arrest lines radiated from a surface defect toward a small rough overload region.", + "readout_selector": "final_prompt_token", + "tracked": [ + "fatigue", + "crack", + "propagation" + ], + "must_be_absent_from_input": true, + "must_be_absent_from_output": true, + "note": "Frozen held-out item. Predetermined terms are used only for the rank endpoint; open-vocabulary generation and ranking cannot use them." + }, + { + "slug": "heldout-v1-assoc-cyclic-02", + "shape": "ASSOCIATION", + "protocol": "lens_eval", + "domain": "materials", + "category": "cyclic", + "target_family": "cyclic", + "phrasing_id": "heldout-cyclic-02", + "title": "Progressive damage under repeated loading 2", + "description": "Held-out unnamed materials description; compare predetermined full-vocabulary ranks and target-free discovery under matched lenses.", + "text": "A bridge hanger separated although every measured load was below its monotonic capacity. Fine parallel striations recorded incremental front advance from a machining groove.", + "readout_selector": "final_prompt_token", + "tracked": [ + "fatigue", + "crack", + "propagation" + ], + "must_be_absent_from_input": true, + "must_be_absent_from_output": true, + "note": "Frozen held-out item. Predetermined terms are used only for the rank endpoint; open-vocabulary generation and ranking cannot use them." + }, + { + "slug": "heldout-v1-assoc-cyclic-03", + "shape": "ASSOCIATION", + "protocol": "lens_eval", + "domain": "materials", + "category": "cyclic", + "target_family": "cyclic", + "phrasing_id": "heldout-cyclic-03", + "title": "Progressive damage under repeated loading 3", + "description": "Held-out unnamed materials description; compare predetermined full-vocabulary ranks and target-free discovery under matched lenses.", + "text": "A wheel axle survived years of repeated bending. The failure face contained thumbnail-shaped beach marks centered on several surface initiation sites.", + "readout_selector": "final_prompt_token", + "tracked": [ + "fatigue", + "crack", + "propagation" + ], + "must_be_absent_from_input": true, + "must_be_absent_from_output": true, + "note": "Frozen held-out item. Predetermined terms are used only for the rank endpoint; open-vocabulary generation and ranking cannot use them." + }, + { + "slug": "heldout-v1-assoc-cyclic-04", + "shape": "ASSOCIATION", + "protocol": "lens_eval", + "domain": "materials", + "category": "cyclic", + "target_family": "cyclic", + "phrasing_id": "heldout-cyclic-04", + "title": "Progressive damage under repeated loading 4", + "description": "Held-out unnamed materials description; compare predetermined full-vocabulary ranks and target-free discovery under matched lenses.", + "text": "A bolted aluminum joint opened after many pressurization cycles. A smooth region with regularly spaced microscopic ridges occupied most of the separated surface.", + "readout_selector": "final_prompt_token", + "tracked": [ + "fatigue", + "crack", + "propagation" + ], + "must_be_absent_from_input": true, + "must_be_absent_from_output": true, + "note": "Frozen held-out item. Predetermined terms are used only for the rank endpoint; open-vocabulary generation and ranking cannot use them." + }, + { + "slug": "heldout-v1-assoc-cyclic-05", + "shape": "ASSOCIATION", + "protocol": "lens_eval", + "domain": "materials", + "category": "cyclic", + "target_family": "cyclic", + "phrasing_id": "heldout-cyclic-05", + "title": "Progressive damage under repeated loading 5", + "description": "Held-out unnamed materials description; compare predetermined full-vocabulary ranks and target-free discovery under matched lenses.", + "text": "A rotating laboratory specimen failed after a long sequence of alternating stresses. Ratchet marks joined multiple surface origins before the remaining ligament broke suddenly.", + "readout_selector": "final_prompt_token", + "tracked": [ + "fatigue", + "crack", + "propagation" + ], + "must_be_absent_from_input": true, + "must_be_absent_from_output": true, + "note": "Frozen held-out item. Predetermined terms are used only for the rank endpoint; open-vocabulary generation and ranking cannot use them." + }, + { + "slug": "heldout-v1-assoc-cleavage-01", + "shape": "ASSOCIATION", + "protocol": "lens_eval", + "domain": "materials", + "category": "cleavage", + "target_family": "cleavage", + "phrasing_id": "heldout-cleavage-01", + "title": "Faceted low-plasticity separation 1", + "description": "Held-out unnamed materials description; compare predetermined full-vocabulary ranks and target-free discovery under matched lenses.", + "text": "A ferritic plate separated abruptly during cold weather with almost no macroscopic distortion. Flat facets and converging river markings crossed the grain interiors.", + "readout_selector": "final_prompt_token", + "tracked": [ + "brittle", + "cleavage", + "transgranular" + ], + "must_be_absent_from_input": true, + "must_be_absent_from_output": true, + "note": "Frozen held-out item. Predetermined terms are used only for the rank endpoint; open-vocabulary generation and ranking cannot use them." + }, + { + "slug": "heldout-v1-assoc-cleavage-02", + "shape": "ASSOCIATION", + "protocol": "lens_eval", + "domain": "materials", + "category": "cleavage", + "target_family": "cleavage", + "phrasing_id": "heldout-cleavage-02", + "title": "Faceted low-plasticity separation 2", + "description": "Held-out unnamed materials description; compare predetermined full-vocabulary ranks and target-free discovery under matched lenses.", + "text": "A low-temperature impact specimen absorbed little energy and retained its original cross section. The shiny failure face contained crystallographic steps and feather patterns.", + "readout_selector": "final_prompt_token", + "tracked": [ + "brittle", + "cleavage", + "transgranular" + ], + "must_be_absent_from_input": true, + "must_be_absent_from_output": true, + "note": "Frozen held-out item. Predetermined terms are used only for the rank endpoint; open-vocabulary generation and ranking cannot use them." + }, + { + "slug": "heldout-v1-assoc-cleavage-03", + "shape": "ASSOCIATION", + "protocol": "lens_eval", + "domain": "materials", + "category": "cleavage", + "target_family": "cleavage", + "phrasing_id": "heldout-cleavage-03", + "title": "Faceted low-plasticity separation 3", + "description": "Held-out unnamed materials description; compare predetermined full-vocabulary ranks and target-free discovery under matched lenses.", + "text": "A body-centered metal failed suddenly below its transition temperature. Microscopy revealed broad planar facets connected by sharp ridges through individual crystallites.", + "readout_selector": "final_prompt_token", + "tracked": [ + "brittle", + "cleavage", + "transgranular" + ], + "must_be_absent_from_input": true, + "must_be_absent_from_output": true, + "note": "Frozen held-out item. Predetermined terms are used only for the rank endpoint; open-vocabulary generation and ranking cannot use them." + }, + { + "slug": "heldout-v1-assoc-cleavage-04", + "shape": "ASSOCIATION", + "protocol": "lens_eval", + "domain": "materials", + "category": "cleavage", + "target_family": "cleavage", + "phrasing_id": "heldout-cleavage-04", + "title": "Faceted low-plasticity separation 4", + "description": "Held-out unnamed materials description; compare predetermined full-vocabulary ranks and target-free discovery under matched lenses.", + "text": "A thick steel section opened under modest loading after rapid cooling. The separated faces showed flat reflective planes and negligible evidence of local necking.", + "readout_selector": "final_prompt_token", + "tracked": [ + "brittle", + "cleavage", + "transgranular" + ], + "must_be_absent_from_input": true, + "must_be_absent_from_output": true, + "note": "Frozen held-out item. Predetermined terms are used only for the rank endpoint; open-vocabulary generation and ranking cannot use them." + }, + { + "slug": "heldout-v1-assoc-cleavage-05", + "shape": "ASSOCIATION", + "protocol": "lens_eval", + "domain": "materials", + "category": "cleavage", + "target_family": "cleavage", + "phrasing_id": "heldout-cleavage-05", + "title": "Faceted low-plasticity separation 5", + "description": "Held-out unnamed materials description; compare predetermined full-vocabulary ranks and target-free discovery under matched lenses.", + "text": "A notched iron alloy snapped during a cold bend test before appreciable permanent curvature developed. River-like markings on the facets converged toward the initiation site.", + "readout_selector": "final_prompt_token", + "tracked": [ + "brittle", + "cleavage", + "transgranular" + ], + "must_be_absent_from_input": true, + "must_be_absent_from_output": true, + "note": "Frozen held-out item. Predetermined terms are used only for the rank endpoint; open-vocabulary generation and ranking cannot use them." + }, + { + "slug": "heldout-v1-assoc-high-temperature-deformation-01", + "shape": "ASSOCIATION", + "protocol": "lens_eval", + "domain": "materials", + "category": "high-temperature-deformation", + "target_family": "high-temperature-deformation", + "phrasing_id": "heldout-high-temperature-deformation-01", + "title": "Time-dependent deformation under heat 1", + "description": "Held-out unnamed materials description; compare predetermined full-vocabulary ranks and target-free discovery under matched lenses.", + "text": "A steam-pipe alloy elongated gradually during ten years of steady load at 600 degrees Celsius. Rounded pores appeared at triple junctions late in life.", + "readout_selector": "final_prompt_token", + "tracked": [ + "creep", + "diffusion", + "cavity" + ], + "must_be_absent_from_input": true, + "must_be_absent_from_output": true, + "note": "Frozen held-out item. Predetermined terms are used only for the rank endpoint; open-vocabulary generation and ranking cannot use them." + }, + { + "slug": "heldout-v1-assoc-high-temperature-deformation-02", + "shape": "ASSOCIATION", + "protocol": "lens_eval", + "domain": "materials", + "category": "high-temperature-deformation", + "target_family": "high-temperature-deformation", + "phrasing_id": "heldout-high-temperature-deformation-02", + "title": "Time-dependent deformation under heat 2", + "description": "Held-out unnamed materials description; compare predetermined full-vocabulary ranks and target-free discovery under matched lenses.", + "text": "A constant-load high-temperature test showed rapid initial strain, a long nearly linear segment, and accelerating extension before rupture.", + "readout_selector": "final_prompt_token", + "tracked": [ + "creep", + "diffusion", + "cavity" + ], + "must_be_absent_from_input": true, + "must_be_absent_from_output": true, + "note": "Frozen held-out item. Predetermined terms are used only for the rank endpoint; open-vocabulary generation and ranking cannot use them." + }, + { + "slug": "heldout-v1-assoc-high-temperature-deformation-03", + "shape": "ASSOCIATION", + "protocol": "lens_eval", + "domain": "materials", + "category": "high-temperature-deformation", + "target_family": "high-temperature-deformation", + "phrasing_id": "heldout-high-temperature-deformation-03", + "title": "Time-dependent deformation under heat 3", + "description": "Held-out unnamed materials description; compare predetermined full-vocabulary ranks and target-free discovery under matched lenses.", + "text": "A turbine component operated under sustained stress near half its melting temperature. Grain-edge pores enlarged and linked even though the applied stress was below the room-temperature yield value.", + "readout_selector": "final_prompt_token", + "tracked": [ + "creep", + "diffusion", + "cavity" + ], + "must_be_absent_from_input": true, + "must_be_absent_from_output": true, + "note": "Frozen held-out item. Predetermined terms are used only for the rank endpoint; open-vocabulary generation and ranking cannot use them." + }, + { + "slug": "heldout-v1-assoc-high-temperature-deformation-04", + "shape": "ASSOCIATION", + "protocol": "lens_eval", + "domain": "materials", + "category": "high-temperature-deformation", + "target_family": "high-temperature-deformation", + "phrasing_id": "heldout-high-temperature-deformation-04", + "title": "Time-dependent deformation under heat 4", + "description": "Held-out unnamed materials description; compare predetermined full-vocabulary ranks and target-free discovery under matched lenses.", + "text": "A fine-grained metal slowly changed shape during a prolonged hot hold. Matter moved away from compressed grain faces and accumulated on faces under tension.", + "readout_selector": "final_prompt_token", + "tracked": [ + "creep", + "diffusion", + "cavity" + ], + "must_be_absent_from_input": true, + "must_be_absent_from_output": true, + "note": "Frozen held-out item. Predetermined terms are used only for the rank endpoint; open-vocabulary generation and ranking cannot use them." + }, + { + "slug": "heldout-v1-assoc-high-temperature-deformation-05", + "shape": "ASSOCIATION", + "protocol": "lens_eval", + "domain": "materials", + "category": "high-temperature-deformation", + "target_family": "high-temperature-deformation", + "phrasing_id": "heldout-high-temperature-deformation-05", + "title": "Time-dependent deformation under heat 5", + "description": "Held-out unnamed materials description; compare predetermined full-vocabulary ranks and target-free discovery under matched lenses.", + "text": "A heat-resistant alloy accumulated measurable permanent strain with increasing service time. Interfacial pores and wedge-shaped separations were concentrated near the final rupture zone.", + "readout_selector": "final_prompt_token", + "tracked": [ + "creep", + "diffusion", + "cavity" + ], + "must_be_absent_from_input": true, + "must_be_absent_from_output": true, + "note": "Frozen held-out item. Predetermined terms are used only for the rank endpoint; open-vocabulary generation and ranking cannot use them." + }, + { + "slug": "heldout-v1-assoc-particle-strengthening-01", + "shape": "ASSOCIATION", + "protocol": "lens_eval", + "domain": "materials", + "category": "particle-strengthening", + "target_family": "particle-strengthening", + "phrasing_id": "heldout-particle-strengthening-01", + "title": "Nonshearable particle obstacles 1", + "description": "Held-out unnamed materials description; compare predetermined full-vocabulary ranks and target-free discovery under matched lenses.", + "text": "A metal matrix contains closely spaced rigid dispersoids that cannot be cut. Moving lattice lines arc between them and leave closed loops around each obstacle.", + "readout_selector": "final_prompt_token", + "tracked": [ + "precipitation", + "bowing", + "strengthening" + ], + "must_be_absent_from_input": true, + "must_be_absent_from_output": true, + "note": "Frozen held-out item. Predetermined terms are used only for the rank endpoint; open-vocabulary generation and ranking cannot use them." + }, + { + "slug": "heldout-v1-assoc-particle-strengthening-02", + "shape": "ASSOCIATION", + "protocol": "lens_eval", + "domain": "materials", + "category": "particle-strengthening", + "target_family": "particle-strengthening", + "phrasing_id": "heldout-particle-strengthening-02", + "title": "Nonshearable particle obstacles 2", + "description": "Held-out unnamed materials description; compare predetermined full-vocabulary ranks and target-free discovery under matched lenses.", + "text": "After aging, a dense population of hard nanoscale particles raises the stress required for plastic flow. Electron microscopy shows rings encircling the particles after deformation.", + "readout_selector": "final_prompt_token", + "tracked": [ + "precipitation", + "bowing", + "strengthening" + ], + "must_be_absent_from_input": true, + "must_be_absent_from_output": true, + "note": "Frozen held-out item. Predetermined terms are used only for the rank endpoint; open-vocabulary generation and ranking cannot use them." + }, + { + "slug": "heldout-v1-assoc-particle-strengthening-03", + "shape": "ASSOCIATION", + "protocol": "lens_eval", + "domain": "materials", + "category": "particle-strengthening", + "target_family": "particle-strengthening", + "phrasing_id": "heldout-particle-strengthening-03", + "title": "Nonshearable particle obstacles 3", + "description": "Held-out unnamed materials description; compare predetermined full-vocabulary ranks and target-free discovery under matched lenses.", + "text": "Reducing the spacing between stable nonshearable particles increases the yield stress. Flexible line defects must curve strongly to pass through the remaining channels.", + "readout_selector": "final_prompt_token", + "tracked": [ + "precipitation", + "bowing", + "strengthening" + ], + "must_be_absent_from_input": true, + "must_be_absent_from_output": true, + "note": "Frozen held-out item. Predetermined terms are used only for the rank endpoint; open-vocabulary generation and ranking cannot use them." + }, + { + "slug": "heldout-v1-assoc-particle-strengthening-04", + "shape": "ASSOCIATION", + "protocol": "lens_eval", + "domain": "materials", + "category": "particle-strengthening", + "target_family": "particle-strengthening", + "phrasing_id": "heldout-particle-strengthening-04", + "title": "Nonshearable particle obstacles 4", + "description": "Held-out unnamed materials description; compare predetermined full-vocabulary ranks and target-free discovery under matched lenses.", + "text": "A dispersion-treated alloy contains hard particles embedded in a softer matrix. Mobile lattice imperfections wrap around the particles rather than passing through them.", + "readout_selector": "final_prompt_token", + "tracked": [ + "precipitation", + "bowing", + "strengthening" + ], + "must_be_absent_from_input": true, + "must_be_absent_from_output": true, + "note": "Frozen held-out item. Predetermined terms are used only for the rank endpoint; open-vocabulary generation and ranking cannot use them." + }, + { + "slug": "heldout-v1-assoc-particle-strengthening-05", + "shape": "ASSOCIATION", + "protocol": "lens_eval", + "domain": "materials", + "category": "particle-strengthening", + "target_family": "particle-strengthening", + "phrasing_id": "heldout-particle-strengthening-05", + "title": "Nonshearable particle obstacles 5", + "description": "Held-out unnamed materials description; compare predetermined full-vocabulary ranks and target-free discovery under matched lenses.", + "text": "Two alloys contain the same particle fraction, but the alloy with finer obstacle spacing requires a larger applied stress to move its line defects.", + "readout_selector": "final_prompt_token", + "tracked": [ + "precipitation", + "bowing", + "strengthening" + ], + "must_be_absent_from_input": true, + "must_be_absent_from_output": true, + "note": "Frozen held-out item. Predetermined terms are used only for the rank endpoint; open-vocabulary generation and ranking cannot use them." + }, + { + "slug": "heldout-v1-assoc-rapid-transformation-01", + "shape": "ASSOCIATION", + "protocol": "lens_eval", + "domain": "materials", + "category": "rapid-transformation", + "target_family": "rapid-transformation", + "phrasing_id": "heldout-rapid-transformation-01", + "title": "Quench-induced coordinated transformation 1", + "description": "Held-out unnamed materials description; compare predetermined full-vocabulary ranks and target-free discovery under matched lenses.", + "text": "A carbon steel quenched too rapidly for long-range solute redistribution develops a hard acicular product with a body-centered cell elongated along one axis.", + "readout_selector": "final_prompt_token", + "tracked": [ + "martensite", + "shear", + "Bain", + "tetragonal" + ], + "must_be_absent_from_input": true, + "must_be_absent_from_output": true, + "note": "Frozen held-out item. Predetermined terms are used only for the rank endpoint; open-vocabulary generation and ranking cannot use them." + }, + { + "slug": "heldout-v1-assoc-rapid-transformation-02", + "shape": "ASSOCIATION", + "protocol": "lens_eval", + "domain": "materials", + "category": "rapid-transformation", + "target_family": "rapid-transformation", + "phrasing_id": "heldout-rapid-transformation-02", + "title": "Quench-induced coordinated transformation 2", + "description": "Held-out unnamed materials description; compare predetermined full-vocabulary ranks and target-free discovery under matched lenses.", + "text": "Severe cooling converts the parent face-centered phase into fine laths almost instantaneously. Carbon remains trapped and the product has unequal axial lattice parameters.", + "readout_selector": "final_prompt_token", + "tracked": [ + "martensite", + "shear", + "Bain", + "tetragonal" + ], + "must_be_absent_from_input": true, + "must_be_absent_from_output": true, + "note": "Frozen held-out item. Predetermined terms are used only for the rank endpoint; open-vocabulary generation and ranking cannot use them." + }, + { + "slug": "heldout-v1-assoc-rapid-transformation-03", + "shape": "ASSOCIATION", + "protocol": "lens_eval", + "domain": "materials", + "category": "rapid-transformation", + "target_family": "rapid-transformation", + "phrasing_id": "heldout-rapid-transformation-03", + "title": "Quench-induced coordinated transformation 3", + "description": "Held-out unnamed materials description; compare predetermined full-vocabulary ranks and target-free discovery under matched lenses.", + "text": "A rapidly cooled steel forms hard plates by coordinated short-distance atomic motion. No composition partitioning is detected across the moving interface.", + "readout_selector": "final_prompt_token", + "tracked": [ + "martensite", + "shear", + "Bain", + "tetragonal" + ], + "must_be_absent_from_input": true, + "must_be_absent_from_output": true, + "note": "Frozen held-out item. Predetermined terms are used only for the rank endpoint; open-vocabulary generation and ranking cannot use them." + }, + { + "slug": "heldout-v1-assoc-rapid-transformation-04", + "shape": "ASSOCIATION", + "protocol": "lens_eval", + "domain": "materials", + "category": "rapid-transformation", + "target_family": "rapid-transformation", + "phrasing_id": "heldout-rapid-transformation-04", + "title": "Quench-induced coordinated transformation 4", + "description": "Held-out unnamed materials description; compare predetermined full-vocabulary ranks and target-free discovery under matched lenses.", + "text": "Quenching suppresses long-range atomic transport and produces packets of highly strained needles. The resulting body-centered unit cell has a c-to-a ratio above one.", + "readout_selector": "final_prompt_token", + "tracked": [ + "martensite", + "shear", + "Bain", + "tetragonal" + ], + "must_be_absent_from_input": true, + "must_be_absent_from_output": true, + "note": "Frozen held-out item. Predetermined terms are used only for the rank endpoint; open-vocabulary generation and ranking cannot use them." + }, + { + "slug": "heldout-v1-assoc-rapid-transformation-05", + "shape": "ASSOCIATION", + "protocol": "lens_eval", + "domain": "materials", + "category": "rapid-transformation", + "target_family": "rapid-transformation", + "phrasing_id": "heldout-rapid-transformation-05", + "title": "Quench-induced coordinated transformation 5", + "description": "Held-out unnamed materials description; compare predetermined full-vocabulary ranks and target-free discovery under matched lenses.", + "text": "An austenitic sample undergoes an abrupt, composition-preserving shape change during cooling. The product is hard, plate-like, and internally twinned.", + "readout_selector": "final_prompt_token", + "tracked": [ + "martensite", + "shear", + "Bain", + "tetragonal" + ], + "must_be_absent_from_input": true, + "must_be_absent_from_output": true, + "note": "Frozen held-out item. Predetermined terms are used only for the rank endpoint; open-vocabulary generation and ranking cannot use them." + }, + { + "slug": "heldout-v1-assoc-line-defect-motion-01", + "shape": "ASSOCIATION", + "protocol": "lens_eval", + "domain": "materials", + "category": "line-defect-motion", + "target_family": "line-defect-motion", + "phrasing_id": "heldout-line-defect-motion-01", + "title": "Motion of linear lattice imperfections 1", + "description": "Held-out unnamed materials description; compare predetermined full-vocabulary ranks and target-free discovery under matched lenses.", + "text": "Plastic flow begins when a linear lattice imperfection travels across a close-packed plane and leaves a permanent surface step.", + "readout_selector": "final_prompt_token", + "tracked": [ + "dislocation", + "glide", + "slip" + ], + "must_be_absent_from_input": true, + "must_be_absent_from_output": true, + "note": "Frozen held-out item. Predetermined terms are used only for the rank endpoint; open-vocabulary generation and ranking cannot use them." + }, + { + "slug": "heldout-v1-assoc-line-defect-motion-02", + "shape": "ASSOCIATION", + "protocol": "lens_eval", + "domain": "materials", + "category": "line-defect-motion", + "target_family": "line-defect-motion", + "phrasing_id": "heldout-line-defect-motion-02", + "title": "Motion of linear lattice imperfections 2", + "description": "Held-out unnamed materials description; compare predetermined full-vocabulary ranks and target-free discovery under matched lenses.", + "text": "During loading of a single crystal, etch-pit positions move along selected crystallographic planes and an irreversible offset accumulates.", + "readout_selector": "final_prompt_token", + "tracked": [ + "dislocation", + "glide", + "slip" + ], + "must_be_absent_from_input": true, + "must_be_absent_from_output": true, + "note": "Frozen held-out item. Predetermined terms are used only for the rank endpoint; open-vocabulary generation and ranking cannot use them." + }, + { + "slug": "heldout-v1-assoc-line-defect-motion-03", + "shape": "ASSOCIATION", + "protocol": "lens_eval", + "domain": "materials", + "category": "line-defect-motion", + "target_family": "line-defect-motion", + "phrasing_id": "heldout-line-defect-motion-03", + "title": "Motion of linear lattice imperfections 3", + "description": "Held-out unnamed materials description; compare predetermined full-vocabulary ranks and target-free discovery under matched lenses.", + "text": "A one-dimensional defect sweeps through the crystal in the direction of its mismatch vector, displacing the material on opposite sides of its plane.", + "readout_selector": "final_prompt_token", + "tracked": [ + "dislocation", + "glide", + "slip" + ], + "must_be_absent_from_input": true, + "must_be_absent_from_output": true, + "note": "Frozen held-out item. Predetermined terms are used only for the rank endpoint; open-vocabulary generation and ranking cannot use them." + }, + { + "slug": "heldout-v1-assoc-line-defect-motion-04", + "shape": "ASSOCIATION", + "protocol": "lens_eval", + "domain": "materials", + "category": "line-defect-motion", + "target_family": "line-defect-motion", + "phrasing_id": "heldout-line-defect-motion-04", + "title": "Motion of linear lattice imperfections 4", + "description": "Held-out unnamed materials description; compare predetermined full-vocabulary ranks and target-free discovery under matched lenses.", + "text": "Once the resolved driving stress exceeds a threshold, mobile lattice lines traverse their favored planes and produce permanent strain.", + "readout_selector": "final_prompt_token", + "tracked": [ + "dislocation", + "glide", + "slip" + ], + "must_be_absent_from_input": true, + "must_be_absent_from_output": true, + "note": "Frozen held-out item. Predetermined terms are used only for the rank endpoint; open-vocabulary generation and ranking cannot use them." + }, + { + "slug": "heldout-v1-assoc-line-defect-motion-05", + "shape": "ASSOCIATION", + "protocol": "lens_eval", + "domain": "materials", + "category": "line-defect-motion", + "target_family": "line-defect-motion", + "phrasing_id": "heldout-line-defect-motion-05", + "title": "Motion of linear lattice imperfections 5", + "description": "Held-out unnamed materials description; compare predetermined full-vocabulary ranks and target-free discovery under matched lenses.", + "text": "High-speed microscopy shows linear imperfections moving across densely packed planes. Each passage creates a small step at the free surface.", + "readout_selector": "final_prompt_token", + "tracked": [ + "dislocation", + "glide", + "slip" + ], + "must_be_absent_from_input": true, + "must_be_absent_from_output": true, + "note": "Frozen held-out item. Predetermined terms are used only for the rank endpoint; open-vocabulary generation and ranking cannot use them." + }, + { + "slug": "heldout-v1-assoc-notch-resistance-01", + "shape": "ASSOCIATION", + "protocol": "lens_eval", + "domain": "materials", + "category": "notch-resistance", + "target_family": "notch-resistance", + "phrasing_id": "heldout-notch-resistance-01", + "title": "Resistance to unstable flaw extension 1", + "description": "Held-out unnamed materials description; compare predetermined full-vocabulary ranks and target-free discovery under matched lenses.", + "text": "Two steels have comparable yield stress, yet one pre-notched plate absorbs several times more energy before sudden separation and survives a much larger flaw.", + "readout_selector": "final_prompt_token", + "tracked": [ + "toughness", + "crack", + "fracture" + ], + "must_be_absent_from_input": true, + "must_be_absent_from_output": true, + "note": "Frozen held-out item. Predetermined terms are used only for the rank endpoint; open-vocabulary generation and ranking cannot use them." + }, + { + "slug": "heldout-v1-assoc-notch-resistance-02", + "shape": "ASSOCIATION", + "protocol": "lens_eval", + "domain": "materials", + "category": "notch-resistance", + "target_family": "notch-resistance", + "phrasing_id": "heldout-notch-resistance-02", + "title": "Resistance to unstable flaw extension 2", + "description": "Held-out unnamed materials description; compare predetermined full-vocabulary ranks and target-free discovery under matched lenses.", + "text": "A sharp starter notch blunts as a plastic zone develops, allowing the specimen to carry additional load before rapid extension occurs.", + "readout_selector": "final_prompt_token", + "tracked": [ + "toughness", + "crack", + "fracture" + ], + "must_be_absent_from_input": true, + "must_be_absent_from_output": true, + "note": "Frozen held-out item. Predetermined terms are used only for the rank endpoint; open-vocabulary generation and ranking cannot use them." + }, + { + "slug": "heldout-v1-assoc-notch-resistance-03", + "shape": "ASSOCIATION", + "protocol": "lens_eval", + "domain": "materials", + "category": "notch-resistance", + "target_family": "notch-resistance", + "phrasing_id": "heldout-notch-resistance-03", + "title": "Resistance to unstable flaw extension 3", + "description": "Held-out unnamed materials description; compare predetermined full-vocabulary ranks and target-free discovery under matched lenses.", + "text": "A compact-tension sample requires substantial work to advance its sharp flaw. Instability begins only at an unusually large stress-intensity value.", + "readout_selector": "final_prompt_token", + "tracked": [ + "toughness", + "crack", + "fracture" + ], + "must_be_absent_from_input": true, + "must_be_absent_from_output": true, + "note": "Frozen held-out item. Predetermined terms are used only for the rank endpoint; open-vocabulary generation and ranking cannot use them." + }, + { + "slug": "heldout-v1-assoc-notch-resistance-04", + "shape": "ASSOCIATION", + "protocol": "lens_eval", + "domain": "materials", + "category": "notch-resistance", + "target_family": "notch-resistance", + "phrasing_id": "heldout-notch-resistance-04", + "title": "Resistance to unstable flaw extension 4", + "description": "Held-out unnamed materials description; compare predetermined full-vocabulary ranks and target-free discovery under matched lenses.", + "text": "A structural alloy containing a long defect remains stable at loads that immediately separate a comparison alloy of similar strength.", + "readout_selector": "final_prompt_token", + "tracked": [ + "toughness", + "crack", + "fracture" + ], + "must_be_absent_from_input": true, + "must_be_absent_from_output": true, + "note": "Frozen held-out item. Predetermined terms are used only for the rank endpoint; open-vocabulary generation and ranking cannot use them." + }, + { + "slug": "heldout-v1-assoc-notch-resistance-05", + "shape": "ASSOCIATION", + "protocol": "lens_eval", + "domain": "materials", + "category": "notch-resistance", + "target_family": "notch-resistance", + "phrasing_id": "heldout-notch-resistance-05", + "title": "Resistance to unstable flaw extension 5", + "description": "Held-out unnamed materials description; compare predetermined full-vocabulary ranks and target-free discovery under matched lenses.", + "text": "An instrumented impact test on a notched bar records high absorbed energy and extensive local deformation before the remaining ligament opens.", + "readout_selector": "final_prompt_token", + "tracked": [ + "toughness", + "crack", + "fracture" + ], + "must_be_absent_from_input": true, + "must_be_absent_from_output": true, + "note": "Frozen held-out item. Predetermined terms are used only for the rank endpoint; open-vocabulary generation and ranking cannot use them." + }, + { + "slug": "heldout-v1-assoc-hot-air-surface-layer-01", + "shape": "ASSOCIATION", + "protocol": "lens_eval", + "domain": "materials", + "category": "hot-air-surface-layer", + "target_family": "hot-air-surface-layer", + "phrasing_id": "heldout-hot-air-surface-layer-01", + "title": "Oxygen-rich high-temperature surface film 1", + "description": "Held-out unnamed materials description; compare predetermined full-vocabulary ranks and target-free discovery under matched lenses.", + "text": "A superalloy held in oxygen-bearing gas gains mass while developing a dense ceramic surface film whose thickness follows an approximately parabolic time dependence.", + "readout_selector": "final_prompt_token", + "tracked": [ + "oxidation", + "oxide", + "scale" + ], + "must_be_absent_from_input": true, + "must_be_absent_from_output": true, + "note": "Frozen held-out item. Predetermined terms are used only for the rank endpoint; open-vocabulary generation and ranking cannot use them." + }, + { + "slug": "heldout-v1-assoc-hot-air-surface-layer-02", + "shape": "ASSOCIATION", + "protocol": "lens_eval", + "domain": "materials", + "category": "hot-air-surface-layer", + "target_family": "hot-air-surface-layer", + "phrasing_id": "heldout-hot-air-surface-layer-02", + "title": "Oxygen-rich high-temperature surface film 2", + "description": "Held-out unnamed materials description; compare predetermined full-vocabulary ranks and target-free discovery under matched lenses.", + "text": "Long furnace exposure covers a steel coupon with several brittle oxygen-rich layers. Continued growth requires ions to cross the existing reaction product.", + "readout_selector": "final_prompt_token", + "tracked": [ + "oxidation", + "oxide", + "scale" + ], + "must_be_absent_from_input": true, + "must_be_absent_from_output": true, + "note": "Frozen held-out item. Predetermined terms are used only for the rank endpoint; open-vocabulary generation and ranking cannot use them." + }, + { + "slug": "heldout-v1-assoc-hot-air-surface-layer-03", + "shape": "ASSOCIATION", + "protocol": "lens_eval", + "domain": "materials", + "category": "hot-air-surface-layer", + "target_family": "hot-air-surface-layer", + "phrasing_id": "heldout-hot-air-surface-layer-03", + "title": "Oxygen-rich high-temperature surface film 3", + "description": "Held-out unnamed materials description; compare predetermined full-vocabulary ranks and target-free discovery under matched lenses.", + "text": "A chromium-containing alloy forms a compact adherent surface film in hot air, sharply slowing additional inward oxygen transport.", + "readout_selector": "final_prompt_token", + "tracked": [ + "oxidation", + "oxide", + "scale" + ], + "must_be_absent_from_input": true, + "must_be_absent_from_output": true, + "note": "Frozen held-out item. Predetermined terms are used only for the rank endpoint; open-vocabulary generation and ranking cannot use them." + }, + { + "slug": "heldout-v1-assoc-hot-air-surface-layer-04", + "shape": "ASSOCIATION", + "protocol": "lens_eval", + "domain": "materials", + "category": "hot-air-surface-layer", + "target_family": "hot-air-surface-layer", + "phrasing_id": "heldout-hot-air-surface-layer-04", + "title": "Oxygen-rich high-temperature surface film 4", + "description": "Held-out unnamed materials description; compare predetermined full-vocabulary ranks and target-free discovery under matched lenses.", + "text": "Repeated heating in air creates an external metal-oxygen reaction layer that thickens during each hold and sheds flakes during cooling.", + "readout_selector": "final_prompt_token", + "tracked": [ + "oxidation", + "oxide", + "scale" + ], + "must_be_absent_from_input": true, + "must_be_absent_from_output": true, + "note": "Frozen held-out item. Predetermined terms are used only for the rank endpoint; open-vocabulary generation and ranking cannot use them." + }, + { + "slug": "heldout-v1-assoc-hot-air-surface-layer-05", + "shape": "ASSOCIATION", + "protocol": "lens_eval", + "domain": "materials", + "category": "hot-air-surface-layer", + "target_family": "hot-air-surface-layer", + "phrasing_id": "heldout-hot-air-surface-layer-05", + "title": "Oxygen-rich high-temperature surface film 5", + "description": "Held-out unnamed materials description; compare predetermined full-vocabulary ranks and target-free discovery under matched lenses.", + "text": "A turbine component exposed to hot combustion gas develops a multilayer surface product containing metal cations and oxygen anions.", + "readout_selector": "final_prompt_token", + "tracked": [ + "oxidation", + "oxide", + "scale" + ], + "must_be_absent_from_input": true, + "must_be_absent_from_output": true, + "note": "Frozen held-out item. Predetermined terms are used only for the rank endpoint; open-vocabulary generation and ranking cannot use them." + } + ] +} diff --git a/jlens_materials/prompts/materials-heldout-v1-smoke.json b/jlens_materials/prompts/materials-heldout-v1-smoke.json new file mode 100644 index 0000000000000000000000000000000000000000..7ebaa006dbf167e446ffc58950732e2236673513 --- /dev/null +++ b/jlens_materials/prompts/materials-heldout-v1-smoke.json @@ -0,0 +1,29 @@ +{ + "format_version": 1, + "study": "materials-heldout-v1-smoke", + "source_manifest_sha256": "8c034cf33d287d379fddf842971914ec035a22b9e31d29f457258bf85c52e203", + "prompts": [ + { + "slug": "heldout-v1-assoc-ductile-01", + "shape": "ASSOCIATION", + "protocol": "lens_eval", + "domain": "materials", + "category": "ductile", + "target_family": "ductile", + "phrasing_id": "heldout-ductile-01", + "title": "Energy-absorbing dimpled separation 1", + "description": "Held-out unnamed materials description; compare predetermined full-vocabulary ranks and target-free discovery under matched lenses.", + "text": "A round aluminum bar stretched into a pronounced waist before separating. The mating faces contained hemispherical pits centered on inclusions and a slanted tearing rim.", + "readout_selector": "final_prompt_token", + "tracked": [ + "ductile", + "nucleation", + "coalescence", + "void" + ], + "must_be_absent_from_input": true, + "must_be_absent_from_output": true, + "note": "Frozen held-out item. Predetermined terms are used only for the rank endpoint; open-vocabulary generation and ranking cannot use them." + } + ] +} diff --git a/jlens_materials/prompts/materials-key-concept-traces-v1.json b/jlens_materials/prompts/materials-key-concept-traces-v1.json new file mode 100644 index 0000000000000000000000000000000000000000..af50ec2c7d341d3b88d56a798627bdd7397484b5 --- /dev/null +++ b/jlens_materials/prompts/materials-key-concept-traces-v1.json @@ -0,0 +1,121 @@ +{ + "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": [ + { + "slug": "paper-v2-assoc-notch-resistance-04", + "shape": "ASSOCIATION", + "protocol": "lens_eval", + "domain": "materials", + "category": "notch-resistance", + "title": "Resistance to notch-driven failure 4", + "description": "Preregistered unnamed materials vignette; compare fixed-band J-lens ranks with the matched logit-lens baseline.", + "text": "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.", + "readout_selector": "final_prompt_token", + "tracked": [ + "toughness", + "crack", + "fracture" + ], + "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." + }, + { + "slug": "paper-v2-assoc-ductile-02", + "shape": "ASSOCIATION", + "protocol": "lens_eval", + "domain": "materials", + "category": "ductile", + "title": "Dimpled tensile failure 2", + "description": "Preregistered unnamed materials vignette; compare fixed-band J-lens ranks with the matched logit-lens baseline.", + "text": "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.", + "readout_selector": "final_prompt_token", + "tracked": [ + "ductile", + "nucleation", + "coalescence", + "void" + ], + "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." + }, + { + "slug": "paper-v2-assoc-line-defect-motion-01", + "shape": "ASSOCIATION", + "protocol": "lens_eval", + "domain": "materials", + "category": "line-defect-motion", + "title": "Crystal line-imperfection motion 1", + "description": "Preregistered unnamed materials vignette; compare fixed-band J-lens ranks with the matched logit-lens baseline.", + "text": "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.", + "readout_selector": "final_prompt_token", + "tracked": [ + "dislocation", + "glide", + "slip" + ], + "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." + }, + { + "slug": "paper-v2-assoc-rapid-transformation-04", + "shape": "ASSOCIATION", + "protocol": "lens_eval", + "domain": "materials", + "category": "rapid-transformation", + "title": "Rapid diffusionless steel transformation 4", + "description": "Preregistered unnamed materials vignette; compare fixed-band J-lens ranks with the matched logit-lens baseline.", + "text": "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.", + "readout_selector": "final_prompt_token", + "tracked": [ + "martensite", + "shear", + "Bain", + "tetragonal" + ], + "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." + }, + { + "slug": "paper-v2-assoc-cleavage-03", + "shape": "ASSOCIATION", + "protocol": "lens_eval", + "domain": "materials", + "category": "cleavage", + "title": "Faceted low-temperature failure 3", + "description": "Preregistered unnamed materials vignette; compare fixed-band J-lens ranks with the matched logit-lens baseline.", + "text": "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.", + "readout_selector": "final_prompt_token", + "tracked": [ + "brittle", + "cleavage", + "transgranular" + ], + "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." + }, + { + "slug": "paper-v2-assoc-hot-air-surface-layer-01", + "shape": "ASSOCIATION", + "protocol": "lens_eval", + "domain": "materials", + "category": "hot-air-surface-layer", + "title": "High-temperature environmental surface layer 1", + "description": "Preregistered unnamed materials vignette; compare fixed-band J-lens ranks with the matched logit-lens baseline.", + "text": "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.", + "readout_selector": "final_prompt_token", + "tracked": [ + "oxidation", + "oxide", + "scale" + ], + "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." + } + ] +} diff --git a/jlens_materials/prompts/materials-mechanism-name-v1.json b/jlens_materials/prompts/materials-mechanism-name-v1.json new file mode 100644 index 0000000000000000000000000000000000000000..de187682c72903ba8ec364ea5c8623ec7ed0f6fc --- /dev/null +++ b/jlens_materials/prompts/materials-mechanism-name-v1.json @@ -0,0 +1,905 @@ +{ + "description": "Frozen 50-item mechanism-versus-eponym materials evaluation: five principles, ten independent wordings each.", + "prompts": [ + { + "slug": "mechanism-name-grain-size-strength-01", + "shape": "ASSOCIATION", + "protocol": "lens_eval", + "domain": "materials", + "category": "grain-size-strength", + "title": "Grain-size strengthening 1", + "description": "Frozen mechanism-versus-eponym vignette; both tracked words are absent from the input and generated output.", + "text": "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.", + "readout_selector": "final_prompt_token", + "tracked": [ + "boundary", + "Hall" + ], + "must_be_absent_from_input": true, + "must_be_absent_from_output": true, + "note": "Paired comparison of physical carrier 'boundary' against eponym token 'Hall'. Primary endpoint is the within-item log10 rank difference, summarized separately for J-lens and logit lens." + }, + { + "slug": "mechanism-name-grain-size-strength-02", + "shape": "ASSOCIATION", + "protocol": "lens_eval", + "domain": "materials", + "category": "grain-size-strength", + "title": "Grain-size strengthening 2", + "description": "Frozen mechanism-versus-eponym vignette; both tracked words are absent from the input and generated output.", + "text": "A polycrystalline metal is processed until its average crystallite diameter is reduced by half. Its resistance to the onset of plastic flow increases.", + "readout_selector": "final_prompt_token", + "tracked": [ + "boundary", + "Hall" + ], + "must_be_absent_from_input": true, + "must_be_absent_from_output": true, + "note": "Paired comparison of physical carrier 'boundary' against eponym token 'Hall'. Primary endpoint is the within-item log10 rank difference, summarized separately for J-lens and logit lens." + }, + { + "slug": "mechanism-name-grain-size-strength-03", + "shape": "ASSOCIATION", + "protocol": "lens_eval", + "domain": "materials", + "category": "grain-size-strength", + "title": "Grain-size strengthening 3", + "description": "Frozen mechanism-versus-eponym vignette; both tracked words are absent from the input and generated output.", + "text": "An ultrafine-grained steel yields at a higher stress than a coarse-grained specimen with the same composition and phase fractions.", + "readout_selector": "final_prompt_token", + "tracked": [ + "boundary", + "Hall" + ], + "must_be_absent_from_input": true, + "must_be_absent_from_output": true, + "note": "Paired comparison of physical carrier 'boundary' against eponym token 'Hall'. Primary endpoint is the within-item log10 rank difference, summarized separately for J-lens and logit lens." + }, + { + "slug": "mechanism-name-grain-size-strength-04", + "shape": "ASSOCIATION", + "protocol": "lens_eval", + "domain": "materials", + "category": "grain-size-strength", + "title": "Grain-size strengthening 4", + "description": "Frozen mechanism-versus-eponym vignette; both tracked words are absent from the input and generated output.", + "text": "Repeated rolling and recrystallization create many small crystallites. Moving line defects encounter interfaces more frequently, and the measured yield stress rises.", + "readout_selector": "final_prompt_token", + "tracked": [ + "boundary", + "Hall" + ], + "must_be_absent_from_input": true, + "must_be_absent_from_output": true, + "note": "Paired comparison of physical carrier 'boundary' against eponym token 'Hall'. Primary endpoint is the within-item log10 rank difference, summarized separately for J-lens and logit lens." + }, + { + "slug": "mechanism-name-grain-size-strength-05", + "shape": "ASSOCIATION", + "protocol": "lens_eval", + "domain": "materials", + "category": "grain-size-strength", + "title": "Grain-size strengthening 5", + "description": "Frozen mechanism-versus-eponym vignette; both tracked words are absent from the input and generated output.", + "text": "A metal with coarse crystallites is compared with an otherwise identical fine-crystallite version. The refined material better resists the start of irreversible strain.", + "readout_selector": "final_prompt_token", + "tracked": [ + "boundary", + "Hall" + ], + "must_be_absent_from_input": true, + "must_be_absent_from_output": true, + "note": "Paired comparison of physical carrier 'boundary' against eponym token 'Hall'. Primary endpoint is the within-item log10 rank difference, summarized separately for J-lens and logit lens." + }, + { + "slug": "mechanism-name-grain-size-strength-06", + "shape": "ASSOCIATION", + "protocol": "lens_eval", + "domain": "materials", + "category": "grain-size-strength", + "title": "Grain-size strengthening 6", + "description": "Frozen mechanism-versus-eponym vignette; both tracked words are absent from the input and generated output.", + "text": "Thermomechanical processing reduces the characteristic crystal-region size without changing chemistry. The resulting sheet shows higher yield strength.", + "readout_selector": "final_prompt_token", + "tracked": [ + "boundary", + "Hall" + ], + "must_be_absent_from_input": true, + "must_be_absent_from_output": true, + "note": "Paired comparison of physical carrier 'boundary' against eponym token 'Hall'. Primary endpoint is the within-item log10 rank difference, summarized separately for J-lens and logit lens." + }, + { + "slug": "mechanism-name-grain-size-strength-07", + "shape": "ASSOCIATION", + "protocol": "lens_eval", + "domain": "materials", + "category": "grain-size-strength", + "title": "Grain-size strengthening 7", + "description": "Frozen mechanism-versus-eponym vignette; both tracked words are absent from the input and generated output.", + "text": "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.", + "readout_selector": "final_prompt_token", + "tracked": [ + "boundary", + "Hall" + ], + "must_be_absent_from_input": true, + "must_be_absent_from_output": true, + "note": "Paired comparison of physical carrier 'boundary' against eponym token 'Hall'. Primary endpoint is the within-item log10 rank difference, summarized separately for J-lens and logit lens." + }, + { + "slug": "mechanism-name-grain-size-strength-08", + "shape": "ASSOCIATION", + "protocol": "lens_eval", + "domain": "materials", + "category": "grain-size-strength", + "title": "Grain-size strengthening 8", + "description": "Frozen mechanism-versus-eponym vignette; both tracked words are absent from the input and generated output.", + "text": "Two copper samples differ mainly in crystallite size. The sample containing more interfaces per unit volume begins plastic flow at the larger stress.", + "readout_selector": "final_prompt_token", + "tracked": [ + "boundary", + "Hall" + ], + "must_be_absent_from_input": true, + "must_be_absent_from_output": true, + "note": "Paired comparison of physical carrier 'boundary' against eponym token 'Hall'. Primary endpoint is the within-item log10 rank difference, summarized separately for J-lens and logit lens." + }, + { + "slug": "mechanism-name-grain-size-strength-09", + "shape": "ASSOCIATION", + "protocol": "lens_eval", + "domain": "materials", + "category": "grain-size-strength", + "title": "Grain-size strengthening 9", + "description": "Frozen mechanism-versus-eponym vignette; both tracked words are absent from the input and generated output.", + "text": "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.", + "readout_selector": "final_prompt_token", + "tracked": [ + "boundary", + "Hall" + ], + "must_be_absent_from_input": true, + "must_be_absent_from_output": true, + "note": "Paired comparison of physical carrier 'boundary' against eponym token 'Hall'. Primary endpoint is the within-item log10 rank difference, summarized separately for J-lens and logit lens." + }, + { + "slug": "mechanism-name-grain-size-strength-10", + "shape": "ASSOCIATION", + "protocol": "lens_eval", + "domain": "materials", + "category": "grain-size-strength", + "title": "Grain-size strengthening 10", + "description": "Frozen mechanism-versus-eponym vignette; both tracked words are absent from the input and generated output.", + "text": "A structural alloy is strengthened by subdividing it into smaller misoriented crystal regions, creating more barriers to the passage of moving line imperfections.", + "readout_selector": "final_prompt_token", + "tracked": [ + "boundary", + "Hall" + ], + "must_be_absent_from_input": true, + "must_be_absent_from_output": true, + "note": "Paired comparison of physical carrier 'boundary' against eponym token 'Hall'. Primary endpoint is the within-item log10 rank difference, summarized separately for J-lens and logit lens." + }, + { + "slug": "mechanism-name-flaw-controlled-failure-01", + "shape": "ASSOCIATION", + "protocol": "lens_eval", + "domain": "materials", + "category": "flaw-controlled-failure", + "title": "Flaw-controlled brittle failure 1", + "description": "Frozen mechanism-versus-eponym vignette; both tracked words are absent from the input and generated output.", + "text": "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.", + "readout_selector": "final_prompt_token", + "tracked": [ + "crack", + "Griffith" + ], + "must_be_absent_from_input": true, + "must_be_absent_from_output": true, + "note": "Paired comparison of physical carrier 'crack' against eponym token 'Griffith'. Primary endpoint is the within-item log10 rank difference, summarized separately for J-lens and logit lens." + }, + { + "slug": "mechanism-name-flaw-controlled-failure-02", + "shape": "ASSOCIATION", + "protocol": "lens_eval", + "domain": "materials", + "category": "flaw-controlled-failure", + "title": "Flaw-controlled brittle failure 2", + "description": "Frozen mechanism-versus-eponym vignette; both tracked words are absent from the input and generated output.", + "text": "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.", + "readout_selector": "final_prompt_token", + "tracked": [ + "crack", + "Griffith" + ], + "must_be_absent_from_input": true, + "must_be_absent_from_output": true, + "note": "Paired comparison of physical carrier 'crack' against eponym token 'Griffith'. Primary endpoint is the within-item log10 rank difference, summarized separately for J-lens and logit lens." + }, + { + "slug": "mechanism-name-flaw-controlled-failure-03", + "shape": "ASSOCIATION", + "protocol": "lens_eval", + "domain": "materials", + "category": "flaw-controlled-failure", + "title": "Flaw-controlled brittle failure 3", + "description": "Frozen mechanism-versus-eponym vignette; both tracked words are absent from the input and generated output.", + "text": "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.", + "readout_selector": "final_prompt_token", + "tracked": [ + "crack", + "Griffith" + ], + "must_be_absent_from_input": true, + "must_be_absent_from_output": true, + "note": "Paired comparison of physical carrier 'crack' against eponym token 'Griffith'. Primary endpoint is the within-item log10 rank difference, summarized separately for J-lens and logit lens." + }, + { + "slug": "mechanism-name-flaw-controlled-failure-04", + "shape": "ASSOCIATION", + "protocol": "lens_eval", + "domain": "materials", + "category": "flaw-controlled-failure", + "title": "Flaw-controlled brittle failure 4", + "description": "Frozen mechanism-versus-eponym vignette; both tracked words are absent from the input and generated output.", + "text": "A brittle solid separates when the elastic energy released by extending a sharp defect exceeds the energy needed to create two new surfaces.", + "readout_selector": "final_prompt_token", + "tracked": [ + "crack", + "Griffith" + ], + "must_be_absent_from_input": true, + "must_be_absent_from_output": true, + "note": "Paired comparison of physical carrier 'crack' against eponym token 'Griffith'. Primary endpoint is the within-item log10 rank difference, summarized separately for J-lens and logit lens." + }, + { + "slug": "mechanism-name-flaw-controlled-failure-05", + "shape": "ASSOCIATION", + "protocol": "lens_eval", + "domain": "materials", + "category": "flaw-controlled-failure", + "title": "Flaw-controlled brittle failure 5", + "description": "Frozen mechanism-versus-eponym vignette; both tracked words are absent from the input and generated output.", + "text": "A polished glass fiber is much stronger than a scratched one of the same diameter because the scratch concentrates tensile loading at its tip.", + "readout_selector": "final_prompt_token", + "tracked": [ + "crack", + "Griffith" + ], + "must_be_absent_from_input": true, + "must_be_absent_from_output": true, + "note": "Paired comparison of physical carrier 'crack' against eponym token 'Griffith'. Primary endpoint is the within-item log10 rank difference, summarized separately for J-lens and logit lens." + }, + { + "slug": "mechanism-name-flaw-controlled-failure-06", + "shape": "ASSOCIATION", + "protocol": "lens_eval", + "domain": "materials", + "category": "flaw-controlled-failure", + "title": "Flaw-controlled brittle failure 6", + "description": "Frozen mechanism-versus-eponym vignette; both tracked words are absent from the input and generated output.", + "text": "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.", + "readout_selector": "final_prompt_token", + "tracked": [ + "crack", + "Griffith" + ], + "must_be_absent_from_input": true, + "must_be_absent_from_output": true, + "note": "Paired comparison of physical carrier 'crack' against eponym token 'Griffith'. Primary endpoint is the within-item log10 rank difference, summarized separately for J-lens and logit lens." + }, + { + "slug": "mechanism-name-flaw-controlled-failure-07", + "shape": "ASSOCIATION", + "protocol": "lens_eval", + "domain": "materials", + "category": "flaw-controlled-failure", + "title": "Flaw-controlled brittle failure 7", + "description": "Frozen mechanism-versus-eponym vignette; both tracked words are absent from the input and generated output.", + "text": "A ceramic component survives a given tensile stress when its largest flaw is short, but fails after processing introduces a much longer sharp flaw.", + "readout_selector": "final_prompt_token", + "tracked": [ + "crack", + "Griffith" + ], + "must_be_absent_from_input": true, + "must_be_absent_from_output": true, + "note": "Paired comparison of physical carrier 'crack' against eponym token 'Griffith'. Primary endpoint is the within-item log10 rank difference, summarized separately for J-lens and logit lens." + }, + { + "slug": "mechanism-name-flaw-controlled-failure-08", + "shape": "ASSOCIATION", + "protocol": "lens_eval", + "domain": "materials", + "category": "flaw-controlled-failure", + "title": "Flaw-controlled brittle failure 8", + "description": "Frozen mechanism-versus-eponym vignette; both tracked words are absent from the input and generated output.", + "text": "The nominal strength of a brittle specimen scales approximately with the inverse square root of the size of its dominant sharp defect.", + "readout_selector": "final_prompt_token", + "tracked": [ + "crack", + "Griffith" + ], + "must_be_absent_from_input": true, + "must_be_absent_from_output": true, + "note": "Paired comparison of physical carrier 'crack' against eponym token 'Griffith'. Primary endpoint is the within-item log10 rank difference, summarized separately for J-lens and logit lens." + }, + { + "slug": "mechanism-name-flaw-controlled-failure-09", + "shape": "ASSOCIATION", + "protocol": "lens_eval", + "domain": "materials", + "category": "flaw-controlled-failure", + "title": "Flaw-controlled brittle failure 9", + "description": "Frozen mechanism-versus-eponym vignette; both tracked words are absent from the input and generated output.", + "text": "A sharp notch in a glass sheet locally magnifies stress, allowing separation to start even though the average applied stress is modest.", + "readout_selector": "final_prompt_token", + "tracked": [ + "crack", + "Griffith" + ], + "must_be_absent_from_input": true, + "must_be_absent_from_output": true, + "note": "Paired comparison of physical carrier 'crack' against eponym token 'Griffith'. Primary endpoint is the within-item log10 rank difference, summarized separately for J-lens and logit lens." + }, + { + "slug": "mechanism-name-flaw-controlled-failure-10", + "shape": "ASSOCIATION", + "protocol": "lens_eval", + "domain": "materials", + "category": "flaw-controlled-failure", + "title": "Flaw-controlled brittle failure 10", + "description": "Frozen mechanism-versus-eponym vignette; both tracked words are absent from the input and generated output.", + "text": "A brittle plate with a pre-existing slit becomes unstable when additional extension lowers the total elastic-plus-surface energy of the system.", + "readout_selector": "final_prompt_token", + "tracked": [ + "crack", + "Griffith" + ], + "must_be_absent_from_input": true, + "must_be_absent_from_output": true, + "note": "Paired comparison of physical carrier 'crack' against eponym token 'Griffith'. Primary endpoint is the within-item log10 rank difference, summarized separately for J-lens and logit lens." + }, + { + "slug": "mechanism-name-resolved-slip-threshold-01", + "shape": "ASSOCIATION", + "protocol": "lens_eval", + "domain": "materials", + "category": "resolved-slip-threshold", + "title": "Resolved slip threshold 1", + "description": "Frozen mechanism-versus-eponym vignette; both tracked words are absent from the input and generated output.", + "text": "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.", + "readout_selector": "final_prompt_token", + "tracked": [ + "dislocation", + "Schmid" + ], + "must_be_absent_from_input": true, + "must_be_absent_from_output": true, + "note": "Paired comparison of physical carrier 'dislocation' against eponym token 'Schmid'. Primary endpoint is the within-item log10 rank difference, summarized separately for J-lens and logit lens." + }, + { + "slug": "mechanism-name-resolved-slip-threshold-02", + "shape": "ASSOCIATION", + "protocol": "lens_eval", + "domain": "materials", + "category": "resolved-slip-threshold", + "title": "Resolved slip threshold 2", + "description": "Frozen mechanism-versus-eponym vignette; both tracked words are absent from the input and generated output.", + "text": "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.", + "readout_selector": "final_prompt_token", + "tracked": [ + "dislocation", + "Schmid" + ], + "must_be_absent_from_input": true, + "must_be_absent_from_output": true, + "note": "Paired comparison of physical carrier 'dislocation' against eponym token 'Schmid'. Primary endpoint is the within-item log10 rank difference, summarized separately for J-lens and logit lens." + }, + { + "slug": "mechanism-name-resolved-slip-threshold-03", + "shape": "ASSOCIATION", + "protocol": "lens_eval", + "domain": "materials", + "category": "resolved-slip-threshold", + "title": "Resolved slip threshold 3", + "description": "Frozen mechanism-versus-eponym vignette; both tracked words are absent from the input and generated output.", + "text": "Two identically loaded crystals have different orientations. The crystal with the larger product of the two direction cosines activates its favored slip system first.", + "readout_selector": "final_prompt_token", + "tracked": [ + "dislocation", + "Schmid" + ], + "must_be_absent_from_input": true, + "must_be_absent_from_output": true, + "note": "Paired comparison of physical carrier 'dislocation' against eponym token 'Schmid'. Primary endpoint is the within-item log10 rank difference, summarized separately for J-lens and logit lens." + }, + { + "slug": "mechanism-name-resolved-slip-threshold-04", + "shape": "ASSOCIATION", + "protocol": "lens_eval", + "domain": "materials", + "category": "resolved-slip-threshold", + "title": "Resolved slip threshold 4", + "description": "Frozen mechanism-versus-eponym vignette; both tracked words are absent from the input and generated output.", + "text": "A crystal remains elastic until the shear component acting along a permitted direction on a permitted plane reaches a material-specific critical value.", + "readout_selector": "final_prompt_token", + "tracked": [ + "dislocation", + "Schmid" + ], + "must_be_absent_from_input": true, + "must_be_absent_from_output": true, + "note": "Paired comparison of physical carrier 'dislocation' against eponym token 'Schmid'. Primary endpoint is the within-item log10 rank difference, summarized separately for J-lens and logit lens." + }, + { + "slug": "mechanism-name-resolved-slip-threshold-05", + "shape": "ASSOCIATION", + "protocol": "lens_eval", + "domain": "materials", + "category": "resolved-slip-threshold", + "title": "Resolved slip threshold 5", + "description": "Frozen mechanism-versus-eponym vignette; both tracked words are absent from the input and generated output.", + "text": "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.", + "readout_selector": "final_prompt_token", + "tracked": [ + "dislocation", + "Schmid" + ], + "must_be_absent_from_input": true, + "must_be_absent_from_output": true, + "note": "Paired comparison of physical carrier 'dislocation' against eponym token 'Schmid'. Primary endpoint is the within-item log10 rank difference, summarized separately for J-lens and logit lens." + }, + { + "slug": "mechanism-name-resolved-slip-threshold-06", + "shape": "ASSOCIATION", + "protocol": "lens_eval", + "domain": "materials", + "category": "resolved-slip-threshold", + "title": "Resolved slip threshold 6", + "description": "Frozen mechanism-versus-eponym vignette; both tracked words are absent from the input and generated output.", + "text": "Rotating a single crystal changes the axial stress required to initiate slip even though the critical resolved shear resistance is unchanged.", + "readout_selector": "final_prompt_token", + "tracked": [ + "dislocation", + "Schmid" + ], + "must_be_absent_from_input": true, + "must_be_absent_from_output": true, + "note": "Paired comparison of physical carrier 'dislocation' against eponym token 'Schmid'. Primary endpoint is the within-item log10 rank difference, summarized separately for J-lens and logit lens." + }, + { + "slug": "mechanism-name-resolved-slip-threshold-07", + "shape": "ASSOCIATION", + "protocol": "lens_eval", + "domain": "materials", + "category": "resolved-slip-threshold", + "title": "Resolved slip threshold 7", + "description": "Frozen mechanism-versus-eponym vignette; both tracked words are absent from the input and generated output.", + "text": "A favorably oriented slip system reaches its critical shear value before any other system, producing the first permanent surface steps.", + "readout_selector": "final_prompt_token", + "tracked": [ + "dislocation", + "Schmid" + ], + "must_be_absent_from_input": true, + "must_be_absent_from_output": true, + "note": "Paired comparison of physical carrier 'dislocation' against eponym token 'Schmid'. Primary endpoint is the within-item log10 rank difference, summarized separately for J-lens and logit lens." + }, + { + "slug": "mechanism-name-resolved-slip-threshold-08", + "shape": "ASSOCIATION", + "protocol": "lens_eval", + "domain": "materials", + "category": "resolved-slip-threshold", + "title": "Resolved slip threshold 8", + "description": "Frozen mechanism-versus-eponym vignette; both tracked words are absent from the input and generated output.", + "text": "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.", + "readout_selector": "final_prompt_token", + "tracked": [ + "dislocation", + "Schmid" + ], + "must_be_absent_from_input": true, + "must_be_absent_from_output": true, + "note": "Paired comparison of physical carrier 'dislocation' against eponym token 'Schmid'. Primary endpoint is the within-item log10 rank difference, summarized separately for J-lens and logit lens." + }, + { + "slug": "mechanism-name-resolved-slip-threshold-09", + "shape": "ASSOCIATION", + "protocol": "lens_eval", + "domain": "materials", + "category": "resolved-slip-threshold", + "title": "Resolved slip threshold 9", + "description": "Frozen mechanism-versus-eponym vignette; both tracked words are absent from the input and generated output.", + "text": "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.", + "readout_selector": "final_prompt_token", + "tracked": [ + "dislocation", + "Schmid" + ], + "must_be_absent_from_input": true, + "must_be_absent_from_output": true, + "note": "Paired comparison of physical carrier 'dislocation' against eponym token 'Schmid'. Primary endpoint is the within-item log10 rank difference, summarized separately for J-lens and logit lens." + }, + { + "slug": "mechanism-name-resolved-slip-threshold-10", + "shape": "ASSOCIATION", + "protocol": "lens_eval", + "domain": "materials", + "category": "resolved-slip-threshold", + "title": "Resolved slip threshold 10", + "description": "Frozen mechanism-versus-eponym vignette; both tracked words are absent from the input and generated output.", + "text": "When the resolved driving force on the most favorably oriented plane-direction pair exceeds its critical resistance, mobile line defects sweep across the crystal.", + "readout_selector": "final_prompt_token", + "tracked": [ + "dislocation", + "Schmid" + ], + "must_be_absent_from_input": true, + "must_be_absent_from_output": true, + "note": "Paired comparison of physical carrier 'dislocation' against eponym token 'Schmid'. Primary endpoint is the within-item log10 rank difference, summarized separately for J-lens and logit lens." + }, + { + "slug": "mechanism-name-lattice-diffusion-creep-01", + "shape": "ASSOCIATION", + "protocol": "lens_eval", + "domain": "materials", + "category": "lattice-diffusion-creep", + "title": "Lattice-diffusion creep 1", + "description": "Frozen mechanism-versus-eponym vignette; both tracked words are absent from the input and generated output.", + "text": "A fine polycrystal held at high temperature and low stress lengthens as atoms diffuse through crystal interiors from compressed faces toward tensile faces.", + "readout_selector": "final_prompt_token", + "tracked": [ + "vacancy", + "Herring" + ], + "must_be_absent_from_input": true, + "must_be_absent_from_output": true, + "note": "Paired comparison of physical carrier 'vacancy' against eponym token 'Herring'. Primary endpoint is the within-item log10 rank difference, summarized separately for J-lens and logit lens." + }, + { + "slug": "mechanism-name-lattice-diffusion-creep-02", + "shape": "ASSOCIATION", + "protocol": "lens_eval", + "domain": "materials", + "category": "lattice-diffusion-creep", + "title": "Lattice-diffusion creep 2", + "description": "Frozen mechanism-versus-eponym vignette; both tracked words are absent from the input and generated output.", + "text": "At elevated temperature, atoms move through the lattice between oppositely loaded grain faces, producing slow elongation without extensive line-defect glide.", + "readout_selector": "final_prompt_token", + "tracked": [ + "vacancy", + "Herring" + ], + "must_be_absent_from_input": true, + "must_be_absent_from_output": true, + "note": "Paired comparison of physical carrier 'vacancy' against eponym token 'Herring'. Primary endpoint is the within-item log10 rank difference, summarized separately for J-lens and logit lens." + }, + { + "slug": "mechanism-name-lattice-diffusion-creep-03", + "shape": "ASSOCIATION", + "protocol": "lens_eval", + "domain": "materials", + "category": "lattice-diffusion-creep", + "title": "Lattice-diffusion creep 3", + "description": "Frozen mechanism-versus-eponym vignette; both tracked words are absent from the input and generated output.", + "text": "A polycrystalline solid changes shape under a small sustained load because empty lattice sites migrate through grain interiors in the direction opposite atomic transport.", + "readout_selector": "final_prompt_token", + "tracked": [ + "vacancy", + "Herring" + ], + "must_be_absent_from_input": true, + "must_be_absent_from_output": true, + "note": "Paired comparison of physical carrier 'vacancy' against eponym token 'Herring'. Primary endpoint is the within-item log10 rank difference, summarized separately for J-lens and logit lens." + }, + { + "slug": "mechanism-name-lattice-diffusion-creep-04", + "shape": "ASSOCIATION", + "protocol": "lens_eval", + "domain": "materials", + "category": "lattice-diffusion-creep", + "title": "Lattice-diffusion creep 4", + "description": "Frozen mechanism-versus-eponym vignette; both tracked words are absent from the input and generated output.", + "text": "Slow deformation at high homologous temperature is controlled by bulk atomic transport, and its rate increases strongly as crystallite size decreases.", + "readout_selector": "final_prompt_token", + "tracked": [ + "vacancy", + "Herring" + ], + "must_be_absent_from_input": true, + "must_be_absent_from_output": true, + "note": "Paired comparison of physical carrier 'vacancy' against eponym token 'Herring'. Primary endpoint is the within-item log10 rank difference, summarized separately for J-lens and logit lens." + }, + { + "slug": "mechanism-name-lattice-diffusion-creep-05", + "shape": "ASSOCIATION", + "protocol": "lens_eval", + "domain": "materials", + "category": "lattice-diffusion-creep", + "title": "Lattice-diffusion creep 5", + "description": "Frozen mechanism-versus-eponym vignette; both tracked words are absent from the input and generated output.", + "text": "Material is transported through crystal interiors from interfaces under compression to interfaces under tension, gradually elongating the specimen.", + "readout_selector": "final_prompt_token", + "tracked": [ + "vacancy", + "Herring" + ], + "must_be_absent_from_input": true, + "must_be_absent_from_output": true, + "note": "Paired comparison of physical carrier 'vacancy' against eponym token 'Herring'. Primary endpoint is the within-item log10 rank difference, summarized separately for J-lens and logit lens." + }, + { + "slug": "mechanism-name-lattice-diffusion-creep-06", + "shape": "ASSOCIATION", + "protocol": "lens_eval", + "domain": "materials", + "category": "lattice-diffusion-creep", + "title": "Lattice-diffusion creep 6", + "description": "Frozen mechanism-versus-eponym vignette; both tracked words are absent from the input and generated output.", + "text": "A fine-grained ceramic deforms under low stress near its melting temperature through diffusion across the volume of each crystallite rather than along interfaces.", + "readout_selector": "final_prompt_token", + "tracked": [ + "vacancy", + "Herring" + ], + "must_be_absent_from_input": true, + "must_be_absent_from_output": true, + "note": "Paired comparison of physical carrier 'vacancy' against eponym token 'Herring'. Primary endpoint is the within-item log10 rank difference, summarized separately for J-lens and logit lens." + }, + { + "slug": "mechanism-name-lattice-diffusion-creep-07", + "shape": "ASSOCIATION", + "protocol": "lens_eval", + "domain": "materials", + "category": "lattice-diffusion-creep", + "title": "Lattice-diffusion creep 7", + "description": "Frozen mechanism-versus-eponym vignette; both tracked words are absent from the input and generated output.", + "text": "Atoms leave compressed grain faces, traverse the crystal lattice, and attach at tensile faces, leading to time-dependent shape change.", + "readout_selector": "final_prompt_token", + "tracked": [ + "vacancy", + "Herring" + ], + "must_be_absent_from_input": true, + "must_be_absent_from_output": true, + "note": "Paired comparison of physical carrier 'vacancy' against eponym token 'Herring'. Primary endpoint is the within-item log10 rank difference, summarized separately for J-lens and logit lens." + }, + { + "slug": "mechanism-name-lattice-diffusion-creep-08", + "shape": "ASSOCIATION", + "protocol": "lens_eval", + "domain": "materials", + "category": "lattice-diffusion-creep", + "title": "Lattice-diffusion creep 8", + "description": "Frozen mechanism-versus-eponym vignette; both tracked words are absent from the input and generated output.", + "text": "The steady deformation rate varies inversely with the square of crystallite size and is controlled by bulk self-diffusion.", + "readout_selector": "final_prompt_token", + "tracked": [ + "vacancy", + "Herring" + ], + "must_be_absent_from_input": true, + "must_be_absent_from_output": true, + "note": "Paired comparison of physical carrier 'vacancy' against eponym token 'Herring'. Primary endpoint is the within-item log10 rank difference, summarized separately for J-lens and logit lens." + }, + { + "slug": "mechanism-name-lattice-diffusion-creep-09", + "shape": "ASSOCIATION", + "protocol": "lens_eval", + "domain": "materials", + "category": "lattice-diffusion-creep", + "title": "Lattice-diffusion creep 9", + "description": "Frozen mechanism-versus-eponym vignette; both tracked words are absent from the input and generated output.", + "text": "A high-temperature specimen elongates as missing-site defects carry mass transport through the interiors of its constituent crystals.", + "readout_selector": "final_prompt_token", + "tracked": [ + "vacancy", + "Herring" + ], + "must_be_absent_from_input": true, + "must_be_absent_from_output": true, + "note": "Paired comparison of physical carrier 'vacancy' against eponym token 'Herring'. Primary endpoint is the within-item log10 rank difference, summarized separately for J-lens and logit lens." + }, + { + "slug": "mechanism-name-lattice-diffusion-creep-10", + "shape": "ASSOCIATION", + "protocol": "lens_eval", + "domain": "materials", + "category": "lattice-diffusion-creep", + "title": "Lattice-diffusion creep 10", + "description": "Frozen mechanism-versus-eponym vignette; both tracked words are absent from the input and generated output.", + "text": "Under a small tensile load, bulk diffusion moves atoms toward faces normal to the loading direction, producing gradual axial extension.", + "readout_selector": "final_prompt_token", + "tracked": [ + "vacancy", + "Herring" + ], + "must_be_absent_from_input": true, + "must_be_absent_from_output": true, + "note": "Paired comparison of physical carrier 'vacancy' against eponym token 'Herring'. Primary endpoint is the within-item log10 rank difference, summarized separately for J-lens and logit lens." + }, + { + "slug": "mechanism-name-diffusionless-lattice-change-01", + "shape": "ASSOCIATION", + "protocol": "lens_eval", + "domain": "materials", + "category": "diffusionless-lattice-change", + "title": "Diffusionless lattice change 1", + "description": "Frozen mechanism-versus-eponym vignette; both tracked words are absent from the input and generated output.", + "text": "Rapid cooling converts a face-centered parent lattice into a body-centered product whose vertical cell dimension differs from the two horizontal dimensions.", + "readout_selector": "final_prompt_token", + "tracked": [ + "tetragonal", + "Bain" + ], + "must_be_absent_from_input": true, + "must_be_absent_from_output": true, + "note": "Paired comparison of physical carrier 'tetragonal' against eponym token 'Bain'. Primary endpoint is the within-item log10 rank difference, summarized separately for J-lens and logit lens." + }, + { + "slug": "mechanism-name-diffusionless-lattice-change-02", + "shape": "ASSOCIATION", + "protocol": "lens_eval", + "domain": "materials", + "category": "diffusionless-lattice-change", + "title": "Diffusionless lattice change 2", + "description": "Frozen mechanism-versus-eponym vignette; both tracked words are absent from the input and generated output.", + "text": "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.", + "readout_selector": "final_prompt_token", + "tracked": [ + "tetragonal", + "Bain" + ], + "must_be_absent_from_input": true, + "must_be_absent_from_output": true, + "note": "Paired comparison of physical carrier 'tetragonal' against eponym token 'Bain'. Primary endpoint is the within-item log10 rank difference, summarized separately for J-lens and logit lens." + }, + { + "slug": "mechanism-name-diffusionless-lattice-change-03", + "shape": "ASSOCIATION", + "protocol": "lens_eval", + "domain": "materials", + "category": "diffusionless-lattice-change", + "title": "Diffusionless lattice change 3", + "description": "Frozen mechanism-versus-eponym vignette; both tracked words are absent from the input and generated output.", + "text": "Long-range solute redistribution is suppressed during cooling, leaving a body-centered cell with an axial ratio different from unity.", + "readout_selector": "final_prompt_token", + "tracked": [ + "tetragonal", + "Bain" + ], + "must_be_absent_from_input": true, + "must_be_absent_from_output": true, + "note": "Paired comparison of physical carrier 'tetragonal' against eponym token 'Bain'. Primary endpoint is the within-item log10 rank difference, summarized separately for J-lens and logit lens." + }, + { + "slug": "mechanism-name-diffusionless-lattice-change-04", + "shape": "ASSOCIATION", + "protocol": "lens_eval", + "domain": "materials", + "category": "diffusionless-lattice-change", + "title": "Diffusionless lattice change 4", + "description": "Frozen mechanism-versus-eponym vignette; both tracked words are absent from the input and generated output.", + "text": "The parent face-centered cell transforms through compression along one crystallographic axis and expansion along the other two, creating a distorted body-centered product.", + "readout_selector": "final_prompt_token", + "tracked": [ + "tetragonal", + "Bain" + ], + "must_be_absent_from_input": true, + "must_be_absent_from_output": true, + "note": "Paired comparison of physical carrier 'tetragonal' against eponym token 'Bain'. Primary endpoint is the within-item log10 rank difference, summarized separately for J-lens and logit lens." + }, + { + "slug": "mechanism-name-diffusionless-lattice-change-05", + "shape": "ASSOCIATION", + "protocol": "lens_eval", + "domain": "materials", + "category": "diffusionless-lattice-change", + "title": "Diffusionless lattice change 5", + "description": "Frozen mechanism-versus-eponym vignette; both tracked words are absent from the input and generated output.", + "text": "A rapid diffusionless transformation produces an acicular phase whose unit cell has two equal short axes and one longer axis.", + "readout_selector": "final_prompt_token", + "tracked": [ + "tetragonal", + "Bain" + ], + "must_be_absent_from_input": true, + "must_be_absent_from_output": true, + "note": "Paired comparison of physical carrier 'tetragonal' against eponym token 'Bain'. Primary endpoint is the within-item log10 rank difference, summarized separately for J-lens and logit lens." + }, + { + "slug": "mechanism-name-diffusionless-lattice-change-06", + "shape": "ASSOCIATION", + "protocol": "lens_eval", + "domain": "materials", + "category": "diffusionless-lattice-change", + "title": "Diffusionless lattice change 6", + "description": "Frozen mechanism-versus-eponym vignette; both tracked words are absent from the input and generated output.", + "text": "Carbon trapped during quenching distorts the body-centered product so that its c dimension is not equal to a and b.", + "readout_selector": "final_prompt_token", + "tracked": [ + "tetragonal", + "Bain" + ], + "must_be_absent_from_input": true, + "must_be_absent_from_output": true, + "note": "Paired comparison of physical carrier 'tetragonal' against eponym token 'Bain'. Primary endpoint is the within-item log10 rank difference, summarized separately for J-lens and logit lens." + }, + { + "slug": "mechanism-name-diffusionless-lattice-change-07", + "shape": "ASSOCIATION", + "protocol": "lens_eval", + "domain": "materials", + "category": "diffusionless-lattice-change", + "title": "Diffusionless lattice change 7", + "description": "Frozen mechanism-versus-eponym vignette; both tracked words are absent from the input and generated output.", + "text": "A coordinated lattice deformation maps the parent cubic arrangement into a body-centered cell elongated in one direction.", + "readout_selector": "final_prompt_token", + "tracked": [ + "tetragonal", + "Bain" + ], + "must_be_absent_from_input": true, + "must_be_absent_from_output": true, + "note": "Paired comparison of physical carrier 'tetragonal' against eponym token 'Bain'. Primary endpoint is the within-item log10 rank difference, summarized separately for J-lens and logit lens." + }, + { + "slug": "mechanism-name-diffusionless-lattice-change-08", + "shape": "ASSOCIATION", + "protocol": "lens_eval", + "domain": "materials", + "category": "diffusionless-lattice-change", + "title": "Diffusionless lattice change 8", + "description": "Frozen mechanism-versus-eponym vignette; both tracked words are absent from the input and generated output.", + "text": "The hard lath-shaped product of severe quenching has a c-to-a lattice ratio measurably greater than one.", + "readout_selector": "final_prompt_token", + "tracked": [ + "tetragonal", + "Bain" + ], + "must_be_absent_from_input": true, + "must_be_absent_from_output": true, + "note": "Paired comparison of physical carrier 'tetragonal' against eponym token 'Bain'. Primary endpoint is the within-item log10 rank difference, summarized separately for J-lens and logit lens." + }, + { + "slug": "mechanism-name-diffusionless-lattice-change-09", + "shape": "ASSOCIATION", + "protocol": "lens_eval", + "domain": "materials", + "category": "diffusionless-lattice-change", + "title": "Diffusionless lattice change 9", + "description": "Frozen mechanism-versus-eponym vignette; both tracked words are absent from the input and generated output.", + "text": "Atoms move cooperatively over short distances during a rapid transformation, generating a body-centered structure with unequal axial dimensions.", + "readout_selector": "final_prompt_token", + "tracked": [ + "tetragonal", + "Bain" + ], + "must_be_absent_from_input": true, + "must_be_absent_from_output": true, + "note": "Paired comparison of physical carrier 'tetragonal' against eponym token 'Bain'. Primary endpoint is the within-item log10 rank difference, summarized separately for J-lens and logit lens." + }, + { + "slug": "mechanism-name-diffusionless-lattice-change-10", + "shape": "ASSOCIATION", + "protocol": "lens_eval", + "domain": "materials", + "category": "diffusionless-lattice-change", + "title": "Diffusionless lattice change 10", + "description": "Frozen mechanism-versus-eponym vignette; both tracked words are absent from the input and generated output.", + "text": "X-ray peaks from the quenched product split because one unit-cell axis differs from the two equivalent perpendicular axes.", + "readout_selector": "final_prompt_token", + "tracked": [ + "tetragonal", + "Bain" + ], + "must_be_absent_from_input": true, + "must_be_absent_from_output": true, + "note": "Paired comparison of physical carrier 'tetragonal' against eponym token 'Bain'. Primary endpoint is the within-item log10 rank difference, summarized separately for J-lens and logit lens." + } + ] +} diff --git a/jlens_materials/prompts/materials-paper-case-studies-v1.json b/jlens_materials/prompts/materials-paper-case-studies-v1.json new file mode 100644 index 0000000000000000000000000000000000000000..cfb05da3840cc99735c390daf6a08c3c34b46557 --- /dev/null +++ b/jlens_materials/prompts/materials-paper-case-studies-v1.json @@ -0,0 +1,231 @@ +{ + "description": "Preregistered exploratory Gemma-4 materials case studies. Four unnamed materials vignettes test latent scientific associations, four constrained-answer items test mechanism/recognition traces, and two matched carrier triplets replicate directed modulation. This pack is designed for documented qualitative examples, not population-level quantitative claims.", + "prompts": [ + { + "slug": "paper-v1-ductile-fracture-replication", + "shape": "ASSOCIATION", + "protocol": "lens_eval", + "domain": "materials", + "title": "Ductile-fracture replication vignette", + "description": "An independently worded fractography vignette tests replication of the ductile microvoid family.", + "text": "A tensile bar exhibited extensive necking before separation. The fracture surface was covered by rounded dimples centered on inclusions, and the final profile had a cup-shaped center with a surrounding shear lip.", + "readout_selector": "final_prompt_token", + "tracked": ["ductile", "void", "nucleation", "coalescence"], + "must_be_absent_from_input": true, + "must_be_absent_from_output": true, + "note": "Replication succeeds qualitatively if the ductile/microvoid family improves strongly under the J-lens relative to the logit lens. Do not infer an ordered mechanism unless nucleation precedes coalescence reproducibly." + }, + { + "slug": "paper-v1-intergranular-replication", + "shape": "ASSOCIATION", + "protocol": "lens_eval", + "domain": "materials", + "title": "Boundary-attack replication vignette", + "description": "An independently worded stainless-steel vignette tests replication of the corrosion and sensitization family.", + "text": "A stainless-steel component spent hours near 650 degrees Celsius, allowing chromium-rich carbides to decorate grain interfaces. After immersion in a chloride solution, narrow connected grooves followed those interfaces while the grain interiors remained nearly untouched.", + "readout_selector": "final_prompt_token", + "tracked": ["boundary", "corrosion", "sensitization", "intergranular"], + "must_be_absent_from_input": true, + "must_be_absent_from_output": true, + "note": "The strongest result would recover both generic corrosion and the specific sensitization/intergranular interpretation without those terms appearing in the prompt or continuation." + }, + { + "slug": "paper-v1-fatigue-fractography", + "shape": "ASSOCIATION", + "protocol": "lens_eval", + "domain": "materials", + "title": "Cyclic-fracture vignette", + "description": "Fractographic and loading clues should evoke the cyclic damage family without naming it.", + "text": "A shaft survived thousands of alternating load cycles before sudden separation. Concentric beach marks spread from a surface notch, and microscopy showed closely spaced parallel ridges advancing across the smooth region.", + "readout_selector": "final_prompt_token", + "tracked": ["fatigue", "crack", "striations", "propagation"], + "must_be_absent_from_input": true, + "must_be_absent_from_output": true, + "note": "This tests whether a coherent fatigue/crack-growth family emerges from loading history and fractographic morphology." + }, + { + "slug": "paper-v1-cleavage-fractography", + "shape": "ASSOCIATION", + "protocol": "lens_eval", + "domain": "materials", + "title": "Low-temperature faceted-fracture vignette", + "description": "A faceted low-temperature fracture should evoke a brittle cleavage interpretation.", + "text": "A body-centered alloy failed at low temperature with almost no necking. Its fracture surface contained flat crystallographic facets joined by river-like markings, while macroscopic plastic deformation was minimal.", + "readout_selector": "final_prompt_token", + "tracked": ["brittle", "cleavage", "transgranular", "fracture"], + "must_be_absent_from_input": true, + "must_be_absent_from_output": true, + "note": "Compare the specific cleavage/transgranular terms with the broader brittle/fracture family." + }, + { + "slug": "paper-v1-hall-petch-constrained", + "shape": "MULTIHOP", + "protocol": "lens_eval", + "domain": "materials", + "title": "Constrained grain-size strengthening", + "description": "A forced binary completion prevents an adverb from invalidating the clean answer control.", + "text": "Two otherwise identical polycrystals have mean grain sizes of 60 micrometers and 6 micrometers. Interfaces impede carrier motion, so reducing the grain size changes the yield stress. Choose exactly one word, higher or lower: the fine-grained specimen's yield stress is", + "readout_selector": "before_answer", + "tracked": ["Hall", "Petch", "strengthening"], + "answer": "higher", + "must_be_absent_from_input": true, + "must_be_absent_from_output": true, + "note": "This repairs the earlier clean-answer failure caused by the first generated token being 'significantly'." + }, + { + "slug": "paper-v1-schmid-constrained", + "shape": "MULTIHOP", + "protocol": "lens_eval", + "domain": "materials", + "title": "Constrained resolved-shear event", + "description": "A one-word microscopic-event request distinguishes slip from the macroscopic answer yield.", + "text": "For a single crystal, the product of applied stress and the two orientation cosines gives a resolved value of 120 MPa on the most favorably oriented system. The critical value is 80 MPa. Answer with one word naming the microscopic deformation event that begins:", + "readout_selector": "before_answer", + "tracked": ["Schmid", "dislocation"], + "answer": "slip", + "must_be_absent_from_input": true, + "must_be_absent_from_output": true, + "note": "This repairs the earlier ambiguity in which the scientifically plausible first token was 'yield'." + }, + { + "slug": "paper-v1-fcc-constrained", + "shape": "RECOGNITION", + "protocol": "lens_eval", + "domain": "materials", + "title": "Constrained diffraction recognition", + "description": "A strict abbreviation request prevents an initial article from invalidating the answer check.", + "text": "A cubic powder pattern permits reflections when the three Miller indices are all odd or all even and excludes mixed parity. Reply only with the standard three-letter Bravais-lattice abbreviation:", + "readout_selector": "before_answer", + "tracked": ["cubic", "extinction", "diffraction"], + "answer": "FCC", + "must_be_absent_from_input": true, + "must_be_absent_from_output": true, + "note": "This repairs the earlier clean-answer failure caused by the first generated token being the article 'a'." + }, + { + "slug": "paper-v1-griffith-constrained", + "shape": "MULTIHOP", + "protocol": "lens_eval", + "domain": "materials", + "title": "Constrained flaw-size scaling", + "description": "A binary comparative answer tests whether the named fracture criterion appears before the scaling result.", + "text": "Two elastic plates have identical modulus and surface energy, but one contains an internal flaw four times as long as the other. Choose exactly one word, higher or lower: the critical remote tensile stress for the plate with the longer flaw is", + "readout_selector": "before_answer", + "tracked": ["Griffith", "crack", "fracture"], + "answer": "lower", + "must_be_absent_from_input": true, + "must_be_absent_from_output": true, + "note": "The answer follows the inverse-square-root flaw-size scaling; Griffith is the predeclared mechanism-level concept." + }, + { + "slug": "paper-v1-pileup-focus-key", + "shape": "MODULATION", + "protocol": "directed_modulation", + "domain": "materials", + "title": "Pile-up focus replication: brass key", + "description": "First new teacher-forced carrier for the directed-modulation replication.", + "user": "Write \"The brass key rested under the folded map.\" Concentrate on a dislocation pile-up and its stress concentration while you write the sentence. Do not write anything else.", + "assistant_prefill": "The brass key rested under the folded map.", + "readout_selector": "assistant_response", + "tracked": ["dislocation", "pile", "stress"], + "must_be_absent_from_output": true, + "condition": "focus", + "control_group": "paper-v1-pileup-brass-key", + "phrasing_id": "focus-02", + "carrier_id": "brass-key", + "target_family": "dislocation-pileup", + "note": "Interpret only relative to the matched suppress and neutral records using the identical carrier and target set." + }, + { + "slug": "paper-v1-pileup-suppress-key", + "shape": "MODULATION", + "protocol": "directed_modulation", + "domain": "materials", + "title": "Pile-up suppression replication: brass key", + "description": "Matched suppress condition for the brass-key carrier.", + "user": "Write \"The brass key rested under the folded map.\" Try not to think about a dislocation pile-up or its stress concentration while you write the sentence. Do not write anything else.", + "assistant_prefill": "The brass key rested under the folded map.", + "readout_selector": "assistant_response", + "tracked": ["dislocation", "pile", "stress"], + "must_be_absent_from_output": true, + "condition": "suppress", + "control_group": "paper-v1-pileup-brass-key", + "phrasing_id": "suppress-02", + "carrier_id": "brass-key", + "target_family": "dislocation-pileup" + }, + { + "slug": "paper-v1-pileup-control-key", + "shape": "MODULATION", + "protocol": "directed_modulation", + "domain": "materials", + "title": "Pile-up neutral control: brass key", + "description": "Matched neutral condition for the brass-key carrier.", + "user": "Write \"The brass key rested under the folded map.\" Do not write anything else.", + "assistant_prefill": "The brass key rested under the folded map.", + "readout_selector": "assistant_response", + "tracked": ["dislocation", "pile", "stress"], + "must_be_absent_from_output": true, + "condition": "control", + "control_group": "paper-v1-pileup-brass-key", + "phrasing_id": "control-02", + "carrier_id": "brass-key", + "target_family": "dislocation-pileup" + }, + { + "slug": "paper-v1-pileup-focus-cup", + "shape": "MODULATION", + "protocol": "directed_modulation", + "domain": "materials", + "title": "Pile-up focus replication: white cup", + "description": "Second new teacher-forced carrier for the directed-modulation replication.", + "user": "Write \"A white cup stood beside the closed window.\" Keep a dislocation pile-up and the stress it concentrates in mind while you write the sentence. Do not write anything else.", + "assistant_prefill": "A white cup stood beside the closed window.", + "readout_selector": "assistant_response", + "tracked": ["dislocation", "pile", "stress"], + "must_be_absent_from_output": true, + "condition": "focus", + "control_group": "paper-v1-pileup-white-cup", + "phrasing_id": "focus-03", + "carrier_id": "white-cup", + "target_family": "dislocation-pileup", + "note": "Interpret only relative to the matched suppress and neutral records using the identical carrier and target set." + }, + { + "slug": "paper-v1-pileup-suppress-cup", + "shape": "MODULATION", + "protocol": "directed_modulation", + "domain": "materials", + "title": "Pile-up suppression replication: white cup", + "description": "Matched suppress condition for the white-cup carrier.", + "user": "Write \"A white cup stood beside the closed window.\" Try not to think about a dislocation pile-up or the stress it concentrates while you write the sentence. Do not write anything else.", + "assistant_prefill": "A white cup stood beside the closed window.", + "readout_selector": "assistant_response", + "tracked": ["dislocation", "pile", "stress"], + "must_be_absent_from_output": true, + "condition": "suppress", + "control_group": "paper-v1-pileup-white-cup", + "phrasing_id": "suppress-03", + "carrier_id": "white-cup", + "target_family": "dislocation-pileup" + }, + { + "slug": "paper-v1-pileup-control-cup", + "shape": "MODULATION", + "protocol": "directed_modulation", + "domain": "materials", + "title": "Pile-up neutral control: white cup", + "description": "Matched neutral condition for the white-cup carrier.", + "user": "Write \"A white cup stood beside the closed window.\" Do not write anything else.", + "assistant_prefill": "A white cup stood beside the closed window.", + "readout_selector": "assistant_response", + "tracked": ["dislocation", "pile", "stress"], + "must_be_absent_from_output": true, + "condition": "control", + "control_group": "paper-v1-pileup-white-cup", + "phrasing_id": "control-03", + "carrier_id": "white-cup", + "target_family": "dislocation-pileup" + } + ] +} diff --git a/jlens_materials/prompts/materials-paper-v2-preregistered.json b/jlens_materials/prompts/materials-paper-v2-preregistered.json new file mode 100644 index 0000000000000000000000000000000000000000..e8cfda5dbfd945fca1ac244b7c689dd588db4168 --- /dev/null +++ b/jlens_materials/prompts/materials-paper-v2-preregistered.json @@ -0,0 +1,2621 @@ +{ + "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": [ + { + "slug": "paper-v2-assoc-ductile-01", + "shape": "ASSOCIATION", + "protocol": "lens_eval", + "domain": "materials", + "category": "ductile", + "title": "Dimpled tensile failure 1", + "description": "Preregistered unnamed materials vignette; compare fixed-band J-lens ranks with the matched logit-lens baseline.", + "text": "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.", + "readout_selector": "final_prompt_token", + "tracked": [ + "ductile", + "nucleation", + "coalescence", + "void" + ], + "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." + }, + { + "slug": "paper-v2-assoc-ductile-02", + "shape": "ASSOCIATION", + "protocol": "lens_eval", + "domain": "materials", + "category": "ductile", + "title": "Dimpled tensile failure 2", + "description": "Preregistered unnamed materials vignette; compare fixed-band J-lens ranks with the matched logit-lens baseline.", + "text": "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.", + "readout_selector": "final_prompt_token", + "tracked": [ + "ductile", + "nucleation", + "coalescence", + "void" + ], + "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." + }, + { + "slug": "paper-v2-assoc-ductile-03", + "shape": "ASSOCIATION", + "protocol": "lens_eval", + "domain": "materials", + "category": "ductile", + "title": "Dimpled tensile failure 3", + "description": "Preregistered unnamed materials vignette; compare fixed-band J-lens ranks with the matched logit-lens baseline.", + "text": "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.", + "readout_selector": "final_prompt_token", + "tracked": [ + "ductile", + "nucleation", + "coalescence", + "void" + ], + "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." + }, + { + "slug": "paper-v2-assoc-ductile-04", + "shape": "ASSOCIATION", + "protocol": "lens_eval", + "domain": "materials", + "category": "ductile", + "title": "Dimpled tensile failure 4", + "description": "Preregistered unnamed materials vignette; compare fixed-band J-lens ranks with the matched logit-lens baseline.", + "text": "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.", + "readout_selector": "final_prompt_token", + "tracked": [ + "ductile", + "nucleation", + "coalescence", + "void" + ], + "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." + }, + { + "slug": "paper-v2-assoc-ductile-05", + "shape": "ASSOCIATION", + "protocol": "lens_eval", + "domain": "materials", + "category": "ductile", + "title": "Dimpled tensile failure 5", + "description": "Preregistered unnamed materials vignette; compare fixed-band J-lens ranks with the matched logit-lens baseline.", + "text": "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.", + "readout_selector": "final_prompt_token", + "tracked": [ + "ductile", + "nucleation", + "coalescence", + "void" + ], + "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." + }, + { + "slug": "paper-v2-assoc-boundary-attack-01", + "shape": "ASSOCIATION", + "protocol": "lens_eval", + "domain": "materials", + "category": "boundary-attack", + "title": "Interface-localized stainless-steel attack 1", + "description": "Preregistered unnamed materials vignette; compare fixed-band J-lens ranks with the matched logit-lens baseline.", + "text": "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.", + "readout_selector": "final_prompt_token", + "tracked": [ + "boundary", + "corrosion", + "sensitization" + ], + "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." + }, + { + "slug": "paper-v2-assoc-boundary-attack-02", + "shape": "ASSOCIATION", + "protocol": "lens_eval", + "domain": "materials", + "category": "boundary-attack", + "title": "Interface-localized stainless-steel attack 2", + "description": "Preregistered unnamed materials vignette; compare fixed-band J-lens ranks with the matched logit-lens baseline.", + "text": "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.", + "readout_selector": "final_prompt_token", + "tracked": [ + "boundary", + "corrosion", + "sensitization" + ], + "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." + }, + { + "slug": "paper-v2-assoc-boundary-attack-03", + "shape": "ASSOCIATION", + "protocol": "lens_eval", + "domain": "materials", + "category": "boundary-attack", + "title": "Interface-localized stainless-steel attack 3", + "description": "Preregistered unnamed materials vignette; compare fixed-band J-lens ranks with the matched logit-lens baseline.", + "text": "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.", + "readout_selector": "final_prompt_token", + "tracked": [ + "boundary", + "corrosion", + "sensitization" + ], + "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." + }, + { + "slug": "paper-v2-assoc-boundary-attack-04", + "shape": "ASSOCIATION", + "protocol": "lens_eval", + "domain": "materials", + "category": "boundary-attack", + "title": "Interface-localized stainless-steel attack 4", + "description": "Preregistered unnamed materials vignette; compare fixed-band J-lens ranks with the matched logit-lens baseline.", + "text": "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.", + "readout_selector": "final_prompt_token", + "tracked": [ + "boundary", + "corrosion", + "sensitization" + ], + "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." + }, + { + "slug": "paper-v2-assoc-boundary-attack-05", + "shape": "ASSOCIATION", + "protocol": "lens_eval", + "domain": "materials", + "category": "boundary-attack", + "title": "Interface-localized stainless-steel attack 5", + "description": "Preregistered unnamed materials vignette; compare fixed-band J-lens ranks with the matched logit-lens baseline.", + "text": "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.", + "readout_selector": "final_prompt_token", + "tracked": [ + "boundary", + "corrosion", + "sensitization" + ], + "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." + }, + { + "slug": "paper-v2-assoc-cyclic-01", + "shape": "ASSOCIATION", + "protocol": "lens_eval", + "domain": "materials", + "category": "cyclic", + "title": "Cyclic loading and beach-mark morphology 1", + "description": "Preregistered unnamed materials vignette; compare fixed-band J-lens ranks with the matched logit-lens baseline.", + "text": "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.", + "readout_selector": "final_prompt_token", + "tracked": [ + "fatigue", + "crack", + "propagation" + ], + "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." + }, + { + "slug": "paper-v2-assoc-cyclic-02", + "shape": "ASSOCIATION", + "protocol": "lens_eval", + "domain": "materials", + "category": "cyclic", + "title": "Cyclic loading and beach-mark morphology 2", + "description": "Preregistered unnamed materials vignette; compare fixed-band J-lens ranks with the matched logit-lens baseline.", + "text": "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.", + "readout_selector": "final_prompt_token", + "tracked": [ + "fatigue", + "crack", + "propagation" + ], + "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." + }, + { + "slug": "paper-v2-assoc-cyclic-03", + "shape": "ASSOCIATION", + "protocol": "lens_eval", + "domain": "materials", + "category": "cyclic", + "title": "Cyclic loading and beach-mark morphology 3", + "description": "Preregistered unnamed materials vignette; compare fixed-band J-lens ranks with the matched logit-lens baseline.", + "text": "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.", + "readout_selector": "final_prompt_token", + "tracked": [ + "fatigue", + "crack", + "propagation" + ], + "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." + }, + { + "slug": "paper-v2-assoc-cyclic-04", + "shape": "ASSOCIATION", + "protocol": "lens_eval", + "domain": "materials", + "category": "cyclic", + "title": "Cyclic loading and beach-mark morphology 4", + "description": "Preregistered unnamed materials vignette; compare fixed-band J-lens ranks with the matched logit-lens baseline.", + "text": "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.", + "readout_selector": "final_prompt_token", + "tracked": [ + "fatigue", + "crack", + "propagation" + ], + "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." + }, + { + "slug": "paper-v2-assoc-cyclic-05", + "shape": "ASSOCIATION", + "protocol": "lens_eval", + "domain": "materials", + "category": "cyclic", + "title": "Cyclic loading and beach-mark morphology 5", + "description": "Preregistered unnamed materials vignette; compare fixed-band J-lens ranks with the matched logit-lens baseline.", + "text": "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.", + "readout_selector": "final_prompt_token", + "tracked": [ + "fatigue", + "crack", + "propagation" + ], + "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." + }, + { + "slug": "paper-v2-assoc-cleavage-01", + "shape": "ASSOCIATION", + "protocol": "lens_eval", + "domain": "materials", + "category": "cleavage", + "title": "Faceted low-temperature failure 1", + "description": "Preregistered unnamed materials vignette; compare fixed-band J-lens ranks with the matched logit-lens baseline.", + "text": "A body-centered alloy separated at low temperature with negligible necking. The failure face consisted of flat crystallographic facets connected by river-like markings.", + "readout_selector": "final_prompt_token", + "tracked": [ + "brittle", + "cleavage", + "transgranular" + ], + "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." + }, + { + "slug": "paper-v2-assoc-cleavage-02", + "shape": "ASSOCIATION", + "protocol": "lens_eval", + "domain": "materials", + "category": "cleavage", + "title": "Faceted low-temperature failure 2", + "description": "Preregistered unnamed materials vignette; compare fixed-band J-lens ranks with the matched logit-lens baseline.", + "text": "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.", + "readout_selector": "final_prompt_token", + "tracked": [ + "brittle", + "cleavage", + "transgranular" + ], + "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." + }, + { + "slug": "paper-v2-assoc-cleavage-03", + "shape": "ASSOCIATION", + "protocol": "lens_eval", + "domain": "materials", + "category": "cleavage", + "title": "Faceted low-temperature failure 3", + "description": "Preregistered unnamed materials vignette; compare fixed-band J-lens ranks with the matched logit-lens baseline.", + "text": "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.", + "readout_selector": "final_prompt_token", + "tracked": [ + "brittle", + "cleavage", + "transgranular" + ], + "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." + }, + { + "slug": "paper-v2-assoc-cleavage-04", + "shape": "ASSOCIATION", + "protocol": "lens_eval", + "domain": "materials", + "category": "cleavage", + "title": "Faceted low-temperature failure 4", + "description": "Preregistered unnamed materials vignette; compare fixed-band J-lens ranks with the matched logit-lens baseline.", + "text": "A low-temperature bend test produced abrupt separation after almost no permanent curvature. The mating faces displayed large flat facets and feather-like markings.", + "readout_selector": "final_prompt_token", + "tracked": [ + "brittle", + "cleavage", + "transgranular" + ], + "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." + }, + { + "slug": "paper-v2-assoc-cleavage-05", + "shape": "ASSOCIATION", + "protocol": "lens_eval", + "domain": "materials", + "category": "cleavage", + "title": "Faceted low-temperature failure 5", + "description": "Preregistered unnamed materials vignette; compare fixed-band J-lens ranks with the matched logit-lens baseline.", + "text": "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.", + "readout_selector": "final_prompt_token", + "tracked": [ + "brittle", + "cleavage", + "transgranular" + ], + "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." + }, + { + "slug": "paper-v2-assoc-high-temperature-deformation-01", + "shape": "ASSOCIATION", + "protocol": "lens_eval", + "domain": "materials", + "category": "high-temperature-deformation", + "title": "Slow high-temperature deformation 1", + "description": "Preregistered unnamed materials vignette; compare fixed-band J-lens ranks with the matched logit-lens baseline.", + "text": "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.", + "readout_selector": "final_prompt_token", + "tracked": [ + "creep", + "diffusion", + "cavity" + ], + "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." + }, + { + "slug": "paper-v2-assoc-high-temperature-deformation-02", + "shape": "ASSOCIATION", + "protocol": "lens_eval", + "domain": "materials", + "category": "high-temperature-deformation", + "title": "Slow high-temperature deformation 2", + "description": "Preregistered unnamed materials vignette; compare fixed-band J-lens ranks with the matched logit-lens baseline.", + "text": "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.", + "readout_selector": "final_prompt_token", + "tracked": [ + "creep", + "diffusion", + "cavity" + ], + "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." + }, + { + "slug": "paper-v2-assoc-high-temperature-deformation-03", + "shape": "ASSOCIATION", + "protocol": "lens_eval", + "domain": "materials", + "category": "high-temperature-deformation", + "title": "Slow high-temperature deformation 3", + "description": "Preregistered unnamed materials vignette; compare fixed-band J-lens ranks with the matched logit-lens baseline.", + "text": "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.", + "readout_selector": "final_prompt_token", + "tracked": [ + "creep", + "diffusion", + "cavity" + ], + "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." + }, + { + "slug": "paper-v2-assoc-high-temperature-deformation-04", + "shape": "ASSOCIATION", + "protocol": "lens_eval", + "domain": "materials", + "category": "high-temperature-deformation", + "title": "Slow high-temperature deformation 4", + "description": "Preregistered unnamed materials vignette; compare fixed-band J-lens ranks with the matched logit-lens baseline.", + "text": "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.", + "readout_selector": "final_prompt_token", + "tracked": [ + "creep", + "diffusion", + "cavity" + ], + "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." + }, + { + "slug": "paper-v2-assoc-high-temperature-deformation-05", + "shape": "ASSOCIATION", + "protocol": "lens_eval", + "domain": "materials", + "category": "high-temperature-deformation", + "title": "Slow high-temperature deformation 5", + "description": "Preregistered unnamed materials vignette; compare fixed-band J-lens ranks with the matched logit-lens baseline.", + "text": "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.", + "readout_selector": "final_prompt_token", + "tracked": [ + "creep", + "diffusion", + "cavity" + ], + "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." + }, + { + "slug": "paper-v2-assoc-particle-strengthening-01", + "shape": "ASSOCIATION", + "protocol": "lens_eval", + "domain": "materials", + "category": "particle-strengthening", + "title": "Hard-particle obstacle strengthening 1", + "description": "Preregistered unnamed materials vignette; compare fixed-band J-lens ranks with the matched logit-lens baseline.", + "text": "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.", + "readout_selector": "final_prompt_token", + "tracked": [ + "precipitation", + "bowing", + "strengthening" + ], + "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." + }, + { + "slug": "paper-v2-assoc-particle-strengthening-02", + "shape": "ASSOCIATION", + "protocol": "lens_eval", + "domain": "materials", + "category": "particle-strengthening", + "title": "Hard-particle obstacle strengthening 2", + "description": "Preregistered unnamed materials vignette; compare fixed-band J-lens ranks with the matched logit-lens baseline.", + "text": "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.", + "readout_selector": "final_prompt_token", + "tracked": [ + "precipitation", + "bowing", + "strengthening" + ], + "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." + }, + { + "slug": "paper-v2-assoc-particle-strengthening-03", + "shape": "ASSOCIATION", + "protocol": "lens_eval", + "domain": "materials", + "category": "particle-strengthening", + "title": "Hard-particle obstacle strengthening 3", + "description": "Preregistered unnamed materials vignette; compare fixed-band J-lens ranks with the matched logit-lens baseline.", + "text": "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.", + "readout_selector": "final_prompt_token", + "tracked": [ + "precipitation", + "bowing", + "strengthening" + ], + "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." + }, + { + "slug": "paper-v2-assoc-particle-strengthening-04", + "shape": "ASSOCIATION", + "protocol": "lens_eval", + "domain": "materials", + "category": "particle-strengthening", + "title": "Hard-particle obstacle strengthening 4", + "description": "Preregistered unnamed materials vignette; compare fixed-band J-lens ranks with the matched logit-lens baseline.", + "text": "An aged alloy gains yield strength from closely spaced stable particles. Under load, flexible line imperfections bend around each obstacle rather than slicing it.", + "readout_selector": "final_prompt_token", + "tracked": [ + "precipitation", + "bowing", + "strengthening" + ], + "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." + }, + { + "slug": "paper-v2-assoc-particle-strengthening-05", + "shape": "ASSOCIATION", + "protocol": "lens_eval", + "domain": "materials", + "category": "particle-strengthening", + "title": "Hard-particle obstacle strengthening 5", + "description": "Preregistered unnamed materials vignette; compare fixed-band J-lens ranks with the matched logit-lens baseline.", + "text": "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.", + "readout_selector": "final_prompt_token", + "tracked": [ + "precipitation", + "bowing", + "strengthening" + ], + "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." + }, + { + "slug": "paper-v2-assoc-rapid-transformation-01", + "shape": "ASSOCIATION", + "protocol": "lens_eval", + "domain": "materials", + "category": "rapid-transformation", + "title": "Rapid diffusionless steel transformation 1", + "description": "Preregistered unnamed materials vignette; compare fixed-band J-lens ranks with the matched logit-lens baseline.", + "text": "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.", + "readout_selector": "final_prompt_token", + "tracked": [ + "martensite", + "shear", + "Bain", + "tetragonal" + ], + "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." + }, + { + "slug": "paper-v2-assoc-rapid-transformation-02", + "shape": "ASSOCIATION", + "protocol": "lens_eval", + "domain": "materials", + "category": "rapid-transformation", + "title": "Rapid diffusionless steel transformation 2", + "description": "Preregistered unnamed materials vignette; compare fixed-band J-lens ranks with the matched logit-lens baseline.", + "text": "Rapid quenching produces thin laths without allowing carbon to redistribute over long distances. The product lattice is body-centered but has unequal axial dimensions.", + "readout_selector": "final_prompt_token", + "tracked": [ + "martensite", + "shear", + "Bain", + "tetragonal" + ], + "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." + }, + { + "slug": "paper-v2-assoc-rapid-transformation-03", + "shape": "ASSOCIATION", + "protocol": "lens_eval", + "domain": "materials", + "category": "rapid-transformation", + "title": "Rapid diffusionless steel transformation 3", + "description": "Preregistered unnamed materials vignette; compare fixed-band J-lens ranks with the matched logit-lens baseline.", + "text": "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.", + "readout_selector": "final_prompt_token", + "tracked": [ + "martensite", + "shear", + "Bain", + "tetragonal" + ], + "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." + }, + { + "slug": "paper-v2-assoc-rapid-transformation-04", + "shape": "ASSOCIATION", + "protocol": "lens_eval", + "domain": "materials", + "category": "rapid-transformation", + "title": "Rapid diffusionless steel transformation 4", + "description": "Preregistered unnamed materials vignette; compare fixed-band J-lens ranks with the matched logit-lens baseline.", + "text": "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.", + "readout_selector": "final_prompt_token", + "tracked": [ + "martensite", + "shear", + "Bain", + "tetragonal" + ], + "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." + }, + { + "slug": "paper-v2-assoc-rapid-transformation-05", + "shape": "ASSOCIATION", + "protocol": "lens_eval", + "domain": "materials", + "category": "rapid-transformation", + "title": "Rapid diffusionless steel transformation 5", + "description": "Preregistered unnamed materials vignette; compare fixed-band J-lens ranks with the matched logit-lens baseline.", + "text": "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.", + "readout_selector": "final_prompt_token", + "tracked": [ + "martensite", + "shear", + "Bain", + "tetragonal" + ], + "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." + }, + { + "slug": "paper-v2-assoc-line-defect-motion-01", + "shape": "ASSOCIATION", + "protocol": "lens_eval", + "domain": "materials", + "category": "line-defect-motion", + "title": "Crystal line-imperfection motion 1", + "description": "Preregistered unnamed materials vignette; compare fixed-band J-lens ranks with the matched logit-lens baseline.", + "text": "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.", + "readout_selector": "final_prompt_token", + "tracked": [ + "dislocation", + "glide", + "slip" + ], + "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." + }, + { + "slug": "paper-v2-assoc-line-defect-motion-02", + "shape": "ASSOCIATION", + "protocol": "lens_eval", + "domain": "materials", + "category": "line-defect-motion", + "title": "Crystal line-imperfection motion 2", + "description": "Preregistered unnamed materials vignette; compare fixed-band J-lens ranks with the matched logit-lens baseline.", + "text": "Plastic strain begins when mobile line imperfections sweep across selected crystallographic planes and create small surface steps.", + "readout_selector": "final_prompt_token", + "tracked": [ + "dislocation", + "glide", + "slip" + ], + "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." + }, + { + "slug": "paper-v2-assoc-line-defect-motion-03", + "shape": "ASSOCIATION", + "protocol": "lens_eval", + "domain": "materials", + "category": "line-defect-motion", + "title": "Crystal line-imperfection motion 3", + "description": "Preregistered unnamed materials vignette; compare fixed-band J-lens ranks with the matched logit-lens baseline.", + "text": "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.", + "readout_selector": "final_prompt_token", + "tracked": [ + "dislocation", + "glide", + "slip" + ], + "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." + }, + { + "slug": "paper-v2-assoc-line-defect-motion-04", + "shape": "ASSOCIATION", + "protocol": "lens_eval", + "domain": "materials", + "category": "line-defect-motion", + "title": "Crystal line-imperfection motion 4", + "description": "Preregistered unnamed materials vignette; compare fixed-band J-lens ranks with the matched logit-lens baseline.", + "text": "Etch pits tracing mobile line imperfections shift across a crystal during loading. Each passage leaves a step whose direction follows the mismatch vector.", + "readout_selector": "final_prompt_token", + "tracked": [ + "dislocation", + "glide", + "slip" + ], + "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." + }, + { + "slug": "paper-v2-assoc-line-defect-motion-05", + "shape": "ASSOCIATION", + "protocol": "lens_eval", + "domain": "materials", + "category": "line-defect-motion", + "title": "Crystal line-imperfection motion 5", + "description": "Preregistered unnamed materials vignette; compare fixed-band J-lens ranks with the matched logit-lens baseline.", + "text": "Once the resolved driving force exceeds a critical value, a linear lattice defect travels on its favored plane and the specimen acquires permanent deformation.", + "readout_selector": "final_prompt_token", + "tracked": [ + "dislocation", + "glide", + "slip" + ], + "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." + }, + { + "slug": "paper-v2-assoc-notch-resistance-01", + "shape": "ASSOCIATION", + "protocol": "lens_eval", + "domain": "materials", + "category": "notch-resistance", + "title": "Resistance to notch-driven failure 1", + "description": "Preregistered unnamed materials vignette; compare fixed-band J-lens ranks with the matched logit-lens baseline.", + "text": "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.", + "readout_selector": "final_prompt_token", + "tracked": [ + "toughness", + "crack", + "fracture" + ], + "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." + }, + { + "slug": "paper-v2-assoc-notch-resistance-02", + "shape": "ASSOCIATION", + "protocol": "lens_eval", + "domain": "materials", + "category": "notch-resistance", + "title": "Resistance to notch-driven failure 2", + "description": "Preregistered unnamed materials vignette; compare fixed-band J-lens ranks with the matched logit-lens baseline.", + "text": "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.", + "readout_selector": "final_prompt_token", + "tracked": [ + "toughness", + "crack", + "fracture" + ], + "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." + }, + { + "slug": "paper-v2-assoc-notch-resistance-03", + "shape": "ASSOCIATION", + "protocol": "lens_eval", + "domain": "materials", + "category": "notch-resistance", + "title": "Resistance to notch-driven failure 3", + "description": "Preregistered unnamed materials vignette; compare fixed-band J-lens ranks with the matched logit-lens baseline.", + "text": "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.", + "readout_selector": "final_prompt_token", + "tracked": [ + "toughness", + "crack", + "fracture" + ], + "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." + }, + { + "slug": "paper-v2-assoc-notch-resistance-04", + "shape": "ASSOCIATION", + "protocol": "lens_eval", + "domain": "materials", + "category": "notch-resistance", + "title": "Resistance to notch-driven failure 4", + "description": "Preregistered unnamed materials vignette; compare fixed-band J-lens ranks with the matched logit-lens baseline.", + "text": "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.", + "readout_selector": "final_prompt_token", + "tracked": [ + "toughness", + "crack", + "fracture" + ], + "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." + }, + { + "slug": "paper-v2-assoc-notch-resistance-05", + "shape": "ASSOCIATION", + "protocol": "lens_eval", + "domain": "materials", + "category": "notch-resistance", + "title": "Resistance to notch-driven failure 5", + "description": "Preregistered unnamed materials vignette; compare fixed-band J-lens ranks with the matched logit-lens baseline.", + "text": "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.", + "readout_selector": "final_prompt_token", + "tracked": [ + "toughness", + "crack", + "fracture" + ], + "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." + }, + { + "slug": "paper-v2-assoc-hot-air-surface-layer-01", + "shape": "ASSOCIATION", + "protocol": "lens_eval", + "domain": "materials", + "category": "hot-air-surface-layer", + "title": "High-temperature environmental surface layer 1", + "description": "Preregistered unnamed materials vignette; compare fixed-band J-lens ranks with the matched logit-lens baseline.", + "text": "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.", + "readout_selector": "final_prompt_token", + "tracked": [ + "oxidation", + "oxide", + "scale" + ], + "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." + }, + { + "slug": "paper-v2-assoc-hot-air-surface-layer-02", + "shape": "ASSOCIATION", + "protocol": "lens_eval", + "domain": "materials", + "category": "hot-air-surface-layer", + "title": "High-temperature environmental surface layer 2", + "description": "Preregistered unnamed materials vignette; compare fixed-band J-lens ranks with the matched logit-lens baseline.", + "text": "During prolonged furnace exposure, a metal component gains mass and forms a dark brittle surface layer rich in oxygen and metal cations.", + "readout_selector": "final_prompt_token", + "tracked": [ + "oxidation", + "oxide", + "scale" + ], + "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." + }, + { + "slug": "paper-v2-assoc-hot-air-surface-layer-03", + "shape": "ASSOCIATION", + "protocol": "lens_eval", + "domain": "materials", + "category": "hot-air-surface-layer", + "title": "High-temperature environmental surface layer 3", + "description": "Preregistered unnamed materials vignette; compare fixed-band J-lens ranks with the matched logit-lens baseline.", + "text": "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.", + "readout_selector": "final_prompt_token", + "tracked": [ + "oxidation", + "oxide", + "scale" + ], + "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." + }, + { + "slug": "paper-v2-assoc-hot-air-surface-layer-04", + "shape": "ASSOCIATION", + "protocol": "lens_eval", + "domain": "materials", + "category": "hot-air-surface-layer", + "title": "High-temperature environmental surface layer 4", + "description": "Preregistered unnamed materials vignette; compare fixed-band J-lens ranks with the matched logit-lens baseline.", + "text": "Elevated-temperature air exposure produces a compact protective film on a chromium-containing alloy. The film limits further oxygen ingress by solid-state transport.", + "readout_selector": "final_prompt_token", + "tracked": [ + "oxidation", + "oxide", + "scale" + ], + "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." + }, + { + "slug": "paper-v2-assoc-hot-air-surface-layer-05", + "shape": "ASSOCIATION", + "protocol": "lens_eval", + "domain": "materials", + "category": "hot-air-surface-layer", + "title": "High-temperature environmental surface layer 5", + "description": "Preregistered unnamed materials vignette; compare fixed-band J-lens ranks with the matched logit-lens baseline.", + "text": "A turbine component operated in oxygen-bearing gas develops a thick external reaction product that can spall during thermal cycling.", + "readout_selector": "final_prompt_token", + "tracked": [ + "oxidation", + "oxide", + "scale" + ], + "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." + }, + { + "shape": "MODULATION", + "protocol": "directed_modulation", + "domain": "materials", + "description": "Preregistered matched directed-modulation triplet.", + "assistant_prefill": "The amber notebook rested beside the ceramic bowl.", + "readout_selector": "assistant_response", + "tracked": [ + "dislocation", + "pile", + "stress" + ], + "must_be_absent_from_output": true, + "control_group": "paper-v2-pileup-directed-modulation", + "phrasing_id": "triplet-01", + "carrier_id": "carrier-01", + "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.", + "slug": "paper-v2-mod-focus-01", + "title": "Directed focus 01", + "user": "Write exactly \"The amber notebook rested beside the ceramic bowl.\" Concentrate on a dislocation pile-up and its stress concentration while you write it. Do not write anything else.", + "condition": "focus" + }, + { + "shape": "MODULATION", + "protocol": "directed_modulation", + "domain": "materials", + "description": "Preregistered matched directed-modulation triplet.", + "assistant_prefill": "The amber notebook rested beside the ceramic bowl.", + "readout_selector": "assistant_response", + "tracked": [ + "dislocation", + "pile", + "stress" + ], + "must_be_absent_from_output": true, + "control_group": "paper-v2-pileup-directed-modulation", + "phrasing_id": "triplet-01", + "carrier_id": "carrier-01", + "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.", + "slug": "paper-v2-mod-suppress-01", + "title": "Directed suppression 01", + "user": "Write exactly \"The amber notebook rested beside the ceramic bowl.\" Try not to think about a dislocation pile-up or its stress concentration while you write it. Do not write anything else.", + "condition": "suppress" + }, + { + "shape": "MODULATION", + "protocol": "directed_modulation", + "domain": "materials", + "description": "Preregistered matched directed-modulation triplet.", + "assistant_prefill": "The amber notebook rested beside the ceramic bowl.", + "readout_selector": "assistant_response", + "tracked": [ + "dislocation", + "pile", + "stress" + ], + "must_be_absent_from_output": true, + "control_group": "paper-v2-pileup-directed-modulation", + "phrasing_id": "triplet-01", + "carrier_id": "carrier-01", + "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.", + "slug": "paper-v2-mod-control-01", + "title": "Neutral carrier control 01", + "user": "Write exactly \"The amber notebook rested beside the ceramic bowl.\" Do not write anything else.", + "condition": "control" + }, + { + "shape": "MODULATION", + "protocol": "directed_modulation", + "domain": "materials", + "description": "Preregistered matched directed-modulation triplet.", + "assistant_prefill": "A silver spoon lay beneath the folded cloth.", + "readout_selector": "assistant_response", + "tracked": [ + "dislocation", + "pile", + "stress" + ], + "must_be_absent_from_output": true, + "control_group": "paper-v2-pileup-directed-modulation", + "phrasing_id": "triplet-02", + "carrier_id": "carrier-02", + "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.", + "slug": "paper-v2-mod-focus-02", + "title": "Directed focus 02", + "user": "Write exactly \"A silver spoon lay beneath the folded cloth.\" Keep a dislocation pile-up and the stress it concentrates in mind while copying it. Do not write anything else.", + "condition": "focus" + }, + { + "shape": "MODULATION", + "protocol": "directed_modulation", + "domain": "materials", + "description": "Preregistered matched directed-modulation triplet.", + "assistant_prefill": "A silver spoon lay beneath the folded cloth.", + "readout_selector": "assistant_response", + "tracked": [ + "dislocation", + "pile", + "stress" + ], + "must_be_absent_from_output": true, + "control_group": "paper-v2-pileup-directed-modulation", + "phrasing_id": "triplet-02", + "carrier_id": "carrier-02", + "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.", + "slug": "paper-v2-mod-suppress-02", + "title": "Directed suppression 02", + "user": "Write exactly \"A silver spoon lay beneath the folded cloth.\" Keep your attention away from a dislocation pile-up and the stress it concentrates while copying it. Do not write anything else.", + "condition": "suppress" + }, + { + "shape": "MODULATION", + "protocol": "directed_modulation", + "domain": "materials", + "description": "Preregistered matched directed-modulation triplet.", + "assistant_prefill": "A silver spoon lay beneath the folded cloth.", + "readout_selector": "assistant_response", + "tracked": [ + "dislocation", + "pile", + "stress" + ], + "must_be_absent_from_output": true, + "control_group": "paper-v2-pileup-directed-modulation", + "phrasing_id": "triplet-02", + "carrier_id": "carrier-02", + "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.", + "slug": "paper-v2-mod-control-02", + "title": "Neutral carrier control 02", + "user": "Write exactly \"A silver spoon lay beneath the folded cloth.\" Do not write anything else.", + "condition": "control" + }, + { + "shape": "MODULATION", + "protocol": "directed_modulation", + "domain": "materials", + "description": "Preregistered matched directed-modulation triplet.", + "assistant_prefill": "The green folder remained inside the wooden drawer.", + "readout_selector": "assistant_response", + "tracked": [ + "dislocation", + "pile", + "stress" + ], + "must_be_absent_from_output": true, + "control_group": "paper-v2-pileup-directed-modulation", + "phrasing_id": "triplet-03", + "carrier_id": "carrier-03", + "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.", + "slug": "paper-v2-mod-focus-03", + "title": "Directed focus 03", + "user": "Write exactly \"The green folder remained inside the wooden drawer.\" Maintain an internal focus on a dislocation pile-up and its local stress concentration as you write. Do not write anything else.", + "condition": "focus" + }, + { + "shape": "MODULATION", + "protocol": "directed_modulation", + "domain": "materials", + "description": "Preregistered matched directed-modulation triplet.", + "assistant_prefill": "The green folder remained inside the wooden drawer.", + "readout_selector": "assistant_response", + "tracked": [ + "dislocation", + "pile", + "stress" + ], + "must_be_absent_from_output": true, + "control_group": "paper-v2-pileup-directed-modulation", + "phrasing_id": "triplet-03", + "carrier_id": "carrier-03", + "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.", + "slug": "paper-v2-mod-suppress-03", + "title": "Directed suppression 03", + "user": "Write exactly \"The green folder remained inside the wooden drawer.\" Avoid internally focusing on a dislocation pile-up or its local stress concentration as you write. Do not write anything else.", + "condition": "suppress" + }, + { + "shape": "MODULATION", + "protocol": "directed_modulation", + "domain": "materials", + "description": "Preregistered matched directed-modulation triplet.", + "assistant_prefill": "The green folder remained inside the wooden drawer.", + "readout_selector": "assistant_response", + "tracked": [ + "dislocation", + "pile", + "stress" + ], + "must_be_absent_from_output": true, + "control_group": "paper-v2-pileup-directed-modulation", + "phrasing_id": "triplet-03", + "carrier_id": "carrier-03", + "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.", + "slug": "paper-v2-mod-control-03", + "title": "Neutral carrier control 03", + "user": "Write exactly \"The green folder remained inside the wooden drawer.\" Do not write anything else.", + "condition": "control" + }, + { + "shape": "MODULATION", + "protocol": "directed_modulation", + "domain": "materials", + "description": "Preregistered matched directed-modulation triplet.", + "assistant_prefill": "A small lantern stood near the closed window.", + "readout_selector": "assistant_response", + "tracked": [ + "dislocation", + "pile", + "stress" + ], + "must_be_absent_from_output": true, + "control_group": "paper-v2-pileup-directed-modulation", + "phrasing_id": "triplet-04", + "carrier_id": "carrier-04", + "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.", + "slug": "paper-v2-mod-focus-04", + "title": "Directed focus 04", + "user": "Write exactly \"A small lantern stood near the closed window.\" Mentally attend to a dislocation pile-up and the concentrated stress near its head while reproducing it. Do not write anything else.", + "condition": "focus" + }, + { + "shape": "MODULATION", + "protocol": "directed_modulation", + "domain": "materials", + "description": "Preregistered matched directed-modulation triplet.", + "assistant_prefill": "A small lantern stood near the closed window.", + "readout_selector": "assistant_response", + "tracked": [ + "dislocation", + "pile", + "stress" + ], + "must_be_absent_from_output": true, + "control_group": "paper-v2-pileup-directed-modulation", + "phrasing_id": "triplet-04", + "carrier_id": "carrier-04", + "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.", + "slug": "paper-v2-mod-suppress-04", + "title": "Directed suppression 04", + "user": "Write exactly \"A small lantern stood near the closed window.\" Mentally suppress thoughts of a dislocation pile-up and the concentrated stress near its head while reproducing it. Do not write anything else.", + "condition": "suppress" + }, + { + "shape": "MODULATION", + "protocol": "directed_modulation", + "domain": "materials", + "description": "Preregistered matched directed-modulation triplet.", + "assistant_prefill": "A small lantern stood near the closed window.", + "readout_selector": "assistant_response", + "tracked": [ + "dislocation", + "pile", + "stress" + ], + "must_be_absent_from_output": true, + "control_group": "paper-v2-pileup-directed-modulation", + "phrasing_id": "triplet-04", + "carrier_id": "carrier-04", + "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.", + "slug": "paper-v2-mod-control-04", + "title": "Neutral carrier control 04", + "user": "Write exactly \"A small lantern stood near the closed window.\" Do not write anything else.", + "condition": "control" + }, + { + "shape": "MODULATION", + "protocol": "directed_modulation", + "domain": "materials", + "description": "Preregistered matched directed-modulation triplet.", + "assistant_prefill": "The paper ticket waited under the glass jar.", + "readout_selector": "assistant_response", + "tracked": [ + "dislocation", + "pile", + "stress" + ], + "must_be_absent_from_output": true, + "control_group": "paper-v2-pileup-directed-modulation", + "phrasing_id": "triplet-05", + "carrier_id": "carrier-05", + "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.", + "slug": "paper-v2-mod-focus-05", + "title": "Directed focus 05", + "user": "Write exactly \"The paper ticket waited under the glass jar.\" Hold the idea of a dislocation pile-up and its stress concentration in mind during the writing task. Do not write anything else.", + "condition": "focus" + }, + { + "shape": "MODULATION", + "protocol": "directed_modulation", + "domain": "materials", + "description": "Preregistered matched directed-modulation triplet.", + "assistant_prefill": "The paper ticket waited under the glass jar.", + "readout_selector": "assistant_response", + "tracked": [ + "dislocation", + "pile", + "stress" + ], + "must_be_absent_from_output": true, + "control_group": "paper-v2-pileup-directed-modulation", + "phrasing_id": "triplet-05", + "carrier_id": "carrier-05", + "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.", + "slug": "paper-v2-mod-suppress-05", + "title": "Directed suppression 05", + "user": "Write exactly \"The paper ticket waited under the glass jar.\" Do not hold the idea of a dislocation pile-up or its stress concentration in mind during the writing task. Do not write anything else.", + "condition": "suppress" + }, + { + "shape": "MODULATION", + "protocol": "directed_modulation", + "domain": "materials", + "description": "Preregistered matched directed-modulation triplet.", + "assistant_prefill": "The paper ticket waited under the glass jar.", + "readout_selector": "assistant_response", + "tracked": [ + "dislocation", + "pile", + "stress" + ], + "must_be_absent_from_output": true, + "control_group": "paper-v2-pileup-directed-modulation", + "phrasing_id": "triplet-05", + "carrier_id": "carrier-05", + "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.", + "slug": "paper-v2-mod-control-05", + "title": "Neutral carrier control 05", + "user": "Write exactly \"The paper ticket waited under the glass jar.\" Do not write anything else.", + "condition": "control" + }, + { + "shape": "MODULATION", + "protocol": "directed_modulation", + "domain": "materials", + "description": "Preregistered matched directed-modulation triplet.", + "assistant_prefill": "A blue scarf hung behind the narrow door.", + "readout_selector": "assistant_response", + "tracked": [ + "dislocation", + "pile", + "stress" + ], + "must_be_absent_from_output": true, + "control_group": "paper-v2-pileup-directed-modulation", + "phrasing_id": "triplet-06", + "carrier_id": "carrier-06", + "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.", + "slug": "paper-v2-mod-focus-06", + "title": "Directed focus 06", + "user": "Write exactly \"A blue scarf hung behind the narrow door.\" Direct your attention to a dislocation pile-up and the stress concentration it creates while writing. Do not write anything else.", + "condition": "focus" + }, + { + "shape": "MODULATION", + "protocol": "directed_modulation", + "domain": "materials", + "description": "Preregistered matched directed-modulation triplet.", + "assistant_prefill": "A blue scarf hung behind the narrow door.", + "readout_selector": "assistant_response", + "tracked": [ + "dislocation", + "pile", + "stress" + ], + "must_be_absent_from_output": true, + "control_group": "paper-v2-pileup-directed-modulation", + "phrasing_id": "triplet-06", + "carrier_id": "carrier-06", + "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.", + "slug": "paper-v2-mod-suppress-06", + "title": "Directed suppression 06", + "user": "Write exactly \"A blue scarf hung behind the narrow door.\" Direct your attention away from a dislocation pile-up and the stress concentration it creates while writing. Do not write anything else.", + "condition": "suppress" + }, + { + "shape": "MODULATION", + "protocol": "directed_modulation", + "domain": "materials", + "description": "Preregistered matched directed-modulation triplet.", + "assistant_prefill": "A blue scarf hung behind the narrow door.", + "readout_selector": "assistant_response", + "tracked": [ + "dislocation", + "pile", + "stress" + ], + "must_be_absent_from_output": true, + "control_group": "paper-v2-pileup-directed-modulation", + "phrasing_id": "triplet-06", + "carrier_id": "carrier-06", + "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.", + "slug": "paper-v2-mod-control-06", + "title": "Neutral carrier control 06", + "user": "Write exactly \"A blue scarf hung behind the narrow door.\" Do not write anything else.", + "condition": "control" + }, + { + "shape": "MODULATION", + "protocol": "directed_modulation", + "domain": "materials", + "description": "Preregistered matched directed-modulation triplet.", + "assistant_prefill": "The brass compass sat beside the blank card.", + "readout_selector": "assistant_response", + "tracked": [ + "dislocation", + "pile", + "stress" + ], + "must_be_absent_from_output": true, + "control_group": "paper-v2-pileup-directed-modulation", + "phrasing_id": "triplet-07", + "carrier_id": "carrier-07", + "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.", + "slug": "paper-v2-mod-focus-07", + "title": "Directed focus 07", + "user": "Write exactly \"The brass compass sat beside the blank card.\" While copying, actively think about a dislocation pile-up and its associated stress concentration. Do not write anything else.", + "condition": "focus" + }, + { + "shape": "MODULATION", + "protocol": "directed_modulation", + "domain": "materials", + "description": "Preregistered matched directed-modulation triplet.", + "assistant_prefill": "The brass compass sat beside the blank card.", + "readout_selector": "assistant_response", + "tracked": [ + "dislocation", + "pile", + "stress" + ], + "must_be_absent_from_output": true, + "control_group": "paper-v2-pileup-directed-modulation", + "phrasing_id": "triplet-07", + "carrier_id": "carrier-07", + "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.", + "slug": "paper-v2-mod-suppress-07", + "title": "Directed suppression 07", + "user": "Write exactly \"The brass compass sat beside the blank card.\" While copying, actively avoid thinking about a dislocation pile-up and its associated stress concentration. Do not write anything else.", + "condition": "suppress" + }, + { + "shape": "MODULATION", + "protocol": "directed_modulation", + "domain": "materials", + "description": "Preregistered matched directed-modulation triplet.", + "assistant_prefill": "The brass compass sat beside the blank card.", + "readout_selector": "assistant_response", + "tracked": [ + "dislocation", + "pile", + "stress" + ], + "must_be_absent_from_output": true, + "control_group": "paper-v2-pileup-directed-modulation", + "phrasing_id": "triplet-07", + "carrier_id": "carrier-07", + "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.", + "slug": "paper-v2-mod-control-07", + "title": "Neutral carrier control 07", + "user": "Write exactly \"The brass compass sat beside the blank card.\" Do not write anything else.", + "condition": "control" + }, + { + "shape": "MODULATION", + "protocol": "directed_modulation", + "domain": "materials", + "description": "Preregistered matched directed-modulation triplet.", + "assistant_prefill": "A white candle stood on the stone shelf.", + "readout_selector": "assistant_response", + "tracked": [ + "dislocation", + "pile", + "stress" + ], + "must_be_absent_from_output": true, + "control_group": "paper-v2-pileup-directed-modulation", + "phrasing_id": "triplet-08", + "carrier_id": "carrier-08", + "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.", + "slug": "paper-v2-mod-focus-08", + "title": "Directed focus 08", + "user": "Write exactly \"A white candle stood on the stone shelf.\" Keep your thoughts centered on a dislocation pile-up and concentrated stress as you produce the sentence. Do not write anything else.", + "condition": "focus" + }, + { + "shape": "MODULATION", + "protocol": "directed_modulation", + "domain": "materials", + "description": "Preregistered matched directed-modulation triplet.", + "assistant_prefill": "A white candle stood on the stone shelf.", + "readout_selector": "assistant_response", + "tracked": [ + "dislocation", + "pile", + "stress" + ], + "must_be_absent_from_output": true, + "control_group": "paper-v2-pileup-directed-modulation", + "phrasing_id": "triplet-08", + "carrier_id": "carrier-08", + "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.", + "slug": "paper-v2-mod-suppress-08", + "title": "Directed suppression 08", + "user": "Write exactly \"A white candle stood on the stone shelf.\" Keep your thoughts from dwelling on a dislocation pile-up or concentrated stress as you produce the sentence. Do not write anything else.", + "condition": "suppress" + }, + { + "shape": "MODULATION", + "protocol": "directed_modulation", + "domain": "materials", + "description": "Preregistered matched directed-modulation triplet.", + "assistant_prefill": "A white candle stood on the stone shelf.", + "readout_selector": "assistant_response", + "tracked": [ + "dislocation", + "pile", + "stress" + ], + "must_be_absent_from_output": true, + "control_group": "paper-v2-pileup-directed-modulation", + "phrasing_id": "triplet-08", + "carrier_id": "carrier-08", + "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.", + "slug": "paper-v2-mod-control-08", + "title": "Neutral carrier control 08", + "user": "Write exactly \"A white candle stood on the stone shelf.\" Do not write anything else.", + "condition": "control" + }, + { + "shape": "MODULATION", + "protocol": "directed_modulation", + "domain": "materials", + "description": "Preregistered matched directed-modulation triplet.", + "assistant_prefill": "The red envelope remained beneath the old clock.", + "readout_selector": "assistant_response", + "tracked": [ + "dislocation", + "pile", + "stress" + ], + "must_be_absent_from_output": true, + "control_group": "paper-v2-pileup-directed-modulation", + "phrasing_id": "triplet-09", + "carrier_id": "carrier-09", + "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.", + "slug": "paper-v2-mod-focus-09", + "title": "Directed focus 09", + "user": "Write exactly \"The red envelope remained beneath the old clock.\" Concentrate on a dislocation pile-up and its stress concentration while you write it. Do not write anything else.", + "condition": "focus" + }, + { + "shape": "MODULATION", + "protocol": "directed_modulation", + "domain": "materials", + "description": "Preregistered matched directed-modulation triplet.", + "assistant_prefill": "The red envelope remained beneath the old clock.", + "readout_selector": "assistant_response", + "tracked": [ + "dislocation", + "pile", + "stress" + ], + "must_be_absent_from_output": true, + "control_group": "paper-v2-pileup-directed-modulation", + "phrasing_id": "triplet-09", + "carrier_id": "carrier-09", + "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.", + "slug": "paper-v2-mod-suppress-09", + "title": "Directed suppression 09", + "user": "Write exactly \"The red envelope remained beneath the old clock.\" Try not to think about a dislocation pile-up or its stress concentration while you write it. Do not write anything else.", + "condition": "suppress" + }, + { + "shape": "MODULATION", + "protocol": "directed_modulation", + "domain": "materials", + "description": "Preregistered matched directed-modulation triplet.", + "assistant_prefill": "The red envelope remained beneath the old clock.", + "readout_selector": "assistant_response", + "tracked": [ + "dislocation", + "pile", + "stress" + ], + "must_be_absent_from_output": true, + "control_group": "paper-v2-pileup-directed-modulation", + "phrasing_id": "triplet-09", + "carrier_id": "carrier-09", + "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.", + "slug": "paper-v2-mod-control-09", + "title": "Neutral carrier control 09", + "user": "Write exactly \"The red envelope remained beneath the old clock.\" Do not write anything else.", + "condition": "control" + }, + { + "shape": "MODULATION", + "protocol": "directed_modulation", + "domain": "materials", + "description": "Preregistered matched directed-modulation triplet.", + "assistant_prefill": "A clay cup waited near the folded map.", + "readout_selector": "assistant_response", + "tracked": [ + "dislocation", + "pile", + "stress" + ], + "must_be_absent_from_output": true, + "control_group": "paper-v2-pileup-directed-modulation", + "phrasing_id": "triplet-10", + "carrier_id": "carrier-10", + "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.", + "slug": "paper-v2-mod-focus-10", + "title": "Directed focus 10", + "user": "Write exactly \"A clay cup waited near the folded map.\" Keep a dislocation pile-up and the stress it concentrates in mind while copying it. Do not write anything else.", + "condition": "focus" + }, + { + "shape": "MODULATION", + "protocol": "directed_modulation", + "domain": "materials", + "description": "Preregistered matched directed-modulation triplet.", + "assistant_prefill": "A clay cup waited near the folded map.", + "readout_selector": "assistant_response", + "tracked": [ + "dislocation", + "pile", + "stress" + ], + "must_be_absent_from_output": true, + "control_group": "paper-v2-pileup-directed-modulation", + "phrasing_id": "triplet-10", + "carrier_id": "carrier-10", + "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.", + "slug": "paper-v2-mod-suppress-10", + "title": "Directed suppression 10", + "user": "Write exactly \"A clay cup waited near the folded map.\" Keep your attention away from a dislocation pile-up and the stress it concentrates while copying it. Do not write anything else.", + "condition": "suppress" + }, + { + "shape": "MODULATION", + "protocol": "directed_modulation", + "domain": "materials", + "description": "Preregistered matched directed-modulation triplet.", + "assistant_prefill": "A clay cup waited near the folded map.", + "readout_selector": "assistant_response", + "tracked": [ + "dislocation", + "pile", + "stress" + ], + "must_be_absent_from_output": true, + "control_group": "paper-v2-pileup-directed-modulation", + "phrasing_id": "triplet-10", + "carrier_id": "carrier-10", + "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.", + "slug": "paper-v2-mod-control-10", + "title": "Neutral carrier control 10", + "user": "Write exactly \"A clay cup waited near the folded map.\" Do not write anything else.", + "condition": "control" + }, + { + "shape": "MODULATION", + "protocol": "directed_modulation", + "domain": "materials", + "description": "Preregistered matched directed-modulation triplet.", + "assistant_prefill": "The black pencil lay across the yellow pad.", + "readout_selector": "assistant_response", + "tracked": [ + "dislocation", + "pile", + "stress" + ], + "must_be_absent_from_output": true, + "control_group": "paper-v2-pileup-directed-modulation", + "phrasing_id": "triplet-11", + "carrier_id": "carrier-11", + "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.", + "slug": "paper-v2-mod-focus-11", + "title": "Directed focus 11", + "user": "Write exactly \"The black pencil lay across the yellow pad.\" Maintain an internal focus on a dislocation pile-up and its local stress concentration as you write. Do not write anything else.", + "condition": "focus" + }, + { + "shape": "MODULATION", + "protocol": "directed_modulation", + "domain": "materials", + "description": "Preregistered matched directed-modulation triplet.", + "assistant_prefill": "The black pencil lay across the yellow pad.", + "readout_selector": "assistant_response", + "tracked": [ + "dislocation", + "pile", + "stress" + ], + "must_be_absent_from_output": true, + "control_group": "paper-v2-pileup-directed-modulation", + "phrasing_id": "triplet-11", + "carrier_id": "carrier-11", + "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.", + "slug": "paper-v2-mod-suppress-11", + "title": "Directed suppression 11", + "user": "Write exactly \"The black pencil lay across the yellow pad.\" Avoid internally focusing on a dislocation pile-up or its local stress concentration as you write. Do not write anything else.", + "condition": "suppress" + }, + { + "shape": "MODULATION", + "protocol": "directed_modulation", + "domain": "materials", + "description": "Preregistered matched directed-modulation triplet.", + "assistant_prefill": "The black pencil lay across the yellow pad.", + "readout_selector": "assistant_response", + "tracked": [ + "dislocation", + "pile", + "stress" + ], + "must_be_absent_from_output": true, + "control_group": "paper-v2-pileup-directed-modulation", + "phrasing_id": "triplet-11", + "carrier_id": "carrier-11", + "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.", + "slug": "paper-v2-mod-control-11", + "title": "Neutral carrier control 11", + "user": "Write exactly \"The black pencil lay across the yellow pad.\" Do not write anything else.", + "condition": "control" + }, + { + "shape": "MODULATION", + "protocol": "directed_modulation", + "domain": "materials", + "description": "Preregistered matched directed-modulation triplet.", + "assistant_prefill": "A linen pouch rested beside the copper tray.", + "readout_selector": "assistant_response", + "tracked": [ + "dislocation", + "pile", + "stress" + ], + "must_be_absent_from_output": true, + "control_group": "paper-v2-pileup-directed-modulation", + "phrasing_id": "triplet-12", + "carrier_id": "carrier-12", + "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.", + "slug": "paper-v2-mod-focus-12", + "title": "Directed focus 12", + "user": "Write exactly \"A linen pouch rested beside the copper tray.\" Mentally attend to a dislocation pile-up and the concentrated stress near its head while reproducing it. Do not write anything else.", + "condition": "focus" + }, + { + "shape": "MODULATION", + "protocol": "directed_modulation", + "domain": "materials", + "description": "Preregistered matched directed-modulation triplet.", + "assistant_prefill": "A linen pouch rested beside the copper tray.", + "readout_selector": "assistant_response", + "tracked": [ + "dislocation", + "pile", + "stress" + ], + "must_be_absent_from_output": true, + "control_group": "paper-v2-pileup-directed-modulation", + "phrasing_id": "triplet-12", + "carrier_id": "carrier-12", + "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.", + "slug": "paper-v2-mod-suppress-12", + "title": "Directed suppression 12", + "user": "Write exactly \"A linen pouch rested beside the copper tray.\" Mentally suppress thoughts of a dislocation pile-up and the concentrated stress near its head while reproducing it. Do not write anything else.", + "condition": "suppress" + }, + { + "shape": "MODULATION", + "protocol": "directed_modulation", + "domain": "materials", + "description": "Preregistered matched directed-modulation triplet.", + "assistant_prefill": "A linen pouch rested beside the copper tray.", + "readout_selector": "assistant_response", + "tracked": [ + "dislocation", + "pile", + "stress" + ], + "must_be_absent_from_output": true, + "control_group": "paper-v2-pileup-directed-modulation", + "phrasing_id": "triplet-12", + "carrier_id": "carrier-12", + "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.", + "slug": "paper-v2-mod-control-12", + "title": "Neutral carrier control 12", + "user": "Write exactly \"A linen pouch rested beside the copper tray.\" Do not write anything else.", + "condition": "control" + }, + { + "shape": "MODULATION", + "protocol": "directed_modulation", + "domain": "materials", + "description": "Preregistered matched directed-modulation triplet.", + "assistant_prefill": "The round mirror leaned against the gray box.", + "readout_selector": "assistant_response", + "tracked": [ + "dislocation", + "pile", + "stress" + ], + "must_be_absent_from_output": true, + "control_group": "paper-v2-pileup-directed-modulation", + "phrasing_id": "triplet-13", + "carrier_id": "carrier-13", + "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.", + "slug": "paper-v2-mod-focus-13", + "title": "Directed focus 13", + "user": "Write exactly \"The round mirror leaned against the gray box.\" Hold the idea of a dislocation pile-up and its stress concentration in mind during the writing task. Do not write anything else.", + "condition": "focus" + }, + { + "shape": "MODULATION", + "protocol": "directed_modulation", + "domain": "materials", + "description": "Preregistered matched directed-modulation triplet.", + "assistant_prefill": "The round mirror leaned against the gray box.", + "readout_selector": "assistant_response", + "tracked": [ + "dislocation", + "pile", + "stress" + ], + "must_be_absent_from_output": true, + "control_group": "paper-v2-pileup-directed-modulation", + "phrasing_id": "triplet-13", + "carrier_id": "carrier-13", + "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.", + "slug": "paper-v2-mod-suppress-13", + "title": "Directed suppression 13", + "user": "Write exactly \"The round mirror leaned against the gray box.\" Do not hold the idea of a dislocation pile-up or its stress concentration in mind during the writing task. Do not write anything else.", + "condition": "suppress" + }, + { + "shape": "MODULATION", + "protocol": "directed_modulation", + "domain": "materials", + "description": "Preregistered matched directed-modulation triplet.", + "assistant_prefill": "The round mirror leaned against the gray box.", + "readout_selector": "assistant_response", + "tracked": [ + "dislocation", + "pile", + "stress" + ], + "must_be_absent_from_output": true, + "control_group": "paper-v2-pileup-directed-modulation", + "phrasing_id": "triplet-13", + "carrier_id": "carrier-13", + "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.", + "slug": "paper-v2-mod-control-13", + "title": "Neutral carrier control 13", + "user": "Write exactly \"The round mirror leaned against the gray box.\" Do not write anything else.", + "condition": "control" + }, + { + "shape": "MODULATION", + "protocol": "directed_modulation", + "domain": "materials", + "description": "Preregistered matched directed-modulation triplet.", + "assistant_prefill": "A wooden token sat inside the shallow dish.", + "readout_selector": "assistant_response", + "tracked": [ + "dislocation", + "pile", + "stress" + ], + "must_be_absent_from_output": true, + "control_group": "paper-v2-pileup-directed-modulation", + "phrasing_id": "triplet-14", + "carrier_id": "carrier-14", + "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.", + "slug": "paper-v2-mod-focus-14", + "title": "Directed focus 14", + "user": "Write exactly \"A wooden token sat inside the shallow dish.\" Direct your attention to a dislocation pile-up and the stress concentration it creates while writing. Do not write anything else.", + "condition": "focus" + }, + { + "shape": "MODULATION", + "protocol": "directed_modulation", + "domain": "materials", + "description": "Preregistered matched directed-modulation triplet.", + "assistant_prefill": "A wooden token sat inside the shallow dish.", + "readout_selector": "assistant_response", + "tracked": [ + "dislocation", + "pile", + "stress" + ], + "must_be_absent_from_output": true, + "control_group": "paper-v2-pileup-directed-modulation", + "phrasing_id": "triplet-14", + "carrier_id": "carrier-14", + "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.", + "slug": "paper-v2-mod-suppress-14", + "title": "Directed suppression 14", + "user": "Write exactly \"A wooden token sat inside the shallow dish.\" Direct your attention away from a dislocation pile-up and the stress concentration it creates while writing. Do not write anything else.", + "condition": "suppress" + }, + { + "shape": "MODULATION", + "protocol": "directed_modulation", + "domain": "materials", + "description": "Preregistered matched directed-modulation triplet.", + "assistant_prefill": "A wooden token sat inside the shallow dish.", + "readout_selector": "assistant_response", + "tracked": [ + "dislocation", + "pile", + "stress" + ], + "must_be_absent_from_output": true, + "control_group": "paper-v2-pileup-directed-modulation", + "phrasing_id": "triplet-14", + "carrier_id": "carrier-14", + "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.", + "slug": "paper-v2-mod-control-14", + "title": "Neutral carrier control 14", + "user": "Write exactly \"A wooden token sat inside the shallow dish.\" Do not write anything else.", + "condition": "control" + }, + { + "shape": "MODULATION", + "protocol": "directed_modulation", + "domain": "materials", + "description": "Preregistered matched directed-modulation triplet.", + "assistant_prefill": "The violet ribbon lay below the framed sketch.", + "readout_selector": "assistant_response", + "tracked": [ + "dislocation", + "pile", + "stress" + ], + "must_be_absent_from_output": true, + "control_group": "paper-v2-pileup-directed-modulation", + "phrasing_id": "triplet-15", + "carrier_id": "carrier-15", + "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.", + "slug": "paper-v2-mod-focus-15", + "title": "Directed focus 15", + "user": "Write exactly \"The violet ribbon lay below the framed sketch.\" While copying, actively think about a dislocation pile-up and its associated stress concentration. Do not write anything else.", + "condition": "focus" + }, + { + "shape": "MODULATION", + "protocol": "directed_modulation", + "domain": "materials", + "description": "Preregistered matched directed-modulation triplet.", + "assistant_prefill": "The violet ribbon lay below the framed sketch.", + "readout_selector": "assistant_response", + "tracked": [ + "dislocation", + "pile", + "stress" + ], + "must_be_absent_from_output": true, + "control_group": "paper-v2-pileup-directed-modulation", + "phrasing_id": "triplet-15", + "carrier_id": "carrier-15", + "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.", + "slug": "paper-v2-mod-suppress-15", + "title": "Directed suppression 15", + "user": "Write exactly \"The violet ribbon lay below the framed sketch.\" While copying, actively avoid thinking about a dislocation pile-up and its associated stress concentration. Do not write anything else.", + "condition": "suppress" + }, + { + "shape": "MODULATION", + "protocol": "directed_modulation", + "domain": "materials", + "description": "Preregistered matched directed-modulation triplet.", + "assistant_prefill": "The violet ribbon lay below the framed sketch.", + "readout_selector": "assistant_response", + "tracked": [ + "dislocation", + "pile", + "stress" + ], + "must_be_absent_from_output": true, + "control_group": "paper-v2-pileup-directed-modulation", + "phrasing_id": "triplet-15", + "carrier_id": "carrier-15", + "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.", + "slug": "paper-v2-mod-control-15", + "title": "Neutral carrier control 15", + "user": "Write exactly \"The violet ribbon lay below the framed sketch.\" Do not write anything else.", + "condition": "control" + }, + { + "shape": "MODULATION", + "protocol": "directed_modulation", + "domain": "materials", + "description": "Preregistered matched directed-modulation triplet.", + "assistant_prefill": "A clear bottle stood next to the woven basket.", + "readout_selector": "assistant_response", + "tracked": [ + "dislocation", + "pile", + "stress" + ], + "must_be_absent_from_output": true, + "control_group": "paper-v2-pileup-directed-modulation", + "phrasing_id": "triplet-16", + "carrier_id": "carrier-16", + "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.", + "slug": "paper-v2-mod-focus-16", + "title": "Directed focus 16", + "user": "Write exactly \"A clear bottle stood next to the woven basket.\" Keep your thoughts centered on a dislocation pile-up and concentrated stress as you produce the sentence. Do not write anything else.", + "condition": "focus" + }, + { + "shape": "MODULATION", + "protocol": "directed_modulation", + "domain": "materials", + "description": "Preregistered matched directed-modulation triplet.", + "assistant_prefill": "A clear bottle stood next to the woven basket.", + "readout_selector": "assistant_response", + "tracked": [ + "dislocation", + "pile", + "stress" + ], + "must_be_absent_from_output": true, + "control_group": "paper-v2-pileup-directed-modulation", + "phrasing_id": "triplet-16", + "carrier_id": "carrier-16", + "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.", + "slug": "paper-v2-mod-suppress-16", + "title": "Directed suppression 16", + "user": "Write exactly \"A clear bottle stood next to the woven basket.\" Keep your thoughts from dwelling on a dislocation pile-up or concentrated stress as you produce the sentence. Do not write anything else.", + "condition": "suppress" + }, + { + "shape": "MODULATION", + "protocol": "directed_modulation", + "domain": "materials", + "description": "Preregistered matched directed-modulation triplet.", + "assistant_prefill": "A clear bottle stood next to the woven basket.", + "readout_selector": "assistant_response", + "tracked": [ + "dislocation", + "pile", + "stress" + ], + "must_be_absent_from_output": true, + "control_group": "paper-v2-pileup-directed-modulation", + "phrasing_id": "triplet-16", + "carrier_id": "carrier-16", + "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.", + "slug": "paper-v2-mod-control-16", + "title": "Neutral carrier control 16", + "user": "Write exactly \"A clear bottle stood next to the woven basket.\" Do not write anything else.", + "condition": "control" + }, + { + "shape": "MODULATION", + "protocol": "directed_modulation", + "domain": "materials", + "description": "Preregistered matched directed-modulation triplet.", + "assistant_prefill": "The bronze key rested above the sealed letter.", + "readout_selector": "assistant_response", + "tracked": [ + "dislocation", + "pile", + "stress" + ], + "must_be_absent_from_output": true, + "control_group": "paper-v2-pileup-directed-modulation", + "phrasing_id": "triplet-17", + "carrier_id": "carrier-17", + "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.", + "slug": "paper-v2-mod-focus-17", + "title": "Directed focus 17", + "user": "Write exactly \"The bronze key rested above the sealed letter.\" Concentrate on a dislocation pile-up and its stress concentration while you write it. Do not write anything else.", + "condition": "focus" + }, + { + "shape": "MODULATION", + "protocol": "directed_modulation", + "domain": "materials", + "description": "Preregistered matched directed-modulation triplet.", + "assistant_prefill": "The bronze key rested above the sealed letter.", + "readout_selector": "assistant_response", + "tracked": [ + "dislocation", + "pile", + "stress" + ], + "must_be_absent_from_output": true, + "control_group": "paper-v2-pileup-directed-modulation", + "phrasing_id": "triplet-17", + "carrier_id": "carrier-17", + "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.", + "slug": "paper-v2-mod-suppress-17", + "title": "Directed suppression 17", + "user": "Write exactly \"The bronze key rested above the sealed letter.\" Try not to think about a dislocation pile-up or its stress concentration while you write it. Do not write anything else.", + "condition": "suppress" + }, + { + "shape": "MODULATION", + "protocol": "directed_modulation", + "domain": "materials", + "description": "Preregistered matched directed-modulation triplet.", + "assistant_prefill": "The bronze key rested above the sealed letter.", + "readout_selector": "assistant_response", + "tracked": [ + "dislocation", + "pile", + "stress" + ], + "must_be_absent_from_output": true, + "control_group": "paper-v2-pileup-directed-modulation", + "phrasing_id": "triplet-17", + "carrier_id": "carrier-17", + "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.", + "slug": "paper-v2-mod-control-17", + "title": "Neutral carrier control 17", + "user": "Write exactly \"The bronze key rested above the sealed letter.\" Do not write anything else.", + "condition": "control" + }, + { + "shape": "MODULATION", + "protocol": "directed_modulation", + "domain": "materials", + "description": "Preregistered matched directed-modulation triplet.", + "assistant_prefill": "A cotton glove lay beside the empty vase.", + "readout_selector": "assistant_response", + "tracked": [ + "dislocation", + "pile", + "stress" + ], + "must_be_absent_from_output": true, + "control_group": "paper-v2-pileup-directed-modulation", + "phrasing_id": "triplet-18", + "carrier_id": "carrier-18", + "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.", + "slug": "paper-v2-mod-focus-18", + "title": "Directed focus 18", + "user": "Write exactly \"A cotton glove lay beside the empty vase.\" Keep a dislocation pile-up and the stress it concentrates in mind while copying it. Do not write anything else.", + "condition": "focus" + }, + { + "shape": "MODULATION", + "protocol": "directed_modulation", + "domain": "materials", + "description": "Preregistered matched directed-modulation triplet.", + "assistant_prefill": "A cotton glove lay beside the empty vase.", + "readout_selector": "assistant_response", + "tracked": [ + "dislocation", + "pile", + "stress" + ], + "must_be_absent_from_output": true, + "control_group": "paper-v2-pileup-directed-modulation", + "phrasing_id": "triplet-18", + "carrier_id": "carrier-18", + "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.", + "slug": "paper-v2-mod-suppress-18", + "title": "Directed suppression 18", + "user": "Write exactly \"A cotton glove lay beside the empty vase.\" Keep your attention away from a dislocation pile-up and the stress it concentrates while copying it. Do not write anything else.", + "condition": "suppress" + }, + { + "shape": "MODULATION", + "protocol": "directed_modulation", + "domain": "materials", + "description": "Preregistered matched directed-modulation triplet.", + "assistant_prefill": "A cotton glove lay beside the empty vase.", + "readout_selector": "assistant_response", + "tracked": [ + "dislocation", + "pile", + "stress" + ], + "must_be_absent_from_output": true, + "control_group": "paper-v2-pileup-directed-modulation", + "phrasing_id": "triplet-18", + "carrier_id": "carrier-18", + "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.", + "slug": "paper-v2-mod-control-18", + "title": "Neutral carrier control 18", + "user": "Write exactly \"A cotton glove lay beside the empty vase.\" Do not write anything else.", + "condition": "control" + }, + { + "shape": "MODULATION", + "protocol": "directed_modulation", + "domain": "materials", + "description": "Preregistered matched directed-modulation triplet.", + "assistant_prefill": "The narrow ruler remained under the green book.", + "readout_selector": "assistant_response", + "tracked": [ + "dislocation", + "pile", + "stress" + ], + "must_be_absent_from_output": true, + "control_group": "paper-v2-pileup-directed-modulation", + "phrasing_id": "triplet-19", + "carrier_id": "carrier-19", + "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.", + "slug": "paper-v2-mod-focus-19", + "title": "Directed focus 19", + "user": "Write exactly \"The narrow ruler remained under the green book.\" Maintain an internal focus on a dislocation pile-up and its local stress concentration as you write. Do not write anything else.", + "condition": "focus" + }, + { + "shape": "MODULATION", + "protocol": "directed_modulation", + "domain": "materials", + "description": "Preregistered matched directed-modulation triplet.", + "assistant_prefill": "The narrow ruler remained under the green book.", + "readout_selector": "assistant_response", + "tracked": [ + "dislocation", + "pile", + "stress" + ], + "must_be_absent_from_output": true, + "control_group": "paper-v2-pileup-directed-modulation", + "phrasing_id": "triplet-19", + "carrier_id": "carrier-19", + "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.", + "slug": "paper-v2-mod-suppress-19", + "title": "Directed suppression 19", + "user": "Write exactly \"The narrow ruler remained under the green book.\" Avoid internally focusing on a dislocation pile-up or its local stress concentration as you write. Do not write anything else.", + "condition": "suppress" + }, + { + "shape": "MODULATION", + "protocol": "directed_modulation", + "domain": "materials", + "description": "Preregistered matched directed-modulation triplet.", + "assistant_prefill": "The narrow ruler remained under the green book.", + "readout_selector": "assistant_response", + "tracked": [ + "dislocation", + "pile", + "stress" + ], + "must_be_absent_from_output": true, + "control_group": "paper-v2-pileup-directed-modulation", + "phrasing_id": "triplet-19", + "carrier_id": "carrier-19", + "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.", + "slug": "paper-v2-mod-control-19", + "title": "Neutral carrier control 19", + "user": "Write exactly \"The narrow ruler remained under the green book.\" Do not write anything else.", + "condition": "control" + }, + { + "shape": "MODULATION", + "protocol": "directed_modulation", + "domain": "materials", + "description": "Preregistered matched directed-modulation triplet.", + "assistant_prefill": "A ceramic bird stood near the folded towel.", + "readout_selector": "assistant_response", + "tracked": [ + "dislocation", + "pile", + "stress" + ], + "must_be_absent_from_output": true, + "control_group": "paper-v2-pileup-directed-modulation", + "phrasing_id": "triplet-20", + "carrier_id": "carrier-20", + "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.", + "slug": "paper-v2-mod-focus-20", + "title": "Directed focus 20", + "user": "Write exactly \"A ceramic bird stood near the folded towel.\" Mentally attend to a dislocation pile-up and the concentrated stress near its head while reproducing it. Do not write anything else.", + "condition": "focus" + }, + { + "shape": "MODULATION", + "protocol": "directed_modulation", + "domain": "materials", + "description": "Preregistered matched directed-modulation triplet.", + "assistant_prefill": "A ceramic bird stood near the folded towel.", + "readout_selector": "assistant_response", + "tracked": [ + "dislocation", + "pile", + "stress" + ], + "must_be_absent_from_output": true, + "control_group": "paper-v2-pileup-directed-modulation", + "phrasing_id": "triplet-20", + "carrier_id": "carrier-20", + "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.", + "slug": "paper-v2-mod-suppress-20", + "title": "Directed suppression 20", + "user": "Write exactly \"A ceramic bird stood near the folded towel.\" Mentally suppress thoughts of a dislocation pile-up and the concentrated stress near its head while reproducing it. Do not write anything else.", + "condition": "suppress" + }, + { + "shape": "MODULATION", + "protocol": "directed_modulation", + "domain": "materials", + "description": "Preregistered matched directed-modulation triplet.", + "assistant_prefill": "A ceramic bird stood near the folded towel.", + "readout_selector": "assistant_response", + "tracked": [ + "dislocation", + "pile", + "stress" + ], + "must_be_absent_from_output": true, + "control_group": "paper-v2-pileup-directed-modulation", + "phrasing_id": "triplet-20", + "carrier_id": "carrier-20", + "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.", + "slug": "paper-v2-mod-control-20", + "title": "Neutral carrier control 20", + "user": "Write exactly \"A ceramic bird stood near the folded towel.\" Do not write anything else.", + "condition": "control" + }, + { + "shape": "MODULATION", + "protocol": "directed_modulation", + "domain": "materials", + "description": "Preregistered matched directed-modulation triplet.", + "assistant_prefill": "The orange marker lay inside the metal tin.", + "readout_selector": "assistant_response", + "tracked": [ + "dislocation", + "pile", + "stress" + ], + "must_be_absent_from_output": true, + "control_group": "paper-v2-pileup-directed-modulation", + "phrasing_id": "triplet-21", + "carrier_id": "carrier-21", + "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.", + "slug": "paper-v2-mod-focus-21", + "title": "Directed focus 21", + "user": "Write exactly \"The orange marker lay inside the metal tin.\" Hold the idea of a dislocation pile-up and its stress concentration in mind during the writing task. Do not write anything else.", + "condition": "focus" + }, + { + "shape": "MODULATION", + "protocol": "directed_modulation", + "domain": "materials", + "description": "Preregistered matched directed-modulation triplet.", + "assistant_prefill": "The orange marker lay inside the metal tin.", + "readout_selector": "assistant_response", + "tracked": [ + "dislocation", + "pile", + "stress" + ], + "must_be_absent_from_output": true, + "control_group": "paper-v2-pileup-directed-modulation", + "phrasing_id": "triplet-21", + "carrier_id": "carrier-21", + "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.", + "slug": "paper-v2-mod-suppress-21", + "title": "Directed suppression 21", + "user": "Write exactly \"The orange marker lay inside the metal tin.\" Do not hold the idea of a dislocation pile-up or its stress concentration in mind during the writing task. Do not write anything else.", + "condition": "suppress" + }, + { + "shape": "MODULATION", + "protocol": "directed_modulation", + "domain": "materials", + "description": "Preregistered matched directed-modulation triplet.", + "assistant_prefill": "The orange marker lay inside the metal tin.", + "readout_selector": "assistant_response", + "tracked": [ + "dislocation", + "pile", + "stress" + ], + "must_be_absent_from_output": true, + "control_group": "paper-v2-pileup-directed-modulation", + "phrasing_id": "triplet-21", + "carrier_id": "carrier-21", + "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.", + "slug": "paper-v2-mod-control-21", + "title": "Neutral carrier control 21", + "user": "Write exactly \"The orange marker lay inside the metal tin.\" Do not write anything else.", + "condition": "control" + }, + { + "shape": "MODULATION", + "protocol": "directed_modulation", + "domain": "materials", + "description": "Preregistered matched directed-modulation triplet.", + "assistant_prefill": "A glass bead rested beside the paper label.", + "readout_selector": "assistant_response", + "tracked": [ + "dislocation", + "pile", + "stress" + ], + "must_be_absent_from_output": true, + "control_group": "paper-v2-pileup-directed-modulation", + "phrasing_id": "triplet-22", + "carrier_id": "carrier-22", + "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.", + "slug": "paper-v2-mod-focus-22", + "title": "Directed focus 22", + "user": "Write exactly \"A glass bead rested beside the paper label.\" Direct your attention to a dislocation pile-up and the stress concentration it creates while writing. Do not write anything else.", + "condition": "focus" + }, + { + "shape": "MODULATION", + "protocol": "directed_modulation", + "domain": "materials", + "description": "Preregistered matched directed-modulation triplet.", + "assistant_prefill": "A glass bead rested beside the paper label.", + "readout_selector": "assistant_response", + "tracked": [ + "dislocation", + "pile", + "stress" + ], + "must_be_absent_from_output": true, + "control_group": "paper-v2-pileup-directed-modulation", + "phrasing_id": "triplet-22", + "carrier_id": "carrier-22", + "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.", + "slug": "paper-v2-mod-suppress-22", + "title": "Directed suppression 22", + "user": "Write exactly \"A glass bead rested beside the paper label.\" Direct your attention away from a dislocation pile-up and the stress concentration it creates while writing. Do not write anything else.", + "condition": "suppress" + }, + { + "shape": "MODULATION", + "protocol": "directed_modulation", + "domain": "materials", + "description": "Preregistered matched directed-modulation triplet.", + "assistant_prefill": "A glass bead rested beside the paper label.", + "readout_selector": "assistant_response", + "tracked": [ + "dislocation", + "pile", + "stress" + ], + "must_be_absent_from_output": true, + "control_group": "paper-v2-pileup-directed-modulation", + "phrasing_id": "triplet-22", + "carrier_id": "carrier-22", + "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.", + "slug": "paper-v2-mod-control-22", + "title": "Neutral carrier control 22", + "user": "Write exactly \"A glass bead rested beside the paper label.\" Do not write anything else.", + "condition": "control" + }, + { + "shape": "MODULATION", + "protocol": "directed_modulation", + "domain": "materials", + "description": "Preregistered matched directed-modulation triplet.", + "assistant_prefill": "The brown wallet waited below the desk lamp.", + "readout_selector": "assistant_response", + "tracked": [ + "dislocation", + "pile", + "stress" + ], + "must_be_absent_from_output": true, + "control_group": "paper-v2-pileup-directed-modulation", + "phrasing_id": "triplet-23", + "carrier_id": "carrier-23", + "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.", + "slug": "paper-v2-mod-focus-23", + "title": "Directed focus 23", + "user": "Write exactly \"The brown wallet waited below the desk lamp.\" While copying, actively think about a dislocation pile-up and its associated stress concentration. Do not write anything else.", + "condition": "focus" + }, + { + "shape": "MODULATION", + "protocol": "directed_modulation", + "domain": "materials", + "description": "Preregistered matched directed-modulation triplet.", + "assistant_prefill": "The brown wallet waited below the desk lamp.", + "readout_selector": "assistant_response", + "tracked": [ + "dislocation", + "pile", + "stress" + ], + "must_be_absent_from_output": true, + "control_group": "paper-v2-pileup-directed-modulation", + "phrasing_id": "triplet-23", + "carrier_id": "carrier-23", + "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.", + "slug": "paper-v2-mod-suppress-23", + "title": "Directed suppression 23", + "user": "Write exactly \"The brown wallet waited below the desk lamp.\" While copying, actively avoid thinking about a dislocation pile-up and its associated stress concentration. Do not write anything else.", + "condition": "suppress" + }, + { + "shape": "MODULATION", + "protocol": "directed_modulation", + "domain": "materials", + "description": "Preregistered matched directed-modulation triplet.", + "assistant_prefill": "The brown wallet waited below the desk lamp.", + "readout_selector": "assistant_response", + "tracked": [ + "dislocation", + "pile", + "stress" + ], + "must_be_absent_from_output": true, + "control_group": "paper-v2-pileup-directed-modulation", + "phrasing_id": "triplet-23", + "carrier_id": "carrier-23", + "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.", + "slug": "paper-v2-mod-control-23", + "title": "Neutral carrier control 23", + "user": "Write exactly \"The brown wallet waited below the desk lamp.\" Do not write anything else.", + "condition": "control" + }, + { + "shape": "MODULATION", + "protocol": "directed_modulation", + "domain": "materials", + "description": "Preregistered matched directed-modulation triplet.", + "assistant_prefill": "A steel clip lay across the plain postcard.", + "readout_selector": "assistant_response", + "tracked": [ + "dislocation", + "pile", + "stress" + ], + "must_be_absent_from_output": true, + "control_group": "paper-v2-pileup-directed-modulation", + "phrasing_id": "triplet-24", + "carrier_id": "carrier-24", + "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.", + "slug": "paper-v2-mod-focus-24", + "title": "Directed focus 24", + "user": "Write exactly \"A steel clip lay across the plain postcard.\" Keep your thoughts centered on a dislocation pile-up and concentrated stress as you produce the sentence. Do not write anything else.", + "condition": "focus" + }, + { + "shape": "MODULATION", + "protocol": "directed_modulation", + "domain": "materials", + "description": "Preregistered matched directed-modulation triplet.", + "assistant_prefill": "A steel clip lay across the plain postcard.", + "readout_selector": "assistant_response", + "tracked": [ + "dislocation", + "pile", + "stress" + ], + "must_be_absent_from_output": true, + "control_group": "paper-v2-pileup-directed-modulation", + "phrasing_id": "triplet-24", + "carrier_id": "carrier-24", + "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.", + "slug": "paper-v2-mod-suppress-24", + "title": "Directed suppression 24", + "user": "Write exactly \"A steel clip lay across the plain postcard.\" Keep your thoughts from dwelling on a dislocation pile-up or concentrated stress as you produce the sentence. Do not write anything else.", + "condition": "suppress" + }, + { + "shape": "MODULATION", + "protocol": "directed_modulation", + "domain": "materials", + "description": "Preregistered matched directed-modulation triplet.", + "assistant_prefill": "A steel clip lay across the plain postcard.", + "readout_selector": "assistant_response", + "tracked": [ + "dislocation", + "pile", + "stress" + ], + "must_be_absent_from_output": true, + "control_group": "paper-v2-pileup-directed-modulation", + "phrasing_id": "triplet-24", + "carrier_id": "carrier-24", + "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.", + "slug": "paper-v2-mod-control-24", + "title": "Neutral carrier control 24", + "user": "Write exactly \"A steel clip lay across the plain postcard.\" Do not write anything else.", + "condition": "control" + } + ] +} diff --git a/jlens_materials/prompts/materials-qualitative-pack.json b/jlens_materials/prompts/materials-qualitative-pack.json new file mode 100644 index 0000000000000000000000000000000000000000..45742e83009fa43018e154aac45a07bb75168321 --- /dev/null +++ b/jlens_materials/prompts/materials-qualitative-pack.json @@ -0,0 +1,228 @@ +{ + "description": "Curated materials-science case studies for close qualitative inspection. These items exercise fixed score positions and controls, but the set is intentionally too small for paper-scale quantitative claims.", + "prompts": [ + { + "slug": "materials-hall-petch", + "shape": "MULTIHOP", + "protocol": "lens_eval", + "domain": "materials", + "title": "Grain refinement to yield strength", + "description": "Tests whether the named strengthening law surfaces before a one-word comparative answer.", + "text": "Two otherwise identical polycrystalline samples have average crystallite diameters of 50 micrometers and 5 micrometers. In the finer sample, interfaces interrupt carrier motion much more frequently. Its yield stress should therefore be", + "readout_selector": "before_answer", + "tracked": ["Hall", "Petch", "strengthening"], + "answer": "higher", + "must_be_absent_from_input": true, + "must_be_absent_from_output": true, + "note": "Inspect whether Hall/Petch rises before the answer, whether onset is sustained in the fixed band, and whether its J-lens rank improves materially over the matched logit-lens rank." + }, + { + "slug": "materials-orowan-spacing", + "shape": "MULTIHOP", + "protocol": "lens_eval", + "domain": "materials", + "title": "Obstacle spacing to bypass stress", + "description": "A particle-spacing change should evoke the bypass mechanism before the direction of the stress change.", + "text": "A metal contains a fixed volume fraction of hard, non-shearable nanoscale particles. When their center-to-center spacing is reduced, lattice line defects must bow more sharply between them. The stress required for passage should", + "readout_selector": "before_answer", + "tracked": ["Orowan", "bowing", "precipitation", "strengthening"], + "answer": "increase", + "acceptable_answers": ["rise"], + "must_be_absent_from_input": true, + "must_be_absent_from_output": true, + "note": "Look for Orowan as the specific latent mechanism rather than only generic strengthening. Compare it with the Hall-Petch case to test mechanism specificity." + }, + { + "slug": "materials-martensite-quench", + "shape": "MULTIHOP", + "protocol": "lens_eval", + "domain": "materials", + "title": "Rapid quench to martensite", + "description": "Tests whether transformation-mechanism concepts appear before the product name.", + "text": "Austenitic steel is cooled so rapidly that long-range atomic redistribution is suppressed. The product is hard and needle-like, and its body-centered unit cell is elongated so that c divided by a is not equal to one. Its crystal symmetry is", + "readout_selector": "before_answer", + "tracked": ["martensite", "shear", "Bain"], + "answer": "tetragonal", + "must_be_absent_from_input": true, + "must_be_absent_from_output": true, + "note": "The answer is the lattice symmetry; shear and Bain encode the transformation mechanism. Gemma tokenizes martensite into multiple pieces, so the report should explicitly list that exact phase name as dropped rather than silently approximating it." + }, + { + "slug": "materials-schmid-slip", + "shape": "MULTIHOP", + "protocol": "lens_eval", + "domain": "materials", + "title": "Resolved stress to slip", + "description": "An orientation-factor calculation should evoke the named criterion before the deformation event.", + "text": "A single crystal under uniaxial load has cos(phi) = 0.8 and cos(lambda) = 0.5. Multiplying these orientation factors by the applied stress gives 120 MPa, above the critical resolved value of 80 MPa. The crystal will begin to", + "readout_selector": "before_answer", + "tracked": ["Schmid", "dislocation"], + "answer": "slip", + "must_be_absent_from_input": true, + "must_be_absent_from_output": true, + "note": "Check whether Schmid appears as a law-level concept and whether dislocation appears as the physical carrier before the clean one-token answer." + }, + { + "slug": "materials-nabarro-herring", + "shape": "MULTIHOP", + "protocol": "lens_eval", + "domain": "materials", + "title": "High-temperature deformation mechanism", + "description": "The transport path and loading regime imply a named creep mechanism before the transport-process answer.", + "text": "A fine-grained polycrystal is held near its melting temperature under low stress. Atoms migrate through grain interiors from compressed boundaries toward tensile boundaries, producing slow shape change. The dominant transport process is", + "readout_selector": "before_answer", + "tracked": ["creep", "Nabarro", "Herring"], + "answer": "diffusion", + "must_be_absent_from_input": true, + "must_be_absent_from_output": true, + "note": "Compare generic creep with the more specific Nabarro concept. If only creep surfaces, the representation may be scientifically broad but not mechanism-specific." + }, + { + "slug": "materials-screw-character", + "shape": "MULTIHOP", + "protocol": "lens_eval", + "domain": "materials", + "title": "Line-defect character", + "description": "Geometric information should evoke the standard defect vocabulary before the character label.", + "text": "For a line defect in a crystal, the displacement vector associated with the lattice mismatch is parallel to the defect's line direction. Its character is", + "readout_selector": "before_answer", + "tracked": ["Burgers", "dislocation"], + "answer": "screw", + "must_be_absent_from_input": true, + "must_be_absent_from_output": true, + "note": "This separates recovery of the formal descriptor Burgers from the generic object class dislocation. Inspect their relative onset order rather than only their best ranks." + }, + { + "slug": "materials-fcc-extinctions", + "shape": "RECOGNITION", + "protocol": "lens_eval", + "domain": "materials", + "title": "Diffraction selection-rule recognition", + "description": "A raw reflection rule must be mapped to a crystal-structure label.", + "text": "A powder diffraction pattern contains reflections only when h, k, and l are all odd or all even; mixed-parity reflections are systematically absent. The corresponding Bravais lattice is", + "readout_selector": "before_answer", + "tracked": ["cubic", "extinction", "close-packed"], + "answer": "FCC", + "acceptable_answers": ["face"], + "must_be_absent_from_input": true, + "must_be_absent_from_output": true, + "note": "This is a recognition test rather than a prose association. Check that cubic rises from the extinction rule and is not merely present at the final layer." + }, + { + "slug": "materials-ductile-fractography", + "shape": "ASSOCIATION", + "protocol": "lens_eval", + "domain": "materials", + "title": "Unnamed ductile-fracture vignette", + "description": "A fractographic description evokes a failure mechanism without naming it.", + "text": "The tensile specimen formed a pronounced waist before separation. Its failure surface contains rounded microscopic pits, each surrounding a second-phase particle, joined into a cup-and-cone profile.", + "readout_selector": "final_prompt_token", + "tracked": ["ductile", "microvoid", "void", "nucleation", "coalescence"], + "must_be_absent_from_input": true, + "must_be_absent_from_output": true, + "note": "A strong result distinguishes the specific microvoid/coalescence mechanism from the broader adjective ductile. The generated-output absence control prevents counting a concept the model simply says next." + }, + { + "slug": "materials-intergranular-attack", + "shape": "ASSOCIATION", + "protocol": "lens_eval", + "domain": "materials", + "title": "Unnamed boundary-localized degradation", + "description": "A processing and microscopy vignette should evoke a specific degradation mode.", + "text": "An austenitic stainless steel was held in a temperature range where carbides formed at crystallite edges. After exposure to an aggressive solution, attack traces connected paths along those edges while the grain interiors remain largely intact.", + "readout_selector": "final_prompt_token", + "tracked": ["intergranular", "boundary", "corrosion", "sensitization"], + "must_be_absent_from_input": true, + "must_be_absent_from_output": true, + "note": "Inspect specificity: intergranular plus sensitization is more informative than generic corrosion. Tokenizer-level input checks catch accidental leakage." + }, + { + "slug": "materials-ductile-brittle-swap", + "shape": "PROBE_SWAP", + "protocol": "probe_swap", + "domain": "materials", + "category": "failure-response", + "title": "Ductile-to-brittle causal counterfactual", + "description": "Tests whether replacing a latent failure-response coordinate changes the declared downstream answer.", + "text": "A pure metal has many easy close-packed slip systems and accommodates extensive glide before separation. In a tensile test beyond yield it normally shows substantial", + "readout_selector": "before_answer", + "tracked": ["ductile"], + "answer": "elongation", + "swap_from": "ductile", + "swap_to": "brittle", + "swap_answer": "fracture", + "must_be_absent_from_input": true, + "must_be_absent_from_output": true, + "note": "Grade the clean and counterfactual output tokens. A target-rank improvement without the declared answer change is not causal success." + }, + { + "slug": "materials-pileup-focus", + "shape": "MODULATION", + "protocol": "directed_modulation", + "domain": "materials", + "title": "Focus on a dislocation pile-up", + "description": "Teacher-forced carrier with a materials side concept.", + "user": "Write \"The blue notebook remained beside the furnace.\" Concentrate on a dislocation pile-up and its stress concentration while you write the sentence. Do not write anything else.", + "assistant_prefill": "The blue notebook remained beside the furnace.", + "readout_selector": "assistant_response", + "tracked": ["dislocation", "pile", "stress"], + "must_be_absent_from_output": true, + "condition": "focus", + "control_group": "materials-pileup-notebook", + "phrasing_id": "focus-01", + "carrier_id": "blue-notebook", + "target_family": "dislocation-pileup", + "note": "Compare only against the matched suppress and neutral records; the absolute rank by itself is not the modulation result." + }, + { + "slug": "materials-pileup-suppress", + "shape": "MODULATION", + "protocol": "directed_modulation", + "domain": "materials", + "title": "Suppress a dislocation pile-up", + "description": "Matched suppress condition using the identical carrier and target set.", + "user": "Write \"The blue notebook remained beside the furnace.\" Try not to think about a dislocation pile-up or its stress concentration while you write the sentence. Do not write anything else.", + "assistant_prefill": "The blue notebook remained beside the furnace.", + "readout_selector": "assistant_response", + "tracked": ["dislocation", "pile", "stress"], + "must_be_absent_from_output": true, + "condition": "suppress", + "control_group": "materials-pileup-notebook", + "phrasing_id": "suppress-01", + "carrier_id": "blue-notebook", + "target_family": "dislocation-pileup" + }, + { + "slug": "materials-pileup-control", + "shape": "MODULATION", + "protocol": "directed_modulation", + "domain": "materials", + "title": "Neutral carrier control", + "description": "Matched carrier with no materials side task.", + "user": "Write \"The blue notebook remained beside the furnace.\" Do not write anything else.", + "assistant_prefill": "The blue notebook remained beside the furnace.", + "readout_selector": "assistant_response", + "tracked": ["dislocation", "pile", "stress"], + "must_be_absent_from_output": true, + "condition": "control", + "control_group": "materials-pileup-notebook", + "phrasing_id": "control-01", + "carrier_id": "blue-notebook", + "target_family": "dislocation-pileup" + }, + { + "slug": "materials-defect-report", + "shape": "REPORT_SWAP", + "protocol": "verbal_report", + "domain": "materials", + "title": "Crystal-defect verbal-report swap", + "description": "Dynamically select the clean one-word report, then try sibling defect coordinates.", + "user": "Think of a crystalline defect. Answer in one word.", + "readout_selector": "final_prompt_token", + "tracked": ["vacancy", "dislocation", "interstitial", "twin", "boundary"], + "swap_to": "interstitial", + "candidates": ["vacancy", "dislocation", "interstitial", "twin", "boundary"], + "note": "Inspect every recorded candidate trial. The primary diagnostic is whether the one-word report changes to the installed sibling, not whether its internal rank merely improves." + } + ] +} diff --git a/jlens_materials/prompts/materials-reasoning-pilot-v1.json b/jlens_materials/prompts/materials-reasoning-pilot-v1.json new file mode 100644 index 0000000000000000000000000000000000000000..b06d933baa787e5a5957fe5550932172288ceb96 --- /dev/null +++ b/jlens_materials/prompts/materials-reasoning-pilot-v1.json @@ -0,0 +1,409 @@ +{ + "description": "Frozen chat-formatted clean-answer pilot.", + "prompts": [ + { + "slug": "reason-hall-01", + "shape": "MULTIHOP", + "protocol": "lens_eval", + "domain": "materials", + "category": "grain-size", + "title": "Grain-size comparison", + "description": "Chat-formatted one-word reasoning pilot with a preregistered latent mechanism.", + "user": "Two otherwise identical polycrystals have mean grain sizes 50 and 5 micrometers. Grain interfaces impede carrier motion. Which specimen has the larger yield stress? Reply with exactly one word: fine or coarse.", + "readout_selector": "before_answer", + "tracked": [ + "Hall", + "Petch", + "strengthening", + "dislocation" + ], + "answer": "fine", + "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." + }, + { + "slug": "reason-hall-02", + "shape": "MULTIHOP", + "protocol": "lens_eval", + "domain": "materials", + "category": "grain-size", + "title": "Grain-size comparison", + "description": "Chat-formatted one-word reasoning pilot with a preregistered latent mechanism.", + "user": "Two otherwise identical polycrystals have mean grain sizes 100 and 10 micrometers. Grain interfaces impede carrier motion. Which specimen has the larger yield stress? Reply with exactly one word: fine or coarse.", + "readout_selector": "before_answer", + "tracked": [ + "Hall", + "Petch", + "strengthening", + "dislocation" + ], + "answer": "fine", + "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." + }, + { + "slug": "reason-hall-03", + "shape": "MULTIHOP", + "protocol": "lens_eval", + "domain": "materials", + "category": "grain-size", + "title": "Grain-size comparison", + "description": "Chat-formatted one-word reasoning pilot with a preregistered latent mechanism.", + "user": "Two otherwise identical polycrystals have mean grain sizes 40 and 4 micrometers. Grain interfaces impede carrier motion. Which specimen has the larger yield stress? Reply with exactly one word: fine or coarse.", + "readout_selector": "before_answer", + "tracked": [ + "Hall", + "Petch", + "strengthening", + "dislocation" + ], + "answer": "fine", + "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." + }, + { + "slug": "reason-hall-04", + "shape": "MULTIHOP", + "protocol": "lens_eval", + "domain": "materials", + "category": "grain-size", + "title": "Grain-size comparison", + "description": "Chat-formatted one-word reasoning pilot with a preregistered latent mechanism.", + "user": "Two otherwise identical polycrystals have mean grain sizes 80 and 8 micrometers. Grain interfaces impede carrier motion. Which specimen has the larger yield stress? Reply with exactly one word: fine or coarse.", + "readout_selector": "before_answer", + "tracked": [ + "Hall", + "Petch", + "strengthening", + "dislocation" + ], + "answer": "fine", + "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." + }, + { + "slug": "reason-griffith-01", + "shape": "MULTIHOP", + "protocol": "lens_eval", + "domain": "materials", + "category": "flaw-scaling", + "title": "Flaw-size scaling", + "description": "Chat-formatted one-word reasoning pilot with a preregistered latent mechanism.", + "user": "Two elastic plates have the same modulus and surface energy. One contains an internal elliptical flaw 2 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.", + "readout_selector": "before_answer", + "tracked": [ + "Griffith", + "crack", + "fracture" + ], + "answer": "lower", + "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." + }, + { + "slug": "reason-griffith-02", + "shape": "MULTIHOP", + "protocol": "lens_eval", + "domain": "materials", + "category": "flaw-scaling", + "title": "Flaw-size scaling", + "description": "Chat-formatted one-word reasoning pilot with a preregistered latent mechanism.", + "user": "Two elastic plates have the same modulus and surface energy. One contains an internal elliptical flaw 4 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.", + "readout_selector": "before_answer", + "tracked": [ + "Griffith", + "crack", + "fracture" + ], + "answer": "lower", + "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." + }, + { + "slug": "reason-griffith-03", + "shape": "MULTIHOP", + "protocol": "lens_eval", + "domain": "materials", + "category": "flaw-scaling", + "title": "Flaw-size scaling", + "description": "Chat-formatted one-word reasoning pilot with a preregistered latent mechanism.", + "user": "Two elastic plates have the same modulus and surface energy. One contains an internal elliptical flaw 9 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.", + "readout_selector": "before_answer", + "tracked": [ + "Griffith", + "crack", + "fracture" + ], + "answer": "lower", + "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." + }, + { + "slug": "reason-griffith-04", + "shape": "MULTIHOP", + "protocol": "lens_eval", + "domain": "materials", + "category": "flaw-scaling", + "title": "Flaw-size scaling", + "description": "Chat-formatted one-word reasoning pilot with a preregistered latent mechanism.", + "user": "Two elastic plates have the same modulus and surface energy. One contains an internal elliptical flaw 16 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.", + "readout_selector": "before_answer", + "tracked": [ + "Griffith", + "crack", + "fracture" + ], + "answer": "lower", + "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." + }, + { + "slug": "reason-schmid-01", + "shape": "MULTIHOP", + "protocol": "lens_eval", + "domain": "materials", + "category": "resolved-threshold", + "title": "Resolved-force threshold", + "description": "Chat-formatted one-word reasoning pilot with a preregistered latent mechanism.", + "user": "A single crystal has a resolved driving value of 120 MPa on its most favorably oriented system; the critical value is 80 MPa. Does microscopic plastic motion begin, or does the crystal remain elastic? Reply with exactly one word: slip or elastic.", + "readout_selector": "before_answer", + "tracked": [ + "Schmid", + "dislocation" + ], + "answer": "slip", + "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." + }, + { + "slug": "reason-schmid-02", + "shape": "MULTIHOP", + "protocol": "lens_eval", + "domain": "materials", + "category": "resolved-threshold", + "title": "Resolved-force threshold", + "description": "Chat-formatted one-word reasoning pilot with a preregistered latent mechanism.", + "user": "A single crystal has a resolved driving value of 60 MPa on its most favorably oriented system; the critical value is 80 MPa. Does microscopic plastic motion begin, or does the crystal remain elastic? Reply with exactly one word: slip or elastic.", + "readout_selector": "before_answer", + "tracked": [ + "Schmid", + "dislocation" + ], + "answer": "elastic", + "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." + }, + { + "slug": "reason-schmid-03", + "shape": "MULTIHOP", + "protocol": "lens_eval", + "domain": "materials", + "category": "resolved-threshold", + "title": "Resolved-force threshold", + "description": "Chat-formatted one-word reasoning pilot with a preregistered latent mechanism.", + "user": "A single crystal has a resolved driving value of 95 MPa on its most favorably oriented system; the critical value is 70 MPa. Does microscopic plastic motion begin, or does the crystal remain elastic? Reply with exactly one word: slip or elastic.", + "readout_selector": "before_answer", + "tracked": [ + "Schmid", + "dislocation" + ], + "answer": "slip", + "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." + }, + { + "slug": "reason-schmid-04", + "shape": "MULTIHOP", + "protocol": "lens_eval", + "domain": "materials", + "category": "resolved-threshold", + "title": "Resolved-force threshold", + "description": "Chat-formatted one-word reasoning pilot with a preregistered latent mechanism.", + "user": "A single crystal has a resolved driving value of 45 MPa on its most favorably oriented system; the critical value is 65 MPa. Does microscopic plastic motion begin, or does the crystal remain elastic? Reply with exactly one word: slip or elastic.", + "readout_selector": "before_answer", + "tracked": [ + "Schmid", + "dislocation" + ], + "answer": "elastic", + "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." + }, + { + "slug": "reason-particle-01", + "shape": "MULTIHOP", + "protocol": "lens_eval", + "domain": "materials", + "category": "particle-spacing", + "title": "Obstacle-spacing comparison", + "description": "Chat-formatted one-word reasoning pilot with a preregistered latent mechanism.", + "user": "A metal contains a fixed volume fraction of hard nonshearable particles. The center-to-center particle spacing decreases, so moving lattice line defects must curve between the obstacles. Is the required applied stress higher or lower? Reply with exactly one word.", + "readout_selector": "before_answer", + "tracked": [ + "Orowan", + "bowing", + "precipitation", + "strengthening" + ], + "answer": "higher", + "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." + }, + { + "slug": "reason-particle-02", + "shape": "MULTIHOP", + "protocol": "lens_eval", + "domain": "materials", + "category": "particle-spacing", + "title": "Obstacle-spacing comparison", + "description": "Chat-formatted one-word reasoning pilot with a preregistered latent mechanism.", + "user": "A metal contains a fixed volume fraction of hard nonshearable particles. The center-to-center particle spacing increases, so moving lattice line defects must curve between the obstacles. Is the required applied stress higher or lower? Reply with exactly one word.", + "readout_selector": "before_answer", + "tracked": [ + "Orowan", + "bowing", + "precipitation", + "strengthening" + ], + "answer": "lower", + "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." + }, + { + "slug": "reason-particle-03", + "shape": "MULTIHOP", + "protocol": "lens_eval", + "domain": "materials", + "category": "particle-spacing", + "title": "Obstacle-spacing comparison", + "description": "Chat-formatted one-word reasoning pilot with a preregistered latent mechanism.", + "user": "A metal contains a fixed volume fraction of hard nonshearable particles. The center-to-center particle spacing halves, so moving lattice line defects must curve between the obstacles. Is the required applied stress higher or lower? Reply with exactly one word.", + "readout_selector": "before_answer", + "tracked": [ + "Orowan", + "bowing", + "precipitation", + "strengthening" + ], + "answer": "higher", + "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." + }, + { + "slug": "reason-particle-04", + "shape": "MULTIHOP", + "protocol": "lens_eval", + "domain": "materials", + "category": "particle-spacing", + "title": "Obstacle-spacing comparison", + "description": "Chat-formatted one-word reasoning pilot with a preregistered latent mechanism.", + "user": "A metal contains a fixed volume fraction of hard nonshearable particles. The center-to-center particle spacing doubles, so moving lattice line defects must curve between the obstacles. Is the required applied stress higher or lower? Reply with exactly one word.", + "readout_selector": "before_answer", + "tracked": [ + "Orowan", + "bowing", + "precipitation", + "strengthening" + ], + "answer": "lower", + "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." + }, + { + "slug": "reason-intensity-01", + "shape": "MULTIHOP", + "protocol": "lens_eval", + "domain": "materials", + "category": "intensity-threshold", + "title": "Stress-intensity threshold", + "description": "Chat-formatted one-word reasoning pilot with a preregistered latent mechanism.", + "user": "A notched plate has applied mode-I intensity 20 MPa square-root-meter and a critical intensity 40 MPa square-root-meter. Is rapid extension stable or unstable? Reply with exactly one word.", + "readout_selector": "before_answer", + "tracked": [ + "toughness", + "crack", + "fracture" + ], + "answer": "stable", + "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." + }, + { + "slug": "reason-intensity-02", + "shape": "MULTIHOP", + "protocol": "lens_eval", + "domain": "materials", + "category": "intensity-threshold", + "title": "Stress-intensity threshold", + "description": "Chat-formatted one-word reasoning pilot with a preregistered latent mechanism.", + "user": "A notched plate has applied mode-I intensity 50 MPa square-root-meter and a critical intensity 40 MPa square-root-meter. Is rapid extension stable or unstable? Reply with exactly one word.", + "readout_selector": "before_answer", + "tracked": [ + "toughness", + "crack", + "fracture" + ], + "answer": "unstable", + "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." + }, + { + "slug": "reason-intensity-03", + "shape": "MULTIHOP", + "protocol": "lens_eval", + "domain": "materials", + "category": "intensity-threshold", + "title": "Stress-intensity threshold", + "description": "Chat-formatted one-word reasoning pilot with a preregistered latent mechanism.", + "user": "A notched plate has applied mode-I intensity 15 MPa square-root-meter and a critical intensity 30 MPa square-root-meter. Is rapid extension stable or unstable? Reply with exactly one word.", + "readout_selector": "before_answer", + "tracked": [ + "toughness", + "crack", + "fracture" + ], + "answer": "stable", + "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." + }, + { + "slug": "reason-intensity-04", + "shape": "MULTIHOP", + "protocol": "lens_eval", + "domain": "materials", + "category": "intensity-threshold", + "title": "Stress-intensity threshold", + "description": "Chat-formatted one-word reasoning pilot with a preregistered latent mechanism.", + "user": "A notched plate has applied mode-I intensity 45 MPa square-root-meter and a critical intensity 35 MPa square-root-meter. Is rapid extension stable or unstable? Reply with exactly one word.", + "readout_selector": "before_answer", + "tracked": [ + "toughness", + "crack", + "fracture" + ], + "answer": "unstable", + "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." + } + ] +} diff --git a/jlens_materials/prompts/materials-report-swap-dose-v1.json b/jlens_materials/prompts/materials-report-swap-dose-v1.json new file mode 100644 index 0000000000000000000000000000000000000000..ddc34b97f701d424c5030c730f2042e9466fff84 --- /dev/null +++ b/jlens_materials/prompts/materials-report-swap-dose-v1.json @@ -0,0 +1,70 @@ +{ + "description": "Exploratory verbal-report intervention dose study across five materials categories.", + "prompts": [ + { + "slug": "dose-report-defect", + "shape": "REPORT_SWAP", + "protocol": "verbal_report", + "domain": "materials", + "category": "defect", + "title": "Crystalline defect report", + "description": "Dynamically select the clean report and install sibling defect coordinates.", + "user": "Think of a crystalline defect. Answer in one word.", + "readout_selector": "final_prompt_token", + "tracked": ["vacancy", "dislocation", "interstitial", "twin", "boundary"], + "candidates": ["vacancy", "dislocation", "interstitial", "twin", "boundary"] + }, + { + "slug": "dose-report-failure", + "shape": "REPORT_SWAP", + "protocol": "verbal_report", + "domain": "materials", + "category": "failure-mode", + "title": "Failure-mode report", + "description": "Dynamically select the clean report and install sibling failure-mode coordinates.", + "user": "Think of a materials failure mode. Answer in one word.", + "readout_selector": "final_prompt_token", + "tracked": ["ductile", "brittle", "fatigue", "cleavage", "creep"], + "candidates": ["ductile", "brittle", "fatigue", "cleavage", "creep"] + }, + { + "slug": "dose-report-lattice", + "shape": "REPORT_SWAP", + "protocol": "verbal_report", + "domain": "materials", + "category": "lattice", + "title": "Crystal-lattice report", + "description": "Dynamically select the clean report and install sibling lattice coordinates.", + "user": "Think of a common crystal lattice. Answer using one abbreviation.", + "readout_selector": "final_prompt_token", + "tracked": ["FCC", "BCC", "HCP"], + "candidates": ["FCC", "BCC", "HCP"] + }, + { + "slug": "dose-report-deformation", + "shape": "REPORT_SWAP", + "protocol": "verbal_report", + "domain": "materials", + "category": "deformation", + "title": "Deformation-mechanism report", + "description": "Dynamically select the clean report and install sibling deformation coordinates.", + "user": "Think of a material deformation mechanism. Answer in one word.", + "readout_selector": "final_prompt_token", + "tracked": ["slip", "twinning", "creep", "diffusion"], + "candidates": ["slip", "twinning", "creep", "diffusion"] + }, + { + "slug": "dose-report-strengthening", + "shape": "REPORT_SWAP", + "protocol": "verbal_report", + "domain": "materials", + "category": "strengthening", + "title": "Strengthening-mechanism report", + "description": "Dynamically select the clean report and install sibling strengthening coordinates.", + "user": "Think of a metal strengthening mechanism. Answer in one word.", + "readout_selector": "final_prompt_token", + "tracked": ["precipitation", "solution", "grain", "work"], + "candidates": ["precipitation", "solution", "grain", "work"] + } + ] +} diff --git a/jlens_materials/prompts/mechanics-paper-example.json b/jlens_materials/prompts/mechanics-paper-example.json new file mode 100644 index 0000000000000000000000000000000000000000..ff563bcb00a000d951d331c965af70398786b254 --- /dev/null +++ b/jlens_materials/prompts/mechanics-paper-example.json @@ -0,0 +1,118 @@ +{ + "description": "Small schema example only. Expand each evaluation distribution to at least 50 independent items before quantitative reporting.", + "prompts": [ + { + "slug": "griffith-length-example", + "shape": "MULTIHOP", + "protocol": "lens_eval", + "domain": "mechanics", + "title": "Brittle flaw scaling", + "description": "Input-absent mechanism before the expected answer.", + "text": "A brittle elastic plate contains a sharp internal flaw. Its failure load follows from balancing stored work released during advance 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 + }, + { + "slug": "fatigue-striations-example", + "shape": "MULTIHOP", + "protocol": "lens_eval", + "domain": "mechanics", + "title": "Repeated loading to fractographic feature", + "description": "The mechanism is latent; the requested output is a different token.", + "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 + }, + { + "slug": "ductile-counterfactual-example", + "shape": "PROBE_SWAP", + "protocol": "probe_swap", + "domain": "materials", + "category": "failure-response", + "title": "Failure-response counterfactual", + "description": "Grade the downstream answer, not target-direction rank movement.", + "text": "A metal can accommodate extensive dislocation glide before separation. Under a tensile test beyond yield it will exhibit substantial", + "readout_selector": "before_answer", + "tracked": ["ductile"], + "answer": "elongation", + "swap_from": "ductile", + "swap_to": "brittle", + "swap_answer": "fracture", + "must_be_absent_from_input": true, + "must_be_absent_from_output": true + }, + { + "slug": "plasticity-focus-example", + "shape": "MODULATION", + "protocol": "directed_modulation", + "domain": "mechanics", + "title": "Focus condition", + "description": "Teacher-forced carrier with an instructed side concept.", + "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", "plasticity", "yield"], + "must_be_absent_from_output": true, + "condition": "focus", + "control_group": "plasticity-notebook", + "phrasing_id": "concentrate-01", + "carrier_id": "notebook", + "target_family": "crack-tip-plasticity" + }, + { + "slug": "plasticity-suppress-example", + "shape": "MODULATION", + "protocol": "directed_modulation", + "domain": "mechanics", + "title": "Suppress condition", + "description": "Matched carrier and targets with a suppress instruction.", + "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", "plasticity", "yield"], + "must_be_absent_from_output": true, + "condition": "suppress", + "control_group": "plasticity-notebook", + "phrasing_id": "suppress-01", + "carrier_id": "notebook", + "target_family": "crack-tip-plasticity" + }, + { + "slug": "plasticity-control-example", + "shape": "MODULATION", + "protocol": "directed_modulation", + "domain": "mechanics", + "title": "Neutral control", + "description": "Matched carrier without a side-task instruction.", + "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", "plasticity", "yield"], + "must_be_absent_from_output": true, + "condition": "control", + "control_group": "plasticity-notebook", + "phrasing_id": "control-01", + "carrier_id": "notebook", + "target_family": "crack-tip-plasticity" + }, + { + "slug": "failure-mode-report-example", + "shape": "REPORT_SWAP", + "protocol": "verbal_report", + "domain": "materials", + "title": "Dynamic verbal-report swap", + "description": "The clean greedy report is selected dynamically as the source.", + "user": "Think of a metal failure mode. Answer in one word.", + "readout_selector": "final_prompt_token", + "tracked": ["fatigue", "creep", "corrosion", "fracture", "buckling"], + "swap_to": "creep", + "candidates": ["fatigue", "creep", "corrosion", "fracture", "buckling"] + } + ] +} diff --git a/jlens_materials/prompts/protein.json b/jlens_materials/prompts/protein.json new file mode 100644 index 0000000000000000000000000000000000000000..2f1a71803eb0b6850dd1a0c10b40aa84391d807c --- /dev/null +++ b/jlens_materials/prompts/protein.json @@ -0,0 +1,148 @@ +{ + "prompts": [ + { + "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, when the backbone carbonyl oxygen of one residue accepts a hydrogen bond from the amide of the residue four positions later, and this repeats down the chain, the local fold that results is the alpha", + "readout_at": -1, + "tracked": [ + "hydrogen", + "bond", + "backbone", + "helix", + "spiral" + ], + "note": "Two-hop: i,i+4 H-bond pattern -> alpha helix. 'hydrogen'/'bond'/'backbone' are the unspoken intermediates before the answer 'helix'." + }, + { + "slug": "protein-gfp-recognition", + "shape": "RECOGNITION", + "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." + }, + { + "slug": "protein-collagen-recognition", + "shape": "RECOGNITION", + "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." + }, + { + "slug": "protein-transmembrane-recognition", + "shape": "RECOGNITION", + "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'." + }, + { + "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_at": -1, + "tracked": [ + "amyloid", + "fibril", + "aggregate", + "misfold", + "beta", + "plaque" + ], + "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." + }, + { + "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 are driven out of contact with solvent and pack together in the interior, and this burial of hydrophobic residues is the main force that drives the chain back to its native", + "readout_at": -1, + "tracked": [ + "hydrophobic", + "core", + "collapse", + "fold", + "structure", + "native" + ], + "note": "Anfinsen-style hydrophobic-collapse chain: the intermediates 'hydrophobic' and 'core' should precede the answer 'fold'/'structure' across layers." + }, + { + "slug": "protein-modulation", + "shape": "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_at": -1, + "tracked": [ + "disulfide", + "cysteine", + "bridge", + "bond", + "sulfur", + "cross" + ], + "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." + }, + { + "slug": "protein-report-swap", + "shape": "REPORT_SWAP", + "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.", + "readout_at": -1, + "tracked": [ + "glycine", + "alanine", + "leucine", + "serine", + "proline" + ], + "swap_from": "glycine", + "swap_to": "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." + } + ] +} \ No newline at end of file diff --git a/jlens_materials/runs/comparison_Qwen25-05B-Instruct_vs_gpt2.json b/jlens_materials/runs/comparison_Qwen25-05B-Instruct_vs_gpt2.json new file mode 100644 index 0000000000000000000000000000000000000000..5985356c1951c7af557190773fa526c54f820ba9 --- /dev/null +++ b/jlens_materials/runs/comparison_Qwen25-05B-Instruct_vs_gpt2.json @@ -0,0 +1,198 @@ +{ + "tag": "Qwen25-05B-Instruct_vs_gpt2", + "models": [ + "Qwen/Qwen2.5-0.5B-Instruct", + "gpt2" + ], + "provider": "anthropic", + "offline": true, + "hitrate": { + "shapes": [ + "MULTIHOP", + "ASSOCIATION", + "RECOGNITION", + "MODULATION", + "REPORT_SWAP" + ], + "labels": [ + "Qwen2.5-0.5B-Instruct", + "gpt2" + ], + "values": [ + [ + 0.625, + 0.2, + 0.07142857142857142, + 0.5555555555555556, + 0.5 + ], + [ + 0.4782608695652174, + 0.3, + 0.0, + NaN, + NaN + ] + ] + }, + "swaps": [ + { + "model": "Qwen2.5-0.5B-Instruct", + "slug": "fracture-report-swap", + "source": "fatigue", + "target": "creep", + "alpha": 1.0, + "source_rank_clean": 86, + "source_rank_swapped": 7933, + "target_rank_clean": 324, + "target_rank_swapped": 154 + } + ], + "analysis": "[offline template] Comparison of Qwen2.5-0.5B-Instruct, gpt2. Highest overall top-5 surfacing rate: Qwen2.5-0.5B-Instruct. See the grouped-bar and concept-rank figures for the per-shape and per-concept breakdown.", + "per_prompt": [ + { + "slug": "fracture-griffith", + "title": "Griffith criterion (multi-hop)", + "shape": "MULTIHOP", + "domain": "fracture", + "figure": "figures/compare/Qwen25-05B-Instruct_vs_gpt2/fracture-griffith__ranks.png", + "cross": "[offline template] Qwen2.5-0.5B-Instruct: 3 concept(s) in lens top-5 (energy, surface, crack); gpt2: 2 concept(s) in lens top-5 (energy, surface).", + "models": [ + "Qwen2.5-0.5B-Instruct", + "gpt2" + ] + }, + { + "slug": "fracture-fatigue", + "title": "Cyclic loading -> failure mode (multi-hop)", + "shape": "MULTIHOP", + "domain": "fracture", + "figure": "figures/compare/Qwen25-05B-Instruct_vs_gpt2/fracture-fatigue__ranks.png", + "cross": "[offline template] Qwen2.5-0.5B-Instruct: 3 concept(s) in lens top-5 (fatigue, crack, cyclic); gpt2: 1 concept(s) in lens top-5 (crack).", + "models": [ + "Qwen2.5-0.5B-Instruct", + "gpt2" + ] + }, + { + "slug": "fracture-toughness-chain", + "title": "Stress-intensity factor chain (multi-hop)", + "shape": "MULTIHOP", + "domain": "fracture", + "figure": "figures/compare/Qwen25-05B-Instruct_vs_gpt2/fracture-toughness-chain__ranks.png", + "cross": "[offline template] Qwen2.5-0.5B-Instruct: 4 concept(s) in lens top-5 (stress, intensity, toughness, propagate); gpt2: 3 concept(s) in lens top-5 (stress, intensity, toughness).", + "models": [ + "Qwen2.5-0.5B-Instruct", + "gpt2" + ] + }, + { + "slug": "fracture-brittle-assoc", + "title": "Brittle fracture (association, unnamed)", + "shape": "ASSOCIATION", + "domain": "fracture", + "figure": "figures/compare/Qwen25-05B-Instruct_vs_gpt2/fracture-brittle-assoc__ranks.png", + "cross": "[offline template] Qwen2.5-0.5B-Instruct: 1 concept(s) in lens top-5 (glass); gpt2: 2 concept(s) in lens top-5 (glass, shatter).", + "models": [ + "Qwen2.5-0.5B-Instruct", + "gpt2" + ] + }, + { + "slug": "fracture-ductile-assoc", + "title": "Ductile failure (association, unnamed)", + "shape": "ASSOCIATION", + "domain": "fracture", + "figure": "figures/compare/Qwen25-05B-Instruct_vs_gpt2/fracture-ductile-assoc__ranks.png", + "cross": "[offline template] Qwen2.5-0.5B-Instruct: 0 concept(s) in lens top-5; gpt2: 0 concept(s) in lens top-5.", + "models": [ + "Qwen2.5-0.5B-Instruct", + "gpt2" + ] + }, + { + "slug": "fracture-stress-recognition", + "title": "Stress state from a tensor (recognition)", + "shape": "RECOGNITION", + "domain": "fracture", + "figure": "figures/compare/Qwen25-05B-Instruct_vs_gpt2/fracture-stress-recognition__ranks.png", + "cross": "[offline template] Qwen2.5-0.5B-Instruct: 1 concept(s) in lens top-5 (mode); gpt2: 0 concept(s) in lens top-5.", + "models": [ + "Qwen2.5-0.5B-Instruct", + "gpt2" + ] + }, + { + "slug": "protein-helix-multihop", + "title": "Backbone H-bonds -> secondary structure (multi-hop)", + "shape": "MULTIHOP", + "domain": "protein", + "figure": "figures/compare/Qwen25-05B-Instruct_vs_gpt2/protein-helix-multihop__ranks.png", + "cross": "[offline template] Qwen2.5-0.5B-Instruct: 3 concept(s) in lens top-5 (hydrogen, bond, backbone); gpt2: 3 concept(s) in lens top-5 (hydrogen, bond, backbone).", + "models": [ + "Qwen2.5-0.5B-Instruct", + "gpt2" + ] + }, + { + "slug": "protein-gfp-recognition", + "title": "GFP sequence -> function (recognition)", + "shape": "RECOGNITION", + "domain": "protein", + "figure": "figures/compare/Qwen25-05B-Instruct_vs_gpt2/protein-gfp-recognition__ranks.png", + "cross": "[offline template] Qwen2.5-0.5B-Instruct: 0 concept(s) in lens top-5; gpt2: 0 concept(s) in lens top-5.", + "models": [ + "Qwen2.5-0.5B-Instruct", + "gpt2" + ] + }, + { + "slug": "protein-collagen-recognition", + "title": "Gly-X-Y repeat -> collagen (recognition)", + "shape": "RECOGNITION", + "domain": "protein", + "figure": "figures/compare/Qwen25-05B-Instruct_vs_gpt2/protein-collagen-recognition__ranks.png", + "cross": "[offline template] Qwen2.5-0.5B-Instruct: 0 concept(s) in lens top-5; gpt2: 0 concept(s) in lens top-5.", + "models": [ + "Qwen2.5-0.5B-Instruct", + "gpt2" + ] + }, + { + "slug": "protein-transmembrane-recognition", + "title": "Hydrophobic stretch -> transmembrane (recognition)", + "shape": "RECOGNITION", + "domain": "protein", + "figure": "figures/compare/Qwen25-05B-Instruct_vs_gpt2/protein-transmembrane-recognition__ranks.png", + "cross": "[offline template] Qwen2.5-0.5B-Instruct: 0 concept(s) in lens top-5; gpt2: 0 concept(s) in lens top-5.", + "models": [ + "Qwen2.5-0.5B-Instruct", + "gpt2" + ] + }, + { + "slug": "protein-amyloid-assoc", + "title": "Amyloid aggregation (association, unnamed)", + "shape": "ASSOCIATION", + "domain": "protein", + "figure": "figures/compare/Qwen25-05B-Instruct_vs_gpt2/protein-amyloid-assoc__ranks.png", + "cross": "[offline template] Qwen2.5-0.5B-Instruct: 1 concept(s) in lens top-5 (plaque); gpt2: 1 concept(s) in lens top-5 (plaque).", + "models": [ + "Qwen2.5-0.5B-Instruct", + "gpt2" + ] + }, + { + "slug": "protein-fold-multihop", + "title": "Hydrophobic collapse -> tertiary fold (multi-hop)", + "shape": "MULTIHOP", + "domain": "protein", + "figure": "figures/compare/Qwen25-05B-Instruct_vs_gpt2/protein-fold-multihop__ranks.png", + "cross": "[offline template] Qwen2.5-0.5B-Instruct: 2 concept(s) in lens top-5 (structure, native); gpt2: 2 concept(s) in lens top-5 (structure, native).", + "models": [ + "Qwen2.5-0.5B-Instruct", + "gpt2" + ] + } + ] +} \ No newline at end of file diff --git a/jlens_materials/runs/gemma4-12b-it-heldout-v1-seed0.json b/jlens_materials/runs/gemma4-12b-it-heldout-v1-seed0.json new file mode 100644 index 0000000000000000000000000000000000000000..e1050d0fa76aa2c991cf1492fe2990fafef0c25e --- /dev/null +++ b/jlens_materials/runs/gemma4-12b-it-heldout-v1-seed0.json @@ -0,0 +1,157431 @@ +{ + "format_version": 2, + "created_at": "2026-08-25T09:51:19.652946+00:00", + "model": "google/gemma-4-12B-it", + 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Predetermined terms are used only for the rank endpoint; open-vocabulary generation and ranking cannot use them.", + "prompt_text": "A round aluminum bar stretched into a pronounced waist before separating. The mating faces contained hemispherical pits centered on inclusions and a slanted tearing rim.", + "prompt_tail": "luminum bar stretched into a pronounced waist before separating. The mating faces contained hemispherical pits centered on inclusions and a slanted tearing rim.", + "readout_selector": "final_prompt_token", + "readout_at": -1, + "score_positions": [ + 28 + ], + "band": [ + 38.0, + 92.0 + ], + "band_source": "preregistered_cli_or_config", + "condition": null, + "control_group": null, + "phrasing_id": "heldout-ductile-01", + "carrier_id": null, + "target_family": "ductile", + "category": "ductile", + "answer": null, + "baseline": { + "required": false, + "correct": null, + "expected": [] + }, + "generated_completion": " slanted", + "generated_token_ids": [ + 169434 + ], + "protocol_violations": [], + "valid_for_metrics": true, + "excluded_reasons": [], + "discovered": false, + "open_vocabulary_config": { + "layer_readout_top": 32, + "surprising_top": 64, + "candidate_generation_uses_tracked_terms": false, + "matched_logit_open_vocabulary_stored": true + }, + "tracked_dropped": [], + "tracked": [ + { + "label": "ductile", + "surfaces": [ + "ductile" + ], + "token_ids": [ + 185335 + ] + }, + { + "label": "nucleation", + "surfaces": [ + "nucleation" + ], + "token_ids": [ + 87211 + ] + }, + { + "label": "coalescence", + "surfaces": [ + "coalescence" + ], + "token_ids": [ + 216792 + ] + }, + { + "label": "void", + "surfaces": [ + "void" + ], + "token_ids": [ + 2325, + 4117, + 61177, + 70562 + ] + } + ], + "emergence": [ + { + "label": "ductile", + "best_rank": 891, + "best_depth": 68.1, + "onset_depth": null, + "reached_top1": false, + "best_pos": 28, + "logit_lens_best_rank": 2954 + }, + { + "label": "nucleation", + "best_rank": 33097, + "best_depth": 68.1, + "onset_depth": null, + "reached_top1": false, + "best_pos": 28, + "logit_lens_best_rank": 2833 + }, + { + "label": "coalescence", + "best_rank": 2837, + "best_depth": 87.2, + "onset_depth": null, + "reached_top1": false, + "best_pos": 28, + "logit_lens_best_rank": 1353 + }, + { + "label": "void", + "best_rank": 3076, + "best_depth": 76.6, + "onset_depth": null, + "reached_top1": false, + "best_pos": 28, + "logit_lens_best_rank": 920 + } + ], + "layer_readouts": [ + { + "layer": 0, + "depth": 0.0, + "top_tokens": [ + "orna", + "ement", + "supernatural", + "ily", + "Supernatural", + "oji", + "bou", + "Lorraine", + "jor", + "oport", + "Gui", + "Jang", + "Noor", + "plong", + "Cher", + "Abr", + "Supreme", + "olong", + "Mar", + "lak", + "jang" + ] + }, + { + "layer": 2, + "depth": 4.3, + "top_tokens": [ + "ement", + "ily", + "oji", + "orna", + "ugas", + "mathbb", + "mem", + "oms", + "iores", + "lak", + "Arora", + "ool", + "zym", + "ORS", + "gh", + "ingles", + "aboo", + "ORT", + "ORN", + "ht", + "ancher", + "Gui", + "luch", + "otrop" + ] + }, + { + "layer": 4, + "depth": 8.5, + "top_tokens": [ + "Kham", + "Fres", + "handful", + "crawled", + "WORTH", + "TECHNICAL", + "Happen", + "hse", + "Birthday", + "RECOMM", + "happen", + "WHAT", + "EVERY", + "Scores", + "HAS", + "Hassel", + "GRAM", + "headline", + "lias" + ] + }, + { + "layer": 6, + "depth": 12.8, + "top_tokens": [ + "Za", + "neuron", + "Printers", + "Hd", + "Printer", + "Za", + "squre", + "slik", + "ceo", + "rectMode", + "magnesium", + "innervation", + "Magnesium", + "DecodingError", + "Dryer", + "creases", + "prus", + "Writers" + ] + }, + { + "layer": 8, + "depth": 17.0, + "top_tokens": [ + "normalization", + "fqsen", + "Christi", + "Birthday", + "Lobkowicz", + "nvidia", + "NORMAL", + "Xiv", + "Za", + "pewno", + "odes", + "nascita", + "Highlights", + "Venez", + "KD" + ] + }, + { + "layer": 9, + "depth": 19.1, + "top_tokens": [ + "Kristall", + "atth", + "rewrite", + "pewno", + "Chrys", + "NORMAL", + "Saldo", + "Saudi", + "Hd", + "uzz", + "Soda", + "chrysanthemum", + "pencil", + "Lens", + "Cameras", + "UD", + "Lambda" + ] + }, + { + "layer": 11, + "depth": 23.4, + "top_tokens": [ + "Kristall", + "Chrys", + "Saldo", + "Birthday", + "pewno", + "sampel", + "mas", + "chrysanthemum", + "Ink", + "Hd", + "worsen", + "pencil", + "Saudi", + "Lazada", + "Sadd", + "rangea", + "Za", + "Lens", + "saa", + "XXV", + "domin", + "keV" + ] + }, + { + "layer": 13, + "depth": 27.7, + "top_tokens": [ + "bert", + "haya", + "WORTH", + "handful", + "WHIT", + "registro", + "berth", + "TECHNICAL", + "Lives", + "OFFICE", + "haf", + "COR", + "wah", + "FAF", + "Official", + "genesis", + "ForegroundColor", + "hetero", + "ForEach", + "Nickname", + "WHIT", + "nutric", + "enl", + "nho", + "Instance" + ] + }, + { + "layer": 15, + "depth": 31.9, + "top_tokens": [ + "ebel", + "t", + "creases", + "slik", + "energetic", + "exuber", + "Hd", + "sufferings", + "vc", + "Hd", + "softness", + "sene", + "cephal", + "compassion", + "sellers", + "genu", + "sensation", + "soften", + "impediments", + "s", + "incompar", + "salva" + ] + }, + { + "layer": 17, + "depth": 36.2, + "top_tokens": [ + "med", + "s", + "gehend", + "t", + "uette", + "dir", + "skept", + "nesses", + "ujos", + "en", + "mselves", + "uelles", + "ures", + "og", + "ebel", + "bulb", + "eness", + "est", + "enburg", + "domin", + "esia", + "oq", + "natural" + ] 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Predetermined terms are used only for the rank endpoint; open-vocabulary generation and ranking cannot use them.", + "prompt_text": "A wheel axle survived years of repeated bending. The failure face contained thumbnail-shaped beach marks centered on several surface initiation sites.", + "prompt_tail": "A wheel axle survived years of repeated bending. The failure face contained thumbnail-shaped beach marks centered on several surface initiation sites.", + "readout_selector": "final_prompt_token", + "readout_at": -1, + "score_positions": [ + 25 + ], + "band": [ + 38.0, + 92.0 + ], + "band_source": "preregistered_cli_or_config", + "condition": null, + "control_group": null, + "phrasing_id": "heldout-cyclic-03", + "carrier_id": null, + "target_family": "cyclic", + "category": "cyclic", + "answer": null, + "baseline": { + "required": false, + "correct": null, + "expected": [] + }, + "generated_completion": "\n", + "generated_token_ids": [ + 107 + ], + "protocol_violations": [], + "valid_for_metrics": true, + "excluded_reasons": [], + "discovered": false, + "open_vocabulary_config": { + "layer_readout_top": 32, + "surprising_top": 64, + "candidate_generation_uses_tracked_terms": false, + "matched_logit_open_vocabulary_stored": true + }, + "tracked_dropped": [], + "tracked": [ + { + "label": "fatigue", + "surfaces": [ + "fatigue" + ], + 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"figures/gemma4-12b-it-mechanism-name-v1-seed2/materials/mechanism-name-grain-size-strength-01__stream.png", + "trajectory": "figures/gemma4-12b-it-mechanism-name-v1-seed2/materials/mechanism-name-grain-size-strength-01__trajectory.png", + "emergence": "figures/gemma4-12b-it-mechanism-name-v1-seed2/materials/mechanism-name-grain-size-strength-01__emergence.png", + "heatmap": "figures/gemma4-12b-it-mechanism-name-v1-seed2/materials/mechanism-name-grain-size-strength-01__heatmap.png" + } + }, + { + "slug": "mechanism-name-grain-size-strength-02", + "shape": "ASSOCIATION", + "protocol": "lens_eval", + "domain": "materials", + "title": "Grain-size strengthening 2", + "description": "Frozen mechanism-versus-eponym vignette; both tracked words are absent from the input and generated output.", + "note": "Paired comparison of physical carrier 'boundary' against eponym token 'Hall'. Primary endpoint is the within-item log10 rank difference, summarized separately for J-lens and logit lens.", + "prompt_text": "A polycrystalline metal is processed until its average crystallite diameter is reduced by half. Its resistance to the onset of plastic flow increases.", + "prompt_tail": "A polycrystalline metal is processed until its average crystallite diameter is reduced by half. Its resistance to the onset of plastic flow increases.", + "readout_selector": "final_prompt_token", + "readout_at": -1, + "score_positions": [ + 26 + ], + "band": [ + 38.0, + 92.0 + ], + "band_source": "preregistered_cli_or_config", + "condition": null, + "control_group": null, + "phrasing_id": null, + "carrier_id": null, + "target_family": null, + "category": "grain-size-strength", + "answer": null, + "baseline": { + "required": false, + "correct": null, + "expected": [] + }, + "generated_completion": " resistance", + "generated_token_ids": [ + 8047 + ], + "protocol_violations": [], + "valid_for_metrics": true, + "excluded_reasons": [], + "discovered": false, + "open_vocabulary_config": { + "layer_readout_top": 32, + "surprising_top": 64, + "candidate_generation_uses_tracked_terms": false, + "matched_logit_open_vocabulary_stored": true + }, + "tracked_dropped": [], + "tracked": [ + { + "label": "boundary", + "surfaces": [ + "boundary" + ], + "token_ids": [ + 10134, + 63689, + 86526, 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"score": 1.0, + "best_rank": 1, + "best_depth": 63.8, + "near_token": "to", + "output_checked": true + } + ], + "figures": { + "grid": "figures/gemma4-12b-it-mechanism-name-v1-seed2/materials/mechanism-name-grain-size-strength-09__grid.png", + "stream": "figures/gemma4-12b-it-mechanism-name-v1-seed2/materials/mechanism-name-grain-size-strength-09__stream.png", + "trajectory": "figures/gemma4-12b-it-mechanism-name-v1-seed2/materials/mechanism-name-grain-size-strength-09__trajectory.png", + "emergence": "figures/gemma4-12b-it-mechanism-name-v1-seed2/materials/mechanism-name-grain-size-strength-09__emergence.png", + "heatmap": "figures/gemma4-12b-it-mechanism-name-v1-seed2/materials/mechanism-name-grain-size-strength-09__heatmap.png" + } + }, + { + "slug": "mechanism-name-grain-size-strength-10", + "shape": "ASSOCIATION", + "protocol": "lens_eval", + "domain": "materials", + "title": "Grain-size strengthening 10", + "description": "Frozen mechanism-versus-eponym vignette; both tracked words are absent from the input and generated output.", + "note": "Paired comparison of physical carrier 'boundary' against eponym token 'Hall'. Primary endpoint is the within-item log10 rank difference, summarized separately for J-lens and logit lens.", + "prompt_text": "A structural alloy is strengthened by subdividing it into smaller misoriented crystal regions, creating more barriers to the passage of moving line imperfections.", + "prompt_tail": "structural alloy is strengthened by subdividing it into smaller misoriented crystal regions, creating more barriers to the passage of moving line imperfections.", + "readout_selector": "final_prompt_token", + "readout_at": -1, + "score_positions": [ + 27 + ], + "band": [ + 38.0, + 92.0 + ], + "band_source": "preregistered_cli_or_config", + "condition": null, + "control_group": null, + "phrasing_id": null, + "carrier_id": null, + "target_family": null, + "category": "grain-size-strength", + "answer": null, + "baseline": { + "required": false, + "correct": null, + "expected": [] + }, + "generated_completion": "\n\n", + "generated_token_ids": [ + 108 + ], + "protocol_violations": [], 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"title": "Flaw-controlled brittle failure 1", + "description": "Frozen mechanism-versus-eponym vignette; both tracked words are absent from the input and generated output.", + "note": "Paired comparison of physical carrier 'crack' against eponym token 'Griffith'. Primary endpoint is the within-item log10 rank difference, summarized separately for J-lens and logit lens.", + "prompt_text": "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.", + "prompt_tail": "arry 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.", + "readout_selector": "final_prompt_token", + "readout_at": -1, + "score_positions": [ + 34 + ], + "band": [ + 38.0, + 92.0 + ], + "band_source": "preregistered_cli_or_config", + "condition": null, + "control_group": null, + "phrasing_id": null, + "carrier_id": null, + "target_family": null, + "category": "flaw-controlled-failure", + "answer": null, + "baseline": { + "required": false, + "correct": null, + "expected": [] + }, + "generated_completion": "\n", + "generated_token_ids": [ + 107 + ], + "protocol_violations": [], + "valid_for_metrics": true, + "excluded_reasons": [], + "discovered": false, + "open_vocabulary_config": { + "layer_readout_top": 32, + "surprising_top": 64, + "candidate_generation_uses_tracked_terms": false, + "matched_logit_open_vocabulary_stored": true + }, + "tracked_dropped": [], + "tracked": [ + { + "label": "crack", + "surfaces": [ + "crack" + ], + "token_ids": [ + 11890, + 91888, + 78772, + 141677 + ] + }, + { + "label": "Griffith", + "surfaces": [ + "Griffith" + ], + "token_ids": [ + 53755 + ] + } + ], + "emergence": [ + { + "label": "crack", + "best_rank": 12, + "best_depth": 72.3, + "onset_depth": null, + "reached_top1": false, + "best_pos": 34, + "logit_lens_best_rank": 18 + }, + { + "label": "Griffith", + "best_rank": 2831, + "best_depth": 46.8, + "onset_depth": null, + "reached_top1": false, + "best_pos": 34, + "logit_lens_best_rank": 353 + } + ], + "layer_readouts": [ + { + "layer": 0, + "depth": 0.0, + "top_tokens": [ + "unica", + "odium", + "wadi", + "ahs", + "udi", + "Fountain", + "odi", + "graisse", + "ervo", + "ayani", + "Kodi", + "overheard", + "elli", + "wit", + "hacerse", + "ayya", + "avil", + "arpur", + "Burbank", + "Toto", + "quilla", + "rodeo", + "aville", + "Beverungen", + "zk", + "Sever", + "opa" + ] + }, + { + "layer": 2, + "depth": 4.3, + "top_tokens": [ + "graisse", + "Fountain", + "unica", + "odium", + "ahs", + "quilla", + "wadi", + "udi", 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"grid": "figures/gemma4-12b-it-mechanism-name-v1-seed2/materials/mechanism-name-flaw-controlled-failure-01__grid.png", + "stream": "figures/gemma4-12b-it-mechanism-name-v1-seed2/materials/mechanism-name-flaw-controlled-failure-01__stream.png", + "trajectory": "figures/gemma4-12b-it-mechanism-name-v1-seed2/materials/mechanism-name-flaw-controlled-failure-01__trajectory.png", + "emergence": "figures/gemma4-12b-it-mechanism-name-v1-seed2/materials/mechanism-name-flaw-controlled-failure-01__emergence.png", + "heatmap": "figures/gemma4-12b-it-mechanism-name-v1-seed2/materials/mechanism-name-flaw-controlled-failure-01__heatmap.png" + } + }, + { + "slug": "mechanism-name-flaw-controlled-failure-02", + "shape": "ASSOCIATION", + "protocol": "lens_eval", + "domain": "materials", + "title": "Flaw-controlled brittle failure 2", + "description": "Frozen mechanism-versus-eponym vignette; both tracked words are absent from the input and generated output.", + "note": "Paired comparison of physical 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"best_depth": 68.1, + "near_token": "tip", + "output_checked": true + } + ], + "figures": { + "grid": "figures/gemma4-12b-it-mechanism-name-v1-seed2/materials/mechanism-name-flaw-controlled-failure-05__grid.png", + "stream": "figures/gemma4-12b-it-mechanism-name-v1-seed2/materials/mechanism-name-flaw-controlled-failure-05__stream.png", + "trajectory": "figures/gemma4-12b-it-mechanism-name-v1-seed2/materials/mechanism-name-flaw-controlled-failure-05__trajectory.png", + "emergence": "figures/gemma4-12b-it-mechanism-name-v1-seed2/materials/mechanism-name-flaw-controlled-failure-05__emergence.png", + "heatmap": "figures/gemma4-12b-it-mechanism-name-v1-seed2/materials/mechanism-name-flaw-controlled-failure-05__heatmap.png" + } + }, + { + "slug": "mechanism-name-flaw-controlled-failure-06", + "shape": "ASSOCIATION", + "protocol": "lens_eval", + "domain": "materials", + "title": "Flaw-controlled brittle failure 6", + "description": "Frozen mechanism-versus-eponym vignette; both tracked words are absent from the input and generated output.", + "note": "Paired comparison of physical carrier 'crack' against eponym token 'Griffith'. Primary endpoint is the within-item log10 rank difference, summarized separately for J-lens and logit lens.", + "prompt_text": "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.", + "prompt_tail": "astic plate containing a central slit is loaded in tension. Instability begins when the energy-release rate reaches the material's surface-formation resistance.", + "readout_selector": "final_prompt_token", + "readout_at": -1, + "score_positions": [ + 31 + ], + "band": [ + 38.0, + 92.0 + ], + "band_source": "preregistered_cli_or_config", + "condition": null, + "control_group": null, + "phrasing_id": null, + "carrier_id": null, + "target_family": null, + "category": "flaw-controlled-failure", + "answer": null, + "baseline": { + "required": false, + "correct": null, + "expected": [] + }, + "generated_completion": "\n", + "generated_token_ids": [ + 107 + ], + "protocol_violations": [], + "valid_for_metrics": true, + "excluded_reasons": [], + "discovered": false, + "open_vocabulary_config": { + "layer_readout_top": 32, + "surprising_top": 64, + "candidate_generation_uses_tracked_terms": false, + "matched_logit_open_vocabulary_stored": true + }, + "tracked_dropped": [], + "tracked": [ + { + "label": "crack", + "surfaces": [ + "crack" + ], + 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Primary endpoint is the within-item log10 rank difference, summarized separately for J-lens and logit lens.", + "prompt_text": "A ceramic component survives a given tensile stress when its largest flaw is short, but fails after processing introduces a much longer sharp flaw.", + "prompt_tail": "A ceramic component survives a given tensile stress when its largest flaw is short, but fails after processing introduces a much longer sharp flaw.", + "readout_selector": "final_prompt_token", + "readout_at": -1, + "score_positions": [ + 26 + ], + "band": [ + 38.0, + 92.0 + ], + "band_source": "preregistered_cli_or_config", + "condition": null, + "control_group": null, + "phrasing_id": null, + "carrier_id": null, + "target_family": null, + "category": "flaw-controlled-failure", + "answer": null, + "baseline": { + "required": false, + "correct": null, + "expected": [] + }, + "generated_completion": "\n\n", + "generated_token_ids": [ + 108 + ], + "protocol_violations": [], + "valid_for_metrics": 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"figures/gemma4-12b-it-mechanism-name-v1-seed2/materials/mechanism-name-flaw-controlled-failure-08__trajectory.png", + "emergence": "figures/gemma4-12b-it-mechanism-name-v1-seed2/materials/mechanism-name-flaw-controlled-failure-08__emergence.png", + "heatmap": "figures/gemma4-12b-it-mechanism-name-v1-seed2/materials/mechanism-name-flaw-controlled-failure-08__heatmap.png" + } + }, + { + "slug": "mechanism-name-flaw-controlled-failure-09", + "shape": "ASSOCIATION", + "protocol": "lens_eval", + "domain": "materials", + "title": "Flaw-controlled brittle failure 9", + "description": "Frozen mechanism-versus-eponym vignette; both tracked words are absent from the input and generated output.", + "note": "Paired comparison of physical carrier 'crack' against eponym token 'Griffith'. Primary endpoint is the within-item log10 rank difference, summarized separately for J-lens and logit lens.", + "prompt_text": "A sharp notch in a glass sheet locally magnifies stress, allowing separation to start even though the average applied stress is modest.", + "prompt_tail": "A sharp notch in a glass sheet locally magnifies stress, allowing separation to start even though the average applied stress is modest.", + "readout_selector": "final_prompt_token", + "readout_at": -1, + "score_positions": [ + 25 + ], + "band": [ + 38.0, + 92.0 + ], + "band_source": "preregistered_cli_or_config", + "condition": null, + "control_group": null, + "phrasing_id": null, + "carrier_id": null, + "target_family": null, + "category": "flaw-controlled-failure", + "answer": null, + "baseline": { + "required": false, + "correct": null, + "expected": [] + }, + "generated_completion": "\n\n", + "generated_token_ids": [ + 108 + ], + "protocol_violations": [], + "valid_for_metrics": true, + 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"figures/gemma4-12b-it-mechanism-name-v1-seed2/materials/mechanism-name-flaw-controlled-failure-09__trajectory.png", + "emergence": "figures/gemma4-12b-it-mechanism-name-v1-seed2/materials/mechanism-name-flaw-controlled-failure-09__emergence.png", + "heatmap": "figures/gemma4-12b-it-mechanism-name-v1-seed2/materials/mechanism-name-flaw-controlled-failure-09__heatmap.png" + } + }, + { + "slug": "mechanism-name-flaw-controlled-failure-10", + "shape": "ASSOCIATION", + "protocol": "lens_eval", + "domain": "materials", + "title": "Flaw-controlled brittle failure 10", + "description": "Frozen mechanism-versus-eponym vignette; both tracked words are absent from the input and generated output.", + "note": "Paired comparison of physical carrier 'crack' against eponym token 'Griffith'. 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Primary endpoint is the within-item log10 rank difference, summarized separately for J-lens and logit lens.", + "prompt_text": "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.", + "prompt_tail": "al is pulled along an axis that places 120 MPa of resolved shear on its favored slip system. 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output.", + "note": "Paired comparison of physical carrier 'dislocation' against eponym token 'Schmid'. Primary endpoint is the within-item log10 rank difference, summarized separately for J-lens and logit lens.", + "prompt_text": "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.", + "prompt_tail": "iaxial stress is projected onto a crystal's slip-plane normal and slip direction. Plastic flow starts when that resolved component exceeds a critical threshold.", + "readout_selector": "final_prompt_token", + "readout_at": -1, + "score_positions": [ + 31 + ], + "band": [ + 38.0, + 92.0 + ], + "band_source": "preregistered_cli_or_config", + "condition": null, + "control_group": null, + "phrasing_id": null, + "carrier_id": null, + "target_family": null, + "category": "resolved-slip-threshold", + "answer": null, + "baseline": { + "required": false, + "correct": null, + "expected": [] + }, + "generated_completion": "\n", + "generated_token_ids": [ + 107 + ], + "protocol_violations": [], + "valid_for_metrics": true, + "excluded_reasons": [], + "discovered": false, + "open_vocabulary_config": { + "layer_readout_top": 32, + "surprising_top": 64, + "candidate_generation_uses_tracked_terms": false, + "matched_logit_open_vocabulary_stored": true + }, + "tracked_dropped": [], + "tracked": [ + { + "label": "dislocation", + "surfaces": [ + "dislocation" + ], + "token_ids": [ + 25810 + ] + }, + { + "label": "Schmid", + "surfaces": [ + "Schmid" + ], + "token_ids": [ + 145866 + ] + } + ], + "emergence": [ + { + "label": "dislocation", + "best_rank": 325, + "best_depth": 87.2, + "onset_depth": null, + "reached_top1": false, + "best_pos": 31, + "logit_lens_best_rank": 174 + }, + { + "label": "Schmid", + "best_rank": 11647, + "best_depth": 87.2, + "onset_depth": null, + "reached_top1": false, + "best_pos": 31, + "logit_lens_best_rank": 2781 + } + ], + "layer_readouts": [ + { + "layer": 0, + "depth": 0.0, + "top_tokens": [ + "odium", + "unica", + "ilah", + "iber", + "ervo", + "elli", + "wadi", + "Kodi", + "Beverungen", + "odi", + "udi", + "ayya", + "hacerse", + "aforementioned", + "owan", + "Burbank", + "inking", + "Eber", + "ovel", + "nev", + "unun", + "ahs", + "wild", + "bakt", + "avil", + "omata" + ] + }, + { + "layer": 2, + "depth": 4.3, + "top_tokens": [ + "graisse", + "Fountain", + "unica", + "odium", + "ahs", + "quilla", + "wadi", + "udi", + "ayani", + 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"top_tokens": [ + "Zhejiang", + "Guangzhou", + "Erika", + "gently", + "Sore", + "kernels", + "Panasonic", + "ZrO", + "kPa", + "gateway", + "Bangkok", + "Spring", + "matte", + "Xia", + "SPRING", + "Zeta", + "lessly", + "Aurora", + "Pam", + "soreness", + "spring", + "Iraq", + "turquoise", + "waveguide", + "Xinjiang", + "Springfield", + "crammed", + "sensitively" + ] + }, + { + "layer": 9, + "depth": 19.1, + "top_tokens": [ + "flare", + "sociale", + "authenticate", + "infect", + "Infect", + "squares", + "rehabilitated", + "Europ", + "Inflater", + "pneumatic", + "calmed", + "vesicular", + "conferred", + "Adriatic", + "directors", + "Aluminum", + "videre" + ] + }, + { + "layer": 11, + "depth": 23.4, + "top_tokens": [ + "flare", + "sociale", + "Infect", + "infect", + "Europ", + "Inflater", + "squares", + "pneumatic", + "authenticate", + "calmed", + "rehabilitated", + "vesicular", + "directors", + "Aluminum", + "tesla", + "mt", + "reefs" + ] + }, + { + "layer": 13, + "depth": 27.7, + 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slip threshold 3", + "description": "Frozen mechanism-versus-eponym vignette; both tracked words are absent from the input and generated output.", + "note": "Paired comparison of physical carrier 'dislocation' against eponym token 'Schmid'. Primary endpoint is the within-item log10 rank difference, summarized separately for J-lens and logit lens.", + "prompt_text": "Two identically loaded crystals have different orientations. The crystal with the larger product of the two direction cosines activates its favored slip system first.", + "prompt_tail": "entically loaded crystals have different orientations. The crystal with the larger product of the two direction cosines activates its favored slip system first.", + "readout_selector": "final_prompt_token", + "readout_at": -1, + "score_positions": [ + 26 + ], + "band": [ + 38.0, + 92.0 + ], + "band_source": "preregistered_cli_or_config", + "condition": null, + "control_group": null, + "phrasing_id": null, + "carrier_id": null, + "target_family": null, + "category": "resolved-slip-threshold", + "answer": null, + "baseline": { + "required": false, + "correct": null, + "expected": [] + }, + "generated_completion": "\n", + "generated_token_ids": [ + 107 + ], + "protocol_violations": [], + "valid_for_metrics": true, + "excluded_reasons": [], + "discovered": false, + "open_vocabulary_config": { + "layer_readout_top": 32, + "surprising_top": 64, + "candidate_generation_uses_tracked_terms": false, + "matched_logit_open_vocabulary_stored": true + }, + "tracked_dropped": [], + "tracked": [ + { + "label": "dislocation", + "surfaces": [ + 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"near_token": "The", + "output_checked": true + } + ], + "figures": { + "grid": "figures/gemma4-12b-it-mechanism-name-v1-seed2/materials/mechanism-name-resolved-slip-threshold-03__grid.png", + "stream": "figures/gemma4-12b-it-mechanism-name-v1-seed2/materials/mechanism-name-resolved-slip-threshold-03__stream.png", + "trajectory": "figures/gemma4-12b-it-mechanism-name-v1-seed2/materials/mechanism-name-resolved-slip-threshold-03__trajectory.png", + "emergence": "figures/gemma4-12b-it-mechanism-name-v1-seed2/materials/mechanism-name-resolved-slip-threshold-03__emergence.png", + "heatmap": "figures/gemma4-12b-it-mechanism-name-v1-seed2/materials/mechanism-name-resolved-slip-threshold-03__heatmap.png" + } + }, + { + "slug": "mechanism-name-resolved-slip-threshold-04", + "shape": "ASSOCIATION", + "protocol": "lens_eval", + "domain": "materials", + "title": "Resolved slip threshold 4", + "description": "Frozen mechanism-versus-eponym vignette; both tracked words are absent from the input and generated output.", + "note": "Paired comparison of physical carrier 'dislocation' against eponym token 'Schmid'. Primary endpoint is the within-item log10 rank difference, summarized separately for J-lens and logit lens.", + "prompt_text": "A crystal remains elastic until the shear component acting along a permitted direction on a permitted plane reaches a material-specific critical value.", + "prompt_tail": "A crystal remains elastic until the shear component acting along a permitted direction on a permitted plane reaches a material-specific critical value.", + "readout_selector": "final_prompt_token", + "readout_at": -1, + "score_positions": [ + 25 + ], + "band": [ + 38.0, + 92.0 + ], + "band_source": "preregistered_cli_or_config", + "condition": null, + "control_group": null, + "phrasing_id": null, + "carrier_id": null, + "target_family": null, + "category": "resolved-slip-threshold", + "answer": null, + "baseline": { + "required": false, + "correct": null, + "expected": [] + }, + "generated_completion": "", + "generated_token_ids": [ + 106 + ], + "protocol_violations": [], + 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Once this projected driving force is large enough, line imperfections begin to glide.", + "readout_selector": "final_prompt_token", + "readout_at": -1, + "score_positions": [ + 33 + ], + "band": [ + 38.0, + 92.0 + ], + "band_source": "preregistered_cli_or_config", + "condition": null, + "control_group": null, + "phrasing_id": null, + "carrier_id": null, + "target_family": null, + "category": "resolved-slip-threshold", + "answer": null, + "baseline": { + "required": false, + "correct": null, + "expected": [] + }, + "generated_completion": "\n", + "generated_token_ids": [ + 107 + ], + "protocol_violations": [], + "valid_for_metrics": true, + "excluded_reasons": [], + "discovered": false, + "open_vocabulary_config": { + "layer_readout_top": 32, + "surprising_top": 64, + "candidate_generation_uses_tracked_terms": false, + "matched_logit_open_vocabulary_stored": true + }, + "tracked_dropped": [], + "tracked": [ + { + "label": "dislocation", + "surfaces": [ + "dislocation" + ], + 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"heatmap": "figures/gemma4-12b-it-mechanism-name-v1-seed2/materials/mechanism-name-resolved-slip-threshold-05__heatmap.png" + } + }, + { + "slug": "mechanism-name-resolved-slip-threshold-06", + "shape": "ASSOCIATION", + "protocol": "lens_eval", + "domain": "materials", + "title": "Resolved slip threshold 6", + "description": "Frozen mechanism-versus-eponym vignette; both tracked words are absent from the input and generated output.", + "note": "Paired comparison of physical carrier 'dislocation' against eponym token 'Schmid'. Primary endpoint is the within-item log10 rank difference, summarized separately for J-lens and logit lens.", + "prompt_text": "Rotating a single crystal changes the axial stress required to initiate slip even though the critical resolved shear resistance is unchanged.", + "prompt_tail": "Rotating a single crystal changes the axial stress required to initiate slip even though the critical resolved shear resistance is unchanged.", + "readout_selector": "final_prompt_token", + "readout_at": -1, + "score_positions": [ + 22 + ], + "band": [ + 38.0, + 92.0 + ], + "band_source": "preregistered_cli_or_config", + "condition": null, + "control_group": null, + "phrasing_id": null, + "carrier_id": null, + "target_family": null, + "category": "resolved-slip-threshold", + "answer": null, + "baseline": { + "required": false, + "correct": null, + "expected": [] + }, + "generated_completion": "\n", + "generated_token_ids": [ + 107 + ], + "protocol_violations": [], + "valid_for_metrics": true, + 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"figures/gemma4-12b-it-mechanism-name-v1-seed2/materials/mechanism-name-resolved-slip-threshold-06__emergence.png", + "heatmap": "figures/gemma4-12b-it-mechanism-name-v1-seed2/materials/mechanism-name-resolved-slip-threshold-06__heatmap.png" + } + }, + { + "slug": "mechanism-name-resolved-slip-threshold-07", + "shape": "ASSOCIATION", + "protocol": "lens_eval", + "domain": "materials", + "title": "Resolved slip threshold 7", + "description": "Frozen mechanism-versus-eponym vignette; both tracked words are absent from the input and generated output.", + "note": "Paired comparison of physical carrier 'dislocation' against eponym token 'Schmid'. Primary endpoint is the within-item log10 rank difference, summarized separately for J-lens and logit lens.", + "prompt_text": "A favorably oriented slip system reaches its critical shear value before any other system, producing the first permanent surface steps.", + "prompt_tail": "A favorably oriented slip system reaches its critical shear value before any other system, producing the first permanent surface steps.", + "readout_selector": "final_prompt_token", + "readout_at": -1, + "score_positions": [ + 22 + ], + "band": [ + 38.0, + 92.0 + ], + "band_source": "preregistered_cli_or_config", + "condition": null, + "control_group": null, + "phrasing_id": null, + "carrier_id": null, + "target_family": null, + "category": "resolved-slip-threshold", + "answer": null, + "baseline": { + "required": false, + "correct": null, + "expected": [] + }, + "generated_completion": "", + "generated_token_ids": [ + 106 + ], + "protocol_violations": [], + "valid_for_metrics": true, + "excluded_reasons": 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"near_token": "an", + "output_checked": true + }, + { + "concept": "permissible", + "score": 1.0, + "best_rank": 1, + "best_depth": 72.3, + "near_token": "allowed", + "output_checked": true + } + ], + "figures": { + "grid": "figures/gemma4-12b-it-mechanism-name-v1-seed2/materials/mechanism-name-resolved-slip-threshold-09__grid.png", + "stream": "figures/gemma4-12b-it-mechanism-name-v1-seed2/materials/mechanism-name-resolved-slip-threshold-09__stream.png", + "trajectory": "figures/gemma4-12b-it-mechanism-name-v1-seed2/materials/mechanism-name-resolved-slip-threshold-09__trajectory.png", + "emergence": "figures/gemma4-12b-it-mechanism-name-v1-seed2/materials/mechanism-name-resolved-slip-threshold-09__emergence.png", + "heatmap": "figures/gemma4-12b-it-mechanism-name-v1-seed2/materials/mechanism-name-resolved-slip-threshold-09__heatmap.png" + } + }, + { + "slug": "mechanism-name-resolved-slip-threshold-10", + "shape": "ASSOCIATION", + "protocol": "lens_eval", + "domain": "materials", + "title": "Resolved slip threshold 10", + "description": "Frozen mechanism-versus-eponym vignette; both tracked words are absent from the input and generated output.", + "note": "Paired comparison of physical carrier 'dislocation' against eponym token 'Schmid'. Primary endpoint is the within-item log10 rank difference, summarized separately for J-lens and logit lens.", + "prompt_text": "When the resolved driving force on the most favorably oriented plane-direction pair exceeds its critical resistance, mobile line defects sweep across the crystal.", + "prompt_tail": "en the resolved driving force on the most favorably oriented plane-direction pair exceeds its critical resistance, mobile line defects sweep across the crystal.", + "readout_selector": "final_prompt_token", + "readout_at": -1, + "score_positions": [ + 27 + ], + "band": [ + 38.0, + 92.0 + ], + "band_source": "preregistered_cli_or_config", + "condition": null, + "control_group": null, + "phrasing_id": null, + "carrier_id": null, + "target_family": null, + "category": "resolved-slip-threshold", + "answer": null, + "baseline": { + "required": false, + "correct": null, + "expected": [] + }, + "generated_completion": "", + "generated_token_ids": [ + 101 + ], + "protocol_violations": [], + "valid_for_metrics": true, + "excluded_reasons": [], + "discovered": false, + "open_vocabulary_config": { + "layer_readout_top": 32, + "surprising_top": 64, + "candidate_generation_uses_tracked_terms": false, + "matched_logit_open_vocabulary_stored": true + }, + "tracked_dropped": [], + "tracked": [ + { + "label": "dislocation", + "surfaces": [ + "dislocation" + ], + "token_ids": [ + 25810 + ] + }, + { + "label": "Schmid", + "surfaces": [ + "Schmid" + ], + "token_ids": [ + 145866 + ] + } + ], + "emergence": [ + { + "label": "dislocation", + "best_rank": 1161, + "best_depth": 83.0, + "onset_depth": null, + "reached_top1": false, + "best_pos": 27, + "logit_lens_best_rank": 327 + }, + { + "label": "Schmid", + "best_rank": 33711, + "best_depth": 76.6, + "onset_depth": null, + "reached_top1": false, + "best_pos": 27, + "logit_lens_best_rank": 16033 + } + ], + "layer_readouts": [ + { + "layer": 0, + "depth": 0.0, + "top_tokens": [ + "odium", + "unica", + "ilah", + "iber", + "elli", + 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] + }, + { + "layer": 6, + "depth": 12.8, + "top_tokens": [ + "flare", + "sociale", + "Infect", + "infect", + "Inflater", + "squares", + "authenticate", + "pneumatic", + "calmed", + "Europ", + "rehabilitated", + "vesicular", + "mt", + "conferred", + "Aluminum", + "tesla" + ] + }, + { + "layer": 8, + "depth": 17.0, + "top_tokens": [ + "Fountain", + "odium", + "graisse", + "quilla", + "rodeo", + "ta", + "arita", + "arpur", + "wadi", + "overheard", + "uded", + "unica", + "tox", + "lova", + "ahs", + "Imperio", + "erver", + "ayani", + "Gra", + "ervo", + "FAS", + "alco", + "Sever", + "Thunderbird" + ] + }, + { + "layer": 9, + "depth": 19.1, + "top_tokens": [ + "flare", + "sociale", + "infect", + "authenticate", + "Infect", + "rehabilitated", + "Europ", + "vesicular", + "squares", + "pneumatic", + "Inflater", + "conferred", + "calmed", + "mt", + "ductory", + "remotely", + "appealed", + "nye" + ] + }, + { + "layer": 11, + "depth": 23.4, + "top_tokens": [ + "flare", + "sociale", + "Infect", + 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"top_tokens": [ + "flare", + "sociale", + "infect", + "squares", + "Infect", + "vesicular", + "authenticate", + "directors", + "mt", + "pneumatic", + "conferred", + "Inflater", + "fs", + "verder", + "rehabilitated", + "tesla", + "Europ", + "calmed", + "appealed", + "reefs" + ] + }, + { + "layer": 19, + "depth": 40.4, + "top_tokens": [ + "s", + "eness", + "heartedly", + "med", + "fif", + "shore", + "uette", + "mate", + "rog", + "itten", + "SELS", + "lepas", + "selves", + "uating", + "oubtedly", + "isons", + "ujos", + "gehend", + "credo", + "trach" + ] + }, + { + "layer": 21, + "depth": 44.7, + "top_tokens": [ + "uette", + "fif", + "med", + "Depois", + "Kalau", + "heartedly", + "voila", + "osof", + "fato", + "folds", + "iven", + "rites", + "strokes", + "uating", + "selves", + "credo", + "certeza", + "ocorrer" + ] + }, + { + "layer": 22, + "depth": 46.8, + "top_tokens": [ + "med", + "s", + "uette", + "digamos", + "selves", + "fif", + "Kalau", + "enorm", + "isons", + "folds", + "iven", + 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"trajectory": "figures/gemma4-12b-it-mechanism-name-v1-seed2/materials/mechanism-name-resolved-slip-threshold-10__trajectory.png", + "emergence": "figures/gemma4-12b-it-mechanism-name-v1-seed2/materials/mechanism-name-resolved-slip-threshold-10__emergence.png", + "heatmap": "figures/gemma4-12b-it-mechanism-name-v1-seed2/materials/mechanism-name-resolved-slip-threshold-10__heatmap.png" + } + }, + { + "slug": "mechanism-name-lattice-diffusion-creep-01", + "shape": "ASSOCIATION", + "protocol": "lens_eval", + "domain": "materials", + "title": "Lattice-diffusion creep 1", + "description": "Frozen mechanism-versus-eponym vignette; both tracked words are absent from the input and generated output.", + "note": "Paired comparison of physical carrier 'vacancy' against eponym token 'Herring'. Primary endpoint is the within-item log10 rank difference, summarized separately for J-lens and logit lens.", + "prompt_text": "A fine polycrystal held at high temperature and low stress lengthens as atoms diffuse through crystal interiors from compressed faces toward tensile faces.", + "prompt_tail": "A fine polycrystal held at high temperature and low stress lengthens as atoms diffuse through crystal interiors from compressed faces toward tensile faces.", + "readout_selector": "final_prompt_token", + "readout_at": -1, + "score_positions": [ + 26 + ], + "band": [ + 38.0, + 92.0 + ], + "band_source": "preregistered_cli_or_config", + "condition": null, + "control_group": null, + "phrasing_id": null, + "carrier_id": null, + "target_family": null, + "category": "lattice-diffusion-creep", + "answer": null, + "baseline": { + "required": false, + "correct": null, + "expected": [] + }, + "generated_completion": "\n\n", + "generated_token_ids": [ + 108 + ], + "protocol_violations": [], + 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Primary endpoint is the within-item log10 rank difference, summarized separately for J-lens and logit lens.", + "prompt_text": "Slow deformation at high homologous temperature is controlled by bulk atomic transport, and its rate increases strongly as crystallite size decreases.", + "prompt_tail": "Slow deformation at high homologous temperature is controlled by bulk atomic transport, and its rate increases strongly as crystallite size decreases.", + "readout_selector": "final_prompt_token", + "readout_at": -1, + "score_positions": [ + 24 + ], + "band": [ + 38.0, + 92.0 + ], + "band_source": "preregistered_cli_or_config", + "condition": null, + "control_group": null, + "phrasing_id": null, + "carrier_id": null, + "target_family": null, + "category": "lattice-diffusion-creep", + "answer": null, + "baseline": { + "required": false, + "correct": null, + "expected": [] + }, + "generated_completion": "\n\n", + "generated_token_ids": [ + 108 + ], + "protocol_violations": [], + 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Primary endpoint is the within-item log10 rank difference, summarized separately for J-lens and logit lens.", + "prompt_text": "A fine-grained ceramic deforms under low stress near its melting temperature through diffusion across the volume of each crystallite rather than along interfaces.", + "prompt_tail": "fine-grained ceramic deforms under low stress near its melting temperature through diffusion across the volume of each crystallite rather than along interfaces.", + "readout_selector": "final_prompt_token", + "readout_at": -1, + "score_positions": [ + 28 + ], + "band": [ + 38.0, + 92.0 + ], + "band_source": "preregistered_cli_or_config", + "condition": null, + "control_group": null, + "phrasing_id": null, + "carrier_id": null, + "target_family": null, + "category": "lattice-diffusion-creep", + "answer": null, + "baseline": { + "required": false, + "correct": null, + "expected": [] + }, + "generated_completion": "\n\n", + "generated_token_ids": [ + 108 + ], + "protocol_violations": [], + "valid_for_metrics": true, + "excluded_reasons": [], + "discovered": false, + "open_vocabulary_config": { + "layer_readout_top": 32, + "surprising_top": 64, + "candidate_generation_uses_tracked_terms": false, + "matched_logit_open_vocabulary_stored": true + }, + "tracked_dropped": [], + "tracked": [ + { + "label": "vacancy", + "surfaces": [ + "vacancy" + ], + "token_ids": [ + 43976, + 153380, + 190211 + ] + }, + { + "label": "Herring", + "surfaces": [ + "Herring" + ], + "token_ids": [ + 187758, + 104598 + ] + } + ], + "emergence": [ + { + "label": "vacancy", + "best_rank": 6612, + "best_depth": 87.2, + "onset_depth": null, + "reached_top1": false, + "best_pos": 28, + "logit_lens_best_rank": 216 + }, + { + "label": "Herring", + "best_rank": 51358, + "best_depth": 55.3, + "onset_depth": null, + "reached_top1": false, + "best_pos": 28, + "logit_lens_best_rank": 683 + } + ], + "layer_readouts": [ + { + "layer": 0, + "depth": 0.0, + "top_tokens": [ + "unica", + "odium", + "elli", + "iber", + "wadi", + "ervo", + "Beverungen", + "odi", + "ilah", + "udi", + "aforementioned", + "hacerse", + "Burbank", + "inking", + "Kodi", + "ayya", + "ahs", + "eck", + "Eber", + "ovel", + "lassen", + "nev", + "wit", + "bakt", + "avil", + "okok" + ] + }, + { + "layer": 2, + "depth": 4.3, + "top_tokens": [ + "graisse", + "Fountain", + "odium", + "unica", + "ahs", + "quilla", + "wadi", + "overheard", + "udi", + "ayani", + "rodeo", + "arpur", + "odi", + "ServiceID", + "Imperio", + "aville", + "arita", + "ervo", + "Kodi", + "erver", + "cil", + "avil", + "anio", + "uded", + "Toto", + "qat", + "ayya", + "odil", + "zki" + ] + }, + { + "layer": 4, + "depth": 8.5, + "top_tokens": [ + "graisse", + "Fountain", + "unica", + "odium", + "ahs", + "quilla", + "wadi", + "overheard", + "udi", + "ayani", + "arita", + "rodeo", + "arpur", + "odi", + "ervo", + "cil", + "anio", + "aville", + "alco", + "Imperio", + "uded", + "erver", + "qat", + "ServiceID", + "ta", + "empleados", + "odil", + "ayya", + "sel" + ] + }, + { + "layer": 6, + "depth": 12.8, + "top_tokens": [ + "flare", + "sociale", + "Infect", + "infect", + "Inflater", + "authenticate", + "pneumatic", + "calmed", + "Europ", + "squares", + "rehabilitated", + "vesicular", + "mt", + "conferred", + "fs", + "directors", + "Aluminum" + ] + }, + { + "layer": 8, + "depth": 17.0, + "top_tokens": [ + "tration", + "plings", + "Arbor", + "laparoscopic", + "longer", + "PCR", + "Raptors", + "arantine", + "ogenies", + "doubled", + "Alhaji", + "repaid", + "formed", + "hene", + "pneumatic", + "wget", + "Razer", + "KNN", + "crete", + "Bamboo", + "regenerated", + "Reflex", + "itzerland", + "genesis", + "bordered" + ] + }, + { + "layer": 9, + "depth": 19.1, + "top_tokens": [ + "flare", + "sociale", + "authenticate", + "rehabilitated", + "infect", + "Europ", + "Infect", + "vesicular", + "conferred", + "pneumatic", + "calmed", + "squares", + "Inflater", + "remotely", + "ductory", + "mt", + "appealed", + "inflamed" + ] + }, + { + "layer": 11, + 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"arpur" + ] + }, + { + "layer": 17, + "depth": 36.2, + "top_tokens": [ + "sociale", + "infect", + "flare", + "squares", + "Infect", + "vesicular", + "mt", + "pneumatic", + "directors", + "authenticate", + "puis", + "conferred", + "verder", + "rehabilitated", + "fs", + "Inflater", + "tesla", + "reefs", + "chartered" + ] + }, + { + "layer": 19, + "depth": 40.4, + "top_tokens": [ + "s", + "gehend", + "heartedly", + "fif", + "eness", + "Moreover", + "ures", + "brid", + "bulb", + "selves", + "esimerkiksi", + "t", + "med", + "ively", + "digamos", + "sels", + "rog", + "mektedir", + "ujos", + "ermost", + "osof" + ] + }, + { + "layer": 21, + "depth": 44.7, + "top_tokens": [ + "gehend", + "Moreover", + "med", + "heartedly", + "voila", + "esimerkiksi", + "Kalau", + "osof", + "fif", + "fato", + "digamos", + "brid", + "sink", + "selves", + "Furthermore", + "Depois", + "s", + "uri", + "certainly" + ] + }, + { + "layer": 22, + "depth": 46.8, + "top_tokens": [ + "s", + "med", + "gehend", + "selves", + 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"concept": "detailed", + "score": 1.0, + "best_rank": 1, + "best_depth": 72.3, + "near_token": "grained", + "output_checked": true + }, + { + "concept": "aesthetic", + "score": 1.0, + "best_rank": 1, + "best_depth": 72.3, + "near_token": "de", + "output_checked": true + } + ], + "figures": { + "grid": "figures/gemma4-12b-it-mechanism-name-v1-seed2/materials/mechanism-name-lattice-diffusion-creep-06__grid.png", + "stream": "figures/gemma4-12b-it-mechanism-name-v1-seed2/materials/mechanism-name-lattice-diffusion-creep-06__stream.png", + "trajectory": "figures/gemma4-12b-it-mechanism-name-v1-seed2/materials/mechanism-name-lattice-diffusion-creep-06__trajectory.png", + "emergence": "figures/gemma4-12b-it-mechanism-name-v1-seed2/materials/mechanism-name-lattice-diffusion-creep-06__emergence.png", + "heatmap": "figures/gemma4-12b-it-mechanism-name-v1-seed2/materials/mechanism-name-lattice-diffusion-creep-06__heatmap.png" + } + }, + { + "slug": "mechanism-name-lattice-diffusion-creep-07", + "shape": "ASSOCIATION", + "protocol": "lens_eval", + "domain": "materials", + "title": "Lattice-diffusion creep 7", + "description": "Frozen mechanism-versus-eponym vignette; both tracked words are absent from the input and generated output.", + "note": "Paired comparison of physical carrier 'vacancy' against eponym token 'Herring'. Primary endpoint is the within-item log10 rank difference, summarized separately for J-lens and logit lens.", + "prompt_text": "Atoms leave compressed grain faces, traverse the crystal lattice, and attach at tensile faces, leading to time-dependent shape change.", + "prompt_tail": "Atoms leave compressed grain faces, traverse the crystal lattice, and attach at tensile faces, leading to time-dependent shape change.", + "readout_selector": "final_prompt_token", + "readout_at": -1, + "score_positions": [ + 26 + ], + "band": [ + 38.0, + 92.0 + ], + "band_source": "preregistered_cli_or_config", + "condition": null, + "control_group": null, + "phrasing_id": null, + "carrier_id": null, + "target_family": null, + "category": "lattice-diffusion-creep", + "answer": null, + "baseline": { + "required": false, + "correct": null, + "expected": [] + }, + "generated_completion": "\n", + "generated_token_ids": [ + 107 + ], + "protocol_violations": [], + "valid_for_metrics": true, + "excluded_reasons": [], + "discovered": false, + "open_vocabulary_config": { + "layer_readout_top": 32, + "surprising_top": 64, + "candidate_generation_uses_tracked_terms": false, + "matched_logit_open_vocabulary_stored": true + }, + "tracked_dropped": [], + "tracked": [ + { + "label": "vacancy", + "surfaces": [ + "vacancy" + ], + "token_ids": [ + 43976, + 153380, + 190211 + ] + }, + { + "label": "Herring", + "surfaces": [ + "Herring" + ], + "token_ids": [ + 187758, + 104598 + ] + } + ], + "emergence": [ + { + "label": "vacancy", + "best_rank": 7196, + "best_depth": 76.6, + "onset_depth": null, + "reached_top1": false, + "best_pos": 26, + "logit_lens_best_rank": 3926 + }, + { + "label": "Herring", + "best_rank": 51767, + "best_depth": 51.1, + "onset_depth": null, + "reached_top1": false, + "best_pos": 26, + "logit_lens_best_rank": 1681 + } + ], + "layer_readouts": [ + { + "layer": 0, + "depth": 0.0, + "top_tokens": [ + "odium", + "unica", + "elli", + "odi", + "ilah", + "ervo", + "wadi", + "Kodi", + "udi", 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"flare", + "sociale", + "Infect", + "infect", + "Inflater", + "squares", + "authenticate", + "pneumatic", + "calmed", + "Europ", + "rehabilitated", + "vesicular", + "mt", + "conferred", + "Aluminum", + "fs" + ] + }, + { + "layer": 8, + "depth": 17.0, + "top_tokens": [ + "Fountain", + "odium", + "quilla", + "ta", + "rodeo", + "graisse", + "tox", + "lova", + "arpur", + "annya", + "sever", + "omba", + "arita", + "uded", + "Selangor", + "tery", + "overheard", + "Maldonado", + "Sever", + "ushima", + "Alamos", + "FAS", + "Thunderbird", + "Sever", + "poved" + ] + }, + { + "layer": 9, + "depth": 19.1, + "top_tokens": [ + "flare", + "sociale", + "authenticate", + "infect", + "Infect", + "rehabilitated", + "Europ", + "vesicular", + "Inflater", + "squares", + "conferred", + "pneumatic", + "calmed", + "mt", + "remotely", + "appealed", + "ductory", + "Aluminum" + ] + }, + { + "layer": 11, + "depth": 23.4, + "top_tokens": [ + "flare", + "sociale", + "Infect", + "infect", + "Europ", + "Inflater", + 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1.0, + "best_rank": 3, + "best_depth": 51.1, + "near_token": "The", + "output_checked": true + } + ], + "figures": { + "grid": "figures/gemma4-12b-it-mechanism-name-v1-seed2/materials/mechanism-name-lattice-diffusion-creep-08__grid.png", + "stream": "figures/gemma4-12b-it-mechanism-name-v1-seed2/materials/mechanism-name-lattice-diffusion-creep-08__stream.png", + "trajectory": "figures/gemma4-12b-it-mechanism-name-v1-seed2/materials/mechanism-name-lattice-diffusion-creep-08__trajectory.png", + "emergence": "figures/gemma4-12b-it-mechanism-name-v1-seed2/materials/mechanism-name-lattice-diffusion-creep-08__emergence.png", + "heatmap": "figures/gemma4-12b-it-mechanism-name-v1-seed2/materials/mechanism-name-lattice-diffusion-creep-08__heatmap.png" + } + }, + { + "slug": "mechanism-name-lattice-diffusion-creep-09", + "shape": "ASSOCIATION", + "protocol": "lens_eval", + "domain": "materials", + "title": "Lattice-diffusion creep 9", + "description": "Frozen mechanism-versus-eponym vignette; 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"figures/gemma4-12b-it-mechanism-name-v1-seed2/materials/mechanism-name-lattice-diffusion-creep-09__heatmap.png" + } + }, + { + "slug": "mechanism-name-lattice-diffusion-creep-10", + "shape": "ASSOCIATION", + "protocol": "lens_eval", + "domain": "materials", + "title": "Lattice-diffusion creep 10", + "description": "Frozen mechanism-versus-eponym vignette; both tracked words are absent from the input and generated output.", + "note": "Paired comparison of physical carrier 'vacancy' against eponym token 'Herring'. Primary endpoint is the within-item log10 rank difference, summarized separately for J-lens and logit lens.", + "prompt_text": "Under a small tensile load, bulk diffusion moves atoms toward faces normal to the loading direction, producing gradual axial extension.", + "prompt_tail": "Under a small tensile load, bulk diffusion moves atoms toward faces normal to the loading direction, producing gradual axial extension.", + "readout_selector": "final_prompt_token", + "readout_at": -1, + "score_positions": [ + 23 + ], + "band": [ + 38.0, + 92.0 + ], + "band_source": "preregistered_cli_or_config", + "condition": null, + "control_group": null, + "phrasing_id": null, + "carrier_id": null, + "target_family": null, + "category": "lattice-diffusion-creep", + "answer": null, + "baseline": { + "required": false, + "correct": null, + "expected": [] + }, + "generated_completion": "\n", + "generated_token_ids": [ + 107 + ], + "protocol_violations": [], + "valid_for_metrics": true, + 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Answer in one word.\n<|turn>model\n<|channel>thought\n", + "prompt_tail": "<|turn>user\nThink of a metal strengthening mechanism. 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Answer in one word.\n<|turn>model\n<|channel>thought\n", + "source": "Vacancy", + "target": "twin", + "alpha": 0.5, + "method": "pinv", + "band_layers": [ + 19, + 21, + 22, + 24, + 26, + 28, + 30, + 32, + 34, + 36, + 38, + 39, + 41, + 43 + ], + "swap_positions": [ + 0, + 1, + 2, + 3, + 4, + 5, + 6, + 7, + 8, + 9, + 10, + 11, + 12, + 13, + 14, + 15, + 16, + 17, + 18, + 19, + 20, + 21, + 22, + 23, + 24 + ], + "clean_top": [ + [ + "Vacancy", + 0.9998879432678223 + ], + [ + "Vac", + 5.8288133004680276e-05 + ], + [ + "Dis", + 5.143909584148787e-05 + ], + [ + "F", + 2.057822939605103e-06 + ], + [ + "Interstitial", + 2.0535010492039873e-07 + ], + [ + "Sch", + 4.6906382777933686e-08 + ], + [ + "vacancy", + 1.6465689256506266e-08 + ], + [ + "Point", + 1.453092046688198e-08 + ] + ], + "swapped_top": [ + [ + "Vacancy", + 0.8506219387054443 + ], + [ + "Dis", + 0.14781592786312103 + ], + [ + "F", + 0.0007286715554073453 + ], + [ + "Vac", + 0.0006845235475338995 + ], + [ + "Point", + 0.00011174539395142347 + ], + [ + "<|channel>", + 3.3552056265762076e-05 + ], + [ + "J", + 1.3633884918817785e-06 + ], + [ + "Void", + 9.517985404272622e-07 + ] + ], + "source_rank_clean": 0, + "source_rank_swapped": 0, + "target_rank_clean": 89369, + "target_rank_swapped": 80657, + "expected_answer": null, + "swap_answer": null, + "baseline_correct": null, + "counterfactual_correct": null, + "causal_success": null, + "target_top1": false, + "protocol_success": false, + "dynamic_source": true + }, + { + "prompt": "<|turn>user\nThink of a crystalline defect. 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Answer using one abbreviation.\n<|turn>model\n<|channel>thought\n", + "prompt_tail": "<|turn>user\nThink of a common crystal lattice. 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Answer in one word.\n<|turn>model\n<|channel>thought\n", + "source": "Vacancy", + "target": "twin", + "alpha": 1.0, + "method": "pinv", + "band_layers": [ + 19, + 21, + 22, + 24, + 26, + 28, + 30, + 32, + 34, + 36, + 38, + 39, + 41, + 43 + ], + "swap_positions": [ + 0, + 1, + 2, + 3, + 4, + 5, + 6, + 7, + 8, + 9, + 10, + 11, + 12, + 13, + 14, + 15, + 16, + 17, + 18, + 19, + 20, + 21, + 22, + 23, + 24 + ], + "clean_top": [ + [ + "Vacancy", + 0.9998879432678223 + ], + [ + "Vac", + 5.8288133004680276e-05 + ], + [ + "Dis", + 5.143909584148787e-05 + ], + [ + "F", + 2.057822939605103e-06 + ], + [ + "Interstitial", + 2.0535010492039873e-07 + ], + [ + "Sch", + 4.6906382777933686e-08 + ], + [ + "vacancy", + 1.6465689256506266e-08 + ], + [ + "Point", + 1.453092046688198e-08 + ] + ], + "swapped_top": [ + [ + "Vacancy", + 0.5767031311988831 + ], + [ + "Dis", + 0.42192500829696655 + ], + [ + "F", + 0.0010790464002639055 + ], + [ + "Vac", + 0.00019346235785633326 + ], + [ + "Point", + 8.320712368004024e-05 + ], + [ + "<|channel>", + 1.2367634553811513e-05 + ], + [ + "J", + 1.6672519222993287e-06 + ], + [ + "Void", + 9.297796736973396e-07 + ] + ], + "source_rank_clean": 0, + "source_rank_swapped": 0, + "target_rank_clean": 89369, + "target_rank_swapped": 62911, + "expected_answer": null, + "swap_answer": null, + "baseline_correct": null, + "counterfactual_correct": null, + "causal_success": null, + "target_top1": false, + "protocol_success": false, + "dynamic_source": true + }, + { + "prompt": "<|turn>user\nThink of a crystalline defect. 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"Frozen mechanism-versus-eponym vignette; both tracked words are absent from the input and generated output.", + "note": "Paired comparison of physical carrier 'crack' against eponym token 'Griffith'. 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Primary endpoint is the within-item log10 rank difference, summarized separately for J-lens and logit lens.", + "prompt_text": "A favorably oriented slip system reaches its critical shear value before any other system, producing the first permanent surface steps.", + "prompt_tail": "A favorably oriented slip system reaches its critical shear value before any other system, producing the first permanent surface steps.", + "readout_selector": "final_prompt_token", + "readout_at": -1, + "score_positions": [ + 22 + ], + "band": [ + 38.0, + 92.0 + ], + "band_source": "preregistered_cli_or_config", + "condition": null, + "control_group": null, + "phrasing_id": null, + "carrier_id": null, + "target_family": null, + "category": "resolved-slip-threshold", + "answer": null, + "baseline": { + "required": false, + "correct": null, + "expected": [] + }, + "generated_completion": "\n\n", + "generated_token_ids": [ + 108 + ], + "protocol_violations": [], + "valid_for_metrics": true, + 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"figures/gemma4-31b-it-mechanism-name-v1-seed0/materials/mechanism-name-lattice-diffusion-creep-01__emergence.png", + "heatmap": "figures/gemma4-31b-it-mechanism-name-v1-seed0/materials/mechanism-name-lattice-diffusion-creep-01__heatmap.png" + } + }, + { + "slug": "mechanism-name-lattice-diffusion-creep-02", + "shape": "ASSOCIATION", + "protocol": "lens_eval", + "domain": "materials", + "title": "Lattice-diffusion creep 2", + "description": "Frozen mechanism-versus-eponym vignette; both tracked words are absent from the input and generated output.", + "note": "Paired comparison of physical carrier 'vacancy' against eponym token 'Herring'. Primary endpoint is the within-item log10 rank difference, summarized separately for J-lens and logit lens.", + "prompt_text": "At elevated temperature, atoms move through the lattice between oppositely loaded grain faces, producing slow elongation without extensive line-defect glide.", + "prompt_tail": "At elevated temperature, atoms move through the lattice between oppositely loaded grain faces, producing slow elongation without extensive line-defect glide.", + "readout_selector": "final_prompt_token", + "readout_at": -1, + "score_positions": [ + 26 + ], + "band": [ + 38.0, + 92.0 + ], + "band_source": "preregistered_cli_or_config", + "condition": null, + "control_group": null, + "phrasing_id": null, + "carrier_id": null, + "target_family": null, + "category": "lattice-diffusion-creep", + "answer": null, + "baseline": { + "required": false, + "correct": null, + "expected": [] + }, + "generated_completion": "e", + "generated_token_ids": [ + 236744 + ], + "protocol_violations": [], + 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Primary endpoint is the within-item log10 rank difference, summarized separately for J-lens and logit lens.", + "prompt_text": "Material is transported through crystal interiors from interfaces under compression to interfaces under tension, gradually elongating the specimen.", + "prompt_tail": "Material is transported through crystal interiors from interfaces under compression to interfaces under tension, gradually elongating the specimen.", + "readout_selector": "final_prompt_token", + "readout_at": -1, + "score_positions": [ + 21 + ], + "band": [ + 38.0, + 92.0 + ], + "band_source": "preregistered_cli_or_config", + "condition": null, + "control_group": null, + "phrasing_id": null, + "carrier_id": null, + "target_family": null, + "category": "lattice-diffusion-creep", + "answer": null, + "baseline": { + "required": false, + "correct": null, + "expected": [] + }, + "generated_completion": "", + "generated_token_ids": [ + 100 + ], + "protocol_violations": [], + "valid_for_metrics": 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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.", + "prompt_text": "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.", + "prompt_tail": "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.", + "readout_selector": "final_prompt_token", + "readout_at": -1, + "score_positions": [ + 31 + ], + "band": [ + 38.0, + 92.0 + ], + "band_source": "preregistered_cli_or_config", + "condition": null, + "control_group": null, + "phrasing_id": null, + "carrier_id": null, + "target_family": null, + "category": "notch-resistance", + "answer": null, + "baseline": { + "required": false, + "correct": null, + "expected": [] + }, + "generated_completion": " This", + "generated_token_ids": [ + 1174 + ], + 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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.", + "prompt_text": "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.", + "prompt_tail": "ith a sharp starter notch resists sudden extension under increasing tensile load. 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Watch 'energy' / 'surface' / 'Griffith' surface before 'crack'.", + "note": "Griffith's result sigma_c ~ sqrt(2 E gamma / (pi a)). The answer token is 'crack (length)'; 'energy', 'surface' and the eponym 'Griffith' are unspoken intermediates the derivation passes through -- the fracture analogue of Mars->color->red.", + "prompt_text": "In linear elastic fracture mechanics, the critical stress for a brittle solid to fail is set by balancing the released strain energy against the energy needed to create new surface. The critical stress therefore scales inversely with the square root of the", + "prompt_tail": "balancing the released strain energy against the energy needed to create new surface. The critical stress therefore scales inversely with the square root of the", + "readout_at": -1, + "band": [ + 68.3, + 87.8 + ], + "discovered": false, + "tracked_dropped": [], + "emergence": [ + { + "label": "energy", + "best_rank": 0, + "best_depth": 70.7, + "reached_top1": true, + "best_pos": 21 + }, + { + "label": "surface", + "best_rank": 0, + "best_depth": 82.9, + "reached_top1": true, + "best_pos": 29 + }, + { + "label": "Griffith", + "best_rank": 1, + "best_depth": 87.8, + "reached_top1": false, + "best_pos": 18 + }, + { + "label": "crack", + "best_rank": 0, + "best_depth": 85.4, + "reached_top1": true, + "best_pos": 10 + }, + { + "label": "length", + "best_rank": 0, + "best_depth": 68.3, + "reached_top1": true, + "best_pos": 40 + } + ], + "layer_readouts": [ + { + "layer": 0, + "depth": 0.0, + "top_tokens": [ + "Tua", + "Dz", + "Brest", + "Eddy", + "Darm", + "Dar", + "DTS", + "Tann" + ] + }, + { + "layer": 1, + "depth": 2.4, + "top_tokens": [ + "d", + "l", + "or", + "al", + 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"fracture", + "title": "Cyclic loading -> failure mode (multi-hop)", + "description": "Cyclic sub-yield loading. 'fatigue' should lead 'crack', with 'Paris'/'striations' as deeper-layer associates.", + "note": "The answer is 'fatigue'. Whether 'Paris' (the Paris crack-growth law) or 'striations' (the fractographic fingerprint) appear in deeper layers probes how much associated fracture knowledge the workspace pulls in beyond the one-word answer.", + "prompt_text": "A steel component is loaded repeatedly far below its yield stress for millions of cycles. Tiny cracks nucleate at the surface and grow a little on every cycle until the part suddenly breaks. This progressive mode of failure is called", + "prompt_tail": "lions of cycles. Tiny cracks nucleate at the surface and grow a little on every cycle until the part suddenly breaks. This progressive mode of failure is called", + "readout_at": -1, + "band": [ + 82.9, + 90.2 + ], + "discovered": false, + "tracked_dropped": [ + "striations" + ], + "emergence": [ + { + "label": "fatigue", + "best_rank": 0, + "best_depth": 85.4, + "reached_top1": true, + "best_pos": 12 + }, + { + "label": "crack", + "best_rank": 0, + "best_depth": 82.9, + "reached_top1": true, + "best_pos": 17 + }, + { + "label": "cyclic", + "best_rank": 1, + "best_depth": 90.2, + "reached_top1": false, + "best_pos": 12 + }, + { + "label": "Paris", + "best_rank": 172, + "best_depth": 100.0, + "reached_top1": false, + "best_pos": 43 + } + ], + "layer_readouts": [ + { + "layer": 0, + "depth": 0.0, + "top_tokens": [ + "everybody", + "Indians", + "Hindus", + "issimo", + "heed", + "Calcutta", + "الناس", + "forthwith" + ] + }, + { + "layer": 1, + "depth": 2.4, + "top_tokens": [ + "etc", + "or", + "c", + "l", + "a", + "t", + "o", + "about" + ] + }, + { + "layer": 2, + "depth": 4.9, + "top_tokens": [ + 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[ + "EXAMPLE", + "뭐라고", + "XXXXXXXXXXXXXXXX", + "sogenannte", + "something", + "BECAUSE", + "ذریع", + "ــــــــ" + ] + }, + { + "layer": 27, + "depth": 65.9, + "top_tokens": [ + "xxxxxxxx", + "XXXXXXXXXXXXXXXX", + "뭐라고", + "something", + "XXX", + "XXXXXXXX", + "blah", + "ــــــــ" + ] + }, + { + "layer": 28, + "depth": 68.3, + "top_tokens": [ + "imperialism", + "XXX", + "catastrophe", + "harassment", + "xxxxxxxx", + "hysteria", + "piracy", + "뭐라고" + ] + }, + { + "layer": 29, + "depth": 70.7, + "top_tokens": [ + "something", + "อะไร", + "BECAUSE", + "뭐라고", + "XXX", + "harassment", + "FAILURE", + "WHY" + ] + }, + { + "layer": 30, + "depth": 73.2, + "top_tokens": [ + "deterioration", + "degradation", + "harassment", + "FAILURE", + "corrosion", + "something", + "อะไร", + "burnout" + ] + }, + { + "layer": 31, + "depth": 75.6, + "top_tokens": [ + "deterioration", + "fatigue", + "corrosion", + "degradation", + "FAILURE", + "fracture", + "creep", + "뭐라고" + ] + }, + { + "layer": 32, + "depth": 78.0, + "top_tokens": [ + "deterioration", + "fatigue", + "corrosion", + "degradation", + "creep", + "fracture", + "Fatigue", + "exhaustion" + ] + }, + { + "layer": 33, + "depth": 80.5, + "top_tokens": [ + "fatigue", + "creep", + "corrosion", + "deterioration", + "degradation", + "fracture", + "疲", + "exhaustion" + ] + }, + { + "layer": 34, + "depth": 82.9, + "top_tokens": [ + "疲", + "fatigue", + "exhaustion", + "уста", + "Fatigue", + "fatigued", + "wear", + "tiredness" + ] + }, + { + "layer": 35, + "depth": 85.4, + "top_tokens": [ + "fatigue", + "疲", + "Fatigue", + "fatig", + "exhaustion", + "уста", + "wear", + "Fat" + ] + }, + { + "layer": 36, + "depth": 87.8, + "top_tokens": [ + "fatigue", + "疲", + "Fatigue", + "wear", + "creep", + "уста", + "exhaustion", + "fatig" + ] + }, + { + "layer": 37, + "depth": 90.2, + "top_tokens": [ + "fatigue", + "疲", + "Fatigue", + "wear", + "Fat", + "уста", + "exhaustion", + "fatig" + ] + }, + { + "layer": 38, + "depth": 92.7, + "top_tokens": [ + "fatigue", + "Fatigue", + "疲", + "Fat", + "wear", + "fatig", + "fat", + "fat" + ] + }, + { + "layer": 39, + "depth": 95.1, + "top_tokens": [ + "fatigue", + "Fatigue", + "疲", + "fat", + "creep", + "Fat", + "fatig", + "fracture" + ] + }, + { + "layer": 40, + "depth": 97.6, + "top_tokens": [ + "fatigue", + "creep", + "fat", + "fracture", + "high", + "cyclic", + "failure", + "Fatigue" + ] + }, + { + "layer": 41, + "depth": 100.0, + "top_tokens": [ + "fatigue", + "high", + "High", + "creep", + "endurance", + "failure", + "cyclic", + "crack" + ] + } + ], + "surprising": [ + { + "concept": "axially", + "score": 4.0, + "best_rank": 1, + "best_depth": 82.9, + "near_token": "loaded" + }, + { + "concept": "unexpectedly", + "score": 4.0, + "best_rank": 2, + "best_depth": 87.8, + "near_token": "breaks" + }, + { + "concept": "undergoes", + "score": 3.0, + "best_rank": 1, + "best_depth": 90.2, + "near_token": "component" + }, + { + "concept": "surfaces", + "score": 2.0, + "best_rank": 2, + "best_depth": 82.9, + "near_token": "at" + }, + { + "concept": "ciclo", + "score": 2.0, + "best_rank": 8, + "best_depth": 82.9, + "near_token": "of" + }, + { + "concept": "beautiful", + "score": 1.0, + "best_rank": 1, + "best_depth": 82.9, + "near_token": "A" + }, + { + "concept": "wear", + "score": 1.0, + "best_rank": 1, + "best_depth": 82.9, + "near_token": "for" + }, + { + "concept": "Comput", + "score": 1.0, + "best_rank": 1, + "best_depth": 85.4, + "near_token": "" + }, + { + "concept": "Despite", + "score": 1.0, + "best_rank": 1, + "best_depth": 85.4, + "near_token": "." + }, + { + "concept": "veces", + "score": 1.0, + "best_rank": 1, + "best_depth": 90.2, + "near_token": "A" + }, + { + "concept": "donc", + "score": 1.0, + "best_rank": 2, + "best_depth": 82.9, + "near_token": "" + }, + { + "concept": "growth", + "score": 1.0, + "best_rank": 2, + "best_depth": 82.9, + "near_token": "and" + } + ], + "figures": { + "grid": "figures/gemma4-e4b-it/fracture/fracture-fatigue__grid.png", + "stream": "figures/gemma4-e4b-it/fracture/fracture-fatigue__stream.png", + "trajectory": "figures/gemma4-e4b-it/fracture/fracture-fatigue__trajectory.png", + "emergence": "figures/gemma4-e4b-it/fracture/fracture-fatigue__emergence.png", + "heatmap": "figures/gemma4-e4b-it/fracture/fracture-fatigue__heatmap.png" + } + }, + { + "slug": "fracture-toughness-chain", + "shape": "MULTIHOP", + "domain": "fracture", + "title": "Stress-intensity factor chain (multi-hop)", + "description": "Chain: geometry+load -> stress intensity K -> compare to K_Ic -> propagate. Track K / toughness / propagate in order.", + "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_text": "An engineer has a plate with an edge crack under tension. She first computes the stress intensity factor at the crack tip, then compares it against the material's fracture toughness. If the first exceeds the second, the crack will", + "prompt_tail": "mputes the stress intensity factor at the crack tip, then compares it against the material's fracture toughness. If the first exceeds the second, the crack will", + "readout_at": -1, + "band": [ + 75.6, + 100.0 + ], + "discovered": false, + "tracked_dropped": [], + "emergence": [ + { + "label": "stress", + "best_rank": 0, + "best_depth": 75.6, + "reached_top1": true, + "best_pos": 10 + }, + { + "label": "intensity", + "best_rank": 0, + "best_depth": 87.8, + "reached_top1": true, + "best_pos": 17 + }, + { + "label": "toughness", + "best_rank": 0, + "best_depth": 85.4, + "reached_top1": true, + "best_pos": 29 + }, + { + "label": "propagate", + "best_rank": 0, + "best_depth": 85.4, + "reached_top1": true, + "best_pos": 45 + }, + { + "label": "grow", + "best_rank": 0, + "best_depth": 80.5, + "reached_top1": true, + "best_pos": 45 + }, + { + "label": "unstable", + "best_rank": 5, + "best_depth": 100.0, + "reached_top1": false, + "best_pos": 45 + } + ], + "layer_readouts": [ + { + "layer": 0, + "depth": 0.0, + "top_tokens": [ + "programme", + "everybody", + "persons", + "people", + "colours", + "therefor", + "consequent", + "leness" + ] + }, + { + "layer": 1, + "depth": 2.4, + "top_tokens": [ + "l", + "etc", + "nt", + "in", + "ve", + "ar", + "nd", + "le" + ] + }, + { + "layer": 2, + "depth": 4.9, + "top_tokens": [ + "etc", + "gonna", + "t", + "y", + "l", + "ay", + "nt", + "ar" + ] + }, + { + "layer": 3, + "depth": 7.3, + "top_tokens": [ + "etc", + "thats", + "gonna", + "nt", + "t", + "a", + "alot", + "l" + ] + }, + { + "layer": 4, + "depth": 9.8, + "top_tokens": [ + "or", + "etc", + "a", + "y", + "gonna", + "in", + "at", + "an" + ] + }, + { + "layer": 5, + "depth": 12.2, + "top_tokens": [ + "gonna", + "eted", + "iance", + "kids", + "iest", + "etc", + "forever", + "alot" + ] + }, + { + "layer": 6, + "depth": 14.6, + "top_tokens": [ + "gonna", + "guys", + "anybody", + "alot", + "guy", + "kids", + "kinda", + "thats" + ] + }, + { + "layer": 7, + "depth": 17.1, + "top_tokens": [ + "gonna", + "anybody", + "alot", + "everybody", + "guys", + "pretty", + "basically", + "anyways" + ] + }, + { + "layer": 8, + "depth": 19.5, + "top_tokens": [ + "gonna", + "alot", + "also", + "will", + "anyways", + "getting", + "to", + "eventually" + ] + }, + { + "layer": 9, + "depth": 22.0, + "top_tokens": [ + "gonna", + "also", + "will", + "to", + "getting", + "anyways", + "eventually", + "atleast" + ] + }, + { + "layer": 10, + "depth": 24.4, + "top_tokens": [ + "alot", + "will", + "gonna", + "getting", + "aswell", + "to", + "sadly", + "eventually" + ] + }, + { + "layer": 11, + "depth": 26.8, + "top_tokens": [ + "gonna", + "will", + "someday", + "alot", + "anyways", + "to", + "forever", + "recieve" + ] + }, + { + "layer": 12, + "depth": 29.3, + "top_tokens": [ + "gonna", + "alot", + "will", + "anyways", + "someday", + "aswell", + "to", + "sway" + ] + }, + { + "layer": 13, + "depth": 31.7, + "top_tokens": [ + "gonna", + "anyways", + "will", + "aswell", + "alot", + "eventually", + "هيكون", + "collide" + ] + }, + { + "layer": 14, + "depth": 34.1, + "top_tokens": [ + "predicted", + "predicting", + "detonation", + "crash", + "predict", + "doom", + "prediction", + "rotation" + ] + }, + { + "layer": 15, + "depth": 36.6, + "top_tokens": [ + "predicted", + "predicting", + "prediction", + "predicted", + "predict", + "doom", + "Predicted", + "instability" + ] + }, + { + "layer": 16, + "depth": 39.0, + "top_tokens": [ + "predicted", + "predicting", + "predicted", + "Predicted", + "predicts", + "prediction", + "predict", + "instability" + ] + }, + { + "layer": 17, + "depth": 41.5, + "top_tokens": [ + "predicted", + "detonation", + "catastrophic", + "lethal", + "destruction", + "predicting", + "doom", + "destabil" + ] + }, + { + "layer": 18, + "depth": 43.9, + "top_tokens": [ + "predicted", + "predicting", + "detonation", + "destruction", + "triggering", + "prediction", + "predicted", + "predicts" + ] + }, + { + "layer": 19, + "depth": 46.3, + "top_tokens": [ + "predicted", + "predicting", + "prediction", + "predicts", + "Predicted", + "destruction", + "predicted", + "predict" + ] + }, + { + "layer": 20, + "depth": 48.8, + "top_tokens": [ + "predicted", + "predicts", + "will", + "predict", + "predicting", + "gonna", + "destruction", + "outcome" + ] + }, + { + "layer": 21, + "depth": 51.2, + "top_tokens": [ + "will", + "predicted", + "Will", + "predict", + "will", + "predicting", + "Will", + "predicts" + ] + }, + { + "layer": 22, + "depth": 53.7, + "top_tokens": [ + "will", + "will", + "Will", + "WILL", + "Will", + "akan", + "WILL", + "would" + ] + }, + { + "layer": 23, + "depth": 56.1, + "top_tokens": [ + "was", + "had", + "would", + "became", + "will", + "then", + "got", + "has" + ] + }, + { + "layer": 24, + "depth": 58.5, + "top_tokens": [ + "became", + "would", + "was", + "becomes", + "will", + "resulted", + "seemed", + "is" + ] + }, + { + "layer": 25, + "depth": 61.0, + "top_tokens": [ + "became", + "becomes", + "accordingly", + "become", + "subsequently", + "was", + "would", + "resulted" + ] + }, + { + "layer": 26, + "depth": 63.4, + "top_tokens": [ + "become", + "becomes", + "became", + "likely", + "becoming", + "subsequently", + "deemed", + "allegedly" + ] + }, + { + "layer": 27, + "depth": 65.9, + "top_tokens": [ + "become", + "becomes", + "became", + "eventually", + "likely", + "allegedly", + "subsequently", + "deteriorate" + ] + }, + { + "layer": 28, + "depth": 68.3, + "top_tokens": [ + "become", + "deteriorate", + "eventually", + "escalate", + "continue", + "intensify", + "become", + "explode" + ] + }, + { + "layer": 29, + "depth": 70.7, + "top_tokens": [ + "likely", + "probably", + "become", + "deteriorate", + "eventually", + "explode", + "provavelmente", + "continue" + ] + }, + { + "layer": 30, + "depth": 73.2, + "top_tokens": [ + "probably", + "become", + "likely", + "exacerbate", + "deteriorate", + "violate", + "worsen", + "explode" + ] + }, + { + "layer": 31, + "depth": 75.6, + "top_tokens": [ + "deteriorate", + "accelerate", + "likely", + "exacerbate", + "probably", + "undergo", + "worsen", + "become" + ] + }, + { + "layer": 32, + "depth": 78.0, + "top_tokens": [ + "exacerbate", + "worsen", + "deteriorate", + "propagate", + "accelerate", + "proceed", + "likely", + "destabil" + ] + }, + { + "layer": 33, + "depth": 80.5, + "top_tokens": [ + "propagate", + "grow", + "growth", + "propagates", + "crack", + "grows", + "propagation", + "成長" + ] + }, + { + "layer": 34, + "depth": 82.9, + "top_tokens": [ + "propagate", + "grow", + "propagation", + "propagates", + "crack", + "growth", + "grows", + "propagating" + ] + }, + { + "layer": 35, + "depth": 85.4, + "top_tokens": [ + "propagate", + "grow", + "crack", + "growth", + "propagation", + "propagating", + "propagates", + "grows" + ] + }, + { + "layer": 36, + "depth": 87.8, + "top_tokens": [ + "propagate", + "grow", + "crack", + "propagating", + "growth", + "propagates", + "propagation", + "fracture" + ] + }, + { + "layer": 37, + "depth": 90.2, + "top_tokens": [ + "propagate", + "grow", + "crack", + "propagates", + "propagation", + "propagating", + "initiate", + "fracture" + ] + }, + { + "layer": 38, + "depth": 92.7, + "top_tokens": [ + "propagate", + "grow", + "fracture", + "propagates", + "propagation", + "propagating", + "crack", + "propagated" + ] + }, + { + "layer": 39, + "depth": 95.1, + "top_tokens": [ + "propagate", + "grow", + "propagation", + "propagates", + "propagated", + "propagating", + "fracture", + "propag" + ] + }, + { + "layer": 40, + "depth": 97.6, + "top_tokens": [ + "propagate", + "grow", + "propagates", + "propagated", + "propagation", + "propagating", + "extend", + "initiate" + ] + }, + { + "layer": 41, + "depth": 100.0, + "top_tokens": [ + "propagate", + "grow", + "extend", + "likely", + "run", + "unstable", + "cause", + "lead" + ] + } + ], + "surprising": [ + { + "concept": "thickness", + "score": 9.0, + "best_rank": 1, + "best_depth": 75.6, + "near_token": "an" + }, + { + "concept": "develops", + "score": 5.0, + "best_rank": 1, + "best_depth": 92.7, + "near_token": "engineer" + }, + { + "concept": "shaped", + "score": 5.0, + "best_rank": 1, + "best_depth": 85.4, + "near_token": "plate" + }, + { + "concept": "sich", + "score": 4.0, + "best_rank": 1, + "best_depth": 87.8, + "near_token": "An" + }, + { + "concept": "radius", + "score": 2.0, + "best_rank": 1, + "best_depth": 75.6, + "near_token": "intensity" + }, + { + "concept": "assumption", + "score": 2.0, + "best_rank": 1, + "best_depth": 75.6, + "near_token": "the" + }, + { + "concept": "examines", + "score": 2.0, + "best_rank": 2, + "best_depth": 75.6, + "near_token": "first" + }, + { + "concept": "rectangular", + "score": 2.0, + "best_rank": 2, + "best_depth": 78.0, + "near_token": "an" + }, + { + "concept": "donc", + "score": 2.0, + "best_rank": 2, + "best_depth": 82.9, + "near_token": "" + }, + { + "concept": "question", + "score": 2.0, + "best_rank": 2, + "best_depth": 82.9, + "near_token": "a" + }, + { + "concept": "understands", + "score": 2.0, + "best_rank": 3, + "best_depth": 75.6, + "near_token": "engineer" + }, + { + "concept": "collection", + "score": 2.0, + "best_rank": 3, + "best_depth": 87.8, + "near_token": "a" + } + ], + "figures": { + "grid": "figures/gemma4-e4b-it/fracture/fracture-toughness-chain__grid.png", + "stream": "figures/gemma4-e4b-it/fracture/fracture-toughness-chain__stream.png", + "trajectory": "figures/gemma4-e4b-it/fracture/fracture-toughness-chain__trajectory.png", + "emergence": "figures/gemma4-e4b-it/fracture/fracture-toughness-chain__emergence.png", + "heatmap": "figures/gemma4-e4b-it/fracture/fracture-toughness-chain__heatmap.png" + } + }, + { + "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?", + "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_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.", + "prompt_tail": "e 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_at": -1, + "band": [ + 41.5, + 85.4 + ], + "discovered": false, + "tracked_dropped": [], + "emergence": [ + { + "label": "brittle", + "best_rank": 1, + "best_depth": 43.9, + "reached_top1": false, + "best_pos": 28 + }, + { + "label": "fracture", + "best_rank": 25, + "best_depth": 39.0, + "reached_top1": false, + "best_pos": 18 + }, + { + "label": "glass", + "best_rank": 0, + "best_depth": 51.2, + "reached_top1": true, + "best_pos": 20 + }, + { + "label": "shatter", + "best_rank": 1, + "best_depth": 41.5, + "reached_top1": false, + "best_pos": 17 + }, + { + "label": "crack", + "best_rank": 3, + "best_depth": 85.4, + "reached_top1": false, + "best_pos": 16 + } + ], + "layer_readouts": [ + { + "layer": 0, + "depth": 0.0, + "top_tokens": [ + "whereas", + "Although", + "which", + "Also", + "Also", + "however", + "Therefore", + "Although" + ] + }, + { + "layer": 1, + "depth": 2.4, + "top_tokens": [ + "t", + "nig", + "ie", + "wards", + "or", + "er", + "ar", + "nt" + ] + }, + { + "layer": 2, + "depth": 4.9, + "top_tokens": [ + "wards", + "ised", + "ie", + "nt", + "l", + "o", + "nd", + "ise" + ] + }, + { + "layer": 3, + "depth": 7.3, + "top_tokens": [ + "wards", + "ised", + "i", + "l", + "colour", + "nt", + "d", + "r" + ] + }, + { + "layer": 4, + "depth": 9.8, + "top_tokens": [ + "l", + "little", + "wards", + "ought", + "whatever", + "colour", + "quite", + "hence" + ] + }, + { + "layer": 5, + "depth": 12.2, + "top_tokens": [ + "thence", + "sure", + "alike", + "ought", + "little", + "coloured", + "minded", + "grim" + ] + }, + { + "layer": 6, + "depth": 14.6, + "top_tokens": [ + "whilst", + "thence", + "alike", + "unto", + "aye", + "colour", + "endeavour", + "coloured" + ] + }, + { + "layer": 7, + "depth": 17.1, + "top_tokens": [ + "whilst", + "Whilst", + "alike", + "sorta", + "kinda", + "quite", + "alot", + "wee" + ] + }, + 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Does the lens read out 'tension'/'crack'/'mode'?", + "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_text": "Consider the Cauchy stress tensor (MPa):\n[[420, 0, 0],\n [ 0, 15, 0],\n [ 0, 0, 10]]\nThe dominant loading state this represents is", + "prompt_tail": "Consider the Cauchy stress tensor (MPa):\n[[420, 0, 0],\n [ 0, 15, 0],\n [ 0, 0, 10]]\nThe dominant loading state this represents is", + "readout_at": -1, + "band": [ + 31.7, + 95.1 + ], + "discovered": false, + "tracked_dropped": [], + "emergence": [ + { + "label": "tension", + "best_rank": 10, + "best_depth": 82.9, + "reached_top1": false, + "best_pos": 46 + }, + { + "label": "tensile", + "best_rank": 0, + "best_depth": 39.0, + "reached_top1": true, + "best_pos": 13 + }, + { + "label": "uniaxial", + "best_rank": 1, + "best_depth": 31.7, + "reached_top1": 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Answer in one word.\n<|turn>model\n", + "readout_at": -1, + "band": [ + 78.0, + 95.1 + ], + "discovered": false, + "tracked_dropped": [], + "emergence": [ + { + "label": "fatigue", + "best_rank": 0, + "best_depth": 85.4, + "reached_top1": true, + "best_pos": 21 + }, + { + "label": "creep", + "best_rank": 12, + "best_depth": 95.1, + "reached_top1": false, + "best_pos": 21 + }, + { + "label": "corrosion", + "best_rank": 0, + "best_depth": 78.0, + "reached_top1": true, + "best_pos": 21 + }, + { + "label": "fracture", + "best_rank": 1, + "best_depth": 80.5, + "reached_top1": false, + "best_pos": 21 + }, + { + "label": "buckling", + "best_rank": 23, + "best_depth": 78.0, + "reached_top1": false, + "best_pos": 21 + } + ], + "layer_readouts": [ + { + "layer": 0, + "depth": 0.0, + "top_tokens": [ + "에서", + "를", + "는", + "³", + "nbsp", + "라는", + "으로", + "T" + ] + }, + { + "layer": 1, + "depth": 2.4, + "top_tokens": [ + "a", + "d", + "r", + "l", + "i", + "or", + "o", + "it" + ] + }, + { + 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"figures/gemma4-e4b-it/protein/protein-transmembrane-recognition__heatmap.png" + } + }, + { + "slug": "protein-amyloid-assoc", + "shape": "ASSOCIATION", + "domain": "protein", + "title": "Amyloid aggregation (association, unnamed)", + "description": "A vignette of misfolding and fibril formation. 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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_at": -1, + "band": [ + 36.6, + 53.7 + ], + "discovered": false, + "tracked_dropped": [ + "fibril", + "misfold" + ], + "emergence": [ + { + "label": "amyloid", + "best_rank": 4, + "best_depth": 36.6, + "reached_top1": false, + "best_pos": 27 + }, + { + "label": "aggregate", + "best_rank": 33, + "best_depth": 48.8, + "reached_top1": false, + "best_pos": 25 + }, + { + "label": "beta", + "best_rank": 406, + "best_depth": 100.0, + "reached_top1": false, + "best_pos": 18 + }, + { + "label": "plaque", + "best_rank": 0, + "best_depth": 53.7, + "reached_top1": true, + "best_pos": 27 + } + ], + "layer_readouts": [ + { + "layer": 0, + "depth": 0.0, + "top_tokens": [ + "whereas", + "Although", + "Although", + "About", + "which", + "and", + "This", + "It" + ] + }, + { + "layer": 1, + "depth": 2.4, + "top_tokens": [ + "l", + "i", + 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"this", + "these", + "These" + ] + }, + { + "layer": 33, + "depth": 80.5, + "top_tokens": [ + "they", + "They", + "These", + "This", + "these", + "this", + "Their", + "their" + ] + }, + { + "layer": 34, + "depth": 82.9, + "top_tokens": [ + "they", + "Their", + "They", + "These", + "This", + "this", + "these", + "Like" + ] + }, + { + "layer": 35, + "depth": 85.4, + "top_tokens": [ + "Their", + "These", + "This", + "They", + "they", + "The", + "They", + "Like" + ] + }, + { + "layer": 36, + "depth": 87.8, + "top_tokens": [ + "They", + "Their", + "This", + "they", + "These", + "this", + "Like", + "The" + ] + }, + { + "layer": 37, + "depth": 90.2, + "top_tokens": [ + "This", + "These", + "They", + "Their", + "this", + "The", + "Like", + "Suddenly" + ] + }, + { + "layer": 38, + "depth": 92.7, + "top_tokens": [ + "This", + "They", + "The", + "These", + "Like", + "Their", + "this", + "Suddenly" + ] + }, + { + "layer": 39, + "depth": 95.1, + "top_tokens": [ + "This", + "The", + "These", + "They", + "Like", + "Their", + "It", + "Suddenly" + ] + }, + { + "layer": 40, + "depth": 97.6, + "top_tokens": [ + "This", + "The", + "These", + "Like", + "They", + "It", + "Their", + "this" + ] + }, + { + "layer": 41, + "depth": 100.0, + "top_tokens": [ + "This", + "The", + "These", + "It", + "They", + "In", + "Like", + "A" + ] + } + ], + "surprising": [ + { + "concept": "clumps", + "score": 17.0, + "best_rank": 1, + "best_depth": 36.6, + "near_token": "into" + }, + { + "concept": "interlocking", + "score": 9.0, + "best_rank": 1, + "best_depth": 39.0, + "near_token": "into" + }, + { + "concept": "rigidity", + "score": 8.0, + "best_rank": 1, + "best_depth": 36.6, + "near_token": "rigid" + }, + { + "concept": "scientists", + "score": 7.0, + "best_rank": 1, + "best_depth": 36.6, + "near_token": "the" + }, + { + "concept": "formations", + "score": 7.0, + "best_rank": 1, + "best_depth": 36.6, + "near_token": "into" + }, + { + "concept": "horizontal", + "score": 7.0, + "best_rank": 1, + "best_depth": 39.0, + "near_token": "edge" + }, + { + "concept": "exodus", + "score": 6.0, + "best_rank": 1, + "best_depth": 39.0, + "near_token": "abandoned" + }, + { + "concept": "anatomy", + "score": 6.0, + "best_rank": 1, + "best_depth": 39.0, + "near_token": "folds" + }, + { + "concept": "biochemical", + "score": 5.0, + "best_rank": 1, + "best_depth": 46.3, + "near_token": "their" + }, + { + "concept": "molecular", + "score": 5.0, + "best_rank": 1, + "best_depth": 36.6, + "near_token": "a" + }, + { + "concept": "teeming", + "score": 5.0, + "best_rank": 1, + "best_depth": 39.0, + "near_token": "They" + }, + { + "concept": "chemists", + "score": 4.0, + "best_rank": 1, + "best_depth": 36.6, + "near_token": "They" + } + ], + "figures": { + "grid": "figures/gemma4-e4b-it/protein/protein-amyloid-assoc__grid.png", + "stream": "figures/gemma4-e4b-it/protein/protein-amyloid-assoc__stream.png", + "trajectory": "figures/gemma4-e4b-it/protein/protein-amyloid-assoc__trajectory.png", + "emergence": "figures/gemma4-e4b-it/protein/protein-amyloid-assoc__emergence.png", + "heatmap": "figures/gemma4-e4b-it/protein/protein-amyloid-assoc__heatmap.png" + } + }, + { + "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.", + "note": "Anfinsen-style hydrophobic-collapse chain: the intermediates 'hydrophobic' and 'core' should precede the answer 'fold'/'structure' across layers.", + "prompt_text": "A denatured protein is returned to water. Its nonpolar side chains are driven out of contact with solvent and pack together in the interior, and this burial of hydrophobic residues is the main force that drives the chain back to its native", + "prompt_tail": "ut of contact with solvent and pack together in the interior, and this burial of hydrophobic residues is the main force that drives the chain back to its native", + "readout_at": -1, + "band": [ + 46.3, + 100.0 + ], + "discovered": false, + "tracked_dropped": [], + "emergence": [ + { + "label": "hydrophobic", + "best_rank": 0, + "best_depth": 46.3, + "reached_top1": true, + "best_pos": 11 + }, + { + "label": "core", + "best_rank": 1, + "best_depth": 100.0, + "reached_top1": false, + "best_pos": 26 + }, + { + "label": "collapse", + "best_rank": 6, + "best_depth": 97.6, + "reached_top1": false, + "best_pos": 41 + }, + { + "label": "fold", + "best_rank": 0, + "best_depth": 82.9, + "reached_top1": true, + "best_pos": 45 + }, + { + "label": "structure", + "best_rank": 0, + 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"biochemical", + "phenotype", + "phylogenetic" + ] + }, + { + "layer": 17, + "depth": 41.5, + "top_tokens": [ + "physiological", + "biochem", + "morphology", + "biochemical", + "phenotype", + "biochemistry", + "physiologically", + "physiology" + ] + }, + { + "layer": 18, + "depth": 43.9, + "top_tokens": [ + "physiological", + "biochemical", + "phenotype", + "morphology", + "biochemistry", + "biochem", + "physicochemical", + "physiologically" + ] + }, + { + "layer": 19, + "depth": 46.3, + "top_tokens": [ + "physiological", + "phenotype", + "biochemical", + "physicochemical", + "phylogenetic", + "morphology", + "optimized", + "biochemistry" + ] + }, + { + "layer": 20, + "depth": 48.8, + "top_tokens": [ + "biochemical", + "physiological", + "morphology", + "phenotype", + "organism", + "biological", + "molecular", + "physicochemical" + ] + }, + { + "layer": 21, + "depth": 51.2, + "top_tokens": [ + "phenotype", + "biological", + "biochemical", + "organism", + "physiological", + 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"folded", + "structure", + "state" + ] + }, + { + "layer": 39, + "depth": 95.1, + "top_tokens": [ + "folded", + "fold", + "folding", + "state", + "folds", + "conformation", + "structure", + "folded" + ] + }, + { + "layer": 40, + "depth": 97.6, + "top_tokens": [ + "folded", + "state", + "structure", + "fold", + "conformation", + "folding", + "configuration", + "folds" + ] + }, + { + "layer": 41, + "depth": 100.0, + "top_tokens": [ + "state", + "structure", + "conformation", + "folded", + "fold", + "three", + "configuration", + "globular" + ] + } + ], + "surprising": [ + { + "concept": "because", + "score": 51.0, + "best_rank": 1, + "best_depth": 58.5, + "near_token": "is" + }, + { + "concept": "was", + "score": 26.0, + "best_rank": 1, + "best_depth": 56.1, + "near_token": "side" + }, + { + "concept": "became", + "score": 13.0, + "best_rank": 1, + "best_depth": 58.5, + "near_token": "is" + }, + { + "concept": "covalent", + "score": 8.0, + "best_rank": 1, + "best_depth": 82.9, + "near_token": "non" + }, + { + "concept": "which", + "score": 7.0, + "best_rank": 1, + "best_depth": 56.1, + "near_token": "is" + }, + { + "concept": "becomes", + "score": 7.0, + "best_rank": 1, + "best_depth": 63.4, + "near_token": "Its" + }, + { + "concept": "seawater", + "score": 6.0, + "best_rank": 1, + "best_depth": 46.3, + "near_token": "water" + }, + { + "concept": "proteins", + "score": 6.0, + "best_rank": 1, + "best_depth": 46.3, + "near_token": "side" + }, + { + "concept": "example", + "score": 6.0, + "best_rank": 1, + "best_depth": 61.0, + "near_token": "atured" + }, + { + "concept": "again", + "score": 6.0, + "best_rank": 1, + "best_depth": 61.0, + "near_token": "returned" + }, + { + "concept": "reason", + "score": 6.0, + "best_rank": 1, + "best_depth": 65.9, + "near_token": "the" + }, + { + "concept": "thought", + "score": 6.0, + "best_rank": 1, + "best_depth": 82.9, + "near_token": "den" + } + ], + "figures": { + "grid": "figures/gemma4-e4b-it/protein/protein-fold-multihop__grid.png", + "stream": "figures/gemma4-e4b-it/protein/protein-fold-multihop__stream.png", + "trajectory": "figures/gemma4-e4b-it/protein/protein-fold-multihop__trajectory.png", + "emergence": "figures/gemma4-e4b-it/protein/protein-fold-multihop__emergence.png", + "heatmap": "figures/gemma4-e4b-it/protein/protein-fold-multihop__heatmap.png" + } + }, + { + "slug": "protein-modulation", + "shape": "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?", + "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_text": "<|turn>user\nWrite \"The rain tapped against the window all afternoon.\" Concentrate on disulfide bonds while you write the sentence. Don't write anything else.\n<|turn>model\nThe rain tapped against the window all afternoon.", + "prompt_tail": ".\" Concentrate on disulfide bonds while you write the sentence. Don't write anything else.\n<|turn>model\nThe rain tapped against the window all afternoon.", + "readout_at": -1, + "band": [ + 34.1, + 97.6 + ], + "discovered": false, + "tracked_dropped": [], + "emergence": [ + { + "label": "disulfide", + "best_rank": 0, + "best_depth": 36.6, + "reached_top1": true, + "best_pos": 18 + }, + { + "label": "cysteine", + "best_rank": 0, + "best_depth": 51.2, + "reached_top1": true, + "best_pos": 27 + }, + { + "label": "bridge", + "best_rank": 4, + "best_depth": 97.6, + "reached_top1": false, + "best_pos": 18 + }, + { + "label": "bond", + "best_rank": 0, + "best_depth": 82.9, + "reached_top1": true, + "best_pos": 18 + }, + { + "label": "sulfur", + "best_rank": 0, + "best_depth": 34.1, + "reached_top1": true, + "best_pos": 18 + }, + { + "label": "cross", + "best_rank": 15, + "best_depth": 97.6, + "reached_top1": false, + "best_pos": 38 + } + ], + "layer_readouts": [ + { + "layer": 0, + "depth": 0.0, + "top_tokens": [ + "things", + "whereas", + "and", + "which", + "yes", + "asons", + "however", + "ately" + ] + }, + { + "layer": 1, + "depth": 2.4, + "top_tokens": [ + "o", + "i", + "t", + "a", + "nt", + "or", + "wards", + "etc" + ] + }, + { + "layer": 2, + "depth": 4.9, + "top_tokens": [ + "l", + "ie", + "wards", + "o", + "lly", + "d", + "es", + "ar" + ] + }, + { + "layer": 3, + "depth": 7.3, + "top_tokens": [ + "l", + "o", + "r", + "t", + "or", + "etc", + "probably", + "i" + ] + }, + { + "layer": 4, + "depth": 9.8, + "top_tokens": [ + "basically", + "maybe", + "probably", + "lots", + "really", + "everybody", + "probably", + "pretty" + ] + }, + { + "layer": 5, + "depth": 12.2, + "top_tokens": [ + "because", + "according", + "probably", + "basically", + "sure", + "soon", + "anyway", + "somewhere" + ] + }, + { + "layer": 6, + "depth": 14.6, + "top_tokens": [ + "everybody", + "anybody", + "because", + "soon", + "basically", + "however", + "instead", + "somewhere" + ] + }, + { + "layer": 7, + "depth": 17.1, + "top_tokens": [ + "because", + 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"subtly", + "woven", + "grammat" + ] + }, + { + "layer": 20, + "depth": 48.8, + "top_tokens": [ + "however", + "apologise", + "emphasised", + "emphasise", + "although", + "colour", + "characterised", + "poem" + ] + }, + { + "layer": 21, + "depth": 51.2, + "top_tokens": [ + "however", + "unconsciously", + "subconsciously", + "reflecting", + "noticeably", + "nonetheless", + "incidentally", + "hereof" + ] + }, + { + "layer": 22, + "depth": 53.7, + "top_tokens": [ + "though", + "however", + "which", + "nonetheless", + "was", + "fucking", + "because", + "as" + ] + }, + { + "layer": 23, + "depth": 56.1, + "top_tokens": [ + "입니다", + "৷", + "This", + "Though", + "Although", + "Their", + "Which", + "although" + ] + }, + { + "layer": 24, + "depth": 58.5, + "top_tokens": [ + "Their", + "Because", + "This", + "Its", + "Its", + "Because", + "입니다", + "Although" + ] + }, + { + "layer": 25, + "depth": 61.0, + "top_tokens": [ + "때문이다", + "입니다", + "৷", + "Its", + "Meanwhile", + "Their", + "Although", + 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+ "Their", + "disulfide", + "His", + "其", + "Both", + "its" + ] + }, + { + "layer": 33, + "depth": 80.5, + "top_tokens": [ + "Its", + "Its", + "Their", + "Both", + "His", + "The", + "While", + "Our" + ] + }, + { + "layer": 34, + "depth": 82.9, + "top_tokens": [ + "Its", + "disulfide", + "His", + "Its", + "Their", + "Both", + "Bonds", + "While" + ] + }, + { + "layer": 35, + "depth": 85.4, + "top_tokens": [ + "Its", + "Both", + "While", + "His", + "Though", + "Its", + "Through", + "Within" + ] + }, + { + "layer": 36, + "depth": 87.8, + "top_tokens": [ + "Its", + "His", + "Its", + "While", + "Both", + "Though", + "Between", + "Their" + ] + }, + { + "layer": 37, + "depth": 90.2, + "top_tokens": [ + "Its", + "His", + "While", + "Perhaps", + "Both", + "Though", + "Its", + "This" + ] + }, + { + "layer": 38, + "depth": 92.7, + "top_tokens": [ + "Its", + "This", + "While", + "His", + "Through", + "The", + "Because", + "Both" + ] + }, + { + "layer": 39, + "depth": 95.1, + "top_tokens": [ + "Its", + "Within", + "While", + "Because", + "This", + "Between", + "Among", + "The" + ] + }, + { + "layer": 40, + "depth": 97.6, + "top_tokens": [ + "The", + "Its", + "Both", + "While", + "Within", + "This", + "Because", + "Through" + ] + }, + { + "layer": 41, + "depth": 100.0, + "top_tokens": [ + "The", + "Its", + "Dis", + "This", + "While", + "My", + "Both", + "S" + ] + } + ], + "surprising": [ + { + "concept": "because", + "score": 25.0, + "best_rank": 1, + "best_depth": 63.4, + "near_token": "tapped" + }, + { + "concept": "shimmering", + "score": 17.0, + "best_rank": 1, + "best_depth": 34.1, + "near_token": "The" + }, + { + "concept": "molecular", + "score": 15.0, + "best_rank": 1, + "best_depth": 39.0, + "near_token": "you" + }, + { + "concept": "grammat", + "score": 13.0, + "best_rank": 1, + "best_depth": 36.6, + "near_token": "sentence" + }, + { + "concept": "whatsoever", + "score": 13.0, + "best_rank": 1, + "best_depth": 36.6, + "near_token": "else" + }, + { + "concept": "was", + "score": 12.0, + "best_rank": 1, + "best_depth": 56.1, + "near_token": "rain" + }, + { + "concept": "Also", + "score": 12.0, + "best_rank": 1, + "best_depth": 70.7, + "near_token": "<|turn>" + }, + { + "concept": "rhythmic", + "score": 10.0, + "best_rank": 1, + "best_depth": 36.6, + "near_token": "tapped" + }, + { + "concept": "Because", + "score": 9.0, + "best_rank": 1, + "best_depth": 56.1, + "near_token": "" + }, + { + "concept": "chemically", + "score": 8.0, + "best_rank": 1, + "best_depth": 36.6, + "near_token": "Don" + }, + { + "concept": "biochemical", + "score": 8.0, + "best_rank": 1, + "best_depth": 39.0, + "near_token": "while" + }, + { + "concept": "beautifully", + "score": 8.0, + "best_rank": 1, + "best_depth": 61.0, + "near_token": "tapped" + } + ], + "figures": { + "grid": "figures/gemma4-e4b-it/protein/protein-modulation__grid.png", + "stream": "figures/gemma4-e4b-it/protein/protein-modulation__stream.png", + "trajectory": "figures/gemma4-e4b-it/protein/protein-modulation__trajectory.png", + "emergence": "figures/gemma4-e4b-it/protein/protein-modulation__emergence.png", + "heatmap": "figures/gemma4-e4b-it/protein/protein-modulation__heatmap.png" + } + }, + { + "slug": "protein-report-swap", + "shape": "REPORT_SWAP", + "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.", + "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.", + "prompt_text": "<|turn>user\nThink of an amino acid. Answer in one word.\n<|turn>model\n", + "prompt_tail": "<|turn>user\nThink of an amino acid. Answer in one word.\n<|turn>model\n", + "readout_at": -1, + "band": [ + 80.5, + 90.2 + ], + "discovered": false, + "tracked_dropped": [], + "emergence": [ + { + "label": "glycine", + "best_rank": 0, + "best_depth": 87.8, + "reached_top1": true, + "best_pos": 20 + }, + { + "label": "alanine", + "best_rank": 0, + "best_depth": 80.5, + "reached_top1": true, + "best_pos": 20 + }, + { + "label": "leucine", + "best_rank": 8, + "best_depth": 87.8, + "reached_top1": false, + "best_pos": 20 + }, + { + "label": "serine", + "best_rank": 5, + "best_depth": 82.9, + "reached_top1": false, + "best_pos": 20 + }, + { + "label": "proline", + "best_rank": 14, + "best_depth": 90.2, + "reached_top1": false, + "best_pos": 20 + } + ], + "layer_readouts": [ + { + "layer": 0, + "depth": 0.0, + "top_tokens": [ + "에서", + "를", + "는", + "³", + "nbsp", + "라는", + "으로", + "T" + ] + }, + { + "layer": 1, + "depth": 2.4, + "top_tokens": [ + "a", + "d", + "r", + "l", + "i", + "or", + "o", + "it" + ] + }, + { + "layer": 2, + "depth": 4.9, + "top_tokens": [ + "or", + "ty", + "ment", + "al", + "ried", + "emic", + "er", + "nal" + ] + }, + { + "layer": 3, + "depth": 7.3, + "top_tokens": [ + "lli", + "ried", + "or", + "she", + "1", + "pple", + "hey", + "orse" + ] + }, + { + "layer": 4, + "depth": 9.8, + "top_tokens": [ + "ive", + "ish", + "maybe", + "in", + "wards", + "ally", + "d", + "y" + ] + }, + { + "layer": 5, + "depth": 12.2, + "top_tokens": [ + "her", + "herself", + "she", + "elle", + "her", + "udos", + "ally", + "skaya" + ] + }, + { + "layer": 6, + "depth": 14.6, + "top_tokens": [ + "her", + "she", + "herself", + "elle", + "ements", + "ally", + "her", + "she" + ] + }, + { + "layer": 7, + "depth": 17.1, + "top_tokens": [ + "she", + "her", + "she", + "veggies", + "She", + "her", + "etto", + "udos" + ] + }, + { + "layer": 8, + "depth": 19.5, + "top_tokens": [ + "her", + "kids", + "she", + "veggies", + "Yeah", + "herself", + "นะคะ", + "amazing" + ] + }, + { + "layer": 9, + "depth": 22.0, + "top_tokens": [ + "Ferdinand", + "Unfortunately", + "calmness", + "Almighty", + "So", + "amazing", + "So", + "beautiful" + ] + }, + { + "layer": 10, + "depth": 24.4, + "top_tokens": [ + "alot", + "atleast", + "mmmm", + "thats", + "upto", + "Unfortunately", + "The", + "Let" + ] + }, + { + "layer": 11, + "depth": 26.8, + "top_tokens": [ + "kinda", + "alot", + "hmm", + "Whilst", + "wouldn", + "mmmm", + "Anyways", + "thats" + ] + }, + { + "layer": 12, + "depth": 29.3, + "top_tokens": [ + "alot", + "mmmm", + "amazing", + "AMAZING", + "thats", + "kinda", + "maybe", + "Whilst" + ] + }, + { + "layer": 13, + "depth": 31.7, + "top_tokens": [ + "YES", + "mmmm", + "alot", + "thats", + "rumours", + "death", + "Potato", + "Whis" + ] + }, + { + "layer": 14, + "depth": 34.1, + "top_tokens": [ + "UNKNOWN", + "Trivia", + "Joke", + "YES", + "trivia", + "riddle", + "funny", + "xyz" + ] + }, + { + "layer": 15, + "depth": 36.6, + "top_tokens": [ + "riddle", + "YES", + "Seek", + "Jaw", + "Joke", + "Soup", + "FAKE", + "hmm" + ] + }, + { + "layer": 16, + "depth": 39.0, + "top_tokens": [ + "riddle", + "Answer", + "Guess", + "trivia", + "guess", + "joke", + "FAKE", + "guessing" + ] + }, + { + "layer": 17, + "depth": 41.5, + "top_tokens": [ + "ANSWER", + "antwort", + "Answer", + "monos", + "alphabetically", + "riddle", + "answer", + "answer" + ] + }, + { + "layer": 18, + "depth": 43.9, + "top_tokens": [ + "ANSWER", + "Answer", + "NAME", + "antwort", + "monos", + "answer", + "Answer", + "riddle" + ] + }, + { + "layer": 19, + "depth": 46.3, + "top_tokens": [ + "Answer", + "ANSWER", + "answer", + "antwort", + "monos", + "Answer", + "alphabetical", + "Alphabet" + ] + }, + { + "layer": 20, + "depth": 48.8, + "top_tokens": [ + "ANSWER", + "Answer", + "antwort", + "Alphabet", + "alphabetical", + "RANDOM", + "đáp", + "alphabetically" + ] + }, + { + "layer": 21, + "depth": 51.2, + "top_tokens": [ + "Biotechnology", + "Alphabet", + "Amino", + "Energ", + "Answer", + "Bio", + "scegli", + "Anagram" + ] + }, + { + "layer": 22, + "depth": 53.7, + "top_tokens": [ + "Saya", + "Somos", + "Optimize", + "Jump", + "Sulf", + "Optimization", + "Optimizer", + "Watercolor" + ] + }, + { + "layer": 23, + "depth": 56.1, + "top_tokens": [ + "Entropy", + "entropy", + "chaos", + "Là", + "Jeśli", + "Diesel", + "Chaos", + "Fract" + ] + }, + { + "layer": 24, + "depth": 58.5, + "top_tokens": [ + "Often", + "Would", + "Should", + "Maybe", + "Known", + "Might", + "should", + "might" + ] + }, + { + "layer": 25, + "depth": 61.0, + "top_tokens": [ + "Often", + "Maybe", + "Might", + "Perhaps", + "maybe", + "Example", + "might", + "Là" + ] + }, + { + "layer": 26, + "depth": 63.4, + "top_tokens": [ + "Porque", + "Потому", + "vì", + "Might", + "Maybe", + "Often", + "Because", + "Perhaps" + ] + }, + { + "layer": 27, + "depth": 65.9, + "top_tokens": [ + "Garcia", + "Pedro", + "Might", + "Martha", + "Troy", + "Diesel", + "Barry", + "Porque" + ] + }, + { + "layer": 28, + "depth": 68.3, + "top_tokens": [ + "Troy", + "Pedro", + "Nelson", + "Garcia", + "Smith", + "Lisa", + "Lucy", + "Harrison" + ] + }, + { + "layer": 29, + "depth": 70.7, + "top_tokens": [ + "Иван", + "Smith", + "Roboto", + "Superman", + "Джон", + "Picasso", + "Pikachu", + "Hamlet" + ] + }, + { + "layer": 30, + "depth": 73.2, + "top_tokens": [ + "Roboto", + "Enzymes", + "alanine", + "Ethanol", + "Urea", + "Mitochond", + "Chlor", + "Testosterone" + ] + }, + { + "layer": 31, + "depth": 75.6, + "top_tokens": [ + "Roboto", + "alanine", + "Ethanol", + "Amino", + "Nitrogen", + "Freud", + "Enzymes", + "Urea" + ] + }, + { + "layer": 32, + "depth": 78.0, + "top_tokens": [ + "alanine", + "amino", + "Amino", + "Amino", + "phenylalanine", + "methionine", + "lysine", + "氨" + ] + }, + { + "layer": 33, + "depth": 80.5, + "top_tokens": [ + "alanine", + "phenylalanine", + "amino", + "alanine", + "Amino", + "methionine", + "glycine", + "Amino" + ] + }, + { + "layer": 34, + "depth": 82.9, + "top_tokens": [ + "alanine", + "methionine", + "phenylalanine", + "lysine", + "glycine", + "serine", + "Lys", + "amino" + ] + }, + { + "layer": 35, + "depth": 85.4, + "top_tokens": [ + "lysine", + "glycine", + "methionine", + "phenylalanine", + "alanine", + "arginine", + "leucine", + "serine" + ] + }, + { + "layer": 36, + "depth": 87.8, + "top_tokens": [ + "glycine", + "alanine", + "lysine", + "phenylalanine", + "methionine", + "arginine", + "Lys", + "alanine" + ] + }, + { + "layer": 37, + "depth": 90.2, + "top_tokens": [ + "glycine", + "lysine", + "Lys", + "alanine", + "methionine", + "Lys", + "Gly", + "phenylalanine" + ] + }, + { + "layer": 38, + "depth": 92.7, + "top_tokens": [ + "glycine", + "alanine", + "Lys", + "lysine", + "Lys", + "Gly", + "methionine", + "Alan" + ] + }, + { + "layer": 39, + "depth": 95.1, + "top_tokens": [ + "glycine", + "Gly", + "Lys", + "Lys", + "lysine", + "alanine", + "Gly", + "Alan" + ] + }, + { + "layer": 40, + "depth": 97.6, + "top_tokens": [ + "Gly", + "Lys", + "Alan", + "glycine", + "Le", + "Gly", 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Qualitatively, the J-lens does show an interpretable materials-state trajectory in the workspace band: **“ductility”** is top-ranked around 39.0–43.9% depth, then the readout shifts toward consequence/answer concepts such as **“elongation”** at 73.2–75.6%, **“deformation”** at 78.0–85.4%, and finally **“plastic”** from 87.8% onward, matching the actual next-token output. That is a real semantic pattern, but not a clean registered success; moreover, for the tracked **ductile** concept the J-lens peak is rank 5 at 80.5%, while the matched vanilla logit lens is rank 2, so this is not a case where the J-lens clearly outperforms the logit baseline.\n\nThe causal swap does **not** confirm a causal downstream role. The intervention **ductile → brittle** only modestly suppressed the source concept and weakly installed the target: **ductile** full-vocab rank worsened from 20 to 37, while **brittle** improved only from 127 to 119. The declared next-token distribution did not switch toward a brittle-failure answer: clean top tokens were `plastic, strain, neck, uniform, work`, and after the swap they remained essentially `plastic, strain, uniform, neck, plasticity`. So this is best described as a mild representational suppression/installation with no behavioral answer change, not a causal success.\n\nThe exploratory candidate list is useful only as a heuristic. **“ductility,” “ductile,” and “deformation”** are meaningful surfaced concepts consistent with the prompt and the top-token layer sequence; **“atoms”** may reflect inferred microscopic/materials structure but is not the target answer; **“undergoes”** is plausible completion scaffolding rather than a stable concept. By contrast, **“because,” “therefore/Therefore,” “which,” and “was”** look mostly like discourse or syntactic scaffolding, not substantive hidden reasoning. The slice grid also contains many truncated or stem-like tokens such as `deformat`, `fracturi`, `ductilit`, and `metallur`, which should be treated as tokenizer artifacts or single-token limitations rather than clean verbalized concepts." + }, + { + "slug": "materials-pileup-focus", + "title": "Focus on a dislocation pile-up", + "shape": "MODULATION", + "domain": "materials", + "model_used": "openai:gpt-5.5", + "analysis": "This included directed-modulation record shows only a weak, unsustained materials-side signal in the J-lens, and by itself it is not a modulation result because the matched suppress and neutral records are not provided here. Within the fixed workspace band, **stress** reaches J-rank 11 at 43.9% depth, but the matched vanilla logit lens is better at rank 8. **Pile** reaches J-rank 20 at 87.8%, only slightly better than logit rank 23. **Dislocation** reaches only J-rank 101 at 85.4%, while the logit lens is much stronger at rank 13. None has a sustained onset. So the registered readout does not show a clean J-lens-specific emergence of the unspoken concept; it mostly says that the model has some prompt-conditioned availability of the materials words, but not a robust workspace representation beyond the logit baseline.\n\nQualitatively, the slice grid does contain plausible side-concept traces around the instruction/concept positions: tokens like **dislocat**, **pile**, **stacking**, **pressure**, **tensile**, **stress**, **metallur**, and **fracturi** appear in the workspace band. That is consistent with the model attending to the “dislocation pile-up / stress concentration” instruction while producing the neutral carrier sentence. But these are largely near or derived from the prompted side-concept text, and the readout-position top tokens are dominated by generic discourse or carrier-sentence alternatives such as “Its,” “While,” “The,” and literal sentence tokens. I would treat this as a qualitative prompt-conditioning pattern, not a clean demonstration of unspoken reasoning.\n\nThe exploratory candidate list is heuristic only. **because**, **but**, **also/Also** are generic connective continuations and likely reflect instruction-following or discourse defaults rather than materials reasoning, even though “because” peaks near “dislocation.” **motionless**, **was**, and **shimmering** look like carrier-sentence or furnace/description imagery artifacts; **motionless** is an echo of “remained,” and **shimmering** plausibly comes from “blue/furnace” imagery. **writing** is just a morphological/task echo of “Write,” and **whatsoever** is an instruction-control echo of “Do not write anything else.” **implications** is the only candidate that might weakly reflect the “stress concentration/effects” side instruction, but it is too generic to count as a materials concept. None of these candidates should be treated as a paper metric success." + }, + { + "slug": "materials-pileup-suppress", + "title": "Suppress a dislocation pile-up", + "shape": "MODULATION", + "domain": "materials", + "model_used": "openai:gpt-5.5", + "analysis": "This included suppress item is **not a clean J-lens success for the forbidden materials concept**. Within the fixed 38–92% workspace band, **dislocation** never becomes salient: best J rank is 8513 at 46.3%, worse than the matched logit-lens rank 5531. **Stress** is also weak: J rank 1269 at 87.8%, essentially identical to logit rank 1268. **Pile** is the only strong-looking tracked token, reaching J rank 52 at 90.2%, but the logit lens is actually better at rank 37, and there is no sustained onset. This looks more like a late single-token/subword echo of the prompted phrase “pile-up” than a J-specific workspace representation of the full materials concept. The slice grid similarly shows local prompt-conditioned traces around the “dislocation / pile / stress / concentration” positions, but the assistant-response region is dominated by sentence-writing and final-token content such as “sentence,” “write,” “Period,” and copied words from “The blue notebook remained beside the furnace.”\n\nSo the lens suggests the model processed the suppression instruction and the forbidden phrase locally, but it does **not** show a robust unspoken “dislocation pile-up / stress concentration” concept being carried into the response workspace. The clean output absence is satisfied by the completion, but because the target concepts are explicitly in the prompt and the J-lens is not stronger than the logit baseline, this should be read as weak qualitative evidence at most, not as a registered hidden-thought discovery. The best apparent target signal, **pile**, is especially vulnerable to the single-token limitation: it does not establish representation of the compound concept “dislocation pile-up.”\n\nFor the exploratory candidate list, the meaningful entries are mostly task/carrier related: **writing** reflects the instruction to write the sentence; **motionless** is a plausible semantic neighbor of “remained”; **was** is a banal tense/syntax alternative near “notebook”; and **implications** may be a loose association to “its stress concentration,” though it is not a metric result. **grammat** is partly interpretable as “grammar/grammatical sentence,” but it is also a tokenizer fragment. **also**, **Also**, **but**, and **because** look like generic discourse-prior artifacts rather than meaningful reasoning, and **weird** is likely small-model/noise association rather than a reliable concept." + }, + { + "slug": "materials-pileup-control", + "title": "Neutral carrier control", + "shape": "MODULATION", + "domain": "materials", + "model_used": "openai:gpt-5.5", + "analysis": "This included neutral-control item shows no clean materials-side-task signal. In the fixed workspace band (38–92% depth), the registered concepts do not show sustained onset: **dislocation** is weak and worse than vanilla logit lens at its best point (J rank 4809 at 70.7% vs logit rank 3153), **stress** is only modestly better than logit but still low-ranked (J 1641 at 85.4% vs logit 2700), and **pile** becomes fairly high-ranked late (J 59 at 90.2%) but the matched logit lens is actually better (rank 47). So this should not be counted as evidence that the model is representing unspoken materials concepts; if anything, the control behaves as desired. The layer/position grid is dominated by instruction-following and literal-copy content: mid-band tokens like **grammat**, **verbatim**, **whatsoever**, **sentence/wording**, and later the actual carrier sentence and punctuation/period. That looks like “copy exactly, do not add anything,” not hidden reasoning about materials.\n\nThe exploratory candidate list is useful only qualitatively. **verbatim** and **whatsoever** are plausibly meaningful: they reflect the instruction to write exactly the sentence and nothing else. **color** near “blue” and perhaps **motionless** near “remained” are local semantic associations, not task-relevant intermediates. **was** is likely a syntactic alternative for the sentence frame (“notebook was…”) rather than a concept the model computed for a side task. **also/Also**, **because**, and **and** look like generic discourse-token artifacts, and **masterpiece** is best treated as noise or small-model/lens contamination rather than a meaningful hidden concept. Overall, the J-lens reveals a workspace concerned with exact reproduction and output suppression, with no registered clean emergence of **dislocation**, **pile**, or **stress** beyond what the vanilla logit lens already explains." + }, + { + "slug": "materials-defect-report", + "title": "Crystal-defect verbal-report swap", + "shape": "REPORT_SWAP", + "domain": "materials", + "model_used": "openai:gpt-5.5", + "analysis": "Within the fixed workspace band, the lens shows a fairly clean emergence of the chosen verbal report, **“vacancy.”** The readout position first carries generic answer/task structure and defect-related material vocabulary: around 73–78% depth the top tokens include **Dislocations**, **Fracture**, **Distortion**, **Schottky**, and by 78–80.5% it has moved to **defects / imperfection / Vacancy**. The registered concept then locks onto **vacancy**, reaching J-lens rank 1 at **82.9% depth** with sustained onset at **80.5%**, matching the actual completion “Vacancy.” This is not a strong J-lens-over-logit case, however: the matched vanilla logit lens also gives **vacancy rank 1**. The sibling defects are present only as weaker alternatives: **dislocation** reaches J rank 8 at 87.8% versus logit rank 9, and **interstitial** reaches J rank 20 at 90.2% versus logit rank 21. **Twin** and **boundary** remain poor, with twin actually better in the logit lens than the J-lens. So the readout supports “the model is settling on vacancy from a defect-family representation,” not a clean hidden plan to output one of the siblings.\n\nThe causal swap does **not** confirm controllable replacement of the verbal report. The registered success rate is **0/5**. Suppressing the source concept often knocked **vacancy** down substantially, but adding a sibling coordinate did not make the output become **dislocation**, **interstitial**, **twin**, or **boundary**; every swapped trial’s top-1 was **``**. That is suppression or disruption of the report channel, not a successful report swap. The case-only “vacancy” self-target is also not a sibling-concept test, and it failed as well. Thus the primary diagnostic—whether the one-word report changes to the installed sibling—comes out negative.\n\nThe exploratory candidate list should be treated only as a heuristic. Items such as **Example**, **sentence**, **only**, **Answer/Describe/Explain**-like grid tokens are plausibly task-format or instruction-compliance features rather than defect concepts. **Because**, **and**, **also**, **that**, and **about** are mostly function-word or discourse artifacts from all-position top-1 patterns, not meaningful scientific intermediates. **Description** may reflect the prompt style, and **imaginative** is likely noise or a prompt-interpretation artifact. None of these should be counted as protocol evidence; the meaningful content here is the late emergence of the actual report **vacancy**, with weak co-activation of nearby single-token defect alternatives." + } + ], + "synthesis": "Across the supplied items, the clearest workspace-like phenomenon is the **REPORT_SWAP / verbal-report** record for the crystal-defect prompt: the model settles on the single-token report **“vacancy”**, with J-lens rank 1 at **82.9% depth** and sustained onset around **80.5%**. However, this is mainly a readout of the answer the model is about to say, not a hidden intermediate, and the matched vanilla logit lens also ranks **vacancy** at 1. The sibling concepts **dislocation** and **interstitial** appear only as weaker alternatives, and the actual causal report-swap trials fail: installing sibling defect concepts does not make the model output those siblings, instead disrupting the report channel to ``. Thus this is a real semantic settling pattern, but not a successful causal report-swap demonstration and not a J-lens-specific advantage over the logit lens.\n\nThe **MODULATION** records are much weaker. In the focus/suppress/control carrier-sentence setting, the lens shows local prompt-conditioned traces of materials vocabulary such as **dislocation**, **pile**, **stress**, **stacking**, **tensile**, and **metallur**, but the registered tracked concepts do not show robust, sustained J-lens emergence in the workspace band. Often the vanilla logit lens is equal or better: for example, in the focus item **dislocation** is much stronger in the logit lens than the J-lens, and in the suppress/control items **pile** is late and logit-favored. Because these modulation groups are exploratory and sample-insufficient, and because the side concepts are explicitly present in the prompt, these should be treated as qualitative prompt-conditioning patterns rather than registered evidence of unspoken scientific reasoning.\n\nThe **ductile-to-brittle PROBE_SWAP** item gives the most interesting qualitative ordered trajectory: **ductility** appears around **39.0–43.9% depth**, then consequence/answer concepts such as **elongation** at **73.2–75.6%**, **deformation** at **78.0–85.4%**, and finally **plastic** from **87.8% onward**, matching the model’s actual answer. This resembles the paper’s claim that intermediate concepts can appear before the final answer, and that later layers move toward the literal next token. But it is explicitly excluded from registered causal/protocol success because the clean-answer control failed: the expected first token was **“elongation”**, while the model greedily produced **“plastic.”** In addition, the tracked **ductile** signal is not J-lens-superior; its best J rank is 5 while the matched logit-lens rank is 2. The causal swap also fails to install **brittle** strongly or change the answer distribution. No supplied records cleanly test **MULTIHOP**, **ASSOCIATION**, or **RECOGNITION**, so no run-level conclusion should be drawn about those experiment types.\n\nOverall, this run is consistent with the paper’s cautions more than with its strongest positive claims. The lens can reveal interpretable workspace content when the target is a clean single vocabulary token, as with **vacancy**, and it can show plausible semantic trajectories in individual examples, as in the excluded ductility case. But the run does not provide a clean registered demonstration of hidden intermediate scientific concepts being computed in order, nor a successful causal swap. The single-token limitation is substantial in this materials domain: **dislocation pile-up**, **stress concentration**, and related metallurgical concepts are multi-token or fragmented into subword pieces, so isolated tokens like **pile**, **dislocat**, **ductilit**, or **fracturi** should not be overread as full concepts. The weak or absent signals for some intended concepts should also be taken seriously: on this smaller Gemma target, the lens may be faithfully reporting that the model does not robustly maintain those scientific representations.\n\nThe exploratory surfaced candidates are best viewed as a heuristic for noticing patterns, not as a paper metric. Plausible but weak task-related candidates include **writing**, **whatsoever**, and **verbatim**, which reflect instruction compliance; **motionless** and **was**, which are carrier-sentence alternatives around “remained”; and perhaps **implications**, which could loosely relate to “stress concentration” but is too generic to count as a materials concept. Frequent candidates such as **because**, **also/Also**, **but**, and **and** are mostly discourse-prior artifacts. **masterpiece** looks like small-model or lens noise rather than meaningful hidden reasoning. A reader should therefore treat google/gemma-4-E4B-it as useful for exploratory interpretability demos and for debugging the J-lens pipeline, but not as an ideal target for strong scientific-reasoning claims. A larger or more capable model, with more stable domain knowledge and better single-token verbalizations of scientific concepts, would be a more appropriate target for testing the paper’s global-workspace claims in materials science." +} \ No newline at end of file diff --git a/jlens_materials/runs/gemma4-materials-key-concept-traces-v1.json b/jlens_materials/runs/gemma4-materials-key-concept-traces-v1.json new file mode 100644 index 0000000000000000000000000000000000000000..4eb82a8a2b8e30df4b1c8099b16113108060128e --- /dev/null +++ b/jlens_materials/runs/gemma4-materials-key-concept-traces-v1.json @@ -0,0 +1,8771 @@ +{ + "format_version": 2, + "created_at": "2026-07-10T22:09:39.227244+00:00", + "model": "google/gemma-4-E4B-it", + "model_identity": { + "requested_id": "google/gemma-4-E4B-it", + "requested_revision": null, + "model_class": "Gemma4ForConditionalGeneration", + "config_name_or_path": "google/gemma-4-E4B-it", + "model_revision": "a4c2d58be94dda072b918d9db64ee85c8ed34e3f", + "tokenizer_class": "GemmaTokenizer", + "tokenizer_name_or_path": "google/gemma-4-E4B-it", + "vocab_size": 262144, + "n_layers": 42, + "d_model": 2560, + "dtype": "float32" + }, + "tag": "gemma4-materials-key-concept-traces-v1", + "n_layers": 42, + "d_model": 2560, + "lens_n_prompts": 1000, + "lens_provenance": null, + "methodology": { + "recipe": { + "name": "demo", + "n_fit": 12, + "max_seq_len": 64, + "target_layer": null, + "report_layer_count": null, + "skip_first": 16, + "strict_corpus": false, + "description": "Fast exploratory fit. Results from this profile are not a quantitative replication of the paper." + }, + "claims_level": "exploratory demo", + "paper_protocol_complete": false, + "strict_protocol": false, + "workspace_band": [ + 38.0, + 92.0 + ], + "workspace_band_source": "preregistered_cli_or_config", + "score_reduction": "fixed span x fixed band x synonym minimum", + "emergence_threshold": 5, + "emergence_sustain": 2, + "pass_at_k": [ + 1, + 2, + 5, + 10, + 20, + 50, + 100 + ], + "minimum_items_per_shape": 50, + "evaluation_max_seq_len": 512, + "generation_max_new_tokens": 1, + "model_revision": null, + "dtype": "float32", + "layer_stride": 1, + "prompt_source": "jlens_materials/prompts/materials-key-concept-traces-v1.json", + "swaps_enabled": false, + "swap_alpha": 1.0 + }, + "shapes": [ + "ASSOCIATION" + ], + "domains": [ + "materials" + ], + "metrics": { + "by_shape": { + "ASSOCIATION": { + "n_items": 6, + "minimum_required": 50, + "sufficient_sample": false, + "ks": [ + 1, + 2, + 5, + 10, + 20, + 50, + 100 + ], + "jacobian_lens": { + "pass_at_k": [ + 0.0, + 0.09722222222222221, + 0.09722222222222221, + 0.09722222222222221, + 0.15277777777777776, + 0.2083333333333333, + 0.3055555555555555 + ], + "auc_log_k": 0.13470791672688914, + "bootstrap_95": { + "1": { + "mean": 0.0, + "low": 0.0, + "high": 0.0 + }, + "2": { + "mean": 0.09722222222222221, + "low": 0.0, + "high": 0.20833333333333334 + }, + "5": { + "mean": 0.09722222222222221, + "low": 0.0, + "high": 0.20833333333333334 + }, + "10": { + "mean": 0.09722222222222221, + "low": 0.0, + "high": 0.20833333333333334 + }, + "20": { + "mean": 0.15277777777777776, + "low": 0.041666666666666664, + "high": 0.2638888888888889 + }, + "50": { + "mean": 0.2083333333333333, + "low": 0.09722222222222221, + "high": 0.3194444444444444 + }, + "100": { + "mean": 0.3055555555555555, + "low": 0.16666666666666666, + "high": 0.4166666666666667 + } + } + }, + "logit_lens": { + "pass_at_k": [ + 0.0, + 0.0, + 0.0, + 0.05555555555555555, + 0.05555555555555555, + 0.05555555555555555, + 0.05555555555555555 + ], + "auc_log_k": 0.031958749939777514 + } + } + }, + "directed_modulation_controls": {}, + "causal_swaps": { + "n_interventions": 0, + "n_graded": 0, + "minimum_required": 50, + "sufficient_sample": false, + "protocol_success_rate": null, + "n_counterfactual": 0, + "causal_success_rate": null + } + }, + "errors": [], + "insufficient_sample_shapes": [ + "ASSOCIATION" + ], + "prompts": [ + { + "slug": "paper-v2-assoc-notch-resistance-04", + "shape": "ASSOCIATION", + "protocol": "lens_eval", + "domain": "materials", + "title": "Resistance to notch-driven failure 4", + "description": "Preregistered unnamed materials vignette; compare fixed-band J-lens ranks with the matched logit-lens baseline.", + "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.", + "prompt_text": "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.", + "prompt_tail": "-tension test, substantial energy is dissipated near the notch tip before the specimen separates. The material tolerates larger defects than a comparison alloy.", + "readout_selector": "final_prompt_token", + "readout_at": -1, + "score_positions": [ + 31 + ], + "band": [ + 38.0, + 92.0 + ], + "band_source": "preregistered_cli_or_config", + "condition": null, + "control_group": null, + "phrasing_id": null, + "carrier_id": null, + "target_family": null, + "category": "notch-resistance", + "answer": null, + "baseline": { + "required": false, + "correct": null, + "expected": [] + }, + "generated_completion": " This", + "generated_token_ids": [ + 1174 + ], + "protocol_violations": [], + "valid_for_metrics": true, + "excluded_reasons": [], + "discovered": false, + "tracked_dropped": [], + "tracked": [ + { + "label": "toughness", + "surfaces": [ + "toughness" + ], + "token_ids": [ + 89582 + ] + }, + { + "label": "crack", + "surfaces": [ + "crack" + ], + "token_ids": [ + 11890, + 91888, + 78772, + 141677 + ] + }, + { + "label": "fracture", + "surfaces": [ + "fracture" + ], + "token_ids": [ 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microscopic-event request distinguishes slip from the macroscopic answer yield.", + "note": "This repairs the earlier ambiguity in which the scientifically plausible first token was 'yield'.", + "prompt_text": "For a single crystal, the product of applied stress and the two orientation cosines gives a resolved value of 120 MPa on the most favorably oriented system. The critical value is 80 MPa. Answer with one word naming the microscopic deformation event that begins:", + "prompt_tail": "value of 120 MPa on the most favorably oriented system. The critical value is 80 MPa. Answer with one word naming the microscopic deformation event that begins:", + "readout_selector": "before_answer", + "readout_at": -1, + "score_positions": [ + 53 + ], + "band": [ + 38.0, + 92.0 + ], + "band_source": "preregistered_cli_or_config", + "condition": null, + "control_group": null, + "phrasing_id": null, + "carrier_id": null, + "target_family": null, + "category": null, + "answer": "slip", + "baseline": { + "required": true, + "correct": false, + "expected": [ + "slip" + ], + "greedy_token_id": 106, + "greedy_token": "" + }, + "generated_completion": "", + "generated_token_ids": [ + 106 + ], + "protocol_violations": [], + "valid_for_metrics": false, + "excluded_reasons": [ + "clean model did not produce the expected answer" + ], + "discovered": false, + "tracked_dropped": [], + "tracked": [ + { + "label": "Schmid", + "surfaces": [ + "Schmid" + ], + "token_ids": [ + 145866 + ] + }, + { + "label": "dislocation", + "surfaces": [ + "dislocation" + ], + "token_ids": [ + 25810 + 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"best_depth": 39.0, + "near_token": "Miller", + "output_checked": true + } + ], + "surprising_label": "exploratory surfaced candidates", + "figures": { + "grid": "figures/gemma4-materials-paper-v1/materials/paper-v1-fcc-constrained__grid.png", + "stream": "figures/gemma4-materials-paper-v1/materials/paper-v1-fcc-constrained__stream.png", + "trajectory": "figures/gemma4-materials-paper-v1/materials/paper-v1-fcc-constrained__trajectory.png", + "emergence": "figures/gemma4-materials-paper-v1/materials/paper-v1-fcc-constrained__emergence.png", + "heatmap": "figures/gemma4-materials-paper-v1/materials/paper-v1-fcc-constrained__heatmap.png" + } + }, + { + "slug": "paper-v1-griffith-constrained", + "shape": "MULTIHOP", + "protocol": "lens_eval", + "domain": "materials", + "title": "Constrained flaw-size scaling", + "description": "A binary comparative answer tests whether the named fracture criterion appears before the scaling result.", + "note": "The answer follows the inverse-square-root flaw-size scaling; Griffith is the predeclared mechanism-level concept.", + "prompt_text": "Two elastic plates have identical modulus and surface energy, but one contains an internal flaw four times as long as the other. Choose exactly one word, higher or lower: the critical remote tensile stress for the plate with the longer flaw is", + "prompt_tail": "nternal flaw four times as long as the other. 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"materials", + "title": "Pile-up focus replication: brass key", + "description": "First new teacher-forced carrier for the directed-modulation replication.", + "note": "Interpret only relative to the matched suppress and neutral records using the identical carrier and target set.", + "prompt_text": "<|turn>user\nWrite \"The brass key rested under the folded map.\" Concentrate on a dislocation pile-up and its stress concentration while you write the sentence. Do not write anything else.\n<|turn>model\nThe brass key rested under the folded map.", + "prompt_tail": "on pile-up and its stress concentration while you write the sentence. 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Do not write anything else.\n<|turn>model\nA white cup stood beside the closed window.", + "prompt_tail": "and the stress it concentrates in mind while you write the sentence. 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"Dot", + "Write", + "dot", + "Picture", + "Picture", + "Drawings", + "Screenshot", + "笔" + ] + }, + { + "layer": 35, + "depth": 85.4, + "top_tokens": [ + "Picture", + "Feel", + "Write", + "Dot", + "Hopefully", + "PEN", + "Write", + "Print" + ] + }, + { + "layer": 36, + "depth": 87.8, + "top_tokens": [ + "Write", + "Write", + "Period", + "periods", + "period", + "Rush", + "Picture", + "Hopefully" + ] + }, + { + "layer": 37, + "depth": 90.2, + "top_tokens": [ + "Write", + "Write", + "Period", + "periods", + "write", + "Back", + "Feel", + "write" + ] + }, + { + "layer": 38, + "depth": 92.7, + "top_tokens": [ + "Write", + "Write", + "山", + "COPY", + "Copy", + "Feel", + "唯", + "Back" + ] + }, + { + "layer": 39, + "depth": 95.1, + "top_tokens": [ + "COPY", + "Write", + "Write", + "Copy", + "write", + "Back", + "Copy", + "Constit" + ] + }, + { + "layer": 40, + "depth": 97.6, + "top_tokens": [ + "write", + "ほど", + "Write", + "at", + "towards", + "period", + "Write", + "exclusively" + ] + }, + { + "layer": 41, + "depth": 100.0, + "top_tokens": [ + "Write", + "1", + "COPY", + "C", + "High", + "Write", + "This", + "g" + ] + } + ], + "surprising": [ + { + "concept": "also", + "score": 21.0, + "best_rank": 1, + "best_depth": 58.5, + "near_token": "Do", + "output_checked": true + }, + { + "concept": "verbatim", + "score": 19.0, + "best_rank": 1, + "best_depth": 39.0, + "near_token": "not", + "output_checked": true + }, + { + "concept": "motionless", + "score": 16.0, + "best_rank": 1, + "best_depth": 39.0, + "near_token": "stood", + "output_checked": true + }, + { + "concept": "and", + "score": 15.0, + "best_rank": 1, + "best_depth": 56.1, + "near_token": "white", + "output_checked": true + }, + { + "concept": "masterpiece", + "score": 15.0, + "best_rank": 1, + "best_depth": 58.5, + "near_token": "white", + "output_checked": true + }, + { + "concept": "padlock", + "score": 12.0, + "best_rank": 1, + "best_depth": 39.0, + "near_token": "closed", + "output_checked": true + }, + { + "concept": "whatsoever", + "score": 12.0, + "best_rank": 1, + "best_depth": 39.0, + "near_token": "anything", + "output_checked": true + }, + { + "concept": "because", + "score": 12.0, + "best_rank": 2, + "best_depth": 65.9, + "near_token": "<|turn>", + "output_checked": true + }, + { + "concept": "crockery", + "score": 11.0, + "best_rank": 1, + "best_depth": 39.0, + "near_token": "cup", + "output_checked": true + }, + { + "concept": "stillness", + "score": 10.0, + "best_rank": 1, + "best_depth": 39.0, + "near_token": ".\"", + "output_checked": true + }, + { + "concept": "grammat", + "score": 9.0, + "best_rank": 1, + "best_depth": 39.0, + "near_token": "Do", + "output_checked": true + }, + { + "concept": "Also", + "score": 8.0, + "best_rank": 1, + "best_depth": 73.2, + "near_token": "<|turn>", + "output_checked": true + } + ], + "surprising_label": "exploratory surfaced candidates", + "figures": { + "grid": "figures/gemma4-materials-paper-v1/materials/paper-v1-pileup-control-cup__grid.png", + "stream": "figures/gemma4-materials-paper-v1/materials/paper-v1-pileup-control-cup__stream.png", + "trajectory": "figures/gemma4-materials-paper-v1/materials/paper-v1-pileup-control-cup__trajectory.png", + "emergence": "figures/gemma4-materials-paper-v1/materials/paper-v1-pileup-control-cup__emergence.png", + "heatmap": "figures/gemma4-materials-paper-v1/materials/paper-v1-pileup-control-cup__heatmap.png" + } + } + ] +} \ No newline at end of file diff --git a/jlens_materials/runs/gemma4-materials-paper-v1_analysis.json b/jlens_materials/runs/gemma4-materials-paper-v1_analysis.json new file mode 100644 index 0000000000000000000000000000000000000000..0f2747d655b69a78e86a8001c2e0a8b0fda6c9b8 --- /dev/null +++ b/jlens_materials/runs/gemma4-materials-paper-v1_analysis.json @@ -0,0 +1,121 @@ +{ + "model": "google/gemma-4-E4B-it", + "provider": "openai", + "analysis_model": "gpt-5.5", + "offline": false, + "per_prompt": [ + { + "slug": "paper-v1-ductile-fracture-replication", + "title": "Ductile-fracture replication vignette", + "shape": "ASSOCIATION", + "domain": "materials", + "model_used": "openai:gpt-5.5", + "analysis": "This is an included protocol item, and the fixed-band readout shows a real but partial ductile-fracture replication. In the workspace band, **coalescence** is the cleanest signal: J-lens rank **13 at 41.5% depth** versus logit-lens rank **3048**, a very large improvement for an unspoken mechanism implied by rounded dimples/inclusions and cup-and-cone morphology. **Ductile** is also present, though less dramatically: rank **55 at 41.5%** versus logit **171**. **Nucleation** improves too, rank **150 at 51.2%** versus logit **599**, but it is weaker and not sustained. **Void** is not a J-lens success here: rank **6456 at 53.7%**, worse than the matched logit rank **1166**. Since all tracked concepts have sustained onset “None,” this is a transient mid-band emergence, not a stable plateau. Also, the order does **not** support a nucleation-then-coalescence mechanism: coalescence peaks earlier and much more strongly than nucleation, so no ordered mechanistic computation should be inferred.\n\nThe layer-position grid qualitatively supports the same interpretation but should not be treated as the metric. Around the workspace band it shows materials/fractography language such as **ductile**, **ductility**, **metallur-**, **deformat-**, **microstr-**, **contour**, **cup**, **neck**, and **shear lip** near relevant prompt positions, suggesting the model is organizing the vignette as a ductile deformation/fracture case. However, some of this is local lexical association and tokenizer-shaped fragments rather than a clean mechanistic trace. The tracked-rank chart is the stronger evidence: it says the model has the verbalizable concept **microvoid coalescence/ductile fracture**, but the single-token **void** probe fails, likely reflecting either token-choice mismatch or absence of that exact verbalization.\n\nFor the exploratory candidates: **ductile** is meaningful, and its many top-band cells are consistent with the registered ductile signal. **ductility** and **deformation** are also meaningful inferred concepts from “extensive necking,” though they are auxiliary rather than the registered mechanism. **metallurgical** is plausibly domain-contextual but broad. **characteristic** and **indicating** are semi-meaningful diagnostic/explanatory framing words, not fracture mechanisms. **which**, **This**, and **because** are discourse/function-token artifacts. **cracks** is not a reliable positive signal here: ductile dimples are not simply cracks, so this looks like generic fracture-language bleed-through rather than a precise concept." + }, + { + "slug": "paper-v1-intergranular-replication", + "title": "Boundary-attack replication vignette", + "shape": "ASSOCIATION", + "domain": "materials", + "model_used": "openai:gpt-5.5", + "analysis": "Within the fixed workspace band, the lens shows a real but limited corrosion-family interpretation. The clearest absolute signal is **corrosion**: J-lens rank **12 at 41.5% depth**, with nearby early-band support in the slice grid around the chloride/groove region. However, the matched logit-lens rank is **14**, essentially the same, so this is not a strong J-specific hidden-concept result; the surface cues already strongly push “corrosion.” The more interesting J-vs-logit gaps are for **boundary** and **sensitization**: boundary reaches J rank **886 at 39.0%** versus logit rank **4034**, and sensitization reaches J rank **1077 at 41.5%** versus logit rank **15902**. Those are substantial relative improvements, suggesting the transported representation contains some unspoken grain-boundary/sensitization interpretation. But both remain only around rank 10³ and have **no sustained onset**, so this should be called suggestive rather than a clean recovery. **Intergranular** is correctly dropped as multi-token/non-trackable, so the specific intergranular-attack concept cannot be registered by this metric.\n\nThe exploratory candidate list is useful qualitatively but not a paper metric. Plausibly meaningful entries are **metallurgical** as a domain frame, **corrosion** as the main inferred process, **fissures** as a morphology related to the narrow connected grooves, and **unaffected** as a paraphrase of the untouched grain interiors. **Temperatures** is mostly a surface restatement of “650 degrees Celsius,” not an unspoken mechanistic concept. **Because**, **during**, and **subsequently** are discourse/causal-continuation tokens rather than materials concepts, and **and** / **was** are function-word artifacts. Overall, the lens does reveal the model leaning toward corrosion and weakly toward sensitization/boundary attack, but the strongest generic term is not stronger than vanilla logit lens, and the specific mechanism remains sub-top, unsustained, and partly hidden behind the single-token limitation." + }, + { + "slug": "paper-v1-fatigue-fractography", + "title": "Cyclic-fracture vignette", + "shape": "ASSOCIATION", + "domain": "materials", + "model_used": "openai:gpt-5.5", + "analysis": "Within the registered workspace band, this is a **weak-to-moderate protocol signal for the cyclic damage family**, not a clean emergence of the exact answer concept. The best tracked J-lens result is **propagation**, which reaches rank **337 at 41.5% depth**, substantially better than the matched vanilla logit-lens rank **2228**. That is a real J-lens advantage and fits the prompt’s “ridges advancing” / crack-growth semantics. By contrast, **fatigue** only becomes moderately good late, rank **239 at 87.8%**, and the vanilla logit lens is actually slightly better at **190**, so this is not strong evidence that the J-lens uniquely reveals an unspoken “fatigue” concept. **Crack** is also not a J-lens win: rank **1525** versus logit-lens **631**. None of the tracked concepts has a sustained onset, so the rank plot does not show a clean ordered computation of “crack → propagation → fatigue”; it mostly shows a transient propagation signal and a late general rise of fatigue/crack-related vocabulary.\n\nThe layer-position grid qualitatively looks more semantically organized in the workspace band: around the fracture-description positions it contains **fracture, cracks, propagat-/extending/radiating, surface, ridges, microscopy**, and later even **fatigue**. That supports the idea that the model is representing a coherent materials/fractography situation, but this is a qualitative all-position pattern, not the registered metric. At the fixed readout position, the per-layer top tokens are dominated first by observation/microscopy terms and then by discourse tokens like **This**, so the final readout position alone does not strongly verbalize “fatigue failure.”\n\nFor the exploratory candidate list, **cracks** is the most meaningful heuristic hit: it tops many band cells and is scientifically appropriate, though it does not rescue the tracked singular **crack** metric. **metallurgical** is plausibly a meaningful domain cue, and **indicating** is an inference connective consistent with the prompt, but neither is the target concept. **during, because, This, where, was, throughout, years** are mostly discourse, syntax, or paraphrase artifacts; “years” may loosely reflect long duration but is not diagnostic of fatigue. Also, **striations** was dropped as multi-token, which matters here because it is one of the most diagnostic fractographic clues; the single-token limitation likely understates part of the model’s available concept." + }, + { + "slug": "paper-v1-cleavage-fractography", + "title": "Low-temperature faceted-fracture vignette", + "shape": "ASSOCIATION", + "domain": "materials", + "model_used": "openai:gpt-5.5", + "analysis": "This item is **excluded from registered metric interpretation** because the intermediate concept **“fracture”** is already present in the input. Qualitatively, the clearest J-lens signal is for **brittle**: it reaches rank **567 at 43.9% depth** within the fixed workspace band, far stronger than the matched vanilla logit-lens rank (**13,413**). That is a real association signal: the vignette has made the model disposed to say “brittle” even though the word is not in the prompt. However, it is not sustained, and the final-position top tokens are mostly generic descriptors or discourse tokens rather than a stable material-science answer. **Fracture** also improves under the J-lens, rank **1,428 at 85.4%** versus logit rank **3,344**, but because “fracture” is literally in the prompt this cannot count as an unspoken-concept result. **Cleavage** is not convincingly recovered: its best J-lens rank is only **7,687 at 90.2%**, and the vanilla logit lens is actually better at **5,739**, so there is no registered J-lens advantage for the specific cleavage interpretation. The multi-token **transgranular** target is correctly dropped as untrackable.\n\nThe slice grid gives a plausible qualitative picture of materials-domain processing: around the workspace band it surfaces local concepts such as **ductility**, **metallurgical**, **failure**, **negligible**, **fracture**, and occasional **brittle** near relevant prompt positions. But this should not be upgraded into a metric; the scored final-token trajectory only supports a transient **brittle** association, not a clean ordered emergence of “brittle cleavage.” In the exploratory candidate list, **ductility** is the most meaningful heuristic candidate, tied to “almost no necking” and “minimal plastic deformation”; **metallurgical** is a broad but sensible domain label; **failure**, **negligible**, and **temperatures** are mostly paraphrases or local restatements of input content; **indicating** is a plausible explanatory connective but not a scientific concept. **because**, **This**, **which**, and **also** are mainly discourse/syntax artifacts. Overall, the lens shows that the model has some unspoken **brittle-fracture** association, but it does not cleanly verbalize the more specific **cleavage** mechanism in the registered readout." + }, + { + "slug": "paper-v1-hall-petch-constrained", + "title": "Constrained grain-size strengthening", + "shape": "MULTIHOP", + "domain": "materials", + "model_used": "openai:gpt-5.5", + "analysis": "This item is **excluded** from registered success: the clean model’s greedy first token was `**`, not `higher`. Within the fixed workspace band, the registered intermediate concepts do **not** give a clean Hall–Petch reasoning trace. `strengthening` is somewhat J-lens-favored over the matched logit lens, peaking at J rank **3,831** at **41.5%** depth versus logit rank **10,124**, but that is still weak and unsustained. `Hall` is worse for the J-lens than the logit baseline: J rank **16,297** at **53.7%** versus logit rank **1,171**. `Petch` was dropped as multi-token. So there is no registered ordered emergence of “Hall → strengthening → higher.”\n\nQualitatively, the readout does show the model drifting toward the correct comparative answer late: `higher` appears in the top tokens at **85.4%** depth rank 2, **87.8%** rank 5, **90.2%** rank 3, and becomes top-1 only just outside the scored band at **92.7%** and later. The dominant workspace tokens before that are generic comparative/continuation tokens such as `compared`, `comparatively`, `predicted`, and `relative`, not a verbalized materials mechanism. Thus the lens suggests a late answer-direction representation, but not a clean unspoken Hall–Petch explanation; and because the clean-answer control failed, this remains qualitative only.\n\nThe exploratory candidate list is useful only as a heuristic over the slice grid, not a metric. `microstructure`, `diameter`, and `metallurgical` look plausibly meaningful domain associations near grain-size positions; `differing` and `respectively` plausibly reflect parsing the two specimens and their two sizes. `because` may indicate causal framing, but it is generic and not specific to Hall–Petch strengthening. `but`, `was`, `which`, and `will` are mostly syntax/continuation artifacts. Overall, the slice contains materials-flavored context, but the scored readout does not show a robust, ordered, single-token reasoning chain." + }, + { + "slug": "paper-v1-schmid-constrained", + "title": "Constrained resolved-shear event", + "shape": "MULTIHOP", + "domain": "materials", + "model_used": "openai:gpt-5.5", + "analysis": "This item is **excluded**: the clean model did not produce the expected one-word answer **“slip”** and instead emitted ``, so this cannot be counted as a clean protocol success. Within the registered workspace band, the J-lens does not show a clean emergence of the target microscopic event. The readout-position top tokens are dominated by prompt/task words early-to-mid band (“initiating”, “commencement”, “Answer”) and by generic mechanics terms late (“stress”, “strain”, “deformation”). The tracked **dislocation** concept does improve late, reaching J-rank **104 at 90.2% depth**, slightly better than the matched logit-lens rank **151**, but it has no sustained onset and is not the requested event; it is at best a related microscopic mechanism/carrier. **Schmid** is not a J-lens signal here: its best J-rank is only **4292 at 82.9%**, while the vanilla logit lens is much stronger at **391**, so the J-lens is not adding evidence for Schmid-law reasoning. There is also no visible ordered multihop chain ending in “slip”; the model appears to have materials/mechanics associations without the final answer concept surfacing.\n\nThe exploratory candidate list should be treated only as heuristic. **“threshold”** is meaningful: it corresponds to comparing the resolved 120 MPa value against the critical 80 MPa value. **“plane”** is also plausibly meaningful, since resolved shear/slip-system reasoning involves planes even though the prompt says “system” rather than “plane.” **“metallurgical”** is a weak but domain-relevant contextual signal. **“coefficient”** may reflect the orientation-cosine/product computation, but it is not evidence of the correct microscopic event. **“phenomenon”** is mostly a generic paraphrase of “microscopic deformation event,” not a specific solved concept. **“when,” “because,” “which,” “also,”** and likely **“corresponding”** are discourse/relational tokens or prompt-continuation artifacts, not substantive reasoning states. The slice grid contains scattered materials fragments such as “microsco,” “deformat,” and “dislocat,” but these look like generic/subword or neighboring-position associations; they should not be promoted into a metric, and they do not rescue the absence of a clean **slip** signal." + }, + { + "slug": "paper-v1-fcc-constrained", + "title": "Constrained diffraction recognition", + "shape": "RECOGNITION", + "domain": "materials", + "model_used": "openai:gpt-5.5", + "analysis": "This item is **excluded**, so it is not a registered protocol success: the prompt already contains **“cubic”**, and the model’s clean greedy completion was just `'\\n\\n'`, not the expected first token **FCC**. Within the fixed workspace band, the clearest readout is not an unspoken derivation of **FCC**, but a late activation of a broad **cubic/Bravais-lattice answer space**. Around 78.0–92.7% depth the top tokens become **NaCl / isotropic / BCC / FCC / lattice**, then **lattice, cubic, BCC, FCC**, and by the final layers **BCC** dominates while **FCC** remains only rank 2–4 in the displayed top tokens. That is qualitatively consistent with the model recognizing “this is a cubic lattice abbreviation question,” but not with a clean internal computation of “all odd or all even ⇒ FCC.” The tracked concepts reinforce this: **cubic** reaches J-lens rank 1 at 85.4%, but it is in the input and the vanilla logit lens is also rank 1, so it is not a distinctive J-lens discovery. **Diffraction** is only moderate, J rank 180 at 85.4%, and the logit lens is better at rank 149. **Extinction** is essentially absent, J rank 33011 versus logit rank 27160, with no sustained onset. Thus the lens mostly shows a late, somewhat confused lattice-label association, with **BCC incorrectly preferred over FCC**.\n\nThe layer×position grid has plausible qualitative local structure—tokens like **parity**, **symmetry**, **exclusion**, **Miller**, **abbreviation**, **Brav**, and **lattice** appear in relevant columns—but that is an all-position visual pattern, not the registered metric. It suggests the model is representing parts of the prompt semantics, especially “Miller-index parity rule” and “standard Bravais abbreviation,” but the readout position does not show a clean ordered reasoning chain from reflection condition to face-centering. The absence of **extinction/systematic absence** language is notable: if the model had explicitly formed the standard crystallographic rule, one might expect that concept to surface, but here it does not.\n\nFor the exploratory candidate list, several entries look meaningful only as local prompt semantics: **nomenclature** near “standard” reflects the abbreviation/naming instruction; **combinations** near “mixed” and **integers** near “all” plausibly reflect the parity/index condition; **diffraction** near “reflections” is a domain synonym, though not stronger than logit at the answer readout. **Simultaneously** may weakly reflect the “all three indices share parity” constraint, but it is generic. **Description**, **because**, and **This** are mostly discourse/instructional artifacts, and **was** is likely grammatical noise. **Polynomial** near “cubic” is a tokenizer/semantic-neighborhood artifact from the mathematical sense of “cubic,” not evidence of crystallographic reasoning. Overall, the exploratory list supports shallow recognition of the prompt’s vocabulary, not a reliable hidden computation of **FCC**." + }, + { + "slug": "paper-v1-griffith-constrained", + "title": "Constrained flaw-size scaling", + "shape": "MULTIHOP", + "domain": "materials", + "model_used": "openai:gpt-5.5", + "analysis": "This item is **protocol-excluded**: the clean model did not produce the expected first-token answer “lower” and instead greedily produced “**”, so this cannot be counted as a clean J-lens success. Within the fixed workspace band, there is a real but limited materials/fracture signal: **fracture** reaches J-lens rank **101 at 41.5% depth**, much stronger than the matched vanilla logit-lens rank **5632**. That is a meaningful mechanism-adjacent concept, but it is not the registered criterion **Griffith**. **Griffith** itself is weak in the J-lens, best rank only **4746 at 75.6%**, and the vanilla logit lens is actually better there (**1803**), so there is no J-lens-specific evidence that the model represented the Griffith criterion. **crack** improves over the logit baseline (**4136 at 87.8% vs. 12680**) but remains low-ranked and has no sustained onset. The late readout also drifts toward the wrong comparative: “higher” becomes dominant from about **85–100% depth**, while “lower” appears only below it, consistent with the failed clean-answer control rather than with correct inverse-square-root flaw-size scaling.\n\nThe exploratory top-1 list should be treated only as a heuristic, not a metric. **elasticity** is plausibly meaningful but mostly prompt-adjacent, reflecting “elastic plates” or modulus rather than an unspoken inference. **larger** and **compared** are also plausible scaffolding concepts: they track the comparison between the longer/larger flaw and the other plate, but they do not show the Griffith mechanism or the inverse-square-root scaling result. **thickness** and **density** look like generic materials-property intrusions; they are scientific-sounding but irrelevant to the stated criterion and likely small-model/topic-noise artifacts. **was**, **whereas**, **which**, **because**, and **will** are discourse or grammar continuations rather than substantive reasoning states. Overall, the lens shows a localized fracture-domain activation and comparative framing, but not an ordered emergence of “Griffith → lower stress”; the named mechanism is effectively absent in the J-lens, and the final disposition appears wrong." + }, + { + "slug": "paper-v1-pileup-focus-key", + "title": "Pile-up focus replication: brass key", + "shape": "MODULATION", + "domain": "materials", + "model_used": "openai:gpt-5.5", + "analysis": "Within the registered workspace band (38–92% depth), this is only a weak directed-modulation signal. The best J-lens ranks are good for **pile** and **stress**—**pile** reaches rank 14 at 73.2% depth, and **stress** reaches rank 18 at 87.8%—but neither has a sustained onset, and neither beats the matched vanilla logit lens: **pile** is tied with the logit lens at rank 14, while **stress** is worse than the logit lens rank 11. **Dislocation** is not cleanly represented by the J-lens here: its best J rank is only 1563 at 78.0%, and the logit lens is stronger at rank 596. So the chart is consistent with some late availability of the prompted focus concepts, with **pile** preceding **stress**, but it is not a clean J-lens-specific ordered-emergence result, and the key term **dislocation** is essentially absent as a strong verbalizable concept.\n\nThe layer-position grid qualitatively shows metallurgy/focus-related fragments around the instruction region—e.g. **dislocat**, **pile**, **stress**, **tension**, **effects**, **concentr**—but this should not be upgraded into the registered metric. It is useful as a visual sanity check that the model has some local access to the focus instruction while producing the unrelated carrier sentence, but the scored readout does not show a strong advantage over logit lens. Since this is a directed-modulation item, it also should be interpreted only relative to matched suppress and neutral records; those comparisons are not present here, so no modulation claim can be made from this record alone.\n\nFor the exploratory candidates, most are not meaningful evidence of unspoken dislocation-pile-up reasoning. **because**, **but**, **also/Also**, and **was** look like generic discourse or continuation artifacts. **describing** is likely meta-instructional writing/sentence context. **amidst** is a literary carrier-style substitution for spatial wording, not the target concept. **shimmering** may reflect a material/image association with **brass**, but not the pile-up focus; it may also be a small-model/tokenization-style artifact. The only plausibly meaningful heuristic entries are **effects** and perhaps **implications**, because they fit the stress-concentration/consequence frame and appear near the stress-related region, but they remain qualitative and unregistered. Overall: some late, weak focus leakage for **pile** and **stress**, no strong **dislocation** representation, and no clean J-lens-specific success." + }, + { + "slug": "paper-v1-pileup-suppress-key", + "title": "Pile-up suppression replication: brass key", + "shape": "MODULATION", + "domain": "materials", + "model_used": "openai:gpt-5.5", + "analysis": "Within the registered fixed workspace band (38–92%), this included directed-modulation item shows a real but incomplete suppressed-concept signal. The completion cleanly omits the forbidden material-science terms, but they are in the prompt, so this is not an input-absence/clean discovery case. At the readout position, **pile** becomes a strong transient J-lens concept, reaching rank **92 at 73.2% depth**, better than the matched logit-lens rank **212**. **stress** surfaces later, reaching rank **248 at 90.2%**, much stronger than logit-lens rank **1480**. **dislocation**, however, is not recovered: its best J rank is only **6331 at 46.3%**, with the logit lens slightly better at **6069**. None has a sustained onset. So the lens suggests the model is carrying pieces of the “pile-up / stress concentration” suppression content while writing the harmless sentence, but not the full metallurgical concept; in particular, “pile” is only the single-token fragment of “pile-up,” and “dislocation” is effectively absent.\n\nThe apparent ordering, **pile** before **stress**, is qualitatively consistent with the forbidden phrase structure and with a prompted avoidance state, but I would not treat it as a clean multi-step reasoning trace. The layer-position grid also qualitatively shows suppression-clause material such as pile/stress-like tokens in the band, but that all-position pattern is not the registered metric. The exploratory candidate list is mostly heuristic noise or task-frame signal: **also/Also**, **but**, and **because** are generic discourse-token artifacts, with capitalization duplication; **was** is likely grammar/tense carryover; **writing** plausibly reflects the active “write the sentence” task; **whatsoever** plausibly reflects the “do not write anything else” constraint; **implications** may be semantically related to consequences/stress concentration but is not registered evidence; **weird** and **mysterious** may reflect the odd “try not to think” framing, but they are weak qualitative artifacts rather than substantive hidden reasoning. Overall: a genuine transient J-lens signal for unspoken **pile** and **stress**, but no clean representation of **dislocation pile-up** as a full concept." + }, + { + "slug": "paper-v1-pileup-control-key", + "title": "Pile-up neutral control: brass key", + "shape": "MODULATION", + "domain": "materials", + "model_used": "openai:gpt-5.5", + "analysis": "Within the registered workspace band (38–92% depth), this neutral control does **not** show a clean pile-up/materials reasoning signal. The tracked concept **pile** briefly becomes fairly strong in the J-lens, peaking at rank **53** around **70.7% depth**, better than the matched vanilla logit-lens rank **225**. But there is **no sustained onset**, and the top-token slice at that layer is dominated by unrelated multilingual/copying artifacts rather than a coherent “pile-up” representation. **Stress** is also somewhat J-lens-favored, rank **362** at **90.2%** versus logit rank **2069**, but this is late and unsustained. **Dislocation** is not supported: J-lens rank **6685** is worse than the logit-lens rank **3410**. Overall, the lens mostly reflects instruction following and exact copying of “The brass key rested under the folded map,” not unspoken mechanical reasoning.\n\nThe exploratory candidate list should be treated as qualitative only. **“verbatim”** and **“whatsoever”** look meaningful as instruction-following paraphrases of “Do not write anything else,” especially because they appear near the negative-instruction positions; they are not evidence for the registered pile-up concepts. **“tucked”** may be a genuine semantic paraphrase of “rested under the folded map,” but it is a descriptive/story-word association, not a pile-up or stress computation. **“was”** is a generic narrative-tense alternative and only weakly meaningful. **“also/Also,” “and,” “because,” “other,”** and especially **“masterpiece”** look like discourse/default-continuation or tokenizer/model-noise artifacts in this context. Thus the neutral control behaves as it should: some copy/instruction and sentence-semantics structure is visible, but no clean registered emergence of pile-up reasoning." + }, + { + "slug": "paper-v1-pileup-focus-cup", + "title": "Pile-up focus replication: white cup", + "shape": "MODULATION", + "domain": "materials", + "model_used": "openai:gpt-5.5", + "analysis": "Within the registered workspace band, this record gives a selective signal for **pile**, but not a clean three-concept “dislocation pile-up / stress” workspace trace. **Pile** reaches J-lens rank **13 at 78.0% depth**, substantially stronger than the matched vanilla logit-lens rank **92**, so that is the main genuine J-lens finding here. By contrast, **dislocation** peaks only at J rank **388 at 85.4%** while the logit lens is better at **124**, and **stress** peaks at J rank **136 at 82.9%** while the logit lens is also better at **36**. None has a sustained onset. The chart’s late/final-layer improvement should not be promoted into the metric outside the fixed 38–92% band. Since these concepts are present in the instruction but absent from the output sentence, the lens is best read as showing some retained, unspoken task-conditioning around **pile-up**, not as a clean ordered reasoning sequence or a full material-mechanics representation. The directed-modulation claim still needs the matched suppress/neutral records; from this focus item alone we should not infer a replication effect.\n\nThe exploratory candidate list is useful only as a heuristic and mostly looks like carrier/style/top-token noise. **but**, **also/Also**, **because**, and **was** are generic discourse or syntactic continuations, not meaningful hidden concepts. **grammat** is a tokenizer fragment, though plausibly tied to the “write the sentence” control state rather than the materials rationale. **masterpiece** looks like literary-style noise. **amidst** may reflect scenic paraphrasing of “beside,” but not a registered concept. **padlock** near “closed” and **motionless** near “stood” are semantically interpretable local associations from the visible sentence, not evidence of dislocation reasoning. Overall, the record contains one notable J-lens-specific concept, **pile**, while the other tracked concepts and most exploratory tokens are either better captured by the logit lens or are local lexical/style artifacts." + }, + { + "slug": "paper-v1-pileup-suppress-cup", + "title": "Pile-up suppression replication: white cup", + "shape": "MODULATION", + "domain": "materials", + "model_used": "openai:gpt-5.5", + "analysis": "This is a registered included directed-modulation item, and the completion used for output-absence checking contains only “A white cup stood beside the closed window.” So the target materials concepts are unspoken in the output, but they are explicitly present in the prompt; this is not a clean absent-concept discovery, only a suppression/negation readout. Within the fixed 38–92% workspace band, the J-lens does show the forbidden content being represented despite the instruction not to think about it. The strongest signal is **pile**, reaching J-rank **119** at **53.7%** depth versus matched logit-lens rank **405**. **Dislocation** is weaker but still J-favored, J-rank **2289** at **53.7%** versus logit **5113**. **Stress** is also weak-to-moderate, J-rank **1210** at **90.2%** versus logit **2148**. None has a sustained onset, so I would call this a transient suppression/mention trace rather than a stable workspace representation. Also, “pile” is a single-token piece of the hyphenated **pile-up** phrase, so the clearest signal is partly a tokenizer-friendly fragment of the forbidden phrase, not necessarily a robust representation of the full dislocation-pile-up mechanism.\n\nThe slice grid is consistent with this: around the suppress clause there are mid-depth cells containing **dislocat**, **pile**, **stress**, **concentr**, **physics**, and related fragments, while later layers increasingly return to the literal writing/copying task and then next-token behavior. There is no ordered multi-step reasoning to infer here; the model is not computing a materials-science chain, it is parsing a negative instruction that names the forbidden concepts and then producing the neutral sentence. The J-lens is nonetheless more sensitive than the vanilla logit lens for all three tracked terms, especially **pile**, so the result is a genuine but limited modulation signal.\n\nThe exploratory candidate list should be treated only as a heuristic. **writing** and **grammat** mostly reflect the task frame (“Write…”, “write the sentence”) and in the case of **grammat** likely a subword/stem artifact rather than a clean concept. **but**, **also/Also**, and **because** are generic discourse-control tokens and not meaningful evidence of hidden reasoning. **stillness** and **padlock** may be semantic associations to the carrier sentence, especially “closed window,” but they are surface/scene associations, not target materials concepts. **implications** is plausibly related to the “stress it concentrates” clause, but it is diffuse and unregistered. **masterpiece** looks like stylistic/noise spillover. None of these exploratory entries should be counted as a protocol success." + }, + { + "slug": "paper-v1-pileup-control-cup", + "title": "Pile-up neutral control: white cup", + "shape": "MODULATION", + "domain": "materials", + "model_used": "openai:gpt-5.5", + "analysis": "This neutral control does **not** show clean unspoken pile-up/material reasoning. Within the registered workspace band, the best signal is a narrow **pile** blip at **53.7% depth**, where the J-lens reaches **rank 87** versus **logit-lens rank 207**. That is genuinely stronger than the matched logit baseline, but it has **no sustained onset** and does not form an ordered or persistent trajectory; in a neutral “white cup” copy task, I would treat it as a weak single-token association/artifact rather than evidence that the model computed “pile-up.” **Dislocation** and **stress** are weaker: **dislocation** reaches J rank **1730** at 53.7% versus logit **2180**, and **stress** reaches J rank **1590** at 85.4% versus logit **2664**. These are modest J-over-logit improvements but still low-ranked and unsustained. The late layers mostly move toward instruction/output-control tokens such as “Write,” “Period,” and “COPY,” consistent with literal next-token/task representation rather than hidden physical reasoning.\n\nThe exploratory candidate list is best read as a qualitative slice, not a metric. Some entries are plausibly meaningful local associations: **crockery** near “cup” is a reasonable semantic associate; **motionless** and **stillness** relate to “stood”; **padlock** relates to “closed”; **verbatim** and **whatsoever** likely reflect the instruction “Do not write anything else.” These show that the workspace contains paraphrastic or scene-related content, but not pile-up mechanics. By contrast, **also**, **and**, and **because** are mostly discourse/function-token noise, and **masterpiece** looks like a generic writing/literary continuation artifact rather than a controlled concept. Overall, the lens reveals ordinary copying/instruction and scene-association content, with one isolated **pile** token blip that should not be counted as a clean protocol success." + } + ], + "synthesis": "Across this exploratory materials run, the clearest workspace-like phenomena appeared in **ASSOCIATION** items, but they were partial rather than full replications of the paper’s strongest claims. The ductile-fracture vignette was the strongest included example: **coalescence** surfaced in the workspace band at J-rank **13 at 41.5% depth** versus logit-rank **3048**, with weaker but real support for **ductile** and **nucleation**. The cyclic-fracture vignette also had a meaningful J-specific signal for **propagation** at rank **337 vs. 2228**, and the boundary-attack vignette showed suggestive improvements for **boundary** and **sensitization**, though both remained around rank 10³ and were unsustained. These results are consistent with the paper’s claim that unspoken, verbalizable intermediate concepts can appear in the J-lens before the model’s answer, but only in a limited sense here: the signals were transient, often broad, and did not show clean sustained workspace plateaus. The low-temperature brittle-fracture item had a plausible **brittle** signal, but it was excluded from registered interpretation because **fracture** was already in the input.\n\nThe **MULTIHOP** and **RECOGNITION** items did not provide registered support for ordered internal reasoning. All listed multihop items were excluded by clean-answer failures, and the tracked mechanisms were weak or worse than the vanilla logit lens: **Hall** was much better in the logit lens than the J-lens, **Schmid** likewise, and **Griffith** was effectively absent as a J-specific concept. Some adjacent materials terms appeared, such as **fracture** in the flaw-size item and late **dislocation** in the shear item, but these did not form ordered chains such as “Hall–Petch → strengthening → higher,” “Schmid → slip,” or “Griffith → lower.” The diffraction recognition item was also excluded because **cubic** was in the input and the model did not produce the expected **FCC** answer; qualitatively, the lens showed a confused cubic/BCC/FCC answer space rather than a clean derivation of FCC from reflection parity. Thus the paper’s ordered-emergence claim was not demonstrated in this run.\n\nThe **MODULATION** records showed some qualitative suppression/focus leakage, especially for the single token **pile**, and sometimes for **stress**, but not a clean registered modulation result. In several focus or suppress records, **pile** reached strong J-lens ranks, for example rank **13** in the white-cup focus item and rank **119** in the white-cup suppress item, often beating the logit baseline. **Stress** was sometimes J-favored in suppress records, but in focus records it was often equal to or worse than the logit lens, and **dislocation** was generally weak or better captured by the logit lens. Neutral controls also produced isolated **pile** blips, so the modulation evidence should be read cautiously. Because these groups are explicitly sample-insufficient, and because the concepts were named in the prompt rather than absent from both input and output, these are best described as transient traces of prompted or forbidden content, not clean demonstrations of hidden mechanistic thought. No **REPORT_SWAP** analyses were provided in this run.\n\nThe run strongly illustrates the paper’s stated **single-token and model-capability limitations**. Important materials concepts were dropped or weakened because they are not clean single vocabulary tokens: **intergranular**, **transgranular**, **striations**, and **Petch** could not be registered, while **pile** is only a tokenizer-friendly fragment of **pile-up**. Some failures also look like genuine model limitations rather than lens failures: this target model did not reliably solve Hall–Petch, Griffith scaling, resolved-shear slip, or FCC recognition, and the J-lens mostly reported that absence or confusion. That is scientifically useful, but it means google/gemma-4-E4B-it is better suited here for exploratory demonstrations of vocabulary-level associations than for strong interpretability claims about multi-step materials reasoning. A larger or more capable model would be the more appropriate target for testing the paper’s global-workspace claims in this domain.\n\nThe exploratory surfaced candidates should be treated only as a heuristic, not as a paper metric. Plausible but broad signals include **metallurgical** as a domain-frame token and **indicating** as explanatory/diagnostic framing; **whatsoever** is meaningful mainly in modulation prompts as an instruction-following or “do not write anything else” trace. By contrast, the most frequent candidates—**because**, **also/Also**, **was**, **but**, **which**, **This**, and **and**—are mostly discourse, syntax, or continuation artifacts. **masterpiece** looks like stylistic or small-model noise rather than a materials concept. These candidates can help guide qualitative inspection, but they should not be counted as registered workspace successes or used to infer rates from this small exploratory run." +} \ No newline at end of file diff --git a/jlens_materials/runs/gemma4-materials-paper-v2.json b/jlens_materials/runs/gemma4-materials-paper-v2.json new file mode 100644 index 0000000000000000000000000000000000000000..4526b1d6ef6e2f5390c14a418733f25cbe6a5f74 --- /dev/null +++ b/jlens_materials/runs/gemma4-materials-paper-v2.json @@ -0,0 +1,99129 @@ +{ + "format_version": 2, + "created_at": "2026-07-10T21:20:38.444986+00:00", + "model": "google/gemma-4-E4B-it", + "model_identity": { + "requested_id": "google/gemma-4-E4B-it", + "requested_revision": null, + "model_class": "Gemma4ForConditionalGeneration", + "config_name_or_path": "google/gemma-4-E4B-it", + "model_revision": "a4c2d58be94dda072b918d9db64ee85c8ed34e3f", + "tokenizer_class": "GemmaTokenizer", + "tokenizer_name_or_path": "google/gemma-4-E4B-it", + "vocab_size": 262144, + "n_layers": 42, + "d_model": 2560, + "dtype": "float32" + }, + "tag": "gemma4-materials-paper-v2", + "n_layers": 42, + "d_model": 2560, + "lens_n_prompts": 1000, + "lens_provenance": null, + "methodology": { + "recipe": { + "name": "demo", + "n_fit": 12, + "max_seq_len": 64, + "target_layer": null, + "report_layer_count": null, + "skip_first": 16, + "strict_corpus": false, + "description": "Fast exploratory fit. Results from this profile are not a quantitative replication of the paper." + }, + "claims_level": "exploratory demo", + "paper_protocol_complete": false, + "strict_protocol": false, + "workspace_band": [ + 38.0, + 92.0 + ], + "workspace_band_source": "preregistered_cli_or_config", + "score_reduction": "fixed span x fixed band x synonym minimum", + "emergence_threshold": 5, + "emergence_sustain": 2, + "pass_at_k": [ + 1, + 2, + 5, + 10, + 20, + 50, + 100 + ], + "minimum_items_per_shape": 50, + "evaluation_max_seq_len": 512, + "generation_max_new_tokens": 1, + "model_revision": null, + "dtype": "float32", + "layer_stride": 1, + "prompt_source": "prompts/materials-paper-v2-preregistered.json", + "swaps_enabled": false, + "swap_alpha": 1.0 + }, + "shapes": [ + "ASSOCIATION", + "MODULATION" + ], + "domains": [ + "materials" + ], + "metrics": { + "by_shape": { + "ASSOCIATION": { + "n_items": 50, + "minimum_required": 50, + "sufficient_sample": true, + "ks": [ + 1, + 2, + 5, + 10, + 20, + 50, + 100 + ], + "jacobian_lens": { + "pass_at_k": [ + 0.013333333333333332, + 0.029999999999999995, + 0.05, + 0.056666666666666664, + 0.08333333333333331, + 0.14833333333333332, + 0.21166666666666667 + ], + "auc_log_k": 0.07992353330442654, + "bootstrap_95": { + "1": { + "mean": 0.013333333333333332, + "low": 0.0, + "high": 0.03333333333333333 + }, + "2": { + "mean": 0.029999999999999995, + "low": 0.006666666666666666, + "high": 0.055 + }, + "5": { + "mean": 0.05, + "low": 0.02333333333333333, + "high": 0.08333333333333331 + }, + "10": { + "mean": 0.056666666666666664, + "low": 0.026666666666666665, + "high": 0.09166666666666666 + }, + "20": { + "mean": 0.08333333333333331, + "low": 0.04666666666666666, + "high": 0.12333333333333332 + }, + "50": { + "mean": 0.14833333333333332, + "low": 0.10166666666666666, + "high": 0.195 + }, + "100": { + "mean": 0.21166666666666667, + "low": 0.16333333333333333, + "high": 0.26 + } + } + }, + "logit_lens": { + "pass_at_k": [ + 0.006666666666666666, + 0.006666666666666666, + 0.02, + 0.03333333333333333, + 0.06666666666666667, + 0.09999999999999998, + 0.13999999999999999 + ], + "auc_log_k": 0.04983848331165324 + } + } + }, + "directed_modulation_controls": { + "paper-v2-pileup-directed-modulation": { + "focus": { + "n_items": 24, + "hit_rate": 0.041666666666666664, + "bootstrap_95": [ + 0.0, + 0.125 + ], + "minimum_required": 24, + "sufficient_sample": true + }, + "suppress": { + "n_items": 24, + "hit_rate": 0.08333333333333333, + "bootstrap_95": [ + 0.0, + 0.20833333333333334 + ], + "minimum_required": 24, + "sufficient_sample": true + }, + "control": { + "n_items": 24, + "hit_rate": 0.0, + "bootstrap_95": [ + 0.0, + 0.0 + ], + "minimum_required": 24, + "sufficient_sample": true + }, + "summary": { + "n_trials": 72, + "distinct_phrasings": 24, + "paper_phrasing_target": 24 + } + } + }, + "causal_swaps": { + "n_interventions": 0, + "n_graded": 0, + "minimum_required": 50, + "sufficient_sample": false, + "protocol_success_rate": null, + "n_counterfactual": 0, + "causal_success_rate": null + } + }, + "errors": [], + "insufficient_sample_shapes": [], + "prompts": [ + { + "slug": "paper-v2-assoc-ductile-01", + "shape": "ASSOCIATION", + "protocol": "lens_eval", + "domain": "materials", + "title": "Dimpled tensile failure 1", + "description": "Preregistered unnamed materials vignette; compare fixed-band J-lens ranks with the matched logit-lens baseline.", + "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.", + "prompt_text": "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.", + "prompt_tail": "pronounced necking before separation. Its failure face contained rounded dimples centered on inclusions, with a cup-shaped center and a surrounding slanted lip.", + "readout_selector": "final_prompt_token", + "readout_at": -1, + "score_positions": [ + 33 + ], + "band": [ + 38.0, + 92.0 + ], + "band_source": "preregistered_cli_or_config", + "condition": null, + "control_group": null, + "phrasing_id": null, + "carrier_id": null, + "target_family": null, + "category": "ductile", + "answer": null, + "baseline": { + "required": false, + "correct": null, + "expected": [] + }, + "generated_completion": "\n\n", + "generated_token_ids": [ + 108 + ], + "protocol_violations": [], + "valid_for_metrics": true, + "excluded_reasons": [], + "discovered": false, + "tracked_dropped": [], + "tracked": [ + { + "label": "ductile", + "surfaces": [ + "ductile" + ], + "token_ids": [ + 185335 + ] + }, + { + "label": "nucleation", + "surfaces": [ + "nucleation" + ], + "token_ids": [ + 87211 + ] + }, + { + "label": "coalescence", + "surfaces": [ + 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+ "slug": "paper-v2-assoc-boundary-attack-04", + "shape": "ASSOCIATION", + "protocol": "lens_eval", + "domain": "materials", + "title": "Interface-localized stainless-steel attack 4", + "description": "Preregistered unnamed materials vignette; compare fixed-band J-lens ranks with the matched logit-lens baseline.", + "note": "Primary association-suite item. 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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.", + "prompt_text": "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.", + "prompt_tail": "subjected to a sinusoidal load until separation. 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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.", + "prompt_text": "A body-centered alloy separated at low temperature with negligible necking. The failure face consisted of flat crystallographic facets connected by river-like markings.", + "prompt_tail": "entered alloy separated at low temperature with negligible necking. 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Do not write anything else.\n<|turn>model\nA small lantern stood near the closed window.", + "prompt_tail": "up and the concentrated stress near its head while reproducing it. Do not write anything else.\n<|turn>model\nA small lantern stood near the closed window.", + "readout_selector": "assistant_response", + "readout_at": -1, + "score_positions": [ + 49, + 50, + 51, + 52, + 53, + 54, + 55, + 56, + 57 + ], + "band": [ + 38.0, + 92.0 + ], + "band_source": "preregistered_cli_or_config", + "condition": "suppress", + "control_group": "paper-v2-pileup-directed-modulation", + "phrasing_id": "triplet-04", + "carrier_id": "carrier-04", + "target_family": "dislocation-pileup", + "category": null, + "answer": null, + "baseline": { + "required": false, + "correct": null, + "expected": [] + }, + "generated_completion": "A small lantern stood near the closed window.", + "generated_token_ids": [ + 236776, + 1944, + 41865, + 15032, + 3541, + 506, + 6535, + 4771, + 236761 + ], + "protocol_violations": [], + "valid_for_metrics": true, + "excluded_reasons": [], + "discovered": false, + "tracked_dropped": [], + "tracked": [ + { + "label": "dislocation", + "surfaces": [ + 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Do not write anything else.\n<|turn>model\nA blue scarf hung behind the narrow door.", + "prompt_tail": "ocation pile-up and the stress concentration it creates while writing. 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"concept": "masterpiece", + "score": 11.0, + "best_rank": 1, + "best_depth": 56.1, + "near_token": "blue", + "output_checked": true + }, + { + "concept": "draped", + "score": 10.0, + "best_rank": 1, + "best_depth": 39.0, + "near_token": "hung", + "output_checked": true + }, + { + "concept": "other", + "score": 10.0, + "best_rank": 1, + "best_depth": 58.5, + "near_token": "write", + "output_checked": true + }, + { + "concept": "while", + "score": 10.0, + "best_rank": 42, + "best_depth": 82.9, + "near_token": "door", + "output_checked": true + }, + { + "concept": "color", + "score": 9.0, + "best_rank": 1, + "best_depth": 39.0, + "near_token": "blue", + "output_checked": true + }, + { + "concept": "grammat", + "score": 9.0, + "best_rank": 1, + "best_depth": 39.0, + "near_token": "Do", + "output_checked": true + }, + { + "concept": "beautifully", + "score": 9.0, + "best_rank": 1, + "best_depth": 68.3, + "near_token": "hung", + "output_checked": true + } + ], + "surprising_label": 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Do not write anything else.\n<|turn>model\nA white candle stood on the stone shelf.", + "prompt_tail": "islocation pile-up and concentrated stress as you produce the sentence. 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Do not write anything else.\n<|turn>model\nThe violet ribbon lay below the framed sketch.", + "prompt_tail": "ut a dislocation pile-up and its associated stress concentration. 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Do not write anything else.\n<|turn>model\nThe violet ribbon lay below the framed sketch.", + "prompt_tail": "ut a dislocation pile-up and its associated stress concentration. 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+ "output_checked": true + }, + { + "concept": "also", + "score": 14.0, + "best_rank": 1, + "best_depth": 61.0, + "near_token": "Write", + "output_checked": true + }, + { + "concept": "whatsoever", + "score": 12.0, + "best_rank": 1, + "best_depth": 39.0, + "near_token": "anything", + "output_checked": true + }, + { + "concept": "because", + "score": 12.0, + "best_rank": 2, + "best_depth": 65.9, + "near_token": "<|turn>", + "output_checked": true + }, + { + "concept": "Also", + "score": 11.0, + "best_rank": 1, + "best_depth": 73.2, + "near_token": "<|turn>", + "output_checked": true + }, + { + "concept": "and", + "score": 9.0, + "best_rank": 1, + "best_depth": 75.6, + "near_token": "", + "output_checked": true + }, + { + "concept": "other", + "score": 9.0, + "best_rank": 1, + "best_depth": 58.5, + "near_token": "write", + "output_checked": true + }, + { + "concept": "five", + "score": 9.0, + "best_rank": 1, + "best_depth": 70.7, + "near_token": "exactly", + "output_checked": true + } + ], + "surprising_label": "exploratory surfaced candidates", + "figures": { + "grid": "figures/gemma4-materials-paper-v2/materials/paper-v2-mod-control-15__grid.png", + "stream": "figures/gemma4-materials-paper-v2/materials/paper-v2-mod-control-15__stream.png", + "trajectory": "figures/gemma4-materials-paper-v2/materials/paper-v2-mod-control-15__trajectory.png", + "emergence": "figures/gemma4-materials-paper-v2/materials/paper-v2-mod-control-15__emergence.png", + "heatmap": "figures/gemma4-materials-paper-v2/materials/paper-v2-mod-control-15__heatmap.png" + } + }, + { + "slug": "paper-v2-mod-focus-16", + "shape": "MODULATION", + "protocol": "directed_modulation", + "domain": "materials", + "title": "Directed focus 16", + "description": "Preregistered matched directed-modulation triplet.", + "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.", + "prompt_text": "<|turn>user\nWrite exactly \"A clear bottle stood next to the woven basket.\" Keep your thoughts centered on a dislocation pile-up and concentrated stress as you produce the sentence. Do not write anything else.\n<|turn>model\nA clear bottle stood next to the woven basket.", + "prompt_tail": "tion pile-up and concentrated stress as you produce the sentence. 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"near_token": "exactly", + "output_checked": true + } + ], + "surprising_label": "exploratory surfaced candidates", + "figures": { + "grid": "figures/gemma4-materials-paper-v2/materials/paper-v2-mod-control-16__grid.png", + "stream": "figures/gemma4-materials-paper-v2/materials/paper-v2-mod-control-16__stream.png", + "trajectory": "figures/gemma4-materials-paper-v2/materials/paper-v2-mod-control-16__trajectory.png", + "emergence": "figures/gemma4-materials-paper-v2/materials/paper-v2-mod-control-16__emergence.png", + "heatmap": "figures/gemma4-materials-paper-v2/materials/paper-v2-mod-control-16__heatmap.png" + } + }, + { + "slug": "paper-v2-mod-focus-17", + "shape": "MODULATION", + "protocol": "directed_modulation", + "domain": "materials", + "title": "Directed focus 17", + "description": "Preregistered matched directed-modulation triplet.", + "note": "Primary paired endpoint: mean log10 best-rank contrast for focus versus suppress and neutral, computed within carrier and compared 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Do not write anything else.\n<|turn>model\nThe bronze key rested above the sealed letter.", + "prompt_tail": "location pile-up and its stress concentration while you write it. 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"output_checked": true + } + ], + "surprising_label": "exploratory surfaced candidates", + "figures": { + "grid": "figures/gemma4-materials-paper-v2/materials/paper-v2-mod-control-17__grid.png", + "stream": "figures/gemma4-materials-paper-v2/materials/paper-v2-mod-control-17__stream.png", + "trajectory": "figures/gemma4-materials-paper-v2/materials/paper-v2-mod-control-17__trajectory.png", + "emergence": "figures/gemma4-materials-paper-v2/materials/paper-v2-mod-control-17__emergence.png", + "heatmap": "figures/gemma4-materials-paper-v2/materials/paper-v2-mod-control-17__heatmap.png" + } + }, + { + "slug": "paper-v2-mod-focus-18", + "shape": "MODULATION", + "protocol": "directed_modulation", + "domain": "materials", + "title": "Directed focus 18", + "description": "Preregistered matched directed-modulation triplet.", + "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.", + "prompt_text": "<|turn>user\nWrite exactly \"A cotton glove lay beside the empty vase.\" Keep a dislocation pile-up and the stress it concentrates in mind while copying it. Do not write anything else.\n<|turn>model\nA cotton glove lay beside the empty vase.", + "prompt_tail": "ation pile-up and the stress it concentrates in mind while copying it. Do not write anything else.\n<|turn>model\nA cotton glove lay beside the empty vase.", + "readout_selector": "assistant_response", + "readout_at": -1, + "score_positions": [ + 45, + 46, + 47, + 48, + 49, + 50, + 51, + 52, + 53 + ], + "band": [ + 38.0, + 92.0 + ], + "band_source": "preregistered_cli_or_config", + "condition": "focus", + "control_group": "paper-v2-pileup-directed-modulation", + "phrasing_id": "triplet-18", + "carrier_id": "carrier-18", + "target_family": "dislocation-pileup", + "category": null, + "answer": null, + "baseline": { + "required": false, + "correct": null, + "expected": [] + }, + "generated_completion": "A cotton glove lay beside the empty vase.", + "generated_token_ids": [ + 236776, + 14538, + 57688, + 6267, + 28383, + 506, + 7738, + 37468, + 236761 + ], + "protocol_violations": [], + "valid_for_metrics": true, + "excluded_reasons": [], + "discovered": false, + "tracked_dropped": [], + "tracked": [ + { + "label": "dislocation", + "surfaces": [ + 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Do not write anything else.\n<|turn>model\nThe brown wallet waited below the desk lamp.", + "readout_selector": "assistant_response", + "readout_at": -1, + "score_positions": [ + 45, + 46, + 47, + 48, + 49, + 50, + 51, + 52, + 53 + ], + "band": [ + 38.0, + 92.0 + ], + "band_source": "preregistered_cli_or_config", + "condition": "focus", + "control_group": "paper-v2-pileup-directed-modulation", + "phrasing_id": "triplet-23", + "carrier_id": "carrier-23", + "target_family": "dislocation-pileup", + "category": null, + "answer": null, + "baseline": { + "required": false, + "correct": null, + "expected": [] + }, + "generated_completion": "The brown wallet waited below the desk lamp.", + "generated_token_ids": [ + 818, + 8864, + 27173, + 35033, + 3426, + 506, + 15348, + 15123, + 236761 + ], + "protocol_violations": [], + "valid_for_metrics": true, + "excluded_reasons": [], + "discovered": false, + "tracked_dropped": [], + "tracked": [ + { + "label": "dislocation", + "surfaces": [ + 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matched directed-modulation triplet.", + "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.", + "prompt_text": "<|turn>user\nWrite exactly \"The brown wallet waited below the desk lamp.\" While copying, actively avoid thinking about a dislocation pile-up and its associated stress concentration. Do not write anything else.\n<|turn>model\nThe brown wallet waited below the desk lamp.", + "prompt_tail": "bout a dislocation pile-up and its associated stress concentration. 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"therefore", + "correspondingly", + "become", + "becomes", + "also", + "likewise" + ] + }, + { + "layer": 28, + "depth": 68.3, + "top_tokens": [ + "accordingly", + "consequently", + "therefore", + "thus", + "correspondingly", + "Consequently", + "increase", + "increased" + ] + }, + { + "layer": 29, + "depth": 70.7, + "top_tokens": [ + "therefore", + "thus", + "consequently", + "accordingly", + "hence", + "Consequently", + "因此", + "correspondingly" + ] + }, + { + "layer": 30, + "depth": 73.2, + "top_tokens": [ + "therefore", + "thus", + "consequently", + "accordingly", + "increase", + "correspondingly", + "Consequently", + "increases" + ] + }, + { + "layer": 31, + "depth": 75.6, + "top_tokens": [ + "therefore", + "accordingly", + "consequently", + "thus", + "increase", + "correspondingly", + "Consequently", + "因此" + ] + }, + { + "layer": 32, + "depth": 78.0, + "top_tokens": [ + "therefore", + "increase", + "consequently", + "accordingly", + "thus", + "increases", + "correspondingly", + "increased" + ] + }, + { + "layer": 33, + "depth": 80.5, + "top_tokens": [ + "increase", + "increases", + "therefore", + "decrease", + "increasing", + "increased", + "consequently", + "thus" + ] + }, + { + "layer": 34, + "depth": 82.9, + "top_tokens": [ + "increase", + "increases", + "increasing", + "increase", + "increased", + "Increase", + "Increase", + "增加" + ] + }, + { + "layer": 35, + "depth": 85.4, + "top_tokens": [ + "increase", + "increases", + "increase", + "increasing", + "increased", + "Increase", + "Increase", + "增加" + ] + }, + { + "layer": 36, + "depth": 87.8, + "top_tokens": [ + "increase", + "increases", + "increasing", + "increase", + "rise", + "decrease", + "increased", + "Increase" + ] + }, + { + "layer": 37, + "depth": 90.2, + "top_tokens": [ + "increase", + "increases", + "rise", + "increasing", + "increase", + "decrease", + "be", + "increased" + ] + }, + { + "layer": 38, + "depth": 92.7, + "top_tokens": [ + "increase", + "increases", + "rise", + "increasing", + "decrease", + "increase", + "be", + "增加" + ] + }, + { + "layer": 39, + "depth": 95.1, + "top_tokens": [ + "increase", + "rise", + "increases", + "decrease", + "increasing", + "be", + "increase", + "remain" + ] + }, + { + "layer": 40, + "depth": 97.6, + "top_tokens": [ + "increase", + "be", + "decrease", + "rise", + "remain", + "increases", + "depend", + "become" + ] + }, + { + "layer": 41, + "depth": 100.0, + "top_tokens": [ + "increase", + "be", + "therefore", + "thus", + "scale", + "consequently", + "remain", + "rise" + ] + } + ], + "surprising": [ + { + "concept": "because", + "score": 24.0, + "best_rank": 1, + "best_depth": 61.0, + "near_token": "more", + "output_checked": true + }, + { + "concept": "was", + "score": 23.0, + "best_rank": 1, + "best_depth": 56.1, + "near_token": "required", + "output_checked": true + }, + { + "concept": "this", + "score": 22.0, + "best_rank": 1, + "best_depth": 56.1, + "near_token": "for", + "output_checked": true + }, + { + "concept": "metallurgical", + "score": 18.0, + "best_rank": 1, + "best_depth": 39.0, + "near_token": "contains", + "output_checked": true + }, + { + "concept": "and", + "score": 15.0, + "best_rank": 1, + "best_depth": 56.1, + "near_token": "fixed", + "output_checked": true + }, + { + "concept": "density", + "score": 14.0, + "best_rank": 1, + "best_depth": 39.0, + "near_token": "fraction", + "output_checked": true + }, + { + "concept": "they", + "score": 14.0, + "best_rank": 1, + "best_depth": 48.8, + "near_token": "their", + "output_checked": true + }, + { + "concept": "percentage", + "score": 13.0, + "best_rank": 1, + "best_depth": 39.0, + "near_token": "of", + "output_checked": true + }, + { + "concept": "became", + "score": 13.0, + "best_rank": 1, + "best_depth": 58.5, + "near_token": "must", + "output_checked": true + }, + { + "concept": "distance", + "score": 12.0, + "best_rank": 1, + "best_depth": 39.0, + "near_token": "to", + "output_checked": true + }, + { + "concept": "Consequently", + "score": 12.0, + "best_rank": 1, + "best_depth": 51.2, + "near_token": ".", + "output_checked": true + }, + { + "concept": "alloy", + "score": 11.0, + "best_rank": 1, + "best_depth": 39.0, + "near_token": "metal", + "output_checked": true + } + ], + "surprising_label": "exploratory surfaced candidates", + "figures": { + "grid": "figures/gemma4-materials-qual/materials/materials-orowan-spacing__grid.png", + "stream": "figures/gemma4-materials-qual/materials/materials-orowan-spacing__stream.png", + "trajectory": "figures/gemma4-materials-qual/materials/materials-orowan-spacing__trajectory.png", + "emergence": "figures/gemma4-materials-qual/materials/materials-orowan-spacing__emergence.png", + "heatmap": "figures/gemma4-materials-qual/materials/materials-orowan-spacing__heatmap.png" + } + }, + { + "slug": "materials-martensite-quench", + "shape": "MULTIHOP", + "protocol": "lens_eval", + "domain": "materials", + "title": "Rapid quench to martensite", + "description": "Tests whether transformation-mechanism concepts appear before the product name.", + "note": "The answer is the lattice symmetry; shear and Bain encode the transformation mechanism. Gemma tokenizes martensite into multiple pieces, so the report should explicitly list that exact phase name as dropped rather than silently approximating it.", + "prompt_text": "Austenitic steel is cooled so rapidly that long-range atomic redistribution is suppressed. The product is hard and needle-like, and its body-centered unit cell is elongated so that c divided by a is not equal to one. Its crystal symmetry is", + "prompt_tail": "uppressed. The product is hard and needle-like, and its body-centered unit cell is elongated so that c divided by a is not equal to one. Its crystal symmetry is", + "readout_selector": "before_answer", + "readout_at": -1, + "score_positions": [ + 51 + ], + "band": [ + 38.0, + 92.0 + ], + "band_source": "preregistered_cli_or_config", + "condition": null, + "control_group": null, + "phrasing_id": null, + "carrier_id": null, + "target_family": null, + "category": null, + "answer": "tetragonal", + "baseline": { + "required": true, + "correct": false, + "expected": [ + "tetragonal" + ], + "greedy_token_id": 3718, + "greedy_token": "lower" + }, + "generated_completion": " lower", + "generated_token_ids": [ + 3718 + ], + "protocol_violations": [], + "valid_for_metrics": false, + "excluded_reasons": [ + "clean model did not produce the expected answer" + ], + "discovered": false, + "tracked_dropped": [ + "martensite" + ], + "tracked": [ + { + "label": "shear", + "surfaces": [ + "shear" + ], + "token_ids": [ + 26151, + 120896, + 114142 + ] + }, + { + "label": "Bain", + "surfaces": [ + "Bain" + ], + "token_ids": [ + 104962, + 78036 + ] + } + ], + "emergence": [ + { + "label": "shear", + "best_rank": 511, + "best_depth": 82.9, + "onset_depth": null, + "reached_top1": false, + "best_pos": 51, + "logit_lens_best_rank": 537 + }, + { + "label": "Bain", + "best_rank": 3515, + "best_depth": 87.8, + "onset_depth": null, + "reached_top1": false, + "best_pos": 51, + "logit_lens_best_rank": 828 + } + ], + "layer_readouts": [ + { + "layer": 0, + "depth": 0.0, + "top_tokens": [ + "therefore", + "because", + "according", + "there", + "accordingly", + "people", + "thereon", + "leness" + ] + }, + { + "layer": 1, + "depth": 2.4, + "top_tokens": [ + "a", + "i", + "nt", + "etc", + "l", + "o", + "or", + "t" + ] + }, + { + "layer": 2, + "depth": 4.9, + "top_tokens": [ + "ment", + "lly", + "y", + "er", + "ty", + "wards", + "eded", + "ally" + ] + }, + { + "layer": 3, + "depth": 7.3, + "top_tokens": [ + "ally", + "atres", + "atre", + "ity", + "atic", + "eded", + "ly", + "ained" + ] + }, + { + "layer": 4, + "depth": 9.8, + "top_tokens": [ + "ally", + "attained", + "thence", + "arily", + "eded", + "regards", + "therein", + "ly" + ] + }, + { + "layer": 5, + "depth": 12.2, + "top_tokens": [ + "attained", + "oneness", + "therein", + "regard", + "regards", + "therefore", + "she", + "regarded" + ] + }, + { + "layer": 6, + "depth": 14.6, + "top_tokens": [ + "centre", + "programme", + "endeavour", + "colours", + "realised", + "centred", + "honours", + "programmes" + ] + }, + { + "layer": 7, + "depth": 17.1, + "top_tokens": [ + "centre", + "realised", + "behaviour", + "centred", + "recognised", + "colour", + "analysed", + "honours" + ] + }, + { + "layer": 8, + "depth": 19.5, + "top_tokens": [ + "attained", + "she", + "her", + "achieved", + "lustre", + "notable", + "predominantly", + "predominance" + ] + }, + { + "layer": 9, + "depth": 22.0, + "top_tokens": [ + "attained", + "femininity", + "therefore", + "also", + "oneness", + "her", + "she", + "Austro" + ] + }, + { + "layer": 10, + "depth": 24.4, + "top_tokens": [ + "attained", + "notable", + "noted", + "therefore", + "predominantly", + "predominance", + "notably", + "rarity" + ] + }, + { + "layer": 11, + "depth": 26.8, + "top_tokens": [ + "attained", + "femininity", + "feminine", + "attainment", + "ableness", + "oneness", + "isotropy", + "coloration" + ] + }, + { + "layer": 12, + "depth": 29.3, + "top_tokens": [ + "femininity", + "attained", + "isotropy", + "uniformity", + "feminine", + "predominance", + "promulgated", + "oneness" + ] + }, + { + "layer": 13, + "depth": 31.7, + "top_tokens": [ + "isotropy", + "anisotropic", + "isotropic", + "crystallography", + "femininity", + "anisotropy", + "homogeneity", + "geometrical" + ] + }, + { + "layer": 14, + "depth": 34.1, + "top_tokens": [ + "isotropy", + "anisotropic", + "crystallography", + "isotropic", + "anisotropy", + "geometrical", + "geomet", + "morphology" + ] + }, + { + "layer": 15, + "depth": 36.6, + "top_tokens": [ + "crystallography", + "anisotropic", + "isotropy", + "isotropic", + "geomet", + "morphology", + "anisotropy", + "anisotrop" + ] + }, + { + "layer": 16, + "depth": 39.0, + "top_tokens": [ + "anisotropic", + "isotropy", + "geomet", + "isotropic", + "crystallography", + "morphology", + "geometrical", + "metamorphic" + ] + }, + { + "layer": 17, + "depth": 41.5, + "top_tokens": [ + "isotropy", + "geomet", + "anisotropic", + "isotropic", + "geometrical", + "geometric", + "anisotropy", + "geometri" + ] + }, + { + "layer": 18, + "depth": 43.9, + "top_tokens": [ + "classification", + "isotropy", + "geometrical", + "geomet", + "taxonom", + "anisotropic", + "hermaph", + "isotropic" + ] + }, + { + "layer": 19, + "depth": 46.3, + "top_tokens": [ + "classification", + "isotropy", + "taxonom", + "anisotropic", + "morphology", + "hermaph", + "morphogenesis", + "femininity" + ] + }, + { + "layer": 20, + "depth": 48.8, + "top_tokens": [ + "classification", + "anthropology", + "femininity", + "morphology", + "quaternary", + "taxonom", + "characteristic", + "geometrical" + ] + }, + { + "layer": 21, + "depth": 51.2, + "top_tokens": [ + "differs", + "classifying", + "classification", + "is", + "belonged", + "primitive", + "classifies", + "prokary" + ] + }, + { + "layer": 22, + "depth": 53.7, + "top_tokens": [ + "is", + "was", + "her", + "which", + "only", + "are", + "she", + "his" + ] + }, + { + "layer": 23, + "depth": 56.1, + "top_tokens": [ + "because", + "only", + "both", + "not", + "also", + "was", + "actually", + "which" + ] + }, + { + "layer": 24, + "depth": 58.5, + "top_tokens": [ + "because", + "rather", + "seems", + "was", + "often", + "is", + "not", + "became" + ] + }, + { + "layer": 25, + "depth": 61.0, + "top_tokens": [ + "rather", + "instead", + "essentially", + "often", + "mainly", + "not", + "primarily", + "likely" + ] + }, + { + "layer": 26, + "depth": 63.4, + "top_tokens": [ + "rather", + "instead", + "not", + "often", + "somewhat", + "essentially", + "typically", + "generally" + ] + }, + { + "layer": 27, + "depth": 65.9, + "top_tokens": [ + "somewhat", + "largely", + "generally", + "rather", + "typically", + "often", + "not", + "essentially" + ] + }, + { + "layer": 28, + "depth": 68.3, + "top_tokens": [ + "somewhat", + "slightly", + "largely", + "changed", + "replaced", + "mostly", + "typically", + "generally" + ] + }, + { + "layer": 29, + "depth": 70.7, + "top_tokens": [ + "that", + "similar", + "therefore", + "modified", + "reminiscent", + "somewhat", + "changed", + "characterized" + ] + }, + { + "layer": 30, + "depth": 73.2, + "top_tokens": [ + "that", + "similar", + "therefore", + "changed", + "modified", + "altered", + "somewhat", + "characterized" + ] + }, + { + "layer": 31, + "depth": 75.6, + "top_tokens": [ + "that", + "altered", + "modified", + "distorted", + "similar", + "influenced", + "therefore", + "changed" + ] + }, + { + "layer": 32, + "depth": 78.0, + "top_tokens": [ + "that", + "distorted", + "anisotropic", + "altered", + "that", + "tilted", + "deformed", + "therefore" + ] + }, + { + "layer": 33, + "depth": 80.5, + "top_tokens": [ + "that", + "distorted", + "anisotropic", + "tilted", + "altered", + "tetragonal", + "asymmetrical", + "elongated" + ] + }, + { + "layer": 34, + "depth": 82.9, + "top_tokens": [ + "distorted", + "tetragonal", + "hexagonal", + "anisotropic", + "asymmetrical", + "deformed", + "tilted", + "skewed" + ] + }, + { + "layer": 35, + "depth": 85.4, + "top_tokens": [ + "distorted", + "tetragonal", + "hexagonal", + "asymmetrical", + "anisotropic", + "deformed", + "tilted", + "rhomb" + ] + }, + { + "layer": 36, + "depth": 87.8, + "top_tokens": [ + "hexagonal", + "distorted", + "tetragonal", + "cubic", + "that", + "anisotropic", + "deformed", + "asymmetrical" + ] + }, + { + "layer": 37, + "depth": 90.2, + "top_tokens": [ + "tetragonal", + "hexagonal", + "distorted", + "cubic", + "deformed", + "tetr", + "anisotropic", + "tilted" + ] + }, + { + "layer": 38, + "depth": 92.7, + "top_tokens": [ + "distorted", + "tetragonal", + "hexagonal", + "cubic", + "deformed", + "low", + "lowered", + "tetr" + ] + }, + { + "layer": 39, + "depth": 95.1, + "top_tokens": [ + "distorted", + "tetragonal", + "hexagonal", + "lower", + "lowered", + "low", + "reduced", + "cubic" + ] + }, + { + "layer": 40, + "depth": 97.6, + "top_tokens": [ + "distorted", + "lower", + "tetragonal", + "reduced", + "hexagonal", + "lowered", + "low", + "that" + ] + }, + { + "layer": 41, + "depth": 100.0, + "top_tokens": [ + "lower", + "that", + "distorted", + "tetragonal", + "low", + "reduced", + "hexagonal", + "lowered" + ] + } + ], + "surprising": [ + { + "concept": "because", + "score": 61.0, + "best_rank": 1, + "best_depth": 48.8, + "near_token": "one", + "output_checked": true + }, + { + "concept": "was", + "score": 46.0, + "best_rank": 1, + "best_depth": 56.1, + "near_token": "body", + "output_checked": true + }, + { + "concept": "metallurgical", + "score": 35.0, + "best_rank": 1, + "best_depth": 39.0, + "near_token": "steel", + "output_checked": true + }, + { + "concept": "microstructure", + "score": 30.0, + "best_rank": 1, + "best_depth": 41.5, + "near_token": "long", + "output_checked": true + }, + { + "concept": "structure", + "score": 26.0, + "best_rank": 1, + "best_depth": 65.9, + "near_token": "centered", + "output_checked": true + }, + { + "concept": "which", + "score": 19.0, + "best_rank": 1, + "best_depth": 56.1, + "near_token": "is", + "output_checked": true + }, + { + "concept": "ratio", + "score": 17.0, + "best_rank": 1, + "best_depth": 39.0, + "near_token": "divided", + "output_checked": true + }, + { + "concept": "This", + "score": 16.0, + "best_rank": 1, + "best_depth": 68.3, + "near_token": ".", + "output_checked": true + }, + { + "concept": "toughness", + "score": 14.0, + "best_rank": 1, + "best_depth": 39.0, + "near_token": "hard", + "output_checked": true + }, + { + "concept": "martens", + "score": 13.0, + "best_rank": 1, + "best_depth": 43.9, + "near_token": "its", + "output_checked": true + }, + { + "concept": "whereas", + "score": 12.0, + "best_rank": 1, + "best_depth": 51.2, + "near_token": "one", + "output_checked": true + }, + { + "concept": "represents", + "score": 12.0, + "best_rank": 1, + "best_depth": 58.5, + "near_token": "its", + "output_checked": true + } + ], + "surprising_label": "exploratory surfaced candidates", + "figures": { + "grid": "figures/gemma4-materials-qual/materials/materials-martensite-quench__grid.png", + "stream": "figures/gemma4-materials-qual/materials/materials-martensite-quench__stream.png", + "trajectory": "figures/gemma4-materials-qual/materials/materials-martensite-quench__trajectory.png", + "emergence": "figures/gemma4-materials-qual/materials/materials-martensite-quench__emergence.png", + "heatmap": "figures/gemma4-materials-qual/materials/materials-martensite-quench__heatmap.png" + } + }, + { + "slug": "materials-schmid-slip", + "shape": "MULTIHOP", + "protocol": "lens_eval", + "domain": "materials", + "title": "Resolved stress to slip", + "description": "An orientation-factor calculation should evoke the named criterion before the deformation event.", + "note": "Check whether Schmid appears as a law-level concept and whether dislocation appears as the physical carrier before the clean one-token answer.", + "prompt_text": "A single crystal under uniaxial load has cos(phi) = 0.8 and cos(lambda) = 0.5. Multiplying these orientation factors by the applied stress gives 120 MPa, above the critical resolved value of 80 MPa. The crystal will begin to", + "prompt_tail": "lambda) = 0.5. Multiplying these orientation factors by the applied stress gives 120 MPa, above the critical resolved value of 80 MPa. 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"top_tokens": [ + "This", + "During", + "What", + "Such", + "Let", + "Assuming", + "Their", + "During" + ] + }, + { + "layer": 39, + "depth": 95.1, + "top_tokens": [ + "During", + "This", + "Such", + "What", + "Under", + "During", + "When", + "Their" + ] + }, + { + "layer": 40, + "depth": 97.6, + "top_tokens": [ + "During", + "This", + "uring", + "Such", + "Under", + "et", + "he", + "What" + ] + }, + { + "layer": 41, + "depth": 100.0, + "top_tokens": [ + "This", + "Such", + "Under", + "During", + "The", + "Let", + "et", + "nt" + ] + } + ], + "surprising": [ + { + "concept": "metallurgical", + "score": 53.0, + "best_rank": 1, + "best_depth": 39.0, + "near_token": "austenitic", + "output_checked": true + }, + { + "concept": "and", + "score": 22.0, + "best_rank": 1, + "best_depth": 56.1, + "near_token": "carbides", + "output_checked": true + }, + { + "concept": "corrosion", + "score": 21.0, + "best_rank": 1, + "best_depth": 39.0, + "near_token": "attack", + "output_checked": true + }, + { + "concept": "unaffected", + "score": 21.0, + "best_rank": 1, + "best_depth": 43.9, + "near_token": "remain", + "output_checked": true + }, + { + "concept": "environment", + "score": 21.0, + "best_rank": 1, + "best_depth": 58.5, + "near_token": "an", + "output_checked": true + }, + { + "concept": "remained", + "score": 20.0, + "best_rank": 1, + "best_depth": 63.4, + "near_token": "grain", + "output_checked": true + }, + { + "concept": "during", + "score": 18.0, + "best_rank": 1, + "best_depth": 48.8, + "near_token": "held", + "output_checked": true + }, + { + "concept": "but", + "score": 18.0, + "best_rank": 1, + "best_depth": 65.9, + "near_token": "formed", + "output_checked": true + }, + { + "concept": "these", + "score": 18.0, + "best_rank": 1, + "best_depth": 65.9, + "near_token": "along", + "output_checked": true + }, + { + "concept": "because", + "score": 18.0, + "best_rank": 2, + "best_depth": 58.5, + "near_token": "solution", + "output_checked": true + }, + { + "concept": "between", + "score": 16.0, + "best_rank": 1, + "best_depth": 65.9, + "near_token": "connected", + "output_checked": true + }, + { + "concept": "microstructure", + "score": 15.0, + "best_rank": 1, + "best_depth": 39.0, + "near_token": "ite", + "output_checked": true + } + ], + "surprising_label": "exploratory surfaced candidates", + "figures": { + "grid": "figures/gemma4-materials-qual/materials/materials-intergranular-attack__grid.png", + "stream": "figures/gemma4-materials-qual/materials/materials-intergranular-attack__stream.png", + "trajectory": "figures/gemma4-materials-qual/materials/materials-intergranular-attack__trajectory.png", + "emergence": "figures/gemma4-materials-qual/materials/materials-intergranular-attack__emergence.png", + "heatmap": "figures/gemma4-materials-qual/materials/materials-intergranular-attack__heatmap.png" + } + } + ] +} \ No newline at end of file diff --git a/jlens_materials/runs/gemma4-materials-qual_analysis.json b/jlens_materials/runs/gemma4-materials-qual_analysis.json new file mode 100644 index 0000000000000000000000000000000000000000..0ecaa820ad6606aa38da7789dac66fd8901568a4 --- /dev/null +++ b/jlens_materials/runs/gemma4-materials-qual_analysis.json @@ -0,0 +1,81 @@ +{ + "model": "google/gemma-4-E4B-it", + "provider": "openai", + "analysis_model": "gpt-5.5", + "offline": false, + "per_prompt": [ + { + "slug": "materials-hall-petch", + "title": "Grain refinement to yield strength", + "shape": "MULTIHOP", + "domain": "materials", + "model_used": "openai:gpt-5.5", + "analysis": "This is **not a registered protocol success**: the clean-answer control failed because the model’s greedy first token was **“significantly”**, not the expected **“higher”**. Qualitatively, however, the readout looks like the model is moving toward the phrase **“significantly higher”**: in the readout-position top tokens, the mid/late band first carries materials-property content around depths **39–51%** (“resistance,” “metallurgical,” “toughness,” “tensile,” “hardening”), then causal/transition tokens around **53–61%** (“will,” “because,” “due,” “accordingly”), then comparative-answer tokens from about **66–88%** (“more,” “stronger,” “greater,” and **“higher”**, with “higher” top-ranked at **80.5–87.8%**). The last layers then switch to the literal next token **“significantly”** from about **90% onward**, matching the actual greedy output. So there is a real qualitative answer-emergence pattern, but it is excluded from the clean metric.\n\nFor the named law, the evidence is weak. **Hall** does not cleanly surface: its best recorded J-lens rank in the fixed band is only **9005 at 56.1%**, versus a matched logit-lens rank of **11294**. That is technically better than the logit baseline, but still extremely low and not sustained. **Petch** was dropped as multi-token/untrackable, which is exactly the single-token limitation of this protocol. **Strengthening** is the one more meaningful tracked signal: it reaches J-lens rank **479 at 48.8%**, materially better than the matched logit rank **2726**, and it occurs in the same mid-band region where the top tokens are metallurgical/material-property words. But there is no sustained onset, and “strengthening” is a generic concept rather than the specific **Hall–Petch** law. The lens therefore suggests the model has some generic grain-size/material-strength association, not a clean unspoken representation of the named law.\n\nThe exploratory candidate list is useful only heuristically. **“microstructure”** near “crystall-,” **“differing”** near “have,” **“sizes”** near “ite,” and perhaps **“respectively”** near “and” look semantically meaningful as reconstruction of the sample comparison, though “sizes” is partly a tokenizer/local-context artifact. **“because”** and **“Consequently”** reflect causal-discourse structure, not the scientific mechanism. **“toughness”** is a plausible materials-domain activation but not the correct target concept and may reflect a small-model/property-neighbor artifact. **“was,” “are,” and “which”** are basically syntactic/noise candidates. The grid also shows some distracting transport/electrical vocabulary around “carrier motion,” so the model may be mixing “carriers” with conductivity-style concepts rather than cleanly representing dislocation/grain-boundary Hall–Petch reasoning." + }, + { + "slug": "materials-orowan-spacing", + "title": "Obstacle spacing to bypass stress", + "shape": "MULTIHOP", + "domain": "materials", + "model_used": "openai:gpt-5.5", + "analysis": "This is a clean included item for the answer direction: the model greedily says “increase.” However, the J-lens does **not** cleanly reveal the intended specific bypass mechanism, because **“Orowan” was dropped as multi-token and is therefore not trackable** in this protocol. Among tracked substitutes, **strengthening** is the only reasonably strong concept, reaching J-rank **208 at 82.9% depth**, but the matched vanilla logit lens is similar (**281**), so this is not a distinctive J-lens discovery. **Bowing** is weak in the J-lens, rank **3844 at 78.0%**, and the logit lens is actually better (**749**). **Precipitation** improves over the logit baseline, J-rank **5291 vs. logit 53852**, but it is still not a strong or sustained surfaced concept. Thus the main registered signal is generic “strengthening/increase,” not a clean latent “Orowan bypass” mechanism.\n\nThe layer grid has a plausible qualitative materials-mechanics texture in the workspace band: tokens such as “metallur/metallurgical,” “density,” “spacing,” “curvatur,” “bends,” “stiffer,” “pressure,” and “resistan” appear around the prompt positions describing particles, spacing, bowing, and required stress. But much of this mirrors or paraphrases the input rather than showing an unspoken intermediate. At the readout position, the top tokens transition from vague reasoning/discourse tokens to “therefore,” and then to the literal answer **“increase”** by about **80.5% depth**, so the late layers look more like answer commitment than an ordered emergence of a hidden mechanism.\n\nThe exploratory candidate list should be treated only as a heuristic. **“metallurgical”** is probably meaningful as a domain label; **“density,” “percentage,” and “distance”** are partly meaningful paraphrases of volume fraction/spacing but are close to input-derived restatements rather than new reasoning. **“because”** may reflect explanatory framing, not a scientific concept. **“was,” “this,” “and,” “they,” and “became”** are mostly function-word/coreference/discourse artifacts. Overall, this item shows the model knows the answer direction and some generic strengthening context, but it does **not** provide a clean J-lens success for the specific Orowan-bypass intermediate." + }, + { + "slug": "materials-martensite-quench", + "title": "Rapid quench to martensite", + "shape": "MULTIHOP", + "domain": "materials", + "model_used": "openai:gpt-5.5", + "analysis": "This item is **excluded as a registered protocol success** because the clean model did not answer “tetragonal”; its greedy token was **“lower”**. Qualitatively, the J-lens does show that the model has some relevant crystallographic content before the final token: at the readout position, **“tetragonal”** enters the top tokens late in the workspace, rank 6 at 80.5%, rank 2 at 82.9–85.4%, rank 3 at 87.8%, and rank 1 at 90.2%, before the final layers pivot toward **“lower”**. It also strongly represents deformation language such as **“distorted”**, and earlier workspace tokens include **anisotropic/isotropy/crystallography/morphology**. So the lens suggests a partially correct internal description of an elongated, distorted cell, but not a clean answer pathway.\n\nThe registered mechanism concepts do **not** provide a clean ordered multihop signal. **Shear** reaches only J-rank 512 at 82.9%, essentially the same as the matched logit-lens rank 538, with no sustained onset. **Bain** is weaker in the J-lens: J-rank 3516 at 87.8%, while the vanilla logit lens is better at rank 829. Thus there is no convincing J-lens advantage for the intended transformation-mechanism intermediates, and no ordered emergence of “shear” then “Bain” before the product/symmetry. **Martensite** is correctly dropped from the registered tracking because it is multi-token in this tokenizer; the visible **“martens”** fragment is at most a suggestive subword/phase-prefix artifact, not evidence that the single concept “martensite” was cleanly tracked.\n\nThe exploratory candidate list should be treated only as a heuristic. **Metallurgical**, **microstructure**, **structure**, and **ratio** look meaningfully related to the prompt context; **toughness** is domain-relevant but likely cued by “hard” rather than an unspoken reasoning step. **Martens** is interesting but tokenizer-limited and not a valid registered concept. **Because**, **was**, **which**, and **This** are mostly syntactic/discourse tokens and should not be interpreted as scientific intermediates. Overall, this is a qualitative small-model pattern: the J-lens exposes relevant crystallography and a late transient **tetragonal** representation, but it does not validate the intended mechanism-chain hypothesis for this prompt." + }, + { + "slug": "materials-schmid-slip", + "title": "Resolved stress to slip", + "shape": "MULTIHOP", + "domain": "materials", + "model_used": "openai:gpt-5.5", + "analysis": "This item is **excluded as a registered success** because the clean-answer control failed: the model’s greedy next token was **“yield”**, not the expected **“slip.”** Qualitatively, the J-lens still shows a real materials concept: **dislocation** becomes strong in the workspace band, with sustained onset around **41.5% depth** and **rank 1 at 43.9%**, substantially beating the matched vanilla logit lens (**rank 17**). The layer slice agrees: around 41.5–51.2%, the readout position is dominated by “dislocation”/“Dislocations”/“dislocations,” before later layers move toward deformation-event tokens such as **undergo**, **deform**, **deformation**, **slip**, and finally **yield**. This is a plausible unspoken physical-carrier representation, but because the model ultimately answers “yield,” it should be treated as a qualitative pattern, not a clean protocol success.\n\nBy contrast, **Schmid** does **not** surface as a law-level intermediate in the J-lens. Its best reported J-lens rank is only **808 at 87.8% depth**, with no sustained onset, and the vanilla logit lens is actually better (**rank 145**). That is the opposite of the strongest J-lens evidence: the named criterion is not clearly represented/verbalizable here, even though the prompt’s calculation is essentially a Schmid-factor/resolved-shear-stress setup. The late trajectory mostly reflects convergence toward answer-like deformation tokens: “deform” dominates around 78–90%, “slip” reaches top ranks around the band edge/just after it, and the final layers switch to the literal model output **“yield.”**\n\nThe exploratory candidate list should be read only as a heuristic. **“threshold”** is plausibly meaningful, since the prompt compares 120 MPa to a critical value of 80 MPa. **“metallurgical”** is a broad domain tag and consistent with the slice, but not a specific reasoning step. **“equals,” “zero,” “because,”** and **“for”** look mostly like arithmetic/connective or local-token continuations rather than concepts. **“hydrostatic”** is likely a domain-association error: the prompt is uniaxial/resolved shear, not hydrostatic stress. **“which,” “where,”** and **“was”** are generic positional/top-1 artifacts and should not be interpreted as reasoning content." + }, + { + "slug": "materials-nabarro-herring", + "title": "High-temperature deformation mechanism", + "shape": "MULTIHOP", + "domain": "materials", + "model_used": "openai:gpt-5.5", + "analysis": "This is an included, clean item: the model’s greedy answer is the expected single token **“diffusion”**, and the tracked unspoken mechanism tokens are absent from the generated completion. The J-lens does show a real materials-domain concept, **creep**, but it is broad rather than mechanism-specific. In the fixed workspace band, **creep** rises sharply only late, peaking at **J-rank 2 at 80.5% depth**, while the answer token **diffusion** is already top-ranked from about **75.6% depth** onward at the readout position. This is therefore not a clean “intermediate before final answer” ordering: the transport-process answer has already crystallized before or alongside the generic creep concept. Also, the matched vanilla logit lens is actually stronger for **creep** (**rank 1**), so this is not a distinctive J-lens advantage. The specific **Nabarro-Herring** mechanism is not recovered: **Nabarro** is dropped as multi-token/not trackable, and **Herring** remains weak (**J-rank 1104 at 87.8%**, with the logit lens better at **490**). So the lens suggests the model has a broad “diffusion/creep” interpretation, but not a clearly verbalized single-token representation of **Nabarro-Herring creep**.\n\nThe exploratory candidates should be treated only as qualitative hints. **microstructure** is plausibly meaningful as a materials-domain abstraction from “fine-grained polycrystal,” but it is a broad context cue, not the target reasoning step. **deformation** is also meaningful, compressing “slow shape change,” and **metallurgical** is a plausible domain cue. **mechanism** may reflect the task frame or “dominant process” framing, but it is not evidence for the named mechanism. **migratory** is mostly a lexical echo of “migrate,” not an independent concept. **analysis** looks generic/noisy, and the function words **and**, **because**, **was**, and **but** are sentence-continuation artifacts rather than scientific intermediates. Overall, the run shows a genuine but generic creep/diffusion workspace signal, with no clean registered evidence that the model represented the specific Nabarro-Herring mechanism as an unspoken intermediate." + }, + { + "slug": "materials-screw-character", + "title": "Line-defect character", + "shape": "MULTIHOP", + "domain": "materials", + "model_used": "openai:gpt-5.5", + "analysis": "This item is excluded: the model did not give the clean answer “screw,” instead greedily continuing with “determined.” Within the fixed workspace band, the J-lens does not show a clean registered recovery of the intended materials concepts. At the readout position, “Burgers” only reaches J-rank 101 at 87.8% depth, and “dislocation” only reaches J-rank 181 at 85.4%, with no sustained onset for either. Both are also worse than the matched vanilla logit lens baselines: “Burgers” logit-rank 41 and “dislocation” logit-rank 133. The top readout tokens are mostly generic continuation/classification language—“classification,” “characterized,” “classified,” “determined,” “defined”—ending in the literal wrong next-token basin “determined.” So the lens mainly reports a model settling on “the character is determined/characterized/classified,” not an unspoken computation of “Burgers vector parallel to line direction ⇒ screw dislocation.”\n\nThere is some qualitative domain activation in the position-slice grid, but it should not be promoted to a protocol success. The exploratory “dislocation” candidate is the most meaningful: it appears as a top-1 token at some prompt positions around the mid-band, consistent with recognizing the object class from “line defect”/“crystal.” However, this does not translate into a strong final readout-position signal. “Burgers” is not comparably surfaced, and there is no ordered emergence of “Burgers” before “dislocation”; if anything, both improve only late and weakly. “Perpendicular” is also semantically notable but suspicious: it is the wrong geometric relation for the stated screw case and may reflect confusion with the edge-dislocation contrast or generic line-defect associations. “microstructure,” “metallurgical,” and “morphology” are plausible materials-domain context words, but they look diffuse and generic rather than evidence of the specific reasoning chain. “because,” “was,” “which,” “often,” and much of “represents” are continuation/scaffolding tokens or syntactic noise, not meaningful hidden concepts. Overall, this is best read as a weak-model/failed-answer case where the J-lens faithfully shows absence of the intended single-token intermediate reasoning, aside from scattered qualitative domain vocabulary." + }, + { + "slug": "materials-fcc-extinctions", + "title": "Diffraction selection-rule recognition", + "shape": "RECOGNITION", + "domain": "materials", + "model_used": "openai:gpt-5.5", + "analysis": "This item is **excluded as a registered success** because the clean model did not answer with the expected first token (`FCC` or `face`); it greedily produced `a`. Qualitatively, the lens does show a late move toward the broad category **cubic**: `cubic` becomes top-1 at 85.4% depth and is sustained from about 82.9% through the late workspace / pre-final layers. But this is not a J-lens-specific discovery: the matched vanilla logit lens also ranks `cubic` at 1, so the signal is real but not stronger than the baseline. More importantly, **cubic is not the registered answer**; the correct recognition is specifically face-centered cubic / FCC. I do not see clean evidence that the model has the more precise **FCC / face-centered** concept here. The tracked intermediate **extinction** is only weakly present: J-lens improves over logit lens substantially at its best point, rank 6,194 vs logit rank 26,140 at 48.8%, but that is still far outside an interpretable top-rank signal and has no sustained onset. The slice grid mostly shows prompt reconstruction and parity-rule vocabulary—`indices`, `integers`, `all`, `odd`, `even`, `mixed`, `parity`, `absent`—followed late by “lattice is cubic.” That is a plausible partial recognition, not a clean unspoken reasoning chain from extinction rule to FCC.\n\nThe exploratory candidate list should be treated only as a heuristic. Some entries are plausibly meaningful local paraphrases of the rule: **combinations** fits “mixed-parity combinations,” **integers** fits Miller-index triples `h,k,l`, and **simultaneously** loosely captures the all-odd/all-even constraint. **because** may reflect causal explanation framing, but not a substantive crystallographic concept. **was**, **information**, and **for** are generic syntactic/filler tokens. **spectroscopic** looks like domain-neighbor noise from diffraction/spectra rather than the relevant concept. **geomet** is a subword/tokenizer fragment of “geometric” and should not be counted as a clean concept. **perpendicular** is at best a loose geometry association around indices/planes, not evidence for the extinction-to-FCC recognition. Overall, the lens reveals that the model becomes disposed to say **cubic**, but not that it cleanly computed **face-centered cubic / FCC**; the registered protocol therefore remains a failure/exclusion despite a qualitative late cubic pattern." + }, + { + "slug": "materials-ductile-fractography", + "title": "Unnamed ductile-fracture vignette", + "shape": "ASSOCIATION", + "domain": "materials", + "model_used": "openai:gpt-5.5", + "analysis": "Within the registered workspace band, this is a real but transient J-lens signal for the ductile-fracture interpretation. The broad concept **ductile** reaches J-rank 193 at 41.5% depth versus logit-lens rank 38,170, and the more specific mechanism terms **coalescence** and **nucleation** reach J-ranks 383 at 39.0% and 401 at 43.9%, versus logit ranks 46,648 and 25,643. Those are substantially stronger than the matched vanilla logit lens, and the generated completion was only `'ound'`, so these are not simply next-token output leakage. However, none has a sustained onset, and the ordering is not a clean physical reasoning sequence: **coalescence** appears before **nucleation**, while **void** is weak in the J-lens, only rank 7,186 at 53.7%, and is actually better in the logit lens at rank 3,381. Since **microvoid** was dropped as multi-token, the lens cannot directly test the canonical “microvoid nucleation and coalescence” phrase. So the honest read is: the model has an unspoken association to ductile fracture and void-coalescence terminology, but this is not a clean ordered mechanistic trace.\n\nThe slice grid supports a qualitative materials/fractography interpretation, with mid-band tokens like **metallurgical**, **fracture**, **rupture**, **phase**, **particle**, **cluster**, **saucer**, **cone**, **morpholo-**, and **character-** appearing near relevant prompt positions. But much of this is local semantic echoing or subword morphology, not a registered metric. In the exploratory candidate list, **metallurgical** and **fracture** look meaningful as domain/failure-concept readouts; **saucer** is plausibly a morphology association to “cup”/cup-and-cone but surface-level; **clusters** may reflect joined pits/particles but is coarse. **indicating**, **occurred**, and perhaps **because** are generic explanatory scaffolding rather than mechanism concepts. **which**, **was**, and **during** are mostly syntactic/discourse artifacts. The late layers turning toward **ound** and similar fragments are consistent with the final-token regime, not workspace concept tracking." + }, + { + "slug": "materials-intergranular-attack", + "title": "Unnamed boundary-localized degradation", + "shape": "ASSOCIATION", + "domain": "materials", + "model_used": "openai:gpt-5.5", + "analysis": "Within the registered workspace band, the clearest signal is the unspoken concept **corrosion**: the J-lens brings it to rank **41 at 41.5% depth**, versus matched vanilla logit-lens rank **8955**, so this is a real J-lens gain over baseline. **Boundary** is weaker but still meaningfully above logit baseline, reaching rank **1613 at 39.0%** versus **9629**. **Sensitization** is only a faint signal: rank **5974 at 41.5%** versus **18127** is an improvement, but not a clean retrieval of the mechanism. None of the tracked concepts has a sustained onset, and the exact target phrase **intergranular corrosion** cannot be scored because **intergranular** was dropped as multi-token. Qualitatively, the grid shows metallurgy/corrosion/boundary/interior/unaffected vocabulary at relevant positions, but the per-readout top tokens also include off-domain degradation words such as **necrosis/pathogen/pathology**, so this is not a crisp top-token verbalization of the named degradation mode. The fair read is: the model has a strong generic **corrosion** association and some **boundary-localized** structure, but only weak evidence for the more specific **sensitization / intergranular** diagnosis.\n\nThe exploratory candidates are useful only as a heuristic. **metallurgical** looks meaningful as a domain scaffold around “austenitic stainless steel,” but it is not the target degradation concept. **corrosion** is the most meaningful candidate and agrees with the registered rank signal. **unaffected** and **remained** plausibly reflect the “grain interiors remain largely intact” contrast, though **remained** is close to an input morphological variant rather than a new concept. **environment** is a reasonable abstraction of “aggressive solution,” but again generic. **during** is mostly temporal/syntactic, tied to “held,” not a degradation concept. **and**, **but**, **these** are function-word/discourse artifacts; **because** may weakly reflect causal framing, but should not be counted as conceptual evidence. Overall, this item is an included protocol signal for unspoken **corrosion**, with partial boundary localization, but not a clean registered success for the full specialized concept **intergranular corrosion due to sensitization**." + } + ], + "synthesis": "Across these materials prompts, the clearest workspace-like phenomena appeared in the **ASSOCIATION** items, but only as item-level qualitative/registered-band signals, not as group-level evidence because the run is exploratory and the ASSOCIATION group is sample-insufficient. The strongest J-lens-over-logit effects were for unspoken domain concepts in the unnamed vignettes: in the ductile-fracture item, **ductile** reached J-rank 193 versus logit-rank 38,170, **coalescence** reached 383 versus 46,648, and **nucleation** reached 401 versus 25,643, though the ordering was not mechanistic and **void** was weak. In the boundary-localized degradation item, **corrosion** was the cleanest signal, reaching J-rank 41 at 41.5% depth versus logit-rank 8,955, with weaker but meaningful **boundary** evidence at rank 1,613 versus 9,629. These are the best examples in this run of unspoken scientific concepts surfacing in the fixed workspace band before being literally output, but they remain broad associations rather than clean reconstructions of full named mechanisms such as microvoid nucleation/coalescence or intergranular corrosion by sensitization.\n\nThe **MULTIHOP** items showed more mixed, mostly qualitative behavior. Several prompts were excluded from registered success because the model did not give the expected clean answer: grain refinement produced **“significantly”** rather than “higher,” rapid quench produced **“lower”** rather than “tetragonal,” resolved stress produced **“yield”** rather than “slip,” and line-defect character produced **“determined”** rather than “screw.” Some of these excluded items still had real-looking workspace content: grain refinement moved from materials-property words around 39–51% depth to causal tokens around 53–61% and comparative-answer tokens around 66–88%, with **higher** top-ranked at 80.5–87.8% before the final-layer pivot to **significantly**; rapid quench showed late **tetragonal** ranks 6→2→1 around 80.5–90.2% before the wrong final token; and resolved stress had a strong excluded **dislocation** signal, rank 1 at 43.9% versus logit rank 17. But the registered multihop mechanism claims were not cleanly supported. **Schmid**, **Bain**, **Burgers**, **bowing**, and **Herring** were weak or better in the vanilla logit lens, and in the included high-temperature deformation case **diffusion** was already top-ranked before or alongside **creep**, while **creep** itself was stronger in the logit lens. Thus the run gives some evidence for generic materials concepts and answer commitment in the workspace band, but not for reliable ordered emergence of specific multistep scientific intermediates.\n\nThe **RECOGNITION** item was also excluded: the model answered **“a”** rather than `FCC` or `face`. The lens did reveal a late disposition to say **cubic**, with `cubic` top-ranked around 85.4% and sustained late, but the vanilla logit lens also ranked it first, and **cubic** is not the registered target concept. The specific face-centered-cubic / FCC recognition was not cleanly present, and **extinction** remained far outside a strong interpretable rank despite improving over the logit baseline. There were no supplied **MODULATION** or **REPORT_SWAP** items in this materials run, so this synthesis cannot say whether this target model exhibits those phenomena. Relative to the paper’s claims, the run partially matches the broad observation that the mid-to-late band contains verbalizable content and that the last layers pivot to the literal next token, but it does not reproduce the stronger multihop claim that intermediates appear cleanly in the order computed.\n\nThe single-token limitation was a major constraint here. Many of the most scientifically precise targets were dropped or degraded because they are multi-token or subword concepts in this tokenizer: **Petch**, **Orowan**, **martensite**, **Nabarro**, **microvoid**, and **intergranular** could not be cleanly scored, and visible fragments such as **martens** should not be treated as registered concept recovery. The model-capability limitation also matters: this small target often failed the clean answer controls and often surfaced only generic neighbors such as **strengthening**, **creep**, **cubic**, **metallurgical**, or **microstructure**, while the named law or mechanism was absent or no better than the logit lens. That is consistent with the paper’s caution that the lens can only reveal concepts the model actually has; here it often faithfully reports partial domain association rather than expert mechanism-level reasoning.\n\nThe exploratory surfaced candidates should be read only as a heuristic, not as a paper metric. Among the frequent candidates, **metallurgical** (×7) and **microstructure** (×5) are plausible broad materials-domain signals, and **threshold** (×1) was plausibly meaningful in the resolved-stress prompt. **respectively** (×1) may reflect sample-comparison structure in the grain-refinement case, but it is not a scientific mechanism. **perpendicular** (×2) is semantically related to geometry but suspicious because it was sometimes the wrong contrast, especially for the screw-dislocation case. **during** (×2) is mostly temporal scaffolding. The very frequent **because** (×9), **was** (×8), **which** (×6), **and** (×4), **represents** (×2), and **where** (×1) are best treated as discourse, syntax, or local-continuation artifacts unless supported by a separate tracked concept. Overall, this model is useful for exploratory demonstrations of broad workspace readouts in scientific text, but for scientific interpretability of named mechanisms and multistep reasoning, a larger or more capable model—and preferably phrase-level tracking beyond single-token concepts—would be a more appropriate target." +} \ No newline at end of file diff --git a/jlens_materials/runs/gemma4-materials-reasoning-pilot-v1.json b/jlens_materials/runs/gemma4-materials-reasoning-pilot-v1.json new file mode 100644 index 0000000000000000000000000000000000000000..4145d4a5b6a3e59055e95290f6a33c533c766369 --- /dev/null +++ b/jlens_materials/runs/gemma4-materials-reasoning-pilot-v1.json @@ -0,0 +1,16207 @@ +{ + "format_version": 2, + "created_at": "2026-07-10T21:32:28.836108+00:00", + "model": "google/gemma-4-E4B-it", + "model_identity": { + "requested_id": "google/gemma-4-E4B-it", + "requested_revision": null, + "model_class": "Gemma4ForConditionalGeneration", + "config_name_or_path": "google/gemma-4-E4B-it", + "model_revision": "a4c2d58be94dda072b918d9db64ee85c8ed34e3f", + "tokenizer_class": "GemmaTokenizer", + "tokenizer_name_or_path": "google/gemma-4-E4B-it", + "vocab_size": 262144, + "n_layers": 42, + "d_model": 2560, + "dtype": "float32" + }, + "tag": "gemma4-materials-reasoning-pilot-v1", + "n_layers": 42, + "d_model": 2560, + "lens_n_prompts": 1000, + "lens_provenance": null, + "methodology": { + "recipe": { + "name": "demo", + "n_fit": 12, + "max_seq_len": 64, + "target_layer": null, + "report_layer_count": null, + "skip_first": 16, + "strict_corpus": false, + "description": "Fast exploratory fit. Results from this profile are not a quantitative replication of the paper." + }, + "claims_level": "exploratory demo", + "paper_protocol_complete": false, + "strict_protocol": false, + "workspace_band": [ + 38.0, + 92.0 + ], + "workspace_band_source": "preregistered_cli_or_config", + "score_reduction": "fixed span x fixed band x synonym minimum", + "emergence_threshold": 5, + "emergence_sustain": 2, + "pass_at_k": [ + 1, + 2, + 5, + 10, + 20, + 50, + 100 + ], + "minimum_items_per_shape": 50, + "evaluation_max_seq_len": 512, + "generation_max_new_tokens": 4, + "model_revision": null, + "dtype": "float32", + "layer_stride": 1, + "prompt_source": "prompts/materials-reasoning-pilot-v1.json", + "swaps_enabled": false, + "swap_alpha": 1.0 + }, + "shapes": [ + "MULTIHOP" + ], + "domains": [ + "materials" + ], + "metrics": { + "by_shape": { + "MULTIHOP": { + "n_items": 13, + "minimum_required": 50, + "sufficient_sample": false, + "ks": [ + 1, + 2, + 5, + 10, + 20, + 50, + 100 + ], + "jacobian_lens": { + "pass_at_k": [ + 0.0, + 0.0, + 0.07692307692307693, + 0.07692307692307693, + 0.07692307692307693, + 0.11538461538461539, + 0.11538461538461539 + ], + "auc_log_k": 0.06730769230769232, + "bootstrap_95": { + "1": { + "mean": 0.0, + "low": 0.0, + "high": 0.0 + }, + "2": { + "mean": 0.0, + "low": 0.0, + "high": 0.0 + }, + "5": { + "mean": 0.07692307692307693, + "low": 0.0, + "high": 0.19230769230769232 + }, + "10": { + "mean": 0.07692307692307693, + "low": 0.0, + "high": 0.19230769230769232 + }, + "20": { + "mean": 0.07692307692307693, + "low": 0.0, + "high": 0.19230769230769232 + }, + "50": { + "mean": 0.11538461538461539, + "low": 0.0, + "high": 0.23076923076923078 + }, + "100": { + "mean": 0.11538461538461539, + "low": 0.0, + "high": 0.23076923076923078 + } + } + }, + "logit_lens": { + "pass_at_k": [ + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.07692307692307693 + ], + "auc_log_k": 0.005789038378153489 + } + } + }, + "directed_modulation_controls": {}, + "causal_swaps": { + "n_interventions": 0, + "n_graded": 0, + "minimum_required": 50, + "sufficient_sample": false, + "protocol_success_rate": null, + "n_counterfactual": 0, + "causal_success_rate": null + } + }, + "errors": [], + "insufficient_sample_shapes": [ + "MULTIHOP" + ], + "prompts": [ + { + "slug": "reason-hall-01", + "shape": "MULTIHOP", + "protocol": "lens_eval", + "domain": "materials", + "title": "Grain-size comparison", + "description": "Chat-formatted one-word reasoning pilot with a preregistered latent mechanism.", + "note": "Pilot inclusion requires the clean next token to equal the declared one-word answer.", + "prompt_text": "<|turn>user\nTwo otherwise identical polycrystals have mean grain sizes 50 and 5 micrometers. Grain interfaces impede carrier motion. Which specimen has the larger yield stress? Reply with exactly one word: fine or coarse.\n<|turn>model\n", + "prompt_tail": "crometers. Grain interfaces impede carrier motion. Which specimen has the larger yield stress? Reply with exactly one word: fine or coarse.\n<|turn>model\n", + "readout_selector": "before_answer", + "readout_at": -1, + "score_positions": [ + 51 + ], + "band": [ + 38.0, + 92.0 + ], + "band_source": "preregistered_cli_or_config", + "condition": null, + "control_group": null, + "phrasing_id": null, + "carrier_id": null, + "target_family": null, + "category": "grain-size", + "answer": "fine", + "baseline": { + "required": true, + "correct": true, + "expected": [ + "fine" + ], + "greedy_token_id": 35022, + "greedy_token": "fine" + }, + "generated_completion": "fine", + "generated_token_ids": [ + 35022, + 106 + ], + "protocol_violations": [], + "valid_for_metrics": true, + "excluded_reasons": [], + "discovered": false, + "tracked_dropped": [ + "Petch" + ], + "tracked": [ + { + "label": "Hall", + "surfaces": [ + "Hall" + ], + "token_ids": [ + 9519, + 48027, + 11967, + 31300 + ] + }, + { + "label": "strengthening", + "surfaces": [ + "strengthening" + ], + "token_ids": [ + 38080, + 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+ "near_token": "elliptical", + "output_checked": true + }, + { + "concept": "larger", + "score": 16.0, + "best_rank": 1, + "best_depth": 73.2, + "near_token": "times", + "output_checked": true + }, + { + "concept": "also", + "score": 15.0, + "best_rank": 1, + "best_depth": 58.5, + "near_token": "Reply", + "output_checked": true + }, + { + "concept": "only", + "score": 14.0, + "best_rank": 1, + "best_depth": 63.4, + "near_token": "with", + "output_checked": true + }, + { + "concept": "elasticity", + "score": 13.0, + "best_rank": 1, + "best_depth": 39.0, + "near_token": "elastic", + "output_checked": true + }, + { + "concept": "comparison", + "score": 13.0, + "best_rank": 1, + "best_depth": 39.0, + "near_token": "Is", + "output_checked": true + } + ], + "surprising_label": "exploratory surfaced candidates", + "figures": { + "grid": "figures/gemma4-materials-reasoning-pilot-v1/materials/reason-griffith-04__grid.png", + "stream": "figures/gemma4-materials-reasoning-pilot-v1/materials/reason-griffith-04__stream.png", + "trajectory": "figures/gemma4-materials-reasoning-pilot-v1/materials/reason-griffith-04__trajectory.png", + "emergence": "figures/gemma4-materials-reasoning-pilot-v1/materials/reason-griffith-04__emergence.png", + "heatmap": "figures/gemma4-materials-reasoning-pilot-v1/materials/reason-griffith-04__heatmap.png" + } + }, + { + "slug": "reason-schmid-01", + "shape": "MULTIHOP", + "protocol": "lens_eval", + "domain": "materials", + "title": "Resolved-force threshold", + "description": "Chat-formatted one-word reasoning pilot with a preregistered latent mechanism.", + "note": "Pilot inclusion requires the clean next token to equal the declared one-word answer.", + "prompt_text": "<|turn>user\nA single crystal has a resolved driving value of 120 MPa on its most favorably oriented system; the critical value is 80 MPa. Does microscopic plastic motion begin, or does the crystal remain elastic? Reply with exactly one word: slip or elastic.\n<|turn>model\n", + "prompt_tail": "l value is 80 MPa. Does microscopic plastic motion begin, or does the crystal remain elastic? Reply with exactly one word: slip or elastic.\n<|turn>model\n", + "readout_selector": "before_answer", + "readout_at": -1, + "score_positions": [ + 62 + ], + "band": [ + 38.0, + 92.0 + ], + "band_source": "preregistered_cli_or_config", + "condition": null, + "control_group": null, + "phrasing_id": null, + "carrier_id": null, + "target_family": null, + "category": "resolved-threshold", + "answer": "slip", + "baseline": { + "required": true, + "correct": true, + "expected": [ + "slip" + ], + "greedy_token_id": 59722, + "greedy_token": "slip" + }, + "generated_completion": "slip", + "generated_token_ids": [ + 59722, + 106 + ], + "protocol_violations": [], + "valid_for_metrics": true, + "excluded_reasons": [], + "discovered": false, + "tracked_dropped": [], + "tracked": [ + { + "label": "Schmid", + "surfaces": [ + "Schmid" + ], + "token_ids": [ + 145866 + ] + }, + { + "label": "dislocation", + "surfaces": [ + "dislocation" + ], + "token_ids": [ + 25810 + ] + } + ], + "emergence": [ + { + 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Does microscopic plastic motion begin, or does the crystal remain elastic? Reply with exactly one word: slip or elastic.\n<|turn>model\n", + "prompt_tail": "l value is 80 MPa. Does microscopic plastic motion begin, or does the crystal remain elastic? 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Does microscopic plastic motion begin, or does the crystal remain elastic? Reply with exactly one word: slip or elastic.\n<|turn>model\n", + "prompt_tail": "l value is 70 MPa. Does microscopic plastic motion begin, or does the crystal remain elastic? 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73.2, + "top_tokens": [ + "Because", + "Porque", + "เพราะ", + "porque", + "Потому", + "뭔", + "Definitely", + "Vì" + ] + }, + { + "layer": 31, + "depth": 75.6, + "top_tokens": [ + "Porque", + "Definitely", + "Degrad", + "Because", + "Collapse", + "Guilty", + "Shock", + "đấy" + ] + }, + { + "layer": 32, + "depth": 78.0, + "top_tokens": [ + "Collapse", + "Yes", + "deformation", + "collapse", + "yes", + "Definitely", + "rupture", + "eruption" + ] + }, + { + "layer": 33, + "depth": 80.5, + "top_tokens": [ + "collapse", + "deformation", + "creep", + "plasticity", + "deterioration", + "đấy", + "rupture", + "ductile" + ] + }, + { + "layer": 34, + "depth": 82.9, + "top_tokens": [ + "deformation", + "collapse", + "rupture", + "plasticity", + "fracture", + "hysteresis", + "fatigue", + "creep" + ] + }, + { + "layer": 35, + "depth": 85.4, + "top_tokens": [ + "slip", + "Slip", + "slipping", + "plasticity", + "dislocation", + "creep", + "slips", + "glide" + ] + }, + { + "layer": 36, + "depth": 87.8, 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+ "near_token": "crystal", + "output_checked": true + }, + { + "concept": "because", + "score": 34.0, + "best_rank": 1, + "best_depth": 58.5, + "near_token": "has", + "output_checked": true + }, + { + "concept": "threshold", + "score": 28.0, + "best_rank": 1, + "best_depth": 39.0, + "near_token": "critical", + "output_checked": true + }, + { + "concept": "also", + "score": 28.0, + "best_rank": 1, + "best_depth": 56.1, + "near_token": "is", + "output_checked": true + }, + { + "concept": "and", + "score": 26.0, + "best_rank": 1, + "best_depth": 56.1, + "near_token": "A", + "output_checked": true + }, + { + "concept": "but", + "score": 22.0, + "best_rank": 1, + "best_depth": 56.1, + "near_token": "single", + "output_checked": true + }, + { + "concept": "when", + "score": 20.0, + "best_rank": 1, + "best_depth": 63.4, + "near_token": "begin", + "output_checked": true + }, + { + "concept": "this", + "score": 15.0, + "best_rank": 1, + "best_depth": 56.1, + "near_token": "Does", + "output_checked": true + }, + { + "concept": "for", + "score": 13.0, + "best_rank": 1, + "best_depth": 78.0, + "near_token": "value", + "output_checked": true + }, + { + "concept": "deformation", + "score": 12.0, + "best_rank": 1, + "best_depth": 48.8, + "near_token": "plastic", + "output_checked": true + }, + { + "concept": "plane", + "score": 12.0, + "best_rank": 1, + "best_depth": 80.5, + "near_token": "oriented", + "output_checked": true + }, + { + "concept": "which", + "score": 11.0, + "best_rank": 1, + "best_depth": 56.1, + "near_token": "has", + "output_checked": true + } + ], + "surprising_label": "exploratory surfaced candidates", + "figures": { + "grid": "figures/gemma4-materials-reasoning-pilot-v1/materials/reason-schmid-03__grid.png", + "stream": "figures/gemma4-materials-reasoning-pilot-v1/materials/reason-schmid-03__stream.png", + "trajectory": "figures/gemma4-materials-reasoning-pilot-v1/materials/reason-schmid-03__trajectory.png", + "emergence": "figures/gemma4-materials-reasoning-pilot-v1/materials/reason-schmid-03__emergence.png", + "heatmap": "figures/gemma4-materials-reasoning-pilot-v1/materials/reason-schmid-03__heatmap.png" + } + }, + { + "slug": "reason-schmid-04", + "shape": "MULTIHOP", + "protocol": "lens_eval", + "domain": "materials", + "title": "Resolved-force threshold", + "description": "Chat-formatted one-word reasoning pilot with a preregistered latent mechanism.", + "note": "Pilot inclusion requires the clean next token to equal the declared one-word answer.", + "prompt_text": "<|turn>user\nA single crystal has a resolved driving value of 45 MPa on its most favorably oriented system; the critical value is 65 MPa. Does microscopic plastic motion begin, or does the crystal remain elastic? Reply with exactly one word: slip or elastic.\n<|turn>model\n", + "prompt_tail": "l value is 65 MPa. Does microscopic plastic motion begin, or does the crystal remain elastic? Reply with exactly one word: slip or elastic.\n<|turn>model\n", + "readout_selector": "before_answer", + "readout_at": -1, + "score_positions": [ + 61 + ], + "band": [ + 38.0, + 92.0 + ], + "band_source": "preregistered_cli_or_config", + "condition": null, + "control_group": null, + "phrasing_id": null, + "carrier_id": null, + "target_family": null, + "category": "resolved-threshold", + "answer": "elastic", + "baseline": { + "required": true, + "correct": false, + "expected": [ + "elastic" + ], + "greedy_token_id": 59722, + "greedy_token": "slip" + }, + "generated_completion": "slip", + "generated_token_ids": [ + 59722, + 106 + ], + "protocol_violations": [], + "valid_for_metrics": false, + "excluded_reasons": [ + "clean model did not produce the expected answer" + ], + "discovered": false, + "tracked_dropped": [], + "tracked": [ + { + "label": "Schmid", + "surfaces": [ + "Schmid" + ], + "token_ids": [ + 145866 + ] + }, + { + "label": "dislocation", + "surfaces": [ + "dislocation" + ], 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"avoid", + "therefore", + "Avoid", + "avoidance", + "Missouri", + "death", + "however", + "failure" + ] + }, + { + "layer": 12, + "depth": 29.3, + "top_tokens": [ + "avoid", + "mmmm", + "loss", + "Avoid", + "Potato", + "UNKNOWN", + "SOUND", + "Whilst" + ] + }, + { + "layer": 13, + "depth": 31.7, + "top_tokens": [ + "Scientific", + "scientific", + "science", + "scientific", + "cient", + "Science", + "Scient", + "Chemical" + ] + }, + { + "layer": 14, + "depth": 34.1, + "top_tokens": [ + "Scientific", + "scientific", + "scientific", + "Scient", + "scientifically", + "Scientific", + "Science", + "science" + ] + }, + { + "layer": 15, + "depth": 36.6, + "top_tokens": [ + "Scientific", + "scientific", + "scientific", + "Scient", + "science", + "Science", + "scient", + "Scientific" + ] + }, + { + "layer": 16, + "depth": 39.0, + "top_tokens": [ + "Scientific", + "scientific", + "scientific", + "Science", + "hypoxia", + "scientifically", + "Scient", + "science" + ] + }, + { + "layer": 17, + "depth": 41.5, + "top_tokens": [ + "hypoxia", + "ANSWER", + "Scientific", + "scientific", + "scientific", + "pneum", + "abiotic", + "physiological" + ] + }, + { + "layer": 18, + "depth": 43.9, + "top_tokens": [ + "ANSWER", + "Answer", + "hypoxia", + "答案", + "جواب", + "pneum", + "ANSWER", + "Scientific" + ] + }, + { + "layer": 19, + "depth": 46.3, + "top_tokens": [ + "SCIENCE", + "ANSWER", + "Scientific", + "scientific", + "Biotechnology", + "Nuclear", + "Science", + "immunology" + ] + }, + { + "layer": 20, + "depth": 48.8, + "top_tokens": [ + "Incorrect", + "Incorrect", + "incorrect", + "正确", + "ANSWER", + "incorrect", + "正确的", + "correct" + ] + }, + { + "layer": 21, + "depth": 51.2, + "top_tokens": [ + "Biotechnology", + "Scientific", + "과학", + "Incorrect", + "científ", + "mRNA", + "biotechnology", + "과학" + ] + }, + { + "layer": 22, + "depth": 53.7, + "top_tokens": [ + "Unless", + "๘", + "Analyze", + "ịp", + "iguously", + "Initially", + "neq", + "E" + ] + }, + { + "layer": 23, + "depth": 56.1, + "top_tokens": [ + "Because", + "Due", + "Absolutely", + "Both", + "Either", + "Answer", + "Fuck", + "Unless" + ] + }, + { + "layer": 24, + "depth": 58.5, + "top_tokens": [ + "Because", + "Because", + "Due", + "Again", + "Unless", + "because", + "Those", + "This" + ] + }, + { + "layer": 25, + "depth": 61.0, + "top_tokens": [ + "Because", + "Because", + "때문이다", + "因为它", + "Despite", + "because", + "Çünkü", + "Probably" + ] + }, + { + "layer": 26, + "depth": 63.4, + "top_tokens": [ + "Because", + "Because", + "因为它", + "because", + "因為", + "때문이다", + "เพราะ", + "因為" + ] + }, + { + "layer": 27, + "depth": 65.9, + "top_tokens": [ + "Because", + "because", + "때문이다", + "因為", + "Unable", + "因为它", + "Despite", + "vì" + ] + }, + { + "layer": 28, + "depth": 68.3, + "top_tokens": [ + "Again", + "oretically", + "Still", + "아직", + "Because", + "oterapia", + "openia", + "Unable" + ] + }, + { + "layer": 29, + "depth": 70.7, + "top_tokens": [ + "아직", + "inconclusive", + "Neither", + "없습니다", + "belum", + "отсутствие", + "chưa", + "عدم" + ] + }, + { + "layer": 30, + "depth": 73.2, + "top_tokens": [ + "Neither", + "아직", + "Either", + "عدم", + "недостаточно", + "нет", + "belum", + "inconclusive" + ] + }, + { + "layer": 31, + "depth": 75.6, + "top_tokens": [ + "Neither", + "недостаточно", + "Nahi", + "하지", + "belum", + "عدم", + "не", + "نہیں" + ] + }, + { + "layer": 32, + "depth": 78.0, + "top_tokens": [ + "عدم", + "недостаточно", + "не", + "insufficient", + "Neither", + "不会", + "하지", + "tidak" + ] + }, + { + "layer": 33, + "depth": 80.5, + "top_tokens": [ + "elastic", + "insufficient", + "не", + "hysteresis", + "недостаточно", + "inert", + "弹性", + "Elastic" + ] + }, + { + "layer": 34, + "depth": 82.9, + "top_tokens": [ + "elastic", + "hysteresis", + "deformation", + "dormant", + "弹性", + "Elastic", + "inert", + "plasticity" + ] + }, + { + "layer": 35, + "depth": 85.4, + "top_tokens": [ + "elastic", + "Elastic", + "elast", + "elastic", + "elasticity", + "slip", + "deformation", + "Elastic" + ] + }, + { + "layer": 36, + "depth": 87.8, + "top_tokens": [ + "elastic", + "slip", + "deformation", + "ductile", + "Elastic", + "plastic", + "elast", + "creep" + ] + }, + { + "layer": 37, + "depth": 90.2, + "top_tokens": [ + "elastic", + "slip", + "plastic", + "elastic", + "creep", + "inelastic", + "tensile", + "stress" + ] + }, + { + "layer": 38, + "depth": 92.7, + "top_tokens": [ + "elastic", + "slip", + "plastic", + "elastic", + "Elastic", + "inelastic", + "tensile", + "deformation" + ] + }, + { + "layer": 39, + "depth": 95.1, + "top_tokens": [ + "elastic", + "slip", + "elastic", + "slip", + "Slip", + "Elastic", + "plastic", + "inelastic" + ] + }, + { + "layer": 40, + "depth": 97.6, + "top_tokens": [ + "elastic", + "slip", + "plastic", + "no", + "elastic", + "not", + "either", + "yield" + ] + }, + { + "layer": 41, + "depth": 100.0, + "top_tokens": [ + "slip", + "elastic", + "Slip", + "Elastic", + "No", + "plastic", + "sli", + "el" + ] + } + ], + "surprising": [ + { + "concept": "metallurgical", + "score": 34.0, + "best_rank": 1, + "best_depth": 39.0, + "near_token": "crystal", + "output_checked": true + }, + { + "concept": "because", + "score": 30.0, + "best_rank": 1, + "best_depth": 58.5, + "near_token": "has", + "output_checked": true + }, + { + "concept": "threshold", + "score": 29.0, + "best_rank": 1, + "best_depth": 39.0, + "near_token": "critical", + "output_checked": true + }, + { + "concept": "also", + "score": 29.0, + "best_rank": 1, + "best_depth": 56.1, + "near_token": "is", + "output_checked": true + }, + { + "concept": "and", + "score": 24.0, + "best_rank": 1, + "best_depth": 56.1, + "near_token": "A", + "output_checked": true + }, + { + "concept": "when", + "score": 23.0, + "best_rank": 1, + "best_depth": 63.4, + "near_token": "begin", + "output_checked": true + }, + { + "concept": "but", + "score": 20.0, + "best_rank": 1, + "best_depth": 56.1, + "near_token": "single", + "output_checked": true + }, + { + "concept": "this", + "score": 15.0, + "best_rank": 1, + "best_depth": 58.5, + "near_token": "Does", + "output_checked": true + }, + { + "concept": "for", + "score": 15.0, + "best_rank": 1, + "best_depth": 78.0, + "near_token": "value", + "output_checked": true + }, + { + "concept": "whereas", + "score": 14.0, + "best_rank": 4, + "best_depth": 61.0, + "near_token": "its", + "output_checked": true + }, + { + "concept": "deformation", + "score": 12.0, + "best_rank": 1, + "best_depth": 46.3, + "near_token": "plastic", + "output_checked": true + }, + { + "concept": "stress", + "score": 12.0, + "best_rank": 1, + "best_depth": 78.0, + "near_token": "", + "output_checked": true + } + ], + "surprising_label": "exploratory surfaced candidates", + "figures": { + "grid": "figures/gemma4-materials-reasoning-pilot-v1/materials/reason-schmid-04__grid.png", + "stream": "figures/gemma4-materials-reasoning-pilot-v1/materials/reason-schmid-04__stream.png", + "trajectory": "figures/gemma4-materials-reasoning-pilot-v1/materials/reason-schmid-04__trajectory.png", + "emergence": "figures/gemma4-materials-reasoning-pilot-v1/materials/reason-schmid-04__emergence.png", + "heatmap": "figures/gemma4-materials-reasoning-pilot-v1/materials/reason-schmid-04__heatmap.png" + } + }, + { + "slug": "reason-particle-01", + "shape": "MULTIHOP", + "protocol": "lens_eval", + "domain": "materials", + "title": "Obstacle-spacing comparison", + "description": "Chat-formatted one-word reasoning pilot with a preregistered latent mechanism.", + "note": "Pilot inclusion requires the clean next token to equal the declared one-word answer.", + "prompt_text": "<|turn>user\nA metal contains a fixed volume fraction of hard nonshearable particles. The center-to-center particle spacing decreases, so moving lattice line defects must curve between the obstacles. Is the required applied stress higher or lower? Reply with exactly one word.\n<|turn>model\n", + "prompt_tail": " moving lattice line defects must curve between the obstacles. Is the required applied stress higher or lower? Reply with exactly one word.\n<|turn>model\n", + "readout_selector": "before_answer", + "readout_at": -1, + "score_positions": [ + 59 + ], + "band": [ + 38.0, + 92.0 + ], + "band_source": "preregistered_cli_or_config", + "condition": null, + "control_group": null, + "phrasing_id": null, + "carrier_id": null, + "target_family": null, + "category": "particle-spacing", + "answer": "higher", + "baseline": { + "required": true, + "correct": true, + "expected": [ + "higher" + ], + "greedy_token_id": 98458, + "greedy_token": "Higher" + }, + "generated_completion": "Higher", + "generated_token_ids": [ + 98458, + 106 + ], + "protocol_violations": [], + "valid_for_metrics": true, + "excluded_reasons": [], + "discovered": false, + "tracked_dropped": [ + "Orowan" + ], + "tracked": [ + { + "label": "bowing", + "surfaces": [ + "bowing" + ], + "token_ids": [ + 186570 + ] + }, + { + "label": "precipitation", + "surfaces": [ + "precipitation" + ], + "token_ids": [ + 31632, + 44340, + 212775 + ] + }, + { + "label": "strengthening", + "surfaces": [ + "strengthening" + ], + "token_ids": [ + 38080, + 205711 + ] + } + ], + "emergence": [ + { + "label": "bowing", + "best_rank": 28678, + "best_depth": 87.8, + "onset_depth": null, + "reached_top1": false, + "best_pos": 59, + "logit_lens_best_rank": 3438 + }, + { + "label": "precipitation", + "best_rank": 1887, + "best_depth": 73.2, + "onset_depth": null, + "reached_top1": false, + "best_pos": 59, + "logit_lens_best_rank": 18201 + }, + { + "label": "strengthening", + "best_rank": 509, + "best_depth": 73.2, + "onset_depth": null, + "reached_top1": false, + "best_pos": 59, + "logit_lens_best_rank": 135 + } + ], + "layer_readouts": [ + { + "layer": 0, + "depth": 0.0, + "top_tokens": [ + "에서", + "를", + "nbsp", + "는", + "³", + "라는", + "으로", + "을" + ] + }, + { + "layer": 1, + "depth": 2.4, + "top_tokens": [ + "i", + "or", + "l", + "d", + "a", + "nt", + "o", + "r" + ] + }, + { + "layer": 2, + "depth": 4.9, + "top_tokens": [ + "or", + "ty", + "al", 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"surprising_label": "exploratory surfaced candidates", + "figures": { + "grid": "figures/gemma4-materials-reasoning-pilot-v1/materials/reason-particle-01__grid.png", + "stream": "figures/gemma4-materials-reasoning-pilot-v1/materials/reason-particle-01__stream.png", + "trajectory": "figures/gemma4-materials-reasoning-pilot-v1/materials/reason-particle-01__trajectory.png", + "emergence": "figures/gemma4-materials-reasoning-pilot-v1/materials/reason-particle-01__emergence.png", + "heatmap": "figures/gemma4-materials-reasoning-pilot-v1/materials/reason-particle-01__heatmap.png" + } + }, + { + "slug": "reason-particle-02", + "shape": "MULTIHOP", + "protocol": "lens_eval", + "domain": "materials", + "title": "Obstacle-spacing comparison", + "description": "Chat-formatted one-word reasoning pilot with a preregistered latent mechanism.", + "note": "Pilot inclusion requires the clean next token to equal the declared one-word answer.", + "prompt_text": "<|turn>user\nA metal contains a fixed volume fraction of hard nonshearable particles. The center-to-center particle spacing increases, so moving lattice line defects must curve between the obstacles. Is the required applied stress higher or lower? Reply with exactly one word.\n<|turn>model\n", + "prompt_tail": " moving lattice line defects must curve between the obstacles. Is the required applied stress higher or lower? 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Is rapid extension stable or unstable? Reply with exactly one word.\n<|turn>model\n", + "prompt_tail": "Pa square-root-meter and a critical intensity 40 MPa square-root-meter. Is rapid extension stable or unstable? 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applied mode-I intensity 15 MPa square-root-meter and a critical intensity 30 MPa square-root-meter. Is rapid extension stable or unstable? Reply with exactly one word.\n<|turn>model\n", + "prompt_tail": "Pa square-root-meter and a critical intensity 30 MPa square-root-meter. Is rapid extension stable or unstable? Reply with exactly one word.\n<|turn>model\n", + "readout_selector": "before_answer", + "readout_at": -1, + "score_positions": [ + 54 + ], + "band": [ + 38.0, + 92.0 + ], + "band_source": "preregistered_cli_or_config", + "condition": null, + "control_group": null, + "phrasing_id": null, + "carrier_id": null, + "target_family": null, + "category": "intensity-threshold", + "answer": "stable", + "baseline": { + "required": true, + "correct": false, + "expected": [ + "stable" + ], + "greedy_token_id": 2805, + "greedy_token": "Un" + }, + "generated_completion": "Unstable", + "generated_token_ids": [ + 2805, + 24137, + 106 + ], + "protocol_violations": [], + "valid_for_metrics": false, + "excluded_reasons": [ + "clean model did not produce the expected answer" + ], + "discovered": false, + "tracked_dropped": [], + "tracked": [ + { + "label": "toughness", + "surfaces": [ + "toughness" + ], + "token_ids": [ + 89582 + ] + }, + { + "label": "crack", + "surfaces": [ + "crack" + ], + "token_ids": 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applied mode-I intensity 45 MPa square-root-meter and a critical intensity 35 MPa square-root-meter. Is rapid extension stable or unstable? Reply with exactly one word.\n<|turn>model\n", + "prompt_tail": "Pa square-root-meter and a critical intensity 35 MPa square-root-meter. Is rapid extension stable or unstable? 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+ } + ], + "surprising_label": "exploratory surfaced candidates", + "figures": { + "grid": "figures/gemma4-materials-reasoning-pilot-v1/materials/reason-intensity-04__grid.png", + "stream": "figures/gemma4-materials-reasoning-pilot-v1/materials/reason-intensity-04__stream.png", + "trajectory": "figures/gemma4-materials-reasoning-pilot-v1/materials/reason-intensity-04__trajectory.png", + "emergence": "figures/gemma4-materials-reasoning-pilot-v1/materials/reason-intensity-04__emergence.png", + "heatmap": "figures/gemma4-materials-reasoning-pilot-v1/materials/reason-intensity-04__heatmap.png" + } + } + ] +} \ No newline at end of file diff --git a/jlens_materials/runs/gemma4-materials-report-swap-a025.json b/jlens_materials/runs/gemma4-materials-report-swap-a025.json new file mode 100644 index 0000000000000000000000000000000000000000..18295e48ebfac76494224378bca7f5a2fa9e68bc --- /dev/null +++ b/jlens_materials/runs/gemma4-materials-report-swap-a025.json @@ -0,0 +1,7479 @@ +{ + 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Results from this profile are not a quantitative replication of the paper." + }, + "claims_level": "exploratory demo", + "paper_protocol_complete": false, + "strict_protocol": false, + "workspace_band": [ + 38.0, + 92.0 + ], + "workspace_band_source": "preregistered_cli_or_config", + "score_reduction": "fixed span x fixed band x synonym minimum", + "emergence_threshold": 5, + "emergence_sustain": 2, + "pass_at_k": [ + 1, + 2, + 5, + 10, + 20, + 50, + 100 + ], + "minimum_items_per_shape": 50, + "evaluation_max_seq_len": 512, + "generation_max_new_tokens": 1, + "model_revision": null, + "dtype": "float32", + "layer_stride": 1, + "prompt_source": "prompts/materials-report-swap-dose-v1.json", + "swaps_enabled": true, + "swap_alpha": 0.25 + }, + "shapes": [ + "REPORT_SWAP" + ], + "domains": [ + "materials" + ], + "metrics": { + "by_shape": {}, + "directed_modulation_controls": {}, + "causal_swaps": { + "n_interventions": 19, + "n_graded": 19, + "minimum_required": 50, + "sufficient_sample": false, + "protocol_success_rate": 0.0, + "n_counterfactual": 0, + "causal_success_rate": null + } + }, + "errors": [], + "insufficient_sample_shapes": [ + "SWAPS" + ], + "prompts": [ + { + "slug": "dose-report-defect", + "shape": "REPORT_SWAP", + "protocol": "verbal_report", + "domain": "materials", + "title": "Crystalline defect report", + "description": "Dynamically select the clean report and install sibling defect coordinates.", + "note": "", + "prompt_text": "<|turn>user\nThink of a crystalline defect. 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Answer using one abbreviation.\n<|turn>model\n", + "prompt_tail": "<|turn>user\nThink of a common crystal lattice. 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Answer in one word.\n<|turn>model\n", + "prompt_tail": "<|turn>user\nThink of a material deformation mechanism. 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Answer in one word.\n<|turn>model\n", + "prompt_tail": "<|turn>user\nThink of a materials failure mode. 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Answer in one word.\n<|turn>model\n", + "prompt_tail": "<|turn>user\nThink of a material deformation mechanism. 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"tensile", + "score": 19.0, + "best_rank": 1, + "best_depth": 29.3, + "near_token": "stress" + }, + { + "concept": "metallurgical", + "score": 16.0, + "best_rank": 1, + "best_depth": 26.8, + "near_token": "the" + }, + { + "concept": "fissure", + "score": 13.0, + "best_rank": 1, + "best_depth": 26.8, + "near_token": "crack" + }, + { + "concept": "magnitude", + "score": 11.0, + "best_rank": 1, + "best_depth": 24.4, + "near_token": "with" + }, + { + "concept": "entropy", + "score": 10.0, + "best_rank": 1, + "best_depth": 34.1, + "near_token": "energy" + }, + { + "concept": "destabil", + "score": 10.0, + "best_rank": 1, + "best_depth": 39.0, + "near_token": "propagate" + }, + { + "concept": "was", + "score": 9.0, + "best_rank": 1, + "best_depth": 56.1, + "near_token": "crack" + }, + { + "concept": "scenario", + "score": 9.0, + "best_rank": 1, + "best_depth": 61.0, + "near_token": "elastic" + }, + { + "concept": "methodologies", + "score": 8.0, + "best_rank": 1, + "best_depth": 31.7, + "near_token": "," + }, + { + "concept": "represents", + "score": 8.0, + "best_rank": 1, + "best_depth": 58.5, + "near_token": "a" + } + ], + "figures": { + "grid": "figures/gemma4-probe/fracture/griffith-probe__grid.png", + "stream": "figures/gemma4-probe/fracture/griffith-probe__stream.png", + "trajectory": "figures/gemma4-probe/fracture/griffith-probe__trajectory.png", + "emergence": "figures/gemma4-probe/fracture/griffith-probe__emergence.png", + "heatmap": "figures/gemma4-probe/fracture/griffith-probe__heatmap.png" + } + } + ] +} \ No newline at end of file diff --git a/jlens_materials/runs/gpt2lmheadmodel.json b/jlens_materials/runs/gpt2lmheadmodel.json new file mode 100644 index 0000000000000000000000000000000000000000..8967793e52a35b07ac0ff720f1df557e8abed03c --- /dev/null +++ b/jlens_materials/runs/gpt2lmheadmodel.json @@ -0,0 +1,3741 @@ +{ + "model": "gpt2", + "tag": "gpt2lmheadmodel", + "n_layers": 12, + "d_model": 768, + "lens_n_prompts": 12, + "shapes": [ + "ASSOCIATION", + "MULTIHOP", + "RECOGNITION" + ], + "domains": [ + "fracture", + "protein" + ], + "prompts": [ + { + "slug": "fracture-griffith", + "shape": "MULTIHOP", + "domain": "fracture", + "title": "Griffith criterion (multi-hop)", + "description": "Two-hop: the failure law for a brittle solid with a sharp flaw. Watch 'energy' / 'surface' / 'Griffith' surface before 'crack'.", + "note": "Griffith's result sigma_c ~ sqrt(2 E gamma / (pi a)). The answer token is 'crack (length)'; 'energy', 'surface' and the eponym 'Griffith' are unspoken intermediates the derivation passes through -- the fracture analogue of Mars->color->red.", + "prompt_tail": "balancing the released strain energy against the energy needed to create new surface. The critical stress therefore scales inversely with the square root of the", + "readout_at": -1, + "band": [ + 0.0, + 100.0 + ], + "discovered": false, + "tracked_dropped": [], + "emergence": [ + { + "label": "energy", + "best_rank": 0, + "best_depth": 0.0, + "reached_top1": true, + "best_pos": 23 + }, + { + "label": "surface", + "best_rank": 0, + "best_depth": 0.0, + "reached_top1": true, + "best_pos": 31 + }, + { + "label": "Griffith", + "best_rank": 7102, + "best_depth": 9.1, + "reached_top1": false, + "best_pos": 32 + }, + { + "label": "crack", + "best_rank": 19, + "best_depth": 100.0, + "reached_top1": false, + "best_pos": 14 + }, + { + "label": "length", + "best_rank": 11, + "best_depth": 100.0, + "reached_top1": false, + "best_pos": 42 + } + ], + "layer_readouts": [ + { + "layer": 0, + "depth": 0.0, + "top_tokens": [ + "ingred", + "ModLoader", + "cryst", + "enthusi", + "destro", + "FANT", + "glim", + "notor" + ] + }, + { + "layer": 1, + "depth": 9.1, + "top_tokens": [ + "ingred", + "ModLoader", + "nodd", + "cryst", + "enthusi", + "glim", + "destro", + "ponies" + ] + }, + { + "layer": 2, + "depth": 18.2, + "top_tokens": [ + "mathemat", + "enthusi", + "glim", + "ingred", + "tremend", + "cryst", + "ModLoader", + "nodd" + ] + }, + { + "layer": 3, + "depth": 27.3, + "top_tokens": [ + "mathemat", + "proport", + "ingred", + "cryst", + "enthusi", + "sails", + "princ", + "cubes" + ] + }, + { + "layer": 4, + "depth": 36.4, + "top_tokens": [ + "proport", + "mathemat", + "horizont", + "ingred", + "cryst", + "princ", + "enthusi", + "lapt" + ] + }, + { + "layer": 5, + "depth": 45.5, + "top_tokens": [ + "ingred", + "proport", + "quot", + "mathemat", + "lapt", + "cryst", + "multiplier", + "horizont" + ] + }, + { + "layer": 6, + "depth": 54.5, + "top_tokens": [ + "mathemat", + "multiplier", + "multiplication", + "horizont", + "integers", + "ingred", + "theorem", + "quot" + ] + }, + { + "layer": 7, + "depth": 63.6, + "top_tokens": [ + "mathemat", + "squared", + "multiplier", + "numerical", + "magnitude", + "numer", + "multiplication", + "equation" + ] + }, + { + "layer": 8, + "depth": 72.7, + "top_tokens": [ + "multiplier", + "mathemat", + "coefficient", + "exponent", + "coefficients", + "magnitude", + "squared", + "equation" + ] + }, + { + "layer": 9, + "depth": 81.8, + "top_tokens": [ + "mathemat", + "coefficient", + "hardness", + "circumference", + "squared", + "curv", + "thickness", + "gravitational" + ] + }, + { + "layer": 10, + "depth": 90.9, + "top_tokens": [ + "stiffness", + "cube", + "elastic", + "hardness", + "squared", + "coefficient", + "gravitational", + "density" + ] + }, + { + "layer": 11, + "depth": 100.0, + "top_tokens": [ + "stress", + "elastic", + "force", + "strength", + "strain", + "pressure", + "square", + "energy" + ] + } + ], + "surprising": [ + { + "concept": "mathemat", + "score": 49.0, + "best_rank": 1, + "best_depth": 18.2, + "near_token": "the" + }, + { + "concept": "ModLoader", + "score": 43.0, + "best_rank": 1, + "best_depth": 0.0, + "near_token": "," + }, + { + "concept": "multiplier", + "score": 30.0, + "best_rank": 1, + "best_depth": 9.1, + "near_token": "stress" + }, + { + "concept": "ingred", + "score": 15.0, + "best_rank": 1, + "best_depth": 0.0, + "near_token": "needed" + }, + { + "concept": "tremend", + "score": 13.0, + "best_rank": 1, + "best_depth": 27.3, + "near_token": "a" + }, + { + "concept": "destro", + "score": 13.0, + "best_rank": 1, + "best_depth": 27.3, + "near_token": "the" + }, + { + "concept": "subsystem", + "score": 8.0, + "best_rank": 1, + "best_depth": 18.2, + "near_token": "critical" + }, + { + "concept": "inertia", + "score": 8.0, + "best_rank": 1, + "best_depth": 54.5, + "near_token": "against" + }, + { + "concept": "stiffness", + "score": 8.0, + "best_rank": 1, + "best_depth": 81.8, + "near_token": "the" + }, + { + "concept": "squares", + "score": 7.0, + "best_rank": 1, + "best_depth": 0.0, + "near_token": "square" + }, + { + "concept": "creations", + "score": 6.0, + "best_rank": 1, + "best_depth": 0.0, + "near_token": "create" + }, + { + "concept": "Sets", + "score": 4.0, + "best_rank": 1, + "best_depth": 0.0, + "near_token": "set" + } + ], + "figures": { + "grid": "figures/gpt2lmheadmodel/fracture/fracture-griffith__grid.png", + "stream": "figures/gpt2lmheadmodel/fracture/fracture-griffith__stream.png", + "trajectory": "figures/gpt2lmheadmodel/fracture/fracture-griffith__trajectory.png", + "emergence": "figures/gpt2lmheadmodel/fracture/fracture-griffith__emergence.png", + "heatmap": "figures/gpt2lmheadmodel/fracture/fracture-griffith__heatmap.png" + } + }, + { + "slug": "fracture-fatigue", + "shape": "MULTIHOP", + "domain": "fracture", + "title": "Cyclic loading -> failure mode (multi-hop)", + "description": "Cyclic sub-yield loading. 'fatigue' should lead 'crack', with 'Paris'/'striations' as deeper-layer associates.", + "note": "The answer is 'fatigue'. Whether 'Paris' (the Paris crack-growth law) or 'striations' (the fractographic fingerprint) appear in deeper layers probes how much associated fracture knowledge the workspace pulls in beyond the one-word answer.", + "prompt_tail": "lions of cycles. Tiny cracks nucleate at the surface and grow a little on every cycle until the part suddenly breaks. This progressive mode of failure is called", + "readout_at": -1, + "band": [ + 40.0, + 88.0 + ], + "discovered": false, + "tracked_dropped": [ + "cyclic", + "striations" + ], + "emergence": [ + { + "label": "fatigue", + "best_rank": 73, + "best_depth": 9.1, + "reached_top1": false, + "best_pos": 11 + }, + { + "label": "crack", + "best_rank": 3, + "best_depth": 0.0, + "reached_top1": false, + "best_pos": 18 + }, + { + "label": "Paris", + "best_rank": 487, + "best_depth": 72.7, + "reached_top1": false, + "best_pos": 0 + } + ], + "layer_readouts": [ + { + "layer": 0, + "depth": 0.0, + "top_tokens": [ + "call", + "Wraith", + "Call", + "Call", + "calling", + "CARD", + "call", + "called" + ] + }, + { + "layer": 1, + "depth": 9.1, + "top_tokens": [ + "calling", + "call", + "Call", + "calling", + "ModLoader", + "bluff", + "Accessory", + "mages" + ] + }, + { + "layer": 2, + "depth": 18.2, + "top_tokens": [ + "bluff", + "call", + "call", + "Call", + "calling", + "called", + "Miy", + "Call" + ] + }, + { + "layer": 3, + "depth": 27.3, + "top_tokens": [ + "called", + "laun", + "calling", + "called", + "abbrevi", + "GEAR", + "abbre", + "Call" + ] + }, + { + "layer": 4, + "depth": 36.4, + "top_tokens": [ + "called", + "laun", + "called", + "call", + "awa", + "call", + "ometimes", + "calling" + ] + }, + { + "layer": 5, + "depth": 45.5, + "top_tokens": [ + "called", + "called", + "abbre", + "ometimes", + "Calls", + "Tempest", + "call", + "oldown" + ] + }, + { + "layer": 6, + "depth": 54.5, + "top_tokens": [ + "Tempest", + "Despair", + "Vari", + "Cooldown", + "Shutdown", + "Depth", + "Secondary", + "Deaths" + ] + }, + { + "layer": 7, + "depth": 63.6, + "top_tokens": [ + "Sequence", + "phas", + "osis", + "Calling", + "abwe", + "staking", + "escription", + "Depth" + ] + }, + { + "layer": 8, + "depth": 72.7, + "top_tokens": [ + "Mechanical", + "horm", + "cytok", + "extinction", + "bluff", + "guiActiveUn", + "latency", + "millisec" + ] + }, + { + "layer": 9, + "depth": 81.8, + "top_tokens": [ + "horm", + "apopt", + "reper", + "resil", + "deflation", + "extinction", + "compression", + "coma" + ] + }, + { + "layer": 10, + "depth": 90.9, + "top_tokens": [ + "horm", + "apopt", + "structural", + "hydraulic", + "corrosion", + "compression", + "centrif", + "brittle" + ] + }, + { + "layer": 11, + "depth": 100.0, + "top_tokens": [ + "a", + "an", + "the", + "structural", + "de", + "cycl", + "micro", + "failure" + ] + } + ], + "surprising": [ + { + "concept": "mathemat", + "score": 9.0, + "best_rank": 1, + "best_depth": 45.5, + "near_token": "its" + }, + { + "concept": "ModLoader", + "score": 7.0, + "best_rank": 1, + "best_depth": 45.5, + "near_token": "A" + }, + { + "concept": "tremend", + "score": 6.0, + "best_rank": 1, + "best_depth": 45.5, + "near_token": "is" + }, + { + "concept": "seams", + "score": 5.0, + "best_rank": 1, + "best_depth": 54.5, + "near_token": "at" + }, + { + "concept": "loader", + "score": 4.0, + "best_rank": 1, + "best_depth": 45.5, + "near_token": "loaded" + }, + { + "concept": "multiplier", + "score": 4.0, + "best_rank": 1, + "best_depth": 45.5, + "near_token": "stress" + }, + { + "concept": "billions", + "score": 4.0, + "best_rank": 1, + "best_depth": 45.5, + "near_token": "millions" + }, + { + "concept": "disappears", + "score": 4.0, + "best_rank": 1, + "best_depth": 54.5, + "near_token": "suddenly" + }, + { + "concept": "distances", + "score": 3.0, + "best_rank": 1, + "best_depth": 45.5, + "near_token": "far" + }, + { + "concept": "ingred", + "score": 3.0, + "best_rank": 1, + "best_depth": 45.5, + "near_token": "the" + }, + { + "concept": "bit", + "score": 3.0, + "best_rank": 1, + "best_depth": 45.5, + "near_token": "little" + }, + { + "concept": "manufacturing", + "score": 3.0, + "best_rank": 1, + "best_depth": 63.6, + "near_token": "component" + } + ], + "figures": { + "grid": "figures/gpt2lmheadmodel/fracture/fracture-fatigue__grid.png", + "stream": "figures/gpt2lmheadmodel/fracture/fracture-fatigue__stream.png", + "trajectory": "figures/gpt2lmheadmodel/fracture/fracture-fatigue__trajectory.png", + "emergence": "figures/gpt2lmheadmodel/fracture/fracture-fatigue__emergence.png", + "heatmap": "figures/gpt2lmheadmodel/fracture/fracture-fatigue__heatmap.png" + } + }, + { + "slug": "fracture-toughness-chain", + "shape": "MULTIHOP", + "domain": "fracture", + "title": "Stress-intensity factor chain (multi-hop)", + "description": "Chain: geometry+load -> stress intensity K -> compare to K_Ic -> propagate. Track K / toughness / propagate in order.", + "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_tail": "mputes the stress intensity factor at the crack tip, then compares it against the material's fracture toughness. If the first exceeds the second, the crack will", + "readout_at": -1, + "band": [ + 0.0, + 100.0 + ], + "discovered": false, + "tracked_dropped": [], + "emergence": [ + { + "label": "stress", + "best_rank": 0, + "best_depth": 0.0, + "reached_top1": true, + "best_pos": 18 + }, + { + "label": "intensity", + "best_rank": 0, + "best_depth": 0.0, + "reached_top1": true, + "best_pos": 19 + }, + { + "label": "toughness", + "best_rank": 0, + "best_depth": 0.0, + "reached_top1": true, + "best_pos": 34 + }, + { + "label": "propagate", + "best_rank": 6, + "best_depth": 90.9, + "reached_top1": false, + "best_pos": 45 + }, + { + "label": "grow", + "best_rank": 9, + "best_depth": 100.0, + "reached_top1": false, + "best_pos": 45 + }, + { + "label": "unstable", + "best_rank": 705, + "best_depth": 90.9, + "reached_top1": false, + "best_pos": 45 + } + ], + "layer_readouts": [ + { + "layer": 0, + "depth": 0.0, + "top_tokens": [ + "ingred", + "esm", + "ModLoader", + "wagen", + "ometimes", + "Adds", + "VIDE", + "IMAGES" + ] + }, + { + "layer": 1, + "depth": 9.1, + "top_tokens": [ + "ingred", + "rall", + "ebin", + "cryst", + "destro", + "nodd", + "awa", + "ril" + ] + }, + { + "layer": 2, + "depth": 18.2, + "top_tokens": [ + "ingred", + "toget", + "hopefully", + "oun", + "xual", + "doubtless", + "ngth", + "ometimes" + ] + }, + { + "layer": 3, + "depth": 27.3, + "top_tokens": [ + "ingred", + "toget", + "nep", + "RELEASE", + "awa", + "theless", + "xual", + "ende" + ] + }, + { + "layer": 4, + "depth": 36.4, + "top_tokens": [ + "ingred", + "dont", + "happ", + "doesnt", + "wont", + "didnt", + "alot", + "ende" + ] + }, + { + "layer": 5, + "depth": 45.5, + "top_tokens": [ + "ingred", + "happ", + "horizont", + "dx", + "toget", + "ende", + "wont", + "lapt" + ] + }, + { + "layer": 6, + "depth": 54.5, + "top_tokens": [ + "Decay", + "ende", + "ingred", + "crumble", + "likely", + "happ", + "overwrite", + "destro" + ] + }, + { + "layer": 7, + "depth": 63.6, + "top_tokens": [ + "crumble", + "automatically", + "likely", + "dx", + "overwrite", + "be", + "geometry", + "curves" + ] + }, + { + "layer": 8, + "depth": 72.7, + "top_tokens": [ + "crumble", + "tremend", + "deterior", + "deform", + "enlarge", + "shrink", + "ende", + "regress" + ] + }, + { + "layer": 9, + "depth": 81.8, + "top_tokens": [ + "crumble", + "fracture", + "rupture", + "deterior", + "deform", + "explode", + "widen", + "corro" + ] + }, + { + "layer": 10, + "depth": 90.9, + "top_tokens": [ + "fracture", + "crumble", + "rupture", + "shatter", + "weaken", + "explode", + "propagate", + "widen" + ] + }, + { + "layer": 11, + "depth": 100.0, + "top_tokens": [ + "be", + "break", + "crack", + "become", + "expand", + "fracture", + "not", + "rupture" + ] + } + ], + "surprising": [ + { + "concept": "mathemat", + "score": 36.0, + "best_rank": 1, + "best_depth": 9.1, + "near_token": "at" + }, + { + "concept": "ModLoader", + "score": 32.0, + "best_rank": 1, + "best_depth": 0.0, + "near_token": "with" + }, + { + "concept": "ingred", + "score": 24.0, + "best_rank": 1, + "best_depth": 0.0, + "near_token": "then" + }, + { + "concept": "equations", + "score": 10.0, + "best_rank": 1, + "best_depth": 36.4, + "near_token": "utes" + }, + { + "concept": "herself", + "score": 9.0, + "best_rank": 1, + "best_depth": 0.0, + "near_token": "She" + }, + { + "concept": "glim", + "score": 8.0, + "best_rank": 1, + "best_depth": 9.1, + "near_token": "'s" + }, + { + "concept": "multiplier", + "score": 8.0, + "best_rank": 1, + "best_depth": 27.3, + "near_token": "stress" + }, + { + "concept": "graphene", + "score": 7.0, + "best_rank": 1, + "best_depth": 27.3, + "near_token": "material" + }, + { + "concept": "edges", + "score": 5.0, + "best_rank": 1, + "best_depth": 27.3, + "near_token": "edge" + }, + { + "concept": "baseman", + "score": 4.0, + "best_rank": 1, + "best_depth": 18.2, + "near_token": "first" + }, + { + "concept": "cracked", + "score": 4.0, + "best_rank": 1, + "best_depth": 27.3, + "near_token": "crack" + }, + { + "concept": "clipboard", + "score": 4.0, + "best_rank": 1, + "best_depth": 36.4, + "near_token": "has" + } + ], + "figures": { + "grid": "figures/gpt2lmheadmodel/fracture/fracture-toughness-chain__grid.png", + "stream": "figures/gpt2lmheadmodel/fracture/fracture-toughness-chain__stream.png", + "trajectory": "figures/gpt2lmheadmodel/fracture/fracture-toughness-chain__trajectory.png", + "emergence": "figures/gpt2lmheadmodel/fracture/fracture-toughness-chain__emergence.png", + "heatmap": "figures/gpt2lmheadmodel/fracture/fracture-toughness-chain__heatmap.png" + } + }, + { + "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?", + "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_tail": "e 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_at": -1, + "band": [ + 27.3, + 100.0 + ], + "discovered": false, + "tracked_dropped": [], + "emergence": [ + { + "label": "brittle", + "best_rank": 24, + "best_depth": 81.8, + "reached_top1": false, + "best_pos": 20 + }, + { + "label": "fracture", + "best_rank": 143, + "best_depth": 54.5, + "reached_top1": false, + "best_pos": 24 + }, + { + "label": "glass", + "best_rank": 3, + "best_depth": 100.0, + "reached_top1": false, + "best_pos": 29 + }, + { + "label": "shatter", + "best_rank": 2, + "best_depth": 27.3, + "reached_top1": false, + "best_pos": 24 + }, + { + "label": "crack", + "best_rank": 12, + "best_depth": 100.0, + "reached_top1": false, + "best_pos": 32 + } + ], + "layer_readouts": [ + { + "layer": 0, + "depth": 0.0, + "top_tokens": [ + "ModLoader", + "ingred", + "Vault", + "XCOM", + "Heist", + "glim", + "Wraith", + "Wasteland" + ] + }, + { + "layer": 1, + "depth": 9.1, + "top_tokens": [ + "ModLoader", + "cryst", + "ingred", + "Morrow", + "mathemat", + "Clockwork", + "glim", + "CODE" + ] + }, + { + "layer": 2, + "depth": 18.2, + "top_tokens": [ + "ModLoader", + "glim", + "ccording", + "CLASSIFIED", + "mathemat", + "esm", + "ARDIS", + "misunder" + ] + }, + { + "layer": 3, + "depth": 27.3, + "top_tokens": [ + "ingred", + "CODE", + "cryst", + "glim", + "Reborn", + "weap", + "ModLoader", + "mathemat" + ] + }, + { + "layer": 4, + "depth": 36.4, + "top_tokens": [ + "weap", + "ccording", + "ingred", + "Fn", + "blance", + "ensu", + "lapt", + "Swords" + ] + }, + { + "layer": 5, + "depth": 45.5, + "top_tokens": [ + "ingred", + "weap", + "Pastebin", + "lapt", + "confir", + "ccording", + "Subtle", + "Sung" + ] + }, + { + "layer": 6, + "depth": 54.5, + "top_tokens": [ + "Moments", + "Izan", + "Shards", + "Suddenly", + "Eventually", + "Pain", + "Each", + "Slowly" + ] + }, + { + "layer": 7, + "depth": 63.6, + "top_tokens": [ + "Suddenly", + "Dozens", + "Explos", + "Moments", + "Unable", + "Izan", + "Eventually", + "Then" + ] + }, + { + "layer": 8, + "depth": 72.7, + "top_tokens": [ + "Suddenly", + "Dozens", + "Then", + "Each", + "Somehow", + "Eventually", + "Slowly", + "Moments" + ] + }, + { + "layer": 9, + "depth": 81.8, + "top_tokens": [ + "Its", + "Slowly", + "Each", + "Then", + "Eventually", + "Moments", + "It", + "Suddenly" + ] + }, + { + "layer": 10, + "depth": 90.9, + "top_tokens": [ + "Its", + "Each", + "Slowly", + "Then", + "The", + "It", + "Moments", + "With" + ] + }, + { + "layer": 11, + "depth": 100.0, + "top_tokens": [ + "The", + "It", + "A", + "He", + "Then", + "As", + "She", + "His" + ] + } + ], + "surprising": [ + { + "concept": "ingred", + "score": 9.0, + "best_rank": 1, + "best_depth": 27.3, + "near_token": "the" + }, + { + "concept": "disappearing", + "score": 8.0, + "best_rank": 1, + "best_depth": 36.4, + "near_token": "before" + }, + { + "concept": "weap", + "score": 7.0, + "best_rank": 1, + "best_depth": 27.3, + "near_token": "with" + }, + { + "concept": "bends", + "score": 6.0, + "best_rank": 1, + "best_depth": 27.3, + "near_token": "bending" + }, + { + "concept": "tremend", + "score": 6.0, + "best_rank": 1, + "best_depth": 36.4, + "near_token": "a" + }, + { + "concept": "rooft", + "score": 6.0, + "best_rank": 1, + "best_depth": 63.6, + "near_token": "across" + }, + { + "concept": "ModLoader", + "score": 5.0, + "best_rank": 1, + "best_depth": 27.3, + "near_token": "The" + }, + { + "concept": "edges", + "score": 5.0, + "best_rank": 1, + "best_depth": 27.3, + "near_token": "edge" + }, + { + "concept": "taps", + "score": 5.0, + "best_rank": 1, + "best_depth": 36.4, + "near_token": "tapped" + }, + { + "concept": "OHN", + "score": 4.0, + "best_rank": 1, + "best_depth": 27.3, + "near_token": "Without" + }, + { + "concept": "flying", + "score": 4.0, + "best_rank": 1, + "best_depth": 27.3, + "near_token": "flew" + }, + { + "concept": "glim", + "score": 4.0, + "best_rank": 1, + "best_depth": 27.3, + "near_token": "a" + } + ], + "figures": { + "grid": "figures/gpt2lmheadmodel/fracture/fracture-brittle-assoc__grid.png", + "stream": "figures/gpt2lmheadmodel/fracture/fracture-brittle-assoc__stream.png", + "trajectory": "figures/gpt2lmheadmodel/fracture/fracture-brittle-assoc__trajectory.png", + "emergence": "figures/gpt2lmheadmodel/fracture/fracture-brittle-assoc__emergence.png", + "heatmap": "figures/gpt2lmheadmodel/fracture/fracture-brittle-assoc__heatmap.png" + } + }, + { + "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'.", + "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_tail": " 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_at": -1, + "band": [ + 40.0, + 88.0 + ], + "discovered": false, + "tracked_dropped": [ + "ductile", + "necking", + "deformation" + ], + "emergence": [ + { + "label": "plastic", + "best_rank": 45, + "best_depth": 81.8, + "reached_top1": false, + "best_pos": 16 + }, + { + "label": "yield", + "best_rank": 529, + "best_depth": 90.9, + "reached_top1": false, + "best_pos": 38 + } + ], + "layer_readouts": [ + { + "layer": 0, + "depth": 0.0, + "top_tokens": [ + "ModLoader", + "Vault", + "XCOM", + "ingred", + "Heist", + "glim", + "Wasteland", + "Wraith" + ] + }, + { + "layer": 1, + "depth": 9.1, + "top_tokens": [ + "ModLoader", + "cryst", + "ingred", + "Morrow", + "Clockwork", + "mathemat", + "glim", + "Sins" + ] + }, + { + "layer": 2, + "depth": 18.2, + "top_tokens": [ + "ModLoader", + "ingred", + "ccording", + "mathemat", + "ARDIS", + "CLASSIFIED", + "glim", + "esm" + ] + }, + { + "layer": 3, + "depth": 27.3, + "top_tokens": [ + "ingred", + "CODE", + "cryst", + "weap", + "ModLoader", + "alot", + "Cortex", + "awa" + ] + }, + { + "layer": 4, + "depth": 36.4, + "top_tokens": [ + "ingred", + "weap", + "ccording", + "minus", + "Fn", + "surv", + "They", + "laun" + ] + }, + { + "layer": 5, + "depth": 45.5, + "top_tokens": [ + "ingred", + "weap", + "Eventually", + "ccording", + "Then", + "cryst", + "They", + "Pastebin" + ] + }, + { + "layer": 6, + "depth": 54.5, + "top_tokens": [ + "Slowly", + "Eventually", + "Occasionally", + "Worse", + "Its", + "Abandon", + "Thankfully", + "Styles" + ] + }, + { + "layer": 7, + "depth": 63.6, + "top_tokens": [ + "Suddenly", + "Slowly", + "Worse", + "Its", + "Eventually", + "Dozens", + "Somehow", + "Then" + ] + }, + { + "layer": 8, + "depth": 72.7, + "top_tokens": [ + "Suddenly", + "Worse", + "Eventually", + "Then", + "Somehow", + "Slowly", + "Dozens", + "Thankfully" + ] + }, + { + "layer": 9, + "depth": 81.8, + "top_tokens": [ + "Its", + "Slowly", + "Eventually", + "It", + "Then", + "Worse", + "Somehow", + "Suddenly" + ] + }, + { + "layer": 10, + "depth": 90.9, + "top_tokens": [ + "Its", + "Slowly", + "It", + "Eventually", + "Then", + "The", + "There", + "Suddenly" + ] + }, + { + "layer": 11, + "depth": 100.0, + "top_tokens": [ + "The", + "It", + "A", + "I", + "As", + "Then", + "There", + "He" + ] + } + ], + "surprising": [ + { + "concept": "ingred", + "score": 5.0, + "best_rank": 1, + "best_depth": 45.5, + "near_token": "," + }, + { + "concept": "relent", + "score": 5.0, + "best_rank": 1, + "best_depth": 45.5, + "near_token": "finally" + }, + { + "concept": "ModLoader", + "score": 5.0, + "best_rank": 1, + "best_depth": 54.5, + "near_token": "the" + }, + { + "concept": "horizont", + "score": 4.0, + "best_rank": 1, + "best_depth": 45.5, + "near_token": "holding" + }, + { + "concept": "else", + "score": 4.0, + "best_rank": 1, + "best_depth": 45.5, + "near_token": "anyone" + }, + { + "concept": "tremend", + "score": 3.0, + "best_rank": 1, + "best_depth": 45.5, + "near_token": "the" + }, + { + "concept": "loader", + "score": 3.0, + "best_rank": 1, + "best_depth": 45.5, + "near_token": "load" + }, + { + "concept": "climbing", + "score": 3.0, + "best_rank": 1, + "best_depth": 45.5, + "near_token": "climbed" + }, + { + "concept": "weap", + "score": 3.0, + "best_rank": 1, + "best_depth": 45.5, + "near_token": "." + }, + { + "concept": "brim", + "score": 3.0, + "best_rank": 1, + "best_depth": 54.5, + "near_token": "y" + }, + { + "concept": "louder", + "score": 3.0, + "best_rank": 1, + "best_depth": 63.6, + "near_token": "visibly" + }, + { + "concept": "disappearing", + "score": 3.0, + "best_rank": 1, + "best_depth": 63.6, + "near_token": "and" + } + ], + "figures": { + "grid": "figures/gpt2lmheadmodel/fracture/fracture-ductile-assoc__grid.png", + "stream": "figures/gpt2lmheadmodel/fracture/fracture-ductile-assoc__stream.png", + "trajectory": "figures/gpt2lmheadmodel/fracture/fracture-ductile-assoc__trajectory.png", + "emergence": "figures/gpt2lmheadmodel/fracture/fracture-ductile-assoc__emergence.png", + "heatmap": "figures/gpt2lmheadmodel/fracture/fracture-ductile-assoc__heatmap.png" + } + }, + { + "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'?", + "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_tail": "Consider the Cauchy stress tensor (MPa):\n[[420, 0, 0],\n [ 0, 15, 0],\n [ 0, 0, 10]]\nThe dominant loading state this represents is", + "readout_at": -1, + "band": [ + 90.9, + 100.0 + ], + "discovered": false, + "tracked_dropped": [ + "tensile", + "uniaxial" + ], + "emergence": [ + { + "label": "tension", + "best_rank": 6, + "best_depth": 90.9, + "reached_top1": false, + "best_pos": 51 + }, + { + "label": "crack", + "best_rank": 653, + "best_depth": 100.0, + "reached_top1": false, + "best_pos": 8 + }, + { + "label": "opening", + "best_rank": 131, + "best_depth": 100.0, + "reached_top1": false, + "best_pos": 1 + }, + { + "label": "mode", + "best_rank": 15, + "best_depth": 100.0, + "reached_top1": false, + "best_pos": 52 + } + ], + "layer_readouts": [ + { + "layer": 0, + "depth": 0.0, + "top_tokens": [ + "ModLoader", + "ingred", + "destro", + "senal", + "ometimes", + "gobl", + "awa", + "RIP" + ] + }, + { + "layer": 1, + "depth": 9.1, + "top_tokens": [ + "ModLoader", + "ingred", + "destro", + "mathemat", + "senal", + "oun", + "awa", + "nodd" + ] + }, + { + "layer": 2, + "depth": 18.2, + "top_tokens": [ + "ingred", + "senal", + "ModLoader", + "oun", + "ometimes", + "mathemat", + "awa", + "ccording" + ] + }, + { + "layer": 3, + "depth": 27.3, + "top_tokens": [ + "senal", + "oun", + "alot", + "ingred", + "awa", + "tremend", + "ometimes", + "destro" + ] + }, + { + "layer": 4, + "depth": 36.4, + "top_tokens": [ + "senal", + "tremend", + "alot", + "doesnt", + "confir", + "awa", + "ometimes", + "yip" + ] + }, + { + "layer": 5, + "depth": 45.5, + "top_tokens": [ + "senal", + "ometimes", + "eto", + "alot", + "tremend", + "ingred", + "awa", + "doesnt" + ] + }, + { + "layer": 6, + "depth": 54.5, + "top_tokens": [ + "senal", + "tremend", + "ngth", + "yip", + "CLASSIFIED", + "ometimes", + "rael", + "omorphic" + ] + }, + { + "layer": 7, + "depth": 63.6, + "top_tokens": [ + "omorphic", + "senal", + "omorph", + "ModLoader", + "dictated", + "ynchronous", + "CLASSIFIED", + "tremend" + ] + }, + { + "layer": 8, + "depth": 72.7, + "top_tokens": [ + "omorphic", + "omorph", + "senal", + "ModLoader", + "ynchronous", + "CLASSIFIED", + "srf", + "skelet" + ] + }, + { + "layer": 9, + "depth": 81.8, + "top_tokens": [ + "omorphic", + "aerobic", + "senal", + "srf", + "tiss", + "facult", + "maximal", + "equilibrium" + ] + }, + { + "layer": 10, + "depth": 90.9, + "top_tokens": [ + "maximal", + "aerobic", + "analges", + "equilibrium", + "anterior", + "tiss", + "omorphic", + "leptin" + ] + }, + { + "layer": 11, + "depth": 100.0, + "top_tokens": [ + "the", + "a", + "that", + "in", + "an", + "not", + "at", + "C" + ] + } + ], + "surprising": [ + { + "concept": "Notice", + "score": 2.0, + "best_rank": 1, + "best_depth": 90.9, + "near_token": "" + }, + { + "concept": "teasp", + "score": 2.0, + "best_rank": 7, + "best_depth": 90.9, + "near_token": "0" + }, + { + "concept": "URRENT", + "score": 1.0, + "best_rank": 1, + "best_depth": 90.9, + "near_token": "C" + }, + { + "concept": "coils", + "score": 1.0, + "best_rank": 1, + "best_depth": 90.9, + "near_token": "or" + }, + { + "concept": "When", + "score": 1.0, + "best_rank": 1, + "best_depth": 90.9, + "near_token": "):" + }, + { + "concept": "mechanism", + "score": 1.0, + "best_rank": 1, + "best_depth": 90.9, + "near_token": "loading" + }, + { + "concept": "occurs", + "score": 1.0, + "best_rank": 1, + "best_depth": 90.9, + "near_token": "state" + }, + { + "concept": "maximal", + "score": 1.0, + "best_rank": 1, + "best_depth": 90.9, + "near_token": "is" + }, + { + "concept": "Page", + "score": 1.0, + "best_rank": 2, + "best_depth": 90.9, + "near_token": "[[" + }, + { + "concept": "pione", + "score": 1.0, + "best_rank": 2, + "best_depth": 90.9, + "near_token": "[" + }, + { + "concept": "Magikarp", + "score": 1.0, + "best_rank": 3, + "best_depth": 90.9, + "near_token": "" + }, + { + "concept": "EStream", + "score": 1.0, + "best_rank": 4, + "best_depth": 90.9, + "near_token": "0" + } + ], + "figures": { + "grid": "figures/gpt2lmheadmodel/fracture/fracture-stress-recognition__grid.png", + "stream": "figures/gpt2lmheadmodel/fracture/fracture-stress-recognition__stream.png", + "trajectory": "figures/gpt2lmheadmodel/fracture/fracture-stress-recognition__trajectory.png", + "emergence": "figures/gpt2lmheadmodel/fracture/fracture-stress-recognition__emergence.png", + "heatmap": "figures/gpt2lmheadmodel/fracture/fracture-stress-recognition__heatmap.png" + } + }, + { + "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'.", + "note": "Two-hop: i,i+4 H-bond pattern -> alpha helix. 'hydrogen'/'bond'/'backbone' are the unspoken intermediates before the answer 'helix'.", + "prompt_tail": "ne residue accepts a hydrogen bond from the amide of the residue four positions later, and this repeats down the chain, the local fold that results is the alpha", + "readout_at": -1, + "band": [ + 0.0, + 100.0 + ], + "discovered": false, + "tracked_dropped": [ + "helix" + ], + "emergence": [ + { + "label": "hydrogen", + "best_rank": 0, + "best_depth": 0.0, + "reached_top1": true, + "best_pos": 18 + }, + { + "label": "bond", + "best_rank": 0, + "best_depth": 100.0, + "reached_top1": true, + "best_pos": 18 + }, + { + "label": "backbone", + "best_rank": 0, + "best_depth": 0.0, + "reached_top1": true, + "best_pos": 8 + }, + { + "label": "spiral", + "best_rank": 39, + "best_depth": 90.9, + "reached_top1": false, + "best_pos": 35 + } + ], + "layer_readouts": [ + { + "layer": 0, + "depth": 0.0, + "top_tokens": [ + "alpha", + "Alpha", + "Alpha", + "Beta", + "alpha", + "beta", + "Beta", + "beta" + ] + }, + { + "layer": 1, + "depth": 9.1, + "top_tokens": [ + "alpha", + "Alpha", + "Alpha", + "beta", + "Beta", + "Beta", + "alpha", + "beta" + ] + }, + { + "layer": 2, + "depth": 18.2, + "top_tokens": [ + "alpha", + "Alpha", + "alpha", + "Alpha", + "beta", + "beta", + "gamma", + "omega" + ] + }, + { + "layer": 3, + "depth": 27.3, + "top_tokens": [ + "alpha", + "alpha", + "Alpha", + "Alpha", + "gamma", + "beta", + "omega", + "delta" + ] + }, + { + "layer": 4, + "depth": 36.4, + "top_tokens": [ + "alpha", + "alpha", + "Alpha", + "Alpha", + "beta", + "gamma", + "omega", + "delta" + ] + }, + { + "layer": 5, + "depth": 45.5, + "top_tokens": [ + "alpha", + "alpha", + "Alpha", + "Alpha", + "beta", + "gamma", + "tremend", + "omega" + ] + }, + { + "layer": 6, + "depth": 54.5, + "top_tokens": [ + "alpha", + "alpha", + "beta", + "Alpha", + "subscript", + "Centauri", + "gamma", + "Alpha" + ] + }, + { + "layer": 7, + "depth": 63.6, + "top_tokens": [ + "alpha", + "alpha", + "subscript", + "beta", + "gamma", + "Alpha", + "acids", + "delta" + ] + }, + { + "layer": 8, + "depth": 72.7, + "top_tokens": [ + "teasp", + "alpha", + "escription", + "ActionCode", + "StreamerBot", + "hyde", + "quickShip", + "embedreportprint" + ] + }, + { + "layer": 9, + "depth": 81.8, + "top_tokens": [ + "subscript", + "carbohyd", + "teasp", + "allele", + "ゴン", + "oxidation", + "ActionCode", + "residue" + ] + }, + { + "layer": 10, + "depth": 90.9, + "top_tokens": [ + "residue", + "molecule", + "protein", + "helic", + "amino", + "particle", + "carb", + "oxidation" + ] + }, + { + "layer": 11, + "depth": 100.0, + "top_tokens": [ + "of", + "residue", + "and", + "carb", + "channel", + "protein", + "bond", + "particle" + ] + } + ], + "surprising": [ + { + "concept": "ModLoader", + "score": 52.0, + "best_rank": 1, + "best_depth": 0.0, + "near_token": "," + }, + { + "concept": "mathemat", + "score": 32.0, + "best_rank": 1, + "best_depth": 0.0, + "near_token": "this" + }, + { + "concept": "ingred", + "score": 27.0, + "best_rank": 1, + "best_depth": 0.0, + "near_token": "the" + }, + { + "concept": "bonded", + "score": 8.0, + "best_rank": 1, + "best_depth": 18.2, + "near_token": "bond" + }, + { + "concept": "horizont", + "score": 6.0, + "best_rank": 1, + "best_depth": 18.2, + "near_token": "positions" + }, + { + "concept": "LOAD", + "score": 5.0, + "best_rank": 1, + "best_depth": 0.0, + "near_token": "down" + }, + { + "concept": "oun", + "score": 5.0, + "best_rank": 1, + "best_depth": 9.1, + "near_token": "am" + }, + { + "concept": "whence", + "score": 4.0, + "best_rank": 1, + "best_depth": 27.3, + "near_token": "from" + }, + { + "concept": "electrons", + "score": 4.0, + "best_rank": 1, + "best_depth": 81.8, + "near_token": "accepts" + }, + { + "concept": "weap", + "score": 3.0, + "best_rank": 1, + "best_depth": 0.0, + "near_token": "In" + }, + { + "concept": "pione", + "score": 3.0, + "best_rank": 1, + "best_depth": 27.3, + "near_token": "am" + }, + { + "concept": "tremend", + "score": 3.0, + "best_rank": 1, + "best_depth": 36.4, + "near_token": "a" + } + ], + "figures": { + "grid": "figures/gpt2lmheadmodel/protein/protein-helix-multihop__grid.png", + "stream": "figures/gpt2lmheadmodel/protein/protein-helix-multihop__stream.png", + "trajectory": "figures/gpt2lmheadmodel/protein/protein-helix-multihop__trajectory.png", + "emergence": "figures/gpt2lmheadmodel/protein/protein-helix-multihop__emergence.png", + "heatmap": "figures/gpt2lmheadmodel/protein/protein-helix-multihop__heatmap.png" + } + }, + { + "slug": "protein-gfp-recognition", + "shape": "RECOGNITION", + "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.", + "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_tail": "What is this? MSKGEELFTGVVPILVELDGDVNGHKFSVSGEGEGDATYGKLTLKFICTTGKLPVPWPTL", + "readout_at": -1, + "band": [ + 40.0, + 88.0 + ], + "discovered": false, + "tracked_dropped": [ + "GFP" + ], + "emergence": [ + { + "label": "protein", + "best_rank": 1558, + "best_depth": 100.0, + "reached_top1": false, + "best_pos": 3 + }, + { + "label": "fluor", + "best_rank": 2492, + "best_depth": 27.3, + "reached_top1": false, + "best_pos": 6 + }, + { + "label": "green", + "best_rank": 545, + "best_depth": 100.0, + "reached_top1": false, + "best_pos": 3 + }, + { + "label": "sequence", + "best_rank": 2371, + "best_depth": 100.0, + "reached_top1": false, + "best_pos": 3 + }, + { + "label": "amino", + "best_rank": 2161, + "best_depth": 0.0, + "reached_top1": false, + "best_pos": 5 + } + ], + "layer_readouts": [ + { + "layer": 0, + "depth": 0.0, + "top_tokens": [ + "ModLoader", + "ingred", + "emort", + "lvl", + "Lol", + "destro", + "veter", + "lol" + ] + }, + { + "layer": 1, + "depth": 9.1, + "top_tokens": [ + "ingred", + "ModLoader", + "destro", + "yrics", + "ILCS", + "ende", + "RIP", + "enthusi" + ] + }, + { + "layer": 2, + "depth": 18.2, + "top_tokens": [ + "RIP", + "ModLoader", + "ingred", + "yrics", + "aughter", + "gobl", + "VL", + "ensu" + ] + }, + { + "layer": 3, + "depth": 27.3, + "top_tokens": [ + "ILCS", + "RIP", + "UX", + "yrics", + "YR", + "VL", + "ANC", + "LY" + ] + }, + { + "layer": 4, + "depth": 36.4, + "top_tokens": [ + "YR", + "yrics", + "senal", + "UX", + "DR", + "OUP", + "LCS", + "ANS" + ] + }, + { + "layer": 5, + "depth": 45.5, + "top_tokens": [ + "UX", + "YR", + "yrics", + "SK", + "KN", + "DR", + "UNCH", + "OSS" + ] + }, + { + "layer": 6, + "depth": 54.5, + "top_tokens": [ + "yrics", + "YR", + "senal", + "UTH", + "Dialogue", + "UX", + "ACP", + "UNCH" + ] + }, + { + "layer": 7, + "depth": 63.6, + "top_tokens": [ + "YR", + "ADS", + "UTH", + "OV", + "KN", + "OSS", + "YN", + "RIP" + ] + }, + { + "layer": 8, + "depth": 72.7, + "top_tokens": [ + "teasp", + "UTH", + "ActionCode", + "KN", + "ÍÍ", + "YR", + "YN", + "OV" + ] + }, + { + "layer": 9, + "depth": 81.8, + "top_tokens": [ + "FK", + "KN", + "YR", + "WT", + "YC", + "WN", + "OV", + "KC" + ] + }, + { + "layer": 10, + "depth": 90.9, + "top_tokens": [ + "WT", + "FK", + "PT", + "GN", + "KK", + "GV", + "WN", + "KT" + ] + }, + { + "layer": 11, + "depth": 100.0, + "top_tokens": [ + "K", + "Q", + "W", + "X", + "Z", + "F", + "H", + "J" + ] + } + ], + "surprising": [ + { + "concept": "GGGGGGGG", + "score": 32.0, + "best_rank": 1, + "best_depth": 45.5, + "near_token": "G" + }, + { + "concept": "GGGG", + "score": 7.0, + "best_rank": 1, + "best_depth": 45.5, + "near_token": "NG" + }, + { + "concept": "PDATE", + "score": 5.0, + "best_rank": 1, + "best_depth": 63.6, + "near_token": "V" + }, + { + "concept": "Answer", + "score": 4.0, + "best_rank": 1, + "best_depth": 54.5, + "near_token": "?" + }, + { + "concept": "TOR", + "score": 3.0, + "best_rank": 1, + "best_depth": 45.5, + "near_token": "K" + }, + { + "concept": "OOL", + "score": 3.0, + "best_rank": 1, + "best_depth": 54.5, + "near_token": "PT" + }, + { + "concept": "ORK", + "score": 3.0, + "best_rank": 1, + "best_depth": 63.6, + "near_token": "Y" + }, + { + "concept": "StreamerBot", + "score": 3.0, + "best_rank": 1, + "best_depth": 72.7, + "near_token": "K" + }, + { + "concept": "FIELD", + "score": 2.0, + "best_rank": 1, + "best_depth": 45.5, + "near_token": "GE" + }, + { + "concept": "AND", + "score": 2.0, + "best_rank": 1, + "best_depth": 45.5, + "near_token": "EL" + }, + { + "concept": "WAYS", + "score": 2.0, + "best_rank": 1, + "best_depth": 45.5, + "near_token": "IL" + }, + { + "concept": "ModLoader", + "score": 2.0, + "best_rank": 1, + "best_depth": 54.5, + "near_token": "is" + } + ], + "figures": { + "grid": "figures/gpt2lmheadmodel/protein/protein-gfp-recognition__grid.png", + "stream": "figures/gpt2lmheadmodel/protein/protein-gfp-recognition__stream.png", + "trajectory": "figures/gpt2lmheadmodel/protein/protein-gfp-recognition__trajectory.png", + "emergence": "figures/gpt2lmheadmodel/protein/protein-gfp-recognition__emergence.png", + "heatmap": "figures/gpt2lmheadmodel/protein/protein-gfp-recognition__heatmap.png" + } + }, + { + "slug": "protein-collagen-recognition", + "shape": "RECOGNITION", + "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?", + "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_tail": "What is this? GPPGPKGDPGPQGPRGPAGPPGPAGPQGPKGDPGPQGPRGPAGPP", + "readout_at": -1, + "band": [ + 40.0, + 88.0 + ], + "discovered": false, + "tracked_dropped": [ + "helix", + "glycine", + "proline", + "fibril" + ], + "emergence": [ + { + "label": "collagen", + "best_rank": 4416, + "best_depth": 63.6, + "reached_top1": false, + "best_pos": 3 + }, + { + "label": "triple", + "best_rank": 376, + "best_depth": 0.0, + "reached_top1": false, + "best_pos": 5 + } + ], + "layer_readouts": [ + { + "layer": 0, + "depth": 0.0, + "top_tokens": [ + "PP", + "PP", + "PW", + "pse", + "NP", + "ikuman", + "GP", + "retty" + ] + }, + { + "layer": 1, + "depth": 9.1, + "top_tokens": [ + "PP", + "PP", + "pse", + "GP", + "PW", + "CP", + "MP", + "NP" + ] + }, + { + "layer": 2, + "depth": 18.2, + "top_tokens": [ + "PP", + "PP", + "GP", + "MP", + "pse", + "retty", + "ERC", + "TON" + ] + }, + { + "layer": 3, + "depth": 27.3, + "top_tokens": [ + "PP", + "PP", + "TON", + "pse", + "HP", + "GP", + "NG", + "retty" + ] + }, + { + "layer": 4, + "depth": 36.4, + "top_tokens": [ + "PP", + "KK", + "OHN", + "retty", + "PP", + "ometimes", + "OOOO", + "yip" + ] + }, + { + "layer": 5, + "depth": 45.5, + "top_tokens": [ + "PP", + "OHN", + "GGGG", + "KK", + "GGGGGGGG", + "JJ", + "ometimes", + "PS" + ] + }, + { + "layer": 6, + "depth": 54.5, + "top_tokens": [ + "PP", + "GGGG", + "retty", + "OHN", + "GGGGGGGG", + "ometimes", + "olicy", + "ERC" + ] + }, + { + "layer": 7, + "depth": 63.6, + "top_tokens": [ + "PP", + "GGGGGGGG", + "GGGG", + "ÃÂÃÂÃÂÃÂÃÂÃÂÃÂÃÂ", + "retty", + "StreamerBot", + "ÛÛ", + "RandomRedditor" + ] + }, + { + "layer": 8, + "depth": 72.7, + "top_tokens": [ + "StreamerBot", + "ActionCode", + "embedreportprint", + "RandomRedditor", + "ÃÂÃÂÃÂÃÂÃÂÃÂÃÂÃÂ", + "rawdownload", + "reportprint", + "externalToEVA" + ] + }, + { + "layer": 9, + "depth": 81.8, + "top_tokens": [ + "PP", + "PK", + "GGGG", + "GP", + "PG", + "FK", + "FP", + "PDATE" + ] + }, + { + "layer": 10, + "depth": 90.9, + "top_tokens": [ + "PK", + "PG", + "PP", + "GP", + "Q", + "TP", + "FK", + "FG" + ] + }, + { + "layer": 11, + "depth": 100.0, + "top_tokens": [ + "Q", + "G", + "P", + "K", + "GP", + "PG", + "PK", + "PP" + ] + } + ], + "surprising": [ + { + "concept": "GGGGGGGG", + "score": 33.0, + "best_rank": 1, + "best_depth": 45.5, + "near_token": "G" + }, + { + "concept": "PDATE", + "score": 12.0, + "best_rank": 1, + "best_depth": 72.7, + "near_token": "G" + }, + { + "concept": "teasp", + "score": 5.0, + "best_rank": 1, + "best_depth": 63.6, + "near_token": "K" + }, + { + "concept": "StreamerBot", + "score": 5.0, + "best_rank": 1, + "best_depth": 72.7, + "near_token": "GP" + }, + { + "concept": "and", + "score": 4.0, + "best_rank": 1, + "best_depth": 45.5, + "near_token": "<|endoftext|>" + }, + { + "concept": "Answer", + "score": 4.0, + "best_rank": 1, + "best_depth": 54.5, + "near_token": "?" + }, + { + "concept": "GGGG", + "score": 4.0, + "best_rank": 1, + "best_depth": 45.5, + "near_token": "G" + }, + { + "concept": "ADRA", + "score": 4.0, + "best_rank": 1, + "best_depth": 63.6, + "near_token": "DP" + }, + { + "concept": "ActionCode", + "score": 4.0, + "best_rank": 1, + "best_depth": 72.7, + "near_token": "PA" + }, + { + "concept": "ModLoader", + "score": 2.0, + "best_rank": 1, + "best_depth": 54.5, + "near_token": "is" + }, + { + "concept": "OUP", + "score": 2.0, + "best_rank": 1, + "best_depth": 54.5, + "near_token": "PR" + }, + { + "concept": "Lies", + "score": 2.0, + "best_rank": 1, + "best_depth": 63.6, + "near_token": "What" + } + ], + "figures": { + "grid": "figures/gpt2lmheadmodel/protein/protein-collagen-recognition__grid.png", + "stream": "figures/gpt2lmheadmodel/protein/protein-collagen-recognition__stream.png", + "trajectory": "figures/gpt2lmheadmodel/protein/protein-collagen-recognition__trajectory.png", + "emergence": "figures/gpt2lmheadmodel/protein/protein-collagen-recognition__emergence.png", + "heatmap": "figures/gpt2lmheadmodel/protein/protein-collagen-recognition__heatmap.png" + } + }, + { + "slug": "protein-transmembrane-recognition", + "shape": "RECOGNITION", + "domain": "protein", + "title": "Hydrophobic stretch -> transmembrane (recognition)", + "description": "A ~20-residue hydrophobic run. Expect 'membrane'/'hydrophobic'/'transmembrane'/'helix'.", + "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_tail": "What is this? KKPNGFILVGGVLLLGAAIIGGVMLFAYVVKKPN", + "readout_at": -1, + "band": [ + 40.0, + 88.0 + ], + "discovered": false, + "tracked_dropped": [ + "hydrophobic", + "transmembrane", + "helix" + ], + "emergence": [ + { + "label": "membrane", + "best_rank": 1388, + "best_depth": 90.9, + "reached_top1": false, + "best_pos": 3 + }, + { + "label": "lipid", + "best_rank": 2943, + "best_depth": 90.9, + "reached_top1": false, + "best_pos": 2 + } + ], + "layer_readouts": [ + { + "layer": 0, + "depth": 0.0, + "top_tokens": [ + "PN", + "ot", + "Fuk", + "ajor", + "PP", + "Pwr", + "Nish", + "yen" + ] + }, + { + "layer": 1, + "depth": 9.1, + "top_tokens": [ + "PN", + "Fuk", + "ajor", + "ot", + "PP", + "P", + "Pok", + "PT" + ] + }, + { + "layer": 2, + "depth": 18.2, + "top_tokens": [ + "PN", + "ajor", + "JR", + "PG", + "PP", + "ulhu", + "TY", + "Pug" + ] + }, + { + "layer": 3, + "depth": 27.3, + "top_tokens": [ + "PN", + "ajor", + "PG", + "KK", + "TY", + "PI", + "PP", + "JP" + ] + }, + { + "layer": 4, + "depth": 36.4, + "top_tokens": [ + "PN", + "KK", + "EGIN", + "TY", + "FK", + "IZE", + "ajor", + "KO" + ] + }, + { + "layer": 5, + "depth": 45.5, + "top_tokens": [ + "PN", + "KI", + "KK", + "KN", + "GGGGGGGG", + "FK", + "OHN", + "EGIN" + ] + }, + { + "layer": 6, + "depth": 54.5, + "top_tokens": [ + "PN", + "FK", + "GGGG", + "KI", + "GGGGGGGG", + "ADRA", + "KN", + "IX" + ] + }, + { + "layer": 7, + "depth": 63.6, + "top_tokens": [ + "FK", + "KN", + "PN", + "GGGG", + "GGGGGGGG", + "OV", + "IX", + "AZ" + ] + }, + { + "layer": 8, + "depth": 72.7, + "top_tokens": [ + "GGGGGGGG", + "FK", + "ADRA", + "GGGG", + "KN", + "KI", + "KR", + "ActionCode" + ] + }, + { + "layer": 9, + "depth": 81.8, + "top_tokens": [ + "FK", + "YC", + "NN", + "GGGGGGGG", + "GGGG", + "KI", + "KN", + "KK" + ] + }, + { + "layer": 10, + "depth": 90.9, + "top_tokens": [ + "FK", + "KK", + "NN", + "BN", + "YC", + "ZI", + "GG", + "NT" + ] + }, + { + "layer": 11, + "depth": 100.0, + "top_tokens": [ + "Q", + "X", + "Y", + "J", + "V", + "Z", + "W", + "K" + ] + } + ], + "surprising": [ + { + "concept": "GGGG", + "score": 27.0, + "best_rank": 1, + "best_depth": 45.5, + "near_token": "AY" + }, + { + "concept": "GGGGGGGG", + "score": 16.0, + "best_rank": 1, + "best_depth": 45.5, + "near_token": "IG" + }, + { + "concept": "Answer", + "score": 4.0, + "best_rank": 1, + "best_depth": 54.5, + "near_token": "?" + }, + { + "concept": "ModLoader", + "score": 4.0, + "best_rank": 1, + "best_depth": 54.5, + "near_token": "is" + }, + { + "concept": "PDATE", + "score": 3.0, + "best_rank": 1, + "best_depth": 63.6, + "near_token": "V" + }, + { + "concept": "ActionCode", + "score": 3.0, + "best_rank": 1, + "best_depth": 72.7, + "near_token": "K" + }, + { + "concept": "OOOO", + "score": 2.0, + "best_rank": 1, + "best_depth": 45.5, + "near_token": "LL" + }, + { + "concept": "Lies", + "score": 2.0, + "best_rank": 1, + "best_depth": 63.6, + "near_token": "What" + }, + { + "concept": "Collider", + "score": 1.0, + "best_rank": 1, + "best_depth": 45.5, + "near_token": "What" + }, + { + "concept": "DragonMagazine", + "score": 1.0, + "best_rank": 1, + "best_depth": 54.5, + "near_token": "V" + }, + { + "concept": "HHHH", + "score": 1.0, + "best_rank": 1, + "best_depth": 63.6, + "near_token": "I" + }, + { + "concept": "Mayhem", + "score": 1.0, + "best_rank": 1, + "best_depth": 72.7, + "near_token": "is" + } + ], + "figures": { + "grid": "figures/gpt2lmheadmodel/protein/protein-transmembrane-recognition__grid.png", + "stream": "figures/gpt2lmheadmodel/protein/protein-transmembrane-recognition__stream.png", + "trajectory": "figures/gpt2lmheadmodel/protein/protein-transmembrane-recognition__trajectory.png", + "emergence": "figures/gpt2lmheadmodel/protein/protein-transmembrane-recognition__emergence.png", + "heatmap": "figures/gpt2lmheadmodel/protein/protein-transmembrane-recognition__heatmap.png" + } + }, + { + "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?", + "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_tail": " 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_at": -1, + "band": [ + 40.0, + 88.0 + ], + "discovered": false, + "tracked_dropped": [ + "amyloid", + "fibril", + "misfold" + ], + "emergence": [ + { + "label": "aggregate", + "best_rank": 314, + "best_depth": 54.5, + "reached_top1": false, + "best_pos": 5 + }, + { + "label": "beta", + "best_rank": 135, + "best_depth": 100.0, + "reached_top1": false, + "best_pos": 5 + }, + { + "label": "plaque", + "best_rank": 0, + "best_depth": 0.0, + "reached_top1": true, + "best_pos": 29 + } + ], + "layer_readouts": [ + { + "layer": 0, + "depth": 0.0, + "top_tokens": [ + "ingred", + "ModLoader", + "enthusi", + "Colour", + "Heist", + "colours", + "UK", + "XCOM" + ] + }, + { + "layer": 1, + "depth": 9.1, + "top_tokens": [ + "ModLoader", + "ingred", + "mathemat", + "cryst", + "Morrow", + "glim", + "ISBN", + "Clockwork" + ] + }, + { + "layer": 2, + "depth": 18.2, + "top_tokens": [ + "ModLoader", + "mathemat", + "ARDIS", + "BBC", + "surv", + "misunder", + "ccording", + "maths" + ] + }, + { + "layer": 3, + "depth": 27.3, + "top_tokens": [ + "ingred", + "maths", + "ModLoader", + "weap", + "mathemat", + "colours", + "surv", + "cryst" + ] + }, + { + "layer": 4, + "depth": 36.4, + "top_tokens": [ + "weap", + "ModLoader", + "laun", + "surv", + "ingred", + "ensu", + "lapt", + "colours" + ] + }, + { + "layer": 5, + "depth": 45.5, + "top_tokens": [ + "tyres", + "realised", + "colours", + "aluminium", + "ensu", + "maths", + "armour", + "rumours" + ] + }, + { + "layer": 6, + "depth": 54.5, + "top_tokens": [ + "They", + "mathemat", + "Whilst", + "mould", + "Each", + "maths", + "colours", + "realised" + ] + }, + { + "layer": 7, + "depth": 63.6, + "top_tokens": [ + "They", + "Suddenly", + "Then", + "These", + "Each", + "ModLoader", + "Eventually", + "Dozens" + ] + }, + { + "layer": 8, + "depth": 72.7, + "top_tokens": [ + "They", + "Suddenly", + "Eventually", + "Then", + "Their", + "Dozens", + "Each", + "Scientists" + ] + }, + { + "layer": 9, + "depth": 81.8, + "top_tokens": [ + "Their", + "They", + "Each", + "Eventually", + "Occasionally", + "Then", + "These", + "Slowly" + ] + }, + { + "layer": 10, + "depth": 90.9, + "top_tokens": [ + "Their", + "Each", + "They", + "Eventually", + "Slowly", + "Then", + "When", + "Suddenly" + ] + }, + { + "layer": 11, + "depth": 100.0, + "top_tokens": [ + "The", + "They", + "It", + "In", + "A", + "This", + "When", + "As" + ] + } + ], + "surprising": [ + { + "concept": "mathemat", + "score": 7.0, + "best_rank": 1, + "best_depth": 45.5, + "near_token": "the" + }, + { + "concept": "edges", + "score": 4.0, + "best_rank": 1, + "best_depth": 45.5, + "near_token": "edge" + }, + { + "concept": "maze", + "score": 4.0, + "best_rank": 1, + "best_depth": 45.5, + "near_token": "a" + }, + { + "concept": "ModLoader", + "score": 4.0, + "best_rank": 1, + "best_depth": 54.5, + "near_token": "in" + }, + { + "concept": "cryst", + "score": 3.0, + "best_rank": 1, + "best_depth": 45.5, + "near_token": "into" + }, + { + "concept": "individually", + "score": 3.0, + "best_rank": 1, + "best_depth": 45.5, + "near_token": "each" + }, + { + "concept": "adorned", + "score": 3.0, + "best_rank": 1, + "best_depth": 54.5, + "near_token": "bons" + }, + { + "concept": "undergone", + "score": 3.0, + "best_rank": 1, + "best_depth": 63.6, + "near_token": "had" + }, + { + "concept": "fists", + "score": 3.0, + "best_rank": 1, + "best_depth": 63.6, + "near_token": "into" + }, + { + "concept": "resembled", + "score": 3.0, + "best_rank": 1, + "best_depth": 63.6, + "near_token": "that" + }, + { + "concept": "horizont", + "score": 2.0, + "best_rank": 1, + "best_depth": 54.5, + "near_token": "to" + }, + { + "concept": "predecessors", + "score": 2.0, + "best_rank": 1, + "best_depth": 54.5, + "near_token": "its" + } + ], + "figures": { + "grid": "figures/gpt2lmheadmodel/protein/protein-amyloid-assoc__grid.png", + "stream": "figures/gpt2lmheadmodel/protein/protein-amyloid-assoc__stream.png", + "trajectory": "figures/gpt2lmheadmodel/protein/protein-amyloid-assoc__trajectory.png", + "emergence": "figures/gpt2lmheadmodel/protein/protein-amyloid-assoc__emergence.png", + "heatmap": "figures/gpt2lmheadmodel/protein/protein-amyloid-assoc__heatmap.png" + } + }, + { + "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.", + "note": "Anfinsen-style hydrophobic-collapse chain: the intermediates 'hydrophobic' and 'core' should precede the answer 'fold'/'structure' across layers.", + "prompt_tail": "ut of contact with solvent and pack together in the interior, and this burial of hydrophobic residues is the main force that drives the chain back to its native", + "readout_at": -1, + "band": [ + 18.2, + 90.9 + ], + "discovered": false, + "tracked_dropped": [ + "hydrophobic" + ], + "emergence": [ + { + "label": "core", + "best_rank": 29, + "best_depth": 100.0, + "reached_top1": false, + "best_pos": 49 + }, + { + "label": "collapse", + "best_rank": 117, + "best_depth": 90.9, + "reached_top1": false, + "best_pos": 46 + }, + { + "label": "fold", + "best_rank": 27, + "best_depth": 36.4, + "reached_top1": false, + "best_pos": 8 + }, + { + "label": "structure", + "best_rank": 3, + "best_depth": 90.9, + "reached_top1": false, + "best_pos": 46 + }, + { + "label": "native", + "best_rank": 0, + "best_depth": 18.2, + "reached_top1": true, + "best_pos": 50 + } + ], + "layer_readouts": [ + { + "layer": 0, + "depth": 0.0, + "top_tokens": [ + "Native", + "native", + "native", + "Native", + "natives", + "language", + "anguages", + "Languages" + ] + }, + { + "layer": 1, + "depth": 9.1, + "top_tokens": [ + "native", + "native", + "Native", + "Native", + "anguages", + "natives", + "language", + "Languages" + ] + }, + { + "layer": 2, + "depth": 18.2, + "top_tokens": [ + "native", + "native", + "language", + "Native", + "anguages", + "natives", + "Native", + "flora" + ] + }, + { + "layer": 3, + "depth": 27.3, + "top_tokens": [ + "native", + "native", + "Native", + "language", + "Native", + "natives", + "flora", + "tribes" + ] + }, + { + "layer": 4, + "depth": 36.4, + "top_tokens": [ + "native", + "native", + "natives", + "Native", + "language", + "Native", + "tribes", + "flora" + ] + }, + { + "layer": 5, + "depth": 45.5, + "top_tokens": [ + "native", + "language", + "native", + "Native", + "natives", + "flora", + "Native", + "language" + ] + }, + { + "layer": 6, + "depth": 54.5, + "top_tokens": [ + "habitat", + "native", + "flora", + "native", + "language", + "extinction", + "tribe", + "language" + ] + }, + { + "layer": 7, + "depth": 63.6, + "top_tokens": [ + "habitat", + "flora", + "forests", + "ecology", + "homeland", + "language", + "woodland", + "extinction" + ] + }, + { + "layer": 8, + "depth": 72.7, + "top_tokens": [ + "habitat", + "habitats", + "cryst", + "surroundings", + "flora", + "extinction", + "ecosystems", + "ancestor" + ] + }, + { + "layer": 9, + "depth": 81.8, + "top_tokens": [ + "habitat", + "substrate", + "equilibrium", + "habitats", + "soil", + "freshwater", + "morphology", + "surroundings" + ] + }, + { + "layer": 10, + "depth": 90.9, + "top_tokens": [ + "substrate", + "habitat", + "soil", + "equilibrium", + "pH", + "morphology", + "solvent", + "tissues" + ] + }, + { + "layer": 11, + "depth": 100.0, + "top_tokens": [ + "state", + "water", + "form", + "environment", + "location", + "surface", + "pH", + "place" + ] + } + ], + "surprising": [ + { + "concept": "ingred", + "score": 25.0, + "best_rank": 1, + "best_depth": 18.2, + "near_token": "and" + }, + { + "concept": "ModLoader", + "score": 23.0, + "best_rank": 1, + "best_depth": 18.2, + "near_token": "A" + }, + { + "concept": "mathemat", + "score": 20.0, + "best_rank": 1, + "best_depth": 18.2, + "near_token": "this" + }, + { + "concept": "cryst", + "score": 10.0, + "best_rank": 1, + "best_depth": 18.2, + "near_token": "the" + }, + { + "concept": "substrate", + "score": 5.0, + "best_rank": 1, + "best_depth": 63.6, + "near_token": "with" + }, + { + "concept": "relics", + "score": 4.0, + "best_rank": 1, + "best_depth": 36.4, + "near_token": "of" + }, + { + "concept": "exerted", + "score": 4.0, + "best_rank": 1, + "best_depth": 36.4, + "near_token": "force" + }, + { + "concept": "whence", + "score": 4.0, + "best_rank": 1, + "best_depth": 54.5, + "near_token": "," + }, + { + "concept": "habitat", + "score": 4.0, + "best_rank": 1, + "best_depth": 54.5, + "near_token": "native" + }, + { + "concept": "contacts", + "score": 3.0, + "best_rank": 1, + "best_depth": 18.2, + "near_token": "contact" + }, + { + "concept": "awa", + "score": 3.0, + "best_rank": 1, + "best_depth": 27.3, + "near_token": "p" + }, + { + "concept": "redistributed", + "score": 3.0, + "best_rank": 1, + "best_depth": 72.7, + "near_token": "and" + } + ], + "figures": { + "grid": "figures/gpt2lmheadmodel/protein/protein-fold-multihop__grid.png", + "stream": "figures/gpt2lmheadmodel/protein/protein-fold-multihop__stream.png", + "trajectory": "figures/gpt2lmheadmodel/protein/protein-fold-multihop__trajectory.png", + "emergence": "figures/gpt2lmheadmodel/protein/protein-fold-multihop__emergence.png", + "heatmap": "figures/gpt2lmheadmodel/protein/protein-fold-multihop__heatmap.png" + } + } + ] +} \ No newline at end of file diff --git a/jlens_materials/runs/gpt2lmheadmodel_analysis.json b/jlens_materials/runs/gpt2lmheadmodel_analysis.json new file mode 100644 index 0000000000000000000000000000000000000000..512eee7b7be7a69540ecfc034a51ceaa0d431d53 --- /dev/null +++ b/jlens_materials/runs/gpt2lmheadmodel_analysis.json @@ -0,0 +1,105 @@ +{ + "model": "gpt2", + "provider": "anthropic", + "analysis_model": "claude-opus-4-8", + "offline": true, + "per_prompt": [ + { + "slug": "fracture-griffith", + "title": "Griffith criterion (multi-hop)", + "shape": "MULTIHOP", + "domain": "fracture", + "model_used": "offline", + "analysis": "[offline template] Concepts reaching lens rank 1: 'energy' (depth 0.0%), 'surface' (depth 0.0%). Concepts reaching the lens top-5: 'energy' (rank 1, depth 0.0%), 'surface' (rank 1, depth 0.0%). Hidden thoughts (surfaced in the workspace band, not in the prompt or output): mathemat, ModLoader, multiplier, ingred, tremend, destro." + }, + { + "slug": "fracture-fatigue", + "title": "Cyclic loading -> failure mode (multi-hop)", + "shape": "MULTIHOP", + "domain": "fracture", + "model_used": "offline", + "analysis": "[offline template] Concepts reaching the lens top-5: 'crack' (rank 4, depth 0.0%). Not trackable (multi-token, paper limitation section 9.1): cyclic, striations. Hidden thoughts (surfaced in the workspace band, not in the prompt or output): mathemat, ModLoader, tremend, seams, loader, multiplier." + }, + { + "slug": "fracture-toughness-chain", + "title": "Stress-intensity factor chain (multi-hop)", + "shape": "MULTIHOP", + "domain": "fracture", + "model_used": "offline", + "analysis": "[offline template] Concepts reaching lens rank 1: 'stress' (depth 0.0%), 'intensity' (depth 0.0%), 'toughness' (depth 0.0%). Concepts reaching the lens top-5: 'stress' (rank 1, depth 0.0%), 'intensity' (rank 1, depth 0.0%), 'toughness' (rank 1, depth 0.0%). Hidden thoughts (surfaced in the workspace band, not in the prompt or output): mathemat, ModLoader, ingred, equations, herself, glim." + }, + { + "slug": "fracture-brittle-assoc", + "title": "Brittle fracture (association, unnamed)", + "shape": "ASSOCIATION", + "domain": "fracture", + "model_used": "offline", + "analysis": "[offline template] Concepts reaching the lens top-5: 'glass' (rank 4, depth 100.0%), 'shatter' (rank 3, depth 27.3%). Hidden thoughts (surfaced in the workspace band, not in the prompt or output): ingred, disappearing, weap, bends, tremend, rooft." + }, + { + "slug": "fracture-ductile-assoc", + "title": "Ductile failure (association, unnamed)", + "shape": "ASSOCIATION", + "domain": "fracture", + "model_used": "offline", + "analysis": "[offline template] No tracked concept reached the lens top-5 at any depth — either the model lacks these concepts or they are multi-token and untrackable. Not trackable (multi-token, paper limitation section 9.1): ductile, necking, deformation. Hidden thoughts (surfaced in the workspace band, not in the prompt or output): ingred, relent, ModLoader, horizont, else, tremend." + }, + { + "slug": "fracture-stress-recognition", + "title": "Stress state from a tensor (recognition)", + "shape": "RECOGNITION", + "domain": "fracture", + "model_used": "offline", + "analysis": "[offline template] No tracked concept reached the lens top-5 at any depth — either the model lacks these concepts or they are multi-token and untrackable. Not trackable (multi-token, paper limitation section 9.1): tensile, uniaxial. Hidden thoughts (surfaced in the workspace band, not in the prompt or output): Notice, teasp, URRENT, coils, When, mechanism." + }, + { + "slug": "protein-helix-multihop", + "title": "Backbone H-bonds -> secondary structure (multi-hop)", + "shape": "MULTIHOP", + "domain": "protein", + "model_used": "offline", + "analysis": "[offline template] Concepts reaching lens rank 1: 'hydrogen' (depth 0.0%), 'bond' (depth 100.0%), 'backbone' (depth 0.0%). Concepts reaching the lens top-5: 'hydrogen' (rank 1, depth 0.0%), 'bond' (rank 1, depth 100.0%), 'backbone' (rank 1, depth 0.0%). Not trackable (multi-token, paper limitation section 9.1): helix. Hidden thoughts (surfaced in the workspace band, not in the prompt or output): ModLoader, mathemat, ingred, bonded, horizont, LOAD." + }, + { + "slug": "protein-gfp-recognition", + "title": "GFP sequence -> function (recognition)", + "shape": "RECOGNITION", + "domain": "protein", + "model_used": "offline", + "analysis": "[offline template] No tracked concept reached the lens top-5 at any depth — either the model lacks these concepts or they are multi-token and untrackable. Not trackable (multi-token, paper limitation section 9.1): GFP. Hidden thoughts (surfaced in the workspace band, not in the prompt or output): GGGGGGGG, GGGG, PDATE, Answer, TOR, OOL." + }, + { + "slug": "protein-collagen-recognition", + "title": "Gly-X-Y repeat -> collagen (recognition)", + "shape": "RECOGNITION", + "domain": "protein", + "model_used": "offline", + "analysis": "[offline template] No tracked concept reached the lens top-5 at any depth — either the model lacks these concepts or they are multi-token and untrackable. Not trackable (multi-token, paper limitation section 9.1): helix, glycine, proline, fibril. Hidden thoughts (surfaced in the workspace band, not in the prompt or output): GGGGGGGG, PDATE, teasp, StreamerBot, and, Answer." + }, + { + "slug": "protein-transmembrane-recognition", + "title": "Hydrophobic stretch -> transmembrane (recognition)", + "shape": "RECOGNITION", + "domain": "protein", + "model_used": "offline", + "analysis": "[offline template] No tracked concept reached the lens top-5 at any depth — either the model lacks these concepts or they are multi-token and untrackable. Not trackable (multi-token, paper limitation section 9.1): hydrophobic, transmembrane, helix. Hidden thoughts (surfaced in the workspace band, not in the prompt or output): GGGG, GGGGGGGG, Answer, ModLoader, PDATE, ActionCode." + }, + { + "slug": "protein-amyloid-assoc", + "title": "Amyloid aggregation (association, unnamed)", + "shape": "ASSOCIATION", + "domain": "protein", + "model_used": "offline", + "analysis": "[offline template] Concepts reaching lens rank 1: 'plaque' (depth 0.0%). Concepts reaching the lens top-5: 'plaque' (rank 1, depth 0.0%). Not trackable (multi-token, paper limitation section 9.1): amyloid, fibril, misfold. Hidden thoughts (surfaced in the workspace band, not in the prompt or output): mathemat, edges, maze, ModLoader, cryst, individually." + }, + { + "slug": "protein-fold-multihop", + "title": "Hydrophobic collapse -> tertiary fold (multi-hop)", + "shape": "MULTIHOP", + "domain": "protein", + "model_used": "offline", + "analysis": "[offline template] Concepts reaching lens rank 1: 'native' (depth 18.2%). Concepts reaching the lens top-5: 'structure' (rank 4, depth 90.9%), 'native' (rank 1, depth 18.2%). Not trackable (multi-token, paper limitation section 9.1): hydrophobic. Hidden thoughts (surfaced in the workspace band, not in the prompt or output): ingred, ModLoader, mathemat, cryst, substrate, relics." + } + ], + "synthesis": "[offline template] Run over gpt2 (12 layers). By experiment type — MULTIHOP: 5/5 prompts with a top-5 concept; ASSOCIATION: 2/3 prompts with a top-5 concept; RECOGNITION: 0/4 prompts with a top-5 concept. Most frequent unprompted 'hidden thoughts' across prompts: ModLoader (×11), mathemat (×6), ingred (×7), GGGGGGGG (×3), multiplier (×3), GGGG (×3), tremend (×5), PDATE (×3), edges (×3), destro (×1), weap (×3), cryst (×2)." +} \ No newline at end of file diff --git a/jlens_materials/runs/qwen2forcausallm.json b/jlens_materials/runs/qwen2forcausallm.json new file mode 100644 index 0000000000000000000000000000000000000000..9fd9d88783c379adabddb23b8e8511a6a0115d6c --- /dev/null +++ b/jlens_materials/runs/qwen2forcausallm.json @@ -0,0 +1,7575 @@ +{ + "model": "Qwen/Qwen2.5-0.5B-Instruct", + "tag": "qwen2forcausallm", + "n_layers": 24, + "d_model": 896, + "lens_n_prompts": 8, + "shapes": [ + "ASSOCIATION", + "MODULATION", + "MULTIHOP", + "RECOGNITION", + "REPORT_SWAP" + ], + "domains": [ + "fracture", + "protein" + ], + "prompts": [ + { + "slug": "fracture-griffith", + "shape": "MULTIHOP", + "domain": "fracture", + "title": "Griffith criterion (multi-hop)", + "description": "Two-hop: the failure law for a brittle solid with a sharp flaw. Watch 'energy' / 'surface' / 'Griffith' surface before 'crack'.", + "note": "Griffith's result sigma_c ~ sqrt(2 E gamma / (pi a)). The answer token is 'crack (length)'; 'energy', 'surface' and the eponym 'Griffith' are unspoken intermediates the derivation passes through -- the fracture analogue of Mars->color->red.", + "prompt_tail": "balancing the released strain energy against the energy needed to create new surface. The critical stress therefore scales inversely with the square root of the", + "readout_at": -1, + "band": [ + 0.0, + 100.0 + ], + "discovered": false, + "tracked_dropped": [], + "emergence": [ + { + "label": "energy", + "best_rank": 0, + "best_depth": 43.5, + "reached_top1": true, + "best_pos": 20 + }, + { + "label": "surface", + "best_rank": 0, + "best_depth": 0.0, + "reached_top1": true, + "best_pos": 30 + }, + { + "label": "Griffith", + "best_rank": 57, + "best_depth": 100.0, + "reached_top1": false, + "best_pos": 17 + }, + { + "label": "crack", + "best_rank": 0, + "best_depth": 100.0, + "reached_top1": true, + "best_pos": 10 + }, + { + "label": "length", + "best_rank": 6, + "best_depth": 95.7, + "reached_top1": false, + "best_pos": 42 + } + ], + "layer_readouts": [ + { + "layer": 0, + "depth": 0.0, + "top_tokens": [ + "tas", + "s", + "幛", + "iếu", + "mountains", + "irlines", + "many", + "writing" + ] + }, + { + "layer": 1, + "depth": 4.3, + "top_tokens": [ + "s", + "many", + "others", + "the", + "e", + "people", + "following", + "land" + ] + }, + { + "layer": 2, + "depth": 8.7, + "top_tokens": [ + "land", + "mountain", + "s", + "many", + "Mount", + "Mountain", + "company", + "flying" + ] + }, + { + "layer": 3, + "depth": 13.0, + "top_tokens": [ + "flying", + "northern", + "s", + "land", + "provinces", + "north", + "mountain", + "island" + ] + }, + { + "layer": 4, + "depth": 17.4, + "top_tokens": [ + "people", + "others", + "computer", + "s", + "Rings", + "land", + "island", + "plane" + ] + }, + { + "layer": 5, + "depth": 21.7, + "top_tokens": [ + "world", + "hero", + "plane", + "flying", + "people", + "heroes", + "legendary", + "land" + ] + }, + { + "layer": 6, + "depth": 26.1, + "top_tokens": [ + "mathematical", + "physics", + "mathematics", + "numerical", + "planet", + "math", + "orbit", + "cube" + ] + }, + { + "layer": 7, + "depth": 30.4, + "top_tokens": [ + "mathematical", + "numerical", + "computational", + "scientific", + "mathematics", + "dimension", + "space", + "sciences" + ] + }, + { + "layer": 8, + "depth": 34.8, + "top_tokens": [ + "mathematical", + "numerical", + "computational", + "mathematics", + "scientific", + "magnitude", + "space", + "sciences" + ] + }, + { + "layer": 9, + "depth": 39.1, + "top_tokens": [ + "mathematical", + "mathematics", + "numerical", + "computational", + "scientific", + "complexity", + "magnitude", + "science" + ] + }, + { + "layer": 10, + "depth": 43.5, + "top_tokens": [ + "computational", + "numerical", + "mathematical", + "magnitude", + "mathematics", + "complexity", + "calculations", + "dataSize" + ] + }, + { + "layer": 11, + "depth": 47.8, + "top_tokens": [ + "mathematical", + "computational", + "magnitude", + "mathematics", + "numerical", + "complexity", + "scientific", + "calculations" + ] + }, + { + "layer": 12, + "depth": 52.2, + "top_tokens": [ + "computational", + "mathematical", + "numerical", + "mathematics", + "magnitude", + "complexity", + "technological", + "exponential" + ] + }, + { + "layer": 13, + "depth": 56.5, + "top_tokens": [ + "mathematical", + "numerical", + "computational", + "mathematics", + "magnitude", + "exponential", + "logarith", + "calculations" + ] + }, + { + "layer": 14, + "depth": 60.9, + "top_tokens": [ + "magnitude", + "density", + "numerical", + "dataSize", + "geometric", + "diameter", + "computational", + "mathematical" + ] + }, + { + "layer": 15, + "depth": 65.2, + "top_tokens": [ + "density", + "dataSize", + "magnitude", + "diameter", + "密度", + "thickness", + "几何", + "scale" + ] + }, + { + "layer": 16, + "depth": 69.6, + "top_tokens": [ + "density", + "密度", + "dataSize", + "magnitude", + "atomic", + "diameter", + "exponential", + "scaleFactor" + ] + }, + { + "layer": 17, + "depth": 73.9, + "top_tokens": [ + "density", + "magnitude", + "diameter", + "scale", + "topology", + "geometry", + "exponential", + "thickness" + ] + }, + { + "layer": 18, + "depth": 78.3, + "top_tokens": [ + "density", + "diameter", + "magnitude", + "thickness", + "curvature", + "radius", + "dimensions", + "密度" + ] + }, + { + "layer": 19, + "depth": 82.6, + "top_tokens": [ + "density", + "diameter", + "magnitude", + "thickness", + "radius", + "ratio", + "size", + "curvature" + ] + }, + { + "layer": 20, + "depth": 87.0, + "top_tokens": [ + "thickness", + "modulus", + "fracture", + "diameter", + "density", + "cube", + "magnitude", + "curvature" + ] + }, + { + "layer": 21, + "depth": 91.3, + "top_tokens": [ + "fracture", + "density", + "modulus", + "volume", + "deformation", + "strength", + "length", + "thickness" + ] + }, + { + "layer": 22, + "depth": 95.7, + "top_tokens": [ + "strain", + "stress", + "fracture", + "square", + "modulus", + "number", + "thickness", + "load" + ] + }, + { + "layer": 23, + "depth": 100.0, + "top_tokens": [ + "number", + "strain", + "initial", + "area", + "material", + "stress", + "square", + "surface" + ] + } + ], + "surprising": [ + { + "concept": "computational", + "score": 43.0, + "best_rank": 1, + "best_depth": 30.4, + "near_token": "the" + }, + { + "concept": "mathematical", + "score": 30.0, + "best_rank": 1, + "best_depth": 26.1, + "near_token": "the" + }, + { + "concept": "density", + "score": 27.0, + "best_rank": 1, + "best_depth": 30.4, + "near_token": "stress" + }, + { + "concept": "gravitational", + "score": 16.0, + "best_rank": 1, + "best_depth": 69.6, + "near_token": "balancing" + }, + { + "concept": "deformation", + "score": 15.0, + "best_rank": 1, + "best_depth": 30.4, + "near_token": "strain" + }, + { + "concept": "magnitude", + "score": 14.0, + "best_rank": 1, + "best_depth": 43.5, + "near_token": "of" + }, + { + "concept": "balance", + "score": 10.0, + "best_rank": 1, + "best_depth": 8.7, + "near_token": "balancing" + }, + { + "concept": "surfaces", + "score": 10.0, + "best_rank": 1, + "best_depth": 30.4, + "near_token": "surface" + }, + { + "concept": "thresholds", + "score": 10.0, + "best_rank": 1, + "best_depth": 60.9, + "near_token": "critical" + }, + { + "concept": "equilibrium", + "score": 9.0, + "best_rank": 1, + "best_depth": 30.4, + "near_token": "balancing" + }, + { + "concept": "geometric", + "score": 8.0, + "best_rank": 1, + "best_depth": 60.9, + "near_token": "therefore" + }, + { + "concept": "geometry", + "score": 8.0, + "best_rank": 1, + "best_depth": 60.9, + "near_token": "linear" + } + ], + "figures": { + "grid": "figures/qwen2forcausallm/fracture/fracture-griffith__grid.png", + "stream": "figures/qwen2forcausallm/fracture/fracture-griffith__stream.png", + "trajectory": "figures/qwen2forcausallm/fracture/fracture-griffith__trajectory.png", + "emergence": "figures/qwen2forcausallm/fracture/fracture-griffith__emergence.png", + "heatmap": "figures/qwen2forcausallm/fracture/fracture-griffith__heatmap.png" + } + }, + { + "slug": "fracture-fatigue", + "shape": "MULTIHOP", + "domain": "fracture", + "title": "Cyclic loading -> failure mode (multi-hop)", + "description": "Cyclic sub-yield loading. 'fatigue' should lead 'crack', with 'Paris'/'striations' as deeper-layer associates.", + "note": "The answer is 'fatigue'. Whether 'Paris' (the Paris crack-growth law) or 'striations' (the fractographic fingerprint) appear in deeper layers probes how much associated fracture knowledge the workspace pulls in beyond the one-word answer.", + "prompt_tail": "lions of cycles. Tiny cracks nucleate at the surface and grow a little on every cycle until the part suddenly breaks. This progressive mode of failure is called", + "readout_at": -1, + "band": [ + 17.4, + 100.0 + ], + "discovered": false, + "tracked_dropped": [ + "striations" + ], + "emergence": [ + { + "label": "fatigue", + "best_rank": 0, + "best_depth": 95.7, + "reached_top1": true, + "best_pos": 42 + }, + { + "label": "crack", + "best_rank": 2, + "best_depth": 100.0, + "reached_top1": false, + "best_pos": 37 + }, + { + "label": "cyclic", + "best_rank": 2, + "best_depth": 17.4, + "reached_top1": false, + "best_pos": 5 + }, + { + "label": "Paris", + "best_rank": 193, + "best_depth": 8.7, + "reached_top1": false, + "best_pos": 22 + } + ], + "layer_readouts": [ + { + "layer": 0, + "depth": 0.0, + "top_tokens": [ + "Cham", + "phones", + "phone", + "Ville", + "called", + "Sok", + "巴斯", + "summoned" + ] + }, + { + "layer": 1, + "depth": 4.3, + "top_tokens": [ + "called", + "Cham", + "phone", + "Sok", + "summon", + "phones", + "Vo", + "summoned" + ] + }, + { + "layer": 2, + "depth": 8.7, + "top_tokens": [ + "called", + "Cham", + "Vo", + "call", + "Call", + "Sok", + "Cas", + "calling" + ] + }, + { + "layer": 3, + "depth": 13.0, + "top_tokens": [ + "called", + "Cham", + "呼", + "Vo", + "Von", + "Ville", + "Pont", + "itto" + ] + }, + { + "layer": 4, + "depth": 17.4, + "top_tokens": [ + "Cham", + "called", + "telephone", + "Nome", + "呼", + "Vo", + "Pont", + "Caller" + ] + }, + { + "layer": 5, + "depth": 21.7, + "top_tokens": [ + "呼", + "Caller", + "Kansas", + "Vo", + "summons", + "Nome", + "Cham", + "called" + ] + }, + { + "layer": 6, + "depth": 26.1, + "top_tokens": [ + "Nome", + "呼", + "Cham", + "telephone", + "Caption", + "Name", + "Champ", + "Caller" + ] + }, + { + "layer": 7, + "depth": 30.4, + "top_tokens": [ + "Nome", + "呼", + "Cham", + "Patent", + "called", + "Von", + "称之为", + "Doctor" + ] + }, + { + "layer": 8, + "depth": 34.8, + "top_tokens": [ + "called", + "Champ", + "Caller", + "Doctor", + "Cham", + "呼", + "telephone", + "Von" + ] + }, + { + "layer": 9, + "depth": 39.1, + "top_tokens": [ + "Champ", + "Cham", + "Caller", + "Nome", + "Patent", + "Healing", + "Call", + "Doctor" + ] + }, + { + "layer": 10, + "depth": 43.5, + "top_tokens": [ + "Champ", + "Kansas", + "Healing", + "呼", + "called", + "Nome", + "叫做", + "称之为" + ] + }, + { + "layer": 11, + "depth": 47.8, + "top_tokens": [ + "叫做", + "Kansas", + "Champ", + "Name", + "Nome", + "Lambda", + "Healing", + "Galaxy" + ] + }, + { + "layer": 12, + "depth": 52.2, + "top_tokens": [ + "叫做", + "Lambda", + "Origin", + "Galaxy", + "称之为", + "Cortex", + "Gamma", + "终结" + ] + }, + { + "layer": 13, + "depth": 56.5, + "top_tokens": [ + "Lambda", + "Origin", + "Galaxy", + "Gamma", + "Photon", + "Invocation", + "Oxygen", + "Cortex" + ] + }, + { + "layer": 14, + "depth": 60.9, + "top_tokens": [ + "Lazar", + "Adaptive", + "VX", + "Patent", + "patented", + "Scale", + "Photon", + "Destructor" + ] + }, + { + "layer": 15, + "depth": 65.2, + "top_tokens": [ + "命周期", + "Syndrome", + "Adaptive", + "蓄电池", + "Scale", + "哪种", + "MainAxisAlignment", + "Mutation" + ] + }, + { + "layer": 16, + "depth": 69.6, + "top_tokens": [ + "Structural", + "Syndrome", + "Adaptive", + "Modular", + "Mutation", + "Cata", + "Decom", + "Radiation" + ] + }, + { + "layer": 17, + "depth": 73.9, + "top_tokens": [ + "命周期", + "Adaptive", + "Structural", + "Prototype", + "停产", + "Sequential", + "精益", + "Cata" + ] + }, + { + "layer": 18, + "depth": 78.3, + "top_tokens": [ + "Cata", + "fragmentation", + "mutation", + "Mutation", + "corrosion", + "锈", + "Syndrome", + "catastrophic" + ] + }, + { + "layer": 19, + "depth": 82.6, + "top_tokens": [ + "catastrophic", + "exponential", + "mutation", + "corrosion", + "fragmentation", + "Cata", + "fracture", + "premature" + ] + }, + { + "layer": 20, + "depth": 87.0, + "top_tokens": [ + "fracture", + "断裂", + "brittle", + "deformation", + "rupture", + "fract", + "fractures", + "corrosion" + ] + }, + { + "layer": 21, + "depth": 91.3, + "top_tokens": [ + "fracture", + "deformation", + "brittle", + "fatigue", + "fract", + "plastic", + "stress", + "elastic" + ] + }, + { + "layer": 22, + "depth": 95.7, + "top_tokens": [ + "fatigue", + "fracture", + "plastic", + "brittle", + "stress", + "catastrophic", + "creep", + "elastic" + ] + }, + { + "layer": 23, + "depth": 100.0, + "top_tokens": [ + "brittle", + "fatigue", + "creep", + "plastic", + "progressive", + "catastrophic", + "cyclic", + "the" + ] + } + ], + "surprising": [ + { + "concept": "billions", + "score": 11.0, + "best_rank": 1, + "best_depth": 39.1, + "near_token": "of" + }, + { + "concept": "freight", + "score": 9.0, + "best_rank": 1, + "best_depth": 21.7, + "near_token": "loaded" + }, + { + "concept": "geometric", + "score": 9.0, + "best_rank": 4, + "best_depth": 39.1, + "near_token": "is" + }, + { + "concept": "startling", + "score": 8.0, + "best_rank": 1, + "best_depth": 30.4, + "near_token": "suddenly" + }, + { + "concept": "surfaces", + "score": 7.0, + "best_rank": 1, + "best_depth": 34.8, + "near_token": "surface" + }, + { + "concept": "stuff", + "score": 6.0, + "best_rank": 1, + "best_depth": 17.4, + "near_token": "little" + }, + { + "concept": "modulation", + "score": 6.0, + "best_rank": 1, + "best_depth": 17.4, + "near_token": "mode" + }, + { + "concept": "spacecraft", + "score": 6.0, + "best_rank": 1, + "best_depth": 26.1, + "near_token": "the" + }, + { + "concept": "simulations", + "score": 6.0, + "best_rank": 1, + "best_depth": 82.6, + "near_token": "millions" + }, + { + "concept": "seams", + "score": 6.0, + "best_rank": 1, + "best_depth": 69.6, + "near_token": "at" + }, + { + "concept": "onto", + "score": 6.0, + "best_rank": 1, + "best_depth": 73.9, + "near_token": "loaded" + }, + { + "concept": "thus", + "score": 6.0, + "best_rank": 3, + "best_depth": 17.4, + "near_token": "and" + } + ], + "figures": { + "grid": "figures/qwen2forcausallm/fracture/fracture-fatigue__grid.png", + "stream": "figures/qwen2forcausallm/fracture/fracture-fatigue__stream.png", + "trajectory": "figures/qwen2forcausallm/fracture/fracture-fatigue__trajectory.png", + "emergence": "figures/qwen2forcausallm/fracture/fracture-fatigue__emergence.png", + "heatmap": "figures/qwen2forcausallm/fracture/fracture-fatigue__heatmap.png" + } + }, + { + "slug": "fracture-toughness-chain", + "shape": "MULTIHOP", + "domain": "fracture", + "title": "Stress-intensity factor chain (multi-hop)", + "description": "Chain: geometry+load -> stress intensity K -> compare to K_Ic -> propagate. Track K / toughness / propagate in order.", + "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_tail": "mputes the stress intensity factor at the crack tip, then compares it against the material's fracture toughness. If the first exceeds the second, the crack will", + "readout_at": -1, + "band": [ + 4.3, + 100.0 + ], + "discovered": false, + "tracked_dropped": [], + "emergence": [ + { + "label": "stress", + "best_rank": 0, + "best_depth": 91.3, + "reached_top1": true, + "best_pos": 9 + }, + { + "label": "intensity", + "best_rank": 2, + "best_depth": 4.3, + "reached_top1": false, + "best_pos": 17 + }, + { + "label": "toughness", + "best_rank": 0, + "best_depth": 95.7, + "reached_top1": true, + "best_pos": 31 + }, + { + "label": "propagate", + "best_rank": 0, + "best_depth": 95.7, + "reached_top1": true, + "best_pos": 43 + }, + { + "label": "grow", + "best_rank": 5, + "best_depth": 100.0, + "reached_top1": false, + "best_pos": 43 + }, + { + "label": "unstable", + "best_rank": 341, + "best_depth": 87.0, + "reached_top1": false, + "best_pos": 43 + } + ], + "layer_readouts": [ + { + "layer": 0, + "depth": 0.0, + "top_tokens": [ + "s", + "t", + "names", + "m", + "c", + "sie", + "n", + "l" + ] + }, + { + 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"emergence": "figures/qwen2forcausallm/fracture/fracture-toughness-chain__emergence.png", + "heatmap": "figures/qwen2forcausallm/fracture/fracture-toughness-chain__heatmap.png" + } + }, + { + "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?", + "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_tail": "e 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_at": -1, + "band": [ + 91.3, + 100.0 + ], + "discovered": false, + "tracked_dropped": [ + "shatter" + ], + "emergence": [ + { + "label": "brittle", + "best_rank": 135, + "best_depth": 91.3, + "reached_top1": false, + "best_pos": 35 + }, + { + "label": "fracture", + "best_rank": 35, + "best_depth": 91.3, + "reached_top1": false, + "best_pos": 19 + }, + { + "label": "glass", + "best_rank": 0, + "best_depth": 91.3, + "reached_top1": true, + "best_pos": 5 + }, + { + "label": "crack", + "best_rank": 15, + "best_depth": 100.0, + "reached_top1": false, + "best_pos": 19 + } + ], + "layer_readouts": [ + { + "layer": 0, + "depth": 0.0, + "top_tokens": [ + "Rarity", + "Buff", + "Pai", + "atoi", + "Slots", + "Gems", + "鸨", + "Cats" + ] + }, + { + "layer": 1, + "depth": 4.3, + "top_tokens": [ + "Hak", + "Horse", + "Buff", + "Bug", + "Caval", + 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Contrast partner to the brittle vignette: expect 'ductile'/'plastic'/'neck'.", + "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_tail": " 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_at": -1, + "band": [ + 40.0, + 88.0 + ], + "discovered": false, + "tracked_dropped": [ + "ductile", + "necking" + ], + "emergence": [ + { + "label": "plastic", + "best_rank": 12, + "best_depth": 82.6, + "reached_top1": false, + "best_pos": 24 + }, + { + "label": "yield", + "best_rank": 234, + "best_depth": 95.7, + "reached_top1": false, + "best_pos": 8 + }, + { + "label": "deformation", + "best_rank": 29, + "best_depth": 34.8, + "reached_top1": false, + "best_pos": 20 + } + ], + "layer_readouts": [ + { + "layer": 0, + "depth": 0.0, + "top_tokens": [ + "Rarity", + "Buff", + "Pai", + "atoi", + "Profiler", + "鸨", + "MutableList", + "Chance" + ] + }, + { + "layer": 1, + "depth": 4.3, + "top_tokens": [ + "Hak", + "Horse", + "Buff", + "General", + "Main", + "Caval", + "Com", + "Bug" + ] + }, + { + "layer": 2, + "depth": 8.7, + 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Answer in one word.<|im_end|>\n<|im_start|>assistant\n", + "readout_at": -1, + "band": [ + 82.6, + 87.0 + ], + "discovered": false, + "tracked_dropped": [ + "buckling" + ], + "emergence": [ + { + "label": "fatigue", + "best_rank": 65, + "best_depth": 91.3, + "reached_top1": false, + "best_pos": 40 + }, + { + "label": "creep", + "best_rank": 323, + "best_depth": 100.0, + "reached_top1": false, + "best_pos": 40 + }, + { + "label": "corrosion", + "best_rank": 4, + "best_depth": 82.6, + "reached_top1": false, + "best_pos": 40 + }, + { + "label": "fracture", + "best_rank": 2, + "best_depth": 87.0, + "reached_top1": false, + "best_pos": 40 + } + ], + "layer_readouts": [ + { + "layer": 0, + "depth": 0.0, + "top_tokens": [ + "炸弹", + "ustin", + "chedulers", + "rots", + "entence", + "igers", + "ents", + "Ymd" + ] + }, + { + "layer": 1, + "depth": 4.3, + "top_tokens": [ + "s", + "PN", + "VN", + "Wings", + "like", + "兵", + "about", + "sim" + ] + }, + { + "layer": 2, + "depth": 8.7, + "top_tokens": 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Does the lens surface 'collagen'/'triple'/'helix' from the motif alone?", + "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_tail": "What is this? 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Expect 'membrane'/'hydrophobic'/'transmembrane'/'helix'.", + "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_tail": "What is this? 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Does 'amyloid'/'aggregate'/'fibril' rise without being named?", + "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_tail": " 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_at": -1, + "band": [ + 40.0, + 88.0 + ], + "discovered": false, + "tracked_dropped": [ + "amyloid", + "fibril", + "misfold" + ], + "emergence": [ + { + "label": "aggregate", + "best_rank": 152, + "best_depth": 100.0, + "reached_top1": false, + "best_pos": 5 + }, + { + "label": "beta", + "best_rank": 54, + "best_depth": 95.7, + "reached_top1": false, + "best_pos": 4 + }, + { + "label": "plaque", + "best_rank": 2, + "best_depth": 91.3, + "reached_top1": false, + "best_pos": 30 + } + ], + "layer_readouts": [ + { + "layer": 0, + "depth": 0.0, + "top_tokens": [ + "Rarity", + "Buff", + "Pai", + "atoi", + "鸨", + "Profiler", + "MutableList", + "flere" + ] + }, + { + "layer": 1, + "depth": 4.3, + "top_tokens": [ + "Hak", + "Horse", + "Caval", + "Buff", + "Main", + "Bug", + "General", + "Com" + ] + }, + { + "layer": 2, + "depth": 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Answer in one word.' Swap the chosen residue's lens vector for a sibling; check the report changes.", + "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.", + "prompt_tail": ", created by Alibaba Cloud. You are a helpful assistant.<|im_end|>\n<|im_start|>user\nThink of an amino acid. Answer in one word.<|im_end|>\n<|im_start|>assistant\n", + "readout_at": -1, + "band": [ + 40.0, + 88.0 + ], + "discovered": false, + "tracked_dropped": [ + "glycine", + "alanine", + "leucine", + "serine", + "proline" + ], + "emergence": [], + "layer_readouts": [ + { + "layer": 0, + "depth": 0.0, + "top_tokens": [ + "炸弹", + "ustin", + "chedulers", + "rots", + "entence", + "igers", + "ents", + "Ymd" + ] + }, + { + "layer": 1, + "depth": 4.3, + "top_tokens": [ + "s", + "PN", + "Wings", + "VN", + "like", + "about", + "兵", + "sim" + ] + }, + { + "layer": 2, + "depth": 8.7, + "top_tokens": [ + "like", + "like", + "weed", + "LIKE", + "tent", + "columns", + "s", + "Language" + ] + }, + { + "layer": 3, + "depth": 13.0, + "top_tokens": [ + "fortunes", + "hire", + "folio", + "like", + "Songs", + "Chin", + "Ymd", + "sit" + ] + }, + { + "layer": 4, + "depth": 17.4, + "top_tokens": [ + "pls", + "CW", + "NW", + "like", + "wand", + "python", + "vue", + "lvl" + ] + }, + { + "layer": 5, + "depth": 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"喏" + ] + }, + { + "layer": 12, + "depth": 52.2, + "top_tokens": [ + "词语", + "词汇", + "unicorn", + "单词", + "勇气", + "精准", + "İşte", + "驭" + ] + }, + { + "layer": 13, + "depth": 56.5, + "top_tokens": [ + "unicorn", + "智慧", + "expérience", + "İşte", + "勇气", + "moduleName", + "opération", + "ModuleName" + ] + }, + { + "layer": 14, + "depth": 60.9, + "top_tokens": [ + "unicorn", + "İşte", + "typeName", + "glyph", + "神秘", + "oteric", + "moduleName", + "woord" + ] + }, + { + "layer": 15, + "depth": 65.2, + "top_tokens": [ + "unicorn", + "品种", + "typeName", + "İşte", + "Species", + "moduleName", + "有色金属", + "woord" + ] + }, + { + "layer": 16, + "depth": 69.6, + "top_tokens": [ + "unicorn", + "typeName", + "品种", + "黢", + "Species", + "CustomerId", + "Armor", + "GraphQL" + ] + }, + { + "layer": 17, + "depth": 73.9, + "top_tokens": [ + "unicorn", + "Python", + "品种", + "Species", + "Unknown", + "species", + "Oxygen", + "Knife" + ] + }, + { + "layer": 18, + "depth": 78.3, + "top_tokens": [ + 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"best_rank": 2, + "best_depth": 52.2, + "near_token": "user" + }, + { + "concept": "someone", + "score": 3.0, + "best_rank": 9, + "best_depth": 52.2, + "near_token": "." + } + ], + "figures": { + "grid": "figures/qwen2forcausallm/protein/protein-report-swap__grid.png", + "stream": "figures/qwen2forcausallm/protein/protein-report-swap__stream.png" + } + } + ] +} \ No newline at end of file diff --git a/jlens_materials/runs/qwen2forcausallm_analysis.json b/jlens_materials/runs/qwen2forcausallm_analysis.json new file mode 100644 index 0000000000000000000000000000000000000000..9d8563c2bf18820a6ecb3d60bb6ccba6dd0e768c --- /dev/null +++ b/jlens_materials/runs/qwen2forcausallm_analysis.json @@ -0,0 +1,137 @@ +{ + "model": "Qwen/Qwen2.5-0.5B-Instruct", + "provider": "anthropic", + "analysis_model": "claude-opus-4-8", + "offline": true, + "per_prompt": [ + { + "slug": "fracture-griffith", + "title": "Griffith criterion (multi-hop)", + "shape": "MULTIHOP", + "domain": "fracture", + "model_used": "offline", + "analysis": "[offline template] Concepts reaching lens rank 1: 'energy' (depth 43.5%), 'surface' (depth 0.0%), 'crack' (depth 100.0%). Concepts reaching the lens top-5: 'energy' (rank 1, depth 43.5%), 'surface' (rank 1, depth 0.0%), 'crack' (rank 1, depth 100.0%). Hidden thoughts (surfaced in the workspace band, not in the prompt or output): computational, mathematical, density, gravitational, deformation, magnitude." + }, + { + "slug": "fracture-fatigue", + "title": "Cyclic loading -> failure mode (multi-hop)", + "shape": "MULTIHOP", + "domain": "fracture", + "model_used": "offline", + "analysis": "[offline template] Concepts reaching lens rank 1: 'fatigue' (depth 95.7%). Concepts reaching the lens top-5: 'fatigue' (rank 1, depth 95.7%), 'crack' (rank 3, depth 100.0%), 'cyclic' (rank 3, depth 17.4%). Not trackable (multi-token, paper limitation section 9.1): striations. Hidden thoughts (surfaced in the workspace band, not in the prompt or output): billions, freight, geometric, startling, surfaces, stuff." + }, + { + "slug": "fracture-toughness-chain", + "title": "Stress-intensity factor chain (multi-hop)", + "shape": "MULTIHOP", + "domain": "fracture", + "model_used": "offline", + "analysis": "[offline template] Concepts reaching lens rank 1: 'stress' (depth 91.3%), 'toughness' (depth 95.7%), 'propagate' (depth 95.7%). Concepts reaching the lens top-5: 'stress' (rank 1, depth 91.3%), 'intensity' (rank 3, depth 4.3%), 'toughness' (rank 1, depth 95.7%), 'propagate' (rank 1, depth 95.7%). Hidden thoughts (surfaced in the workspace band, not in the prompt or output): computational, density, calculations, thresholds, measurements, weld." + }, + { + "slug": "fracture-brittle-assoc", + "title": "Brittle fracture (association, unnamed)", + "shape": "ASSOCIATION", + "domain": "fracture", + "model_used": "offline", + "analysis": "[offline template] Concepts reaching lens rank 1: 'glass' (depth 91.3%). Concepts reaching the lens top-5: 'glass' (rank 1, depth 91.3%). Not trackable (multi-token, paper limitation section 9.1): shatter. Hidden thoughts (surfaced in the workspace band, not in the prompt or output): hitting, noticing, keyboard, knives, bottom, piercing." + }, + { + "slug": "fracture-ductile-assoc", + "title": "Ductile failure (association, unnamed)", + "shape": "ASSOCIATION", + "domain": "fracture", + "model_used": "offline", + "analysis": "[offline template] No tracked concept reached the lens top-5 at any depth — either the model lacks these concepts or they are multi-token and untrackable. Not trackable (multi-token, paper limitation section 9.1): ductile, necking. Hidden thoughts (surfaced in the workspace band, not in the prompt or output): climbing, slowing, fibers, anymore, taller, into." + }, + { + "slug": "fracture-stress-recognition", + "title": "Stress state from a tensor (recognition)", + "shape": "RECOGNITION", + "domain": "fracture", + "model_used": "offline", + "analysis": "[offline template] Concepts reaching the lens top-5: 'mode' (rank 4, depth 100.0%). Not trackable (multi-token, paper limitation section 9.1): tensile, uniaxial. Hidden thoughts (surfaced in the workspace band, not in the prompt or output): sqrt, computational, blackColor, TeX, mathematical, velocities." + }, + { + "slug": "fracture-modulation", + "title": "Hold 'crack-tip plasticity' while copying (modulation)", + "shape": "MODULATION", + "domain": "fracture", + "model_used": "offline", + "analysis": "[offline template] Concepts reaching lens rank 1: 'crack' (depth 17.4%), 'plastic' (depth 8.7%). Concepts reaching the lens top-5: 'crack' (rank 1, depth 17.4%), 'plastic' (rank 1, depth 8.7%). Not trackable (multi-token, paper limitation section 9.1): plasticity. Hidden thoughts (surfaced in the workspace band, not in the prompt or output): Writing, labs, things, yourself, Flying, board." + }, + { + "slug": "fracture-report-swap", + "title": "Think of a failure mode -> swap (verbal report)", + "shape": "REPORT_SWAP", + "domain": "fracture", + "model_used": "offline", + "analysis": "[offline template] Concepts reaching the lens top-5: 'corrosion' (rank 5, depth 82.6%), 'fracture' (rank 3, depth 87.0%). Not trackable (multi-token, paper limitation section 9.1): buckling. Hidden thoughts (surfaced in the workspace band, not in the prompt or output): Mechanical, Entertainment, humanoid, Describe, involving, words." + }, + { + "slug": "protein-helix-multihop", + "title": "Backbone H-bonds -> secondary structure (multi-hop)", + "shape": "MULTIHOP", + "domain": "protein", + "model_used": "offline", + "analysis": "[offline template] Concepts reaching lens rank 1: 'hydrogen' (depth 30.4%), 'bond' (depth 13.0%), 'backbone' (depth 4.3%). Concepts reaching the lens top-5: 'hydrogen' (rank 1, depth 30.4%), 'bond' (rank 1, depth 13.0%), 'backbone' (rank 1, depth 4.3%). Not trackable (multi-token, paper limitation section 9.1): helix. Hidden thoughts (surfaced in the workspace band, not in the prompt or output): rst, scientific, thus, outcomes, RequestOptions, geometric." + }, + { + "slug": "protein-gfp-recognition", + "title": "GFP sequence -> function (recognition)", + "shape": "RECOGNITION", + "domain": "protein", + "model_used": "offline", + "analysis": "[offline template] No tracked concept reached the lens top-5 at any depth — either the model lacks these concepts or they are multi-token and untrackable. Hidden thoughts (surfaced in the workspace band, not in the prompt or output): Moscow, Secondly, Answers, phrase, Awards, RequestOptions." + }, + { + "slug": "protein-collagen-recognition", + "title": "Gly-X-Y repeat -> collagen (recognition)", + "shape": "RECOGNITION", + "domain": "protein", + "model_used": "offline", + "analysis": "[offline template] No tracked concept reached the lens top-5 at any depth — either the model lacks these concepts or they are multi-token and untrackable. Not trackable (multi-token, paper limitation section 9.1): helix, glycine, proline, fibril. Hidden thoughts (surfaced in the workspace band, not in the prompt or output): Chemical, Toolbox, Secondly, Answers, phrase, RequestOptions." + }, + { + "slug": "protein-transmembrane-recognition", + "title": "Hydrophobic stretch -> transmembrane (recognition)", + "shape": "RECOGNITION", + "domain": "protein", + "model_used": "offline", + "analysis": "[offline template] No tracked concept reached the lens top-5 at any depth — either the model lacks these concepts or they are multi-token and untrackable. Not trackable (multi-token, paper limitation section 9.1): hydrophobic, transmembrane, helix. Hidden thoughts (surfaced in the workspace band, not in the prompt or output): Secondly, Answers, phrase, Logo, RequestOptions, LAG." + }, + { + "slug": "protein-amyloid-assoc", + "title": "Amyloid aggregation (association, unnamed)", + "shape": "ASSOCIATION", + "domain": "protein", + "model_used": "offline", + "analysis": "[offline template] Concepts reaching the lens top-5: 'plaque' (rank 3, depth 91.3%). Not trackable (multi-token, paper limitation section 9.1): amyloid, fibril, misfold. Hidden thoughts (surfaced in the workspace band, not in the prompt or output): onto, plates, fibers, wires, stacks, weren." + }, + { + "slug": "protein-fold-multihop", + "title": "Hydrophobic collapse -> tertiary fold (multi-hop)", + "shape": "MULTIHOP", + "domain": "protein", + "model_used": "offline", + "analysis": "[offline template] Concepts reaching lens rank 1: 'native' (depth 100.0%). Concepts reaching the lens top-5: 'structure' (rank 3, depth 100.0%), 'native' (rank 1, depth 100.0%). Not trackable (multi-token, paper limitation section 9.1): hydrophobic." + }, + { + "slug": "protein-modulation", + "title": "Hold 'disulfide bond' while copying (modulation)", + "shape": "MODULATION", + "domain": "protein", + "model_used": "offline", + "analysis": "[offline template] Concepts reaching lens rank 1: 'bond' (depth 91.3%), 'sulfur' (depth 91.3%). Concepts reaching the lens top-5: 'bridge' (rank 3, depth 100.0%), 'bond' (rank 1, depth 91.3%), 'sulfur' (rank 1, depth 91.3%). Not trackable (multi-token, paper limitation section 9.1): disulfide, cysteine. Hidden thoughts (surfaced in the workspace band, not in the prompt or output): using, intelligent, because." + }, + { + "slug": "protein-report-swap", + "title": "Think of an amino acid -> swap (verbal report)", + "shape": "REPORT_SWAP", + "domain": "protein", + "model_used": "offline", + "analysis": "[offline template] No tracked concept reached the lens top-5 at any depth — either the model lacks these concepts or they are multi-token and untrackable. Not trackable (multi-token, paper limitation section 9.1): glycine, alanine, leucine, serine, proline. Hidden thoughts (surfaced in the workspace band, not in the prompt or output): Brain, Describe, software, Studio, words, Tencent." + } + ], + "synthesis": "[offline template] Run over Qwen/Qwen2.5-0.5B-Instruct (24 layers). By experiment type — MULTIHOP: 5/5 prompts with a top-5 concept; ASSOCIATION: 2/3 prompts with a top-5 concept; RECOGNITION: 1/4 prompts with a top-5 concept; MODULATION: 2/2 prompts with a top-5 concept; REPORT_SWAP: 1/2 prompts with a top-5 concept. Most frequent unprompted 'hidden thoughts' across prompts: computational (×3), density (×2), mathematical (×2), fibers (×3), thresholds (×2), Secondly (×6), Writing (×1), geometric (×3), labs (×1), onto (×2), surfaces (×2), gravitational (×1)." +} \ No newline at end of file diff --git a/jlens_materials/runs/qwen2forcausallm_swaps.json b/jlens_materials/runs/qwen2forcausallm_swaps.json new file mode 100644 index 0000000000000000000000000000000000000000..2d2dcbe6d8eef6f689e863b84271b98d9f098af7 --- /dev/null +++ b/jlens_materials/runs/qwen2forcausallm_swaps.json @@ -0,0 +1,175 @@ +{ + "created_at": "2026-07-08T02:51:29", + "run": "jlens_materials/runs/qwen2forcausallm.json", + "model": "Qwen/Qwen2.5-0.5B-Instruct", + "lens": "/Users/mbuehler/LOCALCODES/Substrates/jlens_materials/lens_qwen2forcausallm.pt", + "method": "pinv", + "tag": "qwen2forcausallm", + "provider": "offline", + "analysis_model": null, + "proposals": [ + { + "slug": "fracture-report-swap", + "title": "Think of a failure mode -> swap (verbal report)", + "domain": "fracture", + "shape": "REPORT_SWAP", + "source": "fracture", + "target": "fatigue", + "source_rank": 3, + "source_depth": 87.0, + "rationale": "'fracture' surfaced strongly in the run (rank 3 at depth 87.0%). 'fatigue' is a same-domain counterfactual target.", + "expected": "The swap should suppress 'fracture', install 'fatigue', and move the next-token distribution toward the target.", + "confidence": 0.89, + "provider": "offline", + "target_token_id": 35609, + "source_token_id": 58804 + } + ], + "approved": [ + { + "slug": "fracture-report-swap", + "title": "Think of a failure mode -> swap (verbal report)", + "domain": "fracture", + "shape": "REPORT_SWAP", + "source": "fracture", + "target": "fatigue", + "source_rank": 3, + "source_depth": 87.0, + "rationale": "'fracture' surfaced strongly in the run (rank 3 at depth 87.0%). 'fatigue' is a same-domain counterfactual target.", + "expected": "The swap should suppress 'fracture', install 'fatigue', and move the next-token distribution toward the target.", + "confidence": 0.89, + "provider": "offline", + "target_token_id": 35609, + "source_token_id": 58804 + } + ], + "results": [ + { + "proposal": { + "slug": "fracture-report-swap", + "title": "Think of a failure mode -> swap (verbal report)", + "domain": "fracture", + "shape": "REPORT_SWAP", + "source": "fracture", + "target": "fatigue", + "source_rank": 3, + "source_depth": 87.0, + "rationale": "'fracture' surfaced strongly in the run (rank 3 at depth 87.0%). 'fatigue' is a same-domain counterfactual target.", + "expected": "The swap should suppress 'fracture', install 'fatigue', and move the next-token distribution toward the target.", + "confidence": 0.89, + "provider": "offline", + "target_token_id": 35609, + "source_token_id": 58804 + }, + "alphas": [ + { + "alpha": 1.0, + "swap": { + "prompt": "<|im_start|>system\nYou are Qwen, created by Alibaba Cloud. You are a helpful assistant.<|im_end|>\n<|im_start|>user\nThink of a metal failure mode. Answer in one word.<|im_end|>\n<|im_start|>assistant\n", + "source": "fracture", + "target": "fatigue", + "alpha": 1.0, + "method": "pinv", + "band_layers": [ + 10, + 11, + 12, + 13, + 14, + 15, + 16, + 17, + 18, + 19, + 20 + ], + "clean_top": [ + [ + "De", + 0.2316262274980545 + ], + [ + "Cr", + 0.12487851828336716 + ], + [ + "F", + 0.0514170303940773 + ], + [ + "Brit", + 0.03872358053922653 + ], + [ + "Fr", + 0.03694424033164978 + ], + [ + "Mel", + 0.027049869298934937 + ], + [ + "Impact", + 0.022980516776442528 + ], + [ + "Break", + 0.022165875881910324 + ] + ], + "swapped_top": [ + [ + "De", + 0.33347177505493164 + ], + [ + "fatigue", + 0.17106041312217712 + ], + [ + "Fat", + 0.08845178037881851 + ], + [ + "疲劳", + 0.03729505091905594 + ], + [ + "F", + 0.033300094306468964 + ], + [ + "D", + 0.028340017423033714 + ], + [ + "P", + 0.022221265360713005 + ], + [ + "Pl", + 0.018454940989613533 + ] + ], + "source_rank_clean": 71, + "source_rank_swapped": 4265, + "target_rank_clean": 85, + "target_rank_swapped": 1 + }, + "metrics": { + "source_suppressed": true, + "target_installed": true, + "target_top1": false, + "target_top5": true, + "top_changed": false, + "source_rank_delta": 4194, + "target_rank_delta": 84 + } + } + ] + } + ], + "plots": [ + "figures/qwen2forcausallm/swaps/fracture-report-swap__swap_effect.png" + ] +} \ No newline at end of file diff --git a/jlens_materials/scripts/amend_answer_code_binding_protocol.py b/jlens_materials/scripts/amend_answer_code_binding_protocol.py new file mode 100644 index 0000000000000000000000000000000000000000..b5a3602d98fdf1168b1b9bd0a2543e889d8bed77 --- /dev/null +++ b/jlens_materials/scripts/amend_answer_code_binding_protocol.py @@ -0,0 +1,75 @@ +#!/usr/bin/env python3 +"""Record the pre-forward contextual-tokenization amendment, exactly once.""" + +from __future__ import annotations + +import json +from datetime import datetime, timezone +from pathlib import Path + +import freeze_lexical_adversarial_representation as shared + +ROOT = Path(__file__).resolve().parents[1] +OUT = ROOT / "experiments" / "answer-code-binding-2026-07-17" +ORIGINAL = OUT / "protocol.json" +AMENDED = OUT / "protocol-amendment-v1.json" +RUNNER = ROOT / "scripts" / "run_answer_code_binding.py" +ATTEMPT = OUT / "execution-attempt-1-pre-forward.json" + + +def main() -> None: + if AMENDED.exists() or ATTEMPT.exists(): + raise FileExistsError("Amendment record already exists; do not overwrite.") + protocol = json.loads(ORIGINAL.read_text()) + original_hash = shared.sha256(ORIGINAL) + original_runner_hash = protocol["inputs"]["runner_sha256"] + protocol["status"] = ( + "prospectively frozen scientific design; protocol amendment v1 was " + "made after execution attempt 1 aborted before any model forward pass" + ) + protocol["amendment"] = { + "amended_at": datetime.now(timezone.utc).isoformat(), + "original_protocol": str(ORIGINAL.relative_to(ROOT)), + "original_protocol_sha256": original_hash, + "reason": ( + "The isolated token `checkpoint` has id 73093, whereas the exact " + "in-prompt substring ` checkpoint` has id 61077. Attempt 1 searched " + "for 73093, found zero matches, and raised before model.forward." + ), + "scientific_design_changed": False, + "prompts_changed": False, + "endpoints_changed": False, + "only_change": ( + "Match the already-frozen marker in its exact contextual " + "tokenization, ` checkpoint`." + ), + "attempt_record": str(ATTEMPT.relative_to(ROOT)), + } + protocol["checkpoint_token_text"] = " checkpoint" + protocol["tokenization_preflight"][" checkpoint"] = [61077] + protocol["inputs"]["runner_sha256_before_amendment"] = original_runner_hash + protocol["inputs"]["runner_sha256"] = shared.sha256(RUNNER) + shared.dump(AMENDED, protocol) + shared.dump(ATTEMPT, { + "study_id": protocol["study_id"], + "attempt": 1, + "outcome": "aborted before any model forward pass", + "original_protocol_sha256": original_hash, + "original_runner_sha256": original_runner_hash, + "model_loaded": True, + "n_prompts_forwarded": 0, + "error": ( + "First prompt had zero matches for isolated checkpoint token id " + "73093; contextual token id is 61077." + ), + "retained_because": ( + "Pre-forward failures and amendments are part of the complete " + "prospective record." + ), + }) + print(f"wrote {AMENDED.relative_to(ROOT)}") + print(f"amended protocol sha256: {shared.sha256(AMENDED)}") + + +if __name__ == "__main__": + main() diff --git a/jlens_materials/scripts/analyze_abstract_physics_semantic.py b/jlens_materials/scripts/analyze_abstract_physics_semantic.py new file mode 100644 index 0000000000000000000000000000000000000000..d38d6a1cd52683f4b9a3af2ded712eaa81a39097 --- /dev/null +++ b/jlens_materials/scripts/analyze_abstract_physics_semantic.py @@ -0,0 +1,371 @@ +#!/usr/bin/env python3 +"""Analyze the semantic-answer abstract-physics replication.""" + +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 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, + contrast_parallelism, + discovery_ccgp, + exact_signflip, + normalize, + replication_law_scores, + safe_dot, +) + + +ROOT = Path(__file__).resolve().parents[1] +OUT = ROOT / "experiments" / "abstract-physics-semantic-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 main() -> None: + protocol = json.loads(PROTOCOL.read_text()) + manifest = json.loads(MANIFEST.read_text()) + raw = json.loads(RAW.read_text()) + for name in ("prompt_manifest", "analyzer"): + row = protocol["inputs"][name] + if sha256(ROOT / row["path"]) != row["sha256"]: + raise RuntimeError(f"{name} fingerprint mismatch") + if raw["provenance"]["protocol_sha256"] != sha256(PROTOCOL): + raise RuntimeError("raw protocol fingerprint mismatch") + if raw["provenance"]["states_sha256"] != sha256(STATES): + raise RuntimeError("state archive fingerprint mismatch") + + 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) + by_id = {row["prompt_id"]: row for row in manifest["prompts"]} + rows = [by_id[prompt_id] for prompt_id in prompt_ids] + 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]), + "answer_order": np.asarray([row["answer_order"] for row in rows]), + } + if positions.tolist() != protocol["positions"]: + raise RuntimeError("position mismatch") + depths = 100.0 * layers / max(int(layers.max()), 1) + decision_index = int(np.flatnonzero(positions == "decision")[0]) + + layer_rows = [] + 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"): + 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]) + values = normalize(states[decision_index, :, selected_index]) + + law_rows = [] + for target in ("physical", "numeric"): + for row in replication_law_scores(values, meta[target], meta): + law_rows.append({"target": target, **row}) + law_frame = pd.DataFrame(law_rows) + physical_laws = law_frame[law_frame["target"] == "physical"].copy() + aucs = physical_laws["auc"].to_numpy(float) + ci = bootstrap( + aucs, + np.random.default_rng(protocol["bootstrap"]["seed"]), + protocol["bootstrap"]["resamples"], + ) + + _, parallel = contrast_parallelism(values, meta, "replication") + cross = parallel.loc[ + ~parallel["same_orientation"], "physical_contrast_cosine" + ].to_numpy(float) + cross_ci = bootstrap( + cross, + np.random.default_rng(protocol["bootstrap"]["seed"] + 1), + protocol["bootstrap"]["resamples"], + ) + + formula_rows = [] + discovery = meta["split"] == "discovery" + replication = meta["split"] == "replication" + for train_surface, test_surface in (("a", "b"), ("b", "a")): + for target in ("physical", "numeric"): + train = discovery & (meta["surface"] == train_surface) + direction, midpoint = centroid_direction( + 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(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 = pd.DataFrame(formula_rows) + + texts = [row["user"].split("†", maxsplit=1)[0] + "†" for row in rows] + control_rows = [] + 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(): + control_values = normalize( + vectorizer.fit_transform(texts).toarray().astype(np.float64) + ) + for target in ("physical", "numeric"): + for row in replication_law_scores( + control_values, meta[target], meta + ): + control_rows.append({"method": method, "target": target, **row}) + controls = pd.DataFrame(control_rows) + + behavior = pd.DataFrame(raw["clean_rows"]) + overall_accuracy = float(behavior["registered_answer_correct"].mean()) + rule = protocol["success_rule"] + 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(aucs.mean()), + "replication_law_bootstrap_95": list(ci), + "replication_laws_above_chance": int(np.sum(aucs > 0.5)), + "replication_laws_total": int(len(aucs)), + "two_sided_exact_signflip_p_auc_minus_half": exact_signflip(aucs - 0.5), + "behavior_accuracy": overall_accuracy, + } + primary["passes_frozen_rule"] = bool( + overall_accuracy >= rule["behavior_accuracy_minimum"] + and aucs.mean() >= rule["mean_replication_auc_minimum"] + and ci[0] > rule["bootstrap_95_lower_above"] + and np.sum(aucs > 0.5) >= rule["positive_replication_laws_minimum"] + ) + + formula_physical = float( + formula.loc[formula["target"] == "physical", "auc"].mean() + ) + control_summary = { + f"{method}_{target}": float( + controls.loc[ + (controls["method"] == method) + & (controls["target"] == target), + "auc", + ].mean() + ) + for method in sorted(set(controls["method"])) + for target in ("physical", "numeric") + } + statistics = { + "study_id": protocol["study_id"], + "protocol_sha256": sha256(PROTOCOL), + "input_fingerprints": { + "manifest": sha256(MANIFEST), + "raw": sha256(RAW), + "states": sha256(STATES), + }, + "primary": primary, + "behavior": { + "overall_accuracy": overall_accuracy, + "discovery_accuracy": float( + behavior.loc[ + behavior["split"] == "discovery", + "registered_answer_correct", + ].mean() + ), + "replication_accuracy": float( + behavior.loc[ + behavior["split"] == "replication", + "registered_answer_correct", + ].mean() + ), + "law_accuracy": { + str(key): float(value) + for key, value in behavior.groupby("law_id")[ + "registered_answer_correct" + ].mean().items() + }, + }, + "parallelism": { + "cross_orientation_mean_physical_contrast_cosine": float( + cross.mean() + ), + "cross_orientation_pair_bootstrap_95": list(cross_ci), + "positive_cross_orientation_pairs": int(np.sum(cross > 0)), + "cross_orientation_pairs": int(len(cross)), + }, + "formula_cross_generalization_physical_auc": formula_physical, + "tfidf_controls": control_summary, + "interpretation_boundary": ( + "Passing would establish an answer-vocabulary-free, cross-law " + "physical-outcome geometry for supplied monotonic equations. " + "Causal use and equation-free natural-language physics remain " + "separate questions." + ), + } + (OUT / "statistics.json").write_text( + json.dumps(statistics, indent=2, sort_keys=True) + "\n" + ) + layer_frame.to_csv(OUT / "layerwise_ccgp.csv", index=False) + law_frame.to_csv(OUT / "replication_law_auc.csv", index=False) + parallel.to_csv(OUT / "replication_parallelism_pairs.csv", index=False) + formula.to_csv(OUT / "formula_cross_generalization.csv", index=False) + controls.to_csv(OUT / "tfidf_controls.csv", index=False) + behavior.to_csv(OUT / "clean_behavior.csv", index=False) + + direction, midpoint = centroid_direction( + values[discovery], meta["physical"][discovery] + ) + numeric_direction, numeric_midpoint = centroid_direction( + values[discovery], meta["numeric"][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), + ) + + FIG.mkdir(parents=True, exist_ok=True) + fig, axes = plt.subplots(1, 3, figsize=(11.0, 3.35)) + for target, color in (("physical", "#16697A"), ("numeric", "#B56576")): + part = layer_frame[ + (layer_frame["position"] == "decision") + & (layer_frame["target"] == target) + ] + axes[0].plot( + part["depth_percent"], + part["discovery_ccgp"], + marker="o", + markersize=2.5, + label=target.capitalize(), + color=color, + ) + axes[0].axhline(0.5, color="#888888", linestyle=":", linewidth=1) + axes[0].axvline( + primary["selected_depth_percent"], + color="#555555", + linestyle="--", + linewidth=1, + ) + axes[0].set(xlabel="Layer depth (%)", ylabel="Discovery CCGP (AUC)") + axes[0].legend(frameon=False, fontsize=8) + order = physical_laws.sort_values(["test_orientation", "auc"]) + axes[1].barh( + order["law_id"], + order["auc"], + color=[ + "#5A7D8C" if value == "direct" else "#7A6AA2" + for value in order["test_orientation"] + ], + ) + axes[1].axvline(0.5, color="#555555", linestyle="--", linewidth=1) + axes[1].set(xlim=(0, 1), xlabel="Held-out physical AUC") + law_accuracy = behavior.groupby("law_id")[ + "registered_answer_correct" + ].mean().reindex(order["law_id"]) + axes[2].barh( + law_accuracy.index, + law_accuracy.values, + color="#5B8C5A", + ) + axes[2].axvline(0.5, color="#555555", linestyle=":", linewidth=1) + axes[2].set(xlim=(0, 1), xlabel="Clean answer accuracy") + fig.tight_layout() + for suffix in ("png", "pdf"): + fig.savefig(FIG / f"abstract-physics-semantic.{suffix}", dpi=300) + plt.close(fig) + + report = [ + "# Semantic-answer abstract-physics result", + "", + "## Frozen primary endpoint", + "", + f"- Clean higher/lower accuracy: **{overall_accuracy:.1%}**.", + 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"- Replication mean held-out-law AUC: **{aucs.mean():.3f}** " + f"(95% law bootstrap {ci[0]:.3f} to {ci[1]:.3f}).", + f"- Laws above chance: **{int(np.sum(aucs > 0.5))}/8**; " + f"exact sign-flip `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", + "", + f"- Cross-form physical AUC: **{formula_physical:.3f}**.", + f"- Cross-orientation physical-contrast cosine: " + f"**{cross.mean():+.3f}**.", + f"- Word TF-IDF physical AUC: " + f"**{control_summary['word_tfidf_physical']:.3f}**.", + f"- Character TF-IDF physical AUC: " + f"**{control_summary['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_candidate_nonsteering.py b/jlens_materials/scripts/analyze_candidate_nonsteering.py new file mode 100644 index 0000000000000000000000000000000000000000..e1d7052097908cd282cb93fe7a646bf2a20f3ab4 --- /dev/null +++ b/jlens_materials/scripts/analyze_candidate_nonsteering.py @@ -0,0 +1,813 @@ +#!/usr/bin/env python3 +"""Run frozen candidate non-steering analyses for the materials J-lens paper.""" + +from __future__ import annotations + +import hashlib +import itertools +import json +import math +import re +from collections import Counter, defaultdict +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 import stats # noqa: E402 + + +ROOT = Path(__file__).resolve().parents[1] +OUT = ROOT / "experiments" / "candidate-nonsteering-2026-07-16" +FIG = OUT / "figures" +PROTOCOL_PATH = OUT / "protocol.json" +STATS_PATH = ROOT / "experiments" / "materials-heldout-v1_statistics.json" +NPZ_PATH = ROOT / "experiments" / "materials-heldout-v1_latent_vectors.npz" +META_PATH = ROOT / "experiments" / "materials-heldout-v1_latent_vectors.meta.json" +RUN_PATHS = [ROOT / "runs" / f"gemma4-e4b-it-heldout-v1-seed{i}.json" for i in range(3)] +SEED = 20260716 +N_BOOT = 30_000 +N_CLASS_PERM = 10_000 +N_RSA_PERM = 30_000 +N_ROLE_PERM = 10_000 +BAND = (38.0, 92.0) + + +def sha256(path: Path) -> str: + return hashlib.sha256(path.read_bytes()).hexdigest() + + +def dump_json(path: Path, payload: object) -> None: + path.write_text(json.dumps(payload, indent=2, allow_nan=False) + "\n") + + +def normalize_rows(values: np.ndarray) -> np.ndarray: + values = np.asarray(values, dtype=float) + norm = np.linalg.norm(values, axis=-1, keepdims=True) + return values / np.maximum(norm, 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], mode: str) -> bool: + if mode == "all": + return False + if token in prompt_words: + return True + if mode == "prompt_exact_removed": + return False + if mode != "prompt_morphology_removed": + raise ValueError(mode) + return any( + min(len(token), len(word)) >= 5 + and (token.startswith(word) or word.startswith(token)) + for word in prompt_words + ) + + +def validate_inputs() -> tuple[dict, list[dict], dict]: + protocol = json.loads(PROTOCOL_PATH.read_text()) + expected = protocol["inputs"] + checks = { + STATS_PATH: expected["heldout_statistics_sha256"], + NPZ_PATH: expected["latent_vectors_sha256"], + META_PATH: expected["latent_metadata_sha256"], + **{RUN_PATHS[i]: expected[f"heldout_seed{i}_sha256"] for i in range(3)}, + } + for path, fingerprint in checks.items(): + if sha256(path) != fingerprint: + raise RuntimeError(f"fingerprint mismatch: {path}") + heldout = json.loads(STATS_PATH.read_text()) + runs = [json.loads(path.read_text()) for path in RUN_PATHS] + return heldout, runs, protocol + + +def macro_f1(y_true: np.ndarray, y_pred: np.ndarray, labels: list[str]) -> float: + scores = [] + for label in labels: + tp = np.sum((y_true == label) & (y_pred == label)) + fp = np.sum((y_true != label) & (y_pred == label)) + fn = np.sum((y_true == label) & (y_pred != label)) + precision = tp / (tp + fp) if tp + fp else 0.0 + recall = tp / (tp + fn) if tp + fn else 0.0 + scores.append(2 * precision * recall / (precision + recall) if precision + recall else 0.0) + return float(np.mean(scores)) + + +def build_fold_features( + records: list[dict], + families: np.ndarray, + folds: np.ndarray, + *, + mode: str, + input_baseline: bool = False, +) -> list[dict]: + output = [] + for heldout_fold in sorted(set(folds)): + train = np.where(folds != heldout_fold)[0] + test = np.where(folds == heldout_fold)[0] + weighted: list[dict[str, float]] = [] + for record in records: + words = token_words(record["prompt"]) + if input_baseline: + weighted.append({word: 1.0 for word in words}) + else: + weighted.append({ + row["token"]: float(row["consensus_score"]) + for row in record["filtered_consensus_candidates"] + if not candidate_removed(row["token"], words, mode) + }) + document_frequency = Counter( + token for index in train for token in weighted[index] + ) + vocabulary = sorted(document_frequency) + lookup = {token: col for col, token in enumerate(vocabulary)} + idf = { + token: math.log(len(train) / count) + for token, count in document_frequency.items() + } + + def matrix(indices: np.ndarray) -> np.ndarray: + values = np.zeros((len(indices), len(vocabulary)), dtype=float) + for row_index, source_index in enumerate(indices): + for token, score in weighted[int(source_index)].items(): + if token in lookup: + values[row_index, lookup[token]] = score * idf[token] + return normalize_rows(values) + + x_train = matrix(train) + x_test = matrix(test) + output.append({ + "fold": int(heldout_fold), + "train": train, + "test": test, + "x_train": x_train, + "x_test": x_test, + "k_test_train": np.einsum("id,jd->ij", x_test, x_train, optimize=True), + "k_train_train": np.einsum("id,jd->ij", x_train, x_train, optimize=True), + "empty_test_vectors": int(np.sum(np.linalg.norm(x_test, axis=1) == 0)), + }) + return output + + +def predict_from_labels( + fold_data: dict, train_labels: np.ndarray, label_names: list[str] +) -> np.ndarray: + similarities = np.empty((len(fold_data["test"]), len(label_names)), dtype=float) + for label_index, label in enumerate(label_names): + mask = train_labels == label + numerator = fold_data["k_test_train"][:, mask].sum(axis=1) + norm_sq = float(fold_data["k_train_train"][np.ix_(mask, mask)].sum()) + similarities[:, label_index] = numerator / math.sqrt(max(norm_sq, 1e-12)) + return np.asarray(label_names)[np.argmax(similarities, axis=1)] + + +def evaluate_folds( + fold_data: list[dict], labels: np.ndarray, label_names: list[str] +) -> tuple[np.ndarray, np.ndarray]: + predictions = np.empty(len(labels), dtype=object) + for fold in fold_data: + predictions[fold["test"]] = predict_from_labels( + fold, labels[fold["train"]], label_names + ) + return predictions, predictions == labels + + +def permutation_accuracies( + fold_data: list[dict], + permuted_labels: np.ndarray, + true_folds: np.ndarray, + label_names: list[str], + *, + batch_size: int = 250, +) -> np.ndarray: + n_permutations = len(permuted_labels) + correct = np.zeros(n_permutations, dtype=float) + label_to_int = {label: index for index, label in enumerate(label_names)} + encoded = np.vectorize(label_to_int.get)(permuted_labels) + for fold in fold_data: + train = fold["train"] + test = fold["test"] + k_qt = fold["k_test_train"] + k_tt = fold["k_train_train"] + for start in range(0, n_permutations, batch_size): + stop = min(start + batch_size, n_permutations) + train_codes = encoded[start:stop, train] + indicators = np.eye(len(label_names), dtype=float)[train_codes] + numerators = np.einsum("ij,bjc->bic", k_qt, indicators, optimize=True) + norm_sq = np.einsum( + "bjc,jk,bkc->bc", indicators, k_tt, indicators, optimize=True + ) + similarities = numerators / np.sqrt(np.maximum(norm_sq[:, None, :], 1e-12)) + prediction_codes = np.argmax(similarities, axis=2) + correct[start:stop] += np.sum( + prediction_codes == encoded[start:stop, test], axis=1 + ) + return correct / len(true_folds) + + +def family_bootstrap(correct: np.ndarray, families: np.ndarray, rng: np.random.Generator) -> list[float]: + names = sorted(set(families)) + means = np.asarray([np.mean(correct[families == name]) for name in names]) + indices = rng.integers(0, len(names), size=(N_BOOT, len(names))) + draws = means[indices].mean(axis=1) + return [float(means.mean()), *[float(v) for v in np.quantile(draws, [0.025, 0.975])]] + + +def analyze_semantic_classification( + heldout: dict, runs: list[dict] +) -> tuple[pd.DataFrame, dict]: + reference = {record["slug"]: record for record in runs[0]["prompts"]} + jac_records = heldout["open_vocabulary"]["methods"]["jacobian"]["per_prompt"] + direct_records = heldout["open_vocabulary"]["methods"]["logit"]["per_prompt"] + slugs = np.asarray([row["slug"] for row in jac_records]) + families = np.asarray([row["family"] for row in jac_records]) + folds = np.asarray([ + int(reference[slug]["phrasing_id"].rsplit("-", 1)[-1]) for slug in slugs + ]) + label_names = sorted(set(families)) + configs: dict[str, list[dict]] = {} + for method, records in (("Jacobian", jac_records), ("direct", direct_records)): + for mode in ("all", "prompt_exact_removed", "prompt_morphology_removed"): + configs[f"{method}::{mode}"] = build_fold_features( + records, families, folds, mode=mode + ) + configs["input_TFIDF::all"] = build_fold_features( + jac_records, families, folds, mode="all", input_baseline=True + ) + + rng = np.random.default_rng(SEED) + permuted_labels = np.empty((N_CLASS_PERM, len(families)), dtype=object) + for iteration in range(N_CLASS_PERM): + candidate = families.copy() + for fold in sorted(set(folds)): + indices = np.where(folds == fold)[0] + candidate[indices] = rng.permutation(candidate[indices]) + permuted_labels[iteration] = candidate + + result_rows = [] + item_rows = [] + permutation_by_config = {} + observed_by_config = {} + correctness_by_config = {} + for config_name, fold_data in configs.items(): + predictions, correct = evaluate_folds(fold_data, families, label_names) + accuracy = float(np.mean(correct)) + permutation = permutation_accuracies( + fold_data, permuted_labels, folds, label_names + ) + permutation_by_config[config_name] = permutation + observed_by_config[config_name] = accuracy + correctness_by_config[config_name] = correct + method, mode = config_name.split("::") + bootstrap_ci = family_bootstrap(correct, families, rng) + result_rows.append({ + "config": config_name, + "method": method, + "filter": mode, + "accuracy": accuracy, + "macro_f1": macro_f1(families, predictions, label_names), + "permutation_p": float((1 + np.sum(permutation >= accuracy - 1e-15)) / (1 + len(permutation))), + "family_bootstrap_mean": bootstrap_ci[0], + "family_bootstrap_low": bootstrap_ci[1], + "family_bootstrap_high": bootstrap_ci[2], + "empty_test_vectors": int(sum(fold["empty_test_vectors"] for fold in fold_data)), + }) + for index, slug in enumerate(slugs): + item_rows.append({ + "config": config_name, + "slug": slug, + "family": families[index], + "fold": int(folds[index]), + "prediction": predictions[index], + "correct": bool(correct[index]), + }) + maximum_null = np.max(np.column_stack(list(permutation_by_config.values())), axis=1) + for row in result_rows: + observed = row["accuracy"] + row["max_config_corrected_p"] = float( + (1 + np.sum(maximum_null >= observed - 1e-15)) / (1 + len(maximum_null)) + ) + + # Paired Jacobian-versus-direct comparisons under identical filters. + paired = {} + for mode in ("all", "prompt_exact_removed", "prompt_morphology_removed"): + j = correctness_by_config[f"Jacobian::{mode}"].astype(int) + d = correctness_by_config[f"direct::{mode}"].astype(int) + wins = int(np.sum((j == 1) & (d == 0))) + losses = int(np.sum((j == 0) & (d == 1))) + paired[mode] = { + "jacobian_only_correct": wins, + "direct_only_correct": losses, + "both_correct": int(np.sum((j == 1) & (d == 1))), + "both_wrong": int(np.sum((j == 0) & (d == 0))), + "exact_two_sided_mcnemar_p": float( + stats.binomtest(wins, wins + losses, 0.5).pvalue + if wins + losses else 1.0 + ), + } + + summary_frame = pd.DataFrame(result_rows).sort_values( + ["accuracy", "config"], ascending=[False, True] + ) + item_frame = pd.DataFrame(item_rows) + summary_frame.to_csv(OUT / "semantic_classification_summary.csv", index=False) + item_frame.to_csv(OUT / "semantic_classification_items.csv", index=False) + per_family = ( + item_frame.groupby(["config", "family"], as_index=False).correct.mean() + ) + per_family.to_csv(OUT / "semantic_classification_by_family.csv", index=False) + payload = { + "status": "retrospective candidate analysis under frozen protocol", + "n_prompts": 50, + "n_families": 10, + "folds": 5, + "results": summary_frame.to_dict(orient="records"), + "paired_jacobian_vs_direct": paired, + "permutation_resamples": N_CLASS_PERM, + "maximum_over_configs_null_95": float(np.quantile(maximum_null, 0.95)), + } + dump_json(OUT / "semantic_classification_statistics.json", payload) + return item_frame, payload + + +def centroid_distances(vectors: np.ndarray, families: np.ndarray, family_order: list[str]) -> np.ndarray: + centroids = normalize_rows(np.asarray([ + normalize_rows(vectors[families == family]).mean(axis=0) + for family in family_order + ])) + similarity = np.einsum("id,jd->ij", centroids, centroids, optimize=True) + return (1.0 - similarity)[np.triu_indices(len(family_order), k=1)] + + +def expert_distances(protocol: dict, family_order: list[str], kind: str) -> tuple[np.ndarray, np.ndarray]: + matrix = np.zeros((len(family_order), len(family_order)), dtype=float) + ontology = protocol["family_ontology"] + for i, first in enumerate(family_order): + for j, second in enumerate(family_order): + if kind == "response_class": + matrix[i, j] = float( + ontology[first]["response_class"] != ontology[second]["response_class"] + ) + elif kind == "multi_attribute": + a = set(ontology[first]["attributes"]) + b = set(ontology[second]["attributes"]) + matrix[i, j] = 1.0 - len(a & b) / len(a | b) + else: + raise ValueError(kind) + return matrix, matrix[np.triu_indices(len(family_order), k=1)] + + +def rank_unit(values: np.ndarray) -> np.ndarray: + ranks = stats.rankdata(values, method="average", axis=-1) + ranks = ranks - ranks.mean(axis=-1, keepdims=True) + return ranks / np.maximum(np.linalg.norm(ranks, axis=-1, keepdims=True), 1e-12) + + +def analyze_ontology_rsa(protocol: dict) -> tuple[pd.DataFrame, dict]: + with np.load(NPZ_PATH, allow_pickle=False) as data: + raw = normalize_rows(data["raw_states"].astype(float)) + transported = normalize_rows(data["transported_states"].astype(float)) + transported_mean = normalize_rows(transported.mean(axis=0)) + target = normalize_rows(data["target_states"].astype(float)) + lexical = normalize_rows(data["lexical_states"].astype(float)) + families = data["families"].astype(str) + depths = data["depths"].astype(float) + source_layers = data["source_layers"].astype(int) + family_order = sorted(set(families)) + j_distances = np.asarray([ + centroid_distances(transported_mean[:, layer], families, family_order) + for layer in range(len(depths)) + ]) + raw_distances = np.asarray([ + centroid_distances(raw[:, layer], families, family_order) + for layer in range(len(depths)) + ]) + target_distances = centroid_distances(target, families, family_order) + lexical_distances = centroid_distances(lexical, families, family_order) + + rng = np.random.default_rng(SEED) + permutations = np.asarray([ + rng.permutation(len(family_order)) for _ in range(N_RSA_PERM) + ]) + rows = [] + payload = {"ontology_permutation_resamples": N_RSA_PERM, "ontologies": {}} + for kind in ("response_class", "multi_attribute"): + matrix, expert = expert_distances(protocol, family_order, kind) + expert_rank = rank_unit(expert[None, :])[0] + j_rank = rank_unit(j_distances) + raw_rank = rank_unit(raw_distances) + target_rank = rank_unit(target_distances[None, :])[0] + lexical_rank = rank_unit(lexical_distances[None, :])[0] + j_corr = np.einsum("ld,d->l", j_rank, expert_rank, optimize=True) + raw_corr = np.einsum("ld,d->l", raw_rank, expert_rank, optimize=True) + target_corr = float(np.einsum("d,d->", target_rank, expert_rank, optimize=True)) + lexical_corr = float(np.einsum("d,d->", lexical_rank, expert_rank, optimize=True)) + + perm_expert = np.empty((N_RSA_PERM, len(expert)), dtype=float) + upper = np.triu_indices(len(family_order), k=1) + for start in range(0, N_RSA_PERM, 1000): + stop = min(start + 1000, N_RSA_PERM) + for offset, permutation in enumerate(permutations[start:stop]): + perm_expert[start + offset] = matrix[np.ix_(permutation, permutation)][upper] + perm_rank = rank_unit(perm_expert) + null_j = np.einsum("pd,ld->pl", perm_rank, j_rank, optimize=True) + null_raw = np.einsum("pd,ld->pl", perm_rank, raw_rank, optimize=True) + null_target = np.einsum("pd,d->p", perm_rank, target_rank, optimize=True) + null_lexical = np.einsum("pd,d->p", perm_rank, lexical_rank, optimize=True) + j_best = int(np.argmax(np.abs(j_corr))) + raw_best = int(np.argmax(np.abs(raw_corr))) + + def corrected_p(null_values: np.ndarray, observed: float) -> float: + maximum = np.max(np.abs(null_values), axis=1) if null_values.ndim == 2 else np.abs(null_values) + return float((1 + np.sum(maximum >= abs(observed) - 1e-15)) / (1 + len(maximum))) + + leave_one = {"Jacobian": [], "raw": [], "target": [], "lexical": []} + for omitted_index, omitted in enumerate(family_order): + keep_family = [i for i in range(len(family_order)) if i != omitted_index] + pair_mask = np.asarray([ + first in keep_family and second in keep_family + for first, second in zip(*upper) + ]) + for method, vector in ( + ("Jacobian", j_distances[j_best]), + ("raw", raw_distances[raw_best]), + ("target", target_distances), + ("lexical", lexical_distances), + ): + value = stats.spearmanr(vector[pair_mask], expert[pair_mask]).statistic + leave_one[method].append({"omitted_family": omitted, "rho": float(value)}) + + kind_payload = { + "family_order": family_order, + "Jacobian": { + "rho_by_layer": [float(v) for v in j_corr], + "best_abs_layer": int(source_layers[j_best]), + "best_abs_depth": float(depths[j_best]), + "best_abs_rho": float(j_corr[j_best]), + "max_layer_corrected_p": corrected_p(null_j, float(j_corr[j_best])), + }, + "raw": { + "rho_by_layer": [float(v) for v in raw_corr], + "best_abs_layer": int(source_layers[raw_best]), + "best_abs_depth": float(depths[raw_best]), + "best_abs_rho": float(raw_corr[raw_best]), + "max_layer_corrected_p": corrected_p(null_raw, float(raw_corr[raw_best])), + }, + "target": {"rho": target_corr, "permutation_p": corrected_p(null_target, target_corr)}, + "lexical": {"rho": lexical_corr, "permutation_p": corrected_p(null_lexical, lexical_corr)}, + "leave_one_family_out": leave_one, + } + payload["ontologies"][kind] = kind_payload + for method, correlations in (("Jacobian", j_corr), ("raw", raw_corr)): + for index, correlation in enumerate(correlations): + rows.append({ + "ontology": kind, "method": method, + "layer": int(source_layers[index]), "depth": float(depths[index]), + "spearman_rho": float(correlation), + }) + rows.extend([ + {"ontology": kind, "method": "target", "layer": np.nan, "depth": np.nan, "spearman_rho": target_corr}, + {"ontology": kind, "method": "lexical", "layer": np.nan, "depth": np.nan, "spearman_rho": lexical_corr}, + ]) + frame = pd.DataFrame(rows) + frame.to_csv(OUT / "ontology_rsa_layer_curves.csv", index=False) + dump_json(OUT / "ontology_rsa_statistics.json", payload) + return frame, payload + + +def first_sustained_depth(depths: np.ndarray, ranks: np.ndarray, threshold: int = 1000) -> float: + hits = ranks <= threshold + for index in range(len(hits) - 1): + if hits[index] and hits[index + 1]: + return float(depths[index]) + return float("nan") + + +def analyze_concept_roles(runs: list[dict], protocol: dict) -> tuple[pd.DataFrame, dict]: + role_lookup = { + concept: role + for role, concepts in protocol["concept_roles"].items() + for concept in concepts + } + indexes = [{record["slug"]: record for record in run["prompts"]} for run in runs] + technical = [] + for seed, index in enumerate(indexes): + for slug, record in index.items(): + for source_key, method in (("jacobian_lens", "Jacobian"), ("logit_lens", "direct")): + for trajectory in record["concept_trajectories"][source_key]: + label = trajectory["label"] + if label not in role_lookup: + raise RuntimeError(f"unassigned concept role: {label}") + depths = np.asarray(trajectory["depths"], dtype=float) + ranks = np.asarray(trajectory["ranks"], dtype=float) + mask = (depths >= BAND[0]) & (depths <= BAND[1]) + depths = depths[mask] + ranks = ranks[mask] + best_index = int(np.argmin(ranks)) + technical.append({ + "seed": seed, "slug": slug, "family": record["target_family"], + "concept": label, "role": role_lookup[label], "method": method, + "best_depth": float(depths[best_index]), + "best_rank": float(ranks[best_index]), + "first_sustained_top1000_depth": first_sustained_depth(depths, ranks), + "sustained_top1000": bool(np.isfinite(first_sustained_depth(depths, ranks))), + }) + technical_frame = pd.DataFrame(technical) + technical_frame.to_csv(OUT / "concept_role_onsets_by_seed.csv", index=False) + units = ( + technical_frame.groupby(["slug", "family", "concept", "role", "method"], as_index=False) + .agg( + best_depth=("best_depth", "mean"), + best_rank=("best_rank", "mean"), + first_sustained_top1000_depth=("first_sustained_top1000_depth", "mean"), + sustained_top1000=("sustained_top1000", "mean"), + ) + ) + units["sustained_top1000"] = units.sustained_top1000 > 0 + units.to_csv(OUT / "concept_role_onsets.csv", index=False) + + rng = np.random.default_rng(SEED) + roles = list(protocol["concept_roles"]) + methods_payload = {} + for method, method_rows in units.groupby("method"): + role_summary = {} + for role in roles: + subset = method_rows[method_rows.role.eq(role)] + onset = subset.first_sustained_top1000_depth.dropna().to_numpy() + role_summary[role] = { + "n_units": int(len(subset)), + "sustained_event_fraction": float(subset.sustained_top1000.mean()), + "n_sustained": int(subset.sustained_top1000.sum()), + "median_sustained_onset_depth": float(np.median(onset)) if len(onset) else None, + "median_best_depth": float(subset.best_depth.median()), + } + + observed_medians = { + role: method_rows[method_rows.role.eq(role)].best_depth.median() + for role in roles + } + observed_stat = max( + abs(observed_medians[a] - observed_medians[b]) + for a, b in itertools.combinations(roles, 2) + ) + null = np.empty(N_ROLE_PERM, dtype=float) + family_concepts = { + family: method_rows[method_rows.family.eq(family)][["concept", "role"]] + .drop_duplicates().to_records(index=False) + for family in sorted(method_rows.family.unique()) + } + for iteration in range(N_ROLE_PERM): + mapping = {} + for family, entries in family_concepts.items(): + concepts = [entry[0] for entry in entries] + shuffled_roles = rng.permutation([entry[1] for entry in entries]) + mapping.update({(family, concept): role for concept, role in zip(concepts, shuffled_roles)}) + perm_roles = np.asarray([ + mapping[(row.family, row.concept)] for row in method_rows.itertuples() + ]) + medians = { + role: np.median(method_rows.best_depth.to_numpy()[perm_roles == role]) + for role in roles if np.any(perm_roles == role) + } + null[iteration] = max( + abs(medians[a] - medians[b]) + for a, b in itertools.combinations(medians, 2) + ) + + bootstrap = {} + family_names = sorted(method_rows.family.unique()) + family_role_values = { + (family, role): method_rows[ + (method_rows.family.eq(family)) & (method_rows.role.eq(role)) + ].best_depth.to_numpy() + for family in family_names for role in roles + } + for role in roles: + estimates = [] + for _ in range(N_BOOT): + selected = rng.choice(family_names, size=len(family_names), replace=True) + arrays = [ + family_role_values[(family, role)] + for family in selected if len(family_role_values[(family, role)]) + ] + if arrays: + estimates.append(float(np.median(np.concatenate(arrays)))) + bootstrap[role] = [float(v) for v in np.quantile(estimates, [0.025, 0.975])] + methods_payload[method] = { + "roles": role_summary, + "family_clustered_best_depth_ci95": bootstrap, + "max_pairwise_median_best_depth_difference": float(observed_stat), + "family_blocked_max_difference_permutation_p": float( + (1 + np.sum(null >= observed_stat - 1e-15)) / (1 + len(null)) + ), + "permutation_resamples": N_ROLE_PERM, + } + payload = { + "registered_band_percent": list(BAND), + "concept_roles": protocol["concept_roles"], + "methods": methods_payload, + } + dump_json(OUT / "concept_role_onset_statistics.json", payload) + return units, payload + + +def plot_semantic_classification(items: pd.DataFrame, payload: dict) -> None: + FIG.mkdir(parents=True, exist_ok=True) + results = pd.DataFrame(payload["results"]) + filters = ["all", "prompt_exact_removed", "prompt_morphology_removed"] + labels = ["All target-free", "Exact prompt\nwords removed", "Morphology-aware\nremoval"] + colors = {"Jacobian": "#147A8A", "direct": "#777777"} + fig, axes = plt.subplots(1, 2, figsize=(10.8, 4.3)) + ax = axes[0] + x = np.arange(len(filters)); width = 0.34 + for offset, method in ((-width / 2, "Jacobian"), (width / 2, "direct")): + values = [ + float(results[(results.method.eq(method)) & (results["filter"].eq(mode))].accuracy.iloc[0]) + for mode in filters + ] + ax.bar(x + offset, values, width, label=method, color=colors[method]) + lexical = float(results[results.method.eq("input_TFIDF")].accuracy.iloc[0]) + ax.axhline(0.1, color="#999999", ls=":", label="chance") + ax.axhline(lexical, color="#B35C44", ls="--", label="prompt TF-IDF") + ax.set_xticks(x, labels); ax.set_ylim(0, 1.02) + ax.set_ylabel("Leave-one-phrasing-out accuracy") + ax.legend(frameon=False, fontsize=8, ncol=2) + ax.text(0.01, 0.98, "A", transform=ax.transAxes, va="top", fontweight="bold") + + ax = axes[1] + config = "Jacobian::prompt_morphology_removed" + subset = items[items.config.eq(config)] + names = sorted(subset.family.unique()) + matrix = pd.crosstab(subset.family, subset.prediction).reindex( + index=names, columns=names, fill_value=0 + ).to_numpy() + image = ax.imshow(matrix, cmap="Blues", vmin=0, vmax=5) + for i in range(len(names)): + for j in range(len(names)): + if matrix[i, j]: + ax.text(j, i, str(matrix[i, j]), ha="center", va="center", fontsize=7, + color="white" if matrix[i, j] >= 3 else "black") + short = [name.replace("high-temperature-deformation", "high-T deform.") + .replace("hot-air-surface-layer", "surface oxide") + .replace("line-defect-motion", "line defect") + .replace("particle-strengthening", "particle strength.") + .replace("rapid-transformation", "transformation") + .replace("notch-resistance", "notch") + .replace("boundary-attack", "boundary attack") for name in names] + ax.set_xticks(range(len(names)), short, rotation=55, ha="right", fontsize=7) + ax.set_yticks(range(len(names)), short, fontsize=7) + ax.set_xlabel("Predicted family"); ax.set_ylabel("True family") + ax.text(-0.14, 1.04, "B", transform=ax.transAxes, ha="left", va="bottom", + fontweight="bold", clip_on=False) + fig.colorbar(image, ax=ax, fraction=0.046, pad=0.04, label="prompts") + fig.tight_layout() + for suffix in ("png", "pdf"): + fig.savefig(FIG / f"semantic-classification.{suffix}", dpi=300, bbox_inches="tight") + plt.close(fig) + + fig, ax = plt.subplots(figsize=(6.4, 3.5)) + x = np.arange(len(filters)) + containers = [] + for offset, method in ((-width / 2, "Jacobian"), (width / 2, "direct")): + values = [ + float(results[(results.method.eq(method)) & (results["filter"].eq(mode))].accuracy.iloc[0]) + for mode in filters + ] + containers.append( + ax.bar(x + offset, values, width, label=method, color=colors[method]) + ) + ax.axhline(0.1, color="#999999", ls=":", label="chance") + ax.axhline(lexical, color="#B35C44", ls="--", label="prompt TF-IDF") + ax.set_xticks(x, labels) + ax.set_ylim(0, 1.02) + ax.set_ylabel("Leave-one-phrasing-out accuracy") + ax.legend(frameon=False, fontsize=8, ncol=2) + for container in containers: + ax.bar_label( + container, + labels=[f"{100 * bar.get_height():.0f}%" for bar in container], + padding=3, + fontsize=8, + ) + fig.tight_layout() + for suffix in ("png", "pdf"): + fig.savefig( + FIG / f"semantic-classification-summary.{suffix}", + dpi=300, bbox_inches="tight", + ) + plt.close(fig) + + +def plot_rsa(frame: pd.DataFrame, payload: dict) -> None: + fig, axes = plt.subplots(1, 2, figsize=(10.8, 4.0), sharey=True) + colors = {"Jacobian": "#147A8A", "raw": "#7562A8"} + for ax, kind, panel in zip(axes, ("response_class", "multi_attribute"), ("A", "B")): + subset = frame[(frame.ontology.eq(kind)) & frame.method.isin(["Jacobian", "raw"])] + for method, group in subset.groupby("method"): + ax.plot(group.depth, group.spearman_rho, marker="o", ms=3, + color=colors[method], label=method) + target = payload["ontologies"][kind]["target"]["rho"] + lexical = payload["ontologies"][kind]["lexical"]["rho"] + ax.axhline(target, color="#4F8A5B", ls="--", label="target state") + ax.axhline(lexical, color="#B35C44", ls=":", label="prompt embedding") + ax.axhline(0, color="#999999", lw=0.7) + ax.set_xlabel("Layer depth (%)") + ax.set_title(kind.replace("_", " ")) + ax.text(0.01, 0.98, panel, transform=ax.transAxes, va="top", fontweight="bold") + axes[0].set_ylabel("Spearman alignment with materials ontology") + axes[1].legend(frameon=False, fontsize=8) + fig.tight_layout() + for suffix in ("png", "pdf"): + fig.savefig(FIG / f"materials-ontology-rsa.{suffix}", dpi=300, bbox_inches="tight") + plt.close(fig) + + +def plot_roles(units: pd.DataFrame, payload: dict) -> None: + roles = list(payload["concept_roles"]) + labels = ["Entity/state", "Process/mechanism", "Response/property"] + colors = {"Jacobian": "#147A8A", "direct": "#777777"} + fig, axes = plt.subplots(1, 2, figsize=(10.5, 4.0)) + for ax, method, panel in zip(axes, ("Jacobian", "direct"), ("A", "B")): + subset = units[units.method.eq(method)] + data = [subset[subset.role.eq(role)].best_depth.to_numpy() for role in roles] + positions = np.arange(len(roles)) + box = ax.boxplot(data, positions=positions, widths=0.55, patch_artist=True, showfliers=False) + for patch in box["boxes"]: + patch.set_facecolor(colors[method]); patch.set_alpha(0.75) + for position, values in zip(positions, data): + jitter = np.linspace(-0.16, 0.16, len(values)) + ax.scatter(np.full(len(values), position) + jitter, values, s=8, + color="#333333", alpha=0.35) + ax.set_xticks(positions, labels, rotation=12) + ax.set_ylim(BAND[0] - 2, BAND[1] + 2) + ax.set_ylabel("Best-rank layer depth (%)") + ax.set_title(method) + ax.text(0.01, 0.98, panel, transform=ax.transAxes, va="top", fontweight="bold") + fig.tight_layout() + for suffix in ("png", "pdf"): + fig.savefig(FIG / f"concept-role-depths.{suffix}", dpi=300, bbox_inches="tight") + plt.close(fig) + + +def write_report(classification: dict, rsa: dict, roles: dict) -> None: + results = {row["config"]: row for row in classification["results"]} + j_clean = results["Jacobian::prompt_morphology_removed"] + d_clean = results["direct::prompt_morphology_removed"] + lexical = results["input_TFIDF::all"] + rsa_multi = rsa["ontologies"]["multi_attribute"] + role_j = roles["methods"]["Jacobian"] + lines = [ + "# Candidate non-steering results", + "", + "These outputs are retrospective candidates under the frozen `PROTOCOL.md`; none has been added to the manuscript.", + "", + "## Target-free mechanism classification", + "", + f"After morphology-aware prompt-word removal, Jacobian target-free vocabulary classifies {j_clean['accuracy']:.1%} of the 50 held-out prompts by mechanism family (macro-F1 {j_clean['macro_f1']:.3f}); direct vocabulary reaches {d_clean['accuracy']:.1%}. The prompt TF-IDF baseline reaches {lexical['accuracy']:.1%}. The Jacobian max-configuration-corrected permutation p-value is {j_clean['max_config_corrected_p']:.4g}.", + f"Under the strict filter, Jacobian alone is correct for {classification['paired_jacobian_vs_direct']['prompt_morphology_removed']['jacobian_only_correct']} prompts and direct alone for {classification['paired_jacobian_vs_direct']['prompt_morphology_removed']['direct_only_correct']} (exact paired p={classification['paired_jacobian_vs_direct']['prompt_morphology_removed']['exact_two_sided_mcnemar_p']:.4g}).", + "", + "## Materials-ontology alignment", + "", + f"For the multi-attribute ontology, the strongest Jacobian layer has rho={rsa_multi['Jacobian']['best_abs_rho']:+.3f} at {rsa_multi['Jacobian']['best_abs_depth']:.1f}% depth (max-layer-corrected p={rsa_multi['Jacobian']['max_layer_corrected_p']:.4g}). Raw states peak at rho={rsa_multi['raw']['best_abs_rho']:+.3f}; the target and prompt-embedding baselines are {rsa_multi['target']['rho']:+.3f} and {rsa_multi['lexical']['rho']:+.3f}.", + "", + "## Concept-role depth", + "", + f"The maximum Jacobian difference among entity/state, process/mechanism, and response/property median best depths is {role_j['max_pairwise_median_best_depth_difference']:.1f} percentage points (family-blocked p={role_j['family_blocked_max_difference_permutation_p']:.4g}). Event rates and exact medians are in `concept_role_onset_statistics.json`.", + "", + "## Decision guidance", + "", + "The lexical-decontaminated classifier is the most directly useful addition if it remains above the corrected null: it turns target-free ribbons into a cross-validated quantitative result. Ontology RSA is valuable only if both frozen ontologies and leave-one-family-out checks agree. Concept-role ordering should remain supplementary unless it is strong, stable, and similar under both readouts.", + "", + ] + (OUT / "RESULTS.md").write_text("\n".join(lines)) + + +def main() -> None: + OUT.mkdir(parents=True, exist_ok=True) + FIG.mkdir(parents=True, exist_ok=True) + heldout, runs, protocol = validate_inputs() + classification_items, classification = analyze_semantic_classification(heldout, runs) + rsa_frame, rsa = analyze_ontology_rsa(protocol) + role_units, roles = analyze_concept_roles(runs, protocol) + plot_semantic_classification(classification_items, classification) + plot_rsa(rsa_frame, rsa) + plot_roles(role_units, roles) + combined = { + "protocol_sha256": sha256(PROTOCOL_PATH), + "semantic_classification": classification, + "ontology_rsa": rsa, + "concept_roles": roles, + } + dump_json(OUT / "candidate_nonsteering_results.json", combined) + write_report(classification, rsa, roles) + print((OUT / "RESULTS.md").read_text()) + + +if __name__ == "__main__": + main() diff --git a/jlens_materials/scripts/analyze_causal_relational_comment3_audit.py b/jlens_materials/scripts/analyze_causal_relational_comment3_audit.py new file mode 100644 index 0000000000000000000000000000000000000000..4cc034c20f310f0e4616fe1b956cc932b18509d5 --- /dev/null +++ b/jlens_materials/scripts/analyze_causal_relational_comment3_audit.py @@ -0,0 +1,500 @@ +#!/usr/bin/env python3 +"""Retrospective Comment 3 sensitivity audit; never modifies the frozen study. + +Run from any directory using Python with NumPy, pandas, and matplotlib. +All selection rules are in the dated ANALYSIS_PLAN.md and law_overlap_taxonomy.json. +No model inference, direction refitting for target subsets, or manuscript edits. +""" +from __future__ import annotations + +import hashlib +import itertools +import json +import math +import os +from pathlib import Path + +import numpy as np +import pandas as pd + +ROOT = Path(__file__).resolve().parents[1] +REPO = ROOT.parent +SOURCE = ROOT / "experiments/causal-relational-knockout-2026-09-02" +OUT = ROOT / "experiments/causal-relational-comment3-audit-2026-09-06" +PORTABLE = REPO / "PAPER_REVISION/Materials_Science_Interpretability/causal_relational_knockout_data" +SCALES = ["4b", "12b", "31b"] +COHORTS = { + "full_24": {"exact", "related", "ambiguous", "unmatched"}, + "exclude_exact": {"related", "ambiguous", "unmatched"}, + "exclude_exact_and_related": {"ambiguous", "unmatched"}, + "conservative_unmatched": {"unmatched"}, +} +METHODS = ["neutralize", "counterfactual_coordinate", "correct_centroid", "wrong_centroid", + "full_counterfactual", "full_counterfactual_direction_restored"] +FACTORS = ["category", "answer_vocabulary", "answer_order", "physical_sign"] +ENDPOINTS = ["neutralize", "neutralize_control_mean", "neutralization_specific", + "counterfactual_coordinate", "counterfactual_control_mean", "counterfactual_specific", + "full_counterfactual", "full_counterfactual_direction_restored", "restoration_reduction"] +BOOTSTRAPS = 30000 +SEED = 20260902 + + +def sha(path): + digest = hashlib.sha256() + with Path(path).open("rb") as stream: + for chunk in iter(lambda: stream.read(8 * 1024 * 1024), b""): + digest.update(chunk) + return digest.hexdigest() + + +def json_write(name, obj): + (OUT / name).write_text(json.dumps(obj, indent=2, allow_nan=False) + "\n") + + +def read_rows(path): + return pd.DataFrame([json.loads(line) for line in path.read_text().splitlines() if line.strip()]) + + +def exact_sign_flip(values): + """Exact two-sided paired sign-flip p via meet-in-the-middle enumeration. + + Assumes sign exchangeability under the null at the stated cluster level. + This is an analysis assumption, not a consequence of target randomization. + """ + x = np.asarray(values, dtype=np.float64) + assert len(x) and np.isfinite(x).all() + observed = abs(x.sum()) + tolerance = 32 * np.finfo(float).eps * max(1.0, np.abs(x).sum()) + if observed <= tolerance: + return 1.0 + + def sums(items): + result = np.array([0.0]) + for item in items: + result = np.concatenate((result - item, result + item)) + return result + + left = sums(x[:len(x) // 2]) + right = np.sort(sums(x[len(x) // 2:])) + threshold = observed - tolerance + lower = np.searchsorted(right, -threshold - left, side="right").sum() + upper = (len(right) - np.searchsorted(right, threshold - left, side="left")).sum() + return float((int(lower) + int(upper)) / (2 ** len(x))) + + +def check_sign_flip(): + rng = np.random.default_rng(614) + cases = [[0], [1], [1, 1], [1, -1], [0, 1, 2], [0, 0, 0], [1, -2, 3, -4]] + cases += [rng.normal(size=n).tolist() for n in range(2, 11)] + for x in cases: + x = np.asarray(x) + signs = np.asarray(list(itertools.product([-1, 1], repeat=len(x)))) + tolerance = 32 * np.finfo(float).eps * max(1.0, np.abs(x).sum()) + expected = np.mean(np.abs((signs * x).sum(axis=1)) >= abs(x.sum()) - tolerance) + assert exact_sign_flip(x) == expected, (x, expected, exact_sign_flip(x)) + assert exact_sign_flip(np.ones(24)) == 2 / 2 ** 24 + return len(cases) + 1 + + +def estimate(values, seed, denominator=None): + x = np.asarray(values, dtype=float) + rng = np.random.default_rng(seed) + ix = rng.integers(0, len(x), size=(BOOTSTRAPS, len(x))) + draws = x[ix].mean(axis=1) + point = float(x.mean()) + undefined = 0 + if denominator is not None: + den = np.asarray(denominator, dtype=float) + ddraws = den[ix].mean(axis=1) + undefined = int((np.abs(ddraws) <= 1e-12).sum()) + draws = np.divide(draws, ddraws, out=np.full_like(draws, np.nan), where=np.abs(ddraws) > 1e-12) + point = float(x.mean() / den.mean()) + lo, hi = np.nanquantile(draws, [.025, .975]) + result = {"estimate": point, "ci95_low": float(lo), "ci95_high": float(hi), "n_clusters": len(x)} + if denominator is None: + result.update(exact_sign_flip_p=exact_sign_flip(x), positive_clusters=int((x > 0).sum()), + zero_clusters=int((x == 0).sum())) + else: + result["undefined_bootstrap_draws"] = undefined + return result + + +def mcnemar(cw, wc): + n = cw + wc + return min(1.0, 2 * sum(math.comb(n, k) for k in range(min(cw, wc) + 1)) / 2 ** n) if n else 1.0 + + +def fitted_direction_audit(spec, frozen): + manifest = json.loads((ROOT / spec["development_manifest"]).read_text()) + prompts = manifest["prompts"] + path = ROOT / spec["development_states"] + if path.suffix == ".npy": + states = np.load(path).astype(float) + else: + with np.load(path) as arrays: + positions = arrays["positions"].astype(str).tolist() + layers = arrays["layers"].astype(int).tolist() + states = arrays["raw_states"][positions.index("final_prompt"), :, layers.index(spec["probe_layer"])].astype(float) + by_id = {p["prompt_id"]: p for p in prompts} + prompts = [by_id[x] for x in arrays["prompt_ids"].astype(str)] + labels = np.array([p["physical_sign"] > 0 for p in prompts]) + assert len(prompts) == 128 and len({p["law_id"] for p in prompts}) == 16 + assert labels.sum() == 64 and states.shape[0] == 128 and np.isfinite(states).all() + unit = states / np.maximum(np.linalg.norm(states, axis=-1, keepdims=True), 1e-12) + pos, neg = unit[labels].mean(0), unit[~labels].mean(0) + direction = pos - neg + direction /= np.linalg.norm(direction) + midpoint = .5 * (pos + neg) + delta = float(np.max(np.abs(direction.astype(np.float32) - frozen["direction"]))) + assert delta < 1e-7 + assert np.allclose(midpoint.astype(np.float32), frozen["development_midpoint"], atol=1e-8, rtol=0) + coord_errors = {} + for key, value in [("midpoint_coordinate", midpoint), ("positive_coordinate", pos), ("negative_coordinate", neg)]: + coord_errors[key] = abs(float(np.sum(value * direction)) - float(frozen[key])) + assert coord_errors[key] < 1e-8 + basis = np.vstack([frozen["direction"], frozen["control_directions"]]).astype(float) + orthogonality = float(np.abs(basis @ basis.T - np.eye(4)).max()) + assert orthogonality < 2e-6 + return {"n_prompts": len(prompts), "n_laws": 16, "positive_count": int(labels.sum()), + "max_direction_error": delta, "coordinate_errors": coord_errors, + "max_basis_orthogonality_error": orthogonality, + "actual_fitted_law_ids": sorted({p["law_id"] for p in prompts}), + "original_split_labels_all_used": sorted({p["split"] for p in prompts})}, manifest + + +def build_scale(scale, manifest): + clean = read_rows(SOURCE / scale / "clean_rows.jsonl") + rows = read_rows(SOURCE / scale / "intervention_rows.jsonl") + expected = set(METHODS + [f"{kind}_control_{i}_{sign}" for kind in ["neutralize", "counterfactual"] + for i in range(3) for sign in ["minus", "plus"]]) + assert len(clean) == 192 and len(rows) == 3456 and clean.prompt_id.is_unique + assert not rows.duplicated(["prompt_id", "intervention"]).any() + assert set(rows.intervention) == expected + assert (rows.groupby("prompt_id").size() == 18).all() + meta = pd.DataFrame(manifest["prompts"]).set_index("prompt_id") + c = clean.set_index("prompt_id").loc[meta.index] + assert set(clean.prompt_id) == set(rows.prompt_id) == set(meta.index) + for column in ["law_id", "category", "numerical_sign", "physical_sign", "answer_vocabulary", "answer_order", "expected_answer", "counterpart_prompt_id"]: + assert (c[column] == meta[column]).all(), column + assert (rows[column].to_numpy() == meta.loc[rows.prompt_id, column].to_numpy()).all(), column + cells = set(itertools.product([-1, 1], ["higher-lower", "greater-smaller"], ["standard", "reversed"])) + for _, frame in clean.groupby("law_id"): + assert set(zip(frame.numerical_sign, frame.answer_vocabulary, frame.answer_order)) == cells + for prompt in manifest["prompts"]: + mate = meta.loc[prompt["counterpart_prompt_id"]] + assert mate.counterpart_prompt_id == prompt["prompt_id"] + assert mate.law_id == prompt["law_id"] and mate.physical_sign == -prompt["physical_sign"] + assert mate.expected_answer == prompt["incorrect_answer"] + before = c.correct_answer_margin.reindex(rows.prompt_id).to_numpy() + movement = before - rows.correct_answer_margin.to_numpy() + margin_error = float(np.max(np.abs(movement - rows.movement_toward_counterfactual))) + assert margin_error < 1e-10 + assert np.allclose(-movement, rows.correct_margin_change, rtol=0, atol=1e-10) + for frame in [clean, rows]: + assert np.isfinite(frame.correct_answer_margin).all() + assert (frame.answer_margin_correct == (frame.correct_answer_margin > 0)).all() + rows["movement"] = movement + rows["margin_change"] = -movement + rows["kl"] = rows.clean_to_intervened_kl + rows["answer_mass"] = rows.registered_answer_probability_mass + distance = rows.pivot(index="prompt_id", columns="intervention", values="replacement_distance") + distance_errors = {} + for prefix, physical in [("neutralize_control_", "neutralize"), ("counterfactual_control_", "counterfactual_coordinate")]: + distance_errors[prefix] = float(distance.filter(regex=f"^{prefix}").sub(distance[physical], axis=0).abs().to_numpy().max()) + assert distance_errors[prefix] < 2e-5 + augmented = [rows] + for prefix in ["neutralize", "counterfactual"]: + control = rows[rows.intervention.str.startswith(prefix + "_control_")].groupby("prompt_id")[["movement", "margin_change", "kl", "answer_mass"]].mean() + control["intervention"] = prefix + "_control_mean" + for col in ["law_id", *FACTORS, "numerical_sign", "expected_answer"]: + control[col] = c[col] + augmented.append(control.reset_index()) + augmented = pd.concat(augmented, ignore_index=True) + law_long = augmented.groupby(["law_id", "intervention"])[["movement", "margin_change", "kl", "answer_mass"]].mean().reset_index() + wide = augmented.pivot(index="prompt_id", columns="intervention", values="movement") + wide["neutralization_specific"] = wide.neutralize - wide.neutralize_control_mean + wide["counterfactual_specific"] = wide.counterfactual_coordinate - wide.counterfactual_control_mean + wide["restoration_reduction"] = wide.full_counterfactual - wide.full_counterfactual_direction_restored + for col in ["law_id", *FACTORS, "numerical_sign", "expected_answer"]: + wide[col] = c[col] + wide["clean_margin"] = c.correct_answer_margin + wide["clean_correct"] = c.answer_margin_correct + margins = rows.pivot(index="prompt_id", columns="intervention", values="correct_answer_margin") + for method in METHODS: + wide[method + "_margin"] = margins[method] + law_wide = wide.groupby("law_id")[ENDPOINTS].mean() + return clean, rows, wide, law_long, law_wide, { + "clean_rows": len(clean), "intervention_rows": len(rows), "law_count": 24, + "factor_cells_per_law": 8, "methods_per_prompt": 18, + "max_reconstructed_margin_error": margin_error, "max_control_distance_error": distance_errors, + "clean_strict_negative": int((c.correct_answer_margin < 0).sum()), + "clean_ties": int((c.correct_answer_margin == 0).sum()), + } + + +def cohort_analysis(frame, scale, cohort, seed): + law = frame.groupby("law_id")[ENDPOINTS].mean() + summary = {"scale": scale, "cohort": cohort, "n_laws": len(law), "n_prompts": len(frame), + "n_canonical_families": frame.canonical_family.nunique(), + "clean_accuracy": float(frame.clean_correct.mean()), + "clean_strict_negative": int((frame.clean_margin < 0).sum()), + "clean_ties": int((frame.clean_margin == 0).sum()), "endpoints": {}} + for i, endpoint in enumerate(ENDPOINTS): + summary["endpoints"][endpoint] = estimate(law[endpoint], seed + i) + for offset, (name, numerator) in enumerate([("coordinate_over_full", "counterfactual_coordinate"), + ("restoration_over_full", "restoration_reduction")]): + summary["endpoints"][name] = estimate(law[numerator], seed + 9 + offset, denominator=law.full_counterfactual) + transitions = [] + for i, method in enumerate(METHODS): + correct = frame[method + "_margin"] > 0 + cw = frame.clean_correct & ~correct + wc = ~frame.clean_correct & correct + delta = (correct.astype(float) - frame.clean_correct.astype(float)).groupby(frame.law_id).mean() + transitions.append({"scale": scale, "cohort": cohort, "intervention": method, + "n_prompts": len(frame), "n_laws": len(law), "clean_accuracy": summary["clean_accuracy"], + "intervention_accuracy": float(correct.mean()), "correct_to_wrong": int(cw.sum()), + "correct_to_wrong_laws": int(frame.loc[cw, "law_id"].nunique()), + "positive_to_strict_negative": int((frame.clean_correct & (frame[method + "_margin"] < 0)).sum()), + "positive_to_strict_negative_laws": int(frame.loc[frame.clean_correct & (frame[method + "_margin"] < 0), "law_id"].nunique()), + "positive_to_tie": int((frame.clean_correct & (frame[method + "_margin"] == 0)).sum()), + "wrong_to_correct_including_ties": int(wc.sum()), + "wrong_to_correct_laws": int(frame.loc[wc, "law_id"].nunique()), + "strict_negative_to_positive": int(((frame.clean_margin < 0) & correct).sum()), + "tie_to_positive": int(((frame.clean_margin == 0) & correct).sum()), + "intervention_ties": int((frame[method + "_margin"] == 0).sum()), + "prompt_mcnemar_descriptive_only": mcnemar(int(cw.sum()), int(wc.sum())), + **{"law_accuracy_delta_" + k: v for k, v in estimate(delta, seed + 30 + i).items()}}) + errors = frame[frame.clean_margin < 0] + repaired = errors[errors.correct_centroid_margin > 0] + summary["repair"] = {"initially_negative_prompts": len(errors), "initially_negative_laws": errors.law_id.nunique(), + "repaired_prompts": len(repaired), "repaired_laws": repaired.law_id.nunique(), + "error_law_ids": sorted(errors.law_id.unique()), "repaired_law_ids": sorted(repaired.law_id.unique()), + "mean_margin_before": float(errors.clean_margin.mean()) if len(errors) else None, + "mean_margin_after": float(errors.correct_centroid_margin.mean()) if len(errors) else None, + "mean_margin_change": float((errors.correct_centroid_margin - errors.clean_margin).mean()) if len(errors) else None} + strata = [] + for fi, factor in enumerate(FACTORS): + for vi, (value, sub) in enumerate(frame.groupby(factor)): + subset = sub.groupby("law_id")[ENDPOINTS].mean() + for ei, endpoint in enumerate(["neutralization_specific", "counterfactual_specific"]): + strata.append({"scale": scale, "cohort": cohort, "factor": factor, "value": str(value), + "endpoint": endpoint, "n_prompts": len(sub), **estimate(subset[endpoint], seed + 100 + fi * 10 + vi * 2 + ei)}) + vocab = frame.groupby(["law_id", "answer_vocabulary"])[["neutralization_specific", "counterfactual_specific"]].mean().unstack() + summary["vocabulary_contrasts_greater_smaller_minus_higher_lower"] = { + endpoint: estimate(vocab[(endpoint, "greater-smaller")] - vocab[(endpoint, "higher-lower")], seed + 200 + i) + for i, endpoint in enumerate(["neutralization_specific", "counterfactual_specific"])} + return summary, transitions, strata + + +def main(): + taxonomy = json.loads((OUT / "law_overlap_taxonomy.json").read_text()) + tax = pd.DataFrame(taxonomy["laws"]).set_index("law_id") + assert tax.level.value_counts().to_dict() == {"ambiguous": 8, "related": 7, "unmatched": 6, "exact": 3} + manifest = json.loads((SOURCE / "prompt_manifest.json").read_text()) + protocol = json.loads((SOURCE / "protocol.json").read_text()) + assert set(tax.index) == {law["law_id"] for law in manifest["laws"]} + validation = {"exact_sign_flip_checks": check_sign_flip(), "scales": {}, "portable_matches": []} + inputs = {SOURCE / name for name in ["protocol.json", "control_amendment.json", "prompt_manifest.json", "statistics.json", "law_level_effects.csv"]} + for scale, spec in protocol["models"].items(): + inputs.update(ROOT / spec[key] for key in ["development_manifest", "development_states"]) + inputs.update(SOURCE / scale / name for name in ["clean_rows.jsonl", "intervention_rows.jsonl", "frozen_direction.npz", "clean_states.npy", "clean_logits.npy", "preflight.json", "run_record.json"]) + inputs.update([Path(__file__).resolve(), OUT / "ANALYSIS_PLAN.md", OUT / "law_overlap_taxonomy.json"]) + inputs.update((REPO / "PAPER_REVISION/Materials_Science_Interpretability").glob("*.tex")) + inputs.update([REPO / "PAPER_REVISION/COMMENTS PEER REVIEW_FINAL.docx", REPO / "PAPER_REVISION/RESPONSES TO REVIEWERS.pdf"]) + for path in sorted(PORTABLE.rglob("*")): + peer = SOURCE / path.relative_to(PORTABLE) + if path.is_file() and peer.is_file(): + inputs.update([path, peer]) + match = sha(path) == sha(peer) + validation["portable_matches"].append({"relative_path": str(path.relative_to(PORTABLE)), "sha256_equal": match}) + assert match, path + before_hashes = {str(path.relative_to(REPO)): sha(path) for path in sorted(inputs)} + originals = {x["scale"]: x for x in json.loads((SOURCE / "statistics.json").read_text())["scales"]} + original_laws = pd.read_csv(SOURCE / "law_level_effects.csv") + summaries, transitions, strata, prompts_all, law_all, errors_all, loo, family_results = [], [], [], [], [], [], [], [] + development_inventory = [] + for si, scale in enumerate(SCALES): + print(f"Auditing {scale} archived inputs", flush=True) + frozen = np.load(SOURCE / scale / "frozen_direction.npz") + direction, dev_manifest = fitted_direction_audit(protocol["models"][scale], frozen) + for target in taxonomy["laws"]: + assert set(target["development_matches"]).issubset(direction["actual_fitted_law_ids"]) + for law in dev_manifest["laws"]: + development_inventory.append({"scale": scale, **law, "fitted_prompt_count": sum(p["law_id"] == law["law_id"] for p in dev_manifest["prompts"])}) + clean, rows, prompt, long, law, checks = build_scale(scale, manifest) + checks["development_direction"] = direction + states = np.load(SOURCE / scale / "clean_states.npy", mmap_mode="r") + logits = np.load(SOURCE / scale / "clean_logits.npy", mmap_mode="r") + assert states.shape == (192, len(frozen["direction"])) and logits.shape[0] == 192 + preflight = json.loads((SOURCE / scale / "preflight.json").read_text()) + token_ids = preflight["answer_token_ids"] + logit_margins = np.array([float(logits[i, token_ids[row.expected_answer]]) - float(logits[i, token_ids[row.incorrect_answer]]) for i, row in clean.iterrows()]) + checks["max_clean_logit_margin_error"] = float(np.abs(logit_margins - clean.correct_answer_margin).max()) + assert checks["max_clean_logit_margin_error"] == 0 + checks["clean_state_shape"] = list(states.shape) + checks["clean_logit_shape"] = list(logits.shape) + expected_law = original_laws[original_laws.scale == scale].set_index(["law_id", "intervention"]) + calculated = long.set_index(["law_id", "intervention"]).loc[expected_law.index] + err = float(np.abs(calculated[["movement", "margin_change", "kl", "answer_mass"]] - expected_law[["movement", "margin_change", "kl", "answer_mass"]]).to_numpy().max()) + assert err < 1e-10 + checks["max_original_law_table_error"] = err + prompt = prompt.join(tax[["level", "canonical_family"]], on="law_id") + for cohort, levels in COHORTS.items(): + prompt["included_" + cohort] = prompt.level.isin(levels) + prompt["scale"] = scale + prompts_all.append(prompt.reset_index()) + long["scale"] = scale + law_all.append(long) + error_cases = prompt[prompt.clean_margin <= 0].copy() + error_cases["baseline_status"] = np.where(error_cases.clean_margin < 0, "strict_negative", "tie") + error_cases["correct_centroid_repaired"] = (error_cases.clean_margin < 0) & (error_cases.correct_centroid_margin > 0) + errors_all.append(error_cases.reset_index()) + for ci, (cohort, levels) in enumerate(COHORTS.items()): + frame = prompt[prompt.level.isin(levels)] + summary, trans, strat = cohort_analysis(frame, scale, cohort, SEED + si * 100 + ci * 10000) + summaries.append(summary); transitions.extend(trans); strata.extend(strat) + if cohort == "full_24": + mapping = [("necessity_margin_drop", "estimate_ci95", "neutralize"), + ("necessity_margin_drop", "activation_control_estimate_ci95", "neutralize_control_mean"), + ("necessity_margin_drop", "specific_estimate_ci95", "neutralization_specific"), + ("counterfactual_coordinate", "estimate_ci95", "counterfactual_coordinate"), + ("counterfactual_coordinate", "activation_control_estimate_ci95", "counterfactual_control_mean"), + ("counterfactual_coordinate", "specific_estimate_ci95", "counterfactual_specific"), + ("full_counterfactual", "estimate_ci95", "full_counterfactual"), + ("full_counterfactual", "direction_restored_estimate_ci95", "full_counterfactual_direction_restored"), + ("full_counterfactual", "restoration_reduction_estimate_ci95", "restoration_reduction"), + ("causal_mediation_fraction", "coordinate_over_full_estimate_ci95", "coordinate_over_full"), + ("causal_mediation_fraction", "restoration_over_full_estimate_ci95", "restoration_over_full")] + ci_errors = [] + for section, key, endpoint in mapping: + new = summary["endpoints"][endpoint] + triple = [new[k] for k in ["estimate", "ci95_low", "ci95_high"]] + ci_errors.append(float(np.max(np.abs(np.array(triple) - originals[scale][section][key])))) + assert max(ci_errors) < 1e-10 + checks["max_original_estimate_and_ci_error"] = max(ci_errors) + assert summary["clean_accuracy"] == originals[scale]["baseline_answer_margin_accuracy"] + for tr in trans: + old = originals[scale]["accuracy_transitions"][tr["intervention"]] + assert tr["correct_to_wrong"] == old["correct_to_wrong"] + assert tr["wrong_to_correct_including_ties"] == old["wrong_to_correct"] + assert abs(tr["prompt_mcnemar_descriptive_only"] - old["exact_mcnemar_p"]) < 1e-12 + assert summary["repair"]["repaired_prompts"] == originals[scale]["error_repair"]["repaired_prompts"] + if cohort == "exclude_exact": + by_law = frame.groupby("law_id")[ENDPOINTS].mean() + for excluded in by_law.index: + remaining = by_law.drop(excluded) + loo.append({"scale": scale, "cohort": cohort, "omitted_law_id": excluded, + "remaining_n_laws": len(remaining), **{k: float(remaining[k].mean()) for k in ENDPOINTS}}) + if cohort == "conservative_unmatched": + # First equal-weight laws, then equal-weight canonical families. + by_law = frame.groupby(["canonical_family", "law_id"])[ENDPOINTS].mean() + by_family = by_law.groupby(level="canonical_family").mean() + for ei, endpoint in enumerate(["neutralization_specific", "counterfactual_specific"]): + family_results.append({"scale": scale, "cohort": cohort, "endpoint": endpoint, + "families": sorted(by_family.index), "family_means": by_family[endpoint].to_dict(), + **estimate(by_family[endpoint], SEED + 90000 + si * 100 + ei)}) + validation["scales"][scale] = checks + # Holm family comprises exactly two primary endpoints at each of three scales. + primary = [(s, e) for s in summaries if s["cohort"] == "exclude_exact" + for e in ["neutralization_specific", "counterfactual_specific"]] + ordered = sorted(primary, key=lambda item: item[0]["endpoints"][item[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 + target_inventory = pd.DataFrame(manifest["laws"]).set_index("law_id").join(tax) + dev_laws = {x["law_id"]: x for x in development_inventory if x["scale"] == "4b"} + target_inventory["development_evidence"] = target_inventory.development_matches.map( + lambda ids: " | ".join(f"{i}: {dev_laws[i]['control']} -> {dev_laws[i]['response']}; {dev_laws[i]['formula_a']}" for i in ids)) + target_inventory["development_matches"] = target_inventory.development_matches.map(lambda x: ";".join(x)) + for cohort, levels in COHORTS.items(): + target_inventory["included_" + cohort] = target_inventory.level.isin(levels) + target_inventory.to_csv(OUT / "overlap_inventory.csv") + pd.DataFrame(development_inventory).to_csv(OUT / "development_law_inventory.csv", index=False) + pd.concat(prompts_all, ignore_index=True).to_csv(OUT / "reconstructed_prompt_effects.csv", index=False) + pd.concat(law_all, ignore_index=True).to_csv(OUT / "reconstructed_law_effects.csv", index=False) + pd.concat(errors_all, ignore_index=True).to_csv(OUT / "all_baseline_errors_and_ties.csv", index=False) + pd.DataFrame(transitions).to_csv(OUT / "accuracy_transitions.csv", index=False) + pd.DataFrame(strata).to_csv(OUT / "stratified_effects.csv", index=False) + pd.DataFrame(loo).to_csv(OUT / "leave_one_law_out.csv", index=False) + flat = [{k: s[k] for k in ["scale", "cohort", "n_laws", "n_prompts", "n_canonical_families", "clean_accuracy", "clean_strict_negative", "clean_ties"]} + | {"endpoint": e} | result for s in summaries for e, result in s["endpoints"].items()] + pd.DataFrame(flat).to_csv(OUT / "cohort_effects.csv", index=False) + pd.DataFrame([{k: s[k] for k in ["scale", "cohort", "n_laws", "n_prompts"]} | s["repair"] for s in summaries]).to_csv(OUT / "repair_summary.csv", index=False) + json_write("cohort_statistics.json", {"analysis": "retrospective sensitivity; frozen axes unchanged", "bootstrap_resamples": BOOTSTRAPS, + "bootstrap_base_seed": SEED, "primary_holm_family_size": 6, "cohorts": summaries, + "conservative_family_level_diagnostics": family_results}) + after_hashes = {str(path.relative_to(REPO)): sha(path) for path in sorted(inputs)} + assert before_hashes == after_hashes + json_write("input_sha256.json", before_hashes) + validation.update(status="passed", source_and_manuscript_hashes_unchanged=True, + n_hashed_inputs=len(inputs), 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") + json_write("validation.json", validation) + plot(summaries, strata) + print(json.dumps({"status": "passed", "output": str(OUT), "primary": [s for s in summaries if s["cohort"] == "exclude_exact"], + "family_diagnostic": family_results}, indent=2), flush=True) + + +def plot(summaries, strata): + os.environ.setdefault("MPLCONFIGDIR", "/tmp/comment3-mpl") + import matplotlib + matplotlib.use("Agg") + import matplotlib.pyplot as plt + colors = ["#85919c", "#147d92", "#805caf", "#bb6a29"] + labels = ["All 24", "Exclude exact (21)", "Exclude exact + related (14)", "Conservative subset (6)"] + plt.rcParams.update({"font.family": "DejaVu Sans", "font.size": 10, "axes.spines.top": False, "axes.spines.right": False}) + fig, axes = plt.subplots(2, 3, figsize=(12, 6.5), sharey="row", layout="constrained") + for si, scale in enumerate(SCALES): + for ei, (endpoint, label) in enumerate([("neutralization_specific", "Neutralization minus controls (logits)"), + ("counterfactual_specific", "Coordinate swap minus controls (logits)")]): + ax = axes[ei, si] + for ci, cohort in enumerate(COHORTS): + result = next(s for s in summaries if s["scale"] == scale and s["cohort"] == cohort)["endpoints"][endpoint] + m, lo, hi = (result[k] for k in ["estimate", "ci95_low", "ci95_high"]) + ax.errorbar(ci, m, yerr=[[m - lo], [hi - m]], fmt="o", color=colors[ci], capsize=4, ms=6) + ax.axhline(0, color="#666666", linewidth=.7) + ax.set_xticks(range(4), ["24", "21", "14", "6"]) + ax.set_xlim(-.5, 3.5) + if ei == 0: + ax.set_title(scale.upper(), fontweight="bold") + if si == 0: + ax.set_ylabel(label) + if ei == 1: + ax.set_xlabel("Relations retained") + handles = [plt.Line2D([], [], marker="o", ls="", color=color, label=label) for color, label in zip(colors, labels)] + fig.legend(handles=handles, loc="outside lower center", ncol=2, frameon=False) + fig.suptitle("Causal effects under retrospective overlap exclusions\nMean and 95% relation-cluster bootstrap interval", fontsize=13) + fig.savefig(OUT / "overlap_sensitivity.png", dpi=180) + plt.close(fig) + fig, axes = plt.subplots(2, 3, figsize=(12, 7), sharey="row", layout="constrained") + groups = [("answer_vocabulary", "higher-lower", "higher /\nlower"), + ("answer_vocabulary", "greater-smaller", "greater /\nsmaller"), + ("physical_sign", "-1", "Negative\noutcome"), + ("physical_sign", "1", "Positive\noutcome")] + colors = ["#147d92", "#147d92", "#805caf", "#805caf"] + for si, scale in enumerate(SCALES): + for ei, (endpoint, label) in enumerate([("neutralization_specific", "Neutralization minus controls (logits)"), + ("counterfactual_specific", "Coordinate swap minus controls (logits)")]): + ax = axes[ei, si] + for gi, (factor, value, _) in enumerate(groups): + result = next(r for r in strata if r["scale"] == scale and r["cohort"] == "exclude_exact" + and r["endpoint"] == endpoint and r["factor"] == factor and r["value"] == value) + m, lo, hi = (result[k] for k in ["estimate", "ci95_low", "ci95_high"]) + ax.errorbar(gi, m, yerr=[[m - lo], [hi - m]], fmt="o", color=colors[gi], capsize=4, ms=6) + ax.axhline(0, color="#666666", linewidth=.7) + ax.axvline(1.5, color="#cccccc", linewidth=.7, linestyle="--") + ax.set_xticks(range(4), [g[2] for g in groups], fontsize=9) + ax.set_xlim(-.5, 3.5) + if ei == 0: + ax.set_title(scale.upper(), fontweight="bold") + if si == 0: + ax.set_ylabel(label) + fig.suptitle("Context dependence after excluding the three repeated relations\n21 relations; mean and 95% relation-cluster bootstrap interval", fontsize=13) + fig.savefig(OUT / "vocabulary_and_polarity.png", dpi=180) + plt.close(fig) + + +if __name__ == "__main__": + main() diff --git a/jlens_materials/scripts/analyze_causal_relational_knockout.py b/jlens_materials/scripts/analyze_causal_relational_knockout.py new file mode 100644 index 0000000000000000000000000000000000000000..5889628bfbd043ab55b1d945f42c5ef1ea68b5ee --- /dev/null +++ b/jlens_materials/scripts/analyze_causal_relational_knockout.py @@ -0,0 +1,648 @@ +#!/usr/bin/env python3 +"""Analyze and plot the frozen causal relational knockout experiment.""" + +from __future__ import annotations + +import json +import math +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 + + +ROOT = Path(__file__).resolve().parents[1] +EXP = ROOT / "experiments" / "causal-relational-knockout-2026-09-02" +SCALES = ["4b", "12b", "31b"] +SCALE_LABELS = {"4b": "4B", "12b": "12B", "31b": "31B"} +COLORS = { + "coordinate": "#178f8f", + "full": "#315f9b", + "control": "#9aa0a4", + "restored": "#7656a8", + "correct": "#d19a2a", + "wrong": "#bb5b4c", +} + + +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, n: int = 30000) -> tuple[float, float, float]: + values = np.asarray(values, dtype=float) + rng = np.random.default_rng(seed) + draws = values[rng.integers(0, len(values), size=(n, len(values)))].mean(axis=1) + return float(values.mean()), float(np.quantile(draws, 0.025)), float(np.quantile(draws, 0.975)) + + +def paired_permutation(values: np.ndarray, *, seed: int, n: int = 100000) -> float: + values = np.asarray(values, dtype=float) + observed = abs(float(values.mean())) + rng = np.random.default_rng(seed) + extreme = 0 + batch = 5000 + for start in range(0, n, batch): + count = min(batch, n - start) + signs = rng.choice(np.asarray([-1.0, 1.0]), size=(count, len(values))) + null = np.abs((signs * values).mean(axis=1)) + extreme += int(np.sum(null >= observed - 1e-15)) + return float((extreme + 1) / (n + 1)) + + +def exact_paired_accuracy_p(correct_to_wrong: int, wrong_to_correct: int) -> float: + """Exact two-sided McNemar/binomial test on discordant prompt outcomes.""" + discordant = correct_to_wrong + wrong_to_correct + if discordant == 0: + return 1.0 + tail = min(correct_to_wrong, wrong_to_correct) + probability = sum(math.comb(discordant, k) for k in range(tail + 1)) / (2 ** discordant) + return float(min(1.0, 2.0 * probability)) + + +def one_sided_sign_p(positive: int, total: int) -> float: + """Exact probability of at least this many positive signs under p=0.5.""" + return float(sum(math.comb(total, k) for k in range(positive, total + 1)) / (2 ** total)) + + +def bootstrap_ratio(numerator: np.ndarray, denominator: np.ndarray, *, seed: int, + n: int = 30000) -> tuple[float, float, float]: + numerator = np.asarray(numerator, dtype=float) + denominator = np.asarray(denominator, dtype=float) + rng = np.random.default_rng(seed) + indices = rng.integers(0, len(numerator), size=(n, len(numerator))) + num = numerator[indices].mean(axis=1) + den = denominator[indices].mean(axis=1) + ratios = np.divide(num, den, out=np.full_like(num, np.nan), where=np.abs(den) > 1e-12) + observed = float(numerator.mean() / denominator.mean()) + return observed, float(np.nanquantile(ratios, 0.025)), float(np.nanquantile(ratios, 0.975)) + + +def aggregate_scale(scale: str, protocol: dict) -> tuple[dict, pd.DataFrame, pd.DataFrame]: + folder = EXP / scale + clean = pd.DataFrame(read_jsonl(folder / "clean_rows.jsonl")) + rows = pd.DataFrame(read_jsonl(folder / "intervention_rows.jsonl")) + control_amendment = json.loads((EXP / "control_amendment.json").read_text()) + n_control_axes = len(control_amendment["construction"]["seeds"]) + expected = len(clean) * (6 + 4 * n_control_axes) + if len(clean) != 192 or len(rows) != expected: + raise RuntimeError(f"incomplete {scale}: clean={len(clean)}, interventions={len(rows)}/{expected}") + + control_neutral = rows[rows["intervention"].str.startswith("neutralize_control_")].groupby( + "prompt_id", as_index=False + ).agg( + movement_toward_counterfactual=("movement_toward_counterfactual", "mean"), + correct_margin_change=("correct_margin_change", "mean"), + clean_to_intervened_kl=("clean_to_intervened_kl", "mean"), + registered_answer_probability_mass=("registered_answer_probability_mass", "mean"), + ) + control_neutral["intervention"] = "neutralize_control_mean" + control_cf = rows[rows["intervention"].str.startswith("counterfactual_control_")].groupby( + "prompt_id", as_index=False + ).agg( + movement_toward_counterfactual=("movement_toward_counterfactual", "mean"), + correct_margin_change=("correct_margin_change", "mean"), + clean_to_intervened_kl=("clean_to_intervened_kl", "mean"), + registered_answer_probability_mass=("registered_answer_probability_mass", "mean"), + ) + control_cf["intervention"] = "counterfactual_control_mean" + metadata = clean.drop( + columns=[ + "correct_answer_margin", "answer_margin_correct", "first_minus_second_logit", + "registered_answer_probability_mass", "global_top_is_registered_answer", + "clean_to_intervened_kl", "state_norm", "n_tokens", + ], + errors="ignore", + ) + rows_augmented = pd.concat( + [rows, control_neutral.merge(metadata, on="prompt_id"), control_cf.merge(metadata, on="prompt_id")], + ignore_index=True, + sort=False, + ) + + law = rows_augmented.groupby(["law_id", "intervention"], as_index=False).agg( + movement=("movement_toward_counterfactual", "mean"), + margin_change=("correct_margin_change", "mean"), + kl=("clean_to_intervened_kl", "mean"), + answer_mass=("registered_answer_probability_mass", "mean"), + ) + wide_movement = law.pivot(index="law_id", columns="intervention", values="movement") + necessity = wide_movement["neutralize"].to_numpy() + necessity_control = wide_movement["neutralize_control_mean"].to_numpy() + necessity_specific = necessity - necessity_control + coordinate = wide_movement["counterfactual_coordinate"].to_numpy() + coordinate_control = wide_movement["counterfactual_control_mean"].to_numpy() + coordinate_specific = coordinate - coordinate_control + full = wide_movement["full_counterfactual"].to_numpy() + restored = wide_movement["full_counterfactual_direction_restored"].to_numpy() + restoration = full - restored + seed = int(protocol["inference"]["bootstrap_seed"]) + SCALES.index(scale) * 100 + + clean_margin = clean.set_index("prompt_id")["correct_answer_margin"] + correct_centroid = rows[rows["intervention"] == "correct_centroid"].set_index("prompt_id") + wrong_mask = clean_margin < 0 + wrong_ids = clean_margin.index[wrong_mask] + repair_before = clean_margin.loc[wrong_ids].to_numpy(dtype=float) + repair_after = correct_centroid.loc[wrong_ids, "correct_answer_margin"].to_numpy(dtype=float) + repaired = int(np.sum(repair_after > 0)) + + intervention_accuracy = { + "clean": float(clean["answer_margin_correct"].mean()) + } + for name, frame in rows.groupby("intervention"): + intervention_accuracy[name] = float(frame["answer_margin_correct"].mean()) + + clean_correct = clean.set_index("prompt_id")["answer_margin_correct"].astype(bool) + accuracy_transitions = {} + for name in ( + "neutralize", + "counterfactual_coordinate", + "wrong_centroid", + "correct_centroid", + "full_counterfactual", + "full_counterfactual_direction_restored", + ): + intervened = rows[rows["intervention"] == name].set_index("prompt_id")[ + "answer_margin_correct" + ].astype(bool).reindex(clean_correct.index) + correct_to_wrong = int(np.sum(clean_correct & ~intervened)) + wrong_to_correct = int(np.sum(~clean_correct & intervened)) + accuracy_transitions[name] = { + "correct_to_wrong": correct_to_wrong, + "wrong_to_correct": wrong_to_correct, + "unchanged_correct": int(np.sum(clean_correct & intervened)), + "unchanged_wrong": int(np.sum(~clean_correct & ~intervened)), + "exact_mcnemar_p": exact_paired_accuracy_p(correct_to_wrong, wrong_to_correct), + } + + full_choice = rows[rows["intervention"] == "full_counterfactual"].set_index( + "prompt_id" + )["answer_margin_correct"].astype(bool).reindex(clean_correct.index) + restored_choice = rows[ + rows["intervention"] == "full_counterfactual_direction_restored" + ].set_index("prompt_id")["answer_margin_correct"].astype(bool).reindex(clean_correct.index) + full_counterfactual_wrong = ~full_choice + restored_original = full_counterfactual_wrong & restored_choice + + strata = {} + for factor in ("category", "answer_vocabulary", "answer_order"): + strata[factor] = {} + for value, frame in rows_augmented.groupby(factor): + stratum_law = frame.groupby(["law_id", "intervention"], as_index=False).agg( + movement=("movement_toward_counterfactual", "mean") + ) + stratum_wide = stratum_law.pivot( + index="law_id", columns="intervention", values="movement" + ) + neutral_specific = ( + stratum_wide["neutralize"] + - stratum_wide["neutralize_control_mean"] + ).to_numpy() + counterfactual_specific = ( + stratum_wide["counterfactual_coordinate"] + - stratum_wide["counterfactual_control_mean"] + ).to_numpy() + factor_seed = seed + 1000 + len(strata[factor]) * 20 + strata[factor][str(value)] = { + "n_laws": int(len(stratum_wide)), + "necessity_specific_estimate_ci95": bootstrap_mean( + neutral_specific, seed=factor_seed + ), + "counterfactual_specific_estimate_ci95": bootstrap_mean( + counterfactual_specific, seed=factor_seed + 1 + ), + } + + result = { + "scale": scale, + "model": protocol["models"][scale]["model"], + "probe_layer": protocol["models"][scale]["probe_layer"], + "n_laws": int(law["law_id"].nunique()), + "n_prompts": int(len(clean)), + "baseline_answer_margin_accuracy": intervention_accuracy["clean"], + "necessity_margin_drop": { + "estimate_ci95": bootstrap_mean(necessity, seed=seed), + "activation_control_estimate_ci95": bootstrap_mean(necessity_control, seed=seed + 1), + "specific_estimate_ci95": bootstrap_mean(necessity_specific, seed=seed + 2), + "specific_permutation_p": paired_permutation( + necessity_specific, + seed=int(protocol["inference"]["permutation_seed"]) + SCALES.index(scale), + n=int(protocol["inference"]["law_sign_permutations"]), + ), + }, + "counterfactual_coordinate": { + "estimate_ci95": bootstrap_mean(coordinate, seed=seed + 3), + "activation_control_estimate_ci95": bootstrap_mean(coordinate_control, seed=seed + 4), + "specific_estimate_ci95": bootstrap_mean(coordinate_specific, seed=seed + 5), + "specific_permutation_p": paired_permutation( + coordinate_specific, + seed=int(protocol["inference"]["permutation_seed"]) + 10 + SCALES.index(scale), + n=int(protocol["inference"]["law_sign_permutations"]), + ), + }, + "full_counterfactual": { + "estimate_ci95": bootstrap_mean(full, seed=seed + 6), + "direction_restored_estimate_ci95": bootstrap_mean(restored, seed=seed + 7), + "restoration_reduction_estimate_ci95": bootstrap_mean(restoration, seed=seed + 8), + "restoration_permutation_p": paired_permutation( + restoration, + seed=int(protocol["inference"]["permutation_seed"]) + 20 + SCALES.index(scale), + n=int(protocol["inference"]["law_sign_permutations"]), + ), + }, + "causal_mediation_fraction": { + "coordinate_over_full_estimate_ci95": bootstrap_ratio( + coordinate, full, seed=seed + 9 + ), + "restoration_over_full_estimate_ci95": bootstrap_ratio( + restoration, full, seed=seed + 10 + ), + }, + "law_level_breadth": { + "neutralization_positive": int(np.sum(necessity > 0)), + "neutralization_specific_positive": int(np.sum(necessity_specific > 0)), + "counterfactual_coordinate_positive": int(np.sum(coordinate > 0)), + "counterfactual_specific_positive": int(np.sum(coordinate_specific > 0)), + "restoration_reduction_positive": int(np.sum(restoration > 0)), + "denominator_laws": int(len(wide_movement)), + "neutralization_specific_one_sided_sign_p": one_sided_sign_p( + int(np.sum(necessity_specific > 0)), len(wide_movement) + ), + "counterfactual_specific_one_sided_sign_p": one_sided_sign_p( + int(np.sum(coordinate_specific > 0)), len(wide_movement) + ), + "restoration_one_sided_sign_p": one_sided_sign_p( + int(np.sum(restoration > 0)), len(wide_movement) + ), + }, + "next_token_kl": { + "neutralize_estimate_ci95": bootstrap_mean( + law.pivot(index="law_id", columns="intervention", values="kl")["neutralize"].to_numpy(), + seed=seed + 11, + ), + "neutralize_control_estimate_ci95": bootstrap_mean( + law.pivot(index="law_id", columns="intervention", values="kl")["neutralize_control_mean"].to_numpy(), + seed=seed + 12, + ), + "counterfactual_coordinate_estimate_ci95": bootstrap_mean( + law.pivot(index="law_id", columns="intervention", values="kl")["counterfactual_coordinate"].to_numpy(), + seed=seed + 13, + ), + "counterfactual_control_estimate_ci95": bootstrap_mean( + law.pivot(index="law_id", columns="intervention", values="kl")["counterfactual_control_mean"].to_numpy(), + seed=seed + 14, + ), + "full_counterfactual_estimate_ci95": bootstrap_mean( + law.pivot(index="law_id", columns="intervention", values="kl")["full_counterfactual"].to_numpy(), + seed=seed + 15, + ), + }, + "error_repair": { + "initially_wrong_prompts": int(len(wrong_ids)), + "repaired_prompts": repaired, + "repair_fraction": float(repaired / len(wrong_ids)) if len(wrong_ids) else None, + "mean_margin_before": float(repair_before.mean()) if len(wrong_ids) else None, + "mean_margin_after": float(repair_after.mean()) if len(wrong_ids) else None, + "mean_margin_change": float((repair_after - repair_before).mean()) if len(wrong_ids) else None, + }, + "answer_margin_accuracy_by_intervention": intervention_accuracy, + "accuracy_transitions": accuracy_transitions, + "counterfactual_restoration_decisions": { + "full_swap_not_original_answer": int(full_counterfactual_wrong.sum()), + "original_answer_recovered_after_coordinate_restoration": int( + restored_original.sum() + ), + "recovery_fraction": float( + restored_original.sum() / full_counterfactual_wrong.sum() + ), + }, + "stratified_specific_effects": strata, + } + + law["scale"] = scale + prompt_export = rows_augmented.copy() + prompt_export["scale"] = scale + return result, law, prompt_export + + +def interval(ax, x: float, triple: list[float] | tuple[float, float, float], color: str, + marker: str = "o", label: str | None = None) -> None: + mean, low, high = triple + ax.errorbar( + [x], [mean], yerr=[[mean - low], [high - mean]], fmt=marker, ms=6.2, + capsize=3.2, color=color, ecolor=color, elinewidth=1.4, label=label, zorder=5, + ) + + +def plot( + results: list[dict], + law_all: pd.DataFrame, + *, + figures_dir: Path | None = None, + font_scale: float = 1.0, + include_supplementary: bool = True, +) -> None: + plt.rcParams.update( + { + "font.family": "DejaVu Sans", + "font.size": 8.5 * font_scale, + "axes.labelsize": 9 * font_scale, + "xtick.labelsize": 8 * font_scale, + "ytick.labelsize": 8 * font_scale, + "axes.spines.top": False, + "axes.spines.right": False, + "pdf.fonttype": 42, + "ps.fonttype": 42, + } + ) + figures = figures_dir or EXP / "figures" + figures.mkdir(parents=True, exist_ok=True) + fig, axes = plt.subplots(2, 2, figsize=(7.35, 5.85), constrained_layout=True) + ax_a, ax_b, ax_c, ax_d = axes.ravel() + x = np.arange(len(results), dtype=float) + law_jitter = np.linspace(-0.045, 0.045, 24) + + for index, result in enumerate(results): + frame = law_all[law_all["scale"] == result["scale"]] + wide = frame.pivot(index="law_id", columns="intervention", values="movement").sort_index() + for position, column, color, marker in ( + (index - 0.11, "neutralize", COLORS["coordinate"], "o"), + (index + 0.11, "neutralize_control_mean", COLORS["control"], "s"), + ): + ax_a.scatter( + position + law_jitter, wide[column].to_numpy(), s=8, marker=marker, + color=color, alpha=0.28, linewidth=0, zorder=2, + ) + interval( + ax_a, index - 0.11, result["necessity_margin_drop"]["estimate_ci95"], + COLORS["coordinate"], label="Relational coordinate" if index == 0 else None, + ) + interval( + ax_a, index + 0.11, result["necessity_margin_drop"]["activation_control_estimate_ci95"], + COLORS["control"], marker="s", + label="Activation-matched control" if index == 0 else None, + ) + ax_a.axhline(0, color="#666666", lw=0.8) + ax_a.set_xticks(x, [SCALE_LABELS[r["scale"]] for r in results]) + ax_a.set_ylabel("Loss of correct-answer margin") + ax_a.legend( + frameon=False, fontsize=7.2 * font_scale, + loc="lower center", bbox_to_anchor=(0.5, 1.015), + ncol=2, borderaxespad=0, + ) + ax_a.text(-0.06, 1.22, "A", transform=ax_a.transAxes, fontweight="bold", va="bottom") + + offsets = [-0.22, 0.0, 0.22] + for index, result in enumerate(results): + frame = law_all[law_all["scale"] == result["scale"]] + wide = frame.pivot(index="law_id", columns="intervention", values="movement").sort_index() + for offset, column, color, marker in ( + (offsets[0], "counterfactual_coordinate", COLORS["coordinate"], "o"), + (offsets[1], "full_counterfactual", COLORS["full"], "D"), + (offsets[2], "counterfactual_control_mean", COLORS["control"], "s"), + ): + ax_b.scatter( + index + offset + law_jitter, wide[column].to_numpy(), s=8, marker=marker, + color=color, alpha=0.25, linewidth=0, zorder=2, + ) + interval( + ax_b, index + offsets[0], result["counterfactual_coordinate"]["estimate_ci95"], + COLORS["coordinate"], label="One-coordinate swap" if index == 0 else None, + ) + interval( + ax_b, index + offsets[1], result["full_counterfactual"]["estimate_ci95"], + COLORS["full"], marker="D", label="Full-state swap" if index == 0 else None, + ) + interval( + ax_b, index + offsets[2], result["counterfactual_coordinate"]["activation_control_estimate_ci95"], + COLORS["control"], marker="s", + label="Activation-matched control" if index == 0 else None, + ) + ax_b.axhline(0, color="#666666", lw=0.8) + ax_b.set_xticks(x, [SCALE_LABELS[r["scale"]] for r in results]) + ax_b.set_ylabel("Movement toward counterfactual answer") + ax_b.legend( + frameon=False, fontsize=7.2 * font_scale, + loc="lower center", bbox_to_anchor=(0.65, 1.015), + ncol=2, borderaxespad=0, + ) + ax_b.text(-0.06, 1.22, "B", transform=ax_b.transAxes, fontweight="bold", va="bottom") + + for index, result in enumerate(results): + interval( + ax_c, index - 0.10, + result["causal_mediation_fraction"]["coordinate_over_full_estimate_ci95"], + COLORS["coordinate"], label="Coordinate / full effect" if index == 0 else None, + ) + interval( + ax_c, index + 0.10, + result["causal_mediation_fraction"]["restoration_over_full_estimate_ci95"], + COLORS["restored"], marker="D", + label="Effect removed by restoring coordinate" if index == 0 else None, + ) + ax_c.axhline(0, color="#666666", lw=0.8) + ax_c.axhline(1, color="#888888", lw=0.8, ls="--") + ax_c.set_xticks(x, [SCALE_LABELS[r["scale"]] for r in results]) + ax_c.set_ylabel("Fraction of full-state effect") + ax_c.legend( + frameon=False, fontsize=7.2 * font_scale, loc="lower right", + bbox_to_anchor=(1.0, 1.015), borderaxespad=0, + ) + ax_c.text(-0.06, 1.19, "C", transform=ax_c.transAxes, fontweight="bold", va="bottom") + + names = [ + "clean", "neutralize", "counterfactual_coordinate", "full_counterfactual", + "full_counterfactual_direction_restored", + ] + display = [ + "Clean", "Neutralized", "Coordinate swap", "Full-state swap", + "Coordinate restored", + ] + scale_colors = ["#5e94b9", "#2a747b", "#846caf"] + for scale_index, result in enumerate(results): + values = [result["answer_margin_accuracy_by_intervention"][name] for name in names] + ax_d.plot( + np.arange(len(names)), values, marker="o", ms=4.5, lw=1.5, + color=scale_colors[scale_index], label=SCALE_LABELS[result["scale"]], + ) + ax_d.axhline(0.5, color="#777777", lw=0.8, ls="--") + ax_d.set_ylim(0, 1.04) + ax_d.set_xticks(np.arange(len(names)), display) + plt.setp( + ax_d.get_xticklabels(), rotation=24, ha="right", rotation_mode="anchor", + ) + ax_d.set_ylabel("Answer-margin accuracy") + ax_d.legend( + frameon=False, fontsize=7.2 * font_scale, ncol=len(results), + loc="upper center", bbox_to_anchor=(0.5, -0.40), borderaxespad=0, + columnspacing=1.2, handletextpad=0.5, + ) + ax_d.text(-0.06, 1.035, "D", transform=ax_d.transAxes, fontweight="bold", va="bottom") + + for extension in ("pdf", "png", "svg"): + fig.savefig( + figures / f"causal-relational-knockout.{extension}", + dpi=400 if extension == "png" else None, bbox_inches="tight", facecolor="white", + ) + plt.close(fig) + + if not include_supplementary: + return + + # A law-level paired audit retains every independent unit without area distortion. + fig, axes = plt.subplots( + 1, len(results), figsize=(7.35, 3.0), sharey=True, constrained_layout=True + ) + axes_array = np.atleast_1d(axes) + for panel_index, (ax, result) in enumerate(zip(axes_array, results)): + frame = law_all[law_all["scale"] == result["scale"]] + wide = frame.pivot(index="law_id", columns="intervention", values="movement").sort_index() + control = wide["neutralize_control_mean"].to_numpy() + physical = wide["neutralize"].to_numpy() + for left, right in zip(control, physical): + color = COLORS["coordinate"] if right > left else COLORS["wrong"] + ax.plot([0, 1], [left, right], color=color, lw=0.75, alpha=0.42, zorder=1) + ax.scatter( + np.zeros(len(control)), control, s=11, color=COLORS["control"], + alpha=0.75, linewidth=0, zorder=2, + ) + ax.scatter( + np.ones(len(physical)), physical, s=13, color=COLORS["coordinate"], + alpha=0.78, linewidth=0, zorder=2, + ) + ax.axhline(0, color="#777777", lw=0.75, ls="--") + ax.set_xlim(-0.28, 1.28) + ax.set_xticks([0, 1], ["Control", "Coordinate"]) + ax.set_title(SCALE_LABELS[result["scale"]], fontsize=9 * font_scale, fontweight="normal") + ax.text( + -0.10, 1.04, chr(ord("A") + panel_index), transform=ax.transAxes, + fontweight="bold", va="bottom", + ) + axes_array[0].set_ylabel("Loss of correct-answer margin") + for extension in ("pdf", "png", "svg"): + fig.savefig( + figures / f"causal-relational-knockout-law-audit.{extension}", + dpi=400 if extension == "png" else None, bbox_inches="tight", facecolor="white", + ) + plt.close(fig) + + # Conditional error repair is shown for every scale with naturally wrong margins. + repair_scales = [ + result["scale"] for result in results + if result["error_repair"]["initially_wrong_prompts"] + ] + if repair_scales: + fig, axes = plt.subplots( + 1, len(repair_scales), figsize=(7.35, 2.75), sharey=True, + constrained_layout=True, + ) + axes_array = np.atleast_1d(axes) + for panel_index, (ax, scale) in enumerate(zip(axes_array, repair_scales)): + clean = pd.DataFrame(read_jsonl(EXP / scale / "clean_rows.jsonl")).set_index("prompt_id") + rows = pd.DataFrame(read_jsonl(EXP / scale / "intervention_rows.jsonl")) + repaired = rows[rows["intervention"] == "correct_centroid"].set_index("prompt_id") + selected = clean[clean["correct_answer_margin"] < 0] + before = selected["correct_answer_margin"].to_numpy() + after = repaired.loc[selected.index, "correct_answer_margin"].to_numpy() + for left, right in zip(before, after): + color = COLORS["coordinate"] if right > 0 else COLORS["control"] + ax.plot([0, 1], [left, right], color=color, lw=1.0, alpha=0.7) + ax.scatter([0, 1], [left, right], s=16, color=color, zorder=3) + ax.axhline(0, color="#555555", lw=0.8) + ax.set_xlim(-0.25, 1.25) + ax.set_xticks([0, 1], ["Clean", "Correct-centroid\nintervention"]) + ax.set_title(SCALE_LABELS[scale], fontsize=9 * font_scale, fontweight="normal") + ax.text( + -0.08, 1.04, chr(ord("A") + panel_index), transform=ax.transAxes, + fontweight="bold", va="bottom", + ) + repaired_count = int(np.sum(after > 0)) + ax.text( + 0.98, 0.04, f"{repaired_count}/{len(before)} cross zero", + transform=ax.transAxes, ha="right", va="bottom", fontsize=7.3 * font_scale, + color="#555555", + ) + axes_array[0].set_ylabel("Correct-answer margin") + for extension in ("pdf", "png", "svg"): + fig.savefig( + figures / f"causal-relational-knockout-error-repair.{extension}", + dpi=400 if extension == "png" else None, bbox_inches="tight", facecolor="white", + ) + plt.close(fig) + + +def main() -> None: + protocol = json.loads((EXP / "protocol.json").read_text()) + results = [] + laws = [] + prompts = [] + for scale in SCALES: + if not (EXP / scale / "run_record.json").exists(): + continue + result, law, prompt = aggregate_scale(scale, protocol) + results.append(result) + laws.append(law) + prompts.append(prompt) + (EXP / scale / "statistics.json").write_text( + json.dumps(result, indent=2) + "\n", encoding="utf-8" + ) + if not results: + raise SystemExit("no complete scale runs") + law_all = pd.concat(laws, ignore_index=True) + prompt_all = pd.concat(prompts, ignore_index=True) + law_all.to_csv(EXP / "law_level_effects.csv", index=False) + prompt_all.to_csv(EXP / "prompt_level_effects.csv", index=False) + summary_rows = [] + stratum_rows = [] + for result in results: + summary_rows.append( + { + "scale": result["scale"], + "clean_accuracy": result["baseline_answer_margin_accuracy"], + "neutralization_margin_loss": result["necessity_margin_drop"]["estimate_ci95"][0], + "neutralization_specific": result["necessity_margin_drop"]["specific_estimate_ci95"][0], + "neutralization_specific_ci_low": result["necessity_margin_drop"]["specific_estimate_ci95"][1], + "neutralization_specific_ci_high": result["necessity_margin_drop"]["specific_estimate_ci95"][2], + "counterfactual_coordinate_movement": result["counterfactual_coordinate"]["estimate_ci95"][0], + "counterfactual_specific": result["counterfactual_coordinate"]["specific_estimate_ci95"][0], + "full_counterfactual_movement": result["full_counterfactual"]["estimate_ci95"][0], + "coordinate_over_full": result["causal_mediation_fraction"]["coordinate_over_full_estimate_ci95"][0], + "restoration_over_full": result["causal_mediation_fraction"]["restoration_over_full_estimate_ci95"][0], + "positive_neutralization_laws": result["law_level_breadth"]["neutralization_specific_positive"], + "positive_counterfactual_laws": result["law_level_breadth"]["counterfactual_specific_positive"], + "initially_wrong_prompts": result["error_repair"]["initially_wrong_prompts"], + "repaired_prompts": result["error_repair"]["repaired_prompts"], + } + ) + for factor, values in result["stratified_specific_effects"].items(): + for level, estimates in values.items(): + stratum_rows.append( + { + "scale": result["scale"], + "factor": factor, + "level": level, + "n_laws": estimates["n_laws"], + "neutralization_specific": estimates["necessity_specific_estimate_ci95"][0], + "neutralization_ci_low": estimates["necessity_specific_estimate_ci95"][1], + "neutralization_ci_high": estimates["necessity_specific_estimate_ci95"][2], + "counterfactual_specific": estimates["counterfactual_specific_estimate_ci95"][0], + "counterfactual_ci_low": estimates["counterfactual_specific_estimate_ci95"][1], + "counterfactual_ci_high": estimates["counterfactual_specific_estimate_ci95"][2], + } + ) + pd.DataFrame(summary_rows).to_csv(EXP / "summary_table.csv", index=False) + pd.DataFrame(stratum_rows).to_csv(EXP / "stratum_effects.csv", index=False) + (EXP / "statistics.json").write_text( + json.dumps({"study_id": protocol["study_id"], "scales": results}, indent=2) + "\n", + encoding="utf-8", + ) + plot(results, law_all) + print(json.dumps(results, indent=2)) + + +if __name__ == "__main__": + main() diff --git a/jlens_materials/scripts/analyze_core_discovery_discipline.py b/jlens_materials/scripts/analyze_core_discovery_discipline.py new file mode 100644 index 0000000000000000000000000000000000000000..b9a3e46187ff6c7ec8ccb82097f49adeda12130d --- /dev/null +++ b/jlens_materials/scripts/analyze_core_discovery_discipline.py @@ -0,0 +1,957 @@ +#!/usr/bin/env python3 +"""Run the archived-data core-discovery discipline audit.""" + +from __future__ import annotations + +import hashlib +import itertools +import json +import math +from pathlib import Path +from typing import Iterable, 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 sklearn.feature_extraction.text import TfidfVectorizer # noqa: E402 +from sklearn.metrics import roc_auc_score # noqa: E402 + +from analyze_graph_topology_rigorous import cosine_layers # noqa: E402 + + +ROOT = Path(__file__).resolve().parents[1] +OUT = ROOT / "experiments" / "core-discovery-discipline-audit-2026-07-18" +PROTOCOL = OUT / "protocol.json" +FIG = OUT / "figures" + + +COLORS = { + "jacobian": "#16697A", + "direct": "#5A7D8C", + "raw": "#7A6AA2", + "word_tfidf": "#D17C38", + "char_tfidf": "#B56576", + "own": "#16697A", + "wrong-mechanism": "#7A6AA2", + "direct_control": "#5B8C5A", + "random": "#8C8C8C", +} + +METHOD_LABELS = { + "jacobian": "Jacobian", + "direct": "Direct", + "raw": "Raw", + "word_tfidf": "Word TF-IDF", + "char_tfidf": "Character TF-IDF", +} + + +def sha256(path: Path) -> str: + return hashlib.sha256(path.read_bytes()).hexdigest() + + +def normalize(values: np.ndarray) -> np.ndarray: + denom = np.linalg.norm(values, axis=-1, keepdims=True) + return values / np.maximum(denom, 1e-12) + + +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(k): json_safe(v) for k, v in value.items()} + if isinstance(value, (list, tuple)): + return [json_safe(v) for v in value] + return value + + +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_interval( + 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) + low, high = np.quantile(means, [0.025, 0.975]) + return float(low), float(high) + + +def validate_inputs(protocol: Mapping[str, object]) -> dict[str, str]: + actual: dict[str, str] = {} + for name, row in protocol["inputs"].items(): # type: ignore[index] + path = ROOT / str(row["path"]) + actual[name] = sha256(path) + expected = str(row["sha256"]) + if actual[name] != expected: + raise RuntimeError( + f"input fingerprint mismatch for {name}: " + f"{actual[name]} != {expected}" + ) + return actual + + +def family_orientations( + rows: Sequence[Mapping[str, object]], +) -> dict[str, str]: + orientations: dict[str, str] = {} + for row in rows: + if str(row["numeric_direction"]) != "increase": + continue + family = str(row["family_id"]) + direct = str(row["expected_outcome"]) == str(row["outcome_positive"]) + value = "direct" if direct else "inverse" + if family in orientations and orientations[family] != value: + raise RuntimeError(f"inconsistent orientation for {family}") + orientations[family] = value + if sorted(orientations.values()).count("direct") != 3: + raise RuntimeError(f"unexpected orientation balance: {orientations}") + if sorted(orientations.values()).count("inverse") != 3: + raise RuntimeError(f"unexpected orientation balance: {orientations}") + return orientations + + +def cross_law_contrast( + similarity: np.ndarray, + rows: Sequence[Mapping[str, object]], + family_a: str, + family_b: str, +) -> tuple[float, list[float]]: + families = np.asarray([str(row["family_id"]) for row in rows]) + variants = np.asarray([str(row["variant"]) for row in rows]) + numeric = np.asarray([str(row["numeric_direction"]) for row in rows]) + physical = np.asarray( + [ + str(row["expected_outcome"]) == str(row["outcome_positive"]) + for row in rows + ], + dtype=bool, + ) + cells = [] + for variant_a in sorted(set(variants)): + for variant_b in sorted(set(variants)): + source = np.flatnonzero( + (families == family_a) & (variants == variant_a) + ) + target = np.flatnonzero( + (families == family_b) & (variants == variant_b) + ) + pair_scores = similarity[np.ix_(source, target)].reshape(-1) + same_numeric = ( + numeric[source, None] == numeric[target][None, :] + ).reshape(-1) + same_physical = ( + physical[source, None] == physical[target][None, :] + ).reshape(-1) + if len(pair_scores) != 16: + raise RuntimeError("each surface cell must contain 16 pairs") + if int(same_numeric.sum()) != 8 or int(same_physical.sum()) != 8: + raise RuntimeError("surface cell is not balanced") + if not np.array_equal(same_physical, ~same_numeric): + raise RuntimeError( + "direct-inverse cell does not oppose physical and numeric" + ) + cells.append( + float( + pair_scores[same_physical].mean() + - pair_scores[same_numeric].mean() + ) + ) + if len(cells) != 9: + raise RuntimeError("expected nine surface-variant cells") + return float(np.mean(cells)), cells + + +def run_geometry_audit( + protocol: Mapping[str, object], + rng: np.random.Generator, +) -> tuple[ + dict[str, object], + pd.DataFrame, + pd.DataFrame, + pd.DataFrame, + dict[str, np.ndarray], +]: + manifest_path = ROOT / str( + protocol["inputs"]["prompt_manifest"]["path"] # type: ignore[index] + ) + states_path = ROOT / str( + protocol["inputs"]["option_free_states"]["path"] # type: ignore[index] + ) + 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)[0] + direct = arrays["direct_decoder_basis"].astype(np.float64)[0] + jacobian = arrays["jacobian_decoder_basis"].astype(np.float64)[:, 0] + if positions.tolist() != ["question_end"]: + raise RuntimeError(f"unexpected state position: {positions.tolist()}") + # The option-free runner retained the historical ``--answer-code`` ID + # suffix even though the stored state stops at the natural question end. + # The scientific stem and metadata are keyed by the suffix-free manifest + # ID. + manifest_ids = [ + prompt_id.removesuffix("--answer-code") for prompt_id in prompt_ids + ] + rows = [metadata[prompt_id] for prompt_id in manifest_ids] + families = np.asarray([str(row["family_id"]) for row in rows]) + numeric = np.asarray( + [str(row["numeric_direction"]) == "increase" for row in rows] + ) + physical = np.asarray( + [ + str(row["expected_outcome"]) == str(row["outcome_positive"]) + for row in rows + ] + ) + stems = [str(row["stem"]) for row in rows] + orientations = family_orientations(rows) + direct_families = sorted( + family for family, value in orientations.items() if value == "direct" + ) + inverse_families = sorted( + family for family, value in orientations.items() if value == "inverse" + ) + family_pairs = list(itertools.product(direct_families, inverse_families)) + if len(family_pairs) != 9: + raise RuntimeError("expected nine direct-inverse mechanism pairs") + + depths = layers / 41.0 * 100.0 + band_low, band_high = protocol["registered_band_percent"] # type: ignore[misc] + band = (depths >= float(band_low)) & (depths <= float(band_high)) + + sim_j_seed = cosine_layers(jacobian) + layered = { + "jacobian": sim_j_seed.mean(axis=0), + "direct": cosine_layers(direct[None, ...])[0], + "raw": cosine_layers(raw[None, ...])[0], + } + word_features = TfidfVectorizer( + lowercase=True, + ngram_range=(1, 2), + sublinear_tf=True, + norm="l2", + ).fit_transform(stems) + char_features = TfidfVectorizer( + lowercase=True, + analyzer="char_wb", + ngram_range=(3, 5), + sublinear_tf=True, + norm="l2", + ).fit_transform(stems) + fixed = { + "word_tfidf": (word_features @ word_features.T).toarray(), + "char_tfidf": (char_features @ char_features.T).toarray(), + } + band_similarity = { + name: values[band].mean(axis=0) for name, values in layered.items() + } + band_similarity.update(fixed) + + pair_rows = [] + for method, similarity in band_similarity.items(): + for family_a, family_b in family_pairs: + value, cells = cross_law_contrast( + similarity, rows, family_a, family_b + ) + pair_rows.append( + { + "method": method, + "direct_family": family_a, + "inverse_family": family_b, + "family_pair": f"{family_a} | {family_b}", + "physical_minus_numeric_similarity": value, + "cell_min": float(np.min(cells)), + "cell_max": float(np.max(cells)), + } + ) + pair_frame = pd.DataFrame(pair_rows) + + layer_rows = [] + for method, similarities in layered.items(): + for layer_index, depth in enumerate(depths): + values = [] + for family_a, family_b in family_pairs: + value, _ = cross_law_contrast( + similarities[layer_index], + rows, + family_a, + family_b, + ) + values.append(value) + layer_rows.append( + { + "method": method, + "layer": int(layers[layer_index]), + "depth_percent": float(depth), + "mean_physical_minus_numeric_similarity": float( + np.mean(values) + ), + "positive_pair_count": int(np.sum(np.asarray(values) > 0)), + } + ) + layer_frame = pd.DataFrame(layer_rows) + + cross_summary: dict[str, object] = {} + n_boot = int(protocol["bootstrap"]["resamples"]) # type: ignore[index] + for method in band_similarity: + values = pair_frame.loc[ + pair_frame["method"] == method, + "physical_minus_numeric_similarity", + ].to_numpy(float) + low, high = bootstrap_interval(values, rng, n_boot) + cross_summary[method] = { + "mean_physical_minus_numeric_similarity": float(values.mean()), + "bootstrap_95": [low, high], + "positive_pair_count": int(np.sum(values > 0)), + "pair_count": int(len(values)), + "two_sided_exact_signflip_p": exact_signflip(values), + } + + # Fixed leave-one-mechanism-out mean-difference readout. + jac_norm = normalize(jacobian) + jac_ensemble = normalize(jac_norm.mean(axis=0)) + vector_methods = { + "jacobian": normalize(jac_ensemble[:, band].mean(axis=1)), + "direct": normalize(normalize(direct)[:, band].mean(axis=1)), + "raw": normalize(normalize(raw)[:, band].mean(axis=1)), + "word_tfidf": word_features.toarray(), + "char_tfidf": char_features.toarray(), + } + readout_rows = [] + labels = {"numeric_direction": numeric, "physical_outcome": physical} + for method, vectors in vector_methods.items(): + vectors = normalize(np.asarray(vectors, dtype=float)) + for target_name, target in labels.items(): + for held_out in sorted(set(families)): + train = families != held_out + test = families == held_out + positive = vectors[train & target].mean(axis=0) + negative = vectors[train & ~target].mean(axis=0) + direction = positive - negative + direction = direction / max(np.linalg.norm(direction), 1e-12) + midpoint = 0.5 * (positive + negative) + # ``einsum`` avoids a noisy Accelerate/BLAS warning observed + # for these small matrix-vector products on macOS while + # producing the identical finite dot products. + scores = np.einsum( + "ij,j->i", vectors[test] - midpoint, direction + ) + auc = float(roc_auc_score(target[test].astype(int), scores)) + readout_rows.append( + { + "method": method, + "target": target_name, + "held_out_family": held_out, + "held_out_orientation": orientations[held_out], + "auc": auc, + "orientation_free_auc": max(auc, 1.0 - auc), + } + ) + readout_frame = pd.DataFrame(readout_rows) + readout_summary: dict[str, object] = {} + for method in vector_methods: + method_rows = readout_frame[readout_frame["method"] == method] + by_target = {} + for target_name in labels: + values = method_rows.loc[ + method_rows["target"] == target_name, "auc" + ].to_numpy(float) + oriented = method_rows.loc[ + method_rows["target"] == target_name, + "orientation_free_auc", + ].to_numpy(float) + by_target[target_name] = { + "mean_auc": float(values.mean()), + "mean_orientation_free_auc": float(oriented.mean()), + "family_aucs": values.tolist(), + } + pivot = method_rows.pivot( + index="held_out_family", columns="target", values="auc" + ) + difference = ( + pivot["numeric_direction"] - pivot["physical_outcome"] + ).to_numpy(float) + readout_summary[method] = { + **by_target, + "mean_numeric_minus_physical_auc": float(difference.mean()), + "two_sided_exact_paired_signflip_p": exact_signflip(difference), + "numeric_better_family_count": int(np.sum(difference > 0)), + } + + summary = { + "prompt_count": len(rows), + "layer_count": len(layers), + "mechanism_count": len(set(families)), + "direct_families": direct_families, + "inverse_families": inverse_families, + "cross_law": cross_summary, + "held_out_readout": readout_summary, + } + plot_payload = { + "depths": depths, + "band": band, + "jacobian_vectors": vector_methods["jacobian"], + } + return summary, pair_frame, layer_frame, readout_frame, plot_payload + + +def steering_control_label(row: Mapping[str, object]) -> str: + control = str(row["control_type"]) + if control == "direct": + return "direct_control" + return control + + +def run_steering_audit( + protocol: Mapping[str, object], + rng: np.random.Generator, +) -> tuple[dict[str, object], pd.DataFrame, pd.DataFrame, pd.DataFrame]: + raw_path = ROOT / str( + protocol["inputs"]["prospective_grain_raw"]["path"] # type: ignore[index] + ) + registration_path = ROOT / str( + protocol["inputs"]["prospective_grain_registration"]["path"] # type: ignore[index] + ) + raw = json.loads(raw_path.read_text()) + registration = json.loads(registration_path.read_text()) + grain = next( + row + for row in registration["families"] + if row["family_id"] == "grain-size-strengthening" + ) + outcome_positive = str(grain["outcome_positive"]) + outcome_negative = str(grain["outcome_negative"]) + preflight = { + str(row["condition_id"]): row for row in raw["preflight"] + } + trajectory_groups: dict[tuple[object, ...], list[Mapping[str, object]]] = {} + for row in raw["intervention_rows"]: + if str(row["target_family_id"]) != "grain-size-strengthening": + continue + key = ( + str(row["condition_id"]), + str(row["prompt_id"]), + steering_control_label(row), + str(row.get("source_family_id")), + str(row.get("lens_seed")), + str(row.get("random_seed")), + ) + trajectory_groups.setdefault(key, []).append(row) + + trajectory_rows = [] + for key, rows in trajectory_groups.items(): + rows = sorted(rows, key=lambda row: float(row["dose_percent"])) + doses = np.asarray([float(row["dose_percent"]) for row in rows]) + if doses.tolist() != [-4.0, -2.0, 0.0, 2.0, 4.0]: + raise RuntimeError(f"incomplete dose trajectory: {key}") + logits = np.asarray([float(row["positive_log_odds"]) for row in rows]) + condition_id = str(key[0]) + expected = str(preflight[condition_id]["expected_outcome"]) + if expected not in (outcome_positive, outcome_negative): + raise RuntimeError(f"unexpected outcome label: {expected}") + expected_sign = 1.0 if expected == outcome_positive else -1.0 + aligned = expected_sign * logits + slope = float(np.polyfit(doses, aligned, 1)[0]) + raw_slope = float(np.polyfit(doses, logits, 1)[0]) + low_dose = float(aligned[doses == 2.0][0] - aligned[doses == -2.0][0]) + odd = float(0.5 * (aligned[-1] - aligned[0])) + even = float(0.5 * (aligned[-1] + aligned[0] - 2.0 * aligned[2])) + pair_id, relation = condition_id.rsplit("--", 1) + trajectory_rows.append( + { + "condition_id": condition_id, + "pair_id": pair_id, + "relation": relation, + "prompt_id": key[1], + "control": key[2], + "source_family_id": key[3], + "lens_seed": key[4], + "random_seed": key[5], + "expected_sign": expected_sign, + "aligned_slope_per_percent": slope, + "raw_higher_minus_lower_slope_per_percent": raw_slope, + "aligned_low_dose_minus2_to_plus2": low_dose, + "strictly_monotone": bool(np.all(np.diff(aligned) > 0)), + "positive_slope": bool(slope > 0), + "positive_low_dose": bool(low_dose > 0), + "odd_endpoint_component": odd, + "even_quadratic_component": even, + "abs_even_over_abs_odd": float( + abs(even) / max(abs(odd), 1e-12) + ), + } + ) + trajectory_frame = pd.DataFrame(trajectory_rows) + + condition_frame = ( + trajectory_frame.groupby( + ["pair_id", "relation", "condition_id", "control"], + as_index=False, + )[ + [ + "aligned_slope_per_percent", + "raw_higher_minus_lower_slope_per_percent", + "aligned_low_dose_minus2_to_plus2", + "abs_even_over_abs_odd", + ] + ] + .mean() + ) + pair_frame = ( + condition_frame.groupby(["pair_id", "control"], as_index=False)[ + [ + "aligned_slope_per_percent", + "aligned_low_dose_minus2_to_plus2", + "abs_even_over_abs_odd", + ] + ] + .mean() + ) + + own_conditions = condition_frame[condition_frame["control"] == "own"] + reversal_rows = [] + for pair_id, group in own_conditions.groupby("pair_id"): + raw_slopes = { + str(row["relation"]): float( + row["raw_higher_minus_lower_slope_per_percent"] + ) + for _, row in group.iterrows() + } + reversal_rows.append( + { + "pair_id": pair_id, + "refinement_raw_slope": raw_slopes["refinement"], + "coarsening_raw_slope": raw_slopes["coarsening"], + "relation_appropriate_reversal": bool( + raw_slopes["refinement"] > 0 + and raw_slopes["coarsening"] < 0 + ), + } + ) + reversal_frame = pd.DataFrame(reversal_rows) + + n_boot = int(protocol["bootstrap"]["resamples"]) # type: ignore[index] + controls = ["own", "wrong-mechanism", "direct_control", "random"] + summaries: dict[str, object] = {} + for control in controls: + rows = pair_frame[pair_frame["control"] == control] + result = {} + for metric in ( + "aligned_slope_per_percent", + "aligned_low_dose_minus2_to_plus2", + ): + values = rows[metric].to_numpy(float) + low, high = bootstrap_interval(values, rng, n_boot) + result[metric] = { + "mean": float(values.mean()), + "bootstrap_95": [low, high], + "positive_pair_count": int(np.sum(values > 0)), + "two_sided_exact_signflip_p": exact_signflip(values), + } + summaries[control] = result + + contrasts: dict[str, object] = {} + own = pair_frame[pair_frame["control"] == "own"].set_index("pair_id") + for control in ("wrong-mechanism", "direct_control", "random"): + other = pair_frame[pair_frame["control"] == control].set_index( + "pair_id" + ) + common = sorted(set(own.index) & set(other.index)) + result = {} + for metric in ( + "aligned_slope_per_percent", + "aligned_low_dose_minus2_to_plus2", + ): + values = ( + own.loc[common, metric].to_numpy(float) + - other.loc[common, metric].to_numpy(float) + ) + low, high = bootstrap_interval(values, rng, n_boot) + result[metric] = { + "mean_difference": float(values.mean()), + "bootstrap_95": [low, high], + "positive_pair_count": int(np.sum(values > 0)), + "two_sided_exact_signflip_p": exact_signflip(values), + } + contrasts[f"own_minus_{control}"] = result + + own_trajectories = trajectory_frame[ + trajectory_frame["control"] == "own" + ] + summary = { + "trajectory_count": int(len(trajectory_frame)), + "own_trajectory_count": int(len(own_trajectories)), + "own_positive_slope_count": int( + own_trajectories["positive_slope"].sum() + ), + "own_positive_low_dose_count": int( + own_trajectories["positive_low_dose"].sum() + ), + "own_strictly_monotone_count": int( + own_trajectories["strictly_monotone"].sum() + ), + "own_median_abs_even_over_abs_odd": float( + own_trajectories["abs_even_over_abs_odd"].median() + ), + "relation_appropriate_reversal_pair_count": int( + reversal_frame["relation_appropriate_reversal"].sum() + ), + "pair_count": int(len(reversal_frame)), + "controls": summaries, + "contrasts": contrasts, + } + return summary, pair_frame, trajectory_frame, reversal_frame + + +def plot_audit( + geometry: Mapping[str, object], + pair_frame: pd.DataFrame, + layer_frame: pd.DataFrame, + readout_frame: pd.DataFrame, + steering: Mapping[str, object], + steering_pairs: pd.DataFrame, +) -> None: + FIG.mkdir(parents=True, exist_ok=True) + fig, axes = plt.subplots(2, 2, figsize=(11.2, 8.0)) + ax = axes[0, 0] + methods = ["jacobian", "direct", "raw", "word_tfidf", "char_tfidf"] + x = np.arange(len(methods)) + for index, method in enumerate(methods): + values = pair_frame.loc[ + pair_frame["method"] == method, + "physical_minus_numeric_similarity", + ].to_numpy(float) + ax.scatter( + np.full_like(values, index, dtype=float), + values, + color=COLORS[method], + s=25, + alpha=0.80, + zorder=3, + ) + ax.plot( + [index - 0.22, index + 0.22], + [values.mean(), values.mean()], + color="black", + lw=1.5, + zorder=4, + ) + ax.axhline(0, color="#777777", lw=0.9) + ax.set_xticks(x, [METHOD_LABELS[m] for m in methods], rotation=22, ha="right") + ax.set_ylabel("same-physical minus same-numerical cosine") + ax.text(-0.10, 1.04, "A", transform=ax.transAxes, fontweight="bold") + + ax = axes[0, 1] + for method in ("jacobian", "direct", "raw"): + rows = layer_frame[layer_frame["method"] == method] + ax.plot( + rows["depth_percent"], + rows["mean_physical_minus_numeric_similarity"], + marker="o", + ms=3, + lw=1.6, + color=COLORS[method], + label=METHOD_LABELS[method], + ) + ax.axhline(0, color="#777777", lw=0.9) + ax.axvspan(38, 92, color="#DDE8EA", alpha=0.45, zorder=0) + ax.set_xlabel("layer depth (%)") + ax.set_ylabel("cross-law contrast") + ax.legend(frameon=False, ncol=3, loc="lower left") + ax.text(-0.10, 1.04, "B", transform=ax.transAxes, fontweight="bold") + + ax = axes[1, 0] + for index, method in enumerate(methods): + rows = readout_frame[readout_frame["method"] == method] + numeric = rows[rows["target"] == "numeric_direction"].set_index( + "held_out_family" + ) + physical = rows[rows["target"] == "physical_outcome"].set_index( + "held_out_family" + ) + families = sorted(set(numeric.index) & set(physical.index)) + for family in families: + ax.plot( + [index - 0.15, index + 0.15], + [numeric.loc[family, "auc"], physical.loc[family, "auc"]], + color="#B8B8B8", + lw=0.7, + zorder=1, + ) + ax.scatter( + np.full(len(families), index - 0.15), + numeric.loc[families, "auc"], + color=COLORS[method], + marker="o", + s=22, + zorder=3, + ) + ax.scatter( + np.full(len(families), index + 0.15), + physical.loc[families, "auc"], + facecolors="white", + edgecolors=COLORS[method], + marker="o", + s=22, + zorder=3, + ) + ax.axhline(0.5, color="#777777", lw=0.9, ls="--") + ax.set_xticks(x, [METHOD_LABELS[m] for m in methods], rotation=22, ha="right") + ax.set_ylabel("held-out-mechanism ROC-AUC") + ax.text( + 0.50, + 1.01, + "filled: numerical direction open: physical outcome", + transform=ax.transAxes, + ha="center", + va="bottom", + fontsize=8, + ) + ax.text(-0.10, 1.08, "C", transform=ax.transAxes, fontweight="bold") + + ax = axes[1, 1] + controls = ["own", "wrong-mechanism", "direct_control", "random"] + labels = ["Matched", "Other mechanisms", "Direct", "Random"] + for index, control in enumerate(controls): + values = steering_pairs.loc[ + steering_pairs["control"] == control, + "aligned_low_dose_minus2_to_plus2", + ].to_numpy(float) + ax.scatter( + np.full_like(values, index, dtype=float), + values, + color=COLORS[control], + s=28, + alpha=0.85, + zorder=3, + ) + ax.plot( + [index - 0.22, index + 0.22], + [values.mean(), values.mean()], + color="black", + lw=1.5, + zorder=4, + ) + ax.axhline(0, color="#777777", lw=0.9) + ax.set_xticks(np.arange(4), labels, rotation=20, ha="right") + ax.set_ylabel("correct-answer log-odds change, -2% to +2%") + ax.text(-0.10, 1.04, "D", transform=ax.transAxes, fontweight="bold") + + for ax in axes.flat: + ax.spines["top"].set_visible(False) + ax.spines["right"].set_visible(False) + ax.grid(axis="y", color="#E6E6E6", lw=0.7, zorder=0) + fig.tight_layout() + fig.savefig(FIG / "core-discovery-discipline-audit.pdf", bbox_inches="tight") + fig.savefig( + FIG / "core-discovery-discipline-audit.png", + dpi=240, + bbox_inches="tight", + ) + plt.close(fig) + + +def fmt_interval(values: Sequence[float]) -> str: + return f"[{values[0]:+.3f}, {values[1]:+.3f}]" + + +def build_report( + protocol_hash: str, + fingerprints: Mapping[str, str], + geometry: Mapping[str, object], + steering: Mapping[str, object], +) -> str: + cross = geometry["cross_law"] # type: ignore[index] + readout = geometry["held_out_readout"] # type: ignore[index] + lines = [ + "# Core-discovery discipline audit", + "", + "This is an analysis-only post hoc robustness audit of archived data. " + "It adds no prompt, model run, mechanism, or paper edit.", + "", + "## Provenance", + "", + f"- Protocol SHA-256: `{protocol_hash}`", + ] + lines.extend( + f"- {name}: `{value}`" for name, value in fingerprints.items() + ) + lines.extend( + [ + "", + "## A. Numerical comparison versus constitutive outcome", + "", + "The cross-law contrast is same-physical similarity minus " + "same-numerical similarity across the nine direct--inverse " + "mechanism pairs. Negative values mean that states group more " + "strongly by the direction of the stated numerical change.", + "", + "| Representation | Mean contrast | 95% pair bootstrap | " + "Positive pairs | Exact p |", + "|---|---:|---:|---:|---:|", + ] + ) + for method in ("jacobian", "direct", "raw", "word_tfidf", "char_tfidf"): + row = cross[method] # type: ignore[index] + lines.append( + f"| {METHOD_LABELS[method]} | " + f"{row['mean_physical_minus_numeric_similarity']:+.4f} | " + f"{fmt_interval(row['bootstrap_95'])} | " + f"{row['positive_pair_count']}/9 | " + f"{row['two_sided_exact_signflip_p']:.4g} |" + ) + lines.extend( + [ + "", + "The fixed leave-one-mechanism-out readout has no selected layer " + "or regularization parameter.", + "", + "| Representation | Numerical AUC | Physical AUC | " + "Physical orientation-free AUC | Paired exact p |", + "|---|---:|---:|---:|---:|", + ] + ) + for method in ("jacobian", "direct", "raw", "word_tfidf", "char_tfidf"): + row = readout[method] # type: ignore[index] + lines.append( + f"| {METHOD_LABELS[method]} | " + f"{row['numeric_direction']['mean_auc']:.3f} | " + f"{row['physical_outcome']['mean_auc']:.3f} | " + f"{row['physical_outcome']['mean_orientation_free_auc']:.3f} | " + f"{row['two_sided_exact_paired_signflip_p']:.4g} |" + ) + + own = steering["controls"]["own"] # type: ignore[index] + contrasts = steering["contrasts"] # type: ignore[index] + lines.extend( + [ + "", + "## B. Low-dose and full-curve causal robustness", + "", + f"- Own-direction trajectories with positive full-curve slope: " + f"{steering['own_positive_slope_count']}/" + f"{steering['own_trajectory_count']}.", + f"- Own-direction trajectories positive from -2% to +2%: " + f"{steering['own_positive_low_dose_count']}/" + f"{steering['own_trajectory_count']}.", + f"- Strictly monotone own-direction trajectories: " + f"{steering['own_strictly_monotone_count']}/" + f"{steering['own_trajectory_count']}.", + f"- Material pairs with positive refinement slope and negative " + f"coarsening slope: " + f"{steering['relation_appropriate_reversal_pair_count']}/" + f"{steering['pair_count']}.", + f"- Mean matched low-dose effect: " + f"{own['aligned_low_dose_minus2_to_plus2']['mean']:+.3f} " + f"{fmt_interval(own['aligned_low_dose_minus2_to_plus2']['bootstrap_95'])}.", + f"- Mean matched slope: " + f"{own['aligned_slope_per_percent']['mean']:+.3f} log-odds per " + f"percent residual norm " + f"{fmt_interval(own['aligned_slope_per_percent']['bootstrap_95'])}.", + f"- Median absolute even/odd ratio: " + f"{steering['own_median_abs_even_over_abs_odd']:.3f}.", + "", + "| Low-dose contrast | Mean difference | 95% pair bootstrap | " + "Positive pairs | Exact p |", + "|---|---:|---:|---:|---:|", + ] + ) + for name in ( + "own_minus_random", + "own_minus_wrong-mechanism", + "own_minus_direct_control", + ): + row = contrasts[name]["aligned_low_dose_minus2_to_plus2"] + lines.append( + f"| {name.replace('_', ' ')} | " + f"{row['mean_difference']:+.3f} | " + f"{fmt_interval(row['bootstrap_95'])} | " + f"{row['positive_pair_count']}/6 | " + f"{row['two_sided_exact_signflip_p']:.4g} |" + ) + lines.extend( + [ + "", + "## Interpretation boundary", + "", + "These analyses can strengthen only the paper's existing bounded " + "claims. They are post hoc checks of archived data, not new " + "prospective confirmation. A negative cross-law contrast supports " + "the numerical-comparison interpretation; a null contrast only " + "shows no detectable physical advantage. Low-dose robustness " + "supports a local causal pathway in the frozen higher/lower grain " + "format and does not repair the already failed vocabulary and " + "inverse-regime transfers.", + "", + ] + ) + return "\n".join(lines) + + +def main() -> None: + protocol = json.loads(PROTOCOL.read_text()) + fingerprints = validate_inputs(protocol) + seed = int(protocol["bootstrap"]["seed"]) + rng = np.random.default_rng(seed) + + geometry, pair_frame, layer_frame, readout_frame, _ = run_geometry_audit( + protocol, rng + ) + steering, steering_pairs, trajectories, reversals = run_steering_audit( + protocol, rng + ) + + OUT.mkdir(parents=True, exist_ok=True) + pair_frame.to_csv(OUT / "cross_law_pair_contrasts.csv", index=False) + layer_frame.to_csv(OUT / "cross_law_layer_trajectories.csv", index=False) + readout_frame.to_csv(OUT / "held_out_readout_auc.csv", index=False) + steering_pairs.to_csv(OUT / "steering_pair_robustness.csv", index=False) + trajectories.to_csv( + OUT / "steering_trajectory_diagnostics.csv", index=False + ) + reversals.to_csv(OUT / "steering_relation_reversals.csv", index=False) + + statistics = { + "study_id": protocol["study_id"], + "status": protocol["status"], + "protocol_sha256": sha256(PROTOCOL), + "input_sha256": fingerprints, + "geometry": geometry, + "steering": steering, + } + (OUT / "statistics.json").write_text( + json.dumps(json_safe(statistics), indent=2, sort_keys=True) + "\n" + ) + plot_audit( + geometry, + pair_frame, + layer_frame, + readout_frame, + steering, + steering_pairs, + ) + report = build_report( + sha256(PROTOCOL), fingerprints, geometry, steering + ) + (OUT / "REPORT.md").write_text(report) + print(json.dumps(json_safe(statistics), indent=2, sort_keys=True)) + + +if __name__ == "__main__": + main() diff --git a/jlens_materials/scripts/analyze_counterfactual_activation_patching.py b/jlens_materials/scripts/analyze_counterfactual_activation_patching.py new file mode 100644 index 0000000000000000000000000000000000000000..34145b7f453ba73c0aab194d3ba28c7684faa025 --- /dev/null +++ b/jlens_materials/scripts/analyze_counterfactual_activation_patching.py @@ -0,0 +1,637 @@ +#!/usr/bin/env python3 +"""Analyze the frozen candidate counterfactual activation-patching run.""" + +from __future__ import annotations + +import json +import math +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 import stats # noqa: E402 + + +ROOT = Path(__file__).resolve().parents[1] +OUT = ROOT / "experiments" / "candidate-activation-patching-2026-07-16" +RAW = OUT / "raw.json" +DISTANCE_RAW = OUT / "distance_controls_raw.json" +PROTOCOL = OUT / "protocol.json" +FIG = OUT / "figures" +SEED = 20260716 +N_BOOT = 30_000 + + +def dump_json(path: Path, payload: object) -> None: + path.write_text(json.dumps(payload, indent=2, allow_nan=False) + "\n") + + +def bootstrap_mean(values: np.ndarray, rng: np.random.Generator) -> dict: + values = np.asarray(values, dtype=float) + draws = values[rng.integers(0, len(values), size=(N_BOOT, len(values)))].mean(axis=1) + return { + "mean": float(values.mean()), + "ci95": [float(value) for value in np.quantile(draws, [0.025, 0.975])], + "n_pairs": int(len(values)), + "positive_pairs": int(np.sum(values > 0)), + } + + +def paired_bootstrap( + left: pd.Series, right: pd.Series, rng: np.random.Generator +) -> dict: + aligned = pd.concat([left.rename("left"), right.rename("right")], axis=1).dropna() + return bootstrap_mean((aligned["left"] - aligned["right"]).to_numpy(), rng) + + +def circular_shift_test(x: np.ndarray, y: np.ndarray) -> dict: + observed = float(stats.spearmanr(x, y).statistic) + null = [] + for reverse in (False, True): + candidate = y[::-1] if reverse else y + for shift in range(len(y)): + if not reverse and shift == 0: + continue + null.append(float(stats.spearmanr(x, np.roll(candidate, shift)).statistic)) + null_array = np.asarray(null) + p_value = (1 + np.sum(np.abs(null_array) >= abs(observed))) / (1 + len(null_array)) + return { + "spearman_rho": observed, + "circular_shift_two_sided_p": float(p_value), + "null_transformations": int(len(null_array)), + } + + +def rowwise_spearman(x: np.ndarray, y: np.ndarray) -> np.ndarray: + rank_x = stats.rankdata(x, axis=1) + rank_y = stats.rankdata(y, axis=1) + rank_x -= rank_x.mean(axis=1, keepdims=True) + rank_y -= rank_y.mean(axis=1, keepdims=True) + numerator = np.sum(rank_x * rank_y, axis=1) + denominator = np.sqrt(np.sum(rank_x**2, axis=1) * np.sum(rank_y**2, axis=1)) + return numerator / np.maximum(denominator, 1e-12) + + +def correlation_difference_bootstrap( + layer_pairs: pd.DataFrame, + separation: pd.DataFrame, + rng: np.random.Generator, +) -> dict: + patch = layer_pairs[layer_pairs["control"] == "matched_reverse"].pivot( + index="pair_id", columns="layer", values="counterfactual_aligned_shift" + ) + jacobian = separation[separation["method"] == "jacobian"].pivot( + index="pair_id", columns="layer", values="relation_separation" + ) + direct = separation[separation["method"] == "direct"].pivot( + index="pair_id", columns="layer", values="relation_separation" + ) + common_pairs = sorted(set(patch.index) & set(jacobian.index) & set(direct.index)) + common_layers = sorted(set(patch.columns) & set(jacobian.columns) & set(direct.columns)) + patch_values = patch.loc[common_pairs, common_layers].to_numpy() + jacobian_values = jacobian.loc[common_pairs, common_layers].to_numpy() + direct_values = direct.loc[common_pairs, common_layers].to_numpy() + indices = rng.integers(0, len(common_pairs), size=(N_BOOT, len(common_pairs))) + patch_boot = patch_values[indices].mean(axis=1) + jacobian_boot = jacobian_values[indices].mean(axis=1) + direct_boot = direct_values[indices].mean(axis=1) + rho_j = rowwise_spearman(patch_boot, jacobian_boot) + rho_d = rowwise_spearman(patch_boot, direct_boot) + difference = rho_j - rho_d + observed_j = float(stats.spearmanr(patch_values.mean(axis=0), jacobian_values.mean(axis=0)).statistic) + observed_d = float(stats.spearmanr(patch_values.mean(axis=0), direct_values.mean(axis=0)).statistic) + return { + "observed_jacobian_minus_direct_rho": observed_j - observed_d, + "pair_bootstrap_ci95": [float(value) for value in np.quantile(difference, [0.025, 0.975])], + "pair_bootstrap_probability_difference_above_zero": float(np.mean(difference > 0)), + "resamples": N_BOOT, + "status": "post hoc falsification audit; not a frozen primary endpoint", + } + + +def relative_onsets(curves: dict[str, np.ndarray], depths: np.ndarray) -> dict: + output = {} + for label, values in curves.items(): + peak = float(np.max(values)) + output[label] = {"peak": peak} + for fraction in (0.1, 0.5): + hits = np.where(values >= fraction * peak)[0] + output[label][f"first_depth_at_{int(100 * fraction)}pct_peak"] = ( + None if len(hits) == 0 else float(depths[hits[0]]) + ) + return output + + +def validate(raw: dict, protocol: dict, patch: pd.DataFrame, readout: pd.DataFrame) -> None: + if raw["provenance"]["protocol_sha256"] != "447327e8f277ca5cfc60944a5514e526aca89d168bf5435575390f512fe54258": + raise RuntimeError("raw output is not tied to the frozen protocol") + if len(raw["clean_prompts"]) != 24 or len(patch) != 2400 or len(readout) != 2400: + raise RuntimeError("unexpected row count") + if patch[["receiver_prompt_id", "control", "layer"]].duplicated().any(): + raise RuntimeError("duplicate patch rows") + if readout[["prompt_id", "method", "lens_seed", "layer"]].fillna(-1).duplicated().any(): + raise RuntimeError("duplicate readout rows") + if not np.isfinite(patch.select_dtypes(include=[np.number]).to_numpy()).all(): + raise RuntimeError("non-finite patch value") + readout_numeric = readout.select_dtypes(include=[np.number]).drop( + columns=["lens_seed"], errors="ignore" + ) + if not np.isfinite(readout_numeric.to_numpy()).all(): + raise RuntimeError("non-finite readout value") + if sorted(patch["layer"].unique()) != protocol["source_layers"]: + raise RuntimeError("patch layers do not match protocol") + + +def pair_band_effects( + patch: pd.DataFrame, band: tuple[float, float] +) -> tuple[pd.DataFrame, pd.DataFrame]: + within = patch[ + patch["depth_percent"].between(band[0], band[1], inclusive="both") + ].copy() + prompt = ( + within.groupby( + [ + "receiver_pair_id", "receiver_condition_id", "receiver_relation", + "receiver_presentation_order", "control", + ], + as_index=False, + )["counterfactual_aligned_shift"] + .mean() + .rename(columns={"counterfactual_aligned_shift": "band_mean_shift"}) + ) + pair = ( + prompt.groupby(["receiver_pair_id", "control"], as_index=False)["band_mean_shift"] + .mean() + .rename(columns={"receiver_pair_id": "pair_id"}) + ) + return prompt, pair + + +def layer_pair_effects(patch: pd.DataFrame) -> pd.DataFrame: + return ( + patch.groupby( + ["receiver_pair_id", "control", "layer", "depth_percent"], as_index=False + )["counterfactual_aligned_shift"] + .mean() + .rename(columns={"receiver_pair_id": "pair_id"}) + ) + + +def readout_separation(readout: pd.DataFrame) -> pd.DataFrame: + collapsed = ( + readout.groupby( + [ + "prompt_id", "pair_id", "relation", "presentation_order", + "method", "layer", "depth_percent", + ], + as_index=False, + )["higher_minus_lower"] + .mean() + ) + pivot = collapsed.pivot_table( + index=["pair_id", "presentation_order", "method", "layer", "depth_percent"], + columns="relation", + values="higher_minus_lower", + ).reset_index() + pivot["relation_separation"] = 0.5 * ( + pivot["refinement"] - pivot["coarsening"] + ) + return ( + pivot.groupby( + ["pair_id", "method", "layer", "depth_percent"], as_index=False + )["relation_separation"] + .mean() + ) + + +def curve_with_ci( + frame: pd.DataFrame, value: str, group: str, rng: np.random.Generator +) -> pd.DataFrame: + rows = [] + for keys, subset in frame.groupby([group, "layer", "depth_percent"], sort=True): + group_value, layer, depth = keys + values = subset.sort_values("pair_id")[value].to_numpy() + summary = bootstrap_mean(values, rng) + rows.append({ + group: group_value, + "layer": int(layer), + "depth_percent": float(depth), + "mean": summary["mean"], + "ci_low": summary["ci95"][0], + "ci_high": summary["ci95"][1], + }) + return pd.DataFrame(rows) + + +def make_figure( + patch_curve: pd.DataFrame, + layer_pairs: pd.DataFrame, + readout_curve: pd.DataFrame, + correlations: dict, + *, + output_dir: Path = FIG, + font_scale: float = 1.0, + legend_y: float = 0.015, +) -> None: + output_dir.mkdir(parents=True, exist_ok=True) + plt.rcParams.update({ + "font.size": 9 * font_scale, + "axes.labelsize": 9 * font_scale, + "xtick.labelsize": 8 * font_scale, + "ytick.labelsize": 8 * font_scale, + "legend.fontsize": 7.5 * font_scale, + "axes.linewidth": 0.8, + }) + # A wide, shallow canvas keeps the three scientific panels readable at + # manuscript width. Explicit positions keep long material labels and the + # two colorbars from creating oversized automatic-layout gutters. + fig = plt.figure(figsize=(12.6, 3.5)) + ax_a = fig.add_axes([0.060, 0.245, 0.245, 0.700]) + ax_b = fig.add_axes([0.400, 0.245, 0.240, 0.700]) + colorbar_b_ax = fig.add_axes([0.647, 0.265, 0.010, 0.660]) + ax_c = fig.add_axes([0.725, 0.245, 0.220, 0.700]) + colorbar_c_ax = fig.add_axes([0.952, 0.265, 0.010, 0.660]) + legend_ax = fig.add_axes([0.060, legend_y, 0.902, 0.120]) + legend_ax.axis("off") + + palette = { + "matched_reverse": "#167D8D", + "cross_material_reverse": "#4C72B0", + "distance_matched_cross_material_same": "#777777", + "distance_matched_order_only": "#B8B8B8", + } + labels = { + "matched_reverse": "same material, relation reversed", + "cross_material_reverse": "new material, relation reversed", + "distance_matched_cross_material_same": "same relation, distance matched", + "distance_matched_order_only": "answer order, distance matched", + } + for control in [ + "matched_reverse", "cross_material_reverse", + "distance_matched_cross_material_same", "distance_matched_order_only", + ]: + subset = patch_curve[patch_curve["control"] == control].sort_values("depth_percent") + line_styles = { + "matched_reverse": "-", + "cross_material_reverse": "--", + "distance_matched_cross_material_same": "-.", + "distance_matched_order_only": ":", + } + ax_a.plot( + subset["depth_percent"], subset["mean"], marker="o", markersize=2.8, + linewidth=1.5, linestyle=line_styles[control], color=palette[control], + label=labels[control], + ) + if control == "matched_reverse": + ax_a.fill_between( + subset["depth_percent"].to_numpy(), subset["ci_low"].to_numpy(), + subset["ci_high"].to_numpy(), color=palette[control], alpha=0.15, + linewidth=0, + ) + ax_a.axhline(0, color="#555555", linewidth=0.7) + ax_a.axvspan(38, 92, color="#999999", alpha=0.08, linewidth=0) + ax_a.set_xlabel("Layer depth (%)") + ax_a.set_ylabel("Shift toward reversed answer\n(log-odds units)") + ax_a.text( + -0.055, 1.04, "A", transform=ax_a.transAxes, + ha="left", va="bottom", fontweight="bold", clip_on=False, + ) + handles, legend_labels = ax_a.get_legend_handles_labels() + legend_ax.legend( + handles, legend_labels, frameon=False, loc="center", ncol=4, + columnspacing=1.6, handlelength=2.6, + ) + + matched = layer_pairs[layer_pairs["control"] == "matched_reverse"] + heat = matched.pivot(index="pair_id", columns="depth_percent", values="counterfactual_aligned_shift") + heat = heat.loc[sorted(heat.index)] + vmax = float(np.quantile(np.abs(heat.to_numpy()), 0.98)) + image = ax_b.imshow( + heat.to_numpy(), aspect="auto", cmap="RdBu_r", vmin=-vmax, vmax=vmax, + interpolation="nearest", extent=[heat.columns.min(), heat.columns.max(), len(heat), 0], + ) + short_names = { + "bronze-36-9": "bronze", + "cobalt-alloy-56-14": "cobalt", + "low-carbon-steel-72-18": "low-carbon steel", + "magnesium-48-12": "magnesium", + "silver-40-10": "silver", + "titanium-64-8": "titanium", + } + short = [short_names.get(name, name) for name in heat.index] + ax_b.set_yticks(np.arange(len(short)) + 0.5, short) + ax_b.set_xlabel("Layer depth (%)") + colorbar = fig.colorbar(image, cax=colorbar_b_ax) + colorbar.ax.set_title("Δ log odds", fontsize=7.5 * font_scale, pad=9) + ax_b.text( + -0.055, 1.04, "B", transform=ax_b.transAxes, + ha="left", va="bottom", fontweight="bold", clip_on=False, + ) + + patch_match = patch_curve[patch_curve["control"] == "matched_reverse"].sort_values("layer") + for method, marker, color, label in [ + ("jacobian", "o", "#167D8D", "Jacobian readout"), + ("direct", "^", "#6F5AA8", "direct readout"), + ]: + read = readout_curve[readout_curve["method"] == method].sort_values("layer") + merged = patch_match.merge(read, on=["layer", "depth_percent"], suffixes=("_patch", "_read")) + scatter = ax_c.scatter( + merged["mean_read"], merged["mean_patch"], c=merged["depth_percent"], + cmap="viridis", marker=marker, s=30, edgecolor="none", label=label, + ) + ax_c.axhline(0, color="#777777", linewidth=0.7) + ax_c.axvline(0, color="#777777", linewidth=0.7) + ax_c.set_xlabel("Readable relation separation\n(log-odds units)") + ax_c.set_ylabel("Reverse-patch shift") + ax_c.yaxis.labelpad = 2 + ax_c.legend(frameon=False, loc="best") + ax_c.text( + 0.97, 0.04, + f"Jacobian ρ={correlations['jacobian']['spearman_rho']:+.2f}\n" + f"direct ρ={correlations['direct']['spearman_rho']:+.2f}", + transform=ax_c.transAxes, ha="right", va="bottom", + ) + ax_c.text( + -0.055, 1.04, "C", transform=ax_c.transAxes, + ha="left", va="bottom", fontweight="bold", clip_on=False, + ) + depth_bar = fig.colorbar(scatter, cax=colorbar_c_ax) + depth_bar.set_label("Depth (%)") + + for suffix in ("png", "pdf"): + fig.savefig(output_dir / f"counterfactual-activation-patching.{suffix}", dpi=300, bbox_inches="tight") + plt.close(fig) + + +def main() -> None: + raw = json.loads(RAW.read_text()) + distance_raw = json.loads(DISTANCE_RAW.read_text()) + protocol = json.loads(PROTOCOL.read_text()) + patch = pd.DataFrame(raw["patch_rows"]) + distance_patch = pd.DataFrame(distance_raw["rows"]) + readout = pd.DataFrame(raw["readout_rows"]) + validate(raw, protocol, patch, readout) + if len(distance_patch) != 1200 or distance_patch[ + ["receiver_prompt_id", "control", "layer"] + ].duplicated().any(): + raise RuntimeError("invalid distance-matched falsification rows") + norm_error = np.abs( + distance_patch["achieved_difference_norm"] + - distance_patch["target_reverse_difference_norm"] + ) + if float(norm_error.max()) > 1e-4: + raise RuntimeError("distance-matched control failed its norm constraint") + rng = np.random.default_rng(SEED) + band = tuple(float(value) for value in protocol["workspace_band_percent"]) + + prompt_band, pair_band = pair_band_effects(patch, band) + distance_prompt_band, distance_pair_band = pair_band_effects(distance_patch, band) + band_state_diagnostics = ( + patch[patch["depth_percent"].between(band[0], band[1], inclusive="both")] + .groupby("control", as_index=False)[ + [ + "receiver_donor_state_cosine", "state_difference_norm", + "receiver_state_norm", "donor_state_norm", + ] + ] + .mean() + ) + layer_pairs = layer_pair_effects(patch) + distance_layer_pairs = layer_pair_effects(distance_patch) + separation = readout_separation(readout) + patch_curve = curve_with_ci( + layer_pairs, "counterfactual_aligned_shift", "control", rng + ) + distance_patch_curve = curve_with_ci( + distance_layer_pairs, "counterfactual_aligned_shift", "control", rng + ) + display_patch_curve = pd.concat( + [patch_curve, distance_patch_curve], ignore_index=True + ) + readout_curve = curve_with_ci( + separation, "relation_separation", "method", rng + ) + + pair_pivot = pair_band.pivot(index="pair_id", columns="control", values="band_mean_shift") + endpoints = { + control: bootstrap_mean(pair_pivot[control].to_numpy(), rng) + for control in protocol["donor_controls"] + } + contrasts = { + "matched_minus_order_only": paired_bootstrap( + pair_pivot["matched_reverse"], pair_pivot["order_only"], rng + ), + "matched_minus_cross_material_same": paired_bootstrap( + pair_pivot["matched_reverse"], pair_pivot["cross_material_same"], rng + ), + "matched_minus_cross_material_reverse": paired_bootstrap( + pair_pivot["matched_reverse"], pair_pivot["cross_material_reverse"], rng + ), + } + distance_pair_pivot = distance_pair_band.pivot( + index="pair_id", columns="control", values="band_mean_shift" + ) + distance_endpoints = { + control: bootstrap_mean(distance_pair_pivot[control].to_numpy(), rng) + for control in distance_pair_pivot.columns + } + distance_contrasts = { + "matched_minus_distance_matched_cross_material_same": paired_bootstrap( + pair_pivot["matched_reverse"], + distance_pair_pivot["distance_matched_cross_material_same"], rng, + ), + "matched_minus_distance_matched_order_only": paired_bootstrap( + pair_pivot["matched_reverse"], + distance_pair_pivot["distance_matched_order_only"], rng, + ), + } + + condition = ( + prompt_band.groupby( + ["receiver_condition_id", "receiver_pair_id", "receiver_relation", "control"], + as_index=False, + )["band_mean_shift"] + .mean() + ) + matched_condition = condition[condition["control"] == "matched_reverse"] + positive_conditions = int(np.sum(matched_condition["band_mean_shift"] > 0)) + sign_test = float(stats.binomtest(positive_conditions, len(matched_condition), 0.5).pvalue) + + mean_matched = patch_curve[patch_curve["control"] == "matched_reverse"] + peak = mean_matched.loc[mean_matched["mean"].idxmax()] + peak_layer = int(peak["layer"]) + peak_rows = patch[(patch["control"] == "matched_reverse") & (patch["layer"] == peak_layer)].copy() + receiver_sign = np.where(peak_rows["receiver_relation"] == "refinement", 1.0, -1.0) + peak_rows["donor_answer_selected"] = ( + -receiver_sign * peak_rows["patched_higher_minus_lower"].to_numpy() > 0 + ) + peak_rows["receiver_clean_answer_selected"] = ( + receiver_sign * peak_rows["clean_higher_minus_lower"].to_numpy() > 0 + ) + + patch_match = mean_matched.sort_values("layer") + correlations = {} + for method in ("jacobian", "direct"): + method_curve = readout_curve[readout_curve["method"] == method].sort_values("layer") + merged = patch_match.merge(method_curve, on=["layer", "depth_percent"], suffixes=("_patch", "_read")) + correlations[method] = circular_shift_test( + merged["mean_patch"].to_numpy(), merged["mean_read"].to_numpy() + ) + + aligned_curves = { + "matched_patch": patch_match.sort_values("layer")["mean"].to_numpy(), + } + ordered_depths = patch_match.sort_values("layer")["depth_percent"].to_numpy() + first_difference = {} + for method in ("jacobian", "direct"): + method_curve = readout_curve[readout_curve["method"] == method].sort_values("layer") + aligned_curves[f"{method}_readout"] = method_curve["mean"].to_numpy() + first_difference[method] = float(stats.spearmanr( + np.diff(aligned_curves["matched_patch"]), + np.diff(aligned_curves[f"{method}_readout"]), + ).statistic) + posthoc_correlation_audit = { + "first_difference_spearman": first_difference, + "relative_onsets": relative_onsets(aligned_curves, ordered_depths), + "jacobian_minus_direct_pair_bootstrap": correlation_difference_bootstrap( + layer_pairs, separation, rng + ), + "interpretation": ( + "The level correlation can be driven by a shared late-layer transition. " + "First differences and relative onsets test whether the transitions coincide exactly." + ), + "status": "post hoc falsification audit added after inspecting the frozen outputs", + } + + clean = pd.DataFrame(raw["clean_prompts"]) + clean_sign = np.where(clean["relation"] == "refinement", 1.0, -1.0) + clean_correct = clean_sign * clean["clean_log_odds"].to_numpy() > 0 + + decision = { + "matched_reverse_ci_above_zero": endpoints["matched_reverse"]["ci95"][0] > 0, + "matched_minus_order_ci_above_zero": contrasts["matched_minus_order_only"]["ci95"][0] > 0, + "matched_minus_same_ci_above_zero": contrasts["matched_minus_cross_material_same"]["ci95"][0] > 0, + "cross_material_reverse_ci_above_zero": endpoints["cross_material_reverse"]["ci95"][0] > 0, + } + decision["causal_localization_candidate_pass"] = all([ + decision["matched_reverse_ci_above_zero"], + decision["matched_minus_order_ci_above_zero"], + decision["matched_minus_same_ci_above_zero"], + ]) + decision["distance_falsification_survives"] = all( + summary["ci95"][0] > 0 for summary in distance_contrasts.values() + ) + + statistics = { + "status": "exploratory causal follow-up under frozen protocol on previously inspected prompts", + "band_percent": list(band), + "n_material_pairs": 6, + "n_conditions": 12, + "n_prompt_orders": 24, + "n_layers": 25, + "clean": { + "physically_correct_prompt_orders": int(clean_correct.sum()), + "n_prompt_orders": int(len(clean)), + "global_top_is_registered_answer": int(clean["clean_top_is_registered_answer"].sum()), + "mean_registered_pair_probability": float(clean["clean_pair_probability"].mean()), + }, + "band_endpoints": endpoints, + "band_contrasts": contrasts, + "distance_matched_falsification": { + "status": "post-hoc falsification protocol frozen before control output", + "maximum_absolute_norm_matching_error": float(norm_error.max()), + "control_endpoints": distance_endpoints, + "matched_reverse_contrasts": distance_contrasts, + }, + "band_state_diagnostics_by_control": band_state_diagnostics.set_index( + "control" + ).to_dict(orient="index"), + "matched_reverse_condition_sign_test": { + "positive_conditions": positive_conditions, + "n_conditions": int(len(matched_condition)), + "exact_two_sided_p": sign_test, + }, + "descriptive_peak": { + "layer": peak_layer, + "depth_percent": float(peak["depth_percent"]), + "mean_shift": float(peak["mean"]), + "ci95": [float(peak["ci_low"]), float(peak["ci_high"])], + "donor_answer_selected_prompt_orders": int(peak_rows["donor_answer_selected"].sum()), + "receiver_clean_answer_selected_prompt_orders": int(peak_rows["receiver_clean_answer_selected"].sum()), + "n_prompt_orders": int(len(peak_rows)), + }, + "readout_causality_correlations": correlations, + "posthoc_readout_causality_audit": posthoc_correlation_audit, + "decision": decision, + "guardrail": ( + "Full-state activation patching tests causal sufficiency in a constrained matched task; " + "it is not a literal chain of thought, a necessity result, or unrestricted materials understanding." + ), + } + + prompt_band.to_csv(OUT / "band_prompt_effects.csv", index=False) + pair_band.to_csv(OUT / "band_pair_effects.csv", index=False) + distance_prompt_band.to_csv(OUT / "distance_control_band_prompt_effects.csv", index=False) + distance_pair_band.to_csv(OUT / "distance_control_band_pair_effects.csv", index=False) + condition.to_csv(OUT / "band_condition_effects.csv", index=False) + band_state_diagnostics.to_csv(OUT / "band_state_diagnostics.csv", index=False) + layer_pairs.to_csv(OUT / "layer_pair_effects.csv", index=False) + patch_curve.to_csv(OUT / "layer_curves.csv", index=False) + distance_patch_curve.to_csv(OUT / "distance_control_layer_curves.csv", index=False) + separation.to_csv(OUT / "readout_relation_separation_by_pair.csv", index=False) + readout_curve.to_csv(OUT / "readout_relation_separation_curves.csv", index=False) + dump_json(OUT / "statistics.json", statistics) + make_figure(display_patch_curve, layer_pairs, readout_curve, correlations) + + matched = endpoints["matched_reverse"] + reverse = endpoints["cross_material_reverse"] + order_contrast = contrasts["matched_minus_order_only"] + same_contrast = contrasts["matched_minus_cross_material_same"] + distance_same = distance_endpoints["distance_matched_cross_material_same"] + distance_order = distance_endpoints["distance_matched_order_only"] + distance_same_contrast = distance_contrasts[ + "matched_minus_distance_matched_cross_material_same" + ] + distance_order_contrast = distance_contrasts[ + "matched_minus_distance_matched_order_only" + ] + lines = [ + "# Candidate counterfactual activation-patching results", + "", + "This is an exploratory causal follow-up under a frozen protocol on a previously inspected prompt cohort. It has not been added to the manuscript.", + "", + "## What was done", + "", + "For six matched materials, the final-prompt-token residual from a refinement prompt was transplanted into its coarsening counterpart and vice versa at each of 25 layers. Same-relation, answer-order-only, and cross-material donors were run as controls. Positive shifts below mean movement toward the answer appropriate to the donor's reversed physical relation.", + "", + "## Registered band result", + "", + f"The same-material reversed-relation patch produced a mean shift of **{matched['mean']:+.3f} log-odds units** (95% pair-bootstrap CI {matched['ci95'][0]:+.3f} to {matched['ci95'][1]:+.3f}) over the frozen 38--92% layer band. It was positive for {positive_conditions}/12 physical conditions (two-sided sign-test p={sign_test:.4g}).", + f"Relative to the answer-order-only donor, the paired gain was **{order_contrast['mean']:+.3f}** (95% CI {order_contrast['ci95'][0]:+.3f} to {order_contrast['ci95'][1]:+.3f}); relative to a different-material donor with the same relation, it was **{same_contrast['mean']:+.3f}** (95% CI {same_contrast['ci95'][0]:+.3f} to {same_contrast['ci95'][1]:+.3f}).", + f"The reversed relation also transferred across materials: **{reverse['mean']:+.3f}** (95% CI {reverse['ci95'][0]:+.3f} to {reverse['ci95'][1]:+.3f}).", + "", + "## State-distance falsification", + "", + f"Reverse donors were farther from the receiver than the original controls, so a second protocol scaled the relation-preserving and order-only donor differences to exactly the reverse donor's state distance. The distance-matched same-relation control was **{distance_same['mean']:+.3f}** (95% CI {distance_same['ci95'][0]:+.3f} to {distance_same['ci95'][1]:+.3f}) and the distance-matched order control was **{distance_order['mean']:+.3f}** (95% CI {distance_order['ci95'][0]:+.3f} to {distance_order['ci95'][1]:+.3f}). The matched reverse advantage remained **{distance_same_contrast['mean']:+.3f}** and **{distance_order_contrast['mean']:+.3f}**, with respective 95% intervals {distance_same_contrast['ci95'][0]:+.3f} to {distance_same_contrast['ci95'][1]:+.3f} and {distance_order_contrast['ci95'][0]:+.3f} to {distance_order_contrast['ci95'][1]:+.3f}.", + "", + "## Layer localization and readout bridge", + "", + f"The descriptive matched-patch curve peaked at layer {peak_layer} ({float(peak['depth_percent']):.1f}% depth) with a mean shift of {float(peak['mean']):+.3f}. Across the 25 registered layers, its Spearman association with readable relation separation was {correlations['jacobian']['spearman_rho']:+.3f} for the Jacobian lens and {correlations['direct']['spearman_rho']:+.3f} for direct unembedding. However, the post-hoc first-difference correlations were {first_difference['jacobian']:+.3f} and {first_difference['direct']:+.3f}; the strong level correlation therefore reflects a broadly shared late-layer transition, not exact layer-by-layer coincidence. Circular-shift and pair-bootstrap sensitivities are stored in `statistics.json` and remain descriptive.", + "", + "## Decision", + "", + f"Frozen causal-localization decision: **{'PASS' if decision['causal_localization_candidate_pass'] else 'FAIL'}**. Cross-material transfer: **{'PASS' if decision['cross_material_reverse_ci_above_zero'] else 'FAIL'}**. Post-hoc state-distance falsification: **{'SURVIVES' if decision['distance_falsification_survives'] else 'DOES NOT SURVIVE'}**.", + "", + "The passing result shows causal sufficiency of a matched internal state for this constrained relation, not a private verbal chain of thought or unrestricted physical understanding. A disjoint prompt cohort is required before treating it as confirmatory.", + ] + (OUT / "RESULTS.md").write_text("\n".join(lines) + "\n") + + print(json.dumps(statistics, indent=2)) + + +if __name__ == "__main__": + main() diff --git a/jlens_materials/scripts/analyze_equation_free_relational_benchmark_amended.py b/jlens_materials/scripts/analyze_equation_free_relational_benchmark_amended.py new file mode 100644 index 0000000000000000000000000000000000000000..0a5f734f739c27ec28dd3118b93e98f8ce9cceaf --- /dev/null +++ b/jlens_materials/scripts/analyze_equation_free_relational_benchmark_amended.py @@ -0,0 +1,93 @@ +#!/usr/bin/env python3 +"""Run the frozen 60-law analysis with a documented zero-MAD control fix. + +The frozen analyzer stopped only when a lexical control assigned identical +contrasts to every calibration-neutral law. The primary hidden-state scale is +nondegenerate. This wrapper leaves the frozen analyzer file and every primary +calculation unchanged, but applies the unit display-scale convention already +used by the submitted 60-law analyzer when a *control* MAD is exactly zero. +""" + +from __future__ import annotations + +import hashlib +import json +import sys +from pathlib import Path + +import numpy as np + + +ROOT = Path(__file__).resolve().parents[1] +sys.path.insert(0, str(ROOT / "scripts")) + +import analyze_equation_free_relational_benchmark as frozen # noqa: E402 + + +OUT = ROOT / "experiments" / "equation-free-relational-physics-2026-08-21" +CONTROL_NAMES = ["output_head", "word_tfidf", "character_tfidf"] +CALL_INDEX = 0 +DEGENERATE_CONTROLS: list[str] = [] + + +def sha256(path: Path) -> str: + return hashlib.sha256(path.read_bytes()).hexdigest() + + +def safe_control_table(scores: np.ndarray, meta: dict) -> tuple: + global CALL_INDEX + name = CONTROL_NAMES[CALL_INDEX] + CALL_INDEX += 1 + table = frozen.aggregate_laws(frozen.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: + DEGENERATE_CONTROLS.append(name) + scale = 1.0 + table["natural_centered_contrast"] = table["mean_contrast"] - center + table["natural_z"] = table["natural_centered_contrast"] / scale + return table, { + **frozen.endpoint_metrics(table, "natural_centered_contrast"), + "natural_neutral_center": center, + "natural_neutral_scale": scale, + "neutral_mad_degenerate": name in DEGENERATE_CONTROLS, + } + + +def main() -> None: + protocol = json.loads((OUT / "protocol.json").read_text()) + frozen_path = ROOT / protocol["inputs"]["analyzer"]["path"] + if sha256(frozen_path) != protocol["inputs"]["analyzer"]["sha256"]: + raise RuntimeError("the frozen analyzer changed; amendment cannot proceed") + frozen.control_table = safe_control_table + frozen.main() + statistics_path = OUT / "statistics.json" + statistics = json.loads(statistics_path.read_text()) + statistics["analysis_amendment"] = { + "script": str(Path(__file__).resolve().relative_to(ROOT)), + "script_sha256": sha256(Path(__file__).resolve()), + "timing": "added after the frozen analyzer encountered a zero-MAD lexical control", + "scope": "control normalization only; primary hidden-state calculations unchanged", + "convention": "replace exactly zero control scale with 1.0 for finite diagnostic z values; AUC is unchanged", + "degenerate_controls": DEGENERATE_CONTROLS, + "frozen_analyzer_sha256": protocol["inputs"]["analyzer"]["sha256"], + } + statistics_path.write_text(json.dumps(statistics, indent=2, sort_keys=True) + "\n") + with (OUT / "REPORT.md").open("a") as handle: + handle.write( + "\nAnalysis amendment: the frozen analyzer encountered an exactly " + f"zero neutral MAD for {', '.join(DEGENERATE_CONTROLS)}. Following " + "the submitted benchmark's existing convention, only the control's " + "diagnostic display scale was set to 1.0; its AUC and every primary " + "hidden-state result are unchanged.\n" + ) + + +if __name__ == "__main__": + main() diff --git a/jlens_materials/scripts/analyze_equation_free_relational_pilot.py b/jlens_materials/scripts/analyze_equation_free_relational_pilot.py new file mode 100644 index 0000000000000000000000000000000000000000..5239de61b88596c68a9b1012c81bd4f9ea8fdea7 --- /dev/null +++ b/jlens_materials/scripts/analyze_equation_free_relational_pilot.py @@ -0,0 +1,308 @@ +#!/usr/bin/env python3 +"""Analyze frozen-direction transfer to natural prompts without supplied equations.""" + +from __future__ import annotations + +import itertools +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, + fit_centroid, + load_development, + matched_pairs, + normalize, + pair_auc, + project, + prompt_arrays, + sha256, + stratified_auc_bootstrap, + tfidf_scores, +) + + +OUT = ROOT / "experiments" / "equation-free-relational-pilot-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 = {x["prompt_id"]: x for x in manifest["prompts"]} + clean_by_id = {x["prompt_id"]: x for x in raw["clean_rows"]} + return { + "manifest": manifest, + "prompts": [by_id[x] for x in ids], + "clean": [clean_by_id[x] for x in ids], + "layer": layer, + "states": states, + } + + +def exact_direct_inverse_p(direct: np.ndarray, inverse: np.ndarray) -> tuple[float, float]: + values = np.concatenate([direct, inverse]) + n_direct = len(direct) + observed = pair_auc(direct, inverse) + statistics = [] + indices = set(range(len(values))) + for selected_tuple in itertools.combinations(range(len(values)), n_direct): + selected = set(selected_tuple) + positive = values[list(selected)] + negative = values[list(indices - selected)] + statistics.append(pair_auc(positive, negative)) + p = float(np.mean(np.asarray(statistics) >= observed - 1e-15)) + return observed, p + + +def exact_ordinal_p(scores: np.ndarray) -> tuple[float, float]: + labels = np.asarray([-1] * 4 + [0] * 4 + [1] * 4, dtype=int) + observed = float(spearmanr(labels, scores).statistic) + all_indices = set(range(12)) + extreme = 0 + total = 0 + for inverse_tuple in itertools.combinations(range(12), 4): + inverse = set(inverse_tuple) + remaining = sorted(all_indices - inverse) + for neutral_tuple in itertools.combinations(remaining, 4): + trial = np.ones(12, dtype=int) + trial[list(inverse)] = -1 + trial[list(neutral_tuple)] = 0 + statistic = float(spearmanr(trial, scores).statistic) + extreme += statistic >= observed - 1e-15 + total += 1 + return observed, float(extreme / total) + + +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([x["physical_sign"] > 0 for x 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) + + frozen_center = float(protocol["frozen_readout"]["primary_center"]) + frozen_scale = float(protocol["frozen_readout"]["primary_scale"]) + laws["frozen_centered_contrast"] = laws["mean_contrast"] - frozen_center + laws["frozen_z"] = laws["frozen_centered_contrast"] / frozen_scale + pairs["frozen_z"] = (pairs["contrast"] - frozen_center) / frozen_scale + natural_neutrals = laws.loc[laws["category"] == "neutral", "mean_contrast"].to_numpy() + local_center = float(np.median(natural_neutrals)) + local_mad = float(np.median(np.abs(natural_neutrals - local_center))) + local_scale = max(1.4826 * local_mad, 1e-12) + laws["pilot_local_z_descriptive"] = (laws["mean_contrast"] - local_center) / local_scale + + direct = laws.loc[laws["category"] == "direct", "frozen_centered_contrast"].to_numpy() + inverse = laws.loc[laws["category"] == "inverse", "frozen_centered_contrast"].to_numpy() + neutral = laws.loc[laws["category"] == "neutral", "frozen_centered_contrast"].to_numpy() + direct_inverse_auc, exact_auc_p = exact_direct_inverse_p(direct, inverse) + direct_neutral_auc = pair_auc(direct, neutral) + neutral_inverse_auc = pair_auc(neutral, inverse) + directional = laws[laws["category"].isin(["direct", "inverse"])].copy() + sign_correct = (directional["frozen_centered_contrast"] > 0) == (directional["category"] == "direct") + sign_accuracy = float(sign_correct.mean()) + directional["sign_correct"] = sign_correct.to_numpy() + laws = laws.merge(directional[["law_id", "sign_correct"]], on="law_id", how="left") + + surface_auc = {} + surface_sign_accuracy = {} + for surface in ("a", "b"): + column = f"surface_{surface}_contrast" + d = laws.loc[laws["category"] == "direct", column].to_numpy() - frozen_center + i = laws.loc[laws["category"] == "inverse", column].to_numpy() - frozen_center + surface_auc[surface] = pair_auc(d, i) + surface_sign_accuracy[surface] = float(np.mean(np.concatenate([d > 0, i < 0]))) + + ordered = laws.sort_values("category", key=lambda x: x.map({"inverse": 0, "neutral": 1, "direct": 2})) + ordinal_scores = ordered["frozen_z"].to_numpy() + rho, ordinal_p = exact_ordinal_p(ordinal_scores) + + 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, protocol["inference"]["bootstrap_seed"] + 1, protocol["inference"]["bootstrap_resamples"]), + "neutral_vs_inverse": stratified_auc_bootstrap(neutral, inverse, protocol["inference"]["bootstrap_seed"] + 2, protocol["inference"]["bootstrap_resamples"]), + } + + output_scores = np.asarray([x["higher_minus_lower_logit"] for x 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 = aggregate_laws(matched_pairs(scores, meta)) + d = table.loc[table["category"] == "direct", "mean_contrast"].to_numpy() + i = table.loc[table["category"] == "inverse", "mean_contrast"].to_numpy() + n = table.loc[table["category"] == "neutral", "mean_contrast"].to_numpy() + controls[name] = { + "direct_vs_inverse_auc": pair_auc(d, i), + "direct_vs_neutral_auc": pair_auc(d, n), + "neutral_vs_inverse_auc": pair_auc(n, i), + } + control_tables[name] = table + + behavior = pd.DataFrame(test["clean"]) + behavior_by_category = {category: float(group["correct"].mean()) for category, group in behavior.groupby("category")} + behavior_by_law = {law_id: float(group["correct"].mean()) for law_id, group in behavior.groupby("law_id")} + behavior_overall = float(behavior["correct"].mean()) + + reference = pd.read_csv(REFERENCE / "law_level_neutral_normalized.csv") + reference = reference[["law_id", "neutral_z"]].rename(columns={"neutral_z": "scaffolded_frozen_z"}) + laws = laws.merge(reference, on="law_id", how="left", validate="one_to_one") + cross_scaffold_rho = float(spearmanr(laws["scaffolded_frozen_z"], laws["frozen_z"]).statistic) + within_category_rhos = {} + for category, group in laws.groupby("category"): + within_category_rhos[category] = float(spearmanr(group["scaffolded_frozen_z"], group["frozen_z"]).statistic) + + rule = protocol["supportive_rule"] + supportive = bool( + direct_inverse_auc >= rule["direct_inverse_auc_minimum"] + and sign_accuracy >= rule["directional_sign_accuracy_minimum"] + and min(surface_auc.values()) >= rule["both_surface_auc_minimum"] + and rho >= rule["ordinal_spearman_minimum"] + ) + summary = { + "study_id": protocol["study_id"], + "analysis_sha256": sha256(Path(__file__).resolve()), + "scope": protocol["design_scope"], + "primary_frozen_transfer": { + "direct_vs_inverse_auc": direct_inverse_auc, + "exact_label_permutation_p": exact_auc_p, + "direct_vs_neutral_auc": direct_neutral_auc, + "neutral_vs_inverse_auc": neutral_inverse_auc, + "bootstrap_95": bootstrap, + "directional_sign_accuracy": sign_accuracy, + "correct_directional_laws": int(sign_correct.sum()), + "directional_laws_total": int(len(sign_correct)), + "surface_direct_inverse_auc": surface_auc, + "surface_sign_accuracy": surface_sign_accuracy, + "ordinal_spearman": rho, + "ordinal_exact_permutation_p": ordinal_p, + "category_median_frozen_z": {category: float(group["frozen_z"].median()) for category, group in laws.groupby("category")}, + "passed_supportive_rule": supportive, + }, + "frozen_reference_calibration": {"center": frozen_center, "scale": frozen_scale}, + "pilot_local_neutral_calibration_descriptive_only": {"center": local_center, "scale": local_scale, "n_neutral_laws": 4}, + "controls": controls, + "behavior": {"overall_accuracy": behavior_overall, "accuracy_by_category": behavior_by_category, "accuracy_by_law": behavior_by_law}, + "cross_scaffold_comparison": {"spearman_all_12_laws": cross_scaffold_rho, "spearman_within_category": within_category_rhos}, + } + + 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") + + prompts_md = ["# Exact equation-free pilot prompts", "", "All prompts below were generated before new model output.", ""] + for law_record in test["manifest"]["laws"]: + prompts_md.extend([f"## {law_record['law_id']}", "", f"Category: `{law_record['category']}`; response: {law_record['response']}; control: {law_record['control']}.", ""]) + for prompt in [x for x in test["manifest"]["prompts"] if x["law_id"] == law_record["law_id"]]: + prompts_md.extend([f"- `{prompt['prompt_id']}`", f" - Expected: `{prompt['expected_answer']}`", f" - Prompt: {prompt['user']}"]) + prompts_md.append("") + (OUT / "PROMPTS.md").write_text("\n".join(prompts_md) + "\n") + + plt.rcParams.update({"font.size": 8, "axes.spines.top": False, "axes.spines.right": False}) + fig, axes = plt.subplots(2, 2, figsize=(10.4, 7.2)) + colors = {"inverse": "#7662a8", "neutral": "#8c8c8c", "direct": "#2a9d8f"} + for x, category in enumerate(("inverse", "neutral", "direct")): + group = laws[laws["category"] == category].sort_values("frozen_z") + axes[0, 0].scatter(np.full(len(group), x), group["frozen_z"], s=36, color=colors[category], edgecolor="white", linewidth=0.5) + for offset, (_, row) in enumerate(group.iterrows()): + axes[0, 0].annotate(row["law_id"].replace("-neutral", ""), (x, row["frozen_z"]), xytext=(5, -5 + 8 * (offset % 2)), textcoords="offset points", fontsize=6) + axes[0, 0].axhline(0, color="0.35", ls="--", lw=1) + axes[0, 0].set_xticks(range(3), ["Inverse", "Neutral", "Direct"]) + axes[0, 0].set_ylabel("Equation-free matched contrast\n(old neutral scale)") + + for category, group in laws.groupby("category"): + axes[0, 1].scatter(group["scaffolded_frozen_z"], group["frozen_z"], s=38, color=colors[category], label=category.capitalize(), edgecolor="white", linewidth=0.5) + axes[0, 1].axhline(0, color="0.7", lw=0.8) + axes[0, 1].axvline(0, color="0.7", lw=0.8) + axes[0, 1].set_xlabel("Original supplied-equation contrast (z)") + axes[0, 1].set_ylabel("Equation-free contrast (z)") + axes[0, 1].legend(frameon=False, fontsize=7) + + methods = ["Hidden", "Output", "Word TF-IDF", "Char TF-IDF"] + values = [direct_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), 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=20, ha="right") + axes[1, 0].set_ylim(0, 1.03) + axes[1, 0].set_ylabel("Direct-versus-inverse AUC") + + categories = ["Inverse", "Neutral", "Direct", "Overall"] + accuracies = [behavior_by_category["inverse"], behavior_by_category["neutral"], behavior_by_category["direct"], behavior_overall] + axes[1, 1].bar(range(4), accuracies, color=[colors["inverse"], colors["neutral"], colors["direct"], "#276b9a"]) + axes[1, 1].set_xticks(range(4), categories) + axes[1, 1].set_ylim(0, 1.03) + axes[1, 1].set_ylabel("Exact one-word answer accuracy") + 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=2.4, w_pad=2.4) + (OUT / "figures").mkdir(exist_ok=True) + fig.savefig(OUT / "figures" / "equation-free-relational-pilot.png", dpi=240, bbox_inches="tight") + fig.savefig(OUT / "figures" / "equation-free-relational-pilot.pdf", bbox_inches="tight") + plt.close(fig) + + incorrect = laws.loc[laws["sign_correct"] == False, "law_id"].tolist() # noqa: E712 + report = [ + "# Equation-free relational-transfer pilot", + "", + "This exploratory pilot removed the equation, law name, direct/inverse label, and explicit two-stage composition instruction. It retained a natural controlled scenario and the one-word response format. The layer, hidden direction, midpoint, neutral center, and scale were reused without pilot refitting.", + "", + f"- Direct versus inverse hidden-state AUC: {direct_inverse_auc:.3f} (exact label-permutation p={exact_auc_p:.5f})", + f"- Direct versus neutral AUC: {direct_neutral_auc:.3f}", + f"- Neutral versus inverse AUC: {neutral_inverse_auc:.3f}", + f"- Frozen-neutral sign accuracy: {int(sign_correct.sum())}/{len(sign_correct)} ({sign_accuracy:.1%})", + f"- Surface A/B direct-versus-inverse AUC: {surface_auc['a']:.3f}/{surface_auc['b']:.3f}", + f"- Inverse-neutral-direct Spearman rho: {rho:.3f} (exact p={ordinal_p:.5f})", + f"- Exact answer accuracy: {behavior_overall:.1%}", + f"- Passed the pre-output supportive pilot rule: {supportive}", + f"- Directional sign errors: {', '.join(incorrect) if incorrect else 'none'}", + "", + "This is evidence about transfer of the previously measured relational coordinate to equation-free natural prompts. Because the laws were deliberately selected as clear associations after the original benchmark was known, it is not an independent population-level confirmation.", + "", + "See `PROMPTS.md`, `prompt_manifest.json`, `raw.json`, `representations.npz`, `matched_pair_contrasts.csv`, and `law_level_results.csv` for the complete audit trail.", + ] + (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_equation_free_relational_pilot_robustness.py b/jlens_materials/scripts/analyze_equation_free_relational_pilot_robustness.py new file mode 100644 index 0000000000000000000000000000000000000000..1b8f8de9a0de4169cd9fbe9094147607f4b2c8d8 --- /dev/null +++ b/jlens_materials/scripts/analyze_equation_free_relational_pilot_robustness.py @@ -0,0 +1,117 @@ +#!/usr/bin/env python3 +"""Post-output robustness audit for the equation-free relational pilot.""" + +from __future__ import annotations + +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 auc(positive: np.ndarray, negative: np.ndarray) -> float: + delta = positive[:, None] - negative[None, :] + return float(np.mean(delta > 0) + 0.5 * np.mean(delta == 0)) + + +def summarize_law_table(table: pd.DataFrame, center: float) -> dict: + direct = table.loc[table["category"] == "direct", "contrast"].to_numpy() + inverse = table.loc[table["category"] == "inverse", "contrast"].to_numpy() + neutral = table.loc[table["category"] == "neutral", "contrast"].to_numpy() + correct = np.concatenate([direct > center, inverse < center]) + return { + "direct_vs_inverse_auc": auc(direct, inverse), + "direct_vs_neutral_auc": auc(direct, neutral), + "neutral_vs_inverse_auc": auc(neutral, inverse), + "directional_sign_accuracy_at_frozen_center": float(correct.mean()), + "correct_directional_laws": int(correct.sum()), + "directional_laws_total": int(len(correct)), + "category_medians": { + "direct": float(np.median(direct)), + "neutral": float(np.median(neutral)), + "inverse": float(np.median(inverse)), + }, + } + + +def grouped_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() + ) + output[str(value)] = summarize_law_table(laws, center) + return output + + +def behavior_metrics(behavior: pd.DataFrame, field: str) -> dict: + return { + str(value): { + "accuracy": float(group["correct"].mean()), + "correct": int(group["correct"].sum()), + "total": int(len(group)), + } + for value, group in behavior.groupby(field, sort=True) + } + + +def main() -> None: + protocol = json.loads((OUT / "protocol.json").read_text()) + center = float(protocol["frozen_readout"]["primary_center"]) + pairs = pd.read_csv(OUT / "matched_pair_contrasts.csv") + behavior = pd.read_csv(OUT / "behavior_rows.csv") + directional_pairs = pairs[pairs["category"].isin(["direct", "inverse"])].copy() + pair_correct = np.where( + directional_pairs["category"] == "direct", + directional_pairs["contrast"] > center, + directional_pairs["contrast"] < center, + ) + pair_errors = directional_pairs.loc[~pair_correct].copy() + summary = { + "study_id": protocol["study_id"], + "status": "post-output robustness audit; not part of the frozen supportive rule", + "frozen_center_reused_without_stratum_refitting": center, + "hidden_state_by_answer_order": grouped_metrics(pairs, "answer_order", center), + "hidden_state_by_surface": grouped_metrics(pairs, "surface", center), + "hidden_state_by_case_index": grouped_metrics(pairs, "case_index", center), + "directional_pair_level": { + "correct": int(pair_correct.sum()), + "total": int(len(pair_correct)), + "accuracy": float(pair_correct.mean()), + "errors": pair_errors.to_dict(orient="records"), + }, + "behavior_by_answer_order": behavior_metrics(behavior, "answer_order"), + "behavior_by_surface": behavior_metrics(behavior, "surface"), + "behavior_by_numerical_sign": behavior_metrics(behavior, "numerical_sign"), + "behavior_by_case_index": behavior_metrics(behavior, "case_index"), + "behavior_unparsed_completions": int(behavior["predicted_answer"].isna().sum()), + } + (OUT / "robustness_statistics.json").write_text( + json.dumps(summary, indent=2, sort_keys=True) + "\n" + ) + order = summary["hidden_state_by_answer_order"] + behavior_order = summary["behavior_by_answer_order"] + report = [ + "# Post-output robustness audit", + "", + "These checks were added after the primary pilot output and are therefore descriptive. The original frozen neutral center is reused in every stratum; no answer-order-, surface-, or case-specific threshold is fitted.", + "", + f"- Directional matched-pair sign accuracy: {summary['directional_pair_level']['correct']}/{summary['directional_pair_level']['total']} ({summary['directional_pair_level']['accuracy']:.1%})", + f"- Standard answer order: hidden AUC {order['standard']['direct_vs_inverse_auc']:.3f}, sign accuracy {order['standard']['directional_sign_accuracy_at_frozen_center']:.1%}, behavioral accuracy {behavior_order['standard']['accuracy']:.1%}", + f"- Reversed answer order: hidden AUC {order['reversed']['direct_vs_inverse_auc']:.3f}, sign accuracy {order['reversed']['directional_sign_accuracy_at_frozen_center']:.1%}, behavioral accuracy {behavior_order['reversed']['accuracy']:.1%}", + f"- Unparsed greedy completions: {summary['behavior_unparsed_completions']}", + "", + "Complete surface, material-case, numerical-direction, and answer-order results are in `robustness_statistics.json`.", + ] + (OUT / "ROBUSTNESS.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_gemma12b_heldout_v1.py b/jlens_materials/scripts/analyze_gemma12b_heldout_v1.py new file mode 100644 index 0000000000000000000000000000000000000000..b71c67db58ec3e4c92ca5bfd3fb6bb356d34f2fb --- /dev/null +++ b/jlens_materials/scripts/analyze_gemma12b_heldout_v1.py @@ -0,0 +1,213 @@ +#!/usr/bin/env python3 +"""Apply the frozen Figure-2 analysis to three Gemma 12B lens fits.""" + +from __future__ import annotations + +import json +import sys +from pathlib import Path + +import numpy as np + + +ROOT = Path(__file__).resolve().parents[1] +sys.path.insert(0, str(ROOT / "scripts")) + +import analyze_materials_heldout_v1 as heldout # noqa: E402 + + +MODEL = "google/gemma-4-12B-it" +MODEL_REVISION = "707f0a3b8a3c7ad586ed01e27eafbad8a27dd0f7" +HUB_REPO = "lamm-mit/gemma4-jacobian-lenses" +RUN_PATHS = [ + ROOT / "runs" / f"gemma4-12b-it-heldout-v1-seed{seed}.json" + for seed in range(3) +] +OUT = ROOT / "experiments" / "gemma12b-heldout-v1-2026-08-25" +FIGURES = OUT / "figures" +EXPECTED_SOURCE_LAYERS = [ + 0, 2, 4, 6, 8, 9, 11, 13, 15, 17, 19, 21, 22, + 24, 26, 28, 30, 32, 34, 36, 38, 39, 41, 43, 45, +] + + +def validate_12b_runs(runs: list[dict], indexes: list[dict[str, dict]]) -> dict: + audit = {"runs": []} + for seed, (run, index, path) in enumerate( + zip(runs, indexes, RUN_PATHS, strict=True) + ): + if run.get("model") != MODEL: + raise ValueError(f"seed {seed}: unexpected model {run.get('model')}") + methodology = run.get("methodology", {}) + if methodology.get("model_revision") != MODEL_REVISION: + raise ValueError(f"seed {seed}: model revision mismatch") + if methodology.get("workspace_band") != [38.0, 92.0]: + raise ValueError(f"seed {seed}: workspace band mismatch") + if methodology.get("generation_max_new_tokens") != 1: + raise ValueError(f"seed {seed}: generation length mismatch") + if methodology.get("prompt_source") != ( + "prompts/materials-heldout-v1-preregistered.json" + ): + raise ValueError(f"seed {seed}: prompt source mismatch") + + provenance = run.get("lens_provenance", {}) + model = provenance.get("model", {}) + recipe = provenance.get("recipe", {}) + corpus = provenance.get("corpus", {}) + hub = provenance.get("hub", {}) + expected_recipe = { + "name": "paper", + "n_fit": 1000, + "max_seq_len": 128, + "target_layer": -2, + "report_layer_count": 25, + } + for key, value in expected_recipe.items(): + if recipe.get(key) != value: + raise ValueError(f"seed {seed}: lens recipe mismatch for {key}") + expected_model = { + "requested_id": MODEL, + "model_revision": MODEL_REVISION, + "n_layers": 48, + "d_model": 3840, + "vocab_size": 262144, + } + for key, value in expected_model.items(): + if model.get(key) != value: + raise ValueError(f"seed {seed}: lens model mismatch for {key}") + if provenance.get("target_layer") != 46: + raise ValueError(f"seed {seed}: target layer is not penultimate") + if provenance.get("source_layers") != EXPECTED_SOURCE_LAYERS: + raise ValueError(f"seed {seed}: source-layer schedule mismatch") + if corpus.get("records") != 1000 or corpus.get("unique_records") != 1000: + raise ValueError(f"seed {seed}: corpus record count mismatch") + if corpus.get("subset") != "wikitext-103-raw-v1": + raise ValueError(f"seed {seed}: corpus subset mismatch") + if corpus.get("seed") != seed: + raise ValueError(f"seed {seed}: lens corpus seed mismatch") + if hub.get("repo_id") != HUB_REPO: + raise ValueError(f"seed {seed}: Hub repository mismatch") + if hub.get("path_in_repo") != f"gemma4-12b-it/paper/seed{seed}.pt": + raise ValueError(f"seed {seed}: Hub lens path mismatch") + if len(index) != 50: + raise ValueError(f"seed {seed}: expected 50 held-out prompts") + audit["runs"].append( + { + "seed": seed, + "path": str(path.relative_to(ROOT)), + "sha256": heldout.sha256(path), + "lens_corpus_sha256": corpus.get("sha256"), + "source_layers": provenance.get("source_layers"), + "target_layer": provenance.get("target_layer"), + } + ) + + reference = indexes[0] + for slug in reference: + reference_items = reference[slug]["emergence"] + expected_direct = [ + int(item["logit_lens_best_rank"]) for item in reference_items + ] + for seed in (1, 2): + observed = [ + int(item["logit_lens_best_rank"]) + for item in indexes[seed][slug]["emergence"] + ] + if observed != expected_direct: + raise ValueError( + f"{slug}: direct-unembedding ranks differ at seed {seed}" + ) + audit["direct_unembedding_identical_across_runs"] = True + return audit + + +def write_csv(path: Path, rows: list[dict]) -> None: + heldout.write_csv(path, rows) + + +def main() -> None: + missing = [path for path in RUN_PATHS if not path.exists()] + if missing: + formatted = "\n".join(f"- {path}" for path in missing) + raise FileNotFoundError( + "Download or generate the three 12B held-out run files first:\n" + + formatted + ) + OUT.mkdir(parents=True, exist_ok=True) + FIGURES.mkdir(parents=True, exist_ok=True) + + runs = [json.loads(path.read_text()) for path in RUN_PATHS] + slugs, indexes = heldout.validate_runs(runs, RUN_PATHS) + audit = validate_12b_runs(runs, indexes) + prompt_rows, concept_rows, ks = heldout.build_controlled_rows( + runs, slugs, indexes + ) + controlled = heldout.build_controlled_statistics( + runs, prompt_rows, concept_rows, ks + ) + + original = { + "FIG_DIR": heldout.FIG_DIR, + "CONTROLLED_FIGURE": heldout.CONTROLLED_FIGURE, + "REPRO_FIGURE": heldout.REPRO_FIGURE, + } + try: + heldout.FIG_DIR = FIGURES + heldout.CONTROLLED_FIGURE = FIGURES / "figure2-12b-three-fit" + heldout.REPRO_FIGURE = FIGURES / "figure-s1-12b-fit-reproducibility" + heldout.plot_controlled(controlled, prompt_rows, concept_rows) + heldout.plot_reproducibility(concept_rows) + finally: + for key, value in original.items(): + setattr(heldout, key, value) + + write_csv(OUT / "prompt_results.csv", prompt_rows) + write_csv(OUT / "concept_results.csv", concept_rows) + statistics = { + "study_id": "gemma12b-heldout-v1-2026-08-25", + "analysis_status": ( + "three-independent-lens replication of the frozen Figure-2 endpoint" + ), + "model": MODEL, + "model_revision": MODEL_REVISION, + "audit": audit, + "controlled": controlled, + "comparison_boundary": ( + "The 12B and submitted 4B endpoints average three independently fitted " + "lenses. The available 31B endpoint uses one lens and must be displayed " + "as a single-fit estimate without lens-fit reproducibility intervals." + ), + } + (OUT / "statistics.json").write_text( + json.dumps(statistics, indent=2, sort_keys=True) + "\n" + ) + + overall = controlled["overall"] + interval = overall["hierarchical_family_bootstrap"] + correlations = controlled["seed_rank_correlations"] + report = [ + "# Gemma 12B held-out Figure-2 replication", + "", + "The exact frozen 50-prompt materials cohort was evaluated with three independently fitted Gemma 12B Jacobian lenses. The analysis is identical to the submitted 4B controlled-recovery endpoint.", + "", + f"- Jacobian recovery AUC: **{overall['j_auc_mean']:.4f}**.", + f"- Direct-unembedding AUC: **{overall['logit_auc_mean']:.4f}**.", + f"- Difference: **{overall['delta_auc']:+.4f}** (family-hierarchical 95% interval {interval['low']:+.4f} to {interval['high']:+.4f}).", + f"- Prompt outcomes: {overall['j_wins']} Jacobian wins, {overall['ties']} ties, and {overall['j_losses']} losses.", + "", + "## Lens-fit rank reproducibility", + "", + f"- seed 0 versus 1: rho={correlations['seed0_vs_seed1']['rho']:.4f}", + f"- seed 0 versus 2: rho={correlations['seed0_vs_seed2']['rho']:.4f}", + f"- seed 1 versus 2: rho={correlations['seed1_vs_seed2']['rho']:.4f}", + "", + statistics["comparison_boundary"], + "", + ] + (OUT / "REPORT.md").write_text("\n".join(report)) + print("\n".join(report)) + print(f"wrote {OUT}") + + +if __name__ == "__main__": + main() diff --git a/jlens_materials/scripts/analyze_gemma31b_compositional_physics_pilot.py b/jlens_materials/scripts/analyze_gemma31b_compositional_physics_pilot.py new file mode 100644 index 0000000000000000000000000000000000000000..7390b38eeeef19dd45aefd86475f97de92816e30 --- /dev/null +++ b/jlens_materials/scripts/analyze_gemma31b_compositional_physics_pilot.py @@ -0,0 +1,324 @@ +#!/usr/bin/env python3 +"""Analyze the frozen 31B two-link compositional-physics pilot.""" + +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-compositional-physics-2026-08-25" +ONE_STEP = ROOT / "experiments" / "gemma4-31b-equation-free-relational-v1" / "frozen_direction.npz" +COLORS = {"context": "#999999", "perturbation": "#2E7E9E", "question": "#239B8F", "final_prompt": "#7153A2"} + + +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_centroid(train_x: np.ndarray, train_y: np.ndarray) -> tuple[np.ndarray, np.ndarray]: + positive = train_x[train_y == 1].mean(axis=0) + negative = train_x[train_y == -1].mean(axis=0) + direction = positive - negative + direction /= max(np.linalg.norm(direction), 1e-12) + midpoint = 0.5 * (positive + negative) + return direction, midpoint + + +def score_direction(values: np.ndarray, direction: np.ndarray, midpoint: np.ndarray) -> np.ndarray: + return np.einsum("ij,j->i", values - 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_centroid(unit_rows(train_x), train_y) + scores = score_direction(unit_rows(test_x), direction, midpoint) + return { + "auc": float(roc_auc_score(test_y == 1, scores)), + "accuracy": float(np.mean((scores > 0) == (test_y == 1))), + "scores": scores, + "direction": direction, + "midpoint": midpoint, + } + + +def exact_chain_parity_p(scores: np.ndarray, meta: pd.DataFrame, observed_auc: float) -> dict: + chains = sorted(meta["chain_id"].unique()) + if len(chains) != 12: + raise RuntimeError(f"expected 12 held-out chains, found {len(chains)}") + numerical = meta["numerical_sign"].to_numpy(dtype=int) + chain_array = meta["chain_id"].to_numpy() + null = [] + for positive_indices in itertools.combinations(range(len(chains)), len(chains) // 2): + positive = {chains[index] for index in positive_indices} + parity = np.asarray([1 if chain_id in positive else -1 for chain_id in chain_array]) + labels = parity * numerical + null.append(float(roc_auc_score(labels == 1, scores))) + null = np.asarray(null) + return { + "assignments": int(len(null)), + "p_one_sided": float(np.mean(null >= observed_auc - 1e-12)), + "null_mean": float(null.mean()), + "null_95": [float(np.quantile(null, 0.025)), float(np.quantile(null, 0.975))], + } + + +def lexical_control(meta: pd.DataFrame, target: str, analyzer: str) -> dict: + prefixes = [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) + development = meta["split"].to_numpy() == "development" + test = ~development + train_x = vectorizer.fit_transform(np.asarray(prefixes)[development]).toarray() + test_x = vectorizer.transform(np.asarray(prefixes)[test]).toarray() + result = evaluate( + train_x, + meta.loc[development, target].to_numpy(dtype=int), + test_x, + meta.loc[test, target].to_numpy(dtype=int), + ) + return {"auc": result["auc"], "accuracy": result["accuracy"]} + + +def answer_prefix_identity(states: np.ndarray, meta: pd.DataFrame, positions: list[str], layers: list[int]) -> dict: + position_index = positions.index("question") + differences = [] + keys = ["chain_id", "surface", "numerical_sign"] + for _, group in meta.groupby(keys, sort=True): + semantic = int(group.loc[group["answer_format"] == "semantic", "row_index"].iloc[0]) + arbitrary = int(group.loc[group["answer_format"] == "arbitrary", "row_index"].iloc[0]) + differences.append(np.abs(states[position_index, semantic] - states[position_index, arbitrary])) + values = np.stack(differences) + return { + "maximum_absolute_state_difference": float(values.max()), + "mean_absolute_state_difference": float(values.mean()), + "layers_checked": layers, + } + + +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") + layers = [int(value) for value in protocol["layers_zero_indexed"]] + positions = list(protocol["positions"]) + meta = pd.DataFrame(manifest["prompts"]).reset_index(names="row_index") + rows = pd.DataFrame(raw["rows"]) + if len(meta) != len(rows) or states.shape[1] != len(meta): + raise RuntimeError("manifest, row, and state counts differ") + + # Before the suffix, semantic/arbitrary variants have an identical causal prefix. + prefix_identity = answer_prefix_identity(states, meta, positions, layers) + + # Use one copy of each prefix for answer-free analyses. + answer_free = meta["answer_format"].eq("semantic").to_numpy() + dev = meta["split"].eq("development").to_numpy() & answer_free + test = meta["split"].eq("test").to_numpy() & answer_free + targets = ["link1_sign", "link2_sign", "net_law_sign", "numerical_sign", "physical_sign"] + metric_rows = [] + cached = {} + for position_index, position in enumerate(positions): + for layer_index, layer in enumerate(layers): + for target in targets: + result = evaluate( + states[position_index, dev, layer_index], + meta.loc[dev, target].to_numpy(dtype=int), + states[position_index, test, layer_index], + meta.loc[test, target].to_numpy(dtype=int), + ) + metric_rows.append( + { + "position": position, + "layer": layer, + "target": target, + "auc": result["auc"], + "accuracy": result["accuracy"], + } + ) + cached[(position, layer, target)] = result + metrics = pd.DataFrame(metric_rows) + metrics.to_csv(OUT / "layerwise_decoding.csv", index=False) + + primary = cached[("question", 49, "physical_sign")] + primary_meta = meta.loc[test].reset_index(drop=True) + surface_auc = {} + for surface in ("a", "b"): + selected = primary_meta["surface"].eq(surface).to_numpy() + labels = primary_meta.loc[selected, "physical_sign"].to_numpy(dtype=int) + surface_auc[surface] = float(roc_auc_score(labels == 1, primary["scores"][selected])) + exact_null = exact_chain_parity_p(primary["scores"], primary_meta, primary["auc"]) + + lexical_meta = meta.loc[answer_free].reset_index(drop=True) + lexical = { + analyzer: lexical_control(lexical_meta, "physical_sign", analyzer) + for analyzer in ("word", "character") + } + + # Apply the previously frozen one-step layer-49 direction with no refitting. + one_step = np.load(ONE_STEP) + old_direction = one_step["direction"].astype(np.float64) + old_direction /= np.linalg.norm(old_direction) + old_midpoint = one_step["midpoint"].astype(np.float64) + question_index = positions.index("question") + layer49_index = layers.index(49) + test_question = unit_rows(states[question_index, test, layer49_index]) + old_scores = score_direction(test_question, old_direction, old_midpoint) + test_labels = meta.loc[test, "physical_sign"].to_numpy(dtype=int) + zero_shot_auc = float(roc_auc_score(test_labels == 1, old_scores)) + zero_shot_accuracy = float(np.mean((old_scores > 0) == (test_labels == 1))) + direction_cosine = float(np.einsum("i,i->", primary["direction"], old_direction)) + + # Transfer the final-state physical variable across answer vocabularies. + final_index = positions.index("final_prompt") + cross_vocab = {} + for train_format, test_format in (("semantic", "arbitrary"), ("arbitrary", "semantic")): + train_mask = meta["split"].eq("development").to_numpy() & meta["answer_format"].eq(train_format).to_numpy() + test_mask = meta["split"].eq("test").to_numpy() & meta["answer_format"].eq(test_format).to_numpy() + result = evaluate( + states[final_index, train_mask, layer49_index], + meta.loc[train_mask, "physical_sign"].to_numpy(dtype=int), + states[final_index, test_mask, layer49_index], + meta.loc[test_mask, "physical_sign"].to_numpy(dtype=int), + ) + cross_vocab[f"{train_format}_to_{test_format}"] = { + "auc": result["auc"], "accuracy": result["accuracy"] + } + + test_rows = rows[rows["split"].eq("test")] + behavior = { + "overall_pairwise_accuracy": float(test_rows["pairwise_correct"].mean()), + "by_answer_format": { + key: float(group["pairwise_correct"].mean()) + for key, group in test_rows.groupby("answer_format") + }, + "by_surface": { + key: float(group["pairwise_correct"].mean()) + for key, group in test_rows.groupby("surface") + }, + "global_top_is_allowed_pair": float(test_rows["global_top_is_pair_member"].mean()), + } + + primary_rule = protocol["success_criteria"] + passed = bool( + primary["auc"] >= primary_rule["primary_auc_minimum"] + and min(surface_auc.values()) >= primary_rule["each_surface_auc_minimum"] + and exact_null["p_one_sided"] <= primary_rule["exact_chain_parity_permutation_p_maximum"] + and lexical["word"]["auc"] <= primary_rule["word_tfidf_auc_maximum_for_nonlexical_support"] + ) + + statistics = { + "study_id": protocol["study_id"], + "analysis_status": "frozen pilot analysis", + "primary": { + "answer_free_question_layer49_physical_outcome_auc": primary["auc"], + "accuracy": primary["accuracy"], + "surface_auc": surface_auc, + "exact_balanced_chain_parity_null": exact_null, + "passed_all_frozen_criteria": passed, + }, + "zero_shot_one_step_direction_on_two_link_test": { + "auc": zero_shot_auc, + "accuracy_using_one_step_midpoint": zero_shot_accuracy, + "new_composition_direction_vs_one_step_direction_cosine": direction_cosine, + }, + "lexical_controls": lexical, + "answer_prefix_identity": prefix_identity, + "cross_answer_vocabulary_at_final_state": cross_vocab, + "behavior": behavior, + "guardrail": ( + "The prompts require composition of familiar monotonic mechanisms. They do not " + "test discovery of unknown laws, quantitative coefficients, or causal necessity." + ), + } + (OUT / "statistics.json").write_text(json.dumps(statistics, indent=2) + "\n") + + # Compact diagnostic figure. + fig, axes = plt.subplots(2, 2, figsize=(11.8, 8.0)) + ax = axes[0, 0] + for position in ("context", "perturbation", "question", "final_prompt"): + selected = metrics[(metrics["target"] == "physical_sign") & (metrics["position"] == position)] + ax.plot(selected["layer"], selected["auc"], marker="o", ms=4, color=COLORS[position], label=position.replace("_", " ")) + 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 physical-outcome AUC") + ax.legend(frameon=False, ncol=2) + + ax = axes[0, 1] + stage = metrics[(metrics["layer"] == 49) & metrics["position"].isin(["context", "perturbation", "question"])] + pivot = stage.pivot(index="target", columns="position", values="auc").loc[targets, ["context", "perturbation", "question"]] + image = ax.imshow(pivot.to_numpy(), vmin=0.4, vmax=1.0, cmap="viridis", aspect="auto") + ax.set_xticks(range(3), ["context", "after change", "after question"]) + ax.set_yticks(range(len(targets)), ["link 1", "link 2", "net law", "numeric change", "physical outcome"]) + for i in range(len(targets)): + for j in range(3): + ax.text(j, i, f"{pivot.iloc[i, j]:.2f}", ha="center", va="center", color="white" if pivot.iloc[i, j] < 0.78 else "black") + fig.colorbar(image, ax=ax, fraction=0.046, pad=0.04, label="held-out AUC") + + ax = axes[1, 0] + ax.scatter(old_scores, primary["scores"], c=np.where(test_labels > 0, "#239B8F", "#7153A2"), s=35, alpha=0.8, edgecolor="white", linewidth=0.4) + ax.axhline(0, color="#999999", lw=0.8) + ax.axvline(0, color="#999999", lw=0.8) + ax.set_xlabel("frozen one-step direction score") + ax.set_ylabel("development-chain composition score") + ax.text(0.04, 0.96, f"zero-shot AUC = {zero_shot_auc:.3f}\ndirection cosine = {direction_cosine:.3f}", transform=ax.transAxes, va="top") + + ax = axes[1, 1] + labels = ["hidden\ncomposition", "one-step\nzero-shot", "word\nTF-IDF", "character\nTF-IDF", "semantic\nbehavior", "code\nbehavior"] + values = [primary["auc"], zero_shot_auc, lexical["word"]["auc"], lexical["character"]["auc"], behavior["by_answer_format"]["semantic"], behavior["by_answer_format"]["arbitrary"]] + ax.bar(labels, values, color=["#239B8F", "#2E7E9E", "#AAAAAA", "#777777", "#D29B35", "#7153A2"]) + 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-compositional-physics.{suffix}", dpi=240, bbox_inches="tight") + plt.close(fig) + + report = f"""# Gemma-4 31B compositional-physics pilot + +The pilot asks whether a late relational direction composes two familiar +materials mechanisms. Prompts contain no equation, direct/inverse label, or +instruction to multiply signs. A direction is fitted on 12 development chain +families and evaluated on 12 disjoint chain families at an answer-free marker. + +- Primary held-out two-link physical-outcome AUC: **{primary['auc']:.3f}** +- Surface A/B AUC: **{surface_auc['a']:.3f}/{surface_auc['b']:.3f}** +- Exact balanced chain-parity p: **{exact_null['p_one_sided']:.5f}** +- Frozen one-step direction applied zero-shot: **AUC {zero_shot_auc:.3f}** +- New composition direction versus one-step direction cosine: **{direction_cosine:.3f}** +- Word/character TF-IDF AUC: **{lexical['word']['auc']:.3f}/{lexical['character']['auc']:.3f}** +- Semantic/arbitrary-code pairwise behavior: **{behavior['by_answer_format']['semantic']:.3f}/{behavior['by_answer_format']['arbitrary']:.3f}** +- Passed all frozen pilot criteria: **{passed}** + +The context marker is causally before the numerical perturbation, so physical +outcome should be undecodable there when up/down cases are balanced. The +layerwise table and figure show when the composed outcome becomes readable. +This remains a familiar-mechanism composition test, not unknown-law discovery. +""" + (OUT / "REPORT.md").write_text(report) + print(json.dumps(statistics, indent=2)) + + +if __name__ == "__main__": + main() diff --git a/jlens_materials/scripts/analyze_gemma31b_relational_geometry_deep.py b/jlens_materials/scripts/analyze_gemma31b_relational_geometry_deep.py new file mode 100644 index 0000000000000000000000000000000000000000..52643c9f2129f029ee3af9017ee256650b0746fc --- /dev/null +++ b/jlens_materials/scripts/analyze_gemma31b_relational_geometry_deep.py @@ -0,0 +1,310 @@ +#!/usr/bin/env python3 +"""Audit the full-vector geometry behind the 31B equation-free law result. + +This is an exploratory, post-paper analysis of already archived states. It asks +whether matched up-minus-down movements for different constitutive laws share a +cross-domain direction, rather than merely producing correct scalar projections +onto the previously frozen development direction. +""" + +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] +SOURCE = ROOT / "experiments" / "gemma4-31b-equation-free-relational-v1" +OUT = ROOT / "experiments" / "gemma4-31b-relational-geometry-deep-2026-08-25" +SEED = 20260825 +N_PERMUTATIONS = 10_000 + +COLORS = {"inverse": "#6C55A3", "direct": "#249C8F"} + + +def unit_rows(values: np.ndarray) -> np.ndarray: + values = np.asarray(values, dtype=np.float64) + norms = np.linalg.norm(values, axis=1, keepdims=True) + return values / np.maximum(norms, 1e-12) + + +def pair_differences(values: np.ndarray, prompts: list[dict]) -> tuple[np.ndarray, pd.DataFrame]: + meta = pd.DataFrame(prompts).reset_index(names="row_index") + keys = ["law_id", "surface", "case_index", "answer_order"] + rows: list[dict] = [] + differences: list[np.ndarray] = [] + for key, group in meta.groupby(keys, sort=True): + if set(group["numerical_sign"]) != {-1, 1} or len(group) != 2: + raise RuntimeError(f"invalid matched cell {key}: {len(group)} rows") + up = group.loc[group["numerical_sign"] == 1].iloc[0] + down = group.loc[group["numerical_sign"] == -1].iloc[0] + differences.append(values[int(up.row_index)] - values[int(down.row_index)]) + rows.append( + { + **dict(zip(keys, key, strict=True)), + "category": up.category, + "domain": up.domain, + "law_sign": int(up.law_sign), + } + ) + return np.vstack(differences), pd.DataFrame(rows) + + +def law_means(differences: np.ndarray, pair_meta: pd.DataFrame) -> tuple[np.ndarray, pd.DataFrame]: + rows: list[dict] = [] + means: list[np.ndarray] = [] + for law_id, group in pair_meta.groupby("law_id", sort=True): + indices = group.index.to_numpy() + means.append(differences[indices].mean(axis=0)) + first = group.iloc[0] + rows.append( + { + "law_id": law_id, + "category": first.category, + "domain": first.domain, + "law_sign": int(first.law_sign), + "n_matched_cells": int(len(group)), + } + ) + return np.vstack(means), pd.DataFrame(rows) + + +def centroid_scores(train_x: np.ndarray, train_y: np.ndarray, test_x: np.ndarray) -> np.ndarray: + positive = train_x[train_y == 1].mean(axis=0) + negative = train_x[train_y == -1].mean(axis=0) + direction = positive - negative + direction /= max(np.linalg.norm(direction), 1e-12) + midpoint = 0.5 * (positive + negative) + return np.einsum("ij,j->i", test_x - midpoint, direction, optimize=True) + + +def leave_one_domain_out(values: np.ndarray, meta: pd.DataFrame, labels: np.ndarray) -> dict: + scores = np.full(len(meta), np.nan, dtype=float) + for domain in sorted(meta["domain"].unique()): + test = meta["domain"].to_numpy() == domain + train = ~test + if len(np.unique(labels[train])) != 2: + raise RuntimeError(f"training fold for {domain} lacks both directional classes") + scores[test] = centroid_scores(values[train], labels[train], values[test]) + return { + "auc": float(roc_auc_score(labels == 1, scores)), + "accuracy": float(np.mean((scores > 0) == (labels == 1))), + "scores": scores, + } + + +def different_domain_cosine(values: np.ndarray, meta: pd.DataFrame, labels: np.ndarray) -> float: + signed = unit_rows(values) * labels[:, None] + gram = np.einsum("ik,jk->ij", signed, signed, optimize=True) + domains = meta["domain"].to_numpy() + mask = np.triu(domains[:, None] != domains[None, :], k=1) + return float(gram[mask].mean()) + + +def permutation_null(values: np.ndarray, meta: pd.DataFrame, labels: np.ndarray) -> dict: + rng = np.random.default_rng(SEED) + aucs = np.empty(N_PERMUTATIONS, dtype=float) + cosines = np.empty(N_PERMUTATIONS, dtype=float) + for index in range(N_PERMUTATIONS): + shuffled = rng.permutation(labels) + aucs[index] = leave_one_domain_out(values, meta, shuffled)["auc"] + cosines[index] = different_domain_cosine(values, meta, shuffled) + return {"auc": aucs, "cosine": cosines} + + +def p_greater(null: np.ndarray, observed: float) -> float: + return float((1 + np.sum(null >= observed)) / (len(null) + 1)) + + +def text_law_vectors(prompts: list[dict], analyzer: str) -> tuple[np.ndarray, pd.DataFrame]: + texts = [row["user"] for row in prompts] + if analyzer == "word": + vectorizer = TfidfVectorizer(ngram_range=(1, 2), min_df=1, sublinear_tf=True) + else: + vectorizer = TfidfVectorizer( + analyzer="char", ngram_range=(3, 5), min_df=2, sublinear_tf=True + ) + matrix = vectorizer.fit_transform(texts).toarray().astype(np.float64) + pair_values, pair_meta = pair_differences(matrix, prompts) + return law_means(pair_values, pair_meta) + + +def main() -> None: + OUT.mkdir(parents=True, exist_ok=True) + figures = OUT / "figures" + figures.mkdir(exist_ok=True) + + manifest = json.loads((SOURCE / "equation_free_prompt_manifest.json").read_text()) + raw_states = np.load(SOURCE / "equation_free_states.npy", mmap_mode="r") + normalized_states = unit_rows(raw_states) + pair_values, pair_meta = pair_differences(normalized_states, manifest["prompts"]) + law_values, law_meta = law_means(pair_values, pair_meta) + + directional = law_meta["category"].isin(["direct", "inverse"]).to_numpy() + x = unit_rows(law_values[directional]) + meta = law_meta.loc[directional].reset_index(drop=True) + labels = meta["law_sign"].to_numpy(dtype=int) + + hidden = leave_one_domain_out(x, meta, labels) + null = permutation_null(x, meta, labels) + observed_cosine = different_domain_cosine(x, meta, labels) + + controls: dict[str, dict] = {} + for analyzer in ("word", "character"): + text_values, text_meta = text_law_vectors(manifest["prompts"], analyzer) + text_directional = text_meta["category"].isin(["direct", "inverse"]).to_numpy() + text_x = unit_rows(text_values[text_directional]) + text_meta = text_meta.loc[text_directional].reset_index(drop=True) + text_labels = text_meta["law_sign"].to_numpy(dtype=int) + result = leave_one_domain_out(text_x, text_meta, text_labels) + controls[analyzer] = {"auc": result["auc"], "accuracy": result["accuracy"]} + + global_positive = x[labels == 1].mean(axis=0) + global_negative = x[labels == -1].mean(axis=0) + geometry_direction = global_positive - global_negative + geometry_direction /= np.linalg.norm(geometry_direction) + frozen = np.load(SOURCE / "frozen_direction.npz")["direction"].astype(np.float64) + frozen /= np.linalg.norm(frozen) + direction_cosine = float(np.einsum("i,i->", geometry_direction, frozen)) + + centered = x - x.mean(axis=0) + centered_gram = np.einsum("ik,jk->ij", centered, centered, optimize=True) + eigenvalues, eigenvectors = np.linalg.eigh(centered_gram) + order = np.argsort(eigenvalues)[::-1] + eigenvalues = np.maximum(eigenvalues[order], 0.0) + eigenvectors = eigenvectors[:, order] + n_components = min(10, len(x) - 1) + coordinates = eigenvectors[:, :n_components] * np.sqrt( + eigenvalues[:n_components] + )[None, :] + explained_variance_ratio = eigenvalues / max(eigenvalues.sum(), 1e-12) + pc1_auc = float(roc_auc_score(labels == 1, coordinates[:, 0])) + pc1_auc = max(pc1_auc, 1.0 - pc1_auc) + centroid_delta = global_positive - global_negative + between = float(np.einsum("i,i->", centroid_delta, centroid_delta)) * len(x) / 4.0 + total = float(np.einsum("ij,ij->", centered, centered)) + between_fraction = float(between / total) + + law_table = meta.copy() + law_table["lodo_score"] = hidden["scores"] + law_table["pc1"] = coordinates[:, 0] + law_table["pc2"] = coordinates[:, 1] + law_table.to_csv(OUT / "directional_law_geometry.csv", index=False) + + statistics = { + "study_id": "gemma4-31b-relational-geometry-deep-2026-08-25", + "status": "exploratory post hoc audit of archived equation-free states", + "source": str(SOURCE.relative_to(ROOT)), + "n_directional_laws": int(len(meta)), + "n_domains": int(meta["domain"].nunique()), + "full_vector_leave_one_domain_out": { + "auc": hidden["auc"], + "accuracy": hidden["accuracy"], + "permutation_p_one_sided": p_greater(null["auc"], hidden["auc"]), + }, + "sign_aligned_cross_domain_cosine": { + "mean": observed_cosine, + "permutation_mean": float(null["cosine"].mean()), + "permutation_95": [ + float(np.quantile(null["cosine"], 0.025)), + float(np.quantile(null["cosine"], 0.975)), + ], + "permutation_p_one_sided": p_greater(null["cosine"], observed_cosine), + }, + "lexical_controls": controls, + "geometry_direction_vs_frozen_development_direction_cosine": direction_cosine, + "pca": { + "pc1_explained_variance": float(explained_variance_ratio[0]), + "first_10_cumulative_variance": float( + explained_variance_ratio[:n_components].sum() + ), + "pc1_directional_auc_orientation_free": pc1_auc, + "direct_inverse_between_centroid_fraction_of_total_sum_squares": between_fraction, + }, + "interpretive_guardrail": ( + "A shared matched-displacement geometry supports cross-domain relational " + "structure at one late layer. It does not by itself establish multistep " + "composition, equation recovery, causal necessity, or unfamiliar-law discovery." + ), + } + (OUT / "statistics.json").write_text(json.dumps(statistics, indent=2) + "\n") + + fig, axes = plt.subplots(1, 3, figsize=(13.0, 4.0)) + for category in ("inverse", "direct"): + selected = meta["category"] == category + axes[0].scatter( + coordinates[selected, 0], + coordinates[selected, 1], + s=44, + color=COLORS[category], + edgecolor="white", + linewidth=0.6, + label=category, + ) + axes[0].axhline(0, color="#bbbbbb", lw=0.7) + axes[0].axvline(0, color="#bbbbbb", lw=0.7) + axes[0].set_xlabel("PC1 of law-level state movement") + axes[0].set_ylabel("PC2") + axes[0].legend(frameon=False) + + names = ["31B hidden\nmovement", "word\nTF-IDF", "character\nTF-IDF"] + values = [hidden["auc"], controls["word"]["auc"], controls["character"]["auc"]] + axes[1].bar(names, values, color=["#249C8F", "#AAAAAA", "#777777"], width=0.65) + axes[1].axhline(0.5, color="#666666", ls="--", lw=1) + axes[1].set_ylim(0.35, 1.03) + axes[1].set_ylabel("leave-one-domain-out direct/inverse AUC") + + axes[2].hist(null["cosine"], bins=40, color="#D7D9DC", edgecolor="white") + axes[2].axvline(observed_cosine, color="#249C8F", lw=2.5) + axes[2].set_xlabel("mean cross-domain cosine after physical-sign alignment") + axes[2].set_ylabel("label permutations") + axes[2].text( + 0.98, + 0.95, + f"observed = {observed_cosine:.3f}\np = {p_greater(null['cosine'], observed_cosine):.4g}", + transform=axes[2].transAxes, + ha="right", + va="top", + ) + 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) + for suffix in ("png", "pdf", "svg"): + fig.savefig(figures / f"gemma31b-relational-full-vector-audit.{suffix}", dpi=240, bbox_inches="tight") + plt.close(fig) + + report = f"""# 31B full-vector relational-geometry audit + +This post hoc audit uses the already archived 960 equation-free states at layer +49. For each law and nuisance-matched cell, it subtracts the down-control state +from the up-control state, averages the eight movements within law, and tests +whether the resulting full vectors generalize across held-out physical domains. + +- Leave-one-domain-out direct/inverse AUC: **{hidden['auc']:.3f}** +- Leave-one-domain-out sign accuracy: **{hidden['accuracy']:.3f}** +- AUC label-permutation p: **{p_greater(null['auc'], hidden['auc']):.5f}** +- Mean sign-aligned cross-domain cosine: **{observed_cosine:.4f}** +- Cosine label-permutation p: **{p_greater(null['cosine'], observed_cosine):.5f}** +- Word/character TF-IDF AUC: **{controls['word']['auc']:.3f}/{controls['character']['auc']:.3f}** +- Full-vector geometry direction versus frozen development direction cosine: **{direction_cosine:.3f}** + +The audit determines whether a cross-domain vector regularity exists at the +registered late layer. It is not a test of multistep physical composition; +that requires new prompts in which two mechanisms must be combined. +""" + (OUT / "REPORT.md").write_text(report) + print(json.dumps(statistics, indent=2)) + + +if __name__ == "__main__": + main() diff --git a/jlens_materials/scripts/analyze_gemma_scale_abstract_physics.py b/jlens_materials/scripts/analyze_gemma_scale_abstract_physics.py new file mode 100644 index 0000000000000000000000000000000000000000..a70493e76655fa32b0f619cb7660f6e26fa91d48 --- /dev/null +++ b/jlens_materials/scripts/analyze_gemma_scale_abstract_physics.py @@ -0,0 +1,883 @@ +#!/usr/bin/env python3 +"""Compare the frozen 4B and 31B abstract-physics cohorts. + +This script reuses the original discovery/replication law split and the +registered centroid endpoints. It evaluates (i) the answer-free decision +marker, (ii) the final prompt state under crossed law orientation and answer +order, (iii) transfer between semantic and arbitrary answer vocabularies, and +(iv) a fixed ~83%-depth audit that does not select a new layer at 31B. +""" + +from __future__ import annotations + +import hashlib +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 + + +ROOT = Path(__file__).resolve().parents[1] +sys.path.insert(0, str(ROOT / "scripts")) + +from analyze_abstract_physics_composition import ( # noqa: E402 + bootstrap, + centroid_direction, + contrast_parallelism, + discovery_ccgp, + exact_signflip, + normalize, + replication_law_scores, + safe_dot, +) +from analyze_final_state_physics_abstraction import ( # noqa: E402 + dual_cross_auc, + replication_law_auc, +) + + +OUT = ROOT / "experiments" / "gemma-scale-abstract-physics-2026-08-23" +COHORTS = { + ("4B", "arbitrary_code"): ROOT + / "experiments" + / "abstract-physics-composition-2026-07-18", + ("4B", "semantic_answer"): ROOT + / "experiments" + / "abstract-physics-semantic-2026-07-18", + ("31B", "arbitrary_code"): ROOT + / "experiments" + / "gemma4-31b-abstract-physics-arbitrary-code-v1", + ("31B", "semantic_answer"): ROOT + / "experiments" + / "gemma4-31b-abstract-physics-semantic-v1", +} +BOOTSTRAP_RESAMPLES = 30000 +BOOTSTRAP_SEED = 20260824 +FIXED_DEPTH_FRACTION = 34 / 41 + + +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 make_meta(rows: list[dict], vocabulary: str) -> dict[str, np.ndarray]: + if vocabulary == "arbitrary_code": + positive_order = np.asarray( + [row["answer_order"] == "a-larger" for row in rows] + ) + code = np.asarray([row["expected_code"] == "A" for row in rows]) + else: + positive_order = np.asarray( + [row["answer_order"] == "higher-first" for row in rows] + ) + code = np.asarray( + [row["expected_answer"] == "higher" for row in rows] + ) + return { + "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 summarize(values: np.ndarray, seed: int) -> dict: + values = np.asarray(values, dtype=float) + ci = bootstrap( + values, + np.random.default_rng(seed), + 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 paired_change(values_4b: np.ndarray, values_31b: np.ndarray, seed: int) -> dict: + differences = np.asarray(values_31b, float) - np.asarray(values_4b, float) + ci = bootstrap( + differences, + np.random.default_rng(seed), + BOOTSTRAP_RESAMPLES, + ) + return { + "mean_31b_minus_4b": float(differences.mean()), + "paired_law_bootstrap_95": list(ci), + "laws_improved": int(np.sum(differences > 0)), + "laws_tied": int(np.sum(differences == 0)), + "laws_worsened": int(np.sum(differences < 0)), + "two_sided_exact_signflip_p": exact_signflip(differences), + } + + +def answer_free_cross_cohort( + train: dict, + test: dict, + layer: int, +) -> pd.DataFrame: + train_index = int(np.flatnonzero(train["layers"] == layer)[0]) + test_index = int(np.flatnonzero(test["layers"] == layer)[0]) + train_position = int(np.flatnonzero(train["positions"] == "decision")[0]) + test_position = int(np.flatnonzero(test["positions"] == "decision")[0]) + train_values = normalize(train["states"][train_position, :, train_index]) + test_values = normalize(test["states"][test_position, :, test_index]) + train_meta, test_meta = train["meta"], test["meta"] + output = [] + for train_orientation, test_orientation in ((True, False), (False, True)): + train_mask = ( + (train_meta["split"] == "discovery") + & (train_meta["orientation"] == train_orientation) + ) + direction, midpoint = centroid_direction( + train_values[train_mask], train_meta["physical"][train_mask] + ) + candidates = sorted( + set( + test_meta["law"][ + (test_meta["split"] == "replication") + & (test_meta["orientation"] == test_orientation) + ] + ) + ) + for law_id in candidates: + mask = ( + (test_meta["split"] == "replication") + & (test_meta["law"] == law_id) + ) + scores = safe_dot(test_values[mask] - midpoint, direction) + labels = test_meta["physical"][mask] + positive = scores[labels] + negative = scores[~labels] + comparison = positive[:, None] - negative[None, :] + auc = float( + np.mean(comparison > 0) + 0.5 * np.mean(comparison == 0) + ) + output.append( + { + "law_id": str(law_id), + "train_orientation": "direct" if train_orientation else "inverse", + "test_orientation": "direct" if test_orientation else "inverse", + "auc": auc, + } + ) + return pd.DataFrame(output) + + +def final_cross_cohort(train: dict, test: dict, layer: int) -> pd.DataFrame: + train_index = int(np.flatnonzero(train["layers"] == layer)[0]) + test_index = int(np.flatnonzero(test["layers"] == layer)[0]) + train_position = int(np.flatnonzero(train["positions"] == "final_prompt")[0]) + test_position = int(np.flatnonzero(test["positions"] == "final_prompt")[0]) + train_values = normalize(train["states"][train_position, :, train_index]) + test_values = normalize(test["states"][test_position, :, test_index]) + train_meta, test_meta = train["meta"], test["meta"] + 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]) + law_aucs = [] + for train_order in (False, True): + train_mask = ( + (train_meta["split"] == "discovery") + & (train_meta["orientation"] != orientation) + & (train_meta["positive_order"] == train_order) + ) + test_mask = ( + replication + & (test_meta["law"] == law_id) + & (test_meta["positive_order"] != train_order) + ) + direction, midpoint = centroid_direction( + train_values[train_mask], train_meta["physical"][train_mask] + ) + scores = safe_dot(test_values[test_mask] - midpoint, direction) + labels = test_meta["physical"][test_mask] + positive = scores[labels] + negative = scores[~labels] + comparison = positive[:, None] - negative[None, :] + law_aucs.append( + float(np.mean(comparison > 0) + 0.5 * np.mean(comparison == 0)) + ) + output.append({"law_id": str(law_id), "auc": float(np.mean(law_aucs))}) + return pd.DataFrame(output) + + +def load_and_analyze(scale: str, vocabulary: str, path: Path, seed: int) -> dict: + protocol_path = path / "protocol.json" + manifest_path = path / "prompt_manifest.json" + raw_path = path / "raw.json" + states_path = path / "representations.npz" + 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"] != sha256(protocol_path): + raise RuntimeError(f"{scale} {vocabulary}: protocol fingerprint mismatch") + if raw["provenance"]["prompt_manifest_sha256"] != sha256(manifest_path): + raise RuntimeError(f"{scale} {vocabulary}: manifest fingerprint mismatch") + if raw["provenance"]["states_sha256"] != sha256(states_path): + raise RuntimeError(f"{scale} {vocabulary}: state fingerprint mismatch") + runner_path = ROOT / raw["provenance"]["runner"] + if runner_path.exists() and sha256(runner_path) != raw["provenance"]["runner_sha256"]: + snapshot = path / "code" / runner_path.name + if not snapshot.exists() or sha256(snapshot) != raw["provenance"]["runner_sha256"]: + raise RuntimeError(f"{scale} {vocabulary}: runner fingerprint mismatch") + + 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) + states = arrays["raw_states"].astype(np.float16) + by_id = {row["prompt_id"]: row for row in manifest["prompts"]} + rows = [by_id[prompt_id] for prompt_id in prompt_ids] + if len(rows) != 256 or len(raw["clean_rows"]) != 256: + raise RuntimeError(f"{scale} {vocabulary}: incomplete prompt cohort") + if positions.tolist() != protocol["positions"]: + raise RuntimeError(f"{scale} {vocabulary}: position mismatch") + meta = make_meta(rows, vocabulary) + discovery_laws = set(meta["law"][meta["split"] == "discovery"]) + replication_laws = set(meta["law"][meta["split"] == "replication"]) + if discovery_laws & replication_laws: + raise RuntimeError(f"{scale} {vocabulary}: discovery/test law leakage") + + decision_position = int(np.flatnonzero(positions == "decision")[0]) + final_position = int(np.flatnonzero(positions == "final_prompt")[0]) + depths = 100.0 * layers / layers.max() + layer_rows = [] + for layer_index, layer in enumerate(layers): + answer_free = normalize(states[decision_position, :, layer_index]) + final = normalize(states[final_position, :, layer_index]) + for target in ("physical", "numeric"): + layer_rows.append( + { + "scale": scale, + "vocabulary": vocabulary, + "position": "answer_free_decision", + "target": target, + "layer": int(layer), + "depth_percent": float(depths[layer_index]), + "discovery_auc": discovery_ccgp(answer_free, meta[target], meta), + } + ) + layer_rows.append( + { + "scale": scale, + "vocabulary": vocabulary, + "position": "final_prompt", + "target": "physical", + "layer": int(layer), + "depth_percent": float(depths[layer_index]), + "discovery_auc": dual_cross_auc(final, meta["physical"], meta, "discovery"), + } + ) + layer_frame = pd.DataFrame(layer_rows) + + answer_selection = layer_frame[ + (layer_frame["position"] == "answer_free_decision") + & (layer_frame["target"] == "physical") + ].sort_values(["discovery_auc", "layer"], ascending=[False, True]) + final_selection = layer_frame[ + (layer_frame["position"] == "final_prompt") + & (layer_frame["target"] == "physical") + ].sort_values(["discovery_auc", "layer"], ascending=[False, True]) + answer_layer = int(answer_selection.iloc[0]["layer"]) + final_layer = int(final_selection.iloc[0]["layer"]) + fixed_layer = int(round(FIXED_DEPTH_FRACTION * int(layers.max()))) + + def answer_laws(layer: int) -> pd.DataFrame: + index = int(np.flatnonzero(layers == layer)[0]) + values = normalize(states[decision_position, :, index]) + return pd.DataFrame(replication_law_scores(values, meta["physical"], meta)) + + def final_laws(layer: int) -> pd.DataFrame: + index = int(np.flatnonzero(layers == layer)[0]) + values = normalize(states[final_position, :, index]) + return pd.DataFrame(replication_law_auc(values, meta["physical"], meta)) + + answer_selected_laws = answer_laws(answer_layer) + answer_fixed_laws = answer_laws(fixed_layer) + final_selected_laws = final_laws(final_layer) + final_fixed_laws = final_laws(fixed_layer) + + answer_values = normalize( + states[decision_position, :, int(np.flatnonzero(layers == answer_layer)[0])] + ) + _, parallel = contrast_parallelism(answer_values, meta, "replication") + cross_orientation = parallel.loc[ + ~parallel["same_orientation"], "physical_contrast_cosine" + ].to_numpy(float) + behavior = pd.DataFrame(raw["clean_rows"]) + correct_column = ( + "registered_code_correct" + if vocabulary == "arbitrary_code" + else "registered_answer_correct" + ) + + summaries = { + "answer_free_selected": { + "selected_layer": answer_layer, + "selected_depth_percent": float(100.0 * answer_layer / layers.max()), + "discovery_selected_auc": float(answer_selection.iloc[0]["discovery_auc"]), + **summarize(answer_selected_laws["auc"].to_numpy(), seed), + }, + "answer_free_fixed_depth": { + "layer": fixed_layer, + "depth_percent": float(100.0 * fixed_layer / layers.max()), + **summarize(answer_fixed_laws["auc"].to_numpy(), seed + 1), + }, + "final_selected": { + "selected_layer": final_layer, + "selected_depth_percent": float(100.0 * final_layer / layers.max()), + "discovery_selected_auc": float(final_selection.iloc[0]["discovery_auc"]), + **summarize(final_selected_laws["auc"].to_numpy(), seed + 2), + }, + "final_fixed_depth": { + "layer": fixed_layer, + "depth_percent": float(100.0 * fixed_layer / layers.max()), + **summarize(final_fixed_laws["auc"].to_numpy(), seed + 3), + }, + "behavior": { + "accuracy": float(behavior[correct_column].mean()), + "discovery_accuracy": float( + behavior.loc[behavior["split"] == "discovery", correct_column].mean() + ), + "replication_accuracy": float( + behavior.loc[behavior["split"] == "replication", correct_column].mean() + ), + }, + "answer_free_parallelism": { + "cross_orientation_mean_cosine": float(cross_orientation.mean()), + "bootstrap_95": list( + bootstrap( + cross_orientation, + np.random.default_rng(seed + 4), + BOOTSTRAP_RESAMPLES, + ) + ), + }, + } + law_frames = [] + for endpoint, layer, frame in ( + ("answer_free_selected", answer_layer, answer_selected_laws), + ("answer_free_fixed_depth", fixed_layer, answer_fixed_laws), + ("final_selected", final_layer, final_selected_laws), + ("final_fixed_depth", fixed_layer, final_fixed_laws), + ): + part = frame.copy() + part.insert(0, "endpoint", endpoint) + part.insert(0, "layer", layer) + part.insert(0, "vocabulary", vocabulary) + part.insert(0, "scale", scale) + law_frames.append(part) + + return { + "scale": scale, + "vocabulary": vocabulary, + "path": path, + "protocol": protocol, + "manifest_sha256": sha256(manifest_path), + "states_sha256": sha256(states_path), + "layers": layers, + "positions": positions, + "states": states, + "meta": meta, + "layer_frame": layer_frame, + "law_frame": pd.concat(law_frames, ignore_index=True), + "summaries": summaries, + "answer_selected_layer": answer_layer, + "final_selected_layer": final_layer, + } + + +def plot_diagnostic(layer_frame: pd.DataFrame, law_frame: pd.DataFrame, summaries: dict) -> None: + colors = {"4B": "#6C5A9E", "31B": "#188F8A"} + fig, axes = plt.subplots(2, 2, figsize=(11.2, 7.3)) + for column, vocabulary in enumerate(("semantic_answer", "arbitrary_code")): + ax = axes[0, column] + for scale in ("4B", "31B"): + part = layer_frame[ + (layer_frame["scale"] == scale) + & (layer_frame["vocabulary"] == vocabulary) + & (layer_frame["position"] == "answer_free_decision") + & (layer_frame["target"] == "physical") + ] + ax.plot( + part["depth_percent"], + part["discovery_auc"], + color=colors[scale], + linewidth=2, + label=scale, + ) + ax.axhline(0.5, color="#777777", linestyle="--", linewidth=1) + ax.set( + title=("Semantic answers" if vocabulary == "semantic_answer" else "Arbitrary A/B codes"), + xlabel="Normalized layer depth (%)", + ylabel="Discovery answer-free CCGP (AUC)", + ylim=(0.35, 1.01), + ) + ax.legend(frameon=False) + + ax = axes[1, 0] + summary_rows = [] + for scale in ("4B", "31B"): + for vocabulary in ("semantic_answer", "arbitrary_code"): + row = summaries[f"{scale}:{vocabulary}"]["answer_free_selected"] + summary_rows.append((scale, vocabulary, row)) + x = np.arange(2) + width = 0.34 + for offset, scale in ((-width / 2, "4B"), (width / 2, "31B")): + values, lows, highs = [], [], [] + for vocabulary in ("semantic_answer", "arbitrary_code"): + row = summaries[f"{scale}:{vocabulary}"]["answer_free_selected"] + values.append(row["mean_law_auc"]) + lows.append(row["law_bootstrap_95"][0]) + highs.append(row["law_bootstrap_95"][1]) + ax.bar(x + offset, values, width, color=colors[scale], label=scale) + ax.errorbar( + x + offset, + values, + yerr=[np.asarray(values) - lows, np.asarray(highs) - np.asarray(values)], + fmt="none", + ecolor="#333333", + capsize=3, + ) + ax.axhline(0.5, color="#777777", linestyle="--", linewidth=1) + ax.set( + xticks=x, + xticklabels=["Semantic", "Arbitrary A/B"], + ylabel="Held-out-law answer-free AUC", + ylim=(0, 1.02), + ) + ax.legend(frameon=False) + + ax = axes[1, 1] + selected = law_frame[law_frame["endpoint"] == "answer_free_selected"] + laws = sorted(selected["law_id"].unique()) + for vocabulary, marker in (("semantic_answer", "o"), ("arbitrary_code", "s")): + four = ( + selected[(selected["scale"] == "4B") & (selected["vocabulary"] == vocabulary)] + .set_index("law_id") + .loc[laws, "auc"] + ) + thirty = ( + selected[(selected["scale"] == "31B") & (selected["vocabulary"] == vocabulary)] + .set_index("law_id") + .loc[laws, "auc"] + ) + ax.scatter(four, thirty, marker=marker, s=48, alpha=0.8, label=vocabulary.replace("_", " ")) + ax.plot([0, 1], [0, 1], color="#777777", linestyle="--", linewidth=1) + ax.set( + xlabel="4B held-out-law AUC", + ylabel="31B held-out-law AUC", + xlim=(-0.02, 1.02), + ylim=(-0.02, 1.02), + ) + ax.legend(frameon=False) + for ax in axes.flat: + ax.spines[["top", "right"]].set_visible(False) + fig.tight_layout() + figure_dir = OUT / "figures" + figure_dir.mkdir(parents=True, exist_ok=True) + fig.savefig(figure_dir / "cross-scale-answer-free-diagnostic.png", dpi=220) + fig.savefig(figure_dir / "cross-scale-answer-free-diagnostic.svg") + plt.close(fig) + + +def plot_transfer_diagnostic( + layerwise_cross: pd.DataFrame, + cross_frame: pd.DataFrame, +) -> None: + colors = {"4B": "#6C5A9E", "31B": "#188F8A"} + styles = { + "arbitrary_code_to_semantic_answer": "-", + "semantic_answer_to_arbitrary_code": "--", + } + fig, axes = plt.subplots(1, 2, figsize=(11.2, 4.1)) + ax = axes[0] + final = layerwise_cross[layerwise_cross["position"] == "final_prompt"] + for scale in ("4B", "31B"): + for direction, style in styles.items(): + part = final[ + (final["scale"] == scale) & (final["direction"] == direction) + ] + ax.plot( + part["depth_percent"], + part["mean_law_auc"], + color=colors[scale], + linestyle=style, + linewidth=2, + label=f"{scale} {direction.replace('_', ' ')}", + ) + ax.axhline(0.5, color="#777777", linestyle=":", linewidth=1) + ax.set( + xlabel="Normalized layer depth (%)", + ylabel="Cross-vocabulary held-out-law AUC", + xlim=(0, 100), + ylim=(0, 1.02), + ) + ax.legend(frameon=False, fontsize=8, ncol=2, loc="lower left") + + ax = axes[1] + selected = cross_frame[cross_frame["position"] == "final_prompt"] + combined = selected.groupby(["scale", "law_id"], as_index=False)["auc"].mean() + four = combined[combined["scale"] == "4B"].set_index("law_id").sort_index() + thirty = combined[combined["scale"] == "31B"].set_index("law_id").sort_index() + if four.index.tolist() != thirty.index.tolist(): + raise RuntimeError("cross-vocabulary paired law mismatch") + ax.scatter(four["auc"], thirty["auc"], s=55, color="#188F8A", alpha=0.85) + ax.plot([0, 1], [0, 1], color="#777777", linestyle=":", linewidth=1) + ax.set( + xlabel="4B final cross-vocabulary law AUC", + ylabel="31B final cross-vocabulary law AUC", + xlim=(-0.02, 1.02), + ylim=(-0.02, 1.02), + ) + for axis in axes: + axis.spines[["top", "right"]].set_visible(False) + fig.tight_layout() + figure_dir = OUT / "figures" + figure_dir.mkdir(parents=True, exist_ok=True) + fig.savefig(figure_dir / "cross-scale-vocabulary-transfer-diagnostic.png", dpi=220) + fig.savefig(figure_dir / "cross-scale-vocabulary-transfer-diagnostic.svg") + plt.close(fig) + + +def main() -> None: + OUT.mkdir(parents=True, exist_ok=True) + results = {} + layer_frames = [] + law_frames = [] + audit = {"inputs": {}, "prompt_identity": {}} + for index, ((scale, vocabulary), path) in enumerate(COHORTS.items()): + result = load_and_analyze(scale, vocabulary, path, BOOTSTRAP_SEED + 20 * index) + results[(scale, vocabulary)] = result + layer_frames.append(result["layer_frame"]) + law_frames.append(result["law_frame"]) + audit["inputs"][f"{scale}:{vocabulary}"] = { + "path": str(path.relative_to(ROOT)), + "manifest_sha256": result["manifest_sha256"], + "states_sha256": result["states_sha256"], + "model": result["protocol"]["model"], + "model_revision": result["protocol"]["model_revision"], + } + + for vocabulary in ("arbitrary_code", "semantic_answer"): + four_manifest = json.loads((COHORTS[("4B", vocabulary)] / "prompt_manifest.json").read_text()) + thirty_manifest = json.loads((COHORTS[("31B", vocabulary)] / "prompt_manifest.json").read_text()) + audit["prompt_identity"][vocabulary] = { + "exact_json_match": four_manifest == thirty_manifest, + "same_prompt_ids": [x["prompt_id"] for x in four_manifest["prompts"]] + == [x["prompt_id"] for x in thirty_manifest["prompts"]], + "same_prompt_text": [x["user"] for x in four_manifest["prompts"]] + == [x["user"] for x in thirty_manifest["prompts"]], + } + + audit["frozen_4b_endpoint_recovery"] = {} + for vocabulary in ("arbitrary_code", "semantic_answer"): + frozen = json.loads( + (COHORTS[("4B", vocabulary)] / "statistics.json").read_text() + )["primary"] + recovered = results[("4B", vocabulary)]["summaries"]["answer_free_selected"] + audit["frozen_4b_endpoint_recovery"][vocabulary] = { + "selected_layer_exact": recovered["selected_layer"] + == frozen["selected_layer"], + "discovery_auc_exact": recovered["discovery_selected_auc"] + == frozen["discovery_selected_ccgp"], + "heldout_mean_auc_exact": recovered["mean_law_auc"] + == frozen["replication_mean_law_auc"], + } + + layer_frame = pd.concat(layer_frames, ignore_index=True) + law_frame = pd.concat(law_frames, ignore_index=True) + layer_frame.to_csv(OUT / "layerwise_cross_scale.csv", index=False) + law_frame.to_csv(OUT / "heldout_law_auc.csv", index=False) + + summaries = { + f"{scale}:{vocabulary}": result["summaries"] + for (scale, vocabulary), result in results.items() + } + paired = {} + for vocabulary in ("semantic_answer", "arbitrary_code"): + for endpoint in ( + "answer_free_selected", + "answer_free_fixed_depth", + "final_selected", + "final_fixed_depth", + ): + four = ( + law_frame[ + (law_frame["scale"] == "4B") + & (law_frame["vocabulary"] == vocabulary) + & (law_frame["endpoint"] == endpoint) + ] + .set_index("law_id") + .sort_index() + ) + thirty = ( + law_frame[ + (law_frame["scale"] == "31B") + & (law_frame["vocabulary"] == vocabulary) + & (law_frame["endpoint"] == endpoint) + ] + .set_index("law_id") + .sort_index() + ) + if four.index.tolist() != thirty.index.tolist(): + raise RuntimeError("paired law identity mismatch") + paired[f"{vocabulary}:{endpoint}"] = paired_change( + four["auc"].to_numpy(), + thirty["auc"].to_numpy(), + BOOTSTRAP_SEED + len(paired), + ) + + cross_rows = [] + cross_summaries = {} + for scale in ("4B", "31B"): + arbitrary = results[(scale, "arbitrary_code")] + semantic = results[(scale, "semantic_answer")] + for position, function, layer_key in ( + ("answer_free", answer_free_cross_cohort, "answer_selected_layer"), + ("final_prompt", final_cross_cohort, "final_selected_layer"), + ): + for train_name, train, test_name, test in ( + ("arbitrary_code", arbitrary, "semantic_answer", semantic), + ("semantic_answer", semantic, "arbitrary_code", arbitrary), + ): + layer = int(train[layer_key]) + frame = function(train, test, layer) + frame.insert(0, "layer", layer) + frame.insert(0, "test_vocabulary", test_name) + frame.insert(0, "train_vocabulary", train_name) + frame.insert(0, "position", position) + frame.insert(0, "scale", scale) + cross_rows.append(frame) + key = f"{scale}:{position}:{train_name}_to_{test_name}" + cross_summaries[key] = summarize( + frame["auc"].to_numpy(), + BOOTSTRAP_SEED + 100 + len(cross_summaries), + ) + cross_frame = pd.concat(cross_rows, ignore_index=True) + cross_frame.to_csv(OUT / "cross_vocabulary_law_auc.csv", index=False) + + layerwise_cross_rows = [] + for scale in ("4B", "31B"): + arbitrary = results[(scale, "arbitrary_code")] + semantic = results[(scale, "semantic_answer")] + for position, function in ( + ("answer_free", answer_free_cross_cohort), + ("final_prompt", final_cross_cohort), + ): + for train_name, train, test_name, test in ( + ("arbitrary_code", arbitrary, "semantic_answer", semantic), + ("semantic_answer", semantic, "arbitrary_code", arbitrary), + ): + for layer in train["layers"]: + frame = function(train, test, int(layer)) + layerwise_cross_rows.append( + { + "scale": scale, + "position": position, + "direction": f"{train_name}_to_{test_name}", + "layer": int(layer), + "depth_percent": float(100.0 * layer / train["layers"].max()), + "mean_law_auc": float(frame["auc"].mean()), + } + ) + layerwise_cross = pd.DataFrame(layerwise_cross_rows) + layerwise_cross.to_csv(OUT / "layerwise_cross_vocabulary.csv", index=False) + + descriptive_transfer = {} + for scale in ("4B", "31B"): + frame = layerwise_cross[ + (layerwise_cross["scale"] == scale) + & (layerwise_cross["position"] == "final_prompt") + ].pivot(index=["layer", "depth_percent"], columns="direction", values="mean_law_auc") + frame["weaker_direction_auc"] = frame.min(axis=1) + best = frame["weaker_direction_auc"].idxmax() + above = frame[frame["weaker_direction_auc"] >= 0.8].reset_index() + above_layers = above["layer"].astype(int).tolist() + runs: list[list[int]] = [] + for layer in above_layers: + if not runs or layer != runs[-1][-1] + 1: + runs.append([layer]) + else: + runs[-1].append(layer) + longest_run = max(runs, key=len) if runs else [] + depth_by_layer = { + int(row.layer): float(row.depth_percent) + for row in above.itertuples(index=False) + } + descriptive_transfer[scale] = { + "best_common_layer": int(best[0]), + "best_common_depth_percent": float(best[1]), + "best_weaker_direction_auc": float(frame.loc[best, "weaker_direction_auc"]), + "layers_both_directions_at_least_0_8": above_layers, + "depths_both_directions_at_least_0_8": above["depth_percent"].astype(float).tolist(), + "longest_consecutive_layer_run_both_directions_at_least_0_8": longest_run, + "longest_consecutive_depth_span": ( + [depth_by_layer[longest_run[0]], depth_by_layer[longest_run[-1]]] + if longest_run + else [] + ), + } + + paired_cross = {} + for position in ("answer_free", "final_prompt"): + for train_name, test_name in ( + ("arbitrary_code", "semantic_answer"), + ("semantic_answer", "arbitrary_code"), + ): + four = cross_frame[ + (cross_frame["scale"] == "4B") + & (cross_frame["position"] == position) + & (cross_frame["train_vocabulary"] == train_name) + & (cross_frame["test_vocabulary"] == test_name) + ].set_index("law_id").sort_index() + thirty = cross_frame[ + (cross_frame["scale"] == "31B") + & (cross_frame["position"] == position) + & (cross_frame["train_vocabulary"] == train_name) + & (cross_frame["test_vocabulary"] == test_name) + ].set_index("law_id").sort_index() + if four.index.tolist() != thirty.index.tolist(): + raise RuntimeError("paired cross-vocabulary law identity mismatch") + paired_cross[f"{position}:{train_name}_to_{test_name}"] = paired_change( + four["auc"].to_numpy(), + thirty["auc"].to_numpy(), + BOOTSTRAP_SEED + 200 + len(paired_cross), + ) + + frozen_success = {} + for vocabulary in ("semantic_answer", "arbitrary_code"): + result = results[("31B", vocabulary)] + rule = result["protocol"]["success_rule"] + row = summaries[f"31B:{vocabulary}"]["answer_free_selected"] + behavior = summaries[f"31B:{vocabulary}"]["behavior"]["accuracy"] + checks = { + "mean_replication_auc": row["mean_law_auc"] + >= rule["mean_replication_auc_minimum"], + "bootstrap_lower_above_chance": row["law_bootstrap_95"][0] + > rule["bootstrap_95_lower_above"], + "positive_replication_laws": row["laws_above_chance"] + >= rule["positive_replication_laws_minimum"], + } + if "behavior_accuracy_minimum" in rule: + checks["behavior_accuracy"] = ( + behavior >= rule["behavior_accuracy_minimum"] + ) + frozen_success[vocabulary] = { + "passes": bool(all(checks.values())), + "checks": checks, + } + + statistics = { + "study_id": "gemma-scale-abstract-physics-2026-08-23", + "analysis_status": "cross-scale analysis of previously frozen 4B endpoints on exact 31B prompt replications", + "fixed_depth_fraction": FIXED_DEPTH_FRACTION, + "bootstrap_resamples": BOOTSTRAP_RESAMPLES, + "summaries": summaries, + "paired_31b_minus_4b": paired, + "cross_vocabulary": cross_summaries, + "paired_cross_vocabulary_31b_minus_4b": paired_cross, + "descriptive_layerwise_cross_vocabulary": descriptive_transfer, + "frozen_31b_primary_success_rule": frozen_success, + "audit": audit, + "interpretation_boundary": ( + "The 31B answer-free signal is suggestive and transfers across held-out constitutive laws before the answer vocabulary is encountered, but neither cohort passes every element of the conservative frozen success rule. The strong final-prompt cross-vocabulary result shows that the larger model carries a common physical-outcome direction despite changing the output code. The experiment supplies equations and therefore does not test spontaneous discovery of unfamiliar physical laws or causal necessity." + ), + } + (OUT / "statistics.json").write_text( + json.dumps(statistics, indent=2, sort_keys=True) + "\n" + ) + (OUT / "audit.json").write_text(json.dumps(audit, indent=2, sort_keys=True) + "\n") + plot_diagnostic(layer_frame, law_frame, summaries) + plot_transfer_diagnostic(layerwise_cross, cross_frame) + + lines = [ + "# Gemma-scale abstract-physics analysis", + "", + "The exact frozen 4B prompt cohorts and endpoints are compared with their 31B replications.", + "", + "## Answer-free decision marker", + "", + ] + for vocabulary in ("semantic_answer", "arbitrary_code"): + for scale in ("4B", "31B"): + row = summaries[f"{scale}:{vocabulary}"]["answer_free_selected"] + lines.append( + f"- {scale} {vocabulary.replace('_', ' ')}: layer {row['selected_layer']} " + f"({row['selected_depth_percent']:.1f}% depth), discovery AUC " + f"{row['discovery_selected_auc']:.3f}, held-out-law AUC " + f"{row['mean_law_auc']:.3f} ({row['law_bootstrap_95'][0]:.3f} to " + f"{row['law_bootstrap_95'][1]:.3f}), {row['laws_above_chance']}/8 laws above chance." + ) + lines.extend(["", "## Frozen 31B success rule", ""]) + for vocabulary, row in frozen_success.items(): + failed = [name for name, passed in row["checks"].items() if not passed] + lines.append( + f"- {vocabulary.replace('_', ' ')}: **{'PASS' if row['passes'] else 'FAIL'}**" + + (f"; unmet: {', '.join(failed)}." if failed else ".") + ) + lines.extend(["", "## Behavioral accuracy and contrast alignment", ""]) + for vocabulary in ("semantic_answer", "arbitrary_code"): + for scale in ("4B", "31B"): + result = summaries[f"{scale}:{vocabulary}"] + behavior = result["behavior"]["accuracy"] + parallel = result["answer_free_parallelism"] + lines.append( + f"- {scale} {vocabulary.replace('_', ' ')}: behavior {behavior:.1%}; " + f"cross-orientation contrast cosine {parallel['cross_orientation_mean_cosine']:+.3f} " + f"({parallel['bootstrap_95'][0]:+.3f} to {parallel['bootstrap_95'][1]:+.3f})." + ) + lines.extend(["", "## Cross-vocabulary transfer", ""]) + for key, row in cross_summaries.items(): + lines.append( + f"- {key}: {row['mean_law_auc']:.3f} " + f"({row['law_bootstrap_95'][0]:.3f} to {row['law_bootstrap_95'][1]:.3f}); " + f"{row['laws_above_chance']}/8 laws above chance." + ) + lines.extend(["", "## Paired 31B minus 4B cross-vocabulary effects", ""]) + for key, row in paired_cross.items(): + lines.append( + f"- {key}: mean change {row['mean_31b_minus_4b']:+.3f} " + f"({row['paired_law_bootstrap_95'][0]:+.3f} to " + f"{row['paired_law_bootstrap_95'][1]:+.3f}); exact sign-flip " + f"p={row['two_sided_exact_signflip_p']:.4g}." + ) + lines.extend(["", "## Descriptive layerwise stability", ""]) + for scale, row in descriptive_transfer.items(): + span_depths = row["longest_consecutive_depth_span"] + if span_depths: + span = f"{span_depths[0]:.1f}% to {span_depths[1]:.1f}% depth" + else: + span = "no layer" + lines.append( + f"- {scale}: best common layer {row['best_common_layer']} " + f"({row['best_common_depth_percent']:.1f}% depth), where the weaker " + f"transfer direction reached {row['best_weaker_direction_auc']:.3f}; " + f"both directions reached AUC >= 0.8 at {span}." + ) + lines.extend(["", statistics["interpretation_boundary"], ""]) + (OUT / "REPORT.md").write_text("\n".join(lines)) + print("\n".join(lines)) + + +if __name__ == "__main__": + main() diff --git a/jlens_materials/scripts/analyze_gemma_scale_heldout_v1.py b/jlens_materials/scripts/analyze_gemma_scale_heldout_v1.py new file mode 100644 index 0000000000000000000000000000000000000000..27cece12fa89624016abf3356a9d41a1fb1f99bb --- /dev/null +++ b/jlens_materials/scripts/analyze_gemma_scale_heldout_v1.py @@ -0,0 +1,821 @@ +#!/usr/bin/env python3 +"""Reproduce Figure 2 and compare the frozen 4B and single-fit 31B runs. + +This analysis deliberately reuses the exact held-out-v1 prompts and the exact +controlled-rank endpoint from the submitted 4B study. It does not modify the +paper or its figure directory. All outputs are written to a dated experiment +directory so that later Gemma-scale runs can be added without overwriting the +submitted result. +""" + +from __future__ import annotations + +import csv +import hashlib +import itertools +import json +import math +import sys +from collections import defaultdict +from pathlib import Path + +import matplotlib.pyplot as plt +import numpy as np + + +ROOT = Path(__file__).resolve().parents[1] +sys.path.insert(0, str(ROOT)) +sys.path.insert(0, str(ROOT / "scripts")) + +import analyze_materials_heldout_v1 as heldout # noqa: E402 + + +RUN_31B = ROOT / "runs" / "gemma4-31b-it-heldout-v1-seed0.json" +EXPECTED_31B_HASH = "03f5a52699a878611db0317539d3641dfcaf7a47b6d85478735360fb99e3c3e5" +EXPECTED_31B_MODEL = "google/gemma-4-31B-it" +EXPECTED_31B_REVISION = "842da3794eaa0b77d5f08bae87a17459d91ff475" + +OUT_DIR = ROOT / "experiments" / "gemma-scale-heldout-v1-2026-08-23" +FIG_DIR = OUT_DIR / "figures" +STATS_PATH = OUT_DIR / "statistics.json" +PROMPT_31B_CSV = OUT_DIR / "prompt_results_31b.csv" +CONCEPT_31B_CSV = OUT_DIR / "concept_results_31b.csv" +CROSS_SCALE_CSV = OUT_DIR / "cross_scale_prompt_results.csv" +REPORT_PATH = OUT_DIR / "REPORT.md" + +FIGURE_4B = FIG_DIR / "figure2-4b-reproduced" +FIGURE_31B = FIG_DIR / "figure2-31b-single-lens" +FIGURE_COMPARE = FIG_DIR / "figure2-cross-scale-candidate" +ORIGINAL_4B_PNG = ( + ROOT / "figures" / "materials-heldout-v1" / "heldout-controlled-recovery.png" +) +FROZEN_4B_STATS = ROOT / "experiments" / "materials-heldout-v1_statistics.json" + +RNG_SEED = 20260823 +N_BOOT = 20_000 + +DISPLAY_FAMILY = heldout.DISPLAY_FAMILY +COLOR_4B = "#087F8C" +COLOR_31B = "#4F4C9A" +COLOR_DIRECT = "#68747C" +COLOR_NEGATIVE = "#C46C5A" +COLOR_NEUTRAL = "#687078" +COLOR_LIGHT = "#A7ADB2" + + +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_31b( + run: dict, + slugs_4b: list[str], + indexes_4b: list[dict[str, dict]], +) -> dict[str, dict]: + if sha256(RUN_31B) != EXPECTED_31B_HASH: + raise ValueError("31B raw run differs from the frozen downloaded file") + if run.get("model") != EXPECTED_31B_MODEL: + raise ValueError(f"unexpected 31B model: {run.get('model')}") + if run.get("errors"): + raise ValueError("31B run contains errors") + methodology = run.get("methodology", {}) + if not methodology.get("paper_protocol_complete"): + raise ValueError("31B run is not paper-protocol complete") + if not methodology.get("strict_protocol"): + raise ValueError("31B run did not use strict protocol validation") + if methodology.get("workspace_band") != [38.0, 92.0]: + raise ValueError("31B workspace band differs from the frozen 4B band") + if methodology.get("model_revision") != EXPECTED_31B_REVISION: + raise ValueError("31B model revision differs from the frozen revision") + if methodology.get("generation_max_new_tokens") != 1: + raise ValueError("31B generation length differs from the frozen protocol") + + provenance = run.get("lens_provenance", {}) + model = provenance.get("model", {}) + recipe = provenance.get("recipe", {}) + corpus = provenance.get("corpus", {}) + expected_recipe = { + "name": "paper", + "n_fit": 1000, + "max_seq_len": 128, + "target_layer": -2, + "report_layer_count": 25, + } + for key, value in expected_recipe.items(): + if recipe.get(key) != value: + raise ValueError(f"31B lens recipe mismatch for {key}") + if model.get("requested_id") != EXPECTED_31B_MODEL: + raise ValueError("31B lens model ID mismatch") + if model.get("model_revision") != EXPECTED_31B_REVISION: + raise ValueError("31B lens model revision mismatch") + if model.get("n_layers") != 60 or model.get("d_model") != 5376: + raise ValueError("31B architecture metadata mismatch") + if model.get("vocab_size") != 262144: + raise ValueError("31B vocabulary size mismatch") + if provenance.get("target_layer") != 58: + raise ValueError("31B lens is not fitted to the penultimate target layer") + if len(provenance.get("source_layers", [])) != 25: + raise ValueError("31B lens does not contain 25 source layers") + if corpus.get("records") != 1000 or corpus.get("unique_records") != 1000: + raise ValueError("31B lens corpus record count mismatch") + if corpus.get("subset") != "wikitext-103-raw-v1" or corpus.get("seed") != 0: + raise ValueError("31B lens corpus identity or seed mismatch") + + index = heldout.indexed_records(run) + if list(index) != slugs_4b or len(index) != 50: + raise ValueError("31B prompt identities or order differ from the 4B cohort") + reference_index = indexes_4b[0] + for slug in slugs_4b: + record = index[slug] + reference = reference_index[slug] + if not record.get("valid_for_metrics"): + raise ValueError(f"31B prompt excluded from metrics: {slug}") + for key in ("prompt_text", "target_family", "phrasing_id", "score_positions"): + if record.get(key) != reference.get(key): + raise ValueError(f"31B prompt field differs for {slug}: {key}") + labels = [item["label"] for item in record.get("emergence", [])] + reference_labels = [item["label"] for item in reference.get("emergence", [])] + if labels != reference_labels: + raise ValueError(f"31B tracked concepts differ for {slug}") + config = record.get("open_vocabulary_config", {}) + if config.get("surprising_top") != 64: + raise ValueError(f"31B candidate retention differs for {slug}") + if not config.get("matched_logit_open_vocabulary_stored"): + raise ValueError(f"31B direct open-vocabulary baseline missing for {slug}") + return index + + +def build_31b_rows( + run: dict, + slugs: list[str], + index: dict[str, dict], +) -> tuple[list[dict], list[dict], list[int]]: + ks = list(run["metrics"]["by_shape"]["ASSOCIATION"]["ks"]) + prompt_rows: list[dict] = [] + concept_rows: list[dict] = [] + for slug in slugs: + record = index[slug] + family = record.get("target_family") or record["category"] + j_auc = heldout.item_auc( + [int(item["best_rank"]) for item in record["emergence"]], ks + ) + direct_auc = heldout.item_auc( + [int(item["logit_lens_best_rank"]) for item in record["emergence"]], ks + ) + prompt_rows.append({ + "slug": slug, + "family": family, + "phrasing_id": record.get("phrasing_id"), + "prompt": record["prompt_text"], + "generated_completion": record.get("generated_completion"), + "n_resolved_concepts": len(record["emergence"]), + "j_auc": j_auc, + "direct_auc": direct_auc, + "delta_auc": j_auc - direct_auc, + }) + for item in record["emergence"]: + j_rank = int(item["best_rank"]) + 1 + direct_rank = int(item["logit_lens_best_rank"]) + 1 + concept_rows.append({ + "slug": slug, + "family": family, + "concept": item["label"], + "j_rank": j_rank, + "direct_rank": direct_rank, + "j_log_rank": math.log10(j_rank), + "direct_log_rank": math.log10(direct_rank), + "log10_rank_advantage": math.log10(direct_rank) - math.log10(j_rank), + }) + return prompt_rows, concept_rows, ks + + +def grouped_family(rows: list[dict], key: str) -> dict[str, list[float]]: + grouped: dict[str, list[float]] = defaultdict(list) + for row in rows: + grouped[row["family"]].append(float(row[key])) + return grouped + + +def hierarchical_bootstrap(rows: list[dict], key: str, *, seed: int) -> dict: + grouped = grouped_family(rows, key) + families = sorted(grouped) + arrays = [np.asarray(grouped[family], dtype=float) for family in families] + rng = np.random.default_rng(seed) + estimates = np.empty(N_BOOT, dtype=float) + for iteration in range(N_BOOT): + family_indices = rng.integers(0, len(arrays), size=len(arrays)) + means = [] + for family_index in family_indices: + values = arrays[int(family_index)] + prompt_indices = rng.integers(0, len(values), size=len(values)) + means.append(float(np.mean(values[prompt_indices]))) + estimates[iteration] = float(np.mean(means)) + observed = float(np.mean([row[key] for row in rows])) + return { + "observed_mean": observed, + "bootstrap_mean": float(np.mean(estimates)), + "low": float(np.quantile(estimates, 0.025)), + "high": float(np.quantile(estimates, 0.975)), + "n_resamples": N_BOOT, + "unit": "10 mechanism families; five phrasings resampled within family", + } + + +def exact_family_sign_flip(rows: list[dict], key: str) -> dict: + grouped = grouped_family(rows, key) + family_means = np.asarray([ + float(np.mean(grouped[family])) for family in sorted(grouped) + ]) + observed = float(np.mean(family_means)) + null = np.asarray([ + float(np.mean(family_means * np.asarray(signs))) + for signs in itertools.product((-1.0, 1.0), repeat=len(family_means)) + ]) + return { + "observed_mean": observed, + "p_one_sided_positive": 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 family_statistics(rows: list[dict], j_key: str, direct_key: str) -> dict: + grouped: dict[str, list[dict]] = defaultdict(list) + for row in rows: + grouped[row["family"]].append(row) + result = {} + for family, members in sorted(grouped.items()): + j_values = np.asarray([row[j_key] for row in members], dtype=float) + direct_values = np.asarray([row[direct_key] for row in members], dtype=float) + result[family] = { + "n_phrasings": len(members), + "j_auc_mean": float(np.mean(j_values)), + "direct_auc_mean": float(np.mean(direct_values)), + "delta_auc_mean": float(np.mean(j_values - direct_values)), + "prompt_deltas": (j_values - direct_values).tolist(), + } + return result + + +def merge_cross_scale( + prompt_4b: list[dict], + prompt_31b: list[dict], +) -> list[dict]: + index_31b = {row["slug"]: row for row in prompt_31b} + rows = [] + for row_4b in prompt_4b: + row_31b = index_31b[row_4b["slug"]] + rows.append({ + "slug": row_4b["slug"], + "family": row_4b["family"], + "phrasing_id": row_4b["phrasing_id"], + "j_auc_4b": float(row_4b["j_auc_mean_seed"]), + "direct_auc_4b": float(row_4b["logit_auc"]), + "j_advantage_4b": float(row_4b["delta_auc_mean_seed"]), + "j_auc_31b": float(row_31b["j_auc"]), + "direct_auc_31b": float(row_31b["direct_auc"]), + "j_advantage_31b": float(row_31b["delta_auc"]), + "j_scale_change": float(row_31b["j_auc"] - row_4b["j_auc_mean_seed"]), + "direct_scale_change": float(row_31b["direct_auc"] - row_4b["logit_auc"]), + "advantage_scale_change": float( + row_31b["delta_auc"] - row_4b["delta_auc_mean_seed"] + ), + }) + return rows + + +def cross_scale_concept_rows( + concept_4b: list[dict], concept_31b: list[dict] +) -> list[dict]: + index_31b = { + (row["slug"], row["concept"]): row for row in concept_31b + } + rows = [] + for row_4b in concept_4b: + row_31b = index_31b[(row_4b["slug"], row_4b["concept"])] + rows.append({ + "family": row_4b["family"], + "slug": row_4b["slug"], + "concept": row_4b["concept"], + "j_log_rank_4b": float(row_4b["j_log_rank_mean_seed"]), + "direct_log_rank_4b": float(row_4b["logit_log_rank"]), + "j_log_rank_31b": float(row_31b["j_log_rank"]), + "direct_log_rank_31b": float(row_31b["direct_log_rank"]), + }) + return 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=240, + bbox_inches="tight", + facecolor="white", + ) + plt.close(figure) + + +def plot_31b( + run: dict, + rows: list[dict], + families: dict, +) -> None: + plt.rcParams.update({ + "font.family": "DejaVu Sans", + "font.size": 10, + "axes.spines.top": False, + "axes.spines.right": False, + }) + figure = plt.figure(figsize=(13.4, 8.7), constrained_layout=True) + grid = figure.add_gridspec(2, 2, height_ratios=[1.0, 1.05]) + axes = [ + figure.add_subplot(grid[0, 0]), + figure.add_subplot(grid[0, 1]), + figure.add_subplot(grid[1, :]), + ] + + metrics = run["metrics"]["by_shape"]["ASSOCIATION"] + ks = metrics["ks"] + axis = axes[0] + axis.plot( + ks, + metrics["jacobian_lens"]["pass_at_k"], + "o-", + color=COLOR_4B, + linewidth=2.4, + label="Jacobian lens (single 31B fit)", + ) + axis.plot( + ks, + metrics["logit_lens"]["pass_at_k"], + "s-", + color=COLOR_DIRECT, + linewidth=2.0, + label="Direct unembedding", + ) + axis.set_xscale("log") + axis.set_xlabel("Vocabulary cutoff k (lower is stricter)") + axis.set_ylabel("Fraction of predeclared concepts within top k") + axis.set_title("A", loc="left", fontweight="bold") + axis.grid(True, alpha=0.18) + axis.legend(frameon=False, loc="upper left") + + axis = axes[1] + x_values = np.asarray([row["direct_auc"] for row in rows]) + y_values = np.asarray([row["j_auc"] for row in rows]) + limit = max(float(np.max(x_values)), float(np.max(y_values)), 0.02) * 1.08 + axis.scatter( + x_values, y_values, color=COLOR_LIGHT, alpha=0.65, s=36, edgecolor="none" + ) + axis.plot([0, limit], [0, limit], linestyle="--", color=COLOR_NEUTRAL, linewidth=1) + axis.set_xlim(-0.006, limit) + axis.set_ylim(-0.006, limit) + axis.set_xlabel("Direct-unembedding prompt AUC") + axis.set_ylabel("Jacobian-lens prompt AUC (single 31B fit)") + axis.set_title("B", loc="left", fontweight="bold") + axis.grid(True, alpha=0.18) + + axis = axes[2] + ordered = sorted(families, key=lambda family: families[family]["delta_auc_mean"]) + positions = np.arange(len(ordered)) + for position, family in zip(positions, ordered, strict=True): + raw = families[family]["prompt_deltas"] + mean_value = families[family]["delta_auc_mean"] + color = ( + COLOR_4B if mean_value > 0 + else COLOR_NEGATIVE if mean_value < 0 + else COLOR_NEUTRAL + ) + axis.scatter(raw, np.full(len(raw), position), color=COLOR_LIGHT, alpha=0.45, s=25) + axis.plot([0, mean_value], [position, position], color=color, linewidth=2.2, alpha=0.75) + axis.scatter(mean_value, position, marker="D", color=color, s=52, zorder=3) + axis.text( + mean_value + (0.008 if mean_value >= 0 else -0.008), + position, + f"{mean_value:+.3f}", + va="center", + ha="left" if mean_value >= 0 else "right", + fontsize=8, + color=color, + ) + axis.axvline(0, color=COLOR_NEUTRAL, linewidth=1) + axis.set_yticks(positions, [DISPLAY_FAMILY[family] for family in ordered]) + axis.set_xlabel("Jacobian minus direct-unembedding AUC") + axis.set_title("C", loc="left", fontweight="bold") + axis.grid(True, axis="x", alpha=0.18) + values = [value for family in ordered for value in families[family]["prompt_deltas"]] + values += [families[family]["delta_auc_mean"] for family in ordered] + maximum = max(abs(min(values)), abs(max(values)), 0.1) + axis.set_xlim(-1.15 * maximum, 1.15 * maximum) + save_figure(figure, FIGURE_31B) + + +def plot_cross_scale( + controlled_4b: dict, + run_31b: dict, + cross_rows: list[dict], + families_31b: dict, + prompt_correlations: dict, +) -> None: + plt.rcParams.update({ + "font.family": "DejaVu Sans", + "font.size": 10, + "axes.spines.top": False, + "axes.spines.right": False, + }) + figure = plt.figure(figsize=(13.4, 8.7), constrained_layout=True) + grid = figure.add_gridspec(2, 2, height_ratios=[1.0, 1.08]) + axes = [ + figure.add_subplot(grid[0, 0]), + figure.add_subplot(grid[0, 1]), + figure.add_subplot(grid[1, :]), + ] + + axis = axes[0] + ks = controlled_4b["pass_at_k"]["ks"] + metrics_31b = run_31b["metrics"]["by_shape"]["ASSOCIATION"] + axis.plot( + ks, + controlled_4b["pass_at_k"]["j_mean"], + "o-", + color=COLOR_4B, + linewidth=2.3, + label="4B Jacobian (3-fit mean)", + ) + axis.plot( + ks, + controlled_4b["pass_at_k"]["logit"], + "s--", + color=COLOR_4B, + linewidth=1.9, + label="4B direct", + ) + axis.plot( + ks, + metrics_31b["jacobian_lens"]["pass_at_k"], + "o-", + color=COLOR_31B, + linewidth=2.3, + label="31B Jacobian (single fit)", + ) + axis.plot( + ks, + metrics_31b["logit_lens"]["pass_at_k"], + "s--", + color=COLOR_31B, + linewidth=1.9, + label="31B direct", + ) + axis.set_xscale("log") + axis.set_xlabel("Vocabulary cutoff k (lower is stricter)") + axis.set_ylabel("Fraction of predeclared concepts within top k") + axis.set_title("A", loc="left", fontweight="bold") + axis.grid(True, alpha=0.18) + axis.legend(frameon=False, loc="upper left", fontsize=8.8) + + axis = axes[1] + j4 = np.asarray([row["j_auc_4b"] for row in cross_rows]) + j31 = np.asarray([row["j_auc_31b"] for row in cross_rows]) + d4 = np.asarray([row["direct_auc_4b"] for row in cross_rows]) + d31 = np.asarray([row["direct_auc_31b"] for row in cross_rows]) + limit = max(float(np.max([j4.max(), j31.max(), d4.max(), d31.max()])), 0.02) * 1.08 + axis.scatter( + j4, j31, s=42, color=COLOR_31B, alpha=0.62, edgecolor="none", label="Jacobian" + ) + axis.scatter( + d4, d31, s=42, facecolor="white", edgecolor=COLOR_DIRECT, + linewidth=1.2, alpha=0.85, marker="s", label="Direct" + ) + axis.plot([0, limit], [0, limit], linestyle="--", color=COLOR_NEUTRAL, linewidth=1) + axis.set_xlim(-0.006, limit) + axis.set_ylim(-0.006, limit) + axis.set_xlabel("4B prompt AUC") + axis.set_ylabel("31B prompt AUC") + axis.set_title("B", loc="left", fontweight="bold") + axis.grid(True, alpha=0.18) + axis.legend(frameon=False, loc="upper left") + axis.text( + 0.98, + 0.05, + f"Jacobian $r_s$ = {prompt_correlations['jacobian']['rho']:.2f}\n" + f"Direct $r_s$ = {prompt_correlations['direct']['rho']:.2f}", + transform=axis.transAxes, + ha="right", + va="bottom", + color=COLOR_NEUTRAL, + fontsize=9, + ) + + axis = axes[2] + families_4b = controlled_4b["families"] + families = sorted( + families_4b, + key=lambda family: 0.5 * ( + families_4b[family]["delta_auc_mean"] + + families_31b[family]["delta_auc_mean"] + ), + ) + positions = np.arange(len(families)) + for position, family in zip(positions, families, strict=True): + value_4b = families_4b[family]["delta_auc_mean"] + value_31b = families_31b[family]["delta_auc_mean"] + axis.plot( + [value_4b, value_31b], [position, position], + color="#C8CDD1", linewidth=1.5, zorder=1, + ) + axis.scatter( + value_4b, position, s=48, facecolor="white", edgecolor=COLOR_4B, + linewidth=1.4, zorder=3, + ) + axis.scatter( + value_31b, position, s=48, color=COLOR_31B, marker="D", zorder=3, + ) + overall_position = len(families) + 0.35 + overall_4b = float(np.mean([row["j_advantage_4b"] for row in cross_rows])) + overall_31b = float(np.mean([row["j_advantage_31b"] for row in cross_rows])) + axis.axhline(len(families) - 0.35, color="#D7DBDE", linewidth=0.9) + axis.plot( + [overall_4b, overall_31b], [overall_position, overall_position], + color="#9DA5AA", linewidth=2.0, zorder=1, + ) + axis.scatter( + overall_4b, overall_position, s=62, facecolor="white", edgecolor=COLOR_4B, + linewidth=1.6, label="4B (3-fit mean)", zorder=3, + ) + axis.scatter( + overall_31b, overall_position, s=62, color=COLOR_31B, marker="D", + label="31B (single fit)", zorder=3, + ) + axis.axvline(0, color=COLOR_NEUTRAL, linewidth=1) + axis.set_yticks( + list(positions) + [overall_position], + [DISPLAY_FAMILY[family] for family in families] + ["overall"], + ) + axis.set_xlabel("Jacobian minus direct-unembedding AUC") + axis.set_title("C", loc="left", fontweight="bold") + axis.grid(True, axis="x", alpha=0.18) + axis.legend(frameon=False, loc="lower right", ncol=2) + all_values = [ + families_4b[family]["delta_auc_mean"] for family in families + ] + [ + families_31b[family]["delta_auc_mean"] for family in families + ] + [overall_4b, overall_31b] + maximum = max(abs(min(all_values)), abs(max(all_values)), 0.08) + axis.set_xlim(-1.18 * maximum, 1.18 * maximum) + axis.set_ylim(-0.65, overall_position + 0.75) + save_figure(figure, FIGURE_COMPARE) + + +def figure_pixel_check(original: Path, reproduced: Path) -> dict: + first = plt.imread(original) + second = plt.imread(reproduced) + same_shape = first.shape == second.shape + maximum_difference = ( + float(np.max(np.abs(first.astype(float) - second.astype(float)))) + if same_shape else None + ) + mean_difference = ( + float(np.mean(np.abs(first.astype(float) - second.astype(float)))) + if same_shape else None + ) + return { + "original": str(original.relative_to(ROOT)), + "reproduced": str(reproduced.relative_to(ROOT)), + "original_sha256": sha256(original), + "reproduced_sha256": sha256(reproduced), + "same_shape": same_shape, + "maximum_normalized_pixel_difference": maximum_difference, + "mean_normalized_pixel_difference": mean_difference, + "pixel_identical": bool(same_shape and maximum_difference == 0.0), + "note": "Numeric endpoints are audited separately; raster differences can reflect font and antialiasing state.", + } + + +def build_report(stats: dict) -> str: + result_31b = stats["single_fit_31b"]["overall"] + scale = stats["cross_scale"] + lens_effect = result_31b["hierarchical_family_bootstrap"] + j_scale = scale["hierarchical_bootstrap"]["jacobian_scale_change"] + d_scale = scale["hierarchical_bootstrap"]["direct_scale_change"] + return "\n".join([ + "# Gemma held-out-v1 scale comparison", + "", + "## Status and scope", + "", + "This is a locked-endpoint cross-scale analysis of the completed 31B run. It reproduces the submitted Figure 2 from the untouched 4B archives, applies the identical controlled-rank endpoint to the single 31B lens, and constructs a provisional comparison figure. It does not modify the manuscript. Because only one 31B lens was fitted, the analysis estimates prompt/family variation but cannot estimate 31B lens-fit reproducibility.", + "", + "## Input validation", + "", + f"- 31B raw run SHA-256: `{stats['inputs']['run_31b']['sha256']}`.", + f"- Exact model revision: `{stats['inputs']['run_31b']['model_revision']}`.", + "- Fifty of fifty prompt identities, prompt strings, family labels, phrasing identifiers, score positions, and predeclared concepts match the frozen 4B cohort.", + "- The 31B lens records 1,000 unique WikiText-103 fitting records, the penultimate target layer, 25 source layers, strict paper protocol, and the same 38--92% readout band.", + "", + "## Single-fit 31B replication", + "", + f"The 31B Jacobian recovery AUC is **{result_31b['j_auc_mean']:.4f}**, versus **{result_31b['direct_auc_mean']:.4f}** for direct unembedding. The paired difference is **{result_31b['delta_auc']:+.4f}**; its family-hierarchical 95% interval is **{lens_effect['low']:+.4f} to {lens_effect['high']:+.4f}**. Prompt outcomes are {result_31b['j_wins']} Jacobian wins, {result_31b['ties']} ties, and {result_31b['j_losses']} losses.", + "", + "## Cross-scale comparison", + "", + f"Relative to the 4B three-fit mean, 31B Jacobian AUC changes by **{j_scale['observed_mean']:+.4f}** (family-hierarchical 95% interval **{j_scale['low']:+.4f} to {j_scale['high']:+.4f}**). Direct-unembedding AUC changes by **{d_scale['observed_mean']:+.4f}** (**{d_scale['low']:+.4f} to {d_scale['high']:+.4f}**).", + "", + f"Prompt-level cross-scale Spearman correlation is {scale['prompt_correlations']['jacobian']['rho']:.3f} for the Jacobian endpoint and {scale['prompt_correlations']['direct']['rho']:.3f} for direct unembedding. Concept-rank correlation is {scale['concept_rank_correlations']['jacobian']['rho']:.3f} and {scale['concept_rank_correlations']['direct']['rho']:.3f}, respectively.", + "", + "These results should be interpreted as cross-scale readability, not as a universal Jacobian advantage. The single 31B fit is an independent lens checkpoint, but not a seed-reproducibility cohort.", + "", + "## Figures", + "", + "- `figures/figure2-4b-reproduced.*`: current Figure 2 regenerated from the three frozen 4B runs.", + "- `figures/figure2-31b-single-lens.*`: the identical Figure 2 logic applied to the one 31B lens.", + "- `figures/figure2-cross-scale-candidate.*`: provisional comparison; not inserted into the paper.", + "", + ]) + + +def main() -> None: + OUT_DIR.mkdir(parents=True, exist_ok=True) + FIG_DIR.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("recomputed 4B controlled statistics differ from frozen Figure 2 statistics") + + original_figure_stem = heldout.CONTROLLED_FIGURE + original_figure_dir = heldout.FIG_DIR + try: + heldout.CONTROLLED_FIGURE = FIGURE_4B + heldout.FIG_DIR = FIG_DIR + heldout.plot_controlled(controlled_4b, prompt_4b, concept_4b) + finally: + heldout.CONTROLLED_FIGURE = original_figure_stem + heldout.FIG_DIR = original_figure_dir + + run_31b = json.loads(RUN_31B.read_text()) + index_31b = validate_31b(run_31b, slugs, indexes_4b) + prompt_31b, concept_31b, ks_31b = build_31b_rows( + run_31b, slugs, index_31b + ) + if ks_31b != ks_4b: + raise ValueError("31B vocabulary cutoffs differ from the 4B endpoint") + + families_31b = family_statistics(prompt_31b, "j_auc", "direct_auc") + overall_31b = { + "n_prompts": len(prompt_31b), + "n_families": len(families_31b), + "n_resolved_prompt_concepts": len(concept_31b), + "j_auc_mean": float(np.mean([row["j_auc"] for row in prompt_31b])), + "direct_auc_mean": float(np.mean([row["direct_auc"] for row in prompt_31b])), + "delta_auc": float(np.mean([row["delta_auc"] for row in prompt_31b])), + "relative_auc_gain": float( + np.mean([row["j_auc"] for row in prompt_31b]) + / np.mean([row["direct_auc"] for row in prompt_31b]) - 1 + ), + "j_wins": int(sum(row["delta_auc"] > 1e-12 for row in prompt_31b)), + "ties": int(sum(abs(row["delta_auc"]) <= 1e-12 for row in prompt_31b)), + "j_losses": int(sum(row["delta_auc"] < -1e-12 for row in prompt_31b)), + "hierarchical_family_bootstrap": hierarchical_bootstrap( + prompt_31b, "delta_auc", seed=RNG_SEED + ), + "exact_family_sign_flip": exact_family_sign_flip(prompt_31b, "delta_auc"), + } + + cross_rows = merge_cross_scale(prompt_4b, prompt_31b) + concepts_cross = cross_scale_concept_rows(concept_4b, concept_31b) + prompt_correlations = { + "jacobian": heldout.family_clustered_spearman( + cross_rows, "j_auc_4b", "j_auc_31b", seed=RNG_SEED + 1 + ), + "direct": heldout.family_clustered_spearman( + cross_rows, "direct_auc_4b", "direct_auc_31b", seed=RNG_SEED + 2 + ), + } + concept_correlations = { + "jacobian": heldout.family_clustered_spearman( + concepts_cross, "j_log_rank_4b", "j_log_rank_31b", seed=RNG_SEED + 3 + ), + "direct": heldout.family_clustered_spearman( + concepts_cross, "direct_log_rank_4b", "direct_log_rank_31b", seed=RNG_SEED + 4 + ), + } + + plot_31b(run_31b, prompt_31b, families_31b) + plot_cross_scale( + controlled_4b, run_31b, cross_rows, families_31b, prompt_correlations + ) + + stats = { + "analysis_status": "locked-endpoint cross-scale analysis; not inserted into paper", + "analysis_date": "2026-08-23", + "analysis_seed": RNG_SEED, + "bootstrap_resamples": N_BOOT, + "inputs": { + "runs_4b": [ + {"path": str(path.relative_to(ROOT)), "sha256": sha256(path)} + for path in heldout.RUN_PATHS + ], + "run_31b": { + "path": str(RUN_31B.relative_to(ROOT)), + "sha256": sha256(RUN_31B), + "model": EXPECTED_31B_MODEL, + "model_revision": EXPECTED_31B_REVISION, + }, + }, + "figure2_reproduction": figure_pixel_check( + ORIGINAL_4B_PNG, FIGURE_4B.with_suffix(".png") + ), + "figure2_numeric_reproduction": { + "frozen_statistics_path": str(FROZEN_4B_STATS.relative_to(ROOT)), + "frozen_statistics_sha256": sha256(FROZEN_4B_STATS), + "exact_controlled_json_match": True, + "note": "All submitted Figure 2 controlled endpoints were recomputed with the original analyzer and match the frozen statistics exactly.", + }, + "submitted_4b": controlled_4b, + "single_fit_31b": { + "overall": overall_31b, + "families": families_31b, + "pass_at_k": { + "ks": ks_31b, + "jacobian": run_31b["metrics"]["by_shape"]["ASSOCIATION"]["jacobian_lens"]["pass_at_k"], + "direct": run_31b["metrics"]["by_shape"]["ASSOCIATION"]["logit_lens"]["pass_at_k"], + }, + }, + "cross_scale": { + "hierarchical_bootstrap": { + "jacobian_scale_change": hierarchical_bootstrap( + cross_rows, "j_scale_change", seed=RNG_SEED + 10 + ), + "direct_scale_change": hierarchical_bootstrap( + cross_rows, "direct_scale_change", seed=RNG_SEED + 11 + ), + "change_in_jacobian_advantage": hierarchical_bootstrap( + cross_rows, "advantage_scale_change", seed=RNG_SEED + 12 + ), + }, + "exact_family_sign_flip": { + "jacobian_scale_change": exact_family_sign_flip( + cross_rows, "j_scale_change" + ), + "direct_scale_change": exact_family_sign_flip( + cross_rows, "direct_scale_change" + ), + "change_in_jacobian_advantage": exact_family_sign_flip( + cross_rows, "advantage_scale_change" + ), + }, + "prompt_correlations": prompt_correlations, + "concept_rank_correlations": concept_correlations, + }, + "limitations": [ + "Only one independently fitted 31B lens is available.", + "Prompt/family uncertainty does not measure lens-fit uncertainty.", + "The 31B analysis reuses the frozen 4B prompts and endpoints; it is a cross-scale replication, not a new held-out prompt cohort.", + "No manuscript figure is replaced by this analysis.", + ], + } + + write_csv(PROMPT_31B_CSV, prompt_31b) + write_csv(CONCEPT_31B_CSV, concept_31b) + write_csv(CROSS_SCALE_CSV, cross_rows) + STATS_PATH.write_text(json.dumps(stats, indent=2) + "\n") + REPORT_PATH.write_text(build_report(stats)) + + print(json.dumps({ + "figure2_reproduction": stats["figure2_reproduction"], + "single_fit_31b": overall_31b, + "cross_scale": stats["cross_scale"], + }, indent=2)) + for path in ( + STATS_PATH, + PROMPT_31B_CSV, + CONCEPT_31B_CSV, + CROSS_SCALE_CSV, + REPORT_PATH, + FIGURE_4B.with_suffix(".pdf"), + FIGURE_31B.with_suffix(".pdf"), + FIGURE_COMPARE.with_suffix(".pdf"), + ): + print(f"wrote {path}") + + +if __name__ == "__main__": + main() diff --git a/jlens_materials/scripts/analyze_graph_topology_rigorous.py b/jlens_materials/scripts/analyze_graph_topology_rigorous.py new file mode 100644 index 0000000000000000000000000000000000000000..0ba898f51647c2bf24b249e712439f2770642f8c --- /dev/null +++ b/jlens_materials/scripts/analyze_graph_topology_rigorous.py @@ -0,0 +1,2663 @@ +#!/usr/bin/env python3 +"""Rigorous graph controls for materials-mechanism representations. + +This script implements the frozen protocol in +``experiments/graph-topology-rigorous-2026-07-17``. It uses only archived +representations and writes complete row-level tables, statistics, and figures. +""" + +from __future__ import annotations + +import hashlib +import itertools +import json +import math +import re +import warnings +from collections import Counter, defaultdict +from pathlib import Path +from typing import Iterable, Mapping, Sequence + +import matplotlib + +matplotlib.use("Agg") +import matplotlib.pyplot as plt # noqa: E402 +from matplotlib.colors import ListedColormap # noqa: E402 +from matplotlib.lines import Line2D # noqa: E402 +import networkx as nx # noqa: E402 +import numpy as np # noqa: E402 +import pandas as pd # noqa: E402 +from scipy.stats import rankdata # noqa: E402 +from sklearn.feature_extraction.text import TfidfVectorizer # noqa: E402 +from sklearn.metrics import adjusted_rand_score, roc_auc_score # noqa: E402 + +from analyze_graph_topology import ( # noqa: E402 + FAMILY_SHORT, + candidate_removed, + community_labels, + normalize, + token_words, + vocabulary_features, +) + + +ROOT = Path(__file__).resolve().parents[1] +OUT = ROOT / "experiments" / "graph-topology-rigorous-2026-07-17" +FIG = OUT / "figures" +PROTOCOL_PATH = OUT / "protocol.json" +AMENDMENT_PATH = OUT / "protocol-amendment-v1.json" +AMENDMENT_V2_PATH = OUT / "protocol-amendment-v2.json" +AMENDMENT_V2_CORRECTION_PATH = OUT / "protocol-amendment-v2-correction.json" +HELDOUT_NPZ = ROOT / "experiments" / "materials-heldout-v1_latent_vectors.npz" +HELDOUT_STATS = ROOT / "experiments" / "materials-heldout-v1_statistics.json" +HELDOUT_PROMPTS = ROOT / "prompts" / "materials-heldout-v1-preregistered.json" +REPLICATION_DIR = ( + ROOT + / "experiments" + / "late-physics-representation-replication-2026-07-17" +) +REPLICATION_NPZ = REPLICATION_DIR / "representations.npz" +REPLICATION_MANIFEST = REPLICATION_DIR / "prompt_manifest.json" +REPLICATION_PROTOCOL = REPLICATION_DIR / "protocol.json" + +SEED = 20260717 +N_PERM = 50_000 +N_BOOT = 50_000 +BAND = (38.0, 92.0) +LATE_BAND = (80.0, 96.0) + +COLORS = { + "jacobian": "#16697A", + "raw": "#6C5B7B", + "direct": "#B56576", + "token_embedding": "#D17C38", + "word_tfidf": "#5B8C5A", + "char_tfidf": "#73777D", + "target_free_jacobian": "#247BA0", + "target_free_direct": "#C06C84", + "chance": "#9AA0A6", +} + + +def sha256(path: Path) -> str: + return hashlib.sha256(path.read_bytes()).hexdigest() + + +def json_safe(value: object) -> object: + """Recursively convert NumPy scalars before serializing audit artifacts.""" + + 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_inputs(protocol: Mapping[str, object]) -> None: + lookup = { + "heldout_vectors": HELDOUT_NPZ, + "heldout_statistics": HELDOUT_STATS, + "heldout_prompts": HELDOUT_PROMPTS, + "replication_vectors": REPLICATION_NPZ, + "replication_manifest": REPLICATION_MANIFEST, + "replication_protocol": REPLICATION_PROTOCOL, + } + for name, path in lookup.items(): + expected = str(protocol["inputs"][name]["sha256"]) # type: ignore[index] + actual = sha256(path) + if actual != expected: + raise RuntimeError( + f"input fingerprint mismatch for {path}: {actual} != {expected}" + ) + + +def cosine_layers(values: np.ndarray) -> np.ndarray: + """Return pairwise cosine matrices for ``[..., item, layer, feature]``.""" + + values = normalize(values) + with warnings.catch_warnings(): + warnings.simplefilter("ignore", RuntimeWarning) + output = np.einsum( + "...ild,...jld->...lij", values, values, optimize=True + ) + if not np.all(np.isfinite(output)): + raise FloatingPointError("non-finite layered cosine matrix") + return output + + +def cosine_matrix(values: np.ndarray) -> np.ndarray: + values = normalize(values) + with warnings.catch_warnings(): + warnings.simplefilter("ignore", RuntimeWarning) + output = np.einsum("id,jd->ij", values, values, optimize=True) + if not np.all(np.isfinite(output)): + raise FloatingPointError("non-finite cosine matrix") + return output + + +def prompt_feature_matrices(texts: Sequence[str]) -> dict[str, np.ndarray]: + word = TfidfVectorizer( + lowercase=True, + ngram_range=(1, 2), + sublinear_tf=True, + norm="l2", + ).fit_transform(texts) + char = TfidfVectorizer( + lowercase=True, + analyzer="char_wb", + ngram_range=(3, 5), + sublinear_tf=True, + norm="l2", + ).fit_transform(texts) + token_sets = [token_words(text) for text in texts] + jaccard = np.zeros((len(texts), len(texts)), dtype=float) + lengths = np.asarray([len(words) for words in token_sets], dtype=float) + for i, first in enumerate(token_sets): + for j in range(i, len(texts)): + second = token_sets[j] + union = first | second + value = len(first & second) / len(union) if union else 0.0 + jaccard[i, j] = jaccard[j, i] = value + return { + "word_tfidf": (word @ word.T).toarray(), + "char_tfidf": (char @ char.T).toarray(), + "token_jaccard": jaccard, + "length_difference": np.abs(lengths[:, None] - lengths[None, :]), + } + + +def cross_fold_edges( + similarity: np.ndarray, + folds: np.ndarray, + *, + neighbors_per_fold: int = 1, +) -> np.ndarray: + """Select a fixed number of directed neighbors in every other fold.""" + + edges: list[tuple[int, int]] = [] + for source in range(len(folds)): + for target_fold in sorted(set(folds)): + if target_fold == folds[source]: + continue + candidates = np.flatnonzero(folds == target_fold) + ordered = sorted( + candidates.tolist(), + key=lambda target: (-float(similarity[source, target]), target), + ) + edges.extend( + (source, target) + for target in ordered[: min(neighbors_per_fold, len(ordered))] + ) + return np.asarray(edges, dtype=np.int16) + + +def relation_edges( + similarity: np.ndarray, + families: np.ndarray, + variants: np.ndarray, + triplets: np.ndarray, +) -> np.ndarray: + """Within-family, cross-variant nearest edges excluding the same case.""" + + edges: list[tuple[int, int]] = [] + variant_values = sorted(set(variants)) + for source in range(len(families)): + for target_variant in variant_values: + if target_variant == variants[source]: + continue + candidates = np.flatnonzero( + (families == families[source]) + & (variants == target_variant) + & (triplets != triplets[source]) + ) + if not len(candidates): + raise RuntimeError("empty disjoint relation candidate set") + target = min( + candidates.tolist(), + key=lambda candidate: ( + -float(similarity[source, candidate]), + candidate, + ), + ) + edges.append((source, target)) + return np.asarray(edges, dtype=np.int16) + + +def case_preserving_exact_permutations( + families: np.ndarray, + variants: np.ndarray, + triplets: np.ndarray, + outcomes: np.ndarray, +) -> np.ndarray: + """Enumerate case labels while preserving the counterfactual sign flip.""" + + family_names = sorted(set(families)) + family_configurations: list[ + tuple[np.ndarray, list[np.ndarray]] + ] = [] + for family in family_names: + family_indices = np.flatnonzero(families == family) + family_triplets = sorted(set(triplets[family_indices])) + if len(family_triplets) != 4: + raise RuntimeError(f"{family} does not contain four material cases") + for triplet in family_triplets: + case = (families == family) & (triplets == triplet) + anchor = outcomes[case & (variants == "anchor")] + paraphrase = outcomes[ + case & (variants == "physics_paraphrase") + ] + counterfactual = outcomes[ + case & (variants == "lexical_counterfactual") + ] + if not ( + len(anchor) == len(paraphrase) == len(counterfactual) == 1 + and anchor[0] == paraphrase[0] + and anchor[0] != counterfactual[0] + ): + raise RuntimeError( + "material case does not have the registered sign transform" + ) + configurations = [] + for positive in itertools.combinations(range(4), 2): + positive_set = set(positive) + assignment = np.zeros(len(families), dtype=np.int8) + for index, triplet in enumerate(family_triplets): + case = (families == family) & (triplets == triplet) + base = int(index in positive_set) + assignment[ + case & (variants != "lexical_counterfactual") + ] = base + assignment[ + case & (variants == "lexical_counterfactual") + ] = 1 - base + configurations.append(assignment) + family_configurations.append((family_indices, configurations)) + combinations = list( + itertools.product(range(6), repeat=len(family_names)) + ) + output = np.empty((len(combinations), len(families)), dtype=np.int8) + for row_index, choice in enumerate(combinations): + row = np.empty(len(families), dtype=np.int8) + for family_index, configuration_index in enumerate(choice): + indices, configurations = family_configurations[family_index] + row[indices] = configurations[configuration_index][indices] + output[row_index] = row + if len(output) != 6 ** len(family_names): + raise RuntimeError("incorrect exact case-permutation count") + return output + + +def relation_ranking_summary( + similarity: np.ndarray, + families: np.ndarray, + variants: np.ndarray, + triplets: np.ndarray, + outcomes: np.ndarray, +) -> tuple[dict[str, object], pd.DataFrame]: + """Evaluate the complete three-candidate ranking behind each graph edge.""" + + rows: list[dict[str, object]] = [] + for source in range(len(families)): + for target_variant in sorted(set(variants)): + if target_variant == variants[source]: + continue + candidates = np.flatnonzero( + (families == families[source]) + & (variants == target_variant) + & (triplets != triplets[source]) + ) + same = outcomes[candidates] == outcomes[source] + if len(candidates) != 3 or int(np.sum(same)) not in (1, 2): + raise RuntimeError( + "relation ranking requires one or two positives among three candidates" + ) + positives = candidates[same] + negatives = candidates[~same] + ordered = sorted( + candidates.tolist(), + key=lambda candidate: ( + -float(similarity[source, candidate]), + candidate, + ), + ) + rank = min(ordered.index(int(positive)) + 1 for positive in positives) + positive_scores = similarity[source, positives] + negative_scores = similarity[source, negatives] + comparisons = positive_scores[:, None] - negative_scores[None, :] + auc = float( + np.mean(comparisons > 0) + + 0.5 * np.mean(comparisons == 0) + ) + rows.append( + { + "source_index": source, + "family": str(families[source]), + "source_variant": str(variants[source]), + "target_variant": str(target_variant), + "n_same_outcome_candidates": int(len(positives)), + "first_positive_rank": rank, + "top1_correct": bool( + outcomes[ordered[0]] == outcomes[source] + ), + "reciprocal_rank": 1.0 / rank, + "pairwise_auc": auc, + } + ) + frame = pd.DataFrame(rows) + family = ( + frame.groupby("family")[ + ["top1_correct", "reciprocal_rank", "pairwise_auc"] + ] + .mean() + .sort_index() + ) + leave_one_out = [] + for omitted in family.index: + retained = frame[frame["family"] != omitted] + leave_one_out.append( + { + "omitted_family": str(omitted), + "top1_accuracy": float(retained["top1_correct"].mean()), + "mean_reciprocal_rank": float( + retained["reciprocal_rank"].mean() + ), + "pairwise_auc": float(retained["pairwise_auc"].mean()), + } + ) + output = { + "n_rankings": int(len(frame)), + "candidates_per_ranking": 3, + "same_outcome_candidates_range": [1, 2], + "top1_accuracy": float(frame["top1_correct"].mean()), + "mean_reciprocal_rank": float(frame["reciprocal_rank"].mean()), + "pairwise_auc": float(frame["pairwise_auc"].mean()), + "family_metrics": { + str(name): { + "top1_accuracy": float(row["top1_correct"]), + "mean_reciprocal_rank": float(row["reciprocal_rank"]), + "pairwise_auc": float(row["pairwise_auc"]), + } + for name, row in family.iterrows() + }, + "leave_one_family_out": leave_one_out, + "leave_one_family_out_range": { + metric: [ + float(min(row[metric] for row in leave_one_out)), + float(max(row[metric] for row in leave_one_out)), + ] + for metric in ( + "top1_accuracy", + "mean_reciprocal_rank", + "pairwise_auc", + ) + }, + "chance": { + "top1_accuracy": float( + frame["n_same_outcome_candidates"].mean() / 3.0 + ), + "pairwise_auc": 0.5, + }, + } + return output, frame + + +def edge_precision(edges: np.ndarray, labels: np.ndarray) -> float: + return float(np.mean(labels[edges[:, 0]] == labels[edges[:, 1]])) + + +def per_query_precision(edges: np.ndarray, labels: np.ndarray) -> np.ndarray: + output = np.zeros(len(labels), dtype=float) + counts = np.zeros(len(labels), dtype=int) + for source, target in edges: + output[source] += labels[source] == labels[target] + counts[source] += 1 + if np.any(counts == 0): + raise RuntimeError("graph contains a node with no directed edges") + return output / counts + + +def family_means( + values: np.ndarray, families: np.ndarray +) -> tuple[list[str], np.ndarray]: + names = sorted(set(families)) + return names, np.asarray( + [np.mean(values[families == family]) for family in names], dtype=float + ) + + +def plus_one_p(null: np.ndarray, observed: float) -> float: + return float((1 + np.sum(null >= observed - 1e-15)) / (1 + len(null))) + + +def sign_flip_test(values: np.ndarray) -> float: + values = np.asarray(values, dtype=float) + signs = np.asarray(list(itertools.product((-1.0, 1.0), repeat=len(values)))) + observed = float(np.mean(values)) + null = np.mean(signs * values[None, :], axis=1) + return float(np.mean(null >= observed - 1e-15)) + + +def bootstrap_mean(values: np.ndarray, rng: np.random.Generator) -> list[float]: + values = np.asarray(values, dtype=float) + indices = rng.integers(0, len(values), size=(N_BOOT, len(values))) + samples = values[indices].mean(axis=1) + return [float(value) for value in np.quantile(samples, (0.025, 0.975))] + + +def permute_within_blocks( + labels: np.ndarray, + blocks: Sequence[tuple[object, ...]], + *, + rng: np.random.Generator, + n: int = N_PERM, +) -> np.ndarray: + output = np.empty((n, len(labels)), dtype=labels.dtype) + unique = sorted(set(blocks)) + indices = [ + np.asarray([i for i, block in enumerate(blocks) if block == value]) + for value in unique + ] + for iteration in range(n): + row = labels.copy() + for block_indices in indices: + row[block_indices] = rng.permutation(row[block_indices]) + output[iteration] = row + return output + + +def precision_null(edges: np.ndarray, permutations: np.ndarray) -> np.ndarray: + return np.mean( + permutations[:, edges[:, 0]] == permutations[:, edges[:, 1]], + axis=1, + ) + + +def layer_scan_null( + edges_by_layer: Sequence[np.ndarray], + permutations: np.ndarray, +) -> np.ndarray: + values = np.empty((len(permutations), len(edges_by_layer)), dtype=np.float32) + for layer, edges in enumerate(edges_by_layer): + values[:, layer] = precision_null(edges, permutations) + return values + + +def prompt_covariate_design( + feature_matrices: Mapping[str, np.ndarray], + folds: np.ndarray, + *, + upper_only: bool = True, + cross_fold_only: bool = False, +) -> tuple[np.ndarray, np.ndarray, np.ndarray]: + if upper_only: + first, second = np.triu_indices(len(folds), k=1) + else: + first, second = np.where(~np.eye(len(folds), dtype=bool)) + if cross_fold_only: + keep = folds[first] != folds[second] + first, second = first[keep], second[keep] + length = feature_matrices["length_difference"] + length_scaled = length / max(1.0, float(np.max(length))) + columns = [ + np.ones(len(first)), + feature_matrices["word_tfidf"][first, second], + feature_matrices["char_tfidf"][first, second], + feature_matrices["token_jaccard"][first, second], + length_scaled[first, second], + (folds[first] == folds[second]).astype(float), + ] + return np.column_stack(columns), first, second + + +def residualize_similarity( + similarity: np.ndarray, + design: np.ndarray, + first: np.ndarray, + second: np.ndarray, +) -> tuple[np.ndarray, np.ndarray]: + y = np.asarray(similarity[first, second], dtype=float) + with warnings.catch_warnings(): + warnings.simplefilter("ignore", RuntimeWarning) + coefficient = np.linalg.lstsq(design, y, rcond=None)[0] + residual = y - np.einsum("ij,j->i", design, coefficient) + if not np.all(np.isfinite(residual)): + raise FloatingPointError("non-finite prompt-residual similarity") + matrix = np.zeros_like(similarity, dtype=float) + matrix[first, second] = residual + matrix[second, first] = residual + np.fill_diagonal(matrix, np.max(residual) + 1.0) + return matrix, coefficient + + +def partial_family_coefficient( + similarity: np.ndarray, + design: np.ndarray, + first: np.ndarray, + second: np.ndarray, + labels: np.ndarray, +) -> float: + same = (labels[first] == labels[second]).astype(float) + full = np.column_stack((design, same)) + with warnings.catch_warnings(): + warnings.simplefilter("ignore", RuntimeWarning) + coefficient = np.linalg.lstsq( + full, similarity[first, second], rcond=None + )[0][-1] + if not np.isfinite(coefficient): + raise FloatingPointError("non-finite partial family coefficient") + return float(coefficient) + + +def partial_coefficient_null( + similarity: np.ndarray, + design: np.ndarray, + first: np.ndarray, + second: np.ndarray, + permutations: np.ndarray, +) -> np.ndarray: + y = similarity[first, second] + with warnings.catch_warnings(): + warnings.simplefilter("ignore", RuntimeWarning) + projection = np.einsum( + "ij,jk->ik", design, np.linalg.pinv(design), optimize=True + ) + y_residual = y - np.einsum( + "ij,j->i", projection, y, optimize=True + ) + if not np.all(np.isfinite(y_residual)): + raise FloatingPointError("non-finite residualized dyadic outcome") + output = np.empty(len(permutations), dtype=float) + chunk = 500 + for start in range(0, len(permutations), chunk): + stop = min(len(permutations), start + chunk) + same = ( + permutations[start:stop, first] + == permutations[start:stop, second] + ).astype(float) + with warnings.catch_warnings(): + warnings.simplefilter("ignore", RuntimeWarning) + same_residual = same - np.einsum( + "ij,jk->ik", same, projection, optimize=True + ) + numerator = np.einsum( + "ij,j->i", same_residual, y_residual, optimize=True + ) + denominator = np.sum(same_residual * same_residual, axis=1) + output[start:stop] = numerator / np.maximum(denominator, 1e-12) + if not np.all(np.isfinite(output)): + raise FloatingPointError("non-finite partial-coefficient null") + return output + + +def hard_negative_rows( + similarities: Mapping[str, np.ndarray], + lexical_score: np.ndarray, + families: np.ndarray, + folds: np.ndarray, + slugs: np.ndarray, +) -> pd.DataFrame: + rows: list[dict[str, object]] = [] + for source in range(len(families)): + for target_fold in sorted(set(folds)): + if target_fold == folds[source]: + continue + true_matches = np.flatnonzero( + (folds == target_fold) & (families == families[source]) + ) + if len(true_matches) != 1: + raise RuntimeError("cross-phrasing target is not unique") + target = int(true_matches[0]) + candidates = np.flatnonzero( + (folds == target_fold) & (families != families[source]) + ) + negative = min( + candidates.tolist(), + key=lambda index: (-float(lexical_score[source, index]), index), + ) + lexical_margin = float( + lexical_score[source, target] - lexical_score[source, negative] + ) + base = { + "source": source, + "source_slug": str(slugs[source]), + "family": str(families[source]), + "source_fold": int(folds[source]), + "target_fold": int(target_fold), + "true_target": target, + "true_slug": str(slugs[target]), + "hard_negative": negative, + "hard_negative_slug": str(slugs[negative]), + "hard_negative_family": str(families[negative]), + "prompt_only_margin": lexical_margin, + "lexical_trap": bool(lexical_margin < 0), + } + for method, similarity in similarities.items(): + rows.append( + { + **base, + "method": method, + "state_margin": float( + similarity[source, target] + - similarity[source, negative] + ), + } + ) + return pd.DataFrame(rows) + + +def summarize_hard_negatives( + frame: pd.DataFrame, + rng: np.random.Generator, +) -> dict[str, object]: + output: dict[str, object] = {} + for subset_name, subset in ( + ("all", frame), + ("lexical_traps", frame[frame["lexical_trap"]]), + ): + method_results = {} + for method, rows in subset.groupby("method"): + family = rows.groupby("family")["state_margin"].mean().sort_index() + values = family.to_numpy(dtype=float) + method_results[str(method)] = { + "n_comparisons": int(len(rows)), + "n_families": int(len(values)), + "mean_family_margin": float(np.mean(values)), + "family_bootstrap_95": bootstrap_mean(values, rng), + "one_sided_exact_family_sign_flip_p": sign_flip_test(values), + "positive_families": int(np.sum(values > 0)), + "family_margins": { + str(name): float(value) + for name, value in family.items() + }, + } + output[subset_name] = method_results + return output + + +def stability_scores( + similarities: np.ndarray, + folds: np.ndarray, + band_mask: np.ndarray, +) -> np.ndarray: + """Frequency for every directed cross-fold pair. + + ``similarities`` is ``[replicate, layer, item, item]``. + """ + + counts = np.zeros((len(folds), len(folds)), dtype=float) + total = 0 + for replicate in range(similarities.shape[0]): + for layer in np.flatnonzero(band_mask): + edges = cross_fold_edges(similarities[replicate, layer], folds) + counts[edges[:, 0], edges[:, 1]] += 1 + total += 1 + return counts / total + + +def stability_auc_null( + scores: np.ndarray, + labels: np.ndarray, + folds: np.ndarray, + permutations: np.ndarray, +) -> tuple[float, np.ndarray]: + first, second = np.where(folds[:, None] != folds[None, :]) + values = scores[first, second] + observed_binary = labels[first] == labels[second] + observed = float(roc_auc_score(observed_binary, values)) + ranks = rankdata(values, method="average") + n_pos = int(np.sum(observed_binary)) + n_total = len(values) + output = np.empty(len(permutations), dtype=float) + chunk = 500 + base = n_pos * (n_pos + 1) / 2.0 + denominator = n_pos * (n_total - n_pos) + for start in range(0, len(permutations), chunk): + stop = min(len(permutations), start + chunk) + same = ( + permutations[start:stop, first] + == permutations[start:stop, second] + ) + with warnings.catch_warnings(): + warnings.simplefilter("ignore", RuntimeWarning) + rank_sum = np.einsum( + "ij,j->i", same, ranks, optimize=True + ) + output[start:stop] = (rank_sum - base) / denominator + if not np.all(np.isfinite(output)): + raise FloatingPointError("non-finite stability AUC null") + return observed, output + + +def bh_adjust(p_values: np.ndarray) -> np.ndarray: + values = np.asarray(p_values, dtype=float) + order = np.argsort(values) + ranked = values[order] + adjusted = ranked * len(values) / np.arange(1, len(values) + 1) + adjusted = np.minimum.accumulate(adjusted[::-1])[::-1] + output = np.empty_like(adjusted) + output[order] = np.minimum(adjusted, 1.0) + return output + + +def concept_documents(records: Sequence[Mapping[str, object]]) -> tuple[np.ndarray, list[str]]: + documents: list[dict[str, float]] = [] + for record in records: + words = token_words(str(record["prompt"])) + filtered = [ + row + for row in record["filtered_consensus_candidates"] # type: ignore[index] + if not candidate_removed(str(row["token"]), words) + ][:20] + documents.append( + { + str(row["token"]): float(row["consensus_score"]) + for row in filtered + } + ) + frequency = Counter(token for document in documents for token in document) + vocabulary = sorted(frequency) + lookup = {token: index for index, token in enumerate(vocabulary)} + matrix = np.zeros((len(documents), len(vocabulary)), dtype=float) + for row, document in enumerate(documents): + for token, score in document.items(): + matrix[row, lookup[token]] = score * math.log( + (1 + len(documents)) / (1 + frequency[token]) + ) + return matrix, vocabulary + + +def concept_enrichment( + matrix: np.ndarray, + vocabulary: Sequence[str], + families: np.ndarray, + permutations: np.ndarray, +) -> pd.DataFrame: + family_names = sorted(set(families)) + family_codes = {family: index for index, family in enumerate(family_names)} + observed_codes = np.asarray([family_codes[value] for value in families]) + perm_codes = np.empty_like(permutations, dtype=np.int16) + for family, code in family_codes.items(): + perm_codes[permutations == family] = code + global_mean = np.mean(matrix, axis=0) + observed_means = np.vstack( + [np.mean(matrix[observed_codes == code], axis=0) for code in range(10)] + ) + observed_score = np.max(observed_means - global_mean[None, :], axis=0) + observed_family = np.argmax(observed_means - global_mean[None, :], axis=0) + exceed = np.zeros(len(vocabulary), dtype=np.int64) + for token_index in range(len(vocabulary)): + support = np.flatnonzero(matrix[:, token_index] > 0) + weights = matrix[support, token_index] + if not len(support): + continue + scores = np.full((len(permutations), 10), -global_mean[token_index]) + for code in range(10): + scores[:, code] += np.sum( + (perm_codes[:, support] == code) * weights[None, :], + axis=1, + ) / 5.0 + null = np.max(scores, axis=1) + exceed[token_index] = int( + np.sum(null >= observed_score[token_index] - 1e-15) + ) + p_values = (1 + exceed) / (1 + len(permutations)) + q_values = bh_adjust(p_values) + rows = [] + for index, token in enumerate(vocabulary): + family_index = int(observed_family[index]) + family = family_names[family_index] + rows.append( + { + "token": token, + "assigned_family": family, + "family_mean": float(observed_means[family_index, index]), + "global_mean": float(global_mean[index]), + "enrichment": float(observed_score[index]), + "prompt_support": int(np.sum(matrix[:, index] > 0)), + "permutation_p": float(p_values[index]), + "bh_q": float(q_values[index]), + "fdr_05": bool(q_values[index] <= 0.05), + } + ) + return pd.DataFrame(rows).sort_values( + ["fdr_05", "bh_q", "enrichment", "token"], + ascending=[False, True, False, True], + ) + + +def graph_from_directed( + edges: np.ndarray, + similarity: np.ndarray, +) -> nx.Graph: + graph = nx.Graph() + graph.add_nodes_from(range(similarity.shape[0])) + for first, second in edges: + weight = float(similarity[first, second]) + if graph.has_edge(int(first), int(second)): + graph[int(first)][int(second)]["weight"] = max( + graph[int(first)][int(second)]["weight"], weight + ) + else: + graph.add_edge(int(first), int(second), weight=weight) + return graph + + +def graph_summary( + similarity: np.ndarray, + labels: np.ndarray, + folds: np.ndarray, + permutations: np.ndarray, + *, + neighbors_per_fold: int = 1, +) -> tuple[dict[str, object], np.ndarray]: + edges = cross_fold_edges( + similarity, folds, neighbors_per_fold=neighbors_per_fold + ) + observed = edge_precision(edges, labels) + null = precision_null(edges, permutations) + query = per_query_precision(edges, labels) + names, family = family_means(query, labels) + graph = graph_from_directed(edges, similarity) + partition = community_labels(graph) + return ( + { + "directed_precision": observed, + "permutation_p": plus_one_p(null, observed), + "null_mean": float(np.mean(null)), + "null_95": [float(value) for value in np.quantile(null, (0.025, 0.975))], + "n_directed_edges": int(len(edges)), + "n_undirected_edges": int(graph.number_of_edges()), + "louvain_ari": float(adjusted_rand_score(labels, partition)), + "n_communities": int(len(set(partition))), + "family_precision": { + name: float(value) for name, value in zip(names, family) + }, + }, + edges, + ) + + +def undirected_edge_sensitivity( + edges: np.ndarray, + labels: np.ndarray, + permutations: np.ndarray, +) -> dict[str, object]: + directed = {(int(first), int(second)) for first, second in edges} + union = sorted( + {tuple(sorted((first, second))) for first, second in directed} + ) + mutual = sorted( + { + tuple(sorted((first, second))) + for first, second in directed + if (second, first) in directed + } + ) + output: dict[str, object] = {} + for name, values in (("union", union), ("mutual", mutual)): + array = np.asarray(values, dtype=np.int16) + if not len(array): + output[name] = { + "n_edges": 0, + "homophily": None, + "permutation_p": None, + } + continue + observed = edge_precision(array, labels) + null = precision_null(array, permutations) + output[name] = { + "n_edges": int(len(array)), + "homophily": observed, + "permutation_p": plus_one_p(null, observed), + "null_95": [ + float(value) for value in np.quantile(null, (0.025, 0.975)) + ], + } + return output + + +def edge_jaccard(first: np.ndarray, second: np.ndarray) -> float: + first_set = {(int(a), int(b)) for a, b in first} + second_set = {(int(a), int(b)) for a, b in second} + return float(len(first_set & second_set) / len(first_set | second_set)) + + +def relation_edge_diagnostics( + method: str, + similarity: np.ndarray, + edges: np.ndarray, + metadata: Sequence[Mapping[str, object]], +) -> pd.DataFrame: + rows = [] + for source, target in edges: + first = metadata[int(source)] + second = metadata[int(target)] + rows.append( + { + "method": method, + "source_index": int(source), + "target_index": int(target), + "family": str(first["family_id"]), + "source_variant": str(first["variant"]), + "target_variant": str(second["variant"]), + "source_presentation_order": str(first["presentation_order"]), + "target_presentation_order": str(second["presentation_order"]), + "same_presentation_order": bool( + first["presentation_order"] == second["presentation_order"] + ), + "source_numeric_direction": str(first["numeric_direction"]), + "target_numeric_direction": str(second["numeric_direction"]), + "same_numeric_direction": bool( + first["numeric_direction"] == second["numeric_direction"] + ), + "source_expected_outcome": str(first["expected_outcome"]), + "target_expected_outcome": str(second["expected_outcome"]), + "same_outcome": bool( + first["expected_outcome"] == second["expected_outcome"] + ), + "cosine": float(similarity[int(source), int(target)]), + } + ) + return pd.DataFrame(rows) + + +def deterministic_binary_similarity( + values: Sequence[object], +) -> np.ndarray: + values = np.asarray(values) + similarity = (values[:, None] == values[None, :]).astype(float) + # A tiny deterministic term makes ties reproducible without changing the + # binary grouping represented by the baseline. + index = np.arange(len(values), dtype=float) + similarity -= 1e-9 * index[None, :] + return similarity + + +def relation_family_contrasts( + fixed_edges: Mapping[str, np.ndarray], + outcomes: np.ndarray, + families: np.ndarray, + rng: np.random.Generator, +) -> dict[str, object]: + query = { + method: per_query_precision(edges, outcomes) + for method, edges in fixed_edges.items() + } + output = {} + for first, second in ( + ("jacobian_band", "direct_band"), + ("jacobian_band", "raw_band"), + ("jacobian_band", "word_tfidf"), + ("jacobian_band", "char_tfidf"), + ("jacobian_late", "direct_late"), + ("jacobian_late", "raw_late"), + ): + names, values = family_means(query[first] - query[second], families) + output[f"{first}_minus_{second}"] = { + "mean_difference": float(np.mean(values)), + "family_bootstrap_95": bootstrap_mean(values, rng), + "one_sided_exact_family_sign_flip_p": sign_flip_test(values), + "positive_families": int(np.sum(values > 0)), + "family_differences": { + name: float(value) for name, value in zip(names, values) + }, + } + return output + + +def analyze_heldout( + protocol: Mapping[str, object], + rng: np.random.Generator, +) -> tuple[dict[str, object], dict[str, object]]: + with np.load(HELDOUT_NPZ, allow_pickle=False) as data: + transported = data["transported_states"].astype(np.float64) + raw = data["raw_states"].astype(np.float64) + lexical = data["lexical_states"].astype(np.float64) + target = data["target_states"].astype(np.float64) + families = data["families"].astype(str) + depths = data["depths"].astype(float) + layers = data["source_layers"].astype(int) + slugs = data["slugs"].astype(str) + phrasing_ids = data["phrasing_ids"].astype(str) + folds = np.asarray( + [int(value.rsplit("-", 1)[-1]) for value in phrasing_ids], + dtype=np.int16, + ) + prompt_manifest = json.loads(HELDOUT_PROMPTS.read_text()) + prompt_by_slug = { + str(row["slug"]): str(row["text"]) + for row in prompt_manifest["prompts"] + } + texts = [prompt_by_slug[str(slug)] for slug in slugs] + features = prompt_feature_matrices(texts) + + sim_j_seed = cosine_layers(transported) + sim_j = np.mean(sim_j_seed, axis=0) + sim_raw = cosine_layers(raw[None, ...])[0] + sim_lexical = cosine_matrix(lexical) + sim_target = cosine_matrix(target) + band = (depths >= BAND[0]) & (depths <= BAND[1]) + band_j = np.mean(sim_j[band], axis=0) + band_raw = np.mean(sim_raw[band], axis=0) + + heldout = json.loads(HELDOUT_STATS.read_text()) + jac_records = heldout["open_vocabulary"]["methods"]["jacobian"]["per_prompt"] + direct_records = heldout["open_vocabulary"]["methods"]["logit"]["per_prompt"] + if [row["slug"] for row in jac_records] != slugs.tolist(): + raise RuntimeError("heldout target-free records do not match vector order") + vocabulary_j, vocabulary_j_words = vocabulary_features(jac_records) + vocabulary_d, vocabulary_d_words = vocabulary_features(direct_records) + sim_vocabulary_j = cosine_matrix(vocabulary_j) + sim_vocabulary_d = cosine_matrix(vocabulary_d) + + label_blocks = [(int(fold),) for fold in folds] + permutations = permute_within_blocks( + families, label_blocks, rng=np.random.default_rng(SEED) + ) + + fixed_similarities = { + "jacobian": band_j, + "raw": band_raw, + "token_embedding": sim_lexical, + "target_state": sim_target, + "word_tfidf": features["word_tfidf"], + "char_tfidf": features["char_tfidf"], + "target_free_jacobian": sim_vocabulary_j, + "target_free_direct": sim_vocabulary_d, + } + primary = {} + primary_edges = {} + for method, similarity in fixed_similarities.items(): + primary[method], primary_edges[method] = graph_summary( + similarity, families, folds, permutations + ) + + layer_rows = [] + layer_edges: dict[str, list[np.ndarray]] = {"jacobian": [], "raw": []} + for index, depth in enumerate(depths): + for method, similarity in ( + ("jacobian", sim_j[index]), + ("raw", sim_raw[index]), + ): + edges = cross_fold_edges(similarity, folds) + layer_edges[method].append(edges) + layer_rows.append( + { + "method": method, + "layer": int(layers[index]), + "depth_percent": float(depth), + "directed_precision": edge_precision(edges, families), + } + ) + layer_frame = pd.DataFrame(layer_rows) + layer_frame.to_csv(OUT / "heldout_cross_phrasing_layer_metrics.csv", index=False) + layer_scan = {} + for method in ("jacobian", "raw"): + observed = layer_frame[layer_frame["method"] == method][ + "directed_precision" + ].to_numpy() + null = layer_scan_null(layer_edges[method], permutations) + best = int(np.argmax(observed)) + layer_scan[method] = { + "best_precision": float(observed[best]), + "best_layer": int(layers[best]), + "best_depth_percent": float(depths[best]), + "max_layer_corrected_p": plus_one_p( + np.max(null, axis=1), float(observed[best]) + ), + "band_mean_layer_precision": float(np.mean(observed[band])), + "band_mean_layer_p": plus_one_p( + np.mean(null[:, band], axis=1), float(np.mean(observed[band])) + ), + } + + design, first, second = prompt_covariate_design( + features, folds, upper_only=True + ) + band_residual_j, residual_coefs_j = residualize_similarity( + band_j, design, first, second + ) + band_residual_raw, residual_coefs_raw = residualize_similarity( + band_raw, design, first, second + ) + residual_primary = {} + residual_edges = {} + for method, similarity in ( + ("jacobian", band_residual_j), + ("raw", band_residual_raw), + ): + residual_primary[method], residual_edges[method] = graph_summary( + similarity, families, folds, permutations + ) + + residual_layer_rows = [] + residual_layer_edges: dict[str, list[np.ndarray]] = { + "jacobian": [], + "raw": [], + } + residual_similarities: dict[str, list[np.ndarray]] = { + "jacobian": [], + "raw": [], + } + for index, depth in enumerate(depths): + for method, similarity in ( + ("jacobian", sim_j[index]), + ("raw", sim_raw[index]), + ): + residual, _ = residualize_similarity( + similarity, design, first, second + ) + edges = cross_fold_edges(residual, folds) + residual_layer_edges[method].append(edges) + residual_similarities[method].append(residual) + residual_layer_rows.append( + { + "method": method, + "layer": int(layers[index]), + "depth_percent": float(depth), + "directed_precision": edge_precision(edges, families), + } + ) + residual_frame = pd.DataFrame(residual_layer_rows) + residual_frame.to_csv( + OUT / "heldout_lexical_residual_layer_metrics.csv", index=False + ) + residual_scan = {} + for method in ("jacobian", "raw"): + observed = residual_frame[residual_frame["method"] == method][ + "directed_precision" + ].to_numpy() + null = layer_scan_null(residual_layer_edges[method], permutations) + best = int(np.argmax(observed)) + residual_scan[method] = { + "best_precision": float(observed[best]), + "best_layer": int(layers[best]), + "best_depth_percent": float(depths[best]), + "max_layer_corrected_p": plus_one_p( + np.max(null, axis=1), float(observed[best]) + ), + "band_mean_layer_precision": float(np.mean(observed[band])), + "band_mean_layer_p": plus_one_p( + np.mean(null[:, band], axis=1), float(np.mean(observed[band])) + ), + } + + cross_design, cross_first, cross_second = prompt_covariate_design( + features, folds, upper_only=True, cross_fold_only=True + ) + partial = {} + for method, similarity in ( + ("jacobian", band_j), + ("raw", band_raw), + ): + observed = partial_family_coefficient( + similarity, + cross_design, + cross_first, + cross_second, + families, + ) + null = partial_coefficient_null( + similarity, + cross_design, + cross_first, + cross_second, + permutations, + ) + partial[method] = { + "same_family_coefficient": observed, + "permutation_p": plus_one_p(null, observed), + "null_95": [float(value) for value in np.quantile(null, (0.025, 0.975))], + } + + lexical_score = 0.5 * ( + features["word_tfidf"] + features["char_tfidf"] + ) + hard_frame = hard_negative_rows( + { + "jacobian": band_j, + "raw": band_raw, + "token_embedding": sim_lexical, + "word_tfidf": features["word_tfidf"], + "char_tfidf": features["char_tfidf"], + "lexical_residual_jacobian": band_residual_j, + }, + lexical_score, + families, + folds, + slugs, + ) + hard_frame.to_csv(OUT / "heldout_lexical_hard_negatives.csv", index=False) + hard_summary = summarize_hard_negatives(hard_frame, rng) + + stability_j = stability_scores(sim_j_seed, folds, band) + stability_raw = stability_scores(sim_raw[None, ...], folds, band) + auc_j, auc_j_null = stability_auc_null( + stability_j, families, folds, permutations + ) + auc_raw, auc_raw_null = stability_auc_null( + stability_raw, families, folds, permutations + ) + stability = { + "jacobian": { + "same_family_auc": auc_j, + "permutation_p": plus_one_p(auc_j_null, auc_j), + "null_95": [float(value) for value in np.quantile(auc_j_null, (0.025, 0.975))], + }, + "raw": { + "same_family_auc": auc_raw, + "permutation_p": plus_one_p(auc_raw_null, auc_raw), + "null_95": [float(value) for value in np.quantile(auc_raw_null, (0.025, 0.975))], + }, + } + + family_contrasts = {} + for baseline in ( + "raw", + "token_embedding", + "word_tfidf", + "char_tfidf", + "target_free_direct", + ): + jac_query = per_query_precision(primary_edges["jacobian"], families) + base_query = per_query_precision(primary_edges[baseline], families) + names, values = family_means(jac_query - base_query, families) + family_contrasts[f"jacobian_minus_{baseline}"] = { + "mean_difference": float(np.mean(values)), + "family_bootstrap_95": bootstrap_mean(values, rng), + "one_sided_exact_family_sign_flip_p": sign_flip_test(values), + "positive_families": int(np.sum(values > 0)), + "family_differences": { + name: float(value) for name, value in zip(names, values) + }, + } + + sensitivity = {} + for count in (1, 2, 3): + sensitivity[str(count)] = {} + for method, similarity in ( + ("jacobian", band_j), + ("raw", band_raw), + ("word_tfidf", features["word_tfidf"]), + ("char_tfidf", features["char_tfidf"]), + ): + result, _ = graph_summary( + similarity, + families, + folds, + permutations, + neighbors_per_fold=count, + ) + sensitivity[str(count)][method] = result + lens_seed_results = [] + lens_seed_edges = [] + for seed_index in range(sim_j_seed.shape[0]): + similarity = np.mean(sim_j_seed[seed_index, band], axis=0) + result, edges = graph_summary( + similarity, families, folds, permutations + ) + lens_seed_results.append({"lens_seed": seed_index, **result}) + lens_seed_edges.append(edges) + lens_seed_edge_jaccard = { + f"{first}-{second}": edge_jaccard( + lens_seed_edges[first], lens_seed_edges[second] + ) + for first, second in ((0, 1), (0, 2), (1, 2)) + } + undirected = { + method: undirected_edge_sensitivity( + primary_edges[method], families, permutations + ) + for method in ("jacobian", "raw", "word_tfidf", "char_tfidf") + } + + concept_matrix, concept_words = concept_documents(jac_records) + enrichment = concept_enrichment( + concept_matrix, + concept_words, + families, + permutations, + ) + enrichment.to_csv(OUT / "target_free_concept_enrichment.csv", index=False) + concept_result = { + "candidate_words": len(concept_words), + "fdr_significant_words": int(enrichment["fdr_05"].sum()), + "families_with_fdr_words": sorted( + set(enrichment.loc[enrichment["fdr_05"], "assigned_family"]) + ), + "top_significant_words": enrichment[enrichment["fdr_05"]] + .head(50) + .to_dict(orient="records"), + "jacobian_vocabulary_size": len(vocabulary_j_words), + "direct_vocabulary_size": len(vocabulary_d_words), + } + + payload = { + "primary_cross_phrasing_graph": primary, + "primary_family_contrasts": family_contrasts, + "layer_scan": layer_scan, + "prompt_only_residualized_graph": residual_primary, + "residualized_layer_scan": residual_scan, + "prompt_adjusted_dyadic_coefficient": partial, + "lexical_hard_negatives": hard_summary, + "edge_stability": stability, + "neighbors_per_fold_sensitivity": sensitivity, + "per_lens_seed": lens_seed_results, + "per_lens_seed_directed_edge_jaccard": lens_seed_edge_jaccard, + "undirected_graph_sensitivity": undirected, + "target_free_concept_network": concept_result, + "residualization_coefficients": { + "covariates": [ + "intercept", + "word_tfidf_cosine", + "character_tfidf_cosine", + "token_jaccard", + "scaled_length_difference", + "same_phrasing_fold", + ], + "jacobian": [float(value) for value in residual_coefs_j], + "raw": [float(value) for value in residual_coefs_raw], + }, + } + figure_data = { + "depths": depths, + "layers": layers, + "families": families, + "folds": folds, + "slugs": slugs, + "layer_frame": layer_frame, + "residual_frame": residual_frame, + "band_j": band_j, + "band_residual_j": band_residual_j, + "primary_edges": primary_edges["jacobian"], + "residual_edges": residual_edges["jacobian"], + "hard_frame": hard_frame, + "stability_j": stability_j, + "enrichment": enrichment, + "concept_matrix": concept_matrix, + "concept_words": concept_words, + } + return payload, figure_data + + +def analyze_replication( + rng: np.random.Generator, +) -> tuple[dict[str, object], dict[str, object]]: + manifest = json.loads(REPLICATION_MANIFEST.read_text()) + metadata = {str(row["prompt_id"]): row for row in manifest["prompts"]} + with np.load(REPLICATION_NPZ, allow_pickle=False) as data: + prompt_ids = data["prompt_ids"].astype(str) + layers = data["layers"].astype(int) + raw = data["raw_states"].astype(np.float64) + direct = data["direct_decoder_basis"].astype(np.float64) + jacobian = data["jacobian_decoder_basis"].astype(np.float64) + rows = [metadata[str(prompt_id)] for prompt_id in prompt_ids] + families = np.asarray([str(row["family_id"]) for row in rows]) + outcomes = np.asarray([str(row["expected_outcome"]) for row in rows]) + variants = np.asarray([str(row["variant"]) for row in rows]) + triplets = np.asarray([str(row["triplet_id"]) for row in rows]) + presentation_orders = np.asarray( + [str(row["presentation_order"]) for row in rows] + ) + numeric_directions = np.asarray( + [str(row["numeric_direction"]) for row in rows] + ) + texts = [str(row["user"]) for row in rows] + depths = layers / 41.0 * 100.0 + features = prompt_feature_matrices(texts) + + sim_j_seed = cosine_layers(jacobian) + sim_j = np.mean(sim_j_seed, axis=0) + sim_direct = cosine_layers(direct[None, ...])[0] + sim_raw = cosine_layers(raw[None, ...])[0] + band = (depths >= BAND[0]) & (depths <= BAND[1]) + late = (depths >= LATE_BAND[0]) & (depths <= LATE_BAND[1]) + + blocks = [ + (str(family), str(variant)) + for family, variant in zip(families, variants) + ] + permutations = permute_within_blocks( + outcomes, blocks, rng=np.random.default_rng(SEED + 1) + ) + exact_case_permutations = case_preserving_exact_permutations( + families, + variants, + triplets, + outcomes, + ) + + fixed = { + "jacobian_band": np.mean(sim_j[band], axis=0), + "jacobian_late": np.mean(sim_j[late], axis=0), + "direct_band": np.mean(sim_direct[band], axis=0), + "direct_late": np.mean(sim_direct[late], axis=0), + "raw_band": np.mean(sim_raw[band], axis=0), + "raw_late": np.mean(sim_raw[late], axis=0), + "word_tfidf": features["word_tfidf"], + "char_tfidf": features["char_tfidf"], + "answer_order_only": deterministic_binary_similarity( + presentation_orders + ), + "numeric_direction_oracle": deterministic_binary_similarity( + numeric_directions + ), + } + fixed_results = {} + fixed_edges = {} + exact_nulls = {} + ranking_results = {} + ranking_frames = [] + for method, similarity in fixed.items(): + edges = relation_edges( + similarity, families, variants, triplets + ) + observed = edge_precision(edges, outcomes) + null = precision_null(edges, permutations) + exact_null = precision_null(edges, exact_case_permutations) + exact_nulls[method] = exact_null + query = per_query_precision(edges, outcomes) + names, values = family_means(query, families) + ranking, ranking_frame = relation_ranking_summary( + similarity, + families, + variants, + triplets, + outcomes, + ) + ranking_results[method] = ranking + ranking_frame.insert(0, "method", method) + ranking_frames.append(ranking_frame) + fixed_results[method] = { + "directed_same_outcome_precision": observed, + "permutation_p": plus_one_p(null, observed), + "null_mean": float(np.mean(null)), + "null_95": [float(value) for value in np.quantile(null, (0.025, 0.975))], + "case_preserving_exact_p": plus_one_p(exact_null, observed), + "case_preserving_exact_null_mean": float( + np.mean(exact_null) + ), + "case_preserving_exact_null_95": [ + float(value) + for value in np.quantile(exact_null, (0.025, 0.975)) + ], + "n_case_preserving_exact_assignments": int( + len(exact_case_permutations) + ), + "n_directed_edges": int(len(edges)), + "family_precision": { + name: float(value) for name, value in zip(names, values) + }, + "family_bootstrap_95": bootstrap_mean(values, rng), + "one_sided_exact_family_sign_flip_p": sign_flip_test( + values - float(np.mean(null)) + ), + } + fixed_edges[method] = edges + ranking_frame = pd.concat(ranking_frames, ignore_index=True) + ranking_frame.to_csv( + OUT / "replication_relation_candidate_ranks.csv", + index=False, + ) + + diagnostic_frames = [] + for method, similarity in fixed.items(): + diagnostic_frames.append( + relation_edge_diagnostics( + method, + similarity, + fixed_edges[method], + rows, + ) + ) + edge_frame = pd.concat(diagnostic_frames, ignore_index=True) + edge_frame.to_csv( + OUT / "replication_relation_graph_selected_edges.csv", + index=False, + ) + diagnostics = {} + for method, method_rows in edge_frame.groupby("method"): + variant_table = ( + method_rows.groupby( + ["source_variant", "target_variant"], as_index=False + ) + .agg( + n_edges=("same_outcome", "size"), + same_outcome_precision=("same_outcome", "mean"), + same_presentation_order_rate=( + "same_presentation_order", + "mean", + ), + same_numeric_direction_rate=( + "same_numeric_direction", + "mean", + ), + ) + ) + variant_p = [] + for row in variant_table.itertuples(index=False): + selected = method_rows[ + (method_rows["source_variant"] == row.source_variant) + & (method_rows["target_variant"] == row.target_variant) + ] + edges = selected[["source_index", "target_index"]].to_numpy( + dtype=np.int16 + ) + null = precision_null(edges, permutations) + variant_p.append( + plus_one_p(null, float(row.same_outcome_precision)) + ) + variant_table["permutation_p"] = variant_p + variant_table["bh_q_across_six_pairs"] = bh_adjust( + np.asarray(variant_p) + ) + order_table = ( + method_rows.groupby( + "same_presentation_order", as_index=False + ) + .agg( + n_edges=("same_outcome", "size"), + same_outcome_precision=("same_outcome", "mean"), + ) + ) + order_p = [] + for row in order_table.itertuples(index=False): + selected = method_rows[ + method_rows["same_presentation_order"] + == row.same_presentation_order + ] + edges = selected[["source_index", "target_index"]].to_numpy( + dtype=np.int16 + ) + null = precision_null(edges, permutations) + order_p.append( + plus_one_p(null, float(row.same_outcome_precision)) + ) + order_table["permutation_p"] = order_p + order_table["bh_q_across_two_groups"] = bh_adjust( + np.asarray(order_p) + ) + diagnostics[str(method)] = { + "by_ordered_variant_pair": variant_table.to_dict( + orient="records" + ), + "by_presentation_order_match": order_table.to_dict( + orient="records" + ), + "same_order_rate_among_correct_edges": float( + method_rows.loc[ + method_rows["same_outcome"], + "same_presentation_order", + ].mean() + ), + "same_order_rate_among_incorrect_edges": float( + method_rows.loc[ + ~method_rows["same_outcome"], + "same_presentation_order", + ].mean() + ), + "same_numeric_direction_rate": float( + method_rows["same_numeric_direction"].mean() + ), + } + + layer_rows = [] + layer_edges: dict[str, list[np.ndarray]] = { + "jacobian": [], + "direct": [], + "raw": [], + } + for index, depth in enumerate(depths): + for method, similarity in ( + ("jacobian", sim_j[index]), + ("direct", sim_direct[index]), + ("raw", sim_raw[index]), + ): + edges = relation_edges( + similarity, families, variants, triplets + ) + layer_edges[method].append(edges) + layer_rows.append( + { + "method": method, + "layer": int(layers[index]), + "depth_percent": float(depth), + "directed_same_outcome_precision": edge_precision( + edges, outcomes + ), + } + ) + layer_frame = pd.DataFrame(layer_rows) + layer_frame.to_csv(OUT / "replication_relation_graph_layers.csv", index=False) + layer_scan = {} + for method in ("jacobian", "direct", "raw"): + observed = layer_frame[layer_frame["method"] == method][ + "directed_same_outcome_precision" + ].to_numpy() + null = layer_scan_null(layer_edges[method], permutations) + best = int(np.argmax(observed)) + layer_scan[method] = { + "best_precision": float(observed[best]), + "best_layer": int(layers[best]), + "best_depth_percent": float(depths[best]), + "max_layer_corrected_p": plus_one_p( + np.max(null, axis=1), float(observed[best]) + ), + "workspace_band_mean_layer_precision": float(np.mean(observed[band])), + "workspace_band_mean_layer_p": plus_one_p( + np.mean(null[:, band], axis=1), float(np.mean(observed[band])) + ), + "late_band_mean_layer_precision": float(np.mean(observed[late])), + "late_band_mean_layer_p": plus_one_p( + np.mean(null[:, late], axis=1), float(np.mean(observed[late])) + ), + } + + payload = { + "fixed_graphs": fixed_results, + "paired_family_contrasts": relation_family_contrasts( + fixed_edges, + outcomes, + families, + rng, + ), + "answer_order_and_variant_falsification": diagnostics, + "complete_candidate_ranking": ranking_results, + "layer_scan": layer_scan, + "guardrail": ( + "Within-mechanism relation topology is a post-hoc graph analysis " + "of a cohort whose earlier registered similarity replication failed." + ), + } + figure_data = { + "depths": depths, + "layers": layers, + "families": families, + "outcomes": outcomes, + "variants": variants, + "triplets": triplets, + "layer_frame": layer_frame, + "fixed_results": fixed_results, + "fixed_edges": fixed_edges, + "edge_frame": edge_frame, + "ranking_frame": ranking_frame, + "exact_null_jacobian_band": exact_nulls["jacobian_band"], + "similarity": fixed["jacobian_late"], + } + return payload, figure_data + + +def panel_label(axis: plt.Axes, letter: str) -> None: + axis.text( + -0.12, + 1.04, + letter, + transform=axis.transAxes, + fontsize=10, + fontweight="bold", + va="bottom", + ) + + +def configure_matplotlib() -> None: + plt.rcParams.update( + { + "font.family": "DejaVu Sans", + "font.size": 8.3, + "axes.labelsize": 8.5, + "axes.titlesize": 8.5, + "xtick.labelsize": 7.4, + "ytick.labelsize": 7.4, + "legend.fontsize": 7.3, + "axes.spines.top": False, + "axes.spines.right": False, + "axes.linewidth": 0.7, + "pdf.fonttype": 42, + "ps.fonttype": 42, + "svg.fonttype": "none", + } + ) + + +def draw_mechanism_graph( + axis: plt.Axes, + similarity: np.ndarray, + edges: np.ndarray, + families: np.ndarray, + folds: np.ndarray, +) -> None: + graph = graph_from_directed(edges, similarity) + positions = nx.spring_layout( + graph, + seed=SEED, + weight="weight", + k=0.58, + iterations=600, + ) + same = [ + edge for edge in graph.edges() + if families[edge[0]] == families[edge[1]] + ] + other = [ + edge for edge in graph.edges() + if families[edge[0]] != families[edge[1]] + ] + nx.draw_networkx_edges( + graph, + positions, + edgelist=other, + width=0.45, + alpha=0.28, + edge_color="#B9BDC2", + ax=axis, + ) + nx.draw_networkx_edges( + graph, + positions, + edgelist=same, + width=1.1, + alpha=0.75, + edge_color=COLORS["jacobian"], + ax=axis, + ) + palette = plt.get_cmap("tab10") + color_lookup = { + family: palette(index) + for index, family in enumerate(sorted(set(families))) + } + markers = {1: "o", 2: "s", 3: "^", 4: "D", 5: "P"} + for fold in sorted(set(folds)): + nodes = np.flatnonzero(folds == fold).tolist() + nx.draw_networkx_nodes( + graph, + positions, + nodelist=nodes, + node_color=[color_lookup[families[node]] for node in nodes], + node_shape=markers[int(fold)], + node_size=44, + edgecolors="white", + linewidths=0.45, + ax=axis, + ) + axis.set_axis_off() + + +def plot_mechanism_evidence( + heldout: Mapping[str, object], + data: Mapping[str, object], +) -> None: + configure_matplotlib() + depths = np.asarray(data["depths"]) + layer_frame = data["layer_frame"] + residual_frame = data["residual_frame"] + families = np.asarray(data["families"]) + hard_frame = data["hard_frame"] + fig, axes = plt.subplots(2, 2, figsize=(10.4, 7.0)) + + axis = axes[0, 0] + for method, label in (("jacobian", "Jacobian"), ("raw", "raw state")): + rows = layer_frame[layer_frame["method"] == method] + axis.plot( + rows["depth_percent"], + rows["directed_precision"], + color=COLORS[method], + lw=2.0 if method == "jacobian" else 1.6, + label=label, + ) + primary = heldout["primary_cross_phrasing_graph"] + axis.axhline( + primary["word_tfidf"]["directed_precision"], + color=COLORS["word_tfidf"], + ls="--", + lw=1.25, + label="word TF–IDF", + ) + axis.axhline( + primary["char_tfidf"]["directed_precision"], + color=COLORS["char_tfidf"], + ls=":", + lw=1.35, + label="character TF–IDF", + ) + axis.axhline(0.1, color=COLORS["chance"], lw=0.9, ls=(0, (3, 2))) + axis.axvspan(*BAND, color="#D9DEE3", alpha=0.35, zorder=-2) + axis.set( + xlabel="layer depth (%)", + ylabel="cross-phrasing same-mechanism edges", + xlim=(0, 100), + ylim=(0, max(0.55, float(layer_frame["directed_precision"].max()) + 0.05)), + ) + axis.legend(frameon=False, ncol=2, loc="upper right") + panel_label(axis, "A") + + axis = axes[0, 1] + for method, label in (("jacobian", "Jacobian"), ("raw", "raw state")): + rows = residual_frame[residual_frame["method"] == method] + axis.plot( + rows["depth_percent"], + rows["directed_precision"], + color=COLORS[method], + lw=2.0 if method == "jacobian" else 1.6, + label=label, + ) + axis.axhline(0.1, color=COLORS["chance"], lw=0.9, ls=(0, (3, 2))) + axis.axvspan(*BAND, color="#D9DEE3", alpha=0.35, zorder=-2) + axis.set( + xlabel="layer depth (%)", + ylabel="same-mechanism edges after\nprompt-only residualization", + xlim=(0, 100), + ylim=(0, max(0.4, float(residual_frame["directed_precision"].max()) + 0.05)), + ) + axis.legend(frameon=False, loc="upper right") + panel_label(axis, "B") + + axis = axes[1, 0] + trap = hard_frame[hard_frame["lexical_trap"]].copy() + family_order = sorted(set(families)) + y = np.arange(len(family_order)) + for offset, (method, label, marker) in zip( + (-0.12, 0.12), + ( + ("jacobian", "Jacobian", "o"), + ("raw", "raw state", "s"), + ), + ): + all_values = ( + hard_frame[hard_frame["method"] == method] + .groupby("family")["state_margin"] + .mean() + .reindex(family_order) + ) + trap_values = ( + trap[trap["method"] == method] + .groupby("family")["state_margin"] + .mean() + .reindex(family_order) + ) + axis.scatter( + all_values, + y + offset, + color=COLORS[method], + marker=marker, + s=30, + label=f"{label}: all", + zorder=3, + ) + for index, value in enumerate(trap_values): + if np.isfinite(value): + axis.scatter( + value, + y[index] + offset, + facecolors="none", + edgecolors=COLORS[method], + marker=marker, + s=52, + linewidths=1.0, + zorder=4, + ) + axis.axvline(0, color=COLORS["chance"], lw=0.8) + axis.set_yticks( + y, + [FAMILY_SHORT.get(family, family[:2].upper()) for family in family_order], + ) + axis.set( + xlabel="true-mechanism minus lexical-competitor cosine", + ylabel="mechanism family", + ) + axis.legend( + handles=[ + Line2D( + [0], [0], marker="o", color="none", + markerfacecolor=COLORS["jacobian"], + markeredgecolor=COLORS["jacobian"], + markersize=5, label="Jacobian", + ), + Line2D( + [0], [0], marker="s", color="none", + markerfacecolor=COLORS["raw"], + markeredgecolor=COLORS["raw"], + markersize=5, label="raw state", + ), + Line2D( + [0], [0], marker="o", color="none", + markerfacecolor="none", + markeredgecolor="#55585C", + markersize=6, label="lexical-trap subset", + ), + ], + frameon=False, + loc="upper right", + ncol=1, + handletextpad=0.4, + ) + panel_label(axis, "C") + + axis = axes[1, 1] + folds = np.asarray(data["folds"]) + order = np.asarray( + sorted( + range(len(families)), + key=lambda index: (str(families[index]), int(folds[index])), + ), + dtype=int, + ) + inverse = np.empty(len(order), dtype=int) + inverse[order] = np.arange(len(order)) + adjacency = np.zeros((len(order), len(order)), dtype=int) + for source, target in np.asarray(data["residual_edges"]): + value = 2 if families[source] == families[target] else 1 + adjacency[inverse[source], inverse[target]] = value + axis.imshow( + adjacency, + cmap=ListedColormap(["#FFFFFF", "#D8DCE0", COLORS["jacobian"]]), + vmin=0, + vmax=2, + interpolation="nearest", + aspect="equal", + ) + for boundary in range(5, len(order), 5): + axis.axhline(boundary - 0.5, color="#AEB3B8", lw=0.45) + axis.axvline(boundary - 0.5, color="#AEB3B8", lw=0.45) + ordered_families = [str(families[index]) for index in order] + family_names = sorted(set(ordered_families)) + centers = [ + np.mean( + [ + index + for index, value in enumerate(ordered_families) + if value == family + ] + ) + for family in family_names + ] + axis.set_xticks( + centers, + [FAMILY_SHORT.get(family, family[:2].upper()) for family in family_names], + rotation=90, + ) + axis.set_yticks( + centers, + [FAMILY_SHORT.get(family, family[:2].upper()) for family in family_names], + ) + axis.set( + xlabel="target prompt ordered by mechanism", + ylabel="source prompt ordered by mechanism", + ) + axis.tick_params(length=0) + panel_label(axis, "D") + axis.legend( + handles=[ + Line2D( + [0], [0], marker="s", color="none", + markerfacecolor=COLORS["jacobian"], + markeredgecolor="none", markersize=6, + label="same mechanism", + ), + Line2D( + [0], [0], marker="s", color="none", + markerfacecolor="#D8DCE0", + markeredgecolor="none", markersize=6, + label="other mechanism", + ), + ], + frameon=False, + ncol=2, + loc="upper center", + bbox_to_anchor=(0.5, 1.12), + columnspacing=0.9, + handletextpad=0.25, + ) + + fig.subplots_adjust( + left=0.09, + right=0.985, + top=0.985, + bottom=0.08, + hspace=0.30, + wspace=0.26, + ) + FIG.mkdir(parents=True, exist_ok=True) + for suffix in ("png", "pdf", "svg"): + kwargs = {"dpi": 450} if suffix == "png" else {} + fig.savefig( + FIG / f"mechanism-graph-evidence.{suffix}", + bbox_inches="tight", + **kwargs, + ) + plt.close(fig) + + +def plot_replication_and_concepts( + replication: Mapping[str, object], + rep_data: Mapping[str, object], + heldout_data: Mapping[str, object], +) -> None: + configure_matplotlib() + fig, axes = plt.subplots(2, 2, figsize=(10.5, 7.1)) + axis = axes[0, 0] + frame = rep_data["layer_frame"] + for method, label in ( + ("jacobian", "Jacobian"), + ("direct", "direct decoder"), + ("raw", "raw state"), + ): + rows = frame[frame["method"] == method] + axis.plot( + rows["depth_percent"], + rows["directed_same_outcome_precision"], + color=COLORS[method], + lw=1.9 if method == "jacobian" else 1.4, + label=label, + ) + axis.axhline(0.5, color=COLORS["chance"], lw=0.9, ls=(0, (3, 2))) + axis.axvspan(*BAND, color="#D9DEE3", alpha=0.25, zorder=-2) + axis.axvspan(*LATE_BAND, color="#BFC8D1", alpha=0.25, zorder=-2) + axis.set( + xlabel="layer depth (%)", + ylabel="within-mechanism edges with\nsame physical direction", + xlim=(0, 100), + ylim=(0.25, 0.91), + ) + axis.legend(frameon=False, loc="upper left") + panel_label(axis, "A") + + axis = axes[0, 1] + fixed = replication["fixed_graphs"] + methods = [ + "jacobian_band", + "jacobian_late", + "direct_late", + "raw_late", + "word_tfidf", + "char_tfidf", + "answer_order_only", + "numeric_direction_oracle", + ] + labels = [ + "Jacobian\n38–92%", + "Jacobian\n80–96%", + "direct\n80–96%", + "raw\n80–96%", + "word\nTF–IDF", + "character\nTF–IDF", + "answer\norder only", + "numeric\ndirection", + ] + values = [ + fixed[method]["directed_same_outcome_precision"] for method in methods + ] + bar_colors = [ + COLORS["jacobian"], + COLORS["jacobian"], + COLORS["direct"], + COLORS["raw"], + COLORS["word_tfidf"], + COLORS["char_tfidf"], + "#A58F78", + "#4A4A4A", + ] + positions = np.arange(len(methods)) + bars = axis.barh(positions, values, color=bar_colors, height=0.66) + bars[-1].set_hatch("//") + axis.axvline(0.5, color=COLORS["chance"], lw=0.9, ls=(0, (3, 2))) + axis.set_yticks(positions, labels) + axis.invert_yaxis() + axis.set( + xlabel="same-direction edge fraction", + xlim=(0.25, 1.06), + ) + for bar, value in zip(bars, values): + axis.text( + value + 0.012, + bar.get_y() + bar.get_height() / 2, + f"{value:.2f}", + ha="left", + va="center", + fontsize=7.2, + ) + panel_label(axis, "B") + + axis = axes[1, 0] + family_names = [ + "obstacle-spacing-orowan", + "porosity-modulus", + "pearlite-spacing-strength", + "dislocation-density-strength", + "particle-fraction-modulus", + "crosslink-density-modulus", + ] + family_labels = [ + "Orowan spacing", + "porosity–modulus", + "pearlite spacing", + "dislocation density", + "particle fraction", + "crosslink density", + ] + y = np.arange(len(family_names)) + for offset, (method, label, marker, color) in zip( + (-0.18, -0.06, 0.06, 0.18), + ( + ("jacobian_late", "Jacobian", "o", COLORS["jacobian"]), + ("raw_late", "raw state", "s", COLORS["raw"]), + ("word_tfidf", "word TF–IDF", "^", COLORS["word_tfidf"]), + ("char_tfidf", "character TF–IDF", "D", COLORS["char_tfidf"]), + ), + ): + method_values = [ + fixed[method]["family_precision"][family] + for family in family_names + ] + axis.scatter( + method_values, + y + offset, + marker=marker, + color=color, + s=28, + label=label, + zorder=3, + ) + axis.axvline(0.5, color=COLORS["chance"], lw=0.9, ls=(0, (3, 2))) + axis.set_yticks(y, family_labels) + axis.invert_yaxis() + axis.set( + xlabel="same-direction edge fraction", + ylabel="held-out mechanism", + xlim=(0.2, 1.05), + ) + axis.legend( + frameon=False, + loc="upper center", + bbox_to_anchor=(0.5, 1.16), + ncol=4, + columnspacing=0.9, + handletextpad=0.35, + ) + panel_label(axis, "C") + + axis = axes[1, 1] + edge_frame = rep_data["edge_frame"] + selected = edge_frame[edge_frame["method"] == "jacobian_late"] + variants = ["anchor", "physics_paraphrase", "lexical_counterfactual"] + short = ["anchor", "paraphrase", "counterfactual"] + outcome = np.full((3, 3), np.nan) + order_rate = np.full((3, 3), np.nan) + for row, source_variant in enumerate(variants): + for column, target_variant in enumerate(variants): + if source_variant == target_variant: + continue + values_for_pair = selected[ + (selected["source_variant"] == source_variant) + & (selected["target_variant"] == target_variant) + ] + outcome[row, column] = float( + values_for_pair["same_outcome"].mean() + ) + order_rate[row, column] = float( + values_for_pair["same_presentation_order"].mean() + ) + image = axis.imshow( + outcome, + cmap="Blues", + vmin=0.0, + vmax=1.0, + interpolation="nearest", + ) + axis.set_xticks(np.arange(3), short, rotation=20, ha="right") + axis.set_yticks(np.arange(3), short) + axis.set( + xlabel="target surface variant", + ylabel="source surface variant", + ) + for row in range(3): + for column in range(3): + if not np.isfinite(outcome[row, column]): + continue + color = "white" if outcome[row, column] >= 0.65 else "#202124" + axis.text( + column, + row, + f"{outcome[row, column]:.2f}\n(order {order_rate[row, column]:.2f})", + ha="center", + va="center", + color=color, + fontsize=7.2, + ) + colorbar = fig.colorbar(image, ax=axis, fraction=0.046, pad=0.04) + colorbar.set_label("same-direction edge fraction", fontsize=7.5) + colorbar.ax.tick_params(labelsize=7) + panel_label(axis, "D") + + fig.subplots_adjust( + left=0.11, + right=0.965, + top=0.985, + bottom=0.105, + hspace=0.39, + wspace=0.29, + ) + FIG.mkdir(parents=True, exist_ok=True) + for suffix in ("png", "pdf", "svg"): + kwargs = {"dpi": 450} if suffix == "png" else {} + fig.savefig( + FIG / f"relation-graph-falsification.{suffix}", + bbox_inches="tight", + **kwargs, + ) + plt.close(fig) + + +def plot_relation_ranking_robustness( + replication: Mapping[str, object], + rep_data: Mapping[str, object], +) -> None: + configure_matplotlib() + fig, axes = plt.subplots(1, 2, figsize=(10.4, 3.75)) + + axis = axes[0] + null = np.asarray(rep_data["exact_null_jacobian_band"], dtype=float) + observed = float( + replication["fixed_graphs"]["jacobian_band"][ + "directed_same_outcome_precision" + ] + ) + bins = np.linspace( + float(np.min(null)) - 0.01, + float(np.max(null)) + 0.01, + 22, + ) + axis.hist( + null, + bins=bins, + color="#CBD3D9", + edgecolor="white", + linewidth=0.45, + ) + axis.axvline( + float(np.mean(null)), + color=COLORS["chance"], + lw=1.2, + ls=(0, (3, 2)), + label=f"exact-null mean {np.mean(null):.2f}", + ) + axis.axvline( + observed, + color=COLORS["jacobian"], + lw=2.4, + label=f"observed {observed:.2f}", + ) + axis.set( + xlabel="same-direction edge fraction", + ylabel="balanced case assignments", + xlim=(max(0.0, float(np.min(null)) - 0.03), observed + 0.04), + ) + axis.legend(frameon=False, loc="upper right") + panel_label(axis, "A") + + axis = axes[1] + rankings = replication["complete_candidate_ranking"] + methods = [ + "jacobian_band", + "direct_band", + "raw_band", + "word_tfidf", + "char_tfidf", + "answer_order_only", + "numeric_direction_oracle", + ] + labels = [ + "Jacobian", + "direct decoder", + "raw state", + "word TF–IDF", + "character TF–IDF", + "answer order only", + "numeric direction", + ] + colors = [ + COLORS["jacobian"], + COLORS["direct"], + COLORS["raw"], + COLORS["word_tfidf"], + COLORS["char_tfidf"], + "#A58F78", + "#4A4A4A", + ] + values = np.asarray( + [rankings[method]["pairwise_auc"] for method in methods], + dtype=float, + ) + ranges = [ + rankings[method]["leave_one_family_out_range"]["pairwise_auc"] + for method in methods + ] + lower = values - np.asarray([row[0] for row in ranges]) + upper = np.asarray([row[1] for row in ranges]) - values + positions = np.arange(len(methods)) + bars = axis.barh( + positions, + values, + color=colors, + height=0.64, + xerr=np.vstack((lower, upper)), + error_kw={ + "ecolor": "#202124", + "elinewidth": 0.8, + "capsize": 2.0, + "capthick": 0.8, + }, + ) + bars[-1].set_hatch("//") + axis.axvline(0.5, color=COLORS["chance"], lw=1.0, ls=(0, (3, 2))) + axis.set_yticks(positions, labels) + axis.invert_yaxis() + axis.set( + xlabel="pairwise ranking AUC", + xlim=(0.2, 1.06), + ) + for bar, value in zip(bars, values): + axis.text( + max(0.22, value - 0.07), + bar.get_y() + bar.get_height() / 2, + f"{value:.2f}", + ha="center", + va="center", + color="white", + fontsize=7.2, + ) + panel_label(axis, "B") + + fig.subplots_adjust( + left=0.09, + right=0.985, + top=0.96, + bottom=0.17, + wspace=0.34, + ) + FIG.mkdir(parents=True, exist_ok=True) + for suffix in ("png", "pdf", "svg"): + kwargs = {"dpi": 450} if suffix == "png" else {} + fig.savefig( + FIG / f"relation-ranking-robustness.{suffix}", + bbox_inches="tight", + **kwargs, + ) + plt.close(fig) + + +def write_results( + heldout: Mapping[str, object], + replication: Mapping[str, object], +) -> None: + primary = heldout["primary_cross_phrasing_graph"] + residual = heldout["prompt_only_residualized_graph"] + hard = heldout["lexical_hard_negatives"] + stability = heldout["edge_stability"] + concept = heldout["target_free_concept_network"] + fixed = replication["fixed_graphs"] + rankings = replication["complete_candidate_ranking"] + diagnostics = replication["answer_order_and_variant_falsification"] + contrasts = replication["paired_family_contrasts"] + late_diagnostics = diagnostics["jacobian_late"] + variant_rows = late_diagnostics["by_ordered_variant_pair"] + counterfactual_rows = [ + row + for row in variant_rows + if "lexical_counterfactual" + in (row["source_variant"], row["target_variant"]) + ] + counterfactual_precision = float( + np.average( + [row["same_outcome_precision"] for row in counterfactual_rows], + weights=[row["n_edges"] for row in counterfactual_rows], + ) + ) + counterfactual_max_q = max( + float(row["bh_q_across_six_pairs"]) + for row in counterfactual_rows + ) + seed_jaccards = heldout["per_lens_seed_directed_edge_jaccard"] + jaccard_values = list(seed_jaccards.values()) + undirected = heldout["undirected_graph_sensitivity"]["jacobian"] + versus_lexical = contrasts["jacobian_band_minus_word_tfidf"] + lines = [ + "# Rigorous graph-topology results", + "", + "## Cross-phrasing mechanism graph", + "", + ( + "The frozen-band Jacobian graph placed " + f"**{primary['jacobian']['directed_precision']:.1%}** of its " + "cross-phrasing edges within the correct mechanism family " + f"(blocked permutation p={primary['jacobian']['permutation_p']:.4g}; " + "balanced chance 10%)." + ), + ( + "The matched raw-state value was " + f"{primary['raw']['directed_precision']:.1%}; word and character " + f"TF--IDF reached {primary['word_tfidf']['directed_precision']:.1%} " + f"and {primary['char_tfidf']['directed_precision']:.1%}." + ), + "", + "## Prompt-only residualization and lexical traps", + "", + ( + "After removing the part of pairwise state similarity predictable " + "from word TF--IDF, character TF--IDF, token overlap, prompt length, " + "and phrasing fold, the Jacobian graph retained " + f"**{residual['jacobian']['directed_precision']:.1%}** " + f"same-mechanism edges (p={residual['jacobian']['permutation_p']:.4g})." + ), + ( + "Across all frozen lexical hard negatives, the Jacobian family-mean " + "true-minus-competitor cosine margin was " + f"{hard['all']['jacobian']['mean_family_margin']:+.4f} " + f"(95% family bootstrap " + f"{hard['all']['jacobian']['family_bootstrap_95'][0]:+.4f} to " + f"{hard['all']['jacobian']['family_bootstrap_95'][1]:+.4f})." + ), + ( + f"There were {hard['lexical_traps']['jacobian']['n_comparisons']} " + "comparisons in which prompt-only TF--IDF preferred the wrong " + "mechanism; their Jacobian family-mean margin was " + f"{hard['lexical_traps']['jacobian']['mean_family_margin']:+.4f}." + ), + "", + "## Repeated graph assembly", + "", + ( + "An edge's selection frequency across all registered band layers " + "and all three lens fits identified same-mechanism pairs with " + f"ROC--AUC **{stability['jacobian']['same_family_auc']:.3f}** " + f"(p={stability['jacobian']['permutation_p']:.4g}); raw-state " + f"stability gave {stability['raw']['same_family_auc']:.3f}." + ), + ( + "The three independently fitted lenses selected directed edge sets " + f"with pairwise Jaccard overlap {min(jaccard_values):.3f}--" + f"{max(jaccard_values):.3f}. Treating edges as undirected gave " + f"{undirected['union']['homophily']:.1%} union homophily and " + f"{undirected['mutual']['homophily']:.1%} mutual-edge homophily." + ), + "", + "## Disjoint signed-relation graph", + "", + ( + "Within each of six new mechanisms and after excluding the same " + "material case, the frozen-band Jacobian graph connected prompts " + "with the same physically correct outcome on " + f"**{fixed['jacobian_band']['directed_same_outcome_precision']:.1%}** " + f"of edges (p={fixed['jacobian_band']['permutation_p']:.4g}; " + "chance 50%)." + ), + ( + "Under the stricter exact null that preserves all three surface " + "variants of each material case—including the registered " + "counterfactual sign reversal—and enumerates all 46,656 balanced " + "base-case assignments, the frozen-band result had null mean " + f"{fixed['jacobian_band']['case_preserving_exact_null_mean']:.1%} " + f"and p={fixed['jacobian_band']['case_preserving_exact_p']:.4g}." + ), + ( + "The corresponding direct, raw, word-TF--IDF, and character-TF--IDF " + "values were " + f"{fixed['direct_band']['directed_same_outcome_precision']:.1%}, " + f"{fixed['raw_band']['directed_same_outcome_precision']:.1%}, " + f"{fixed['word_tfidf']['directed_same_outcome_precision']:.1%}, and " + f"{fixed['char_tfidf']['directed_same_outcome_precision']:.1%}." + ), + ( + "The deliberately non-semantic answer-order-only baseline reached " + f"{fixed['answer_order_only']['directed_same_outcome_precision']:.1%}; " + "a numeric-direction oracle reached " + f"{fixed['numeric_direction_oracle']['directed_same_outcome_precision']:.1%}. " + "The oracle is an interpretive ceiling, not model evidence." + ), + ( + "For the four ordered edge types that cross into or out of the " + "lexical-counterfactual variant, the late Jacobian graph retained " + f"**{counterfactual_precision:.1%}** same-direction precision. " + "Every crossing was significant after BH correction across the " + f"six ordered pairs (largest q={counterfactual_max_q:.4g}). " + "Thus the aggregate is not explained by preserving the displayed " + "answer order across surface variants." + ), + ( + "At the family level, the frozen-band Jacobian graph exceeded word " + f"TF--IDF by {versus_lexical['mean_difference']:+.1%} " + f"(exact one-sided six-family sign-flip " + f"p={versus_lexical['one_sided_exact_family_sign_flip_p']:.4g}), " + "but did not outperform matched raw or direct states. This is " + "evidence for model-state geometry, not a Jacobian-specific gain." + ), + ( + "Using all three eligible candidates rather than only the winning " + "edge, the frozen-band Jacobian ranking achieved pairwise AUC " + f"**{rankings['jacobian_band']['pairwise_auc']:.3f}** " + f"(chance 0.5) and mean reciprocal rank " + f"{rankings['jacobian_band']['mean_reciprocal_rank']:.3f}. " + "Its leave-one-mechanism-out AUC range was " + f"{rankings['jacobian_band']['leave_one_family_out_range']['pairwise_auc'][0]:.3f}--" + f"{rankings['jacobian_band']['leave_one_family_out_range']['pairwise_auc'][1]:.3f}." + ), + "", + "## Target-free concept network", + "", + ( + f"{concept['fdr_significant_words']} of " + f"{concept['candidate_words']} archived target-free consensus words " + "survived blocked permutation testing with BH FDR 0.05, spanning " + f"{len(concept['families_with_fdr_words'])} of ten mechanism families." + ), + "", + "## Interpretation boundary", + "", + ( + "The graph evidence is descriptive representational evidence. It " + "does not establish causal use, consciousness, a literal hidden " + "narrative, or unrestricted materials understanding. The disjoint " + "cohort's earlier registered triplet-similarity endpoint failed; " + "the graph result cannot erase that negative result." + ), + "", + "## Reproduce", + "", + "```bash", + "python scripts/analyze_graph_topology_rigorous.py", + "```", + "", + ] + (OUT / "RESULTS.md").write_text("\n".join(lines), encoding="utf-8") + + +def main() -> None: + protocol = json.loads(PROTOCOL_PATH.read_text()) + amendment = json.loads(AMENDMENT_PATH.read_text()) + amendment_v2 = json.loads(AMENDMENT_V2_PATH.read_text()) + amendment_v2_correction = json.loads( + AMENDMENT_V2_CORRECTION_PATH.read_text() + ) + validate_inputs(protocol) + OUT.mkdir(parents=True, exist_ok=True) + rng = np.random.default_rng(SEED) + + heldout, heldout_data = analyze_heldout(protocol, rng) + replication, replication_data = analyze_replication(rng) + payload = { + "study_id": protocol["study_id"], + "status": protocol["status"], + "protocol_sha256": sha256(PROTOCOL_PATH), + "falsification_amendment": { + "study_id": amendment["study_id"], + "status": amendment["status"], + "sha256": sha256(AMENDMENT_PATH), + }, + "ranking_robustness_amendment": { + "study_id": amendment_v2["study_id"], + "status": amendment_v2["status"], + "sha256": sha256(AMENDMENT_V2_PATH), + }, + "ranking_robustness_correction": { + "study_id": amendment_v2_correction["study_id"], + "status": amendment_v2_correction["status"], + "sha256": sha256(AMENDMENT_V2_CORRECTION_PATH), + }, + "input_fingerprints": protocol["inputs"], + "heldout": heldout, + "disjoint_relation_replication": replication, + "guardrail": protocol["guardrail"], + } + (OUT / "statistics.json").write_text( + json.dumps(json_safe(payload), indent=2) + "\n", + encoding="utf-8", + ) + plot_mechanism_evidence(heldout, heldout_data) + plot_replication_and_concepts( + replication, + replication_data, + heldout_data, + ) + plot_relation_ranking_robustness(replication, replication_data) + write_results(heldout, replication) + execution = { + "script": str(Path(__file__).relative_to(ROOT)), + "script_sha256": sha256(Path(__file__)), + "protocol": str(PROTOCOL_PATH.relative_to(ROOT)), + "protocol_sha256": sha256(PROTOCOL_PATH), + "falsification_amendment": str(AMENDMENT_PATH.relative_to(ROOT)), + "falsification_amendment_sha256": sha256(AMENDMENT_PATH), + "ranking_robustness_amendment": str( + AMENDMENT_V2_PATH.relative_to(ROOT) + ), + "ranking_robustness_amendment_sha256": sha256(AMENDMENT_V2_PATH), + "ranking_robustness_correction": str( + AMENDMENT_V2_CORRECTION_PATH.relative_to(ROOT) + ), + "ranking_robustness_correction_sha256": sha256( + AMENDMENT_V2_CORRECTION_PATH + ), + "outputs": { + str(path.relative_to(ROOT)): sha256(path) + for path in sorted(OUT.rglob("*")) + if path.is_file() + and path.name + not in { + "execution_record.json", + "validation.json", + "VALIDATION.md", + } + }, + } + (OUT / "execution_record.json").write_text( + json.dumps(json_safe(execution), indent=2) + "\n", + encoding="utf-8", + ) + print(json.dumps(json_safe(payload), indent=2)) + + +if __name__ == "__main__": + main() diff --git a/jlens_materials/scripts/analyze_graph_topology_robustness.py b/jlens_materials/scripts/analyze_graph_topology_robustness.py new file mode 100644 index 0000000000000000000000000000000000000000..0807d7557d62d4c8bd10719f6dd80e6087271303 --- /dev/null +++ b/jlens_materials/scripts/analyze_graph_topology_robustness.py @@ -0,0 +1,117 @@ +#!/usr/bin/env python3 +"""Robustness checks for the exploratory materials graph analysis.""" + +from __future__ import annotations + +import json + +import networkx as nx +import numpy as np +from sklearn.metrics import adjusted_rand_score + +from analyze_graph_topology import ( + BAND, + NPZ_PATH, + OUT, + edge_homophily, + graph_metrics, + knn_graph, + normalize, +) + + +def edge_jaccard(first: nx.Graph, second: nx.Graph) -> float: + a = {tuple(sorted(edge)) for edge in first.edges()} + b = {tuple(sorted(edge)) for edge in second.edges()} + return float(len(a & b) / len(a | b)) + + +def main() -> None: + with np.load(NPZ_PATH, allow_pickle=False) as data: + transported = normalize(data["transported_states"].astype(float)) + families = data["families"].astype(str) + depths = data["depths"].astype(float) + band = (depths >= BAND[0]) & (depths <= BAND[1]) + + per_seed = [] + seed_graphs = [] + for seed in range(3): + metrics = [ + graph_metrics(transported[seed, :, layer], families, 4) + for layer in range(len(depths)) + ] + homophily = np.asarray([row["homophily"] for row in metrics]) + best = int(np.argmax(homophily)) + per_seed.append({ + "lens_seed": seed, + "band_mean_homophily": float(np.mean(homophily[band])), + "best_homophily": float(homophily[best]), + "best_depth": float(depths[best]), + }) + seed_graphs.append([row["graph"] for row in metrics]) + + primary_layer = int(np.argmin(np.abs(depths - 39.024391))) + edge_stability = {} + for first, second in ((0, 1), (0, 2), (1, 2)): + edge_stability[f"{first}-{second}"] = edge_jaccard( + seed_graphs[first][primary_layer], + seed_graphs[second][primary_layer], + ) + + mean_states = normalize(np.mean(transported, axis=0)) + mutual = {} + for k in (3, 4, 5, 6): + values = [] + edges = [] + for layer in range(len(depths)): + graph, _ = knn_graph(mean_states[:, layer], k, mutual=True) + values.append(edge_homophily(graph, families)) + edges.append(graph.number_of_edges()) + values = np.asarray(values) + best = int(np.nanargmax(values)) + mutual[str(k)] = { + "band_mean_homophily": float(np.nanmean(values[band])), + "best_homophily": float(values[best]), + "best_depth": float(depths[best]), + "band_mean_edge_count": float(np.mean(np.asarray(edges)[band])), + } + + community_layer = int(np.argmin(np.abs(depths - 43.902439))) + graph, _ = knn_graph(mean_states[:, community_layer], 4) + louvain = {} + for resolution in (0.5, 0.75, 1.0, 1.25, 1.5): + scores = [] + counts = [] + for seed in range(50): + communities = nx.algorithms.community.louvain_communities( + graph, + weight="weight", + resolution=resolution, + seed=seed, + ) + partition = np.full(len(families), -1, dtype=int) + for index, community in enumerate(communities): + partition[list(community)] = index + scores.append(float(adjusted_rand_score(families, partition))) + counts.append(len(communities)) + louvain[str(resolution)] = { + "ari_median": float(np.median(scores)), + "ari_min": float(np.min(scores)), + "ari_max": float(np.max(scores)), + "community_count_median": float(np.median(counts)), + } + + payload = { + "status": "post hoc robustness checks after inspecting the primary graph analysis", + "per_lens_seed": per_seed, + "primary_layer_edge_jaccard_between_lens_fits": edge_stability, + "mutual_knn_sensitivity": mutual, + "louvain_seed_and_resolution_sensitivity": louvain, + "interpretation": "The edge topology is highly reproducible across lens fits and persists under mutual-kNN construction. Exact Louvain ARI depends on resolution and random initialization, so the community score should be presented as a sensitivity range rather than a unique value.", + } + (OUT / "robustness_checks.json").write_text(json.dumps(payload, indent=2) + "\n") + print(json.dumps(payload, indent=2)) + + +if __name__ == "__main__": + main() diff --git a/jlens_materials/scripts/analyze_lexical_adversarial_representation.py b/jlens_materials/scripts/analyze_lexical_adversarial_representation.py new file mode 100644 index 0000000000000000000000000000000000000000..5d5419890874f1ad593c2607d3b3c9a06d8ef52b --- /dev/null +++ b/jlens_materials/scripts/analyze_lexical_adversarial_representation.py @@ -0,0 +1,797 @@ +#!/usr/bin/env python3 +"""Analyze the frozen lexical-adversarial materials representation study.""" + +from __future__ import annotations + +import csv +import itertools +import json +import math +import re +from collections import defaultdict +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] +OUT = 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 sha256(path: Path) -> str: + import hashlib + + 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 dump_json(path: Path, payload: dict) -> None: + path.write_text(json.dumps(payload, indent=2) + "\n") + + +def centered_rows(values: np.ndarray) -> np.ndarray: + values = np.asarray(values, dtype=np.float32) + values = values - values.mean(axis=0, keepdims=True) + norms = np.linalg.norm(values, axis=1, keepdims=True) + return values / np.maximum(norms, 1e-12) + + +def uncentered_rows(values: np.ndarray) -> np.ndarray: + values = np.asarray(values, dtype=np.float32) + norms = np.linalg.norm(values, axis=1, keepdims=True) + return values / np.maximum(norms, 1e-12) + + +def similarity_rows( + methods: dict[str, np.ndarray], + layers: np.ndarray, + triplets: list[dict], + prompt_index: dict[str, int], + *, + centered: bool, + accumulator_dtype: type[np.floating] = np.float32, +) -> pd.DataFrame: + output = [] + normalizer = centered_rows if centered else uncentered_rows + for method, array in methods.items(): + for layer_index, layer in enumerate(layers): + values = normalizer(array[:, layer_index]) + for triplet in triplets: + ids = triplet["prompt_ids"] + anchor = values[prompt_index[ids["anchor"]]] + paraphrase = values[prompt_index[ids["physics_paraphrase"]]] + counter = values[prompt_index[ids["lexical_counterfactual"]]] + # The discovery artifact was frozen with float32 accumulation. + # Later cohorts used float64 after an implementation-only + # numerical-stability amendment. The explicit argument lets + # both historical artifacts remain exactly reproducible. + ap = float(np.dot( + anchor.astype(accumulator_dtype, copy=False), + paraphrase.astype(accumulator_dtype, copy=False), + )) + ac = float(np.dot( + anchor.astype(accumulator_dtype, copy=False), + counter.astype(accumulator_dtype, copy=False), + )) + output.append({ + "method": method, + "centered": centered, + "layer": int(layer), + "depth_percent": 100.0 * int(layer) / 41.0, + "triplet_id": triplet["triplet_id"], + "family_id": triplet["family_id"], + "anchor_paraphrase_cosine": ap, + "anchor_counterfactual_cosine": ac, + "physics_minus_lexical_margin": ap - ac, + }) + return pd.DataFrame(output) + + +def two_stage_bootstrap( + frame: pd.DataFrame, + value: str, + *, + n_resamples: int, + seed: int, +) -> dict: + rng = np.random.default_rng(seed) + grouped = { + family: group[value].to_numpy(dtype=float) + for family, group in frame.groupby("family_id", sort=True) + } + families = sorted(grouped) + family_means = np.asarray([grouped[family].mean() for family in families]) + observed = float(family_means.mean()) + draws = np.empty(n_resamples, dtype=float) + for iteration in range(n_resamples): + selected = rng.integers(0, len(families), size=len(families)) + selected_means = [] + for family_index in selected: + values = grouped[families[family_index]] + sample = values[rng.integers(0, len(values), size=len(values))] + selected_means.append(float(sample.mean())) + draws[iteration] = float(np.mean(selected_means)) + return { + "mean": observed, + "ci95": [ + float(np.quantile(draws, 0.025)), + float(np.quantile(draws, 0.975)), + ], + "bootstrap_probability_above_zero": float(np.mean(draws > 0)), + "n_resamples": n_resamples, + "seed": seed, + } + + +def layer_summary_with_ci( + frame: pd.DataFrame, + value: str, + methods: list[str], + *, + n_resamples: int = 5000, +) -> pd.DataFrame: + output = [] + for method in methods: + for layer, group in frame[frame["method"] == method].groupby("layer"): + result = two_stage_bootstrap( + group, value, n_resamples=n_resamples, + seed=20260717 + int(layer) + 1000 * methods.index(method), + ) + output.append({ + "method": method, + "layer": int(layer), + "depth_percent": float(group["depth_percent"].iloc[0]), + **result, + }) + return pd.DataFrame(output) + + +def exact_family_sign_flip(values: np.ndarray) -> float: + observed = abs(float(np.mean(values))) + null = [ + abs(float(np.mean(values * np.asarray(signs)))) + for signs in itertools.product([-1.0, 1.0], repeat=len(values)) + ] + return float(sum(value >= observed - 1e-15 for value in null) / len(null)) + + +def behavior_summary(raw: dict, manifest: dict) -> tuple[pd.DataFrame, dict]: + clean = pd.DataFrame(raw["clean_rows"]) + prompt_meta = pd.DataFrame(manifest["prompts"])[[ + "prompt_id", "presentation_order", "outcome_positive", "outcome_negative" + ]] + clean = clean.merge(prompt_meta, on="prompt_id", how="left", validate="one_to_one") + triplet_consistency = [] + for triplet_id, group in clean.groupby("triplet_id"): + by_variant = group.set_index("variant") + anchor = by_variant.loc["anchor"] + paraphrase = by_variant.loc["physics_paraphrase"] + counter = by_variant.loc["lexical_counterfactual"] + triplet_consistency.append({ + "triplet_id": triplet_id, + "family_id": str(anchor["family_id"]), + "all_three_registered_pair_correct": bool( + anchor["registered_pair_correct"] + and paraphrase["registered_pair_correct"] + and counter["registered_pair_correct"] + ), + "anchor_paraphrase_same_prediction": ( + anchor["predicted_registered_outcome"] + == paraphrase["predicted_registered_outcome"] + ), + "counterfactual_reverses_prediction": ( + anchor["predicted_registered_outcome"] + != counter["predicted_registered_outcome"] + ), + "full_scientific_triplet_consistency": bool( + anchor["registered_pair_correct"] + and paraphrase["registered_pair_correct"] + and counter["registered_pair_correct"] + and anchor["predicted_registered_outcome"] + == paraphrase["predicted_registered_outcome"] + and anchor["predicted_registered_outcome"] + != counter["predicted_registered_outcome"] + ), + }) + triplet_frame = pd.DataFrame(triplet_consistency) + summary = { + "registered_pair_accuracy": float(clean["registered_pair_correct"].mean()), + "global_top_registered_answer_rate": float( + clean["global_top_is_registered_answer"].mean() + ), + "by_variant_accuracy": { + variant: float(group["registered_pair_correct"].mean()) + for variant, group in clean.groupby("variant") + }, + "triplets_all_three_correct": int( + triplet_frame["all_three_registered_pair_correct"].sum() + ), + "triplets_full_scientific_consistency": int( + triplet_frame["full_scientific_triplet_consistency"].sum() + ), + "n_triplets": len(triplet_frame), + "family_accuracy": { + family: float(group["registered_pair_correct"].mean()) + for family, group in clean.groupby("family_id") + }, + } + return clean, {**summary, "triplet_rows": triplet_consistency} + + +def prompt_words(text: str) -> set[str]: + return set(re.findall(r"[a-z][a-z'-]{2,}", text.lower())) + + +def keep_candidate(token: str, removed_words: set[str], stopwords: set[str]) -> bool: + token = token.lower().strip() + if not re.fullmatch(r"[a-z][a-z-]{2,}", token): + return False + if token in stopwords or token in removed_words: + return False + if any( + min(len(token), len(word)) >= 5 + and (token.startswith(word) or word.startswith(token)) + for word in removed_words + ): + return False + return True + + +def target_free_analysis( + raw: dict, + manifest: dict, + protocol: dict, +) -> tuple[pd.DataFrame, pd.DataFrame]: + prompts = {row["prompt_id"]: row for row in manifest["prompts"]} + rows = { + (row["prompt_id"], row["method"], int(row["layer"])): row + for row in raw["target_free_top_tokens"] + } + output = [] + examples = [] + display_k = int(protocol["target_free_display_k"]) + stopwords = set(protocol["frozen_stopwords"]) + for triplet_index, triplet in enumerate(manifest["triplets"]): + ids = triplet["prompt_ids"] + removed = set() + for prompt_id in ids.values(): + removed |= prompt_words(prompts[prompt_id]["user"]) + for method in ["direct", "jacobian_ensemble"]: + for layer in protocol["target_free_layers"]: + candidates = {} + for variant, prompt_id in ids.items(): + source = rows[(prompt_id, method, int(layer))] + filtered = [] + seen = set() + for token in source["tokens"]: + canonical = token.lower().strip() + if canonical in seen: + continue + if keep_candidate(canonical, removed, stopwords): + seen.add(canonical) + filtered.append(canonical) + if len(filtered) == display_k: + break + candidates[variant] = filtered + anchor = set(candidates["anchor"]) + paraphrase = set(candidates["physics_paraphrase"]) + counter = set(candidates["lexical_counterfactual"]) + ap_union = anchor | paraphrase + ac_union = anchor | counter + ap = len(anchor & paraphrase) / len(ap_union) if ap_union else 0.0 + ac = len(anchor & counter) / len(ac_union) if ac_union else 0.0 + output.append({ + "triplet_id": triplet["triplet_id"], + "family_id": triplet["family_id"], + "method": method, + "layer": int(layer), + "depth_percent": 100.0 * int(layer) / 41.0, + "anchor_paraphrase_jaccard": ap, + "anchor_counterfactual_jaccard": ac, + "physics_minus_lexical_jaccard": ap - ac, + "anchor_n": len(anchor), + "paraphrase_n": len(paraphrase), + "counterfactual_n": len(counter), + }) + if triplet_index % 4 == 0 and int(layer) == 28: + for variant in [ + "anchor", "physics_paraphrase", "lexical_counterfactual" + ]: + examples.append({ + "triplet_id": triplet["triplet_id"], + "family_id": triplet["family_id"], + "method": method, + "layer": int(layer), + "variant": variant, + "candidates": " | ".join(candidates[variant]), + }) + return pd.DataFrame(output), pd.DataFrame(examples) + + +def make_figure( + layer_frame: pd.DataFrame, + band_frame: pd.DataFrame, + family_frame: pd.DataFrame, + lexical_rows: pd.DataFrame, + target_free_frame: pd.DataFrame, +) -> 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 = layer_summary_with_ci( + layer_frame, + "physics_minus_lexical_margin", + ["jacobian_ensemble", "direct"], + ) + 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(38, 92, color="#999999", 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") + + family_order = list(dict.fromkeys(family_frame["family_id"])) + x = np.arange(len(family_order)) + offsets = {"jacobian_ensemble": -0.14, "direct": 0.14} + for method in ["jacobian_ensemble", "direct"]: + subset = ( + family_frame[family_frame["method"] == method] + .set_index("family_id") + .loc[family_order] + ) + ax_b.scatter( + x + offsets[method], + subset["physics_minus_lexical_margin"], + s=38, + color=colors[method], + marker="o" if method == "jacobian_ensemble" else "^", + label=labels[method], + ) + ax_b.axhline(0, color="#666666", linewidth=0.8) + short_family = { + "grain-size-strength": "grain\nsize", + "crack-size-fracture": "crack\nsize", + "temperature-diffusion": "temperature–\ndiffusion", + "fiber-angle-stiffness": "fiber\nangle", + "fiber-fraction-stiffness": "fiber\nfraction", + "martensite-hardness": "martensite\nfraction", + } + ax_b.set_xticks(x, [short_family[value] for value in family_order]) + ax_b.set_ylabel("Band-mean physics-equivalence margin") + ax_b.text(0.02, 0.97, "B", transform=ax_b.transAxes, va="top", fontweight="bold") + + jacobian_band = band_frame[ + band_frame["method"] == "jacobian_ensemble" + ].set_index("triplet_id") + lexical = lexical_rows.set_index("triplet_id") + family_codes = {family: index for index, family in enumerate(family_order)} + scatter = ax_c.scatter( + lexical.loc[jacobian_band.index, "word_tfidf_margin"], + jacobian_band["physics_minus_lexical_margin"], + c=[family_codes[value] for value in jacobian_band["family_id"]], + cmap="tab10", + s=34, + edgecolor="white", + linewidth=0.4, + ) + ax_c.axhline(0, color="#666666", linewidth=0.8) + ax_c.axvline(0, color="#666666", linewidth=0.8) + ax_c.set_xlabel("Word TF–IDF margin") + ax_c.set_ylabel("Jacobian band margin") + ax_c.text(0.02, 0.97, "C", transform=ax_c.transAxes, va="top", fontweight="bold") + + target_summary = layer_summary_with_ci( + target_free_frame.rename( + columns={"physics_minus_lexical_jaccard": "margin"} + ), + "margin", + ["jacobian_ensemble", "direct"], + ) + for method in ["jacobian_ensemble", "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="o" if method == "jacobian_ensemble" else "^", + linewidth=1.4, + capsize=2, + label=labels[method], + ) + ax_d.axhline(0, color="#666666", linewidth=0.8) + ax_d.axvspan(38, 92, color="#999999", 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"lexical-adversarial-representation.{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"] != sha256(PROTOCOL_PATH): + raise RuntimeError("raw output does not match the frozen protocol") + if protocol["inputs"]["prompt_manifest_sha256"] != sha256(MANIFEST_PATH): + raise RuntimeError("prompt manifest fingerprint mismatch") + arrays = np.load(STATES_PATH) + prompt_ids = [str(value) for value in arrays["prompt_ids"]] + layers = arrays["layers"].astype(int) + expected_prompt_ids = [row["prompt_id"] for row in manifest["prompts"]] + if prompt_ids != expected_prompt_ids: + raise RuntimeError("state prompt order does not match manifest") + prompt_index = {prompt_id: index for index, prompt_id in enumerate(prompt_ids)} + 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 = similarity_rows( + methods, layers, manifest["triplets"], prompt_index, centered=True + ) + uncentered = similarity_rows( + methods, layers, manifest["triplets"], prompt_index, centered=False + ) + layer_frame = pd.concat([centered, uncentered], ignore_index=True) + layer_frame.to_csv(OUT / "layer_similarity_margins.csv", index=False) + + band = centered[ + centered["depth_percent"].between( + protocol["registered_band_percent"][0], + protocol["registered_band_percent"][1], + ) + ] + band_frame = ( + band.groupby(["method", "triplet_id", "family_id"], as_index=False)[ + "physics_minus_lexical_margin" + ] + .mean() + ) + band_frame.to_csv(OUT / "band_triplet_margins.csv", index=False) + family_frame = ( + band_frame.groupby(["method", "family_id"], as_index=False)[ + "physics_minus_lexical_margin" + ] + .mean() + ) + family_frame.to_csv(OUT / "family_band_margins.csv", index=False) + + bootstrap = { + method: two_stage_bootstrap( + band_frame[band_frame["method"] == method], + "physics_minus_lexical_margin", + n_resamples=30000, + seed=20260717 + method_index, + ) + for method_index, method in enumerate(methods) + } + pivot = band_frame.pivot( + index=["triplet_id", "family_id"], + columns="method", + values="physics_minus_lexical_margin", + ).reset_index() + pivot["jacobian_minus_direct"] = ( + pivot["jacobian_ensemble"] - pivot["direct"] + ) + contrast = two_stage_bootstrap( + pivot, + "jacobian_minus_direct", + n_resamples=30000, + seed=20260817, + ) + contrast_family = ( + pivot.groupby("family_id")["jacobian_minus_direct"].mean().to_numpy() + ) + contrast["exact_family_sign_flip_p"] = exact_family_sign_flip(contrast_family) + + lexical_protocol = protocol["lexical_adversarial_preflight"] + lexical_by_triplet = pd.DataFrame({ + "triplet_id": [ + row["triplet_id"] for row in lexical_protocol["word_tfidf"]["rows"] + ], + "word_tfidf_margin": [ + row["physics_minus_lexical_margin"] + for row in lexical_protocol["word_tfidf"]["rows"] + ], + "character_tfidf_margin": [ + row["physics_minus_lexical_margin"] + for row in lexical_protocol["character_tfidf"]["rows"] + ], + }) + lexical_by_triplet.to_csv(OUT / "lexical_baseline_margins.csv", index=False) + + clean_frame, behavior = 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, examples = target_free_analysis(raw, manifest, protocol) + target_free.to_csv(OUT / "target_free_jaccard_margins.csv", index=False) + examples.to_csv(OUT / "target_free_candidate_examples.csv", index=False) + selected_band_layers = [ + int(layer) for layer in protocol["target_free_layers"] + if protocol["registered_band_percent"][0] + <= 100.0 * int(layer) / 41.0 + <= protocol["registered_band_percent"][1] + ] + target_band = ( + target_free[target_free["layer"].isin(selected_band_layers)] + .groupby(["method", "triplet_id", "family_id"], as_index=False)[ + "physics_minus_lexical_jaccard" + ] + .mean() + ) + target_stats = { + method: two_stage_bootstrap( + target_band[target_band["method"] == method], + "physics_minus_lexical_jaccard", + n_resamples=30000, + seed=20260917 + method_index, + ) + for method_index, method in enumerate(["jacobian_ensemble", "direct"]) + } + + primary_rows = band_frame[ + band_frame["method"] == "jacobian_ensemble" + ] + primary_family = family_frame[ + family_frame["method"] == "jacobian_ensemble" + ] + primary_positive_triplets = int( + (primary_rows["physics_minus_lexical_margin"] > 0).sum() + ) + primary_positive_families = int( + (primary_family["physics_minus_lexical_margin"] > 0).sum() + ) + decision = { + "primary_ci_above_zero": bootstrap["jacobian_ensemble"]["ci95"][0] > 0, + "positive_triplets": primary_positive_triplets, + "triplet_breadth_pass": primary_positive_triplets >= 18, + "positive_families": primary_positive_families, + "family_breadth_pass": primary_positive_families >= 5, + } + decision["frozen_success_rule_pass"] = all([ + decision["primary_ci_above_zero"], + decision["triplet_breadth_pass"], + decision["family_breadth_pass"], + ]) + + statistics = { + "study_id": protocol["study_id"], + "status": protocol["status"], + "dimensions": raw["dimensions"], + "provenance": { + "protocol_sha256": sha256(PROTOCOL_PATH), + "manifest_sha256": sha256(MANIFEST_PATH), + "raw_sha256": sha256(RAW_PATH), + "representations_sha256": sha256(STATES_PATH), + }, + "lexical_preflight": { + key: { + field: value for field, value in result.items() if field != "rows" + } + for key, result in lexical_protocol.items() + }, + "behavior": { + key: value for key, value in behavior.items() if key != "triplet_rows" + }, + "centered_band_bootstrap": bootstrap, + "jacobian_minus_direct": contrast, + "primary_breadth": { + "positive_triplets": primary_positive_triplets, + "n_triplets": len(primary_rows), + "exact_two_sided_sign_p": float(stats.binomtest( + primary_positive_triplets, + len(primary_rows), + p=0.5, + alternative="two-sided", + ).pvalue), + "positive_families": primary_positive_families, + "n_families": len(primary_family), + }, + "family_band_margins": { + method: { + row["family_id"]: float(row["physics_minus_lexical_margin"]) + for _, row in family_frame[family_frame["method"] == method].iterrows() + } + for method in methods + }, + "target_free_selected_layers": selected_band_layers, + "target_free_jaccard_bootstrap": target_stats, + "decision": decision, + "guardrails": protocol["guardrails"], + } + dump_json(OUT / "statistics.json", statistics) + make_figure( + centered[centered["method"].isin(["jacobian_ensemble", "direct"])], + band_frame, + family_frame, + lexical_by_triplet, + target_free, + ) + + primary = bootstrap["jacobian_ensemble"] + direct = bootstrap["direct"] + raw_result = bootstrap["raw_residual"] + target_j = target_stats["jacobian_ensemble"] + target_d = target_stats["direct"] + lines = [ + "# Prospective lexical-adversarial representation results", + "", + "## Frozen question", + "", + ( + "Does Gemma represent a differently worded but physically equivalent " + "materials description as closer than a near-verbatim prompt in " + "which only the physical relation is reversed?" + ), + "", + "## Design check", + "", + ( + f"Word and character TF-IDF selected the lexical counterfactual in " + f"all 24 triplets. Their mean physics-minus-lexical margins were " + f"{lexical_protocol['word_tfidf']['mean_physics_minus_lexical_margin']:+.3f} " + f"and {lexical_protocol['character_tfidf']['mean_physics_minus_lexical_margin']:+.3f}. " + "Negative is the intended lexical trap." + ), + "", + "## Primary result", + "", + ( + f"The frozen centered Jacobian ensemble band margin was " + f"**{primary['mean']:+.4f}** (two-stage 95% CI " + f"{primary['ci95'][0]:+.4f} to {primary['ci95'][1]:+.4f}). " + f"{primary_positive_triplets}/24 triplets and " + f"{primary_positive_families}/6 family means were positive. " + f"Frozen decision: **{'PASS' if decision['frozen_success_rule_pass'] else 'FAIL'}**." + ), + "", + ( + f"The matched direct-decoder-basis result was {direct['mean']:+.4f} " + f"({direct['ci95'][0]:+.4f} to {direct['ci95'][1]:+.4f}); raw " + f"residual states gave {raw_result['mean']:+.4f} " + f"({raw_result['ci95'][0]:+.4f} to {raw_result['ci95'][1]:+.4f}). " + f"The paired Jacobian-minus-direct contrast was " + f"{contrast['mean']:+.4f} ({contrast['ci95'][0]:+.4f} to " + f"{contrast['ci95'][1]:+.4f}; exact family sign-flip " + f"p={contrast['exact_family_sign_flip_p']:.4f})." + ), + "", + "## Behavioral audit", + "", + ( + f"Registered-pair answer accuracy was " + f"{behavior['registered_pair_accuracy']:.1%}; the registered answer " + f"was the global top token in " + f"{behavior['global_top_registered_answer_rate']:.1%} of prompts. " + f"{behavior['triplets_full_scientific_consistency']}/24 triplets " + "gave the same correct decision for anchor and paraphrase and the " + "opposite correct decision for the lexical counterfactual." + ), + "", + "## Target-free vocabulary", + "", + ( + f"Across frozen in-band decoding layers, the target-free top-word " + f"Jaccard margin was {target_j['mean']:+.4f} " + f"({target_j['ci95'][0]:+.4f} to {target_j['ci95'][1]:+.4f}) for " + f"the Jacobian ensemble and {target_d['mean']:+.4f} " + f"({target_d['ci95'][0]:+.4f} to {target_d['ci95'][1]:+.4f}) for " + "direct decoding. Positive means that emergent word neighborhoods " + "follow physical equivalence more than near-verbatim wording." + ), + "", + "## Interpretation boundary", + "", + ( + "A positive similarity margin shows representational organization " + "under a deliberately adversarial lexical test. It is not a causal " + "intervention, a literal hidden chain of thought, or evidence that " + "the model can solve arbitrary materials problems. All prompts, " + "families, layers, clean errors, and target-free candidates are retained." + ), + "", + "## Files", + "", + "- `prompt_manifest.json`: all 72 exact prompts.", + "- `protocol.json` and `PROTOCOL.md`: frozen endpoints and fingerprints.", + "- `raw.json`: clean answers and 720 target-free top-token records.", + "- `representations.npz`: raw, direct-basis, and three-fit Jacobian states.", + "- `layer_similarity_margins.csv`: every triplet-by-layer similarity.", + "- `band_triplet_margins.csv`: primary independent-unit table.", + "- `family_band_margins.csv`: family-level summary.", + "- `target_free_jaccard_margins.csv`: target-free neighborhood comparison.", + "- `target_free_candidate_examples.csv`: fixed first-case examples.", + "- `statistics.json`: complete numerical decision record.", + "- `figures/lexical-adversarial-representation.{png,pdf}`: candidate figure.", + "", + ] + (OUT / "RESULTS.md").write_text("\n".join(lines)) + (OUT / "README.md").write_text( + "\n".join([ + "# Lexical-adversarial materials representation study", + "", + ( + "Prospectively frozen candidate experiment. It is not included " + "in the paper." + ), + "", + "## Reproduce", + "", + "```bash", + "python scripts/run_lexical_adversarial_representation.py --device mps --dtype bfloat16", + "python scripts/analyze_lexical_adversarial_representation.py", + "```", + "", + "Read `PROTOCOL.md` before `RESULTS.md`. The protocol and exact prompt " + "manifest were checksum-locked before any model forward pass.", + "", + ]) + ) + print(json.dumps(statistics, indent=2)) + + +if __name__ == "__main__": + main() diff --git a/jlens_materials/scripts/analyze_mechanism_name_v1.py b/jlens_materials/scripts/analyze_mechanism_name_v1.py new file mode 100644 index 0000000000000000000000000000000000000000..8b24da0abb9eecbf50d88a03f1a969226e0d9269 --- /dev/null +++ b/jlens_materials/scripts/analyze_mechanism_name_v1.py @@ -0,0 +1,305 @@ +#!/usr/bin/env python3 +"""Analyze and visualize the frozen mechanism-versus-eponym experiment.""" + +from __future__ import annotations + +import json +import math +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] +RUN = ROOT / "runs" / "gemma4-materials-mechanism-name-v1.json" +OUT_JSON = ROOT / "experiments" / "gemma4-materials-mechanism-name-v1_statistics.json" +OUT_MD = ROOT / "experiments" / "gemma4-materials-mechanism-name-v1_results.md" +APPENDIX_MD = ROOT / "experiments" / "gemma4-materials-mechanism-name-v1_appendix.md" +FIG_DIR = ROOT / "figures" / "gemma4-materials-mechanism-name-v1" +SEED = 20260710 +N_BOOT = 20_000 + + +PAIRS = { + "grain-size-strength": ("boundary", "Hall"), + "flaw-controlled-failure": ("crack", "Griffith"), + "resolved-slip-threshold": ("dislocation", "Schmid"), + "lattice-diffusion-creep": ("vacancy", "Herring"), + "diffusionless-lattice-change": ("tetragonal", "Bain"), +} + +FAMILY_LABELS = { + "grain-size-strength": "Grain-size strengthening", + "flaw-controlled-failure": "Flaw-controlled failure", + "resolved-slip-threshold": "Resolved slip threshold", + "lattice-diffusion-creep": "Lattice-diffusion creep", + "diffusionless-lattice-change": "Diffusionless lattice change", +} + + +def bootstrap(values: list[float], seed: int = SEED) -> dict: + x = np.asarray(values, dtype=float) + rng = np.random.default_rng(seed) + means = rng.choice(x, size=(N_BOOT, len(x)), replace=True).mean(axis=1) + return { + "n": len(x), + "mean": float(x.mean()), + "low": float(np.quantile(means, 0.025)), + "high": float(np.quantile(means, 0.975)), + } + + +def directional_test(values: list[float]) -> dict: + x = np.asarray(values, dtype=float) + try: + statistic, pvalue = wilcoxon(x, alternative="less", zero_method="wilcox") + statistic, pvalue = float(statistic), float(pvalue) + except ValueError: + statistic, pvalue = None, None + return { + "effect": bootstrap(values), + "mechanism_wins": int(np.sum(x < 0)), + "ties": int(np.sum(np.abs(x) < 1e-12)), + "name_wins": int(np.sum(x > 0)), + "wilcoxon_statistic": statistic, + "wilcoxon_p_one_sided": pvalue, + } + + +def analyze(run: dict) -> tuple[dict, list[dict]]: + rows = [] + for record in run["prompts"]: + family = record.get("category") + if not record.get("valid_for_metrics") or family not in PAIRS: + continue + mechanism, name = PAIRS[family] + emergence = {item["label"]: item for item in record["emergence"]} + mech = emergence[mechanism] + eponym = emergence[name] + row = { + "slug": record["slug"], + "family": family, + "family_label": FAMILY_LABELS[family], + "prompt": record["prompt_text"], + "completion": record.get("generated_completion", ""), + "mechanism": mechanism, + "name": name, + "j_mechanism_rank": int(mech["best_rank"]) + 1, + "j_name_rank": int(eponym["best_rank"]) + 1, + "logit_mechanism_rank": int(mech["logit_lens_best_rank"]) + 1, + "logit_name_rank": int(eponym["logit_lens_best_rank"]) + 1, + "j_contrast": math.log10(int(mech["best_rank"]) + 1) + - math.log10(int(eponym["best_rank"]) + 1), + "logit_contrast": math.log10(int(mech["logit_lens_best_rank"]) + 1) + - math.log10(int(eponym["logit_lens_best_rank"]) + 1), + } + row["j_minus_logit_contrast"] = row["j_contrast"] - row["logit_contrast"] + rows.append(row) + + by_family: dict[str, list[dict]] = defaultdict(list) + for row in rows: + by_family[row["family"]].append(row) + stats = { + "n_valid": len(rows), + "n_total": len(run["prompts"]), + "contrast_definition": "log10(mechanism rank) - log10(eponym rank); negative favors mechanism", + "jacobian_lens": directional_test([row["j_contrast"] for row in rows]), + "logit_lens": directional_test([row["logit_contrast"] for row in rows]), + "j_minus_logit": directional_test([row["j_minus_logit_contrast"] for row in rows]), + "families": {}, + } + for family, family_rows in sorted(by_family.items()): + stats["families"][family] = { + "label": FAMILY_LABELS[family], + "n": len(family_rows), + "mechanism": PAIRS[family][0], + "name": PAIRS[family][1], + "jacobian_lens": directional_test([row["j_contrast"] for row in family_rows]), + "logit_lens": directional_test([row["logit_contrast"] for row in family_rows]), + } + return stats, rows + + +def plot_summary(stats: dict, rows: list[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(1, 2, figsize=(12.6, 4.8), constrained_layout=True) + + ax = axes[0] + j = np.asarray([row["j_contrast"] for row in rows]) + l = np.asarray([row["logit_contrast"] for row in rows]) + for index, (a, b) in enumerate(zip(j, l)): + ax.plot([0, 1], [a, b], color="#b7b7b7", alpha=0.38, lw=0.7) + ax.scatter(np.zeros(len(j)), j, color="#c1553b", s=24, alpha=0.75, label="J-lens items") + ax.scatter(np.ones(len(l)), l, color="#2b7a8c", s=24, alpha=0.75, label="logit-lens items") + ax.scatter([0, 1], [j.mean(), l.mean()], marker="D", s=80, + color=["#8f2f27", "#185c6b"], edgecolor="white", zorder=5) + ax.axhline(0, color="#555555", lw=1) + ax.set_xticks([0, 1], ["Jacobian lens", "Logit lens"]) + ax.set_ylabel("log10 rank(mechanism) - log10 rank(name)") + ax.set_title("A Physical mechanism versus textbook name", loc="left", fontweight="bold") + ax.text(0.02, 0.02, "below zero = physical carrier is easier to read", + transform=ax.transAxes, fontsize=9, color="#555555") + ax.grid(True, axis="y", alpha=0.18) + + ax = axes[1] + families = list(stats["families"]) + y = np.arange(len(families)) + j_mean = [stats["families"][f]["jacobian_lens"]["effect"]["mean"] for f in families] + j_low = [stats["families"][f]["jacobian_lens"]["effect"]["low"] for f in families] + j_high = [stats["families"][f]["jacobian_lens"]["effect"]["high"] for f in families] + l_mean = [stats["families"][f]["logit_lens"]["effect"]["mean"] for f in families] + ax.errorbar(j_mean, y - 0.12, + xerr=[np.asarray(j_mean)-np.asarray(j_low), np.asarray(j_high)-np.asarray(j_mean)], + fmt="o", color="#c1553b", capsize=3, label="Jacobian lens") + ax.scatter(l_mean, y + 0.12, marker="s", color="#2b7a8c", label="Logit lens") + ax.axvline(0, color="#555555", lw=1) + ax.set_yticks(y, [FAMILY_LABELS[f] for f in families]) + ax.set_xlabel("mean mechanism-minus-name log-rank") + ax.set_title("B Effect by engineering principle", loc="left", fontweight="bold") + ax.legend(frameon=False) + ax.grid(True, axis="x", alpha=0.18) + + for suffix in ("png", "pdf"): + fig.savefig(FIG_DIR / f"mechanism-name-summary.{suffix}", dpi=240, + bbox_inches="tight", facecolor="white") + plt.close(fig) + + +def _trajectory_map(record: dict, lens: str) -> dict[str, dict]: + return { + item["label"]: item + for item in record["concept_trajectories"][lens] + } + + +def plot_concept_traces(run: dict) -> None: + representatives = [ + next(record for record in run["prompts"] + if record["slug"] == f"mechanism-name-{family}-01") + for family in PAIRS + ] + fig, axes = plt.subplots(5, 1, figsize=(10.5, 12.2), sharex=True, + constrained_layout=True) + for ax, record in zip(axes, representatives): + family = record["category"] + mechanism, name = PAIRS[family] + j = _trajectory_map(record, "jacobian_lens") + l = _trajectory_map(record, "logit_lens") + for label, color in ((mechanism, "#c1553b"), (name, "#6a51a3")): + jx = np.asarray(j[label]["depths"]) + jy = np.asarray(j[label]["ranks"], dtype=float) + keep = (jx >= 38) & (jx <= 92) & (jy > 0) + ax.plot(jx[keep], jy[keep], "-o", ms=2.8, lw=2, color=color, + label=f"{label}: Jacobian") + lx = np.asarray(l[label]["depths"]) + ly = np.asarray(l[label]["ranks"], dtype=float) + keep = ly > 0 + ax.plot(lx[keep], ly[keep], "--", lw=1.5, color=color, alpha=0.72, + label=f"{label}: logit") + ax.set_yscale("log") + ax.invert_yaxis() + ax.set_ylim(50000, 0.8) + ax.set_yticks([1, 10, 100, 1000, 10000], ["1", "10", "100", "1k", "10k"]) + ax.grid(True, which="major", alpha=0.18) + ax.set_ylabel("rank") + ax.set_title( + f"{FAMILY_LABELS[family]}: physical carrier '{mechanism}' vs name '{name}'", + loc="left", fontsize=10, fontweight="bold" + ) + ax.legend(frameon=False, ncol=4, fontsize=7.5, loc="lower left") + axes[-1].set_xlabel("network depth (%) - moving upward means easier to read") + fig.suptitle( + "Predeclared concept traces through Gemma-4 E4B-it\n" + "Solid = Jacobian lens; dashed = ordinary logit lens; these are readouts, not literal chain of thought", + fontsize=13, fontweight="bold" + ) + for suffix in ("png", "pdf"): + fig.savefig(FIG_DIR / f"mechanism-name-concept-traces.{suffix}", dpi=240, + bbox_inches="tight", facecolor="white") + plt.close(fig) + + +def write_outputs(stats: dict, rows: list[dict]) -> None: + OUT_JSON.write_text(json.dumps(stats, indent=2) + "\n") + j = stats["jacobian_lens"] + l = stats["logit_lens"] + delta = stats["j_minus_logit"] + lines = [ + "# Mechanism-versus-eponym v1: frozen results", + "", + f"Valid items: {stats['n_valid']}/{stats['n_total']}.", + "", + "Negative contrasts mean that the physical carrier is ranked ahead of the textbook surname.", + "", + "| lens | mean contrast | bootstrap 95% CI | carrier wins | ties | name wins | one-sided p |", + "|---|---:|---:|---:|---:|---:|---:|", + f"| Jacobian | {j['effect']['mean']:.3f} | [{j['effect']['low']:.3f}, {j['effect']['high']:.3f}] | {j['mechanism_wins']} | {j['ties']} | {j['name_wins']} | {j['wilcoxon_p_one_sided']:.4g} |", + f"| Logit | {l['effect']['mean']:.3f} | [{l['effect']['low']:.3f}, {l['effect']['high']:.3f}] | {l['mechanism_wins']} | {l['ties']} | {l['name_wins']} | {l['wilcoxon_p_one_sided']:.4g} |", + "", + f"The J-minus-logit contrast is {delta['effect']['mean']:.3f} " + f"(95% CI [{delta['effect']['low']:.3f}, {delta['effect']['high']:.3f}], " + f"one-sided p={delta['wilcoxon_p_one_sided']:.4g}).", + "", + "## Families", + "", + "| family | pair | J mean | logit mean |", + "|---|---|---:|---:|", + ] + for family, item in stats["families"].items(): + lines.append( + f"| {item['label']} | {item['mechanism']} vs {item['name']} | " + f"{item['jacobian_lens']['effect']['mean']:.3f} | " + f"{item['logit_lens']['effect']['mean']:.3f} |" + ) + lines.extend([ + "", + "## Interpretation boundary", + "", + "This experiment distinguishes readable physical carriers from readable eponyms. It does not demonstrate a complete derivation or a human-like hidden chain of thought. The reused lens remains exploratory because its fitting corpus provenance is incomplete.", + ]) + OUT_MD.write_text("\n".join(lines) + "\n") + + appendix = [ + "# Mechanism-versus-eponym v1: complete prompt and result appendix", + "", + "Ranks are one-indexed; rank 1 is strongest. Every prompt and one-token completion is shown.", + "", + ] + for index, row in enumerate(rows, start=1): + appendix.extend([ + f"## {index}. {row['slug']}", + "", + f"**Prompt:** {row['prompt']}", + "", + f"**Generated continuation:** `{row['completion']}`", + "", + f"**Tracked pair:** `{row['mechanism']}` vs `{row['name']}`", + "", + "| readout | mechanism rank | name rank | log10 contrast |", + "|---|---:|---:|---:|", + f"| Jacobian lens | {row['j_mechanism_rank']} | {row['j_name_rank']} | {row['j_contrast']:.3f} |", + f"| Logit lens | {row['logit_mechanism_rank']} | {row['logit_name_rank']} | {row['logit_contrast']:.3f} |", + "", + ]) + APPENDIX_MD.write_text("\n".join(appendix) + "\n") + + +def main() -> None: + run = json.loads(RUN.read_text()) + stats, rows = analyze(run) + write_outputs(stats, rows) + plot_summary(stats, rows) + plot_concept_traces(run) + print(json.dumps(stats, indent=2)) + + +if __name__ == "__main__": + main() diff --git a/jlens_materials/scripts/analyze_multiseed_association.py b/jlens_materials/scripts/analyze_multiseed_association.py new file mode 100644 index 0000000000000000000000000000000000000000..adef242625e2b08037e7788683f84e2931ce144f --- /dev/null +++ b/jlens_materials/scripts/analyze_multiseed_association.py @@ -0,0 +1,562 @@ +#!/usr/bin/env python3 +"""Integrated three-seed analysis of the frozen 50-item association suite. + +The lens-fit seed is treated as a repeated measurement, not as an independent +prompt. Population uncertainty is estimated by resampling mechanism families +and then phrasings within families. +""" + +from __future__ import annotations + +import csv +import itertools +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.insert(0, str(ROOT)) + +import paper_protocol as pp # noqa: E402 + + +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 / "gemma4-paper-multiseed-integrated_statistics.json" +CSV_PATH = EXP_DIR / "gemma4-paper-multiseed-integrated_prompt_results.csv" +RESULTS_PATH = EXP_DIR / "GEMMA4_PAPER_MULTI_SEED_INTEGRATED_ANALYSIS.md" +FIGURE_PATH = FIG_DIR / "integrated-association-analysis" +RNG_SEED = 20260714 +N_BOOT = 20_000 +N_CORR_BOOT = 5_000 + + +def item_auc(ranks: list[int], ks: list[int]) -> float: + pass_at_k = [float(np.mean([0 <= rank < k for rank in ranks])) for k in ks] + return float(pp.log_k_auc(ks, pass_at_k)) + + +def indexed_association_records(run: dict) -> dict[str, dict]: + return { + record["slug"]: record + for record in run["prompts"] + if record.get("shape") == "ASSOCIATION" + } + + +def validate_runs(runs: list[dict]) -> list[str]: + if len(runs) != 3: + raise ValueError("This analysis requires exactly three lens-fit seeds") + slugs = list(indexed_association_records(runs[0])) + if len(slugs) != 50: + raise ValueError(f"Expected 50 association prompts, found {len(slugs)}") + for seed, run in enumerate(runs): + 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") + records = indexed_association_records(run) + if list(records) != slugs: + raise ValueError(f"Seed {seed} prompt order/set differs") + if not all(record.get("valid_for_metrics") for record in records.values()): + raise ValueError(f"Seed {seed} contains excluded association prompts") + return slugs + + +def percentile_summary(values: np.ndarray) -> dict: + return { + "mean": float(np.mean(values)), + "low": float(np.quantile(values, 0.025)), + "high": float(np.quantile(values, 0.975)), + "n_resamples": int(len(values)), + } + + +def hierarchical_effect_bootstrap( + prompt_rows: list[dict], *, seed: int = RNG_SEED +) -> dict: + """Resample families, then phrasings, after averaging lens seeds per prompt.""" + grouped: dict[str, list[float]] = defaultdict(list) + for row in prompt_rows: + grouped[row["family"]].append(float(row["delta_auc_mean_seed"])) + families = sorted(grouped) + arrays = [np.asarray(grouped[family], dtype=float) for family in families] + rng = np.random.default_rng(seed) + estimates = np.empty(N_BOOT, dtype=float) + for index in range(N_BOOT): + chosen = rng.integers(0, len(arrays), size=len(arrays)) + family_estimates = [] + for family_index in chosen: + values = arrays[int(family_index)] + within = rng.integers(0, len(values), size=len(values)) + family_estimates.append(float(np.mean(values[within]))) + estimates[index] = float(np.mean(family_estimates)) + result = percentile_summary(estimates) + result["observed_mean"] = float(np.mean([ + row["delta_auc_mean_seed"] for row in prompt_rows + ])) + result["unit"] = "10 mechanism families; five phrasings resampled within family" + return result + + +def exact_family_sign_flip(family_effects: dict[str, float]) -> dict: + values = np.asarray([family_effects[name] for name in sorted(family_effects)]) + 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_mean": observed, + "p_one_sided": float(np.mean(null >= observed - 1e-15)), + "n_permutations": int(len(null)), + "unit": "mechanism-family mean effect", + } + + +def spearman_value(first: np.ndarray, second: np.ndarray) -> float: + if len(np.unique(first)) < 2 or len(np.unique(second)) < 2: + return float("nan") + result = spearmanr(first, second) + return float(result.statistic) + + +def family_clustered_spearman( + rows: list[dict], first_key: str, second_key: str, *, seed: int +) -> dict: + grouped: dict[str, list[dict]] = defaultdict(list) + for row in rows: + grouped[row["family"]].append(row) + families = sorted(grouped) + observed = spearman_value( + np.asarray([row[first_key] for row in rows], dtype=float), + np.asarray([row[second_key] for row in rows], dtype=float), + ) + rng = np.random.default_rng(seed) + estimates = [] + for _ in range(N_CORR_BOOT): + chosen = rng.integers(0, len(families), size=len(families)) + sampled = [row for index in chosen for row in grouped[families[int(index)]]] + estimate = spearman_value( + np.asarray([row[first_key] for row in sampled], dtype=float), + np.asarray([row[second_key] for row in sampled], dtype=float), + ) + if math.isfinite(estimate): + estimates.append(estimate) + estimates_array = np.asarray(estimates, dtype=float) + return { + "rho": observed, + "family_clustered_low": float(np.quantile(estimates_array, 0.025)), + "family_clustered_high": float(np.quantile(estimates_array, 0.975)), + "n_rows": len(rows), + "n_resamples": len(estimates), + } + + +def build_rows(runs: list[dict], slugs: list[str]) -> tuple[list[dict], list[dict], list[int]]: + indexed = [indexed_association_records(run) for run in runs] + ks = list(runs[0]["metrics"]["by_shape"]["ASSOCIATION"]["ks"]) + prompt_rows = [] + concept_rows = [] + for slug in slugs: + records = [records_by_slug[slug] for records_by_slug in indexed] + reference = records[0] + labels = [[item["label"] for item in record["emergence"]] for record in records] + if labels[1:] != labels[:1] * 2: + raise ValueError(f"Tracked concepts differ across seeds for {slug}") + j_aucs = [ + item_auc([int(item["best_rank"]) for item in record["emergence"]], ks) + for record in records + ] + logit_auc = item_auc( + [int(item["logit_lens_best_rank"]) for item in reference["emergence"]], + ks, + ) + prompt_rows.append({ + "slug": slug, + "family": reference["category"], + "phrasing_id": reference.get("phrasing_id"), + "prompt": reference["prompt_text"], + "n_concepts": len(reference["emergence"]), + "j_auc_seed0": j_aucs[0], + "j_auc_seed1": j_aucs[1], + "j_auc_seed2": j_aucs[2], + "j_auc_mean_seed": float(np.mean(j_aucs)), + "logit_auc": logit_auc, + "delta_auc_mean_seed": float(np.mean(j_aucs) - logit_auc), + }) + for concept_index, label in enumerate(labels[0]): + emergence = [record["emergence"][concept_index] for record in records] + j_ranks = [int(item["best_rank"]) + 1 for item in emergence] + logit_rank = int(emergence[0]["logit_lens_best_rank"]) + 1 + j_log_ranks = [math.log10(rank) for rank in j_ranks] + concept_rows.append({ + "slug": slug, + "family": reference["category"], + "concept": label, + "j_rank_seed0": j_ranks[0], + "j_rank_seed1": j_ranks[1], + "j_rank_seed2": j_ranks[2], + "j_log_rank_seed0": j_log_ranks[0], + "j_log_rank_seed1": j_log_ranks[1], + "j_log_rank_seed2": j_log_ranks[2], + "j_log_rank_mean_seed": float(np.mean(j_log_ranks)), + "logit_rank": logit_rank, + "logit_log_rank": math.log10(logit_rank), + "log10_rank_advantage": float(math.log10(logit_rank) - np.mean(j_log_ranks)), + }) + return prompt_rows, concept_rows, ks + + +def build_statistics(runs: list[dict], prompt_rows: list[dict], concept_rows: list[dict], ks: list[int]) -> dict: + per_seed = [] + for seed in range(3): + j = np.asarray([row[f"j_auc_seed{seed}"] for row in prompt_rows]) + logit = np.asarray([row["logit_auc"] for row in prompt_rows]) + per_seed.append({ + "seed": seed, + "j_auc_mean": float(np.mean(j)), + "logit_auc_mean": float(np.mean(logit)), + "delta_auc": float(np.mean(j - logit)), + "j_wins": int(np.sum(j > logit + 1e-12)), + "ties": int(np.sum(np.abs(j - logit) <= 1e-12)), + "j_losses": int(np.sum(j < logit - 1e-12)), + }) + + family_groups: dict[str, list[dict]] = defaultdict(list) + for row in prompt_rows: + family_groups[row["family"]].append(row) + families = {} + for family, rows in sorted(family_groups.items()): + deltas = np.asarray([row["delta_auc_mean_seed"] for row in rows]) + families[family] = { + "n_phrasings": len(rows), + "j_auc_mean_seed_and_prompt": float(np.mean([ + row["j_auc_mean_seed"] for row in rows + ])), + "logit_auc_mean_prompt": float(np.mean([row["logit_auc"] for row in rows])), + "delta_auc_mean": float(np.mean(deltas)), + "delta_auc_min": float(np.min(deltas)), + "delta_auc_max": float(np.max(deltas)), + } + + mean_j = np.asarray([row["j_auc_mean_seed"] for row in prompt_rows]) + logit = np.asarray([row["logit_auc"] for row in prompt_rows]) + overall = { + "n_prompts": len(prompt_rows), + "n_families": len(families), + "n_declared_concepts": len(concept_rows), + "j_auc_mean_across_seeds_and_prompts": float(np.mean(mean_j)), + "logit_auc_mean_across_prompts": float(np.mean(logit)), + "delta_auc": float(np.mean(mean_j - logit)), + "relative_auc_gain": float(np.mean(mean_j) / np.mean(logit) - 1), + "j_wins": int(np.sum(mean_j > logit + 1e-12)), + "ties": int(np.sum(np.abs(mean_j - logit) <= 1e-12)), + "j_losses": int(np.sum(mean_j < logit - 1e-12)), + "hierarchical_family_bootstrap": hierarchical_effect_bootstrap(prompt_rows), + "exact_family_sign_flip": exact_family_sign_flip({ + family: result["delta_auc_mean"] for family, result in families.items() + }), + } + + prompt_correlations = {} + concept_correlations = {} + for first in range(3): + for second in range(first + 1, 3): + label = f"seed{first}_vs_seed{second}" + prompt_correlations[label] = family_clustered_spearman( + prompt_rows, + f"j_auc_seed{first}", + f"j_auc_seed{second}", + seed=RNG_SEED + 10 * first + second, + ) + concept_correlations[label] = family_clustered_spearman( + concept_rows, + f"j_log_rank_seed{first}", + f"j_log_rank_seed{second}", + seed=RNG_SEED + 100 + 10 * first + second, + ) + prompt_correlations["mean_j_vs_logit"] = family_clustered_spearman( + prompt_rows, + "j_auc_mean_seed", + "logit_auc", + seed=RNG_SEED + 200, + ) + concept_correlations["mean_j_vs_logit"] = family_clustered_spearman( + concept_rows, + "j_log_rank_mean_seed", + "logit_log_rank", + seed=RNG_SEED + 201, + ) + + pass_at_k = { + "ks": ks, + "j_by_seed": [ + runs[seed]["metrics"]["by_shape"]["ASSOCIATION"]["jacobian_lens"]["pass_at_k"] + for seed in range(3) + ], + "j_mean": np.mean(np.asarray([ + runs[seed]["metrics"]["by_shape"]["ASSOCIATION"]["jacobian_lens"]["pass_at_k"] + for seed in range(3) + ]), axis=0).tolist(), + "logit": runs[0]["metrics"]["by_shape"]["ASSOCIATION"]["logit_lens"]["pass_at_k"], + } + return { + "analysis_status": "retrospective integrated robustness analysis", + "analysis_seed": RNG_SEED, + "bootstrap_resamples": N_BOOT, + "correlation_bootstrap_resamples": N_CORR_BOOT, + "runs": [str(path.relative_to(ROOT)) for path in RUN_PATHS], + "overall": overall, + "per_seed": per_seed, + "families": families, + "correlations": { + "prompt_auc": prompt_correlations, + "concept_log10_rank": concept_correlations, + }, + "pass_at_k": pass_at_k, + } + + +def write_prompt_csv(rows: list[dict]) -> None: + with CSV_PATH.open("w", newline="") as handle: + writer = csv.DictWriter(handle, fieldnames=list(rows[0])) + writer.writeheader() + writer.writerows(rows) + + +def plot_results(stats: dict, prompt_rows: list[dict], concept_rows: list[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, + }) + j_color = "#C33C54" + logit_color = "#287C8E" + neutral = "#6E7781" + fig, axes = plt.subplots(2, 2, figsize=(13.4, 10.2), constrained_layout=True) + + pass_data = stats["pass_at_k"] + ks = pass_data["ks"] + ax = axes[0, 0] + for seed, values in enumerate(pass_data["j_by_seed"]): + ax.plot(ks, values, color=j_color, alpha=0.25, linewidth=1.2) + ax.plot(ks, pass_data["j_mean"], "o-", color=j_color, linewidth=2.4, + label="J-lens (mean of 3 fits)") + ax.plot(ks, pass_data["logit"], "s-", color=logit_color, linewidth=2.0, + label="logit lens") + ax.set_xscale("log") + ax.set_xlabel("Allowed vocabulary rank, k") + ax.set_ylabel("Fraction of declared concepts recovered") + ax.set_title("A Recovery of predeclared concepts", loc="left", fontweight="bold") + ax.grid(True, alpha=0.18) + ax.legend(frameon=False, loc="upper left") + + ax = axes[0, 1] + x = np.asarray([row["logit_auc"] for row in prompt_rows]) + y = np.asarray([row["j_auc_mean_seed"] for row in prompt_rows]) + limit = max(float(np.max(x)), float(np.max(y))) * 1.08 + ax.scatter(x, y, s=42, color=neutral, alpha=0.75, edgecolor="none") + ax.plot([0, limit], [0, limit], linestyle="--", color=neutral, linewidth=1) + ax.set_xlim(-0.006, limit) + ax.set_ylim(-0.006, limit) + ax.set_xlabel("Logit-lens prompt AUC") + ax.set_ylabel("Mean three-seed J-lens prompt AUC") + ax.set_title("B Prompt-level comparison", loc="left", fontweight="bold") + overall = stats["overall"] + ci = overall["hierarchical_family_bootstrap"] + ax.text( + 0.04, 0.95, + f"mean difference = {overall['delta_auc']:+.4f}\n" + f"family-hierarchical 95% CI\n{ci['low']:+.4f} to {ci['high']:+.4f}", + transform=ax.transAxes, va="top", + ) + ax.grid(True, alpha=0.18) + + ax = axes[1, 0] + families = sorted(stats["families"], key=lambda name: stats["families"][name]["delta_auc_mean"]) + y_positions = np.arange(len(families)) + for y_position, family in zip(y_positions, families): + raw = [row["delta_auc_mean_seed"] for row in prompt_rows if row["family"] == family] + ax.scatter(raw, np.full(len(raw), y_position), s=24, color=neutral, alpha=0.35) + ax.scatter(stats["families"][family]["delta_auc_mean"], y_position, + marker="D", s=48, color=j_color, zorder=3) + ax.axvline(0, color=neutral, linewidth=1) + ax.set_yticks(y_positions, [name.replace("-", " ") for name in families]) + ax.set_xlabel("J-lens minus logit-lens AUC (right favors J-lens)") + ax.set_title("C Heterogeneity across mechanism families", loc="left", fontweight="bold") + ax.grid(True, axis="x", alpha=0.18) + + ax = axes[1, 1] + variables = ["J seed 0", "J seed 1", "J seed 2", "logit"] + arrays = [ + np.asarray([row[f"j_log_rank_seed{seed}"] for row in concept_rows]) + for seed in range(3) + ] + [np.asarray([row["logit_log_rank"] for row in concept_rows])] + matrix = np.asarray([ + [spearman_value(first, second) for second in arrays] + for first in arrays + ]) + image = ax.imshow(matrix, vmin=0, vmax=1, cmap="viridis") + ax.set_xticks(range(4), variables, rotation=25, ha="right") + ax.set_yticks(range(4), variables) + for row in range(4): + for column in range(4): + value = matrix[row, column] + ax.text(column, row, f"{value:.3f}", ha="center", va="center", + color="white" if value < 0.72 else "black") + ax.set_title("D Rank reproducibility across 150 concepts", loc="left", fontweight="bold") + colorbar = fig.colorbar(image, ax=ax, fraction=0.046, pad=0.04) + colorbar.set_label("Spearman rank correlation") + + fig.suptitle( + "Three lens fits agree closely; the average J-lens advantage is small and family-dependent", + fontsize=15, + fontweight="bold", + ) + fig.text( + 0.5, -0.01, + "Lens-fit seeds are repeated measurements. Population uncertainty resamples 10 mechanism families and five phrasings within each family.", + ha="center", fontsize=9, color=neutral, + ) + 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 fmt_ci(result: dict) -> str: + return f"{result['rho']:.3f} ({result['family_clustered_low']:.3f} to {result['family_clustered_high']:.3f})" + + +def write_results(stats: dict) -> None: + overall = stats["overall"] + bootstrap = overall["hierarchical_family_bootstrap"] + sign_flip = overall["exact_family_sign_flip"] + concept_corr = stats["correlations"]["concept_log10_rank"] + prompt_corr = stats["correlations"]["prompt_auc"] + lines = [ + "# Integrated three-seed analysis of the 50 materials association prompts", + "", + "## Bottom line", + "", + f"Across the 50 prompts, mean J-lens recovery AUC was {overall['j_auc_mean_across_seeds_and_prompts']:.4f}; " + f"logit-lens AUC was {overall['logit_auc_mean_across_prompts']:.4f}. The absolute difference was " + f"{overall['delta_auc']:+.4f} ({100 * overall['relative_auc_gain']:.1f}% relative), with a " + f"family-hierarchical 95% interval of {bootstrap['low']:+.4f} to {bootstrap['high']:+.4f}. " + f"The exact one-sided family sign-flip p-value was {sign_flip['p_one_sided']:.3f}. The interval crosses zero: " + "these paper-protocol lenses do **not** support a universal aggregate J-lens advantage on this suite.", + "", + f"The scientifically stronger positive result is reproducibility. Across all {overall['n_declared_concepts']} declared " + "prompt-concept pairs, independently fitted J-lenses produced almost identical rank orderings:", + "", + f"- seed 0 versus 1: rho = {fmt_ci(concept_corr['seed0_vs_seed1'])};", + f"- seed 0 versus 2: rho = {fmt_ci(concept_corr['seed0_vs_seed2'])};", + f"- seed 1 versus 2: rho = {fmt_ci(concept_corr['seed1_vs_seed2'])}.", + "", + "Intervals above are family-clustered bootstrap intervals. They describe reproducibility, not evidence that a decoded word is causal or that the model understands the mechanism.", + "", + "![Integrated three-seed analysis](../figures/gemma4-paper-multiseed/integrated-association-analysis.png)", + "", + "## What is correlated?", + "", + "The seed-to-seed correlations compare the full-vocabulary ranks assigned to the same declared scientific concept in the same prompt. They answer: *if the lens is refitted on a different WikiText sample, do the same prompt-concept pairs remain easy or hard to read?* The answer is yes.", + "", + f"The mean J-lens and logit-lens concept ranks were only moderately associated: rho = {fmt_ci(concept_corr['mean_j_vs_logit'])}. " + "Thus the methods share some notion of which concepts are intrinsically easy or hard, but the J-lens is not merely a noisy copy of direct unembedding. This correlation does not itself show that either lens is correct.", + "", + "At the coarser prompt-AUC level, correlations are inflated by many prompts with zero recovery for both methods and should be treated as descriptive:", + "", + f"- seed 0 versus 1: rho = {fmt_ci(prompt_corr['seed0_vs_seed1'])};", + f"- seed 0 versus 2: rho = {fmt_ci(prompt_corr['seed0_vs_seed2'])};", + f"- seed 1 versus 2: rho = {fmt_ci(prompt_corr['seed1_vs_seed2'])};", + f"- mean J-lens versus logit lens: rho = {fmt_ci(prompt_corr['mean_j_vs_logit'])}.", + "", + "## Per-seed aggregate results", + "", + "| lens-fit seed | J AUC | logit AUC | difference | J wins | ties | J losses |", + "|---:|---:|---:|---:|---:|---:|---:|", + ] + for row in stats["per_seed"]: + lines.append( + f"| {row['seed']} | {row['j_auc_mean']:.4f} | {row['logit_auc_mean']:.4f} | " + f"{row['delta_auc']:+.4f} | {row['j_wins']} | {row['ties']} | {row['j_losses']} |" + ) + lines.extend([ + "", + "The logit-lens value is identical across rows because no fitted lens is involved in direct unembedding.", + "", + "## Mechanism-family heterogeneity", + "", + "| family | mean J AUC | logit AUC | difference |", + "|---|---:|---:|---:|", + ]) + for family, row in sorted(stats["families"].items()): + lines.append( + f"| {family} | {row['j_auc_mean_seed_and_prompt']:.4f} | " + f"{row['logit_auc_mean_prompt']:.4f} | {row['delta_auc_mean']:+.4f} |" + ) + lines.extend([ + "", + "These five-phrasing family estimates are descriptive. Notch resistance and ductile failure favor the J-lens, whereas cyclic loading and particle strengthening favor direct unembedding. This heterogeneity is why a single pooled correlation or p-value would tell an incomplete story.", + "", + "## Integrated analysis to add after the new experiments", + "", + "The final paper should connect three independently defined prompt-level scores on genuinely held-out prompts:", + "", + "1. **Open-vocabulary semantic agreement:** whether a frozen, unrestricted decoder and blinded ontology recover the correct mechanism family.", + "2. **Counterfactual sensitivity:** whether the readout moves in the physically correct direction when one causal feature is changed.", + "3. **Behavioral performance:** whether the model answers a separate scientific question correctly.", + "", + "We can then estimate family-blocked Spearman correlations among these three scores and the predeclared readout score. The most meaningful paper result would be a positive association between readable mechanism evidence and correct counterfactual/behavioral performance. A correlation only between two lenses is a reproducibility or shared-difficulty result, not evidence of understanding.", + "", + "## Statistical guardrails", + "", + "- Average the three lens-fit seeds within each prompt before population inference.", + "- Resample or permute at the 10-family level; do not count 150 seed-prompt rows as independent.", + "- Treat the three seed values as robustness measurements, not as n=3 biological-style replicates.", + "- Label this analysis retrospective; reserve confirmatory claims for the new held-out prompt suite.", + "- Report effect sizes and clustered intervals even when showing a correlation coefficient.", + "", + "## Artifacts", + "", + "- Exact prompts and per-concept values: [`PAPER_ASSOCIATION_PROMPTS_MULTI_SEED_SI.md`](PAPER_ASSOCIATION_PROMPTS_MULTI_SEED_SI.md)", + "- Prompt-level CSV: [`gemma4-paper-multiseed-integrated_prompt_results.csv`](gemma4-paper-multiseed-integrated_prompt_results.csv)", + "- Machine-readable statistics: [`gemma4-paper-multiseed-integrated_statistics.json`](gemma4-paper-multiseed-integrated_statistics.json)", + "- Figure (vector): [`../figures/gemma4-paper-multiseed/integrated-association-analysis.pdf`](../figures/gemma4-paper-multiseed/integrated-association-analysis.pdf)", + "- Reproduce: `python scripts/analyze_multiseed_association.py`", + "", + ]) + RESULTS_PATH.write_text("\n".join(lines)) + + +def main() -> None: + runs = [json.loads(path.read_text()) for path in RUN_PATHS] + slugs = validate_runs(runs) + prompt_rows, concept_rows, ks = build_rows(runs, slugs) + stats = build_statistics(runs, prompt_rows, concept_rows, ks) + EXP_DIR.mkdir(parents=True, exist_ok=True) + STATS_PATH.write_text(json.dumps(stats, indent=2) + "\n") + write_prompt_csv(prompt_rows) + plot_results(stats, prompt_rows, concept_rows) + write_results(stats) + print(f"wrote {STATS_PATH}") + print(f"wrote {CSV_PATH}") + print(f"wrote {RESULTS_PATH}") + print(f"wrote {FIGURE_PATH.with_suffix('.pdf')}") + + +if __name__ == "__main__": + main() diff --git a/jlens_materials/scripts/analyze_multiseed_prompt.py b/jlens_materials/scripts/analyze_multiseed_prompt.py new file mode 100644 index 0000000000000000000000000000000000000000..a6586127fee8c202f9b577bb75fd7f987ecd41b9 --- /dev/null +++ b/jlens_materials/scripts/analyze_multiseed_prompt.py @@ -0,0 +1,371 @@ +#!/usr/bin/env python3 +"""Analyze one prompt across independently fitted paper-protocol lenses. + +This consumes stored run JSON files only; it does not load Gemma or lens weights. +""" + +from __future__ import annotations + +import argparse +import json +import math +import os +import sys +import tempfile +from pathlib import Path + +os.environ.setdefault("MPLCONFIGDIR", str(Path(tempfile.gettempdir()) / "jlens-matplotlib")) +import matplotlib +matplotlib.use("Agg") +import matplotlib.pyplot as plt +import numpy as np + + +ROOT = Path(__file__).resolve().parents[1] +sys.path.insert(0, str(ROOT)) +import matviz # noqa: E402 + + +DEFAULT_RUNS = [ + ROOT / "runs" / f"gemma4-e4b-it-paper-seed{seed}.json" + for seed in range(3) +] +DEFAULT_SLUG = "paper-v2-assoc-notch-resistance-01" +DEFAULT_CONCEPT = "toughness" + + +def one_indexed(value: object) -> int | None: + if value is None or int(value) < 0: + return None + return int(value) + 1 + + +def average_ranks(values: list[float]) -> np.ndarray: + """Return average ranks, including the midpoint treatment of ties.""" + order = np.argsort(values, kind="mergesort") + ranks = np.empty(len(values), dtype=float) + start = 0 + while start < len(order): + stop = start + 1 + while stop < len(order) and values[order[stop]] == values[order[start]]: + stop += 1 + ranks[order[start:stop]] = (start + stop - 1) / 2.0 + 1.0 + start = stop + return ranks + + +def spearman(values_a: list[float], values_b: list[float]) -> float: + if len(values_a) != len(values_b) or len(values_a) < 2: + raise ValueError("Spearman inputs must have the same length >= 2") + return float(np.corrcoef(average_ranks(values_a), average_ranks(values_b))[0, 1]) + + +def concept_trajectory(record: dict, lens_name: str, label: str) -> dict: + return next( + item for item in record["concept_trajectories"][lens_name] + if item["label"] == label + ) + + +def emergence(record: dict, label: str) -> dict: + return next(item for item in record["emergence"] if item["label"] == label) + + +def load_prompt_records(run_paths: list[Path], slug: str) -> tuple[list[dict], list[dict]]: + runs = [json.loads(path.read_text()) for path in run_paths] + records = [next(item for item in run["prompts"] if item["slug"] == slug) for run in runs] + reference = records[0] + expected = { + "model": runs[0]["model"], + "model_revision": runs[0]["model_identity"]["model_revision"], + "prompt_text": reference["prompt_text"], + "score_positions": reference["score_positions"], + "tracked": [item["label"] for item in reference["tracked"]], + } + for index, (run, record) in enumerate(zip(runs, records)): + observed = { + "model": run["model"], + "model_revision": run["model_identity"]["model_revision"], + "prompt_text": record["prompt_text"], + "score_positions": record["score_positions"], + "tracked": [item["label"] for item in record["tracked"]], + } + if observed != expected: + raise ValueError(f"seed {index} is not comparable") + if not run["methodology"].get("paper_protocol_complete"): + raise ValueError(f"seed {index} is not a complete paper-protocol run") + if not record.get("valid_for_metrics"): + raise ValueError(f"seed {index} excluded {slug}: {record.get('excluded_reasons')}") + return runs, records + + +def top_tokens_at_depth(record: dict, depth: float) -> list[str]: + layer = min(record["layer_readouts"], key=lambda item: abs(float(item["depth"]) - depth)) + return [str(token).strip() for token in layer["top_tokens"]] + + +def plot_prompt(records: list[dict], concept: str, output_base: Path) -> list[Path]: + colors = matviz.SERIES[:3] + trajectories = [concept_trajectory(record, "jacobian_lens", concept) for record in records] + logit = concept_trajectory(records[0], "logit_lens", concept) + tracked = [item["label"] for item in records[0]["tracked"]] + + fig, (ax_trace, ax_specificity) = plt.subplots( + 1, 2, figsize=(11.0, 4.4), gridspec_kw={"width_ratios": [1.65, 1.0]} + ) + band = records[0]["band"] + ax_trace.axvspan(band[0], band[1], color=matviz.GRID, alpha=0.18, zorder=0) + for seed, (trajectory, color) in enumerate(zip(trajectories, colors)): + ax_trace.plot( + trajectory["depths"], trajectory["ranks"], marker="o", ms=4.2, + lw=1.8, color=color, label=f"Jacobian lens, seed {seed}", zorder=3, + ) + ax_trace.plot( + logit["depths"], logit["ranks"], color=matviz.MUTE, lw=1.8, + linestyle="--", marker="s", ms=3.5, label="logit lens", zorder=2, + ) + best = emergence(records[0], concept) + best_rank = one_indexed(best["best_rank"]) + ax_trace.annotate( + f"{concept}: rank {best_rank} in all 3 seeds\nlayer depth {best['best_depth']:.1f}%", + xy=(best["best_depth"], best_rank), xytext=(best["best_depth"] + 8, 8), + arrowprops={"arrowstyle": "->", "color": matviz.INK, "lw": 0.9}, + fontsize=8.5, color=matviz.INK, + ) + ax_trace.set_yscale("log") + ax_trace.invert_yaxis() + ax_trace.set_xlim(0, 100) + ax_trace.set_ylim(300000, 0.8) + ax_trace.set_xlabel("layer depth (reindexed 0–100)") + ax_trace.set_ylabel("vocabulary rank (lower is stronger)") + ax_trace.grid(True, which="both", color=matviz.GRID, lw=0.55, alpha=0.7) + ax_trace.legend(loc="lower left", fontsize=7.5, framealpha=0.92) + ax_trace.set_title("A Transient concept emergence across depth", loc="left", fontweight="bold") + + x = np.arange(len(tracked), dtype=float) + offsets = (-0.17, 0.0, 0.17) + for seed, (record, color, offset) in enumerate(zip(records, colors, offsets)): + ranks = [one_indexed(emergence(record, label)["best_rank"]) for label in tracked] + ax_specificity.scatter( + x + offset, ranks, s=56, color=color, edgecolor=matviz.INK, + linewidth=0.45, label=f"seed {seed}", zorder=4, + ) + logit_ranks = [one_indexed(emergence(records[0], label)["logit_lens_best_rank"]) for label in tracked] + ax_specificity.scatter( + x, logit_ranks, s=62, marker="s", facecolor="none", edgecolor=matviz.MUTE, + linewidth=1.4, label="logit lens", zorder=3, + ) + ax_specificity.set_yscale("log") + ax_specificity.invert_yaxis() + ax_specificity.set_ylim(50000, 0.8) + ax_specificity.set_xticks(x) + ax_specificity.set_xticklabels(tracked) + ax_specificity.set_ylabel("best rank in preregistered band") + ax_specificity.grid(True, axis="y", which="both", color=matviz.GRID, lw=0.55, alpha=0.7) + ax_specificity.set_title("B The readout is mechanism-specific", loc="left", fontweight="bold") + ax_specificity.legend(loc="lower left", fontsize=7.5, framealpha=0.92) + + fig.suptitle( + "One prompt, three independently fitted lenses: latent fracture toughness", + x=0.055, ha="left", fontsize=15, fontweight="bold", + ) + fig.text( + 0.055, 0.93, + "Same Gemma checkpoint and prompt; lenses fitted on three independently shuffled 1,000-record WikiText samples.", + ha="left", color=matviz.MUTE, fontsize=8.5, + ) + fig.subplots_adjust(left=0.08, right=0.985, bottom=0.15, top=0.82, wspace=0.30) + + output_base.parent.mkdir(parents=True, exist_ok=True) + written = [] + for suffix in (".png", ".svg"): + output = output_base.with_suffix(suffix) + fig.savefig(output, dpi=220, bbox_inches="tight") + written.append(output) + plt.close(fig) + return written + + +def build_report( + runs: list[dict], records: list[dict], run_paths: list[Path], concept: str, + figure_path: Path, +) -> tuple[str, dict]: + trajectories = [concept_trajectory(record, "jacobian_lens", concept) for record in records] + pairwise = [] + for first in range(len(trajectories)): + for second in range(first + 1, len(trajectories)): + rho = spearman( + [math.log10(rank) for rank in trajectories[first]["ranks"]], + [math.log10(rank) for rank in trajectories[second]["ranks"]], + ) + pairwise.append({"seeds": [first, second], "spearman_rho": rho}) + + tracked = [item["label"] for item in records[0]["tracked"]] + primary = [] + for seed, (run, record) in enumerate(zip(runs, records)): + item = emergence(record, concept) + j_rank = one_indexed(item["best_rank"]) + logit_rank = one_indexed(item["logit_lens_best_rank"]) + depth = float(item["best_depth"]) + trajectory = concept_trajectory(record, "jacobian_lens", concept) + best_index = trajectory["depths"].index(depth) + primary.append({ + "seed": seed, + "corpus_sha256": run["lens_provenance"]["corpus"]["sha256"], + "jacobian_rank": j_rank, + "logit_rank": logit_rank, + "rank_improvement_ratio": logit_rank / j_rank, + "best_depth": depth, + "source_layer": trajectory["layers"][best_index], + "sustained_top5_onset": item["onset_depth"], + "top_tokens": top_tokens_at_depth(record, depth), + }) + + specificity = [] + for label in tracked: + seed_rows = [] + for seed, record in enumerate(records): + item = emergence(record, label) + seed_rows.append({ + "seed": seed, + "jacobian_rank": one_indexed(item["best_rank"]), + "best_depth": item["best_depth"], + "logit_rank": one_indexed(item["logit_lens_best_rank"]), + }) + specificity.append({"concept": label, "seeds": seed_rows}) + + stats = { + "slug": records[0]["slug"], + "title": records[0]["title"], + "prompt": records[0]["prompt_text"], + "generated_completion": records[0]["generated_completion"], + "score_positions": records[0]["score_positions"], + "workspace_band": records[0]["band"], + "model": runs[0]["model"], + "model_revision": runs[0]["model_identity"]["model_revision"], + "lens_records_per_seed": [run["lens_n_prompts"] for run in runs], + "primary_concept": concept, + "primary_results": primary, + "trajectory_pairwise_spearman": pairwise, + "specificity_results": specificity, + } + + lines = [ + f"# Deep dive: `{concept}` from one compact-tension prompt across three paper lenses", + "", + "## Exact prompt", + "", + f"> {records[0]['prompt_text']}", + "", + f"The predeclared tracked concepts were {', '.join(f'`{label}`' for label in tracked)}. " + f"They were absent from the tokenized input and the clean one-token continuation " + f"(`{records[0]['generated_completion'].strip()}`). The fixed readout was the final prompt " + f"token at position `{records[0]['score_positions'][0]}`, and the preregistered comparison " + f"band was {records[0]['band'][0]:.0f}–{records[0]['band'][1]:.0f}% network depth.", + "", + "## Result", + "", + "| lens seed | Jacobian rank | best depth | source layer | logit-lens rank | rank ratio | peak decoded tokens |", + "|---:|---:|---:|---:|---:|---:|---|", + ] + for row in primary: + tokens = ", ".join(f"`{token}`" for token in row["top_tokens"][:5]) + lines.append( + f"| {row['seed']} | **{row['jacobian_rank']}** | {row['best_depth']:.1f}% | " + f"{row['source_layer']} | {row['logit_rank']:,} | {row['rank_improvement_ratio']:.0f}× | {tokens} |" + ) + relative_figure = figure_path.relative_to(ROOT) + lines.extend([ + "", + f"![Three-seed concept analysis](../{relative_figure.as_posix()})", + "", + "The same localized event appeared in every independently fitted lens: `toughness` rose " + "to rank 2 at source layer 18 (43.9% depth). At that layer, all three seeds decoded the " + "same leading sequence: `robustness`, `toughness`, `capability`, `abilitas`, and `ductility`. " + "The matched logit lens never ranked `toughness` better than 1,276 anywhere in the fixed " + "band, a 638-fold rank contrast.", + "", + "## Trajectory reproducibility", + "", + ]) + for item in pairwise: + lines.append( + f"- Seeds {item['seeds'][0]} and {item['seeds'][1]}: Spearman " + f"$\\rho={item['spearman_rho']:.3f}$ across the full 26-point depth trajectory." + ) + lines.extend([ + "", + "Immediately before the peak, the three ranks were 84, 78, and 86 at 39.0% depth. " + "Immediately after it, they were 465, 522, and 450 at 48.8%. The concept is therefore a " + "sharp, reproducible intermediate-layer event rather than a generally elevated word.", + "", + "## Specificity and limitations", + "", + "| tracked concept | seed-0 J rank | seed-1 J rank | seed-2 J rank | logit rank |", + "|---|---:|---:|---:|---:|", + ]) + for item in specificity: + rows = item["seeds"] + lines.append( + f"| `{item['concept']}` | {rows[0]['jacobian_rank']:,} | {rows[1]['jacobian_rank']:,} | " + f"{rows[2]['jacobian_rank']:,} | {rows[0]['logit_rank']:,} |" + ) + lines.extend([ + "", + "The Jacobian lens selectively elevated the engineering property `toughness`; it did not " + "indiscriminately elevate the related words `crack` and `fracture`. This supports a " + "mechanism-specific readout interpretation for this example.", + "", + "This remains an illustrative case, not a population-level statistical result. Rank 2 " + "occurred at one sampled source layer, so the preregistered two-layer sustained-top-5 " + "criterion was not met. A readable representation also does not establish that the " + "representation caused the model's output, nor does it constitute a literal transcript " + "of internal prose. Population claims must use all 50 prompts with family-clustered " + "uncertainty; causal claims require a separate intervention experiment.", + "", + "## Reproducibility", + "", + f"- Model: `{runs[0]['model']}` at `{runs[0]['model_identity']['model_revision']}`", + "- Lens fit: 1,000 unique WikiText-103 records per seed; 128 tokens; target layer 40; 25 source layers", + f"- Run files: {', '.join(f'`{path.relative_to(ROOT).as_posix()}`' for path in run_paths)}", + "- Ranks in this document are one-indexed; lower is stronger.", + "", + ]) + return "\n".join(lines), stats + + +def main() -> None: + parser = argparse.ArgumentParser() + parser.add_argument("--runs", nargs="+", type=Path, default=DEFAULT_RUNS) + parser.add_argument("--slug", default=DEFAULT_SLUG) + parser.add_argument("--concept", default=DEFAULT_CONCEPT) + parser.add_argument( + "--output-base", type=Path, + default=ROOT / "figures" / "gemma4-paper-multiseed" / "notch-resistance-01-multiseed", + ) + parser.add_argument( + "--report", type=Path, + default=ROOT / "experiments" / "DEEP_DIVE_NOTCH_TOUGHNESS_MULTI_SEED.md", + ) + parser.add_argument( + "--statistics", type=Path, + default=ROOT / "experiments" / "deep-dive-notch-toughness-multiseed_statistics.json", + ) + args = parser.parse_args() + + run_paths = [path.resolve() for path in args.runs] + runs, records = load_prompt_records(run_paths, args.slug) + written = plot_prompt(records, args.concept, args.output_base.resolve()) + report, stats = build_report(runs, records, run_paths, args.concept, written[0].resolve()) + args.report.parent.mkdir(parents=True, exist_ok=True) + args.report.write_text(report + "\n") + args.statistics.parent.mkdir(parents=True, exist_ok=True) + args.statistics.write_text(json.dumps(stats, indent=2) + "\n") + print(f"wrote {args.report}") + print(f"wrote {args.statistics}") + for path in written: + print(f"wrote {path}") + + +if __name__ == "__main__": + main() diff --git a/jlens_materials/scripts/analyze_neutral_anchored_relational_benchmark.py b/jlens_materials/scripts/analyze_neutral_anchored_relational_benchmark.py new file mode 100644 index 0000000000000000000000000000000000000000..a6b0fcc5203413dbe00532fa297b6899d7e4ff5c --- /dev/null +++ b/jlens_materials/scripts/analyze_neutral_anchored_relational_benchmark.py @@ -0,0 +1,584 @@ +#!/usr/bin/env python3 +"""Analyze the frozen 60-law neutral-anchored relational benchmark.""" + +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 scipy.stats import spearmanr # noqa: E402 +from sklearn.feature_extraction.text import TfidfVectorizer # noqa: E402 + + +ROOT = Path(__file__).resolve().parents[1] +DEV = ROOT / "experiments" / "elicited-physics-abstraction-2026-07-18" +OUT = ROOT / "experiments" / "neutral-anchored-relational-physics-2026-07-18" + + +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 fit_centroid(values: np.ndarray, labels: np.ndarray) -> tuple[np.ndarray, np.ndarray]: + positive, negative = values[labels].mean(0), values[~labels].mean(0) + direction = positive - negative + direction /= max(float(np.linalg.norm(direction)), 1e-12) + return direction, 0.5 * (positive + negative) + + +def project(values: np.ndarray, direction: np.ndarray, midpoint: np.ndarray) -> np.ndarray: + return np.einsum( + "ij,j->i", values - midpoint, direction, optimize=False + ) + + +def load_development() -> dict: + manifest = json.loads((DEV / "prompt_manifest.json").read_text()) + with np.load(DEV / "representations.npz", allow_pickle=False) as arrays: + 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 = {x["prompt_id"]: x for x in manifest["prompts"]} + prompts = [by_id[x] for x in ids] + return { + "prompts": prompts, + "positions": positions, + "layers": layers, + "states": states, + } + + +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 = {x["prompt_id"]: x for x in manifest["prompts"]} + clean_by_id = {x["prompt_id"]: x for x in raw["clean_rows"]} + return { + "manifest": manifest, + "prompts": [by_id[x] for x in ids], + "clean": [clean_by_id[x] for x in ids], + "layer": layer, + "states": states, + } + + +def prompt_arrays(prompts: list[dict]) -> dict[str, np.ndarray]: + return { + "law": np.asarray([x["law_id"] for x in prompts]), + "category": np.asarray([x.get("category", "") for x in prompts]), + "domain": np.asarray([x.get("domain", "") for x in prompts]), + "law_sign": np.asarray([x["law_sign"] for x in prompts], dtype=int), + "neutral_role": np.asarray([x.get("neutral_role") or "" for x in prompts]), + "physical": np.asarray([x["physical_sign"] > 0 for x in prompts]), + "numeric": np.asarray([x["numerical_sign"] > 0 for x in prompts]), + "surface": np.asarray([x["surface"] for x in prompts]), + "case": np.asarray([x["case_index"] for x in prompts], dtype=int), + "order": np.asarray([x["answer_order"] for x in prompts]), + } + + +def matched_pairs(scores: np.ndarray, meta: dict) -> pd.DataFrame: + rows = [] + for law_id in sorted(set(meta["law"])): + for surface in ("a", "b"): + for case in (0, 1): + for order in sorted(set(meta["order"])): + base = ( + (meta["law"] == law_id) + & (meta["surface"] == surface) + & (meta["case"] == case) + & (meta["order"] == order) + ) + up, down = base & meta["numeric"], base & ~meta["numeric"] + if up.sum() != 1 or down.sum() != 1: + raise RuntimeError( + f"bad matched cell: {law_id}, {surface}, {case}, {order}" + ) + rows.append( + { + "law_id": str(law_id), + "category": str(meta["category"][up][0]), + "domain": str(meta["domain"][up][0]), + "law_sign": int(meta["law_sign"][up][0]), + "neutral_role": str(meta["neutral_role"][up][0]), + "surface": surface, + "case_index": case, + "answer_order": order, + "contrast": float(scores[up][0] - scores[down][0]), + } + ) + return pd.DataFrame(rows) + + +def aggregate_laws(pairs: pd.DataFrame) -> pd.DataFrame: + rows = [] + for law_id, group in pairs.groupby("law_id", sort=True): + rows.append( + { + "law_id": law_id, + "category": group["category"].iloc[0], + "domain": group["domain"].iloc[0], + "law_sign": int(group["law_sign"].iloc[0]), + "neutral_role": group["neutral_role"].iloc[0], + "mean_contrast": float(group["contrast"].mean()), + "median_contrast": float(group["contrast"].median()), + "contrast_sd": float(group["contrast"].std(ddof=1)), + "surface_a_contrast": float( + group.loc[group["surface"] == "a", "contrast"].mean() + ), + "surface_b_contrast": float( + group.loc[group["surface"] == "b", "contrast"].mean() + ), + "n_pairs": int(len(group)), + } + ) + return pd.DataFrame(rows) + + +def pair_auc(positive: np.ndarray, negative: np.ndarray) -> float: + comparison = positive[:, None] - negative[None, :] + return float(np.mean(comparison > 0) + 0.5 * np.mean(comparison == 0)) + + +def endpoint_metrics(laws: pd.DataFrame, score_column: str) -> dict: + direct = laws.loc[laws["category"] == "direct", score_column].to_numpy() + inverse = laws.loc[laws["category"] == "inverse", score_column].to_numpy() + neutral = laws.loc[ + (laws["category"] == "neutral") + & (laws["neutral_role"] == "validation"), + score_column, + ].to_numpy() + return { + "direct_vs_inverse_auc": pair_auc(direct, inverse), + "direct_vs_validation_neutral_auc": pair_auc(direct, neutral), + "validation_neutral_vs_inverse_auc": pair_auc(neutral, inverse), + } + + +def calibrate(laws: pd.DataFrame, column: str = "mean_contrast") -> tuple[float, float]: + calibration = laws.loc[ + (laws["category"] == "neutral") + & (laws["neutral_role"] == "calibration"), + column, + ].to_numpy() + center = float(np.median(calibration)) + mad = float(np.median(np.abs(calibration - center))) + scale = 1.4826 * mad + if scale <= 1e-12: + raise RuntimeError(f"neutral MAD is degenerate: {scale}") + return center, scale + + +def stratified_auc_bootstrap( + positive: np.ndarray, + negative: np.ndarray, + seed: int, + n: int, +) -> list[float]: + rng = np.random.default_rng(seed) + output = np.empty(n) + for start in range(0, n, 5000): + count = min(5000, n - start) + p = rng.choice(positive, size=(count, len(positive)), replace=True) + q = rng.choice(negative, size=(count, len(negative)), replace=True) + differences = p[:, :, None] - q[:, None, :] + output[start : start + count] = ( + np.mean(differences > 0, axis=(1, 2)) + + 0.5 * np.mean(differences == 0, axis=(1, 2)) + ) + return [float(x) for x in np.quantile(output, [0.025, 0.975])] + + +def ordinal_permutation( + signs: np.ndarray, + scores: np.ndarray, + seed: int, + n: int, +) -> tuple[float, float]: + observed = float(spearmanr(signs, scores).statistic) + rng = np.random.default_rng(seed) + extreme = 0 + for _ in range(n): + permuted = rng.permutation(signs) + statistic = float(spearmanr(permuted, scores).statistic) + extreme += statistic >= observed - 1e-15 + return observed, float((extreme + 1) / (n + 1)) + + +def tfidf_scores(dev: dict, test: dict, method: str) -> np.ndarray: + if method == "word": + vectorizer = TfidfVectorizer( + lowercase=True, ngram_range=(1, 2), sublinear_tf=True + ) + else: + vectorizer = TfidfVectorizer( + lowercase=True, + analyzer="char_wb", + ngram_range=(3, 5), + max_features=30000, + sublinear_tf=True, + ) + dev_values = normalize( + vectorizer.fit_transform([x["user"] for x in dev["prompts"]]).toarray() + ) + test_values = normalize( + vectorizer.transform([x["user"] for x in test["prompts"]]).toarray() + ) + labels = np.asarray([x["physical_sign"] > 0 for x in dev["prompts"]]) + direction, midpoint = fit_centroid(dev_values, labels) + return project(test_values, direction, midpoint) + + +def main() -> None: + protocol = json.loads((OUT / "protocol.json").read_text()) + for name in ("prompt_manifest", "runner", "development_manifest", "development_states"): + record = protocol["inputs"][name] + if sha256(ROOT / record["path"]) != record["sha256"]: + raise RuntimeError(f"frozen input changed: {name}") + dev, test = load_development(), load_test() + if test["layer"] != protocol["frozen_lens"]["layer"]: + raise RuntimeError("captured layer differs from frozen layer") + dev_position = int( + np.flatnonzero( + dev["positions"] == protocol["frozen_lens"]["position"] + )[0] + ) + dev_layer = int( + np.flatnonzero(dev["layers"] == protocol["frozen_lens"]["layer"])[0] + ) + dev_values = normalize(dev["states"][dev_position, :, dev_layer]) + dev_labels = np.asarray( + [x["physical_sign"] > 0 for x 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) + center, scale = calibrate(laws) + laws["neutral_centered_contrast"] = laws["mean_contrast"] - center + laws["neutral_z"] = laws["neutral_centered_contrast"] / scale + pairs["neutral_z"] = (pairs["contrast"] - center) / scale + + endpoints = endpoint_metrics(laws, "neutral_centered_contrast") + directional = laws[laws["category"].isin(["direct", "inverse"])] + calibrated_accuracy = float( + np.mean( + (directional["neutral_centered_contrast"] > 0) + == (directional["category"] == "direct") + ) + ) + ordinal = laws[ + (laws["category"] != "neutral") + | (laws["neutral_role"] == "validation") + ] + rho, ordinal_p = ordinal_permutation( + ordinal["law_sign"].to_numpy(), + ordinal["neutral_z"].to_numpy(), + protocol["inference"]["permutation_seed"], + protocol["inference"]["permutations"], + ) + surface_endpoints = {} + for surface in ("a", "b"): + column = f"surface_{surface}_contrast" + local = laws.copy() + local_center, _ = calibrate(local, column) + local[f"{column}_centered"] = local[column] - local_center + surface_endpoints[surface] = endpoint_metrics( + local, f"{column}_centered" + )["direct_vs_inverse_auc"] + + direct = laws.loc[ + laws["category"] == "direct", "neutral_centered_contrast" + ].to_numpy() + inverse = laws.loc[ + laws["category"] == "inverse", "neutral_centered_contrast" + ].to_numpy() + neutral_validation = laws.loc[ + (laws["category"] == "neutral") + & (laws["neutral_role"] == "validation"), + "neutral_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( + [x["higher_minus_lower_logit"] for x in test["clean"]], dtype=float + ) + controls = {} + control_law_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 = aggregate_laws(matched_pairs(scores, meta)) + calibration_values = table.loc[ + (table["category"] == "neutral") + & (table["neutral_role"] == "calibration"), + "mean_contrast", + ].to_numpy() + control_center = float(np.median(calibration_values)) + control_mad = float( + np.median(np.abs(calibration_values - control_center)) + ) + control_scale = 1.4826 * control_mad + # A text control can assign exactly the same score to both members of + # every neutral pair, making its null MAD exactly zero. AUC is + # threshold-free and remains well-defined; unit scale is used only to + # keep the diagnostic z column finite. + if control_scale <= 1e-12: + control_scale = 1.0 + table["neutral_centered_contrast"] = table["mean_contrast"] - control_center + table["neutral_z"] = table["neutral_centered_contrast"] / control_scale + controls[name] = endpoint_metrics(table, "neutral_centered_contrast") + control_law_tables[name] = table + + behavior = pd.DataFrame(test["clean"]) + behavior_summary = { + category: float(group.loc[group["behavior_evaluable"], "correct"].mean()) + for category, group in behavior.groupby("category") + if bool(group["behavior_evaluable"].any()) + } + 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 calibrated_accuracy + >= rule["calibrated_direct_inverse_accuracy_minimum"] + and min(surface_endpoints.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()), + "neutral_calibration": { + "center": center, + "robust_scale_1.4826_mad": scale, + "n_calibration_laws": 10, + "n_validation_laws": 10, + }, + "primary": { + **endpoints, + "bootstrap_95": bootstrap, + "calibrated_direct_inverse_accuracy": calibrated_accuracy, + "correct_directional_laws": int( + np.sum( + (directional["neutral_centered_contrast"] > 0) + == (directional["category"] == "direct") + ) + ), + "directional_laws_total": int(len(directional)), + "surface_direct_inverse_auc": surface_endpoints, + "ordinal_spearman": rho, + "ordinal_permutation_p": ordinal_p, + "category_median_z": { + category: float(group["neutral_z"].median()) + for category, group in laws.groupby("category") + }, + }, + "controls": controls, + "behavior_accuracy": behavior_summary, + "passed_all_frozen_criteria": passed, + } + pairs.to_csv(OUT / "matched_pair_contrasts.csv", index=False) + laws.to_csv(OUT / "law_level_neutral_normalized.csv", index=False) + for name, table in control_law_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" + ) + + # Publication-quality audit figure. + plt.rcParams.update( + { + "font.size": 8, + "axes.spines.top": False, + "axes.spines.right": False, + } + ) + fig, axes = plt.subplots(2, 2, figsize=(10.8, 7.2)) + category_order = ["inverse", "neutral", "direct"] + colors = {"inverse": "#7662a8", "neutral": "#8c8c8c", "direct": "#2a9d8f"} + rng = np.random.default_rng(20260725) + for index, category in enumerate(category_order): + selected = laws[laws["category"] == category]["neutral_z"].to_numpy() + jitter = rng.uniform(-0.10, 0.10, len(selected)) + axes[0, 0].scatter( + np.full(len(selected), index) + jitter, + selected, + s=22, + alpha=0.8, + color=colors[category], + edgecolor="white", + linewidth=0.4, + ) + axes[0, 0].plot( + [index - 0.22, index + 0.22], + [np.median(selected)] * 2, + color="black", + lw=2, + ) + axes[0, 0].axhline(0, color="0.35", ls="--", lw=1) + axes[0, 0].set_xticks(range(3), ["Inverse", "Neutral", "Direct"]) + axes[0, 0].set_ylabel("Contrast relative to neutral median (robust z)") + + ordered = laws.sort_values("neutral_z") + axes[0, 1].barh( + range(len(ordered)), + ordered["neutral_z"], + color=ordered["category"].map(colors), + height=0.82, + ) + axes[0, 1].axvline(0, color="0.25", lw=1) + axes[0, 1].set_yticks([]) + axes[0, 1].set_xlabel("Neutral-normalized matched contrast") + + endpoint_names = [ + "Direct vs\ninverse", + "Direct vs\nneutral", + "Neutral vs\ninverse", + ] + method_names = ["Hidden", "Output", "Word TF-IDF", "Char TF-IDF"] + method_values = { + "Hidden": list(endpoints.values()), + "Output": list(controls["output_head"].values()), + "Word TF-IDF": list(controls["word_tfidf"].values()), + "Char TF-IDF": list(controls["character_tfidf"].values()), + } + x = np.arange(3) + width = 0.19 + method_colors = ["#276b9a", "#d39b35", "#a7a7a7", "#666666"] + for index, name in enumerate(method_names): + axes[1, 0].bar( + x + (index - 1.5) * width, + method_values[name], + width, + label=name, + color=method_colors[index], + ) + axes[1, 0].axhline(0.5, color="0.45", ls="--", lw=1) + axes[1, 0].set_xticks(x, endpoint_names) + axes[1, 0].set_ylim(0, 1.03) + axes[1, 0].set_ylabel("Pairwise AUC") + legend_handles, legend_labels = axes[1, 0].get_legend_handles_labels() + + surface_values = [ + surface_endpoints["a"], + surface_endpoints["b"], + endpoints["direct_vs_inverse_auc"], + calibrated_accuracy, + ] + axes[1, 1].bar( + range(4), + surface_values, + color=["#5c8fb6", "#5c8fb6", "#276b9a", "#2a9d8f"], + ) + axes[1, 1].axhline(0.5, color="0.45", ls="--", lw=1) + axes[1, 1].set_xticks( + range(4), + ["Explicit\nsurface", "Rearranged\nsurface", "Threshold-free\nAUC", "Neutral-cut\naccuracy"], + fontsize=7, + ) + axes[1, 1].set_ylim(0, 1.03) + axes[1, 1].set_ylabel("Performance") + for axis, label in zip(axes.flat, "ABCD"): + axis.text( + -0.10, + 1.04, + label, + transform=axis.transAxes, + fontsize=10, + fontweight="bold", + ha="left", + va="bottom", + clip_on=False, + ) + fig.legend( + legend_handles, + legend_labels, + frameon=False, + fontsize=7.5, + ncol=4, + loc="lower center", + bbox_to_anchor=(0.5, 0.01), + ) + fig.tight_layout(rect=(0.02, 0.075, 1.0, 0.98), h_pad=2.4, w_pad=2.0) + (OUT / "figures").mkdir(exist_ok=True) + fig.savefig( + OUT / "figures" / "neutral-anchored-relational-physics.png", + dpi=240, + bbox_inches="tight", + ) + fig.savefig( + OUT / "figures" / "neutral-anchored-relational-physics.pdf", + bbox_inches="tight", + ) + plt.close(fig) + + report = [ + "# Neutral-anchored relational physics", + "", + "The empirical null is defined by ten calibration-neutral laws, not by an assumed raw zero.", + ( + f"Neutral center = {center:.6g}; robust scale = {scale:.6g}. " + "Ten different neutral laws validate the calibration." + ), + "", + 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"- Neutral-cut direct/inverse accuracy: {calibrated_accuracy:.3f}", + f"- Explicit/rearranged surface AUC: {surface_endpoints['a']:.3f}/{surface_endpoints['b']:.3f}", + f"- Ordinal Spearman rho: {rho:.3f}; permutation p={ordinal_p:.6g}", + f"- Passed all frozen criteria: {passed}", + "", + "All prompts, raw outputs, matched-pair contrasts, and law-level results 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_patchscope_physics_outcome.py b/jlens_materials/scripts/analyze_patchscope_physics_outcome.py new file mode 100644 index 0000000000000000000000000000000000000000..0ea0b77a75d2a6eb5057637844519e66774f06ef --- /dev/null +++ b/jlens_materials/scripts/analyze_patchscope_physics_outcome.py @@ -0,0 +1,294 @@ +#!/usr/bin/env python3 +"""Select an outcome Patchscope on discovery laws and test replication 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 + +from analyze_abstract_physics_composition import ( + bootstrap, + exact_signflip, +) + + +ROOT = Path(__file__).resolve().parents[1] +OUT = ROOT / "experiments" / "patchscope-physics-outcome-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 law_auc_rows( + frame: pd.DataFrame, + score_column: str, +) -> list[dict]: + output = [] + for law_id, group in frame.groupby("law_id"): + order_aucs = [] + for answer_order, order_group in group.groupby("answer_order"): + auc = float( + roc_auc_score( + (order_group["physical_sign"] == 1).astype(int), + order_group[score_column], + ) + ) + order_aucs.append(auc) + output.append( + { + "law_id": law_id, + "law_name": group["law_name"].iloc[0], + "split": group["split"].iloc[0], + "law_sign": int(group["law_sign"].iloc[0]), + "mean_order_auc": float(np.mean(order_aucs)), + "minimum_order_auc": float(np.min(order_aucs)), + "maximum_order_auc": float(np.max(order_aucs)), + } + ) + return output + + +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"]) + + candidates = [] + law_tables = {} + for (target_id, layer), group in frame.groupby(["target_id", "layer"]): + for method, column in ( + ("patchscope", "patchscope_positive_minus_negative"), + ("direct", "direct_positive_minus_negative"), + ): + laws = pd.DataFrame(law_auc_rows(group, column)) + law_tables[(target_id, int(layer), method)] = laws + discovery = laws[laws["split"] == "discovery"] + candidates.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]), + "method": method, + "discovery_mean_law_auc": float( + discovery["mean_order_auc"].mean() + ), + "discovery_minimum_law_auc": float( + discovery["mean_order_auc"].min() + ), + "discovery_laws_above_chance": int( + np.sum(discovery["mean_order_auc"] > 0.5) + ), + } + ) + candidates = pd.DataFrame(candidates) + patch_candidates = candidates[candidates["method"] == "patchscope"] + selection = patch_candidates.sort_values( + [ + "discovery_mean_law_auc", + "discovery_minimum_law_auc", + "layer", + "target_id", + ], + ascending=[False, False, True, True], + ).iloc[0] + key = ( + str(selection["target_id"]), + int(selection["layer"]), + "patchscope", + ) + selected_laws = law_tables[key] + replication = selected_laws[ + selected_laws["split"] == "replication" + ].copy() + values = replication["mean_order_auc"].to_numpy(float) + ci = bootstrap( + values, + np.random.default_rng(protocol["bootstrap"]["seed"]), + protocol["bootstrap"]["resamples"], + ) + 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_mean_law_auc": float( + selection["discovery_mean_law_auc"] + ), + "discovery_minimum_law_auc": float( + selection["discovery_minimum_law_auc"] + ), + "replication_mean_law_auc": float(values.mean()), + "replication_law_bootstrap_95": list(ci), + "replication_laws_above_chance": int(np.sum(values > 0.5)), + "replication_laws_total": int(len(values)), + "replication_exact_signflip_p_auc_minus_half": exact_signflip( + values - 0.5 + ), + "replication_minimum_law_auc": float(values.min()), + } + rule = protocol["success_rule"] + primary["passes_frozen_rule"] = bool( + values.mean() >= rule["mean_law_auc_minimum"] + and ci[0] > rule["bootstrap_95_lower_above"] + and np.sum(values > 0.5) >= rule["positive_laws_minimum"] + and values.min() >= rule["minimum_law_auc"] + ) + + candidate_replication = [] + for key, laws in law_tables.items(): + target_id, layer, method = key + test = laws[laws["split"] == "replication"] + candidate_replication.append( + { + "target_id": target_id, + "layer": layer, + "method": method, + "replication_mean_law_auc": float( + test["mean_order_auc"].mean() + ), + "replication_minimum_law_auc": float( + test["mean_order_auc"].min() + ), + } + ) + candidate_replication = pd.DataFrame(candidate_replication) + all_candidates = candidates.merge( + candidate_replication, + on=["target_id", "layer", "method"], + how="left", + ) + candidates.to_csv(OUT / "discovery_candidate_selection.csv", index=False) + all_candidates.to_csv( + OUT / "all_candidate_discovery_replication.csv", index=False + ) + selected_laws.to_csv(OUT / "selected_law_auc.csv", index=False) + + direct_laws = law_tables[ + ( + str(selection["target_id"]), + int(selection["layer"]), + "direct", + ) + ] + direct_replication = direct_laws[ + direct_laws["split"] == "replication" + ] + statistics = { + "study_id": protocol["study_id"], + "protocol_sha256": sha256(PROTOCOL), + "raw_sha256": sha256(RAW), + "primary": primary, + "matched_direct_unembedding": { + "replication_mean_law_auc": float( + direct_replication["mean_order_auc"].mean() + ), + "replication_minimum_law_auc": float( + direct_replication["mean_order_auc"].min() + ), + }, + "interpretation_boundary": ( + "A passing result shows that a fixed nonlinear Patchscope translates " + "an arbitrary-code source state into an answer-vocabulary-independent " + "physical outcome across unseen laws and both code mappings. It " + "still does not prove that the decoded variable is causally used in " + "the original A/B decision; that requires interchange intervention." + ), + } + (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=(11.2, 3.7)) + display = patch_candidates + for target_id, group in display.groupby("target_id"): + axes[0].plot( + group["depth_percent"], + group["discovery_mean_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="Layer depth (%)", ylabel="Discovery mean law AUC") + axes[0].legend(frameon=False, fontsize=6.5, ncol=2) + + selected_display = selected_laws.sort_values(["split", "mean_order_auc"]) + axes[1].barh( + selected_display["law_id"], + selected_display["mean_order_auc"], + color=[ + "#16697A" if value == "discovery" else "#7A6AA2" + for value in selected_display["split"] + ], + ) + axes[1].axvline(0.5, color="#555555", linestyle="--", linewidth=1) + axes[1].set(xlim=(0, 1), xlabel="Physical-outcome AUC") + + comparison = all_candidates[ + (all_candidates["target_id"] == selection["target_id"]) + & (all_candidates["layer"] == selection["layer"]) + ] + axes[2].bar( + comparison["method"], + comparison["replication_mean_law_auc"], + color=["#8C8C8C" if value == "direct" else "#5B8C5A" for value in comparison["method"]], + ) + axes[2].axhline(0.5, color="#555555", linestyle="--", linewidth=1) + axes[2].set(ylim=(0, 1), ylabel="Replication mean law AUC") + fig.tight_layout() + for suffix in ("png", "pdf"): + fig.savefig(FIG / f"patchscope-physics-outcome.{suffix}", dpi=300) + plt.close(fig) + + report = [ + "# Patchscope physical-outcome result", + "", + f"- Selected target: **{primary['selected_target_id']}** " + f"({primary['selected_answer_vocabulary']}).", + f"- Selected layer: **{primary['selected_layer']}** " + f"({primary['selected_depth_percent']:.1f}% depth).", + f"- Discovery mean/minimum law AUC: " + f"**{primary['discovery_mean_law_auc']:.3f}/" + f"{primary['discovery_minimum_law_auc']:.3f}**.", + f"- Replication mean law AUC: " + f"**{primary['replication_mean_law_auc']:.3f}** " + f"(95% law bootstrap {ci[0]:.3f} to {ci[1]:.3f}).", + f"- Replication laws above chance: " + f"**{primary['replication_laws_above_chance']}/8**; exact " + f"`p={primary['replication_exact_signflip_p_auc_minus_half']:.4g}`.", + f"- Matched direct-unembedding mean law AUC: " + f"**{statistics['matched_direct_unembedding']['replication_mean_law_auc']:.3f}**.", + 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_relational_grain_steering_v4.py b/jlens_materials/scripts/analyze_relational_grain_steering_v4.py new file mode 100644 index 0000000000000000000000000000000000000000..af02e0b1e74587e9b208136099eec7d1d51e3006 --- /dev/null +++ b/jlens_materials/scripts/analyze_relational_grain_steering_v4.py @@ -0,0 +1,434 @@ +#!/usr/bin/env python3 +"""Analyze the preregistered matched-pair grain relation confirmation.""" + +from __future__ import annotations + +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 scipy import stats # noqa: E402 + +from analyze_semantic_steering_v3 import ( # noqa: E402 + COLORS, + CONTROL_LABELS, + MARKERS, + bootstrap_mean, + control_condition_table, + endpoint_tables, + fmt_ci, + safe_wilcoxon, +) + + +ROOT = Path(__file__).resolve().parents[1] +RAW = ROOT / "experiments/relational-grain-steering-v4_raw.json" +MANIFEST = ROOT / "experiments/relational-grain-steering-v4-preregistration.json" +OUT_MD = ROOT / "experiments/relational-grain-steering-v4_results.md" +OUT_INVENTORY = ROOT / "experiments/relational-grain-steering-v4_prompt_inventory.md" +OUT_PROMPT = ROOT / "experiments/relational-grain-steering-v4_prompt_effects.csv" +OUT_CONDITION = ROOT / "experiments/relational-grain-steering-v4_condition_effects.csv" +OUT_PAIR = ROOT / "experiments/relational-grain-steering-v4_pair_effects.csv" +OUT_CURVES = ROOT / "experiments/relational-grain-steering-v4_curves.csv" +OUT_STATS = ROOT / "experiments/relational-grain-steering-v4_statistics.json" +FIG_DIR = ROOT / "figures/relational-grain-steering-v4" + +PAIR_LABELS = { + "titanium-64-8": "titanium alloy", + "magnesium-48-12": "magnesium alloy", + "low-carbon-steel-72-18": "low-carbon steel", + "silver-40-10": "silver", + "cobalt-alloy-56-14": "cobalt alloy", + "bronze-36-9": "bronze", +} + + +def main() -> None: + raw = json.loads(RAW.read_text()) + manifest = json.loads(MANIFEST.read_text()) + rows_raw = pd.DataFrame(raw["intervention_rows"]) + preflight = pd.DataFrame(raw["preflight"]) + rows, endpoint_rep, prompt_effects, condition_long = endpoint_tables(rows_raw) + prompt_effects["pair_id"] = prompt_effects["condition_id"].str.rsplit("--", n=1).str[0] + prompt_effects["relation"] = prompt_effects["condition_id"].str.rsplit("--", n=1).str[1] + prompt_effects.to_csv(OUT_PROMPT, index=False) + + condition = control_condition_table(condition_long) + condition["pair_id"] = condition["condition_id"].str.rsplit("--", n=1).str[0] + condition["relation"] = condition["condition_id"].str.rsplit("--", n=1).str[1] + condition["expected_sign"] = condition["relation"].map( + {"refinement": 1.0, "coarsening": -1.0} + ) + columns = { + "own_jacobian": "own_expected_aligned", + "mean_wrong_mechanisms": "wrong_expected_aligned", + "direct_decoder": "direct_expected_aligned", + "random_directions": "random_expected_aligned", + } + for source, destination in columns.items(): + condition[destination] = condition[source] * condition["expected_sign"] + condition.to_csv(OUT_CONDITION, index=False) + + pair = ( + condition.groupby("pair_id", as_index=False) + .agg( + own_expected_aligned=("own_expected_aligned", "mean"), + wrong_expected_aligned=("wrong_expected_aligned", "mean"), + direct_expected_aligned=("direct_expected_aligned", "mean"), + random_expected_aligned=("random_expected_aligned", "mean"), + ) + ) + pair.to_csv(OUT_PAIR, index=False) + + # Technical-replicate dose curves with frozen relation alignment. + curves = ( + rows.groupby( + [ + "condition_id", "prompt_id", "presentation_order", "regime", + "control_type", "dose_percent", + ], + as_index=False, + ) + .agg( + delta_log_odds=("delta_log_odds", "mean"), + valid_choice_probability=("valid_choice_probability", "mean"), + global_top_is_valid_choice=("global_top_is_valid_choice", "mean"), + kl_clean_to_intervened=("kl_clean_to_intervened", "mean"), + ) + ) + curves["pair_id"] = curves["condition_id"].str.rsplit("--", n=1).str[0] + curves["relation"] = curves["condition_id"].str.rsplit("--", n=1).str[1] + curves["expected_sign"] = curves["relation"].map( + {"refinement": 1.0, "coarsening": -1.0} + ) + curves["expected_aligned_delta_log_odds"] = ( + curves["delta_log_odds"] * curves["expected_sign"] + ) + curves.to_csv(OUT_CURVES, index=False) + + rng = np.random.default_rng(20260716) + own = pair["own_expected_aligned"].to_numpy() + wrong = pair["wrong_expected_aligned"].to_numpy() + direct = pair["direct_expected_aligned"].to_numpy() + random = pair["random_expected_aligned"].to_numpy() + own_ci = bootstrap_mean(own, rng) + own_random_ci = bootstrap_mean(own - random, rng) + own_wrong_ci = bootstrap_mean(own - wrong, rng) + own_direct_ci = bootstrap_mean(own - direct, rng) + + own_conditions = condition["own_expected_aligned"].to_numpy() + both_correct = [] + for _, group in condition.groupby("pair_id"): + both_correct.append(bool((group["own_expected_aligned"] > 0).all())) + own_prompt = prompt_effects[prompt_effects["control_type"].eq("own")] + order_agreements = [] + for _, group in own_prompt.groupby("condition_id"): + ordered = group.set_index("presentation_order")["endpoint_change_log_odds"] + a = float(ordered["positive-first"]) + b = float(ordered["negative-first"]) + order_agreements.append(a != 0 and b != 0 and np.sign(a) == np.sign(b)) + + own_rows = rows[rows["control_type"].eq("own")] + top_valid_fraction = float(own_rows["global_top_is_valid_choice"].mean()) + criteria = manifest["design"]["success_criteria"] + condition_fraction = float(np.mean(own_conditions > 0)) + pair_fraction = float(np.mean(both_correct)) + order_fraction = float(np.mean(order_agreements)) + passed = bool( + condition_fraction + >= criteria["individual_conditions_expected_aligned_positive_fraction_at_least"] + and pair_fraction + >= criteria["matched_pairs_both_relations_correct_fraction_at_least"] + and order_fraction + >= criteria["presentation_orders_same_nonzero_raw_sign_fraction_at_least"] + and own_random_ci[1] > 0 + and own_wrong_ci[1] > 0 + and own_direct_ci[1] > 0 + and top_valid_fraction + >= criteria["registered_answer_first_token_is_global_top_fraction_at_least"] + ) + + refinement_raw = condition[condition["relation"].eq("refinement")]["own_jacobian"] + coarsening_raw = condition[condition["relation"].eq("coarsening")]["own_jacobian"] + determinate = preflight[preflight["expected_outcome"].notna()] + clean_accuracy = float(determinate["clean_expected_correct"].mean()) + clean_pair_mass = preflight["clean"].map( + lambda value: value["valid_choice_probability"] + ) + seed_endpoint = endpoint_rep[endpoint_rep["control_type"].eq("own")].pivot( + index="prompt_id", columns="direction_id", values="endpoint_change_log_odds" + ) + seed_correlation = seed_endpoint.corr() + seed_correlation_values = seed_correlation.to_numpy() + upper = seed_correlation_values[ + np.triu_indices_from(seed_correlation_values, k=1) + ] + upper = upper[np.isfinite(upper)] + + stats_payload = { + "study_id": raw["study_id"], + "registered_pass": passed, + "design_accounting": { + "matched_material_pairs": int(pair.shape[0]), + "physical_conditions": int(condition.shape[0]), + "prompt_orders": int(preflight.shape[0]), + "raw_intervention_rows": int(rows_raw.shape[0]), + }, + "primary": { + "matched_expected_aligned": own_ci, + "matched_minus_random": own_random_ci, + "matched_minus_wrong_mechanisms": own_wrong_ci, + "matched_minus_direct": own_direct_ci, + "random_expected_aligned": bootstrap_mean(random, rng), + "wrong_expected_aligned": bootstrap_mean(wrong, rng), + "direct_expected_aligned": bootstrap_mean(direct, rng), + "individual_correct_direction_conditions": int((own_conditions > 0).sum()), + "n_conditions": int(len(own_conditions)), + "matched_pairs_both_relations_correct": int(sum(both_correct)), + "n_pairs": int(len(both_correct)), + "presentation_order_agreements": int(sum(order_agreements)), + "n_conditions_with_orders": int(len(order_agreements)), + "refinement_raw_positive": int((refinement_raw > 0).sum()), + "n_refinement": int(len(refinement_raw)), + "coarsening_raw_negative": int((coarsening_raw < 0).sum()), + "n_coarsening": int(len(coarsening_raw)), + "sign_test_conditions_p": float( + stats.binomtest(int((own_conditions > 0).sum()), len(own_conditions), 0.5).pvalue + ), + "wilcoxon_matched_vs_random_p": safe_wilcoxon(own - random), + "wilcoxon_matched_vs_wrong_p": safe_wilcoxon(own - wrong), + "wilcoxon_matched_vs_direct_p": safe_wilcoxon(own - direct), + }, + "integrity": { + "clean_expected_accuracy_prompt_orders": clean_accuracy, + "own_rows_global_top_valid_fraction": top_valid_fraction, + "clean_pair_probability_minimum": float(clean_pair_mass.min()), + "clean_pair_probability_median": float(clean_pair_mass.median()), + "own_rows_minimum_pair_probability": float( + own_rows["valid_choice_probability"].min() + ), + "own_rows_maximum_kl": float(own_rows["kl_clean_to_intervened"].max()), + "median_lens_seed_endpoint_correlation": float(np.median(upper)), + }, + } + OUT_STATS.write_text(json.dumps(stats_payload, indent=2) + "\n") + make_figure(condition, pair, curves) + write_inventory(preflight) + write_report(stats_payload, condition, pair) + print(OUT_MD.read_text()) + + +def make_figure(condition: pd.DataFrame, pair: pd.DataFrame, curves: pd.DataFrame) -> None: + FIG_DIR.mkdir(parents=True, exist_ok=True) + plt.rcParams.update({ + "font.family": "DejaVu Sans", + "font.size": 9, + "axes.spines.top": False, + "axes.spines.right": False, + "axes.linewidth": 0.8, + "pdf.fonttype": 42, + }) + fig, axes = plt.subplots(1, 3, figsize=(13.8, 4.6)) + + # A: raw sign reversal for every matched material pair. + ax = axes[0] + for pair_id, group in condition.groupby("pair_id"): + ordered = group.set_index("relation")["own_jacobian"] + ax.plot( + [0, 1], [ordered["refinement"], ordered["coarsening"]], + color=COLORS["own"], alpha=0.35, linewidth=1.2, marker="o", + markersize=4, + ) + means = condition.groupby("relation")["own_jacobian"].mean() + ax.plot( + [0, 1], [means["refinement"], means["coarsening"]], + color=COLORS["own"], linewidth=3.0, marker="o", markersize=7, + markeredgecolor="white", markeredgewidth=0.8, + ) + ax.axhline(0, color="#888888", linewidth=0.85) + ax.set_xticks([0, 1], ["Refinement", "Coarsening"]) + ax.set_ylabel("Raw endpoint: higher − lower log odds") + ax.grid(axis="y", color="#E6E6E6", linewidth=0.65) + ax.text(-0.14, 1.03, "A", transform=ax.transAxes, fontweight="bold") + + # B: pair-level expected-answer-aligned controls. + ax = axes[1] + plot_columns = { + "own": "own_expected_aligned", + "wrong-mechanism": "wrong_expected_aligned", + "direct": "direct_expected_aligned", + "random": "random_expected_aligned", + } + rng = np.random.default_rng(20260716) + for y, (control, column) in enumerate(plot_columns.items()): + values = pair[column].to_numpy() + jitter = rng.uniform(-0.08, 0.08, size=len(values)) + ax.scatter( + values, y + jitter, color=COLORS[control], marker=MARKERS[control], + s=24, alpha=0.52, zorder=2, + ) + estimate = bootstrap_mean(values, rng) + ax.errorbar( + estimate[0], y, + xerr=[[estimate[0] - estimate[1]], [estimate[2] - estimate[0]]], + color=COLORS[control], marker=MARKERS[control], markersize=7, + linewidth=1.8, capsize=3, markeredgecolor="white", + markeredgewidth=0.7, zorder=4, + ) + ax.axvline(0, color="#888888", linewidth=0.85) + ax.set_yticks(range(4), [CONTROL_LABELS[value] for value in plot_columns], fontsize=8) + ax.invert_yaxis() + ax.set_xlabel("Pair-level change toward correct answer") + ax.grid(axis="x", color="#E6E6E6", linewidth=0.65) + ax.text(-0.14, 1.03, "B", transform=ax.transAxes, fontweight="bold") + + # C: expected-answer-aligned dose response, equal weight per pair. + ax = axes[2] + doses = [-4, -2, 0, 2, 4] + for control in plot_columns: + subset = curves[curves["control_type"].eq(control)] + pair_curve = ( + subset.groupby(["pair_id", "dose_percent"], as_index=False) + .agg(value=("expected_aligned_delta_log_odds", "mean")) + ) + wide = pair_curve.pivot( + index="pair_id", columns="dose_percent", values="value" + ).reindex(columns=doses) + mean = wide.mean(axis=0) + sem = wide.sem(axis=0) + ax.plot( + doses, mean, color=COLORS[control], marker=MARKERS[control], + markersize=4.5, linewidth=2.0 if control == "own" else 1.4, + label=CONTROL_LABELS[control], + ) + ax.fill_between( + doses, mean - sem, mean + sem, color=COLORS[control], + alpha=0.10, linewidth=0, + ) + ax.axhline(0, color="#888888", linewidth=0.85) + ax.axvline(0, color="#D5D5D5", linewidth=0.8) + ax.set_xlabel("Intervention dose (% residual norm)") + ax.set_ylabel("Change toward correct answer") + ax.grid(axis="y", color="#E6E6E6", linewidth=0.65) + ax.legend(frameon=False, fontsize=7.7, loc="upper left") + ax.text(-0.14, 1.03, "C", transform=ax.transAxes, fontweight="bold") + + fig.subplots_adjust(left=0.075, right=0.985, bottom=0.15, top=0.94, wspace=0.38) + fig.savefig(FIG_DIR / "relational_confirmation.png", dpi=300) + fig.savefig(FIG_DIR / "relational_confirmation.pdf") + plt.close(fig) + + +def write_inventory(preflight: pd.DataFrame) -> None: + lines = [ + "# Relational grain steering v4 exact prompt inventory", + "", + "Every prompt below was frozen before v4 execution. Each physical condition " + "appears in two answer-word presentation orders.", + "", + ] + for condition_id, group in preflight.groupby("condition_id", sort=False): + pair_id, relation = condition_id.rsplit("--", 1) + lines.extend([ + f"## {PAIR_LABELS[pair_id]} — {relation}", + "", + f"Pair id: `{pair_id}`. Expected answer: `{group.iloc[0]['expected_outcome']}`.", + "", + ]) + for row in group.itertuples(): + clean = row.clean + lines.extend([ + f"- `{row.presentation_order}` — clean `{clean['choice']}`, " + f"higher-minus-lower log odds {clean['positive_log_odds']:+.3f}, " + f"pair mass {clean['valid_choice_probability']:.3%}.", + f" - {row.user}", + ]) + lines.append("") + OUT_INVENTORY.write_text("\n".join(lines) + "\n") + + +def write_report(stats_payload: dict, condition: pd.DataFrame, pair: pd.DataFrame) -> None: + primary = stats_payload["primary"] + integrity = stats_payload["integrity"] + pair_lines = [ + "| Matched material pair | Matched J | Other mechanisms | Direct | Random | Both relations correct |", + "|---|---:|---:|---:|---:|---:|", + ] + for row in pair.itertuples(): + group = condition[condition["pair_id"].eq(row.pair_id)] + both = bool((group["own_expected_aligned"] > 0).all()) + pair_lines.append( + f"| {PAIR_LABELS[row.pair_id]} | {row.own_expected_aligned:+.3f} | " + f"{row.wrong_expected_aligned:+.3f} | {row.direct_expected_aligned:+.3f} | " + f"{row.random_expected_aligned:+.3f} | {'yes' if both else 'no'} |" + ) + condition_lines = [ + "| Material | Relation | Raw matched endpoint | Expected-aligned endpoint |", + "|---|---|---:|---:|", + ] + for row in condition.sort_values(["pair_id", "relation"]).itertuples(): + condition_lines.append( + f"| {PAIR_LABELS[row.pair_id]} | {row.relation} | " + f"{row.own_jacobian:+.3f} | {row.own_expected_aligned:+.3f} |" + ) + + report = f"""# Relational grain steering v4 confirmatory results + +This study prospectively tested the context-sensitive grain-size result first noticed post hoc in v3. It was frozen before any v4 model output and has not yet been added to the manuscript. + +## What was tested + +Six entirely new materials formed matched pairs. Within each pair, alloy identity, the two grain-size values, all controlled covariates, and the answer words were held fixed; only the direction of change was reversed. The same frozen layer-16 grain-size Jacobian direction was then applied to both prompts. A genuine relation-sensitive result should increase `higher` after refinement but increase `lower` after coarsening. + +- 6 matched material pairs, 12 physical conditions, and 24 exact answer-order prompts. +- 2,400 registered dose rows: three lens seeds, two wrong-mechanism directions across three seeds, direct decoder, ten random controls, and five symmetric doses. +- Primary statistical unit: matched material pair. +- No v4 layer, direction, concept, dose, prompt, or threshold tuning. + +## Preregistered result + +**Verdict: {'PASS' if stats_payload['registered_pass'] else 'FAIL'}.** + +The matched direction moved toward the frozen correct answer by {fmt_ci(primary['matched_expected_aligned'])}. Matched minus random was {fmt_ci(primary['matched_minus_random'])} (Wilcoxon p={primary['wilcoxon_matched_vs_random_p']:.4g}); matched minus the two other materials mechanisms was {fmt_ci(primary['matched_minus_wrong_mechanisms'])} (p={primary['wilcoxon_matched_vs_wrong_p']:.4g}); and matched minus direct decoder was {fmt_ci(primary['matched_minus_direct'])} (p={primary['wilcoxon_matched_vs_direct_p']:.4g}). + +The effect moved in the correct direction in {primary['individual_correct_direction_conditions']}/{primary['n_conditions']} individual conditions and in both relations for {primary['matched_pairs_both_relations_correct']}/{primary['n_pairs']} material pairs. It was raw-positive for {primary['refinement_raw_positive']}/{primary['n_refinement']} refinement conditions and raw-negative for {primary['coarsening_raw_negative']}/{primary['n_coarsening']} coarsening conditions. The two answer-word orders had the same nonzero raw sign in {primary['presentation_order_agreements']}/{primary['n_conditions_with_orders']} conditions. + +{chr(10).join(pair_lines)} + +## Every physical condition + +`Raw matched endpoint` is the change in higher-minus-lower log odds from −4% to +4%. `Expected-aligned` reverses the sign for coarsening, so positive always means movement toward the frozen physically correct answer. + +{chr(10).join(condition_lines)} + +## Integrity + +- Clean expected-answer accuracy across prompt orders: {integrity['clean_expected_accuracy_prompt_orders']:.1%}. +- A registered answer beginning remained global top-1 in {integrity['own_rows_global_top_valid_fraction']:.1%} of matched-direction rows. +- Clean answer-pair probability: minimum {integrity['clean_pair_probability_minimum']:.3%}, median {integrity['clean_pair_probability_median']:.3%}. +- Minimum pair mass under matched interventions: {integrity['own_rows_minimum_pair_probability']:.3%}; maximum clean-to-intervened KL: {integrity['own_rows_maximum_kl']:.4f}. +- Median endpoint correlation across the three independent lens seeds: {integrity['median_lens_seed_endpoint_correlation']:.3f}. + +## Interpretation + +If the registered test passes, the direction is not acting as a fixed `higher`-token bias: its effect reverses when the physical relation reverses, while unrelated materials directions and the direct decoder are controlled at the same layer and dose. This is evidence for a context-dependent causal pathway linking a grain-boundary mechanism representation to a materials decision. It remains a localized intervention result, not a literal chain of thought or a claim of unrestricted understanding. + +## Files + +- Frozen protocol: `experiments/relational-grain-steering-v4-preregistration.json` +- Exact prompts and clean answers: `experiments/relational-grain-steering-v4_prompt_inventory.md` +- Raw rows: `experiments/relational-grain-steering-v4_raw.json` +- Statistics: `experiments/relational-grain-steering-v4_statistics.json` +- Prompt, condition, pair, and curve CSV files: `experiments/relational-grain-steering-v4_*.csv` +- Figure: `figures/relational-grain-steering-v4/relational_confirmation.pdf` +""" + OUT_MD.write_text(report) + + +if __name__ == "__main__": + main() diff --git a/jlens_materials/scripts/analyze_report_swap_dose.py b/jlens_materials/scripts/analyze_report_swap_dose.py new file mode 100644 index 0000000000000000000000000000000000000000..f80f00cccb08c959a871dc8ffd477800daf65d61 --- /dev/null +++ b/jlens_materials/scripts/analyze_report_swap_dose.py @@ -0,0 +1,56 @@ +#!/usr/bin/env python3 +"""Summarize the three-strength materials verbal-report swap experiment.""" + +from __future__ import annotations + +import json +import math +from pathlib import Path + + +ROOT = Path(__file__).resolve().parents[1] +RUNS = {0.25: "gemma4-materials-report-swap-a025.json", 0.5: "gemma4-materials-report-swap-a05.json", 1.0: "gemma4-materials-report-swap-a10.json"} + + +def main(): + summary = {} + for alpha, filename in RUNS.items(): + run = json.loads((ROOT / "runs" / filename).read_text()) + trials = [] + for record in run["prompts"]: + trials.extend(record.get("swap", {}).get("trials", [])) + target_improved = [t for t in trials if t["target_rank_swapped"] < t["target_rank_clean"]] + source_suppressed = [t for t in trials if t["source_rank_swapped"] > t["source_rank_clean"]] + log_changes = [math.log10((t["target_rank_clean"] + 1) / (t["target_rank_swapped"] + 1)) for t in trials] + special = [t for t in trials if t["swapped_top"][0][0].startswith("<") or not t["swapped_top"][0][0].strip()] + summary[str(alpha)] = { + "n_trials": len(trials), + "target_top1": sum(bool(t.get("target_top1")) for t in trials), + "protocol_success": sum(bool(t.get("protocol_success")) for t in trials), + "target_rank_improved": len(target_improved), + "source_rank_suppressed": len(source_suppressed), + "mean_log10_target_rank_gain": sum(log_changes) / len(log_changes), + "special_or_empty_swapped_top1": len(special), + } + out = ROOT / "experiments" / "gemma4-materials-report-swap-dose-v1_results.json" + out.write_text(json.dumps(summary, indent=2) + "\n") + md = [ + "# Materials verbal-report swap dose study", + "", + "Five materials report prompts were evaluated at three intervention strengths. Each eligible sibling target is one trial.", + "", + "| alpha | trials | target top-1 | target rank improved | source suppressed | mean log10 target-rank gain | special/empty top-1 |", + "|---:|---:|---:|---:|---:|---:|---:|", + ] + for alpha, values in summary.items(): + md.append(f"| {alpha} | {values['n_trials']} | {values['target_top1']} | {values['target_rank_improved']} | {values['source_rank_suppressed']} | {values['mean_log10_target_rank_gain']:+.3f} | {values['special_or_empty_swapped_top1']} |") + md.extend([ + "", + "No intervention installed its target as the model's top report. Rank movement without a target report is not causal success. The dose study therefore provides a robust negative result for coordinate transfer under this legacy Gemma lens.", + ]) + (ROOT / "experiments" / "gemma4-materials-report-swap-dose-v1_results.md").write_text("\n".join(md) + "\n") + print(json.dumps(summary, indent=2)) + + +if __name__ == "__main__": + main() diff --git a/jlens_materials/scripts/analyze_semantic_steering_v3.py b/jlens_materials/scripts/analyze_semantic_steering_v3.py new file mode 100644 index 0000000000000000000000000000000000000000..086dbc11297fc13c717a192f23471338d3e906f0 --- /dev/null +++ b/jlens_materials/scripts/analyze_semantic_steering_v3.py @@ -0,0 +1,955 @@ +#!/usr/bin/env python3 +"""Analyze the frozen v3 semantic-answer steering confirmation. + +The physical condition is always the independent statistical unit. Lens +seeds, random seeds, and answer-word presentation orders are technical +replicates that are averaged before family-level inference. +""" + +from __future__ import annotations + +import json +import math +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 import stats # noqa: E402 + + +ROOT = Path(__file__).resolve().parents[1] +RAW = ROOT / "experiments/semantic-steering-v3_raw.json" +MANIFEST = ROOT / "experiments/semantic-steering-v3-preregistration.json" +OUT_MD = ROOT / "experiments/semantic-steering-v3_results.md" +OUT_INVENTORY = ROOT / "experiments/semantic-steering-v3_prompt_inventory.md" +OUT_PROMPT = ROOT / "experiments/semantic-steering-v3_prompt_effects.csv" +OUT_CONDITION = ROOT / "experiments/semantic-steering-v3_condition_effects.csv" +OUT_CURVES = ROOT / "experiments/semantic-steering-v3_curves.csv" +OUT_SPECIFICITY = ROOT / "experiments/semantic-steering-v3_specificity_matrix.csv" +OUT_ALIGNED = ROOT / "experiments/semantic-steering-v3_expected_aligned_secondary.csv" +OUT_STATS = ROOT / "experiments/semantic-steering-v3_statistics.json" +FIG_DIR = ROOT / "figures/semantic-steering-v3" + +FAMILY_LABELS = { + "intergranular-corrosion": "Intergranular corrosion", + "martensitic-transformation": "Martensitic transformation", + "grain-size-strengthening": "Grain-size strengthening", +} +FAMILY_SHORT = { + "intergranular-corrosion": "Corrosion", + "martensitic-transformation": "Transformation", + "grain-size-strengthening": "Grain size", +} +CONDITION_SHORT = { + "long-sensitizing-service": "long sensitizing service", + "stress-relief-carbide-network": "stress-relief carbides", + "aged-high-carbon-sheet": "aged high-carbon sheet", + "failed-stabilization-cycle": "failed stabilization", + "proper-titanium-stabilization": "proper Ti stabilization", + "ultralow-carbon-transient-cycle": "ultralow-C transient", + "desensitized-by-remelting": "desensitized remelt", + "carbon-scavenged-niobium": "Nb carbon scavenging", + "brief-ambiguous-dwell": "brief ambiguous dwell", + "unknown-carbon-short-stress-relief": "unknown-C stress relief", + "laser-self-quenched-track": "laser self-quench", + "induction-hardened-gear-tooth": "induction-hardened gear", + "high-hardenability-gas-quench": "high-hardenability gas quench", + "cryogenic-retained-austenite": "cryogenic conversion", + "isothermal-pearlite-completion": "isothermal pearlite", + "austempered-bainitic-state": "austempered bainite", + "normalized-thick-plain-carbon": "normalized thick section", + "spheroidized-carbide-aggregate": "spheroidized carbides", + "polymer-quenched-shaft-center": "polymer-quenched center", + "compressed-air-thin-plate": "compressed-air plate", + "ecap-refined-aluminum": "ECAP-refined aluminum", + "recrystallized-fine-brass": "fine recrystallized brass", + "pinned-ferritic-steel-grains": "pinned ferritic grains", + "fine-grained-nickel-foil": "fine nickel foil", + "annealed-copper-grain-growth": "annealed copper growth", + "inhibitor-loss-electrical-steel": "inhibitor-loss steel", + "overannealed-alpha-brass": "overannealed brass", + "coarsened-ferritic-plate": "coarsened ferritic plate", + "nearly-equal-grain-sizes": "nearly equal sizes", + "nanocrystalline-crossover": "nanocrystalline crossover", +} +CONTROL_LABELS = { + "own": "Matched Jacobian", + "wrong-mechanism": "Other mechanisms", + "direct": "Direct decoder", + "random": "Random directions", +} +COLORS = { + "own": "#147A8A", + "wrong-mechanism": "#7562A8", + "direct": "#4F8A5B", + "random": "#777777", +} +MARKERS = {"own": "o", "wrong-mechanism": "^", "direct": "s", "random": "D"} +PRESENTATION_EXAMPLES = { + "intergranular-corrosion": "long-sensitizing-service", + "martensitic-transformation": "laser-self-quenched-track", + "grain-size-strengthening": "ecap-refined-aluminum", +} + + +def bootstrap_mean( + values: np.ndarray, rng: np.random.Generator, n_boot: int = 30000 +) -> tuple[float, float, float]: + values = np.asarray(values, dtype=float) + if len(values) == 0: + return (float("nan"), float("nan"), float("nan")) + samples = rng.choice(values, size=(n_boot, len(values)), replace=True).mean(axis=1) + return float(values.mean()), *np.quantile(samples, [0.025, 0.975]).tolist() + + +def fmt_ci(values: tuple[float, float, float]) -> str: + return f"{values[0]:+.3f} [{values[1]:+.3f}, {values[2]:+.3f}]" + + +def safe_wilcoxon(values: np.ndarray) -> float: + values = np.asarray(values, dtype=float) + if len(values) == 0 or np.allclose(values, 0): + return 1.0 + return float(stats.wilcoxon(values, alternative="two-sided").pvalue) + + +def endpoint_tables(rows: pd.DataFrame) -> tuple[pd.DataFrame, pd.DataFrame, pd.DataFrame]: + rows = rows.copy() + rows["direction_id"] = np.select( + [ + rows["method"].eq("semantic_jacobian"), + rows["method"].eq("semantic_direct"), + rows["method"].eq("random"), + ], + [ + "jac-" + rows["source_family_id"].fillna("") + + "-lens-" + rows["lens_seed"].fillna(-1).astype(int).astype(str), + "direct", + "random-" + rows["random_seed"].fillna(-1).astype(int).astype(str), + ], + default="unknown", + ) + clean = rows[rows["dose_percent"].eq(0)][ + ["prompt_id", "direction_id", "positive_log_odds"] + ].rename(columns={"positive_log_odds": "direction_clean_log_odds"}) + rows = rows.merge(clean, on=["prompt_id", "direction_id"], how="left") + rows["delta_log_odds"] = rows["positive_log_odds"] - rows["direction_clean_log_odds"] + + endpoint_records = [] + group_cols = [ + "target_family_id", "condition_id", "prompt_id", "presentation_order", + "regime", "method", "control_type", "source_family_id", "direction_id", + ] + for keys, group in rows.groupby(group_cols, dropna=False): + values = group.set_index("dose_percent")["positive_log_odds"] + minus = group.loc[group["dose_percent"].eq(-4.0)].iloc[0] + plus = group.loc[group["dose_percent"].eq(4.0)].iloc[0] + endpoint_records.append({ + **dict(zip(group_cols, keys)), + "endpoint_change_log_odds": float(values.loc[4.0] - values.loc[-4.0]), + "linear_slope_per_percent": float( + np.polyfit(group["dose_percent"], group["positive_log_odds"], 1)[0] + ), + "endpoint_choice_flip": bool(minus["choice"] != plus["choice"]), + "minus_choice": minus["choice"], + "plus_choice": plus["choice"], + }) + endpoint_rep = pd.DataFrame(endpoint_records) + + prompt_effects = ( + endpoint_rep.groupby( + [ + "target_family_id", "condition_id", "prompt_id", + "presentation_order", "regime", "method", "control_type", + "source_family_id", + ], + as_index=False, + dropna=False, + ) + .agg( + endpoint_change_log_odds=("endpoint_change_log_odds", "mean"), + endpoint_replicate_sd=("endpoint_change_log_odds", "std"), + linear_slope_per_percent=("linear_slope_per_percent", "mean"), + replicate_flip_fraction=("endpoint_choice_flip", "mean"), + n_replicates=("direction_id", "nunique"), + ) + ) + condition_effects_long = ( + prompt_effects.groupby( + [ + "target_family_id", "condition_id", "regime", "method", + "control_type", "source_family_id", + ], + as_index=False, + dropna=False, + ) + .agg( + endpoint_change_log_odds=("endpoint_change_log_odds", "mean"), + presentation_order_sd=("endpoint_change_log_odds", "std"), + linear_slope_per_percent=("linear_slope_per_percent", "mean"), + mean_replicate_flip_fraction=("replicate_flip_fraction", "mean"), + ) + ) + return rows, endpoint_rep, prompt_effects, condition_effects_long + + +def control_condition_table(condition_long: pd.DataFrame) -> pd.DataFrame: + records = [] + for (family_id, condition_id, regime), group in condition_long.groupby( + ["target_family_id", "condition_id", "regime"] + ): + own = group[group["control_type"].eq("own")]["endpoint_change_log_odds"] + wrong = group[group["control_type"].eq("wrong-mechanism")][ + "endpoint_change_log_odds" + ] + direct = group[group["control_type"].eq("direct")]["endpoint_change_log_odds"] + random = group[group["control_type"].eq("random")]["endpoint_change_log_odds"] + records.append({ + "target_family_id": family_id, + "condition_id": condition_id, + "regime": regime, + "own_jacobian": float(own.iloc[0]), + "mean_wrong_mechanisms": float(wrong.mean()), + "direct_decoder": float(direct.iloc[0]), + "random_directions": float(random.iloc[0]), + }) + return pd.DataFrame(records) + + +def analyze() -> None: + raw = json.loads(RAW.read_text()) + manifest = json.loads(MANIFEST.read_text()) + rows_raw = pd.DataFrame(raw["intervention_rows"]) + preflight = pd.DataFrame(raw["preflight"]) + rows, endpoint_rep, prompt_effects, condition_long = endpoint_tables(rows_raw) + prompt_effects.to_csv(OUT_PROMPT, index=False) + condition = control_condition_table(condition_long) + condition.to_csv(OUT_CONDITION, index=False) + + # Dose curves average only technical replicates, retaining prompt orders. + curves = ( + rows.groupby( + [ + "target_family_id", "condition_id", "prompt_id", + "presentation_order", "regime", "control_type", "dose_percent", + ], + as_index=False, + ) + .agg( + delta_log_odds=("delta_log_odds", "mean"), + technical_replicate_sd=("delta_log_odds", "std"), + valid_choice_probability=("valid_choice_probability", "mean"), + global_top_is_valid_choice=("global_top_is_valid_choice", "mean"), + kl_clean_to_intervened=("kl_clean_to_intervened", "mean"), + ) + ) + curves.to_csv(OUT_CURVES, index=False) + + rng = np.random.default_rng(20260716) + criteria = manifest["design"]["family_success_criteria"] + family_summaries = [] + for family_id in FAMILY_LABELS: + subset = condition[condition["target_family_id"].eq(family_id)] + own = subset["own_jacobian"].to_numpy() + wrong = subset["mean_wrong_mechanisms"].to_numpy() + direct = subset["direct_decoder"].to_numpy() + random = subset["random_directions"].to_numpy() + own_prompt = prompt_effects[ + prompt_effects["target_family_id"].eq(family_id) + & prompt_effects["control_type"].eq("own") + ] + agreements = [] + for _, group in own_prompt.groupby("condition_id"): + ordered = group.set_index("presentation_order")["endpoint_change_log_odds"] + required = {"positive-first", "negative-first"} + if required.issubset(ordered.index): + a = float(ordered["positive-first"]) + b = float(ordered["negative-first"]) + agreements.append(a != 0 and b != 0 and np.sign(a) == np.sign(b)) + integrity_rows = rows[ + rows["target_family_id"].eq(family_id) & rows["control_type"].eq("own") + ] + own_minus_random = bootstrap_mean(own - random, rng) + own_minus_wrong = bootstrap_mean(own - wrong, rng) + positive_fraction = float(np.mean(own > 0)) + order_fraction = float(np.mean(agreements)) + top_valid_fraction = float(integrity_rows["global_top_is_valid_choice"].mean()) + passed = bool( + positive_fraction >= criteria["positive_condition_fraction_at_least"] + and order_fraction + >= criteria["presentation_orders_same_nonzero_sign_fraction_at_least"] + and own_minus_random[1] > 0 + and own_minus_wrong[1] > 0 + and top_valid_fraction + >= criteria[ + "registered_answer_first_token_is_global_top_fraction_at_least" + ] + ) + family_summaries.append({ + "family_id": family_id, + "own_jacobian": bootstrap_mean(own, rng), + "mean_wrong_mechanisms": bootstrap_mean(wrong, rng), + "direct_decoder": bootstrap_mean(direct, rng), + "random_directions": bootstrap_mean(random, rng), + "own_minus_random": own_minus_random, + "own_minus_mean_wrong": own_minus_wrong, + "positive_conditions": int((own > 0).sum()), + "n_conditions": int(len(own)), + "positive_condition_fraction": positive_fraction, + "presentation_order_agreement_count": int(sum(agreements)), + "presentation_order_agreement_n": int(len(agreements)), + "presentation_order_agreement_fraction": order_fraction, + "global_top_valid_fraction_own_rows": top_valid_fraction, + "minimum_pair_probability_own_rows": float( + integrity_rows["valid_choice_probability"].min() + ), + "median_pair_probability_own_rows": float( + integrity_rows["valid_choice_probability"].median() + ), + "maximum_kl_own_rows": float(integrity_rows["kl_clean_to_intervened"].max()), + "wilcoxon_own_vs_random_p": safe_wilcoxon(own - random), + "wilcoxon_own_vs_wrong_p": safe_wilcoxon(own - wrong), + "sign_test_positive_p": float( + stats.binomtest(int((own > 0).sum()), len(own), 0.5).pvalue + ), + "registered_pass": passed, + }) + + # Full layer-matched target-by-source specificity matrix. + specificity_records = [] + semantic_conditions = condition_long[ + condition_long["method"].eq("semantic_jacobian") + ] + for target_id in FAMILY_LABELS: + for source_id in FAMILY_LABELS: + values = semantic_conditions[ + semantic_conditions["target_family_id"].eq(target_id) + & semantic_conditions["source_family_id"].eq(source_id) + ]["endpoint_change_log_odds"].to_numpy() + estimate = bootstrap_mean(values, rng) + specificity_records.append({ + "target_family_id": target_id, + "source_family_id": source_id, + "matched": target_id == source_id, + "mean_endpoint_change_log_odds": estimate[0], + "ci95_low": estimate[1], + "ci95_high": estimate[2], + "n_physical_conditions": len(values), + }) + specificity = pd.DataFrame(specificity_records) + specificity.to_csv(OUT_SPECIFICITY, index=False) + + # Aggregate inference weights each physical condition once. + own_all = condition["own_jacobian"].to_numpy() + wrong_all = condition["mean_wrong_mechanisms"].to_numpy() + direct_all = condition["direct_decoder"].to_numpy() + random_all = condition["random_directions"].to_numpy() + aggregate = { + "own_jacobian": bootstrap_mean(own_all, rng), + "mean_wrong_mechanisms": bootstrap_mean(wrong_all, rng), + "direct_decoder": bootstrap_mean(direct_all, rng), + "random_directions": bootstrap_mean(random_all, rng), + "own_minus_random": bootstrap_mean(own_all - random_all, rng), + "own_minus_mean_wrong": bootstrap_mean(own_all - wrong_all, rng), + "own_minus_direct": bootstrap_mean(own_all - direct_all, rng), + "positive_conditions": int((own_all > 0).sum()), + "n_conditions": int(len(own_all)), + "sign_test_positive_p": float( + stats.binomtest(int((own_all > 0).sum()), len(own_all), 0.5).pvalue + ), + "wilcoxon_own_vs_random_p": safe_wilcoxon(own_all - random_all), + "wilcoxon_own_vs_wrong_p": safe_wilcoxon(own_all - wrong_all), + "wilcoxon_own_vs_direct_p": safe_wilcoxon(own_all - direct_all), + } + + # Secondary, explicitly non-preregistered analysis: does the same positive + # mechanism direction improve the frozen expected answer in both positive + # and negative physical contexts? Threshold cases have no expected label. + aligned = condition[~condition["regime"].eq("near-threshold")].copy() + aligned["expected_sign"] = aligned["regime"].map( + {"positive": 1.0, "negative": -1.0} + ) + aligned_columns = { + "own_jacobian": "own_expected_aligned", + "mean_wrong_mechanisms": "wrong_expected_aligned", + "direct_decoder": "direct_expected_aligned", + "random_directions": "random_expected_aligned", + } + for source, destination in aligned_columns.items(): + aligned[destination] = aligned[source] * aligned["expected_sign"] + aligned.to_csv(OUT_ALIGNED, index=False) + secondary_families = [] + for family_id in FAMILY_LABELS: + subset = aligned[aligned["target_family_id"].eq(family_id)] + own = subset["own_expected_aligned"].to_numpy() + wrong = subset["wrong_expected_aligned"].to_numpy() + direct = subset["direct_expected_aligned"].to_numpy() + random = subset["random_expected_aligned"].to_numpy() + positive_context = subset[subset["regime"].eq("positive")]["own_jacobian"] + negative_context = subset[subset["regime"].eq("negative")]["own_jacobian"] + secondary_families.append({ + "family_id": family_id, + "own_expected_aligned": bootstrap_mean(own, rng), + "wrong_expected_aligned": bootstrap_mean(wrong, rng), + "direct_expected_aligned": bootstrap_mean(direct, rng), + "random_expected_aligned": bootstrap_mean(random, rng), + "own_minus_random_expected_aligned": bootstrap_mean(own - random, rng), + "own_minus_wrong_expected_aligned": bootstrap_mean(own - wrong, rng), + "own_minus_direct_expected_aligned": bootstrap_mean(own - direct, rng), + "aligned_positive_conditions": int((own > 0).sum()), + "n_determinate_conditions": int(len(own)), + "positive_context_raw_positive": int((positive_context > 0).sum()), + "n_positive_contexts": int(len(positive_context)), + "negative_context_raw_negative": int((negative_context < 0).sum()), + "n_negative_contexts": int(len(negative_context)), + "sign_test_aligned_p": float( + stats.binomtest(int((own > 0).sum()), len(own), 0.5).pvalue + ), + "wilcoxon_own_vs_random_p": safe_wilcoxon(own - random), + "wilcoxon_own_vs_wrong_p": safe_wilcoxon(own - wrong), + "wilcoxon_own_vs_direct_p": safe_wilcoxon(own - direct), + }) + aligned_own = aligned["own_expected_aligned"].to_numpy() + aligned_wrong = aligned["wrong_expected_aligned"].to_numpy() + aligned_direct = aligned["direct_expected_aligned"].to_numpy() + aligned_random = aligned["random_expected_aligned"].to_numpy() + secondary_aggregate = { + "own_expected_aligned": bootstrap_mean(aligned_own, rng), + "wrong_expected_aligned": bootstrap_mean(aligned_wrong, rng), + "direct_expected_aligned": bootstrap_mean(aligned_direct, rng), + "random_expected_aligned": bootstrap_mean(aligned_random, rng), + "own_minus_random_expected_aligned": bootstrap_mean( + aligned_own - aligned_random, rng + ), + "own_minus_wrong_expected_aligned": bootstrap_mean( + aligned_own - aligned_wrong, rng + ), + "own_minus_direct_expected_aligned": bootstrap_mean( + aligned_own - aligned_direct, rng + ), + "aligned_positive_conditions": int((aligned_own > 0).sum()), + "n_determinate_conditions": int(len(aligned_own)), + "sign_test_aligned_p": float( + stats.binomtest( + int((aligned_own > 0).sum()), len(aligned_own), 0.5 + ).pvalue + ), + "wilcoxon_own_vs_random_p": safe_wilcoxon(aligned_own - aligned_random), + "wilcoxon_own_vs_wrong_p": safe_wilcoxon(aligned_own - aligned_wrong), + "wilcoxon_own_vs_direct_p": safe_wilcoxon(aligned_own - aligned_direct), + } + + determinate = preflight[preflight["expected_outcome"].notna()] + clean_accuracy = float(determinate["clean_expected_correct"].mean()) + clean_top_valid = float(preflight["clean"].map( + lambda value: value["global_top_is_valid_choice"] + ).mean()) + clean_pair_mass = preflight["clean"].map( + lambda value: value["valid_choice_probability"] + ) + own_endpoint_rep = endpoint_rep[endpoint_rep["control_type"].eq("own")] + seed_pivot = own_endpoint_rep.pivot( + index="prompt_id", columns="direction_id", values="endpoint_change_log_odds" + ) + correlation = seed_pivot.corr() + correlation_values = correlation.to_numpy() + seed_values = correlation_values[ + np.triu_indices_from(correlation_values, k=1) + ] + seed_values = seed_values[np.isfinite(seed_values)] + median_seed_correlation = float(np.median(seed_values)) + own_flips = int(own_endpoint_rep["endpoint_choice_flip"].sum()) + own_endpoint_count = int(len(own_endpoint_rep)) + + integrity = { + "clean_expected_accuracy_determinate_prompt_orders": clean_accuracy, + "clean_global_top_valid_fraction_all_prompt_orders": clean_top_valid, + "clean_pair_probability_minimum": float(clean_pair_mass.min()), + "clean_pair_probability_median": float(clean_pair_mass.median()), + "own_jacobian_endpoint_choice_flips": own_flips, + "own_jacobian_endpoint_replicates": own_endpoint_count, + "median_lens_seed_endpoint_correlation": median_seed_correlation, + } + + statistics = { + "study_id": raw["study_id"], + "design_accounting": { + "physical_conditions": int(condition.shape[0]), + "prompt_orders": int(preflight.shape[0]), + "raw_intervention_rows": int(rows_raw.shape[0]), + "lens_seeds": 3, + "random_directions": 10, + "doses": [-4, -2, 0, 2, 4], + }, + "aggregate": aggregate, + "families": family_summaries, + "integrity": integrity, + "secondary_expected_aligned": { + "status": "post hoc secondary analysis; requires new-data confirmation", + "aggregate": secondary_aggregate, + "families": secondary_families, + }, + } + OUT_STATS.write_text(json.dumps(statistics, indent=2) + "\n") + + make_figures(curves, condition, specificity, aligned) + write_inventory(preflight, manifest) + write_report( + raw, manifest, statistics, condition, specificity, prompt_effects, + endpoint_rep, + ) + print(OUT_MD.read_text()) + + +def make_figures( + curves: pd.DataFrame, + condition: pd.DataFrame, + specificity: pd.DataFrame, + aligned: pd.DataFrame, +) -> None: + FIG_DIR.mkdir(parents=True, exist_ok=True) + plt.rcParams.update({ + "font.family": "DejaVu Sans", + "font.size": 9, + "axes.spines.top": False, + "axes.spines.right": False, + "axes.linewidth": 0.8, + "pdf.fonttype": 42, + "ps.fonttype": 42, + }) + doses = [-4, -2, 0, 2, 4] + + # Equal-weight family dose responses across physical conditions. + fig, axes = plt.subplots(1, 3, figsize=(13.2, 4.25), sharex=True) + for index, family_id in enumerate(FAMILY_LABELS): + ax = axes[index] + subset = curves[curves["target_family_id"].eq(family_id)] + for control_type in CONTROL_LABELS: + control = subset[subset["control_type"].eq(control_type)] + condition_curve = ( + control.groupby(["condition_id", "dose_percent"], as_index=False) + .agg(delta_log_odds=("delta_log_odds", "mean")) + ) + wide = condition_curve.pivot( + index="condition_id", columns="dose_percent", values="delta_log_odds" + ).reindex(columns=doses) + mean = wide.mean(axis=0) + sem = wide.sem(axis=0) + ax.plot( + doses, mean, marker=MARKERS[control_type], markersize=4.5, + linewidth=2.0 if control_type == "own" else 1.45, + color=COLORS[control_type], label=CONTROL_LABELS[control_type], + ) + ax.fill_between( + doses, mean - sem, mean + sem, color=COLORS[control_type], + alpha=0.10, linewidth=0, + ) + ax.axhline(0, color="#9A9A9A", linewidth=0.8) + ax.axvline(0, color="#D5D5D5", linewidth=0.8) + ax.grid(axis="y", color="#E6E6E6", linewidth=0.65) + ax.set_title(FAMILY_LABELS[family_id], fontsize=10.5, pad=8) + ax.set_xlabel("Intervention dose (% residual norm)") + if index == 0: + ax.set_ylabel("Change in positive-answer log odds") + ax.text( + -0.13, 1.04, chr(ord("A") + index), transform=ax.transAxes, + fontweight="bold", va="bottom", + ) + handles, labels = axes[0].get_legend_handles_labels() + fig.legend( + handles, labels, frameon=False, ncol=4, loc="upper center", + bbox_to_anchor=(0.5, 0.995), fontsize=8.2, + ) + fig.subplots_adjust(left=0.075, right=0.985, bottom=0.16, top=0.80, wspace=0.27) + fig.savefig(FIG_DIR / "family_dose_response.png", dpi=300) + fig.savefig(FIG_DIR / "family_dose_response.pdf") + plt.close(fig) + + # Target answer by source mechanism specificity matrix. + family_ids = list(FAMILY_LABELS) + matrix = specificity.pivot( + index="target_family_id", columns="source_family_id", + values="mean_endpoint_change_log_odds", + ).reindex(index=family_ids, columns=family_ids) + limit = max(0.25, float(np.abs(matrix.to_numpy()).max())) + fig, ax = plt.subplots(figsize=(6.6, 5.4)) + image = ax.imshow(matrix.to_numpy(), cmap="PRGn", vmin=-limit, vmax=limit) + ax.set_xticks(range(3), [FAMILY_SHORT[value] for value in family_ids], rotation=25, + ha="right") + ax.set_yticks(range(3), [FAMILY_SHORT[value] for value in family_ids]) + ax.set_xlabel("Source steering direction") + ax.set_ylabel("Target scientific answer") + for row_index, target_id in enumerate(family_ids): + for col_index, source_id in enumerate(family_ids): + value = float(matrix.loc[target_id, source_id]) + text_color = "white" if abs(value) > 0.62 * limit else "black" + ax.text( + col_index, row_index, f"{value:+.2f}", ha="center", va="center", + color=text_color, fontsize=10, fontweight="bold" if row_index == col_index else "normal", + ) + if row_index == col_index: + ax.add_patch(plt.Rectangle( + (col_index - 0.48, row_index - 0.48), 0.96, 0.96, + fill=False, edgecolor="black", linewidth=1.8, + )) + colorbar = fig.colorbar(image, ax=ax, fraction=0.046, pad=0.05) + colorbar.set_label("Endpoint change in target-answer log odds") + ax.spines[:].set_visible(False) + fig.subplots_adjust(left=0.21, right=0.88, bottom=0.22, top=0.97) + fig.savefig(FIG_DIR / "cross_mechanism_specificity.png", dpi=300) + fig.savefig(FIG_DIR / "cross_mechanism_specificity.pdf") + plt.close(fig) + + # All physical conditions, exposing heterogeneity rather than only averages. + fig, axes = plt.subplots(1, 3, figsize=(14.8, 7.1), sharex=True) + all_values = condition[ + ["own_jacobian", "mean_wrong_mechanisms", "direct_decoder", "random_directions"] + ].to_numpy() + xlimit = max(0.5, float(np.nanmax(np.abs(all_values))) * 1.18) + offsets = {"own": -0.24, "wrong-mechanism": -0.08, "direct": 0.08, "random": 0.24} + columns = { + "own": "own_jacobian", + "wrong-mechanism": "mean_wrong_mechanisms", + "direct": "direct_decoder", + "random": "random_directions", + } + for index, family_id in enumerate(FAMILY_LABELS): + ax = axes[index] + subset = condition[condition["target_family_id"].eq(family_id)].copy() + subset["regime_order"] = subset["regime"].map( + {"positive": 0, "negative": 1, "near-threshold": 2} + ) + subset = subset.sort_values(["regime_order", "condition_id"]) + y = np.arange(len(subset)) + for control_type, column in columns.items(): + ax.scatter( + subset[column], y + offsets[control_type], + marker=MARKERS[control_type], s=34 if control_type == "own" else 25, + color=COLORS[control_type], label=CONTROL_LABELS[control_type], zorder=3, + ) + ax.axvline(0, color="#8E8E8E", linewidth=0.85) + ax.grid(axis="x", color="#E6E6E6", linewidth=0.65) + ax.set_xlim(-xlimit, xlimit) + ax.set_yticks(y, [CONDITION_SHORT[value] for value in subset["condition_id"]], + fontsize=7.7) + ax.invert_yaxis() + ax.set_title(FAMILY_LABELS[family_id], fontsize=10.5, pad=8) + ax.set_xlabel("Endpoint change in positive-answer log odds") + ax.text( + -0.15, 1.035, chr(ord("A") + index), transform=ax.transAxes, + fontweight="bold", va="bottom", + ) + handles, labels = axes[0].get_legend_handles_labels() + fig.legend( + handles, labels, frameon=False, ncol=4, loc="upper center", + bbox_to_anchor=(0.5, 0.995), fontsize=8.2, + ) + fig.subplots_adjust(left=0.125, right=0.985, bottom=0.11, top=0.86, wspace=0.48) + fig.savefig(FIG_DIR / "condition_endpoints.png", dpi=300) + fig.savefig(FIG_DIR / "condition_endpoints.pdf") + plt.close(fig) + + # Three predeclared, physically transparent examples. + fig, axes = plt.subplots(1, 3, figsize=(13.2, 4.35), sharex=True) + for index, (family_id, condition_id) in enumerate(PRESENTATION_EXAMPLES.items()): + ax = axes[index] + subset = curves[ + curves["target_family_id"].eq(family_id) + & curves["condition_id"].eq(condition_id) + ] + for control_type in CONTROL_LABELS: + line = ( + subset[subset["control_type"].eq(control_type)] + .groupby("dose_percent", as_index=False) + .agg(delta_log_odds=("delta_log_odds", "mean")) + .sort_values("dose_percent") + ) + ax.plot( + line["dose_percent"], line["delta_log_odds"], + marker=MARKERS[control_type], markersize=4.5, + linewidth=2.0 if control_type == "own" else 1.45, + color=COLORS[control_type], label=CONTROL_LABELS[control_type], + ) + ax.axhline(0, color="#9A9A9A", linewidth=0.8) + ax.axvline(0, color="#D5D5D5", linewidth=0.8) + ax.grid(axis="y", color="#E6E6E6", linewidth=0.65) + ax.set_title( + f"{FAMILY_LABELS[family_id]}\n{CONDITION_SHORT[condition_id]}", + fontsize=10.2, pad=8, + ) + ax.set_xlabel("Intervention dose (% residual norm)") + if index == 0: + ax.set_ylabel("Change in positive-answer log odds") + ax.text( + -0.13, 1.04, chr(ord("A") + index), transform=ax.transAxes, + fontweight="bold", va="bottom", + ) + handles, labels = axes[0].get_legend_handles_labels() + fig.legend( + handles, labels, frameon=False, ncol=4, loc="upper center", + bbox_to_anchor=(0.5, 0.995), fontsize=8.2, + ) + fig.subplots_adjust(left=0.075, right=0.985, bottom=0.16, top=0.75, wspace=0.27) + fig.savefig(FIG_DIR / "three_predeclared_examples.png", dpi=300) + fig.savefig(FIG_DIR / "three_predeclared_examples.pdf") + plt.close(fig) + + # Explicitly secondary: frozen expected-answer alignment in determinate cases. + aligned_plot_columns = { + "own": "own_expected_aligned", + "wrong-mechanism": "wrong_expected_aligned", + "direct": "direct_expected_aligned", + "random": "random_expected_aligned", + } + fig, axes = plt.subplots(1, 3, figsize=(13.2, 4.6), sharex=True, sharey=True) + plot_rng = np.random.default_rng(20260716) + for index, family_id in enumerate(FAMILY_LABELS): + ax = axes[index] + subset = aligned[aligned["target_family_id"].eq(family_id)] + for y, (control_type, column) in enumerate(aligned_plot_columns.items()): + values = subset[column].to_numpy() + jitter = plot_rng.uniform(-0.09, 0.09, size=len(values)) + ax.scatter( + values, y + jitter, s=20, marker=MARKERS[control_type], + color=COLORS[control_type], alpha=0.48, zorder=2, + ) + estimate = bootstrap_mean(values, plot_rng) + ax.errorbar( + estimate[0], y, xerr=[[estimate[0] - estimate[1]], + [estimate[2] - estimate[0]]], + marker=MARKERS[control_type], markersize=7, + color=COLORS[control_type], linewidth=1.8, capsize=3, + markeredgecolor="white", markeredgewidth=0.7, zorder=4, + ) + ax.axvline(0, color="#888888", linewidth=0.85) + ax.grid(axis="x", color="#E6E6E6", linewidth=0.65) + ax.set_title(FAMILY_LABELS[family_id], fontsize=10.5, pad=8) + ax.set_xlabel("Change toward frozen correct answer") + ax.text( + -0.13, 1.04, chr(ord("A") + index), transform=ax.transAxes, + fontweight="bold", va="bottom", + ) + axes[0].set_yticks( + range(4), [CONTROL_LABELS[value] for value in aligned_plot_columns], fontsize=8.2 + ) + axes[0].invert_yaxis() + fig.subplots_adjust(left=0.14, right=0.985, bottom=0.15, top=0.87, wspace=0.18) + fig.savefig(FIG_DIR / "expected_answer_alignment_secondary.png", dpi=300) + fig.savefig(FIG_DIR / "expected_answer_alignment_secondary.pdf") + plt.close(fig) + + +def write_inventory(preflight: pd.DataFrame, manifest: dict) -> None: + lines = [ + "# Semantic-steering v3 exact prompt inventory", + "", + "This file records every exact model prompt and its clean semantic-token answer. " + "Each physical condition was asked twice; only the order of the two answer words changed.", + "", + ] + for family in manifest["families"]: + family_id = family["family_id"] + lines.extend([ + f"## {FAMILY_LABELS[family_id]}", + "", + f"Fixed layer: {family['fixed_layer']}. Positive answer: " + f"`{family['outcome_positive']}`. Negative answer: " + f"`{family['outcome_negative']}`.", + "", + ]) + subset = preflight[preflight["family_id"].eq(family_id)] + for condition_id, group in subset.groupby("condition_id", sort=False): + lines.extend([ + f"### {condition_id}", + "", + f"Regime: `{group.iloc[0]['regime']}`. Expected outcome: " + f"`{group.iloc[0]['expected_outcome']}`.", + "", + ]) + for row in group.itertuples(): + clean = row.clean + lines.extend([ + f"- `{row.presentation_order}` — clean choice `{clean['choice']}`, " + f"positive log odds {clean['positive_log_odds']:+.3f}, " + f"pair probability {clean['valid_choice_probability']:.3%}.", + f" - {row.user}", + ]) + lines.append("") + OUT_INVENTORY.write_text("\n".join(lines) + "\n") + + +def write_report( + raw: dict, + manifest: dict, + statistics: dict, + condition: pd.DataFrame, + specificity: pd.DataFrame, + prompt_effects: pd.DataFrame, + endpoint_rep: pd.DataFrame, +) -> None: + aggregate = statistics["aggregate"] + integrity = statistics["integrity"] + family_lines = [ + "| Materials mechanism | Matched Jacobian | Matched − random | Matched − other mechanisms | Positive cases | Order agreement | Valid top word | Verdict |", + "|---|---:|---:|---:|---:|---:|---:|---:|", + ] + for summary in statistics["families"]: + family_lines.append( + f"| {FAMILY_LABELS[summary['family_id']]} | " + f"{fmt_ci(summary['own_jacobian'])} | {fmt_ci(summary['own_minus_random'])} | " + f"{fmt_ci(summary['own_minus_mean_wrong'])} | " + f"{summary['positive_conditions']}/{summary['n_conditions']} | " + f"{summary['presentation_order_agreement_count']}/" + f"{summary['presentation_order_agreement_n']} | " + f"{summary['global_top_valid_fraction_own_rows']:.1%} | " + f"{'PASS' if summary['registered_pass'] else 'FAIL'} |" + ) + + secondary = statistics["secondary_expected_aligned"] + aligned_lines = [ + "| Materials mechanism | Matched direction toward correct answer | Matched − random | Matched − other mechanisms | Correct-direction cases | Positive contexts ↑ | Negative contexts ↓ |", + "|---|---:|---:|---:|---:|---:|---:|", + ] + for summary in secondary["families"]: + aligned_lines.append( + f"| {FAMILY_LABELS[summary['family_id']]} | " + f"{fmt_ci(summary['own_expected_aligned'])} | " + f"{fmt_ci(summary['own_minus_random_expected_aligned'])} | " + f"{fmt_ci(summary['own_minus_wrong_expected_aligned'])} | " + f"{summary['aligned_positive_conditions']}/" + f"{summary['n_determinate_conditions']} | " + f"{summary['positive_context_raw_positive']}/" + f"{summary['n_positive_contexts']} | " + f"{summary['negative_context_raw_negative']}/" + f"{summary['n_negative_contexts']} |" + ) + + matrix = specificity.pivot( + index="target_family_id", columns="source_family_id", + values="mean_endpoint_change_log_odds", + ).reindex(index=FAMILY_LABELS, columns=FAMILY_LABELS) + matrix_lines = [ + "| Target answer \\ source direction | Corrosion | Transformation | Grain size |", + "|---|---:|---:|---:|", + ] + for target_id in FAMILY_LABELS: + matrix_lines.append( + f"| {FAMILY_SHORT[target_id]} | " + + " | ".join(f"{matrix.loc[target_id, source_id]:+.3f}" for source_id in FAMILY_LABELS) + + " |" + ) + + condition_lines = [ + "| Family and new physical condition | Regime | Matched J | Other mechanisms | Direct | Random | Odds multiplier |", + "|---|---|---:|---:|---:|---:|---:|", + ] + for row in condition.sort_values(["target_family_id", "regime", "condition_id"]).itertuples(): + condition_lines.append( + f"| {FAMILY_SHORT[row.target_family_id]} — {row.condition_id} | {row.regime} | " + f"{row.own_jacobian:+.3f} | {row.mean_wrong_mechanisms:+.3f} | " + f"{row.direct_decoder:+.3f} | {row.random_directions:+.3f} | " + f"{math.exp(row.own_jacobian):.2f}× |" + ) + + # Post-hoc examples are explicitly labeled and do not affect inference. + strongest_lines = [] + for family_id in FAMILY_LABELS: + subset = condition[ + condition["target_family_id"].eq(family_id) + & ~condition["regime"].eq("near-threshold") + ].copy() + subset["selectivity"] = subset["own_jacobian"] - subset["mean_wrong_mechanisms"] + row = subset.sort_values(["selectivity", "own_jacobian"], ascending=False).iloc[0] + strongest_lines.append( + f"- {FAMILY_LABELS[family_id]}: `{row['condition_id']}` moved by " + f"{row['own_jacobian']:+.3f} log odds ({math.exp(row['own_jacobian']):.2f}× odds); " + f"the mean wrong-mechanism effect was {row['mean_wrong_mechanisms']:+.3f}." + ) + + report = f"""# Semantic-answer steering v3 confirmatory results + +This frozen study is separate from the manuscript and has not yet been added to it. + +## What was tested + +The experiment asked whether a Jacobian direction derived from a materials mechanism can *causally move the model's scientific answer*, and whether it does so more strongly than unrelated mechanism directions. It used the actual answer words—`grooves` versus `clean`, `hard` versus `soft`, and `higher` versus `lower`—instead of hiding those outcomes behind A/B labels. + +- 30 entirely new physical conditions: 10 per family, each with four clear positive, four clear negative, and two near-threshold cases. +- 60 exact prompts because each condition was asked with both answer-word presentation orders. +- Fixed v2 layers only: corrosion layer 16, transformation layer 24, grain size layer 16. No v3 tuning or layer selection. +- At every prompt: three matched Jacobian directions (lens seeds), six wrong-mechanism directions (two mechanisms times three seeds), one matched direct-decoder direction, ten random directions, and five symmetric doses. +- {statistics['design_accounting']['raw_intervention_rows']:,} raw registered rows; the physical condition—not a row, seed, or wording order—is the statistical unit. + +## Preregistered family results + +{chr(10).join(family_lines)} + +The registered family verdict requires all five conditions simultaneously: at least 8/10 positive physical conditions, at least 8/10 same-sign presentation orders, a 95% bootstrap interval above zero for matched minus random, a 95% interval above zero for matched minus wrong mechanisms, and the next-token global top beginning one of the two registered scientific answers in at least 90% of matched-Jacobian rows. + +## Integrated result + +Across all 30 physical conditions, the matched Jacobian endpoint was {fmt_ci(aggregate['own_jacobian'])}. Random directions produced {fmt_ci(aggregate['random_directions'])}; other material mechanisms produced {fmt_ci(aggregate['mean_wrong_mechanisms'])}; and the direct-decoder control produced {fmt_ci(aggregate['direct_decoder'])}. + +The paired matched-minus-random effect was {fmt_ci(aggregate['own_minus_random'])} (Wilcoxon p={aggregate['wilcoxon_own_vs_random_p']:.4g}); matched minus other mechanisms was {fmt_ci(aggregate['own_minus_mean_wrong'])} (p={aggregate['wilcoxon_own_vs_wrong_p']:.4g}); and matched minus direct was {fmt_ci(aggregate['own_minus_direct'])} (p={aggregate['wilcoxon_own_vs_direct_p']:.4g}). The matched direction moved the intended scientific contrast positively in {aggregate['positive_conditions']}/{aggregate['n_conditions']} physical conditions (exact sign-test p={aggregate['sign_test_positive_p']:.4g}). + +## Secondary context-aligned result + +This analysis was **not preregistered** and is therefore hypothesis-generating. The physical answers and positive/negative regimes were frozen before execution, but the decision to orient each endpoint toward the frozen correct answer was made after observing that the grain-size direction changed sign between refinement and coarsening. Positive values below mean that the same positive mechanism intervention moved toward `higher` after refinement and toward `lower` after coarsening (and analogously toward the frozen correct word in the other families). + +{chr(10).join(aligned_lines)} + +Across all 24 determinate conditions, the matched expected-answer-aligned effect was {fmt_ci(secondary['aggregate']['own_expected_aligned'])}. It was positive in {secondary['aggregate']['aligned_positive_conditions']}/{secondary['aggregate']['n_determinate_conditions']} conditions (exact sign-test p={secondary['aggregate']['sign_test_aligned_p']:.4g}). Matched minus random was {fmt_ci(secondary['aggregate']['own_minus_random_expected_aligned'])} (Wilcoxon p={secondary['aggregate']['wilcoxon_own_vs_random_p']:.4g}); matched minus other mechanisms was {fmt_ci(secondary['aggregate']['own_minus_wrong_expected_aligned'])} (p={secondary['aggregate']['wilcoxon_own_vs_wrong_p']:.4g}); and matched minus direct was {fmt_ci(secondary['aggregate']['own_minus_direct_expected_aligned'])} (p={secondary['aggregate']['wilcoxon_own_vs_direct_p']:.4g}). Grain-size strengthening showed the cleanest relational pattern: all four refinement cases moved toward `higher`, and all four coarsening cases moved toward `lower`. A new, disjoint matched-pair experiment is required before treating this as confirmatory evidence. + +## Cross-mechanism specificity + +Each row below is the target scientific answer; each column is the source mechanism used to build the Jacobian direction. The diagonal is the scientifically matched intervention. All directions were reconstructed at the target row's fixed layer, so this comparison is not confounded by layer or dose. + +{chr(10).join(matrix_lines)} + +## Every physical condition + +Endpoint values are changes in positive-answer log odds from −4% to +4% residual-norm dose. `Odds multiplier` is `exp(endpoint)` and expresses how much the positive-versus-negative answer odds changed across that dose range. + +{chr(10).join(condition_lines)} + +## Strong descriptive examples + +These are selected post hoc by matched-minus-wrong-mechanism effect and are for illustration only; they do not enter a separate hypothesis test. + +{chr(10).join(strongest_lines)} + +## Output integrity and reproducibility + +- Clean accuracy on determinate prompt orders: {integrity['clean_expected_accuracy_determinate_prompt_orders']:.1%}. +- Either scientific answer was the clean global top token in {integrity['clean_global_top_valid_fraction_all_prompt_orders']:.1%} of prompt orders. +- Clean probability mass on the two registered answer beginnings: minimum {integrity['clean_pair_probability_minimum']:.3%}, median {integrity['clean_pair_probability_median']:.3%}. +- Matched-Jacobian endpoint choice flips: {integrity['own_jacobian_endpoint_choice_flips']}/{integrity['own_jacobian_endpoint_replicates']} lens-by-prompt replicates. +- Median endpoint correlation among the three independently fitted lens seeds: {integrity['median_lens_seed_endpoint_correlation']:.3f}. +- The frozen protocol, exact prompt text, clean outputs, every dose row, analysis tables, and figures are all retained below. + +## Interpretation + +A selective positive effect means the frozen mechanism direction is not merely changing generic confidence: moving along that internal direction changes the odds of the corresponding scientific answer more than random vectors or unrelated materials mechanisms. This is causal evidence about a localized representation-to-output pathway. It is not evidence that the plotted/read-out words are a literal private chain of thought, nor by itself proof of complete physical understanding. + +## Files + +- Frozen protocol: `experiments/semantic-steering-v3-preregistration.json` +- Exact prompts and clean answers: `experiments/semantic-steering-v3_prompt_inventory.md` +- Raw rows: `experiments/semantic-steering-v3_raw.json` +- Machine-readable statistics: `experiments/semantic-steering-v3_statistics.json` +- Prompt-order effects: `experiments/semantic-steering-v3_prompt_effects.csv` +- Physical-condition effects: `experiments/semantic-steering-v3_condition_effects.csv` +- Cross-mechanism matrix: `experiments/semantic-steering-v3_specificity_matrix.csv` +- Expected-answer-aligned secondary table: `experiments/semantic-steering-v3_expected_aligned_secondary.csv` +- Dose curves: `experiments/semantic-steering-v3_curves.csv` +- Figures: `figures/semantic-steering-v3/` +""" + OUT_MD.write_text(report) + + +if __name__ == "__main__": + analyze() diff --git a/jlens_materials/scripts/audit_graph_topology_rigorous.py b/jlens_materials/scripts/audit_graph_topology_rigorous.py new file mode 100644 index 0000000000000000000000000000000000000000..462d9e1f909a5df9ecc6963b9b5c826e9695a5ae --- /dev/null +++ b/jlens_materials/scripts/audit_graph_topology_rigorous.py @@ -0,0 +1,263 @@ +#!/usr/bin/env python3 +"""Audit invariants and reported values for the rigorous graph analysis.""" + +from __future__ import annotations + +import hashlib +import json +import math +from pathlib import Path +from typing import Any + +import numpy as np +import pandas as pd + + +ROOT = Path(__file__).resolve().parents[1] +OUT = ROOT / "experiments" / "graph-topology-rigorous-2026-07-17" +STATS = OUT / "statistics.json" +EDGES = OUT / "replication_relation_graph_selected_edges.csv" +RANKS = OUT / "replication_relation_candidate_ranks.csv" +MANIFEST = ( + ROOT + / "experiments" + / "late-physics-representation-replication-2026-07-17" + / "prompt_manifest.json" +) +VECTORS = ( + ROOT + / "experiments" + / "late-physics-representation-replication-2026-07-17" + / "representations.npz" +) + + +def sha256(path: Path) -> str: + return hashlib.sha256(path.read_bytes()).hexdigest() + + +def strict_json(path: Path) -> Any: + return json.loads( + path.read_text(encoding="utf-8"), + parse_constant=lambda value: (_ for _ in ()).throw( + ValueError(f"non-standard JSON constant {value} in {path}") + ), + ) + + +def assert_finite(value: Any, path: str = "root") -> None: + if isinstance(value, dict): + for key, item in value.items(): + assert_finite(item, f"{path}.{key}") + elif isinstance(value, list): + for index, item in enumerate(value): + assert_finite(item, f"{path}[{index}]") + elif isinstance(value, float) and not math.isfinite(value): + raise AssertionError(f"non-finite statistic at {path}: {value}") + + +def main() -> None: + checks: list[dict[str, object]] = [] + + def record(name: str, detail: str) -> None: + checks.append({"check": name, "status": "pass", "detail": detail}) + + statistics = strict_json(STATS) + assert_finite(statistics) + record("strict_json_and_finite_values", "statistics.json is strict and finite") + + for name, entry in statistics["input_fingerprints"].items(): + path = ROOT / entry["path"] + assert path.is_file(), path + assert sha256(path) == entry["sha256"], name + record("input_fingerprints", "all six frozen input SHA-256 hashes match") + + heldout = statistics["heldout"] + primary = heldout["primary_cross_phrasing_graph"] + assert all(row["n_directed_edges"] == 200 for row in primary.values()) + assert primary["jacobian"]["null_95"][1] < primary["jacobian"]["directed_precision"] + assert ( + heldout["prompt_only_residualized_graph"]["jacobian"]["directed_precision"] + > 0.10 + ) + record( + "heldout_graph_design", + "all methods have 50 prompts × 4 cross-phrasing edges; Jacobian exceeds its null", + ) + + jaccards = list( + heldout["per_lens_seed_directed_edge_jaccard"].values() + ) + assert min(jaccards) >= 0.95 + assert ( + heldout["undirected_graph_sensitivity"]["jacobian"]["mutual"][ + "homophily" + ] + > heldout["undirected_graph_sensitivity"]["jacobian"]["union"][ + "homophily" + ] + ) + record( + "lens_and_graph_sensitivity", + f"lens edge Jaccard {min(jaccards):.3f}–{max(jaccards):.3f}; mutual edges are more homophilic", + ) + + manifest = strict_json(MANIFEST) + metadata = {str(row["prompt_id"]): row for row in manifest["prompts"]} + with np.load(VECTORS, allow_pickle=False) as vectors: + prompt_ids = vectors["prompt_ids"].astype(str) + rows = [metadata[prompt_id] for prompt_id in prompt_ids] + + edge_frame = pd.read_csv(EDGES) + methods = sorted(edge_frame["method"].unique()) + assert len(methods) == 10 + assert len(edge_frame) == 10 * 72 * 2 + for method, method_rows in edge_frame.groupby("method"): + assert len(method_rows) == 144, method + assert method_rows.groupby("source_index").size().eq(2).all(), method + assert (method_rows["source_index"] != method_rows["target_index"]).all() + for edge in method_rows.itertuples(index=False): + source = rows[int(edge.source_index)] + target = rows[int(edge.target_index)] + assert source["family_id"] == target["family_id"] + assert source["variant"] != target["variant"] + assert source["triplet_id"] != target["triplet_id"] + assert bool(edge.same_outcome) == ( + source["expected_outcome"] == target["expected_outcome"] + ) + record( + "relation_edge_invariants", + "1,440 audited edges: two per source, cross-variant, same-family, different material case", + ) + + fixed = statistics["disjoint_relation_replication"]["fixed_graphs"] + for method, method_rows in edge_frame.groupby("method"): + observed = float(method_rows["same_outcome"].mean()) + expected = float(fixed[method]["directed_same_outcome_precision"]) + assert math.isclose(observed, expected, abs_tol=1e-12), method + record( + "reported_precision_recalculation", + "all ten graph precisions exactly match row-level selected edges", + ) + assert all( + row["n_case_preserving_exact_assignments"] == 46_656 + for row in fixed.values() + ) + assert ( + fixed["jacobian_band"]["case_preserving_exact_p"] < 0.05 + ) + record( + "case_preserving_exact_null", + "all fixed graphs use 46,656 balanced case-level assignments; Jacobian p<0.05", + ) + + rank_frame = pd.read_csv(RANKS) + assert len(rank_frame) == 10 * 72 * 2 + assert rank_frame["first_positive_rank"].between(1, 3).all() + assert rank_frame["n_same_outcome_candidates"].between(1, 2).all() + assert rank_frame["pairwise_auc"].between(0, 1).all() + rankings = statistics["disjoint_relation_replication"][ + "complete_candidate_ranking" + ] + jacobian_ranking = rankings["jacobian_band"] + assert jacobian_ranking["pairwise_auc"] > 0.5 + assert ( + jacobian_ranking["leave_one_family_out_range"]["pairwise_auc"][0] + > 0.5 + ) + record( + "complete_candidate_rankings", + "1,440 three-candidate rankings audited; Jacobian AUC and every leave-one-family-out AUC exceed chance", + ) + + diagnostics = statistics["disjoint_relation_replication"][ + "answer_order_and_variant_falsification" + ]["jacobian_late"] + crossings = [ + row + for row in diagnostics["by_ordered_variant_pair"] + if "lexical_counterfactual" + in (row["source_variant"], row["target_variant"]) + ] + assert len(crossings) == 4 + assert min(row["same_outcome_precision"] for row in crossings) >= 0.75 + assert max(row["bh_q_across_six_pairs"] for row in crossings) < 0.05 + order_groups = { + bool(row["same_presentation_order"]): row + for row in diagnostics["by_presentation_order_match"] + } + assert ( + order_groups[False]["same_outcome_precision"] + > order_groups[True]["same_outcome_precision"] + ) + record( + "surface_form_falsification", + "all four counterfactual crossings are ≥75% and BH q<0.05; opposite-order edges are more accurate", + ) + + assert fixed["answer_order_only"]["directed_same_outcome_precision"] < 0.5 + assert math.isclose( + fixed["numeric_direction_oracle"]["directed_same_outcome_precision"], + 1.0, + ) + record( + "artifact_baselines", + "answer-order-only is below chance; numeric-direction oracle is the 100% ceiling", + ) + + figures = [ + OUT / "figures" / "mechanism-graph-evidence.png", + OUT / "figures" / "mechanism-graph-evidence.pdf", + OUT / "figures" / "mechanism-graph-evidence.svg", + OUT / "figures" / "relation-graph-falsification.png", + OUT / "figures" / "relation-graph-falsification.pdf", + OUT / "figures" / "relation-graph-falsification.svg", + OUT / "figures" / "relation-ranking-robustness.png", + OUT / "figures" / "relation-ranking-robustness.pdf", + OUT / "figures" / "relation-ranking-robustness.svg", + ] + assert all(path.stat().st_size > 10_000 for path in figures) + record("figure_artifacts", "all three figures exist as non-empty PNG, PDF, and SVG") + + validation = { + "status": "pass", + "n_checks": len(checks), + "checks": checks, + "audited_artifacts": { + str(path.relative_to(ROOT)): sha256(path) + for path in [STATS, EDGES, RANKS, *figures] + }, + } + (OUT / "validation.json").write_text( + json.dumps(validation, indent=2) + "\n", + encoding="utf-8", + ) + lines = [ + "# Graph-analysis validation", + "", + f"**Status: PASS ({len(checks)} checks).**", + "", + ] + lines.extend( + f"- **{row['check']}** — {row['detail']}" for row in checks + ) + lines.extend( + [ + "", + "Reproduce:", + "", + "```bash", + "python scripts/audit_graph_topology_rigorous.py", + "```", + "", + ] + ) + (OUT / "VALIDATION.md").write_text( + "\n".join(lines), + encoding="utf-8", + ) + print(json.dumps(validation, indent=2)) + + +if __name__ == "__main__": + main() diff --git a/jlens_materials/scripts/build_abstract_physics_composition.py b/jlens_materials/scripts/build_abstract_physics_composition.py new file mode 100644 index 0000000000000000000000000000000000000000..b58cc6798da6c9c346580d11a52656350760e361 --- /dev/null +++ b/jlens_materials/scripts/build_abstract_physics_composition.py @@ -0,0 +1,457 @@ +#!/usr/bin/env python3 +"""Freeze a factorial benchmark for abstract constitutive-law composition. + +The benchmark crosses three binary variables: + + law sign direct (+1) versus inverse (-1) + numerical sign increase (+1) versus decrease (-1) + physical sign product of law sign and numerical sign + +Every law also crosses two algebraically equivalent formula forms, two +material cases, and two answer-code orders. The physical decision checkpoint +precedes the answer key, so its causal-mask state cannot contain the later +``A``/``B`` mapping. +""" + +from __future__ import annotations + +import argparse +import hashlib +import json +from datetime import datetime, timezone +from pathlib import Path + + +ROOT = Path(__file__).resolve().parents[1] +OUT = ROOT / "experiments" / "abstract-physics-composition-2026-07-18" +MANIFEST = OUT / "prompt_manifest.json" +PROTOCOL = OUT / "protocol.json" +RUNNER = ROOT / "scripts" / "run_abstract_physics_composition.py" +ANALYZER = ROOT / "scripts" / "analyze_abstract_physics_composition.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) + + +LAWS = [ + { + "law_id": "taylor-strengthening", + "split": "discovery", + "law_sign": 1, + "law_name": "Taylor dislocation strengthening", + "response": "flow stress sigma_y", + "control": "dislocation density rho", + "formula_a": "sigma_y = sigma_0 + C sqrt(rho)", + "formula_b": "(sigma_y - sigma_0)^2 = C^2 rho", + "cases": [ + ("aluminum alloy", "1e12 m^-2", "1e14 m^-2"), + ("copper alloy", "4e12 m^-2", "4e14 m^-2"), + ], + }, + { + "law_id": "voigt-reinforcement", + "split": "discovery", + "law_sign": 1, + "law_name": "longitudinal Voigt stiffness", + "response": "longitudinal modulus E_L", + "control": "stiff reinforcement fraction f", + "formula_a": "E_L = E_m + (E_r - E_m) f, with E_r > E_m", + "formula_b": "(E_L - E_m)/f = E_r - E_m, with E_r > E_m", + "cases": [ + ("unidirectional carbon-epoxy composite", "0.20", "0.60"), + ("aligned silicon-carbide aluminum composite", "0.15", "0.45"), + ], + }, + { + "law_id": "rubber-network", + "split": "discovery", + "law_sign": 1, + "law_name": "rubber-elastic network stiffness", + "response": "shear modulus G", + "control": "network-strand density nu", + "formula_a": "G = nu k_B T", + "formula_b": "G/nu = k_B T", + "cases": [ + ("crosslinked natural rubber at fixed temperature", "2e25 m^-3", "8e25 m^-3"), + ("crosslinked silicone elastomer at fixed temperature", "1e25 m^-3", "5e25 m^-3"), + ], + }, + { + "law_id": "norton-creep", + "split": "discovery", + "law_sign": 1, + "law_name": "Norton power-law creep", + "response": "steady creep rate eps_dot", + "control": "applied stress sigma", + "formula_a": "eps_dot = A sigma^n, with n > 0", + "formula_b": "eps_dot/sigma^n = A, with n > 0", + "cases": [ + ("nickel superalloy at fixed temperature", "120 MPa", "240 MPa"), + ("ferritic steel at fixed temperature", "80 MPa", "200 MPa"), + ], + }, + { + "law_id": "orowan-spacing", + "split": "discovery", + "law_sign": -1, + "law_name": "Orowan obstacle bypass", + "response": "bypass stress tau", + "control": "obstacle spacing lambda", + "formula_a": "tau = C/lambda", + "formula_b": "tau lambda = C", + "cases": [ + ("precipitation-strengthened nickel alloy", "40 nm", "160 nm"), + ("dispersion-strengthened aluminum alloy", "30 nm", "120 nm"), + ], + }, + { + "law_id": "hall-petch", + "split": "discovery", + "law_sign": -1, + "law_name": "Hall-Petch grain-size strengthening", + "response": "yield stress sigma_y", + "control": "grain size d", + "formula_a": "sigma_y = sigma_0 + k/sqrt(d)", + "formula_b": "(sigma_y - sigma_0) sqrt(d) = k", + "cases": [ + ("polycrystalline steel", "5 micrometers", "45 micrometers"), + ("polycrystalline magnesium alloy", "3 micrometers", "27 micrometers"), + ], + }, + { + "law_id": "griffith-strength", + "split": "discovery", + "law_sign": -1, + "law_name": "Griffith fracture strength", + "response": "critical fracture stress sigma_c", + "control": "crack half-length a", + "formula_a": "sigma_c = C/sqrt(a)", + "formula_b": "sigma_c^2 a = C^2", + "cases": [ + ("brittle glass plate", "20 micrometers", "180 micrometers"), + ("silicon-carbide ceramic", "10 micrometers", "90 micrometers"), + ], + }, + { + "law_id": "porosity-modulus", + "split": "discovery", + "law_sign": -1, + "law_name": "porosity-dependent elastic modulus", + "response": "effective elastic modulus E", + "control": "porosity P", + "formula_a": "E/E_0 = (1 - P)^n, with n > 0 and 0 < P < 1", + "formula_b": "(E/E_0)^(1/n) + P = 1, with n > 0", + "cases": [ + ("sintered alumina", "0.03", "0.18"), + ("porous titanium", "0.05", "0.25"), + ], + }, + { + "law_id": "griffith-energy-release", + "split": "replication", + "law_sign": 1, + "law_name": "Griffith energy-release rate at fixed stress", + "response": "energy-release rate G", + "control": "crack half-length a", + "formula_a": "G = C a", + "formula_b": "G/a = C", + "cases": [ + ("brittle polymer sheet at fixed remote stress", "0.2 mm", "1.8 mm"), + ("glass plate at fixed remote stress", "0.1 mm", "0.9 mm"), + ], + }, + { + "law_id": "fick-flux", + "split": "replication", + "law_sign": 1, + "law_name": "Fickian diffusion-flux magnitude", + "response": "diffusive flux magnitude J_mag", + "control": "concentration-gradient magnitude g", + "formula_a": "J_mag = D g", + "formula_b": "J_mag/g = D", + "cases": [ + ("carbon diffusion in austenite at fixed temperature", "2 mol m^-4", "10 mol m^-4"), + ("hydrogen diffusion in nickel at fixed temperature", "3 mol m^-4", "15 mol m^-4"), + ], + }, + { + "law_id": "stress-intensity", + "split": "replication", + "law_sign": 1, + "law_name": "mode-I stress intensity at fixed stress", + "response": "stress-intensity factor K_I", + "control": "crack length a", + "formula_a": "K_I = C sqrt(a)", + "formula_b": "K_I^2/a = C^2", + "cases": [ + ("aluminum panel at fixed geometry factor and stress", "1 mm", "9 mm"), + ("steel plate at fixed geometry factor and stress", "2 mm", "18 mm"), + ], + }, + { + "law_id": "diffusion-distance", + "split": "replication", + "law_sign": 1, + "law_name": "diffusion distance at fixed diffusivity", + "response": "characteristic diffusion distance ell", + "control": "annealing time t", + "formula_a": "ell = sqrt(D t)", + "formula_b": "ell^2/t = D", + "cases": [ + ("carbon in iron at fixed temperature", "1 hour", "9 hours"), + ("boron in silicon at fixed temperature", "2 hours", "18 hours"), + ], + }, + { + "law_id": "coble-grain-size", + "split": "replication", + "law_sign": -1, + "law_name": "Coble grain-boundary-diffusion creep", + "response": "creep rate eps_dot", + "control": "grain size d", + "formula_a": "eps_dot = C/d^3", + "formula_b": "eps_dot d^3 = C", + "cases": [ + ("fine-grained ceramic at fixed stress and temperature", "0.5 micrometers", "4 micrometers"), + ("nanocrystalline metal at fixed stress and temperature", "0.2 micrometers", "1.6 micrometers"), + ], + }, + { + "law_id": "diffusion-time", + "split": "replication", + "law_sign": -1, + "law_name": "diffusive homogenization time at fixed length", + "response": "homogenization time t", + "control": "diffusivity D", + "formula_a": "t = L^2/D", + "formula_b": "t D = L^2", + "cases": [ + ("solute homogenization across a fixed slab", "1e-15 m^2 s^-1", "1e-13 m^2 s^-1"), + ("hydrogen transport across a fixed membrane", "2e-14 m^2 s^-1", "2e-12 m^2 s^-1"), + ], + }, + { + "law_id": "beam-deflection", + "split": "replication", + "law_sign": -1, + "law_name": "elastic beam deflection at fixed load and geometry", + "response": "midspan deflection delta", + "control": "Young modulus E", + "formula_a": "delta = C/E", + "formula_b": "delta E = C", + "cases": [ + ("simply supported composite beam", "20 GPa", "180 GPa"), + ("cantilever alloy beam", "40 GPa", "200 GPa"), + ], + }, + { + "law_id": "capillary-pressure", + "split": "replication", + "law_sign": -1, + "law_name": "Young-Laplace capillary pressure", + "response": "capillary pressure magnitude p_c", + "control": "pore radius r", + "formula_a": "p_c = C/r", + "formula_b": "p_c r = C", + "cases": [ + ("wetting liquid in a ceramic pore at fixed surface tension", "0.2 micrometers", "2 micrometers"), + ("molten metal in a capillary at fixed contact angle", "0.5 micrometers", "5 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 + formula = law[f"formula_{surface}"] + if answer_order == "a-larger": + key = "A means larger and B means smaller" + expected_code = "A" if physical_sign == 1 else "B" + else: + key = "A means smaller and B means larger" + expected_code = "B" if physical_sign == 1 else "A" + 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']}: {formula}. " + "All stated coefficients are positive, and quantities not varied below " + f"remain fixed. § In {system}, {law['control']} changes from {start} " + f"to {end}. ¶ What is the resulting direction of change in " + f"{law['response']}? † Choice key: {key}. Respond with A or B only." + ) + 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, + "expected_code": expected_code, + "user": user, + } + + +def build_manifest() -> dict: + 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 ("a-larger", "a-smaller"): + 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 paired prompt for {row['prompt_id']}") + row["opposite_numerical_prompt_id"] = opposite + return { + "study_id": "abstract-physics-composition-2026-07-18", + "markers": { + "law": "§", + "comparison": "¶", + "decision": "†", + }, + "laws": LAWS, + "prompts": prompts, + "dimensions": { + "n_laws": len(LAWS), + "n_discovery_laws": sum(row["split"] == "discovery" for row in LAWS), + "n_replication_laws": sum(row["split"] == "replication" for row in LAWS), + "n_prompts": len(prompts), + }, + } + + +def main() -> None: + parser = argparse.ArgumentParser() + parser.add_argument("--force", action="store_true") + args = parser.parse_args() + if not RUNNER.exists() or not ANALYZER.exists(): + raise RuntimeError("create the runner and analyzer before freezing") + if (OUT / "raw.json").exists() and args.force: + raise RuntimeError("refusing to overwrite a protocol after raw output exists") + + manifest = build_manifest() + atomic_json(MANIFEST, manifest) + previous = json.loads(PROTOCOL.read_text()) if PROTOCOL.exists() else {} + frozen_at = previous.get("frozen_at", datetime.now(timezone.utc).isoformat()) + protocol = { + "study_id": manifest["study_id"], + "status": "frozen-before-output", + "frozen_at": frozen_at, + "scientific_question": ( + "Does Gemma form a reusable physical-outcome representation that " + "cross-generalizes between direct and inverse constitutive laws, " + "rather than only encoding numerical direction or answer code?" + ), + "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 decision marker" + ), + "tie_break": "earliest layer", + }, + "primary_endpoint": { + "data": "replication laws only", + "representation": "raw residual stream at discovery-selected layer and decision marker", + "metric": ( + "mean of law-level physical-outcome AUCs: a direction trained " + "on discovery direct laws is tested on replication inverse laws, " + "and a direction trained on discovery inverse laws is tested on " + "replication direct laws" + ), + "independent_unit": "held-out constitutive law", + "inference": "30000 law bootstrap resamples and exact sign flip of AUC minus 0.5", + }, + "success_rule": { + "mean_replication_auc_minimum": 0.70, + "bootstrap_95_lower_above": 0.50, + "positive_replication_laws_minimum": 6, + "replication_laws_total": 8, + }, + "secondary_endpoints": [ + "parallelism of physical-outcome contrast vectors across direct and inverse laws", + "formula-form cross-generalization", + "answer-code-order cross-generalization at the final prompt state", + "numerical-direction decoding", + "word and character TF-IDF controls", + "layer-resolved emergence", + ], + "guardrails": [ + "The decision marker occurs before the answer key, so causal masking prevents answer-code leakage.", + "Layer selection uses discovery laws only.", + "Replication laws are never used to choose a layer or classifier hyperparameter.", + "The primary decoder is a parameter-free centroid direction.", + "All prompts and all model outputs are retained.", + "Passing representation tests alone does not establish causal use; interchange intervention is a separate stage.", + ], + "bootstrap": {"seed": 20260718, "resamples": 30000}, + } + atomic_json(PROTOCOL, protocol) + print( + f"frozen {manifest['dimensions']['n_prompts']} prompts across " + f"{manifest['dimensions']['n_laws']} laws" + ) + print(f"manifest sha256: {sha256(MANIFEST)}") + print(f"protocol sha256: {sha256(PROTOCOL)}") + + +if __name__ == "__main__": + main() diff --git a/jlens_materials/scripts/build_causal_relational_knockout.py b/jlens_materials/scripts/build_causal_relational_knockout.py new file mode 100644 index 0000000000000000000000000000000000000000..3ce71a33cdd45478142a5564b985944753d73f4a --- /dev/null +++ b/jlens_materials/scripts/build_causal_relational_knockout.py @@ -0,0 +1,518 @@ +#!/usr/bin/env python3 +"""Freeze a disjoint equation-free cohort for causal knockout and repair.""" + +from __future__ import annotations + +import json +from datetime import datetime, timezone +from pathlib import Path + + +ROOT = Path(__file__).resolve().parents[1] +OUT = ROOT / "experiments" / "causal-relational-knockout-2026-09-02" + + +LAWS = [ + { + "law_id": "elastic-strain-tensile-stress", + "domain": "mechanics", + "law_sign": 1, + "system": "a uniaxially loaded titanium coupon in its linear-elastic regime", + "control": "tensile stress", + "response": "elastic axial strain", + "low": "40 MPa", + "high": "240 MPa", + }, + { + "law_id": "thermal-strain-temperature-rise", + "domain": "thermal mechanics", + "law_sign": 1, + "system": "a freely expanding nickel strip with fixed thermal-expansion coefficient", + "control": "temperature rise above its reference state", + "response": "thermal strain", + "low": "20 kelvin", + "high": "180 kelvin", + }, + { + "law_id": "heat-flux-temperature-gradient", + "domain": "thermal transport", + "law_sign": 1, + "system": "a homogeneous ceramic slab with constant thermal conductivity", + "control": "temperature-gradient magnitude", + "response": "conductive heat-flux magnitude", + "low": "5 kelvin per meter", + "high": "45 kelvin per meter", + }, + { + "law_id": "diffusion-flux-concentration-gradient", + "domain": "mass transport", + "law_sign": 1, + "system": "a single-phase alloy at fixed temperature with constant diffusivity", + "control": "concentration-gradient magnitude", + "response": "diffusive solute-flux magnitude", + "low": "0.2 mole fraction per meter", + "high": "1.8 mole fraction per meter", + }, + { + "law_id": "resistance-conductor-length", + "domain": "electrical materials", + "law_sign": 1, + "system": "a uniform copper trace with fixed cross-section and resistivity", + "control": "conductor length", + "response": "electrical resistance", + "low": "2 centimeters", + "high": "18 centimeters", + }, + { + "law_id": "capacitance-permittivity", + "domain": "dielectrics", + "law_sign": 1, + "system": "a parallel-plate capacitor with fixed area and plate spacing", + "control": "dielectric relative permittivity", + "response": "capacitance", + "low": "2", + "high": "12", + }, + { + "law_id": "oxide-thickness-exposure-time", + "domain": "oxidation", + "law_sign": 1, + "system": "an alloy oxidizing isothermally within one parabolic-growth regime", + "control": "oxidation exposure time", + "response": "oxide-scale thickness", + "low": "1 hour", + "high": "25 hours", + }, + { + "law_id": "creep-rate-applied-stress", + "domain": "high-temperature deformation", + "law_sign": 1, + "system": "a superalloy deforming in one steady-state creep regime at fixed temperature", + "control": "applied uniaxial stress", + "response": "steady creep rate", + "low": "60 MPa", + "high": "180 MPa", + }, + { + "law_id": "dislocation-speed-shear-stress", + "domain": "crystal plasticity", + "law_sign": 1, + "system": "a crystal in a drag-controlled glide regime at fixed temperature", + "control": "resolved shear stress", + "response": "mean dislocation glide speed", + "low": "8 MPa", + "high": "48 MPa", + }, + { + "law_id": "vacancy-concentration-temperature", + "domain": "point defects", + "law_sign": 1, + "system": "a pure crystal below melting with fixed vacancy-formation energy", + "control": "absolute temperature", + "response": "equilibrium vacancy concentration", + "low": "500 kelvin", + "high": "1000 kelvin", + }, + { + "law_id": "sintering-neck-hold-time", + "domain": "powder processing", + "law_sign": 1, + "system": "two identical powder particles in one early-stage isothermal sintering regime", + "control": "sintering hold time", + "response": "interparticle neck radius", + "low": "2 minutes", + "high": "50 minutes", + }, + { + "law_id": "crack-opening-tensile-load", + "domain": "fracture mechanics", + "law_sign": 1, + "system": "a cracked plate under small-scale yielding with fixed crack geometry", + "control": "remote tensile load", + "response": "crack-opening displacement", + "low": "4 kilonewtons", + "high": "28 kilonewtons", + }, + { + "law_id": "fatigue-life-stress-amplitude", + "domain": "fatigue", + "law_sign": -1, + "system": "a polished steel specimen in one high-cycle fatigue regime", + "control": "cyclic stress amplitude", + "response": "fatigue life", + "low": "120 MPa", + "high": "300 MPa", + }, + { + "law_id": "temperature-gradient-conductivity", + "domain": "thermal transport", + "law_sign": -1, + "system": "a homogeneous slab carrying a fixed steady heat flux", + "control": "thermal conductivity", + "response": "required temperature-gradient magnitude", + "low": "2 watts per meter-kelvin", + "high": "20 watts per meter-kelvin", + }, + { + "law_id": "homogenization-time-diffusivity", + "domain": "mass transport", + "law_sign": -1, + "system": "an alloy diffusion couple with fixed homogenization distance", + "control": "solute diffusivity", + "response": "characteristic homogenization time", + "low": "1e-15 square meters per second", + "high": "1e-13 square meters per second", + }, + { + "law_id": "resistivity-carrier-mobility", + "domain": "electronic materials", + "law_sign": -1, + "system": "a semiconductor with fixed carrier concentration and charge", + "control": "carrier mobility", + "response": "electrical resistivity", + "low": "100 square centimeters per volt-second", + "high": "900 square centimeters per volt-second", + }, + { + "law_id": "cantilever-frequency-length", + "domain": "structural dynamics", + "law_sign": -1, + "system": "a uniform cantilever with fixed material, cross-section, and end condition", + "control": "cantilever length", + "response": "fundamental resonance frequency", + "low": "20 millimeters", + "high": "80 millimeters", + }, + { + "law_id": "strength-porosity", + "domain": "porous materials", + "law_sign": -1, + "system": "a brittle ceramic with fixed composition and pore morphology", + "control": "porosity", + "response": "tensile strength", + "low": "2 percent", + "high": "18 percent", + }, + { + "law_id": "corrosion-current-polarization-resistance", + "domain": "corrosion", + "law_sign": -1, + "system": "a metal-electrolyte interface within the linear-polarization regime", + "control": "polarization resistance", + "response": "corrosion-current density", + "low": "2 kilohm square-centimeters", + "high": "20 kilohm square-centimeters", + }, + { + "law_id": "cooling-rate-section-thickness", + "domain": "heat treatment", + "law_sign": -1, + "system": "a metal plate quenched with fixed alloy chemistry and surface conditions", + "control": "section thickness", + "response": "cooling-rate magnitude at the center", + "low": "2 millimeters", + "high": "20 millimeters", + }, + { + "law_id": "rupture-life-temperature", + "domain": "high-temperature deformation", + "law_sign": -1, + "system": "a superalloy held at fixed tensile stress in one creep-rupture regime", + "control": "absolute temperature", + "response": "creep-rupture life", + "low": "850 kelvin", + "high": "1150 kelvin", + }, + { + "law_id": "nucleation-rate-interfacial-energy", + "domain": "phase transformation", + "law_sign": -1, + "system": "a homogeneous nucleation process at fixed undercooling and temperature", + "control": "nucleus-matrix interfacial energy", + "response": "nucleation rate", + "low": "0.05 joules per square meter", + "high": "0.25 joules per square meter", + }, + { + "law_id": "surface-volume-ratio-particle-diameter", + "domain": "nanomaterials", + "law_sign": -1, + "system": "a population of geometrically similar spherical particles", + "control": "particle diameter", + "response": "surface-area-to-volume ratio", + "low": "10 nanometers", + "high": "100 nanometers", + }, + { + "law_id": "buckling-load-column-length", + "domain": "structural mechanics", + "law_sign": -1, + "system": "a slender elastic column with fixed flexural rigidity and end conditions", + "control": "column length", + "response": "critical buckling load", + "low": "0.5 meters", + "high": "2 meters", + }, +] + +VOCABULARIES = { + "higher-lower": ("higher", "lower"), + "greater-smaller": ("greater", "smaller"), +} + +MODEL_SPECS = { + "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", + }, +} + + +def prompt_text(law: dict, start: str, end: str, words: tuple[str, str]) -> str: + return ( + f"For {law['system']}, all other relevant physical conditions are unchanged. " + f"The {law['control']} changes from {start} to {end}. What happens to " + f"{law['response']}? Reply with exactly one lowercase word from this ordered " + f"set: {words[0]}, {words[1]}." + ) + + +def build_prompts() -> list[dict]: + prompts = [] + for law in LAWS: + category = "direct" if law["law_sign"] == 1 else "inverse" + for numerical_sign, start, end in [ + (-1, law["high"], law["low"]), + (1, law["low"], law["high"]), + ]: + physical_sign = int(law["law_sign"] * numerical_sign) + for vocab_id, base_words in VOCABULARIES.items(): + correct = base_words[0] if physical_sign > 0 else base_words[1] + incorrect = base_words[1] if physical_sign > 0 else base_words[0] + for order_id, words in [ + ("standard", base_words), + ("reversed", tuple(reversed(base_words))), + ]: + prompt_id = ( + f"crk24--{law['law_id']}--n{numerical_sign:+d}--" + f"{vocab_id}--{order_id}" + ) + counterpart_id = ( + f"crk24--{law['law_id']}--n{-numerical_sign:+d}--" + f"{vocab_id}--{order_id}" + ) + prompts.append( + { + "prompt_id": prompt_id, + "counterpart_prompt_id": counterpart_id, + "law_id": law["law_id"], + "domain": law["domain"], + "category": category, + "law_sign": law["law_sign"], + "numerical_sign": numerical_sign, + "physical_sign": physical_sign, + "answer_vocabulary": vocab_id, + "answer_order": order_id, + "answer_words": list(base_words), + "presented_words": list(words), + "expected_answer": correct, + "incorrect_answer": incorrect, + "user": prompt_text(law, start, end, words), + } + ) + return prompts + + +def write_inventory(prompts: list[dict]) -> None: + lines = [ + "# 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.", + "", + ] + for row in prompts: + lines.extend( + [ + f"## `{row['prompt_id']}`", + "", + f"- Law identity: `{row['law_id']}`", + f"- Registered category: `{row['category']}`", + f"- Expected answer: `{row['expected_answer']}`", + f"- Matched counterfactual: `{row['counterpart_prompt_id']}`", + f"- Prompt: {row['user']}", + "", + ] + ) + (OUT / "PROMPTS.md").write_text("\n".join(lines), encoding="utf-8") + + +def main() -> None: + OUT.mkdir(parents=True, exist_ok=True) + protocol_path = OUT / "protocol.json" + if protocol_path.exists(): + raise SystemExit(f"Refusing to overwrite frozen protocol: {protocol_path}") + prompts = build_prompts() + assert len(LAWS) == 24 + assert len(prompts) == 192 + assert len({row["prompt_id"] for row in prompts}) == len(prompts) + ids = {row["prompt_id"] for row in prompts} + assert all(row["counterpart_prompt_id"] in ids for row in prompts) + assert not ({row["law_id"] for row in prompts} & { + "taylor-strengthening", "voigt-reinforcement", "rubber-network", + "norton-creep", "orowan-spacing", "hall-petch", "griffith-strength", + "porosity-modulus", "griffith-energy-release", "fick-flux", + "stress-intensity", "diffusion-distance", "coble-grain-size", + "diffusion-time", "beam-deflection", "capillary-pressure", + }) + + manifest = { + "study_id": "causal-relational-knockout-24law-v1", + "created_at": datetime.now(timezone.utc).isoformat(), + "laws": LAWS, + "prompts": prompts, + } + (OUT / "prompt_manifest.json").write_text( + json.dumps(manifest, indent=2) + "\n", encoding="utf-8" + ) + write_inventory(prompts) + + protocol = { + "study_id": manifest["study_id"], + "status": "frozen-before-any-study-model-output", + "frozen_at": datetime.now(timezone.utc).isoformat(), + "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": MODEL_SPECS, + "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.", + ], + } + protocol_path.write_text(json.dumps(protocol, indent=2) + "\n", encoding="utf-8") + (OUT / "PROTOCOL.md").write_text( + "# Frozen causal relational knockout protocol\n\n" + "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.\n", + encoding="utf-8", + ) + print(f"frozen {len(prompts)} prompts across {len(LAWS)} laws -> {OUT}") + + +if __name__ == "__main__": + main() diff --git a/jlens_materials/scripts/build_engineering_appendix.py b/jlens_materials/scripts/build_engineering_appendix.py new file mode 100644 index 0000000000000000000000000000000000000000..185a67ef1df135ce91198df8b9248a10f11ed9a2 --- /dev/null +++ b/jlens_materials/scripts/build_engineering_appendix.py @@ -0,0 +1,147 @@ +#!/usr/bin/env python3 +"""Build a lay-readable, complete prompt and result appendix from raw runs.""" + +from __future__ import annotations + +import json +from pathlib import Path + + +ROOT = Path(__file__).resolve().parents[1] +OUTPUT = ROOT / "experiments" / "ENGINEERING_APPENDIX.md" + +RUNS = [ + ("Initial qualitative cases", "gemma4-materials-qual.json"), + ("Initial causal cases", "gemma4-materials-causal.json"), + ("Independent case-study replication", "gemma4-materials-paper-v1.json"), + ("Preregistered association and modulation study", "gemma4-materials-paper-v2.json"), + ("Closed-answer reasoning pilot", "gemma4-materials-reasoning-pilot-v1.json"), + ("Report-swap dose, alpha 0.25", "gemma4-materials-report-swap-a025.json"), + ("Report-swap dose, alpha 0.5", "gemma4-materials-report-swap-a05.json"), + ("Report-swap dose, alpha 1.0", "gemma4-materials-report-swap-a10.json"), + ("Mechanism-versus-eponym study", "gemma4-materials-mechanism-name-v1.json"), + ("Key concept-trace replication", "gemma4-materials-key-concept-traces-v1.json"), +] + + +def clean(text: object) -> str: + if text is None: + return "" + return str(text).replace("\n", " ").strip() + + +def score_table(record: dict) -> list[str]: + emergence = record.get("emergence") or [] + if not emergence: + return [] + rows = [ + "| concept | Jacobian rank | logit rank | best depth | sustained top-5 onset |", + "|---|---:|---:|---:|---:|", + ] + for item in emergence: + j = item.get("best_rank") + l = item.get("logit_lens_best_rank") + j = "-" if j is None or int(j) < 0 else str(int(j) + 1) + l = "-" if l is None or int(l) < 0 else str(int(l) + 1) + rows.append( + f"| `{item.get('label', '')}` | {j} | {l} | " + f"{item.get('best_depth', '-')} | {item.get('onset_depth', '-')} |" + ) + return rows + + +def swap_table(record: dict) -> list[str]: + swap = record.get("swap") + if not swap: + return [] + lines = ["**Intervention record:**", "", "```json"] + lines.extend(json.dumps(swap, indent=2).splitlines()) + lines.append("```") + return lines + + +def record_section(index: int, record: dict) -> list[str]: + status = "included" if record.get("valid_for_metrics") else "excluded" + excluded = "; ".join(record.get("excluded_reasons") or []) or "none" + tracked = ", ".join( + f"`{item.get('label', '')}`" for item in record.get("tracked", []) + ) or "none" + dropped = ", ".join( + f"`{item.get('label', item) if isinstance(item, dict) else item}`" + for item in record.get("tracked_dropped", []) + ) or "none" + lines = [ + f"### {index}. {record.get('slug', 'unnamed')}", + "", + f"- **Experiment type:** `{record.get('shape', '')}` / `{record.get('protocol', '')}`", + f"- **Metric status:** {status}; exclusion reasons: {excluded}", + f"- **Predetermined readout:** `{record.get('readout_selector', '')}` at token position(s) `{record.get('score_positions', [])}`", + f"- **Tracked concepts:** {tracked}", + f"- **Tokenizer-dropped concepts:** {dropped}", + "", + f"**Exact model input:** {clean(record.get('prompt_text'))}", + "", + f"**Clean generated or teacher-forced continuation:** `{clean(record.get('generated_completion'))}`", + "", + ] + baseline = record.get("baseline") or {} + if baseline.get("required"): + lines.extend([ + f"**Clean-answer check:** expected `{baseline.get('expected')}`; model produced " + f"`{baseline.get('greedy_token', '')}`; correct = `{baseline.get('correct')}`.", + "", + ]) + lines.extend(score_table(record)) + if record.get("emergence"): + lines.append("") + lines.extend(swap_table(record)) + if record.get("swap"): + lines.append("") + return lines + + +def main() -> None: + lines = [ + "# Engineering appendix: every Gemma materials experiment", + "", + "This appendix is generated directly from the stored run JSON files. It is intended for engineers and materials scientists who do not specialize in neural-network interpretability.", + "", + "## Plain-language guide", + "", + "A transformer processes text through a sequence of computational stages called layers. At each layer, its state is a long vector rather than a sentence. A lens converts that vector into a ranked vocabulary list so that we can ask whether a predeclared word such as `toughness` is easy or difficult to read from that state.", + "", + "- **Rank 1** means the selected word is the strongest vocabulary readout at that layer and position. Rank 10 means only nine vocabulary tokens score more highly. Lower is therefore stronger.", + "- The **logit lens** directly reuses the model's final word-output coordinates at every intermediate layer. This is simple, but early layers may use different coordinates.", + "- The **Jacobian lens** first applies an average map describing how a small change at an intermediate layer propagates toward later verbal output. It attempts to correct for those changing coordinates.", + "- A plotted **concept trace** is a sequence of lens ranks through the network. It is a measurement produced by a linear readout, not a transcript of private prose and not proof of a human-like chain of thought.", + "- A concept being readable does not prove that it caused the answer. That is why separate intervention experiments attempt to modify the state and observe whether behavior changes.", + "", + "## Common experimental controls", + "", + "Tracked words are declared before running the prompt. The scored token position and layer band are fixed. Association items require the tracked token to be absent from both the tokenized input and the clean continuation. Both lenses use the same word forms, token positions, and layer band. Invalid clean answers are excluded rather than reinterpreted after inspection.", + "", + "The current 1,000-record Gemma lens is exploratory: its fitting-corpus fingerprint is unavailable and its layer recipe predates the source paper's final protocol. This affects the strength of the scientific claim, not the completeness of the record below.", + "", + ] + for heading, filename in RUNS: + path = ROOT / "runs" / filename + if not path.exists(): + continue + run = json.loads(path.read_text()) + records = run.get("prompts", []) + lines.extend([ + f"## {heading}", + "", + f"Raw run: [`runs/{filename}`](../runs/{filename})", + "", + f"Records: {len(records)}.", + "", + ]) + for index, record in enumerate(records, start=1): + lines.extend(record_section(index, record)) + OUTPUT.write_text("\n".join(lines) + "\n") + print(f"wrote {OUTPUT} ({OUTPUT.stat().st_size:,} bytes)") + + +if __name__ == "__main__": + main() diff --git a/jlens_materials/scripts/build_equation_free_relational_benchmark.py b/jlens_materials/scripts/build_equation_free_relational_benchmark.py new file mode 100644 index 0000000000000000000000000000000000000000..18e7ccbbbcf4a8b4d1c9c07ae84dd5475dd8a490 --- /dev/null +++ b/jlens_materials/scripts/build_equation_free_relational_benchmark.py @@ -0,0 +1,381 @@ +#!/usr/bin/env python3 +"""Freeze the equation-free prompt-format transfer benchmark over all 60 laws.""" + +from __future__ import annotations + +import hashlib +import json +import sys +from datetime import datetime, timezone +from pathlib import Path + + +ROOT = Path(__file__).resolve().parents[1] +sys.path.insert(0, str(ROOT / "scripts")) + +from build_neutral_anchored_relational_benchmark import LAWS as SOURCE_LAWS # noqa: E402 + + +OUT = ROOT / "experiments" / "equation-free-relational-physics-2026-08-21" +MANIFEST = OUT / "prompt_manifest.json" +PROMPTS_MD = OUT / "PROMPTS.md" +PROTOCOL = OUT / "protocol.json" +RUNNER = ROOT / "scripts" / "run_equation_free_relational_benchmark.py" +ANALYZER = ROOT / "scripts" / "analyze_equation_free_relational_benchmark.py" +VALIDATOR = ROOT / "scripts" / "validate_equation_free_relational_benchmark.py" +SOURCE_BUILDER = ROOT / "scripts" / "build_neutral_anchored_relational_benchmark.py" +DEVELOPMENT = ROOT / "experiments" / "elicited-physics-abstraction-2026-07-18" +REFERENCE = ROOT / "experiments" / "neutral-anchored-relational-physics-2026-07-18" + + +ANSWER_ORDERS = { + "standard": ["higher", "lower", "unchanged"], + "reversed": ["unchanged", "lower", "higher"], +} + +# The supplied equations in the source benchmark fixed positive coefficients. +# Once those equations are removed, a few signed quantities would otherwise be +# ambiguous (for example, the Hall coefficient of an n-type material is +# negative even though its magnitude falls with carrier density). These +# wording-only overrides state the positive magnitude intended by the original +# relation without disclosing its monotonic orientation. +NATURAL_OVERRIDES = { + "hall-voltage-field": { + "response": "Hall-voltage magnitude V_H", + "control": "magnetic-field magnitude B", + }, + "hall-coefficient-carriers": { + "response": "single-carrier Hall-coefficient magnitude R_H", + }, + "magnetization-field": { + "response": "magnetization magnitude M", + "control": "magnetic-field strength H", + }, + "settling-speed-viscosity": { + "response": "terminal settling-speed magnitude v_s", + }, + "capillary-rise-surface-tension": { + "case_context": {1: "a wetting molten metal in a fixed capillary"}, + }, + "capillary-rise-radius": { + "response": "capillary-height magnitude h_c", + "case_context": {1: "a wetting molten salt in a capillary"}, + }, +} + + +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 clean_law(source: dict) -> dict: + """Retain experimental metadata but deliberately omit equation and law name.""" + output = { + "law_id": source["law_id"], + "category": source["category"], + "law_sign": source["law_sign"], + "domain": source["domain"], + "response": source["response"], + "control": source["control"], + "cases": [ + {"context": context, "low": low, "high": high} + for context, low, high in source["cases"] + ], + "neutral_role": source.get("neutral_role"), + } + override = NATURAL_OVERRIDES.get(source["law_id"], {}) + for field in ("response", "control"): + if field in override: + output[field] = override[field] + for case_index, context in override.get("case_context", {}).items(): + output["cases"][int(case_index)]["context"] = context + return output + + +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"For {selected['context']}, all other relevant physical conditions " + f"are unchanged. The {item['control']} changes from {start} to {end}. " + f"What happens to {item['response']}? Reply with exactly one lowercase " + f"word from this ordered set: {choices}." + ) + else: + user = ( + f"Consider {selected['context']}. Change only the {item['control']} " + f"from {start} to {end} while keeping every other relevant condition " + f"the same. Determine the resulting trend in {item['response']}. " + f"Answer exactly one lowercase word from this ordered set: {choices}." + ) + physical_sign = int(item["law_sign"] * numerical_sign) + return { + "prompt_id": f"eqfree60--{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"], + "neutral_role": item.get("neutral_role"), + "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": {1: "higher", -1: "lower", 0: "unchanged"}[physical_sign], + "user": user, + } + + +def render_prompt_inventory(manifest: dict) -> str: + lines = [ + "# Exact equation-free 60-law prompts", + "", + "This inventory was generated and frozen before model execution. It contains all 960 exact prompts.", + "", + ] + prompts = manifest["prompts"] + for law_record in manifest["laws"]: + lines.extend( + [ + f"## {law_record['law_id']}", + "", + ( + f"Category: `{law_record['category']}`; domain: `{law_record['domain']}`; " + f"response: {law_record['response']}; control: {law_record['control']}; " + f"neutral role: `{law_record.get('neutral_role') or 'not applicable'}`." + ), + "", + ] + ) + for prompt in (row for row in prompts if row["law_id"] == law_record["law_id"]): + lines.extend( + [ + f"- `{prompt['prompt_id']}`", + f" - Expected: `{prompt['expected_answer']}`", + f" - Prompt: {prompt['user']}", + ] + ) + lines.append("") + return "\n".join(lines) + "\n" + + +def main() -> None: + if (OUT / "raw.json").exists(): + raise RuntimeError("refusing to rebuild after model outputs exist") + required = [ + RUNNER, + ANALYZER, + VALIDATOR, + SOURCE_BUILDER, + DEVELOPMENT / "prompt_manifest.json", + DEVELOPMENT / "representations.npz", + REFERENCE / "statistics.json", + REFERENCE / "law_level_neutral_normalized.csv", + REFERENCE / "prompt_manifest.json", + ] + missing = [str(path) for path in required if not path.exists()] + if missing: + raise RuntimeError(f"missing required inputs: {missing}") + + laws = [clean_law(item) for item in SOURCE_LAWS] + counts = { + category: sum(item["category"] == category for item in laws) + for category in ("direct", "inverse", "neutral") + } + if counts != {"direct": 20, "inverse": 20, "neutral": 20}: + raise RuntimeError(f"unbalanced law inventory: {counts}") + if len({item["law_id"] for item in laws}) != 60: + raise RuntimeError("law ids are not 60 unique values") + neutral_roles = { + role: sum(item.get("neutral_role") == role for item in laws) + for role in ("calibration", "validation") + } + if neutral_roles != {"calibration": 10, "validation": 10}: + raise RuntimeError(f"bad neutral split: {neutral_roles}") + + 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 = ( + "constitutive equation", + "direct-response law", + "inverse-response law", + "stage 1", + "stage 2", + "silently compose", + " = ", + ) + offenders = [ + row["prompt_id"] + for row in prompts + if any(term in row["user"].lower() for term in forbidden) + ] + if offenders: + raise RuntimeError(f"equation/scaffold leakage: {offenders[:5]}") + + manifest = { + "study_id": "equation-free-relational-physics-2026-08-21", + "laws": laws, + "prompts": prompts, + "dimensions": { + "n_laws": 60, + "n_direct": 20, + "n_inverse": 20, + "n_neutral": 20, + "n_neutral_calibration": 10, + "n_neutral_validation": 10, + "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) + PROMPTS_MD.parent.mkdir(parents=True, exist_ok=True) + PROMPTS_MD.write_text(render_prompt_inventory(manifest)) + + frozen_paths = { + "prompt_manifest": MANIFEST, + "prompt_inventory": PROMPTS_MD, + "builder": Path(__file__).resolve(), + "runner": RUNNER, + "analyzer": ANALYZER, + "validator": VALIDATOR, + "source_60_law_builder": SOURCE_BUILDER, + "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", + "reference_prompt_manifest": REFERENCE / "prompt_manifest.json", + } + protocol = { + "study_id": manifest["study_id"], + "status": "full-prompt-format-transfer-frozen-before-model-output", + "frozen_at": datetime.now(timezone.utc).isoformat(), + "model": "google/gemma-4-E4B-it", + "model_revision": "a4c2d58be94dda072b918d9db64ee85c8ed34e3f", + "design_scope": ( + "All 60 law identities are reused from the earlier scaffolded benchmark, " + "but every prompt is newly generated without an equation, law name, " + "law-orientation label, or explicit sign-composition instruction. This " + "isolates prompt-format transfer and is not a fresh-law confirmation." + ), + "prompt_ablation": { + "removed": [ + "constitutive equation", + "law name", + "direct/inverse/neutral label", + "two-stage monotonicity decomposition", + "instruction to compose law sign with numerical sign", + ], + "retained": [ + "response and control names", + "material scenario", + "controlled numerical reversal", + "all-other-conditions-unchanged clause", + "one-word higher/lower/unchanged answer format", + ], + }, + "inputs": { + name: {"path": str(path.relative_to(ROOT)), "sha256": sha256(path)} + for name, path in frozen_paths.items() + }, + "frozen_readout": { + "position": "final_prompt", + "layer": 34, + "state_normalization": "unit L2", + "direction": "positive-minus-negative centroids from the original 16 development laws", + "benchmark_refit": "none", + "old_scaffold_center": 0.013596733322224686, + "old_scaffold_scale": 0.008536733931615473, + }, + "natural_neutral_calibration": { + "center": "median law contrast over the 10 preassigned calibration-neutral laws", + "scale": "1.4826 times MAD over those 10 calibration-neutral laws", + "validation": "the other 10 neutral laws do not contribute to center or scale", + }, + "matched_contrast": ( + "Within each law/surface/case/answer-order cell, frozen-direction " + "score for numerical-up minus score for numerical-down." + ), + "primary_endpoints": [ + "direct-versus-inverse law AUC", + "direct-versus-validation-neutral law AUC", + "validation-neutral-versus-inverse law AUC", + "directional sign accuracy at the natural calibration-neutral median", + "direct-versus-inverse AUC on both natural surfaces", + "inverse-neutral-direct ordinal Spearman correlation using validation neutrals", + ], + "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, + }, + "secondary_endpoints": [ + "directional sign accuracy under the old scaffolded neutral center", + "law-score correlation with the supplied-equation condition", + "answer-order, surface, and material-case strata", + "greedy three-way answer accuracy", + ], + "controls": [ + "output-head higher-minus-lower matched change", + "word TF-IDF direction trained on original development prompts", + "character TF-IDF direction trained on original development prompts", + ], + "inference": { + "bootstrap_seed": 20260822, + "bootstrap_resamples": 50000, + "permutation_seed": 20260823, + "permutations": 100000, + }, + "guardrails": [ + "All 60 original law identities are retained without exclusion.", + "All 960 prompts are written to machine- and human-readable files before execution.", + "No equation, law name, law-orientation label, or decomposition instruction appears in a prompt.", + "No benchmark state refits the layer, hidden direction, or midpoint.", + "Only the 10 assigned natural calibration neutrals define the natural-format center and scale.", + "The old scaffolded center and scale remain a separately reported cross-format endpoint.", + "All outputs and failures are retained.", + ], + } + write_json(PROTOCOL, protocol) + (OUT / "PROTOCOL.md").write_text( + "# Equation-free 60-law relational benchmark\n\n" + "All 60 original law identities are evaluated under natural prompts that " + "omit the equation, law name, orientation label, and explicit two-stage " + "composition scaffold. The layer-34 direction is reused without refitting. " + "Ten preassigned neutral laws calibrate the natural prompt format and ten " + "different neutral laws validate it. All 960 prompts were frozen before " + "model execution and are printed in `PROMPTS.md`.\n" + ) + print(f"frozen {len(prompts)} prompts over 60 laws: {counts}") + print(f"manifest sha256: {sha256(MANIFEST)}") + print(f"prompt inventory sha256: {sha256(PROMPTS_MD)}") + print(f"protocol sha256: {sha256(PROTOCOL)}") + + +if __name__ == "__main__": + main() diff --git a/jlens_materials/scripts/build_gemma31b_compositional_physics_pilot.py b/jlens_materials/scripts/build_gemma31b_compositional_physics_pilot.py new file mode 100644 index 0000000000000000000000000000000000000000..207f8f83463d65a0c66902b9b7b9234398d3c3a7 --- /dev/null +++ b/jlens_materials/scripts/build_gemma31b_compositional_physics_pilot.py @@ -0,0 +1,278 @@ +#!/usr/bin/env python3 +"""Build the frozen 31B equation-free two-link physics-composition pilot.""" + +from __future__ import annotations + +import hashlib +import json +from pathlib import Path + + +ROOT = Path(__file__).resolve().parents[1] +OUT = ROOT / "experiments" / "gemma4-31b-compositional-physics-2026-08-25" +MODEL = "google/gemma-4-31B-it" +MODEL_REVISION = "842da3794eaa0b77d5f08bae87a17459d91ff475" +LAYERS = [0, 8, 16, 24, 32, 40, 45, 49, 53, 57, 59] +MARKERS = {"context": "§", "perturbation": "¶", "question": "†"} + + +def sha256(path: Path) -> str: + return hashlib.sha256(path.read_bytes()).hexdigest() + + +def chain( + chain_id: str, + split: str, + domain: str, + link1: int, + link2: int, + control: str, + low: str, + high: str, + response: str, + context_a: str, + context_b: str, +) -> dict: + return locals() + + +CHAINS = [ + # (+,+): net direct + chain("underaged-precipitation-strength", "development", "precipitation", 1, 1, + "aging time", "2 h", "8 h", "yield strength", + "An Al-Cu alloy remains in the underaged regime. Aging controls the coherent precipitate fraction, and those precipitates obstruct dislocation glide.", + "Consider an underaged aluminum-copper alloy whose coherent-particle population is set by aging and whose strength is controlled by particle resistance to glide."), + chain("strain-hardening", "development", "plasticity", 1, 1, + "accumulated plastic strain", "0.05", "0.25", "flow stress", + "A metal remains in the conventional strain-hardening regime. Plastic deformation stores dislocations, and Taylor strengthening controls the flow stress.", + "Consider ordinary work hardening: deformation builds the dislocation population and dislocation interactions govern the stress needed for further flow."), + chain("carburization-hardness", "development", "surface treatment", 1, 1, + "carburizing time", "1 h", "6 h", "surface hardness", + "A steel remains in the diffusion-controlled carburizing regime before saturation. Carbon uptake controls the near-surface carbon content, and carbon in solid solution controls hardness.", + "Consider a nonsaturated carburizing treatment in which carbon incorporation sets the surface composition and solid-solution carbon governs indentation hardness."), + chain("oxidation-resistance", "test", "oxidation", 1, 1, + "oxidation time", "1 h", "10 h", "electrical resistance across the scale", + "A dense insulating oxide remains in its normal growth regime. Exposure controls scale thickness, and charge must traverse that scale normal to its surface.", + "Consider a compact dielectric oxide that continues to grow normally; its exposure history sets film thickness and current crosses the film through its thickness."), + chain("sinter-neck-stiffness", "test", "sintering", 1, 1, + "sintering dwell time", "5 min", "60 min", "compact stiffness", + "A powder compact remains in initial-stage sintering. Dwell time controls interparticle neck development, and load transfer occurs through those necks.", + "Consider early sintering of a particulate compact, where thermal dwell sets neck development and the neck network carries the elastic load."), + chain("polymer-cure-modulus", "test", "polymers", 1, 1, + "cure time", "10 min", "60 min", "elastic modulus", + "A thermoset remains before vitrification and below full conversion. Cure progression controls crosslink density, and the network connectivity controls elastic modulus.", + "Consider an incompletely cured thermoset in its monotonic conversion regime; reaction progress sets network crosslinking and that network governs stiffness."), + + # (+,-): net inverse + chain("grain-growth-hall-petch", "development", "microstructure", 1, -1, + "annealing time", "5 min", "120 min", "yield strength", + "A polycrystal remains in normal grain growth during annealing. Its yield stress is governed by Hall-Petch grain-boundary strengthening.", + "Consider a single-phase polycrystal undergoing ordinary annealing-driven grain growth while grain-boundary strengthening controls yielding."), + chain("fatigue-crack-load", "development", "fracture", 1, -1, + "fatigue-cycle count", "10000 cycles", "1000000 cycles", "remaining fracture load", + "A component remains in stable Paris-regime fatigue growth. Its remaining load capacity is controlled by the longest crack through linear-elastic fracture mechanics.", + "Consider stable cyclic crack extension in a brittle component, with the critical remaining load set by the dominant crack length."), + chain("creep-void-life", "development", "creep", 1, -1, + "creep exposure time", "100 h", "1000 h", "remaining creep life", + "An alloy remains in tertiary creep with progressive grain-boundary cavitation. The remaining lifetime is governed by the accumulated void population.", + "Consider a high-temperature alloy in a monotonic cavitation regime, where exposure develops boundary voids and those voids control residual creep life."), + chain("irradiation-thermal-conductivity", "test", "irradiation", 1, -1, + "irradiation dose", "0.1 dpa", "5 dpa", "thermal conductivity", + "A crystal remains in a defect-accumulation regime without amorphization. Irradiation sets the point-defect and cluster population, and phonons are scattered by those defects.", + "Consider crystalline irradiation below amorphization: dose controls retained defect density and defect scattering controls heat transport."), + chain("corrosion-pit-fatigue", "test", "corrosion", 1, -1, + "chloride exposure time", "1 day", "30 days", "fatigue strength", + "A metal remains in stable localized pitting. Exposure controls the deepest pit, and cyclic failure initiates from that dominant pit.", + "Consider a chloride-exposed alloy in a monotonic pitting regime, where exposure develops the critical pit and that flaw governs fatigue resistance."), + chain("hydrogen-cohesion", "test", "hydrogen embrittlement", 1, -1, + "hydrogen charging time", "10 min", "120 min", "grain-boundary cohesive strength", + "A steel remains below hydride formation while hydrogen uptake is monotonic. Boundary hydrogen occupancy controls cohesive strength through hydrogen-enhanced decohesion.", + "Consider a hydrogen-charged steel before hydride precipitation; charging sets interfacial hydrogen occupancy and decohesion governs boundary strength."), + + # (-,+): net inverse + chain("recovery-dislocation-strength", "development", "recovery", -1, 1, + "recovery time", "1 min", "60 min", "flow stress", + "A cold-worked metal remains in static recovery without recrystallization. Recovery controls the stored dislocation density, and Taylor strengthening controls flow stress.", + "Consider a deformed alloy during recovery before recrystallization, with the dislocation population set by recovery and glide resistance set by dislocation strengthening."), + chain("viscosity-pressure-drop", "development", "rheology", -1, 1, + "melt temperature", "450 K", "550 K", "extrusion pressure drop", + "A Newtonian polymer melt is extruded at fixed throughput and geometry. Temperature controls melt viscosity, and viscous resistance controls the required pressure drop.", + "Consider fixed-rate extrusion of a Newtonian melt through an unchanged die; thermal state sets viscosity and viscosity sets the pumping pressure."), + chain("stress-relief-distortion", "development", "residual stress", -1, 1, + "stress-relief time", "10 min", "120 min", "machining distortion", + "A welded plate remains in the stress-relief regime without microstructural transformation. The treatment controls residual stress, and elastic unloading of that stress controls machining distortion.", + "Consider subcritical stress relief of a welded component, where the treatment changes locked-in stress and release of that stress governs shape change during machining."), + chain("particle-size-dissolution", "test", "surface kinetics", -1, 1, + "particle radius", "0.5 micrometers", "5 micrometers", "initial dissolution rate", + "A monodisperse powder has fixed total solid mass and remains surface-reaction controlled. Particle size controls specific surface area, and exposed area controls the initial dissolution rate.", + "Consider equal powder mass divided into monodisperse particles under surface-controlled dissolution; particle radius sets area per mass and that area sets the initial rate."), + chain("grain-size-boundary-diffusion", "test", "diffusion", -1, 1, + "grain size", "0.5 micrometers", "50 micrometers", "effective diffusivity", + "A dense polycrystal remains in a grain-boundary-diffusion-dominated regime. Grain size controls boundary area density, and the boundary network carries the fast transport path.", + "Consider a pore-free polycrystal whose transport is dominated by grain boundaries; microstructural scale sets boundary density and the boundaries set effective transport."), + chain("pressure-free-volume-diffusion", "test", "high-pressure transport", -1, 1, + "hydrostatic pressure", "0.1 GPa", "1.0 GPa", "atomic diffusivity", + "A dense amorphous solid remains below structural transformation. Pressure controls free volume, and atomic mobility is governed by the available free volume.", + "Consider an amorphous material under hydrostatic compression without a phase change, where packing sets free volume and free volume controls diffusion."), + + # (-,-): net direct + chain("cooling-grain-strength", "development", "solidification", -1, -1, + "cooling rate", "1 K/s", "100 K/s", "yield strength", + "A cast alloy remains in a grain-refinement-by-cooling regime. Cooling history controls final grain size, and Hall-Petch strengthening governs yielding.", + "Consider solidification where cooling rate sets the grain scale and the resulting polycrystal follows Hall-Petch yielding."), + chain("compaction-porosity-modulus", "development", "powder processing", -1, -1, + "compaction pressure", "50 MPa", "500 MPa", "elastic modulus", + "A powder body remains in monotonic densification without particle fracture. Pressing controls residual porosity, and porosity controls the effective elastic modulus.", + "Consider pressure-assisted compaction in its densification regime; processing pressure sets the pore fraction and the pore fraction governs stiffness."), + chain("grain-refiner-fatigue", "development", "alloy processing", -1, -1, + "grain-refiner addition", "0.01 wt percent", "0.10 wt percent", "fatigue limit", + "An alloy remains in the effective inoculation regime without particle agglomeration. Refiner addition controls final grain size, and grain size controls the fatigue limit through boundary-mediated slip resistance.", + "Consider monotonic grain refinement by an inoculant below its agglomeration limit, with the resulting grain scale governing fatigue resistance."), + chain("temperature-viscosity-settling", "test", "fluid mechanics", -1, -1, + "liquid temperature", "290 K", "350 K", "particle settling speed", + "A dilute suspension remains Newtonian and in the Stokes-settling regime. Temperature controls liquid viscosity, and viscous drag controls terminal settling speed.", + "Consider isolated particles settling at low Reynolds number in a Newtonian liquid; thermal state sets viscosity and Stokes drag sets terminal motion."), + chain("anneal-defects-conductivity", "test", "electronic transport", -1, -1, + "defect-annealing time", "1 min", "60 min", "electrical conductivity", + "A metal remains in point-defect recovery without precipitation. Annealing controls retained defect density, and electron scattering by those defects controls conductivity.", + "Consider defect annealing in a single-phase metal, where recovery sets the scattering-center population and defect scattering governs charge transport."), + chain("crosslink-mobility-modulus", "test", "polymer networks", -1, -1, + "crosslink density", "0.1 mol/L", "1.0 mol/L", "elastic modulus", + "A rubbery network remains above its glass transition. Crosslink density controls segmental mobility, and that mobility controls compliance and therefore the elastic modulus.", + "Consider a rubber-elastic polymer network in which crosslinking sets chain mobility and chain mobility governs mechanical compliance."), +] + + +def prompt_text(item: dict, surface: str, numerical_sign: int, answer_format: str, code_flip: bool) -> tuple[str, str, str]: + context = item[f"context_{surface}"] + start, end = (item["low"], item["high"]) if numerical_sign > 0 else (item["high"], item["low"]) + prefix = ( + f"{context} All unstated conditions remain fixed. {MARKERS['context']} " + f"The {item['control']} changes from {start} to {end}. {MARKERS['perturbation']} " + f"What happens to the {item['response']}? {MARKERS['question']}" + ) + physical_sign = item["link1"] * item["link2"] * numerical_sign + semantic_answer = "higher" if physical_sign > 0 else "lower" + if answer_format == "semantic": + order = ["higher", "lower"] if not code_flip else ["lower", "higher"] + suffix = f" Reply with exactly one lowercase word from this ordered pair: {order[0]}, {order[1]}." + expected = semantic_answer + mapping = {"higher": "higher", "lower": "lower"} + else: + mapping = ({"higher": "A", "lower": "B"} if not code_flip else {"higher": "B", "lower": "A"}) + suffix = ( + f" Use this code: A means {'higher' if mapping['higher'] == 'A' else 'lower'} and " + f"B means {'lower' if mapping['lower'] == 'B' else 'higher'}. " + "Reply with exactly one uppercase letter from this ordered pair: A, B." + ) + expected = mapping[semantic_answer] + return prefix + suffix, expected, semantic_answer + + +def main() -> None: + OUT.mkdir(parents=True, exist_ok=True) + prompts = [] + for chain_index, item in enumerate(CHAINS): + for surface in ("a", "b"): + for numerical_sign in (-1, 1): + for answer_format in ("semantic", "arbitrary"): + code_flip = (chain_index + (surface == "b")) % 2 == 1 + user, expected, semantic_answer = prompt_text( + item, surface, numerical_sign, answer_format, code_flip + ) + physical_sign = item["link1"] * item["link2"] * numerical_sign + prompt_id = ( + f"compose--{item['chain_id']}--s{surface}--n{numerical_sign:+d}--{answer_format}" + ) + prompts.append( + { + "prompt_id": prompt_id, + "chain_id": item["chain_id"], + "split": item["split"], + "domain": item["domain"], + "surface": surface, + "answer_format": answer_format, + "code_flip": code_flip, + "link1_sign": item["link1"], + "link2_sign": item["link2"], + "net_law_sign": item["link1"] * item["link2"], + "numerical_sign": numerical_sign, + "physical_sign": physical_sign, + "control": item["control"], + "response": item["response"], + "expected_answer": expected, + "semantic_answer": semantic_answer, + "user": user, + } + ) + + manifest = { + "study_id": "gemma4-31b-compositional-physics-2026-08-25", + "markers": MARKERS, + "dimensions": { + "chains": len(CHAINS), + "development_chains": sum(row["split"] == "development" for row in CHAINS), + "test_chains": sum(row["split"] == "test" for row in CHAINS), + "surfaces": 2, + "numerical_directions": 2, + "answer_formats": 2, + "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_compositional_physics_pilot.py" + analyzer = ROOT / "scripts" / "analyze_gemma31b_compositional_physics_pilot.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": ["context", "perturbation", "question", "final_prompt"], + "primary_endpoint": ( + "test-chain physical-outcome AUC at the answer-free question marker and layer 49, " + "using a centroid direction fitted only on development chains" + ), + "success_criteria": { + "primary_auc_minimum": 0.80, + "each_surface_auc_minimum": 0.75, + "exact_chain_parity_permutation_p_maximum": 0.05, + "word_tfidf_auc_maximum_for_nonlexical_support": 0.70, + }, + "secondary_endpoints": [ + "layerwise context-to-question emergence of net physical outcome", + "held-out decoding of link-1 sign, link-2 sign, net law sign, and numerical sign", + "zero-shot application of the previously frozen one-step layer-49 direction to held-out two-link chains", + "semantic-to-arbitrary and arbitrary-to-semantic final-state transfer", + "paired answer-format identity before the answer suffix", + "pairwise answer-logit accuracy by answer format and held-out chain", + ], + "guardrails": [ + "No equation, law sign, direct/inverse label, or sign-composition instruction appears in a prompt.", + "Chain identity is disjoint between development and test splits.", + "All four link-sign combinations are balanced 3/3 between development and test.", + "The scientific question marker precedes all semantic or arbitrary answer mappings.", + "This pilot tests composition of familiar mechanisms, not discovery of unfamiliar laws.", + ], + "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 compositional-physics 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_gemma31b_goal_specific_causal_roles.py b/jlens_materials/scripts/build_gemma31b_goal_specific_causal_roles.py new file mode 100644 index 0000000000000000000000000000000000000000..6e847510d555f5a5c49a6a2b006da2ebe02cd25f --- /dev/null +++ b/jlens_materials/scripts/build_gemma31b_goal_specific_causal_roles.py @@ -0,0 +1,465 @@ +#!/usr/bin/env python3 +"""Freeze a prospective 31B causal-role intervention benchmark.""" + +from __future__ import annotations + +import hashlib +import json +from pathlib import Path + + +ROOT = Path(__file__).resolve().parents[1] +OUT = ROOT / "experiments" / "gemma4-31b-goal-specific-causal-roles-2026-09-02" +MODEL = "google/gemma-4-31B-it" +MODEL_REVISION = "842da3794eaa0b77d5f08bae87a17459d91ff475" +PROBE_LAYER = 49 +MARKERS = {"context": "§", "perturbation": "¶", "question": "†"} + + +def sha256(path: Path) -> str: + return hashlib.sha256(path.read_bytes()).hexdigest() + + +def chain( + chain_id: str, + domain: str, + link1: int, + link2: int, + control: str, + low: str, + high: str, + intermediate: str, + response: str, + context_a: str, + context_b: str, +) -> dict: + return locals() + + +# These 24 chain identities are disjoint from all 24 chains used to discover the +# role-specific hypothesis. Six chains instantiate each of the four possible +# sign compositions. Prompts state familiar regimes but provide no equation or +# instruction to multiply signs. +CHAINS = [ + # (+,+): an increase raises the intermediate and the intermediate raises y. + chain( + "solute-misfit-yield-strength", "solid-solution strengthening", 1, 1, + "substitutional solute concentration", "0.2 at percent", "2.0 at percent", + "lattice-misfit strain field density", "yield strength", + "A dilute single-phase alloy remains below its solubility limit. More substitutional solute produces more elastic misfit fields, and those fields impede dislocation glide.", + "Consider a homogeneous substitutional solid solution without precipitation. Solute content sets the population of lattice-distortion fields, which controls resistance to slip.", + ), + chain( + "fiber-fraction-longitudinal-modulus", "composites", 1, 1, + "aligned fiber volume fraction", "10 percent", "50 percent", + "load-bearing fiber fraction", "longitudinal elastic modulus", + "A unidirectional composite is loaded along continuous stiff fibers with intact interfaces. Fiber content controls the load-bearing fiber fraction, which controls axial stiffness.", + "Consider an aligned continuous-fiber composite under iso-strain loading. Added fibers increase the stiff load-carrying phase and thereby govern the longitudinal modulus.", + ), + chain( + "deformation-twin-hardening", "plasticity", 1, 1, + "plastic strain", "0.02", "0.20", + "deformation-twin density", "flow stress", + "A low-stacking-fault-energy alloy remains in a twinning-assisted hardening regime. Plastic strain creates deformation twins, whose boundaries obstruct subsequent slip.", + "Consider monotonic deformation of a twinning-prone metal before damage. Strain builds a twin-boundary network and that network raises resistance to dislocation motion.", + ), + chain( + "boron-hardenability-martensite", "steel processing", 1, 1, + "dissolved boron concentration", "5 ppm", "25 ppm", + "hardenability", "martensite fraction after a fixed quench", + "A low-alloy steel remains below the boron-solubility and embrittlement limits. Dissolved boron suppresses ferrite nucleation, increasing hardenability, which controls martensite formation in a fixed quench.", + "Consider a steel in the effective microalloying range for boron. Boron delays diffusional transformation, thereby setting hardenability and the amount transformed martensitically under one cooling schedule.", + ), + chain( + "cement-hydration-stiffness", "cementitious materials", 1, 1, + "curing time", "1 day", "28 days", + "load-bearing hydrate fraction", "elastic modulus", + "A cement paste remains in ordinary hydration without drying damage. Curing develops load-bearing hydrates, and the connected hydrate skeleton controls stiffness.", + "Consider sealed curing of a cementitious binder in its monotonic hydration regime. Reaction time sets the solid hydrate network, which carries elastic load.", + ), + chain( + "poling-domain-piezoelectricity", "functional ceramics", 1, 1, + "poling electric field", "0.2 kV/mm", "2.0 kV/mm", + "aligned ferroelectric domain fraction", "piezoelectric coefficient", + "A ferroelectric ceramic remains below electrical breakdown and before complete saturation. A stronger poling field aligns more domains, and aligned domains control the macroscopic piezoelectric response.", + "Consider sub-breakdown poling of a multidomain piezoceramic. Field magnitude sets domain alignment and the aligned population governs electromechanical coupling.", + ), + + # (+,-): an increase raises the intermediate, which lowers y. + chain( + "chloride-corrosion-section", "corrosion", 1, -1, + "chloride concentration", "0.01 mol/L", "1.0 mol/L", + "localized corrosion rate", "remaining load-bearing cross-sectional area", + "A steel remains in an active chloride-pitting regime for a fixed exposure time. Chloride increases localized corrosion, and material loss reduces the remaining load-bearing section.", + "Consider a passivity-broken steel exposed for the same duration. Chloride level controls pit growth and pit growth removes structural cross section.", + ), + chain( + "stress-amplitude-crack-life", "fatigue", 1, -1, + "cyclic stress amplitude", "80 MPa", "240 MPa", + "fatigue-crack growth rate", "remaining fatigue life", + "A component remains in stable fatigue-crack propagation under otherwise identical cycling. Stress amplitude controls crack-growth rate, and faster growth consumes the remaining life.", + "Consider two specimens in the Paris-growth regime with the same initial flaw. The applied cyclic amplitude sets propagation speed, which governs cycles remaining before failure.", + ), + chain( + "porosity-stress-fracture", "fracture", 1, -1, + "pore volume fraction", "1 percent", "12 percent", + "local tensile stress concentration", "fracture strength", + "A brittle porous solid retains the same pore shape and matrix chemistry. More porosity raises local tensile stress concentration, which lowers the remote stress required for fracture.", + "Consider a ceramic with geometrically similar pores and unchanged matrix toughness. Pore fraction sets stress amplification and that amplification controls macroscopic failure strength.", + ), + chain( + "hydrogen-traps-ductility", "hydrogen embrittlement", 1, -1, + "diffusible hydrogen concentration", "0.1 wppm", "5 wppm", + "occupied embrittling-trap fraction", "tensile ductility", + "A high-strength steel remains below hydride formation. More diffusible hydrogen fills more embrittling traps, and occupied traps promote premature localization that reduces tensile ductility.", + "Consider hydrogen charging of a steel without a phase change. Hydrogen content controls occupancy of damage-relevant traps, which governs loss of elongation.", + ), + chain( + "sulfur-inclusions-impact", "steel cleanliness", 1, -1, + "sulfur concentration", "0.002 wt percent", "0.05 wt percent", + "manganese-sulfide inclusion density", "Charpy impact energy", + "A steel has sufficient manganese to bind sulfur and otherwise fixed processing. Added sulfur forms more manganese-sulfide inclusions, which nucleate voids and reduce absorbed impact energy.", + "Consider steels differing only in sulfur within the inclusion-forming regime. Sulfur content sets sulfide inclusion density and the inclusions control impact toughness.", + ), + chain( + "irradiation-defects-elongation", "irradiation damage", 1, -1, + "neutron dose", "0.01 dpa", "2 dpa", + "obstacle-defect cluster density", "uniform tensile elongation", + "A structural alloy remains crystalline and below swelling-dominated damage. Irradiation creates more defect clusters, and those obstacles promote localization that reduces uniform elongation.", + "Consider sub-amorphizing irradiation of an alloy. Dose sets the retained cluster population and cluster hardening governs the loss of tensile deformability.", + ), + + # (-,+): an increase lowers the intermediate, and the intermediate raises y. + chain( + "anneal-vacancy-resistivity", "defect annealing", -1, 1, + "annealing time", "1 min", "120 min", + "retained vacancy concentration", "electrical resistivity", + "A quenched metal remains in point-defect recovery without precipitation. Longer annealing removes more vacancies, and retained vacancies increase electron scattering and resistivity.", + "Consider isothermal vacancy annealing in a single-phase conductor. Treatment time lowers the surviving vacancy population, which sets the defect contribution to resistivity.", + ), + chain( + "grain-size-boundary-strength", "grain-boundary strengthening", -1, 1, + "mean grain diameter", "1 micrometer", "100 micrometers", + "grain-boundary area density", "yield strength", + "A polycrystal remains in the conventional Hall-Petch regime. Increasing grain diameter reduces boundary area density, and boundaries impede slip transfer and raise yield strength.", + "Consider a fully dense polycrystal above the inverse Hall-Petch range. Grain scale sets boundary density, which governs resistance to dislocation transmission.", + ), + chain( + "temperature-viscosity-damping", "polymer rheology", -1, 1, + "temperature", "320 K", "420 K", + "melt viscosity", "viscous damping coefficient", + "A polymer melt remains Newtonian with unchanged geometry and oscillation rate. Heating lowers viscosity, and viscous resistance controls the measured damping coefficient.", + "Consider small-amplitude motion in a Newtonian polymer melt. Thermal state sets viscosity and viscosity sets dissipative damping under fixed kinematics.", + ), + chain( + "consolidation-porosity-permeability", "porous materials", -1, 1, + "hot-pressing pressure", "20 MPa", "200 MPa", + "connected porosity", "gas permeability", + "A powder compact densifies monotonically without cracking. Higher pressing pressure closes connected pores, and the connected pore network controls gas permeability.", + "Consider pressure-assisted consolidation with no open cracks. Processing pressure reduces continuous void space, which provides the transport paths for gas flow.", + ), + chain( + "tempering-dislocations-coercivity", "magnetic materials", -1, 1, + "recovery heat-treatment time", "2 min", "60 min", + "dislocation density", "magnetic coercivity", + "A ferromagnetic alloy undergoes recovery without recrystallization or precipitation. Longer treatment removes dislocations, and dislocations pin domain walls and raise coercivity.", + "Consider subcritical recovery of a magnetic metal. Treatment duration lowers the line-defect population, which controls domain-wall pinning and coercive field.", + ), + chain( + "particle-radius-area-reaction", "surface reaction", -1, 1, + "mean particle radius", "0.2 micrometers", "10 micrometers", + "specific surface area", "initial heterogeneous reaction rate", + "A monodisperse powder has fixed mass and remains surface-reaction controlled. Larger particles provide less area per mass, and exposed area controls the initial heterogeneous reaction rate.", + "Consider equal solid mass partitioned into nonporous spherical particles. Particle radius sets specific surface, which sets the initial surface-limited conversion rate.", + ), + + # (-,-): an increase lowers the intermediate, which lowers y. + chain( + "deoxidizer-inclusions-ductility", "metal processing", -1, -1, + "effective deoxidizer addition", "0.005 wt percent", "0.05 wt percent", + "oxide inclusion density", "tensile ductility", + "A molten alloy remains within the effective deoxidation window without forming excess inclusions. More deoxidizer lowers oxide inclusion density, and inclusions nucleate voids that reduce tensile ductility.", + "Consider controlled melt deoxidation below the over-treatment regime. Addition level reduces oxide particles, which otherwise limit elongation by initiating damage.", + ), + chain( + "homogenization-segregation-corrosion", "alloy homogenization", -1, -1, + "homogenization time", "10 min", "10 h", + "microsegregation amplitude", "corrosion resistance", + "A cast alloy homogenizes without grain growth or second-phase precipitation. Longer treatment reduces solute microsegregation, and segregation creates local electrochemical cells that lower corrosion resistance.", + "Consider diffusion homogenization of a cast solid solution. Treatment time smooths composition gradients, which otherwise promote galvanic attack and impair corrosion resistance.", + ), + chain( + "anneal-dislocations-thermal-conductivity", "thermal transport", -1, -1, + "recovery annealing time", "1 min", "90 min", + "stored dislocation density", "thermal conductivity", + "A cold-worked pure metal recovers without recrystallization. Annealing removes dislocations, and dislocations scatter heat carriers and reduce thermal conductivity.", + "Consider recovery of a deformed metallic conductor with unchanged chemistry. Treatment duration lowers line-defect density, which otherwise impedes heat transport.", + ), + chain( + "pressure-pores-toughness", "ceramic consolidation", -1, -1, + "sintering pressure", "5 MPa", "100 MPa", + "residual pore fraction", "fracture toughness", + "A ceramic densifies without abnormal grain growth. Higher sintering pressure reduces residual pores, and pores initiate cracks that lower fracture toughness.", + "Consider pressure-assisted sintering at fixed thermal history. Applied pressure closes voids, which otherwise concentrate stress and impair resistance to crack extension.", + ), + chain( + "purification-impurities-carrier-mobility", "electronic materials", -1, -1, + "zone-refining pass count", "1 pass", "20 passes", + "electrically active impurity concentration", "charge-carrier mobility", + "A semiconductor remains at fixed intentional doping. Additional zone-refining passes reduce unwanted impurities, and impurity scattering lowers charge-carrier mobility.", + "Consider repeated purification of a semiconductor without changing its designed dopant level. Pass count removes background impurities, which otherwise scatter mobile carriers.", + ), + chain( + "stress-relief-residual-fatigue", "residual stress", -1, -1, + "stress-relief time", "5 min", "120 min", + "tensile residual stress", "fatigue life", + "A welded component is stress relieved below transformation temperature. Longer treatment lowers tensile residual stress, and tensile residual stress accelerates fatigue damage and reduces life.", + "Consider subcritical stress relief with unchanged microstructure. Treatment time relaxes locked-in tension, which otherwise promotes cyclic crack initiation and shortens fatigue life.", + ), +] + + +def answer_order(chain_index: int, surface: str, question_type: str) -> list[str]: + offset = chain_index + (surface == "b") + if question_type != "invariant": + return ["higher", "lower"] if offset % 2 == 0 else ["lower", "higher"] + orders = [ + ["higher", "lower", "same"], + ["lower", "same", "higher"], + ["same", "higher", "lower"], + ["lower", "higher", "same"], + ["higher", "same", "lower"], + ["same", "lower", "higher"], + ] + return orders[offset % len(orders)] + + +def main() -> None: + OUT.mkdir(parents=True, exist_ok=True) + if len(CHAINS) != 24: + raise RuntimeError("prospective target cohort must contain 24 chains") + counts = {} + for row in CHAINS: + counts[(row["link1"], row["link2"])] = counts.get((row["link1"], row["link2"]), 0) + 1 + if set(counts.values()) != {6}: + raise RuntimeError(f"link-sign cells are not balanced: {counts}") + + prompts = [] + for chain_index, item in enumerate(CHAINS): + for surface in ("a", "b"): + 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[f'context_{surface}']} All unstated conditions remain fixed. " + f"{MARKERS['context']} The {item['control']} changes from {start} to {end}. " + f"{MARKERS['perturbation']} " + ) + questions = { + "target": (f"What happens to the {item['response']}?", target_sign), + "intermediate": ( + f"What happens to the {item['intermediate']}?", + intermediate_sign, + ), + "invariant": ("What happens to the total specimen mass?", 0), + } + for question_type, (question, query_sign) in questions.items(): + words = answer_order(chain_index, surface, question_type) + expected = "same" if query_sign == 0 else ("higher" if query_sign > 0 else "lower") + prompt_id = ( + f"goalrole--{item['chain_id']}--s{surface}--n{numerical_sign:+d}--{question_type}" + ) + counterpart_id = ( + f"goalrole--{item['chain_id']}--s{surface}--n{-numerical_sign:+d}--{question_type}" + ) + user = ( + prefix + + question + + f" {MARKERS['question']} Reply with exactly one lowercase word from this ordered " + + ("pair: " if len(words) == 2 else "set: ") + + ", ".join(words) + + "." + ) + prompts.append( + { + "prompt_id": prompt_id, + "counterpart_prompt_id": counterpart_id, + "chain_id": item["chain_id"], + "domain": item["domain"], + "surface": surface, + "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, + "control": item["control"], + "intermediate": item["intermediate"], + "response": item["response"], + "answer_words": words, + "expected_answer": expected, + "user": user, + } + ) + + expected_prompts = 24 * 2 * 2 * 3 + if len(prompts) != expected_prompts: + raise RuntimeError(f"expected {expected_prompts} prompts, found {len(prompts)}") + prompt_ids = {row["prompt_id"] for row in prompts} + if len(prompt_ids) != len(prompts): + raise RuntimeError("duplicate prompt ids") + if any(row["counterpart_prompt_id"] not in prompt_ids for row in prompts): + raise RuntimeError("missing numerical-reversal counterpart") + + manifest = { + "study_id": "gemma4-31b-goal-specific-causal-roles-v1", + "status": "frozen-before-new-target-output", + "markers": MARKERS, + "dimensions": { + "target_chains": len(CHAINS), + "link_sign_cells": 4, + "chains_per_link_sign_cell": 6, + "surfaces": 2, + "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_goal_specific_causal_roles.py" + analyzer = ROOT / "scripts" / "analyze_gemma31b_goal_specific_causal_roles.py" + validator = ROOT / "scripts" / "validate_gemma31b_goal_specific_causal_roles.py" + discovery = ROOT / "experiments" / "gemma4-31b-query-gated-physics-2026-08-25" + protocol = { + "study_id": manifest["study_id"], + "status": "frozen-before-new-target-output", + "hypothesis_origin": ( + "Prospective causal test of a post hoc observational finding: independently fitted " + "downstream and intermediate outcome directions generalized on an earlier disjoint " + "cohort with AUC 0.826 and 0.778 and formed an angle of 79.99 degrees." + ), + "model": MODEL, + "model_revision": MODEL_REVISION, + "dtype": "bfloat16", + "probe_layer_zero_indexed": PROBE_LAYER, + "probe_position": "the answer-free question marker", + "development_data": { + "manifest": str((discovery / "prompt_manifest.json").relative_to(ROOT)), + "states": str((discovery / "states.npy").relative_to(ROOT)), + "protocol": str((discovery / "protocol.json").relative_to(ROOT)), + "development_chain_count": 12, + "target_and_intermediate_prompts_each": 24, + }, + "interventions": { + "role_neutralization": ( + "Set one role direction's normalized-state projection to its development midpoint " + "while exactly preserving the other role projection and the receiver state norm." + ), + "role_counterfactual": ( + "Set one role projection to its value in the matched reversed-input prompt while " + "preserving the other role projection and receiver norm." + ), + "full_counterfactual": "Replace the marker state with the matched reversed-input state.", + "role_restoration": ( + "Within the full counterfactual state, restore either role projection to its clean " + "receiver value while preserving the other counterfactual projection and state norm." + ), + "centroid_repair": ( + "Set either role projection to its development centroid for the registered sign; " + "analyze only naturally incorrect clean binary decisions." + ), + "activation_controls": ( + "Four sign-shuffled activation-derived directions orthogonal to the two-role span; " + "apply antithetic plus/minus perturbations at exactly the physical intervention norm." + ), + "dose_matched_other_role": ( + "For each queried role, perturb the other physical-role coordinate by exactly the " + "same Euclidean distance on the same state-norm sphere while preserving the queried-role " + "projection. This is the comparator used in the double-dissociation endpoints." + ), + }, + "independent_unit": "physical chain, after averaging surfaces and numerical directions", + "primary_endpoint": ( + "Dose-matched neutralization double dissociation: for each binary question, compare " + "neutralizing its own role coordinate with an equal-distance, equal-norm movement along " + "the other physical-role coordinate; average target and intermediate contrasts within " + "each of 24 chains." + ), + "confirmatory_endpoints": [ + "the analogous role-specific double dissociation under coordinate counterfactuals", + "the analogous double dissociation when restoring one coordinate inside a full counterfactual state", + "own-role neutralization and reversal effects relative to activation-matched null controls", + "stability of invariant-mass decisions and registered output probability mass", + ], + "success_criteria": { + "primary_chain_bootstrap_ci_lower_above_zero": True, + "primary_one_sided_exact_sign_flip_p_maximum": 0.05, + "minimum_positive_chain_fraction": 0.75, + "both_question_specific_mean_contrasts_positive": True, + "own_role_neutralization_exceeds_activation_control_ci_lower_above_zero": True, + }, + "secondary_endpoints": [ + "prospective held-out AUC of each frozen role direction", + "clean answer accuracy and correct-to-error transitions", + "role-specific mediation fraction relative to full-state counterfactual replacement", + "natural-error repair by the correct role centroid and falsification by the wrong centroid", + "law, surface, numerical-direction, and link-sign-cell heterogeneity", + ], + "guardrails": [ + "All 24 target chain identities are disjoint from the 24 chains used in the observational discovery study.", + "Directions, layer, prompt grammar, endpoints, controls, and success criteria are frozen before target-model output.", + "No prompt supplies an equation or instructs the model to compose signs.", + "The primary unit is the chain, not an individual prompt.", + "A positive result would establish role-selective causal contribution at one layer and token position, not a literal graph, unique necessity, or a complete reasoning trace.", + ], + "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)}, + "validator": {"path": str(validator.relative_to(ROOT)), "sha256": sha256(validator)}, + "development_manifest": {"path": str((discovery / "prompt_manifest.json").relative_to(ROOT)), "sha256": sha256(discovery / "prompt_manifest.json")}, + "development_states": {"path": str((discovery / "states.npy").relative_to(ROOT)), "sha256": sha256(discovery / "states.npy")}, + "development_protocol": {"path": str((discovery / "protocol.json").relative_to(ROOT)), "sha256": sha256(discovery / "protocol.json")}, + }, + } + (OUT / "protocol.json").write_text(json.dumps(protocol, indent=2) + "\n") + + lines = [ + "# Exact prompts for the prospective goal-specific causal-role experiment", + "", + "All 288 prompts below were frozen before any target-cohort model output.", + "", + ] + for row in prompts: + lines.extend([f"## `{row['prompt_id']}`", "", row["user"], ""]) + (OUT / "PROMPTS.md").write_text("\n".join(lines)) + + brief = """# Frozen protocol + +This is a prospective causal test of a post hoc observational hypothesis. Two +directions—one for an intermediate physical variable and one for a downstream +response—are fitted only from the 12 development chains archived in the 31B +query-gating experiment. They are then intervened on in 24 completely new +two-link chains. The primary endpoint is a chain-level double dissociation. + +The experiment directory is deliberately separate from the manuscript. No +result will be added to the paper unless it survives the frozen tests and is +reviewed with the author. +""" + (OUT / "PROTOCOL.md").write_text(brief) + print(json.dumps({"dimensions": manifest["dimensions"], "link_sign_counts": {str(k): v for k, v in counts.items()}}, indent=2)) + + +if __name__ == "__main__": + main() diff --git a/jlens_materials/scripts/build_gemma31b_semantic_streams.py b/jlens_materials/scripts/build_gemma31b_semantic_streams.py new file mode 100644 index 0000000000000000000000000000000000000000..f423419f809555c7a0ec1cb8dd8a11edbd929988 --- /dev/null +++ b/jlens_materials/scripts/build_gemma31b_semantic_streams.py @@ -0,0 +1,554 @@ +#!/usr/bin/env python3 +"""Build the descriptive 31B semantic-stream Supplementary figure. + +The left column exactly reproduces the unfiltered single-fit stream protocol +used for the 4B paper figure. The right column is necessarily single-fit +because only one 31B Jacobian lens exists. It therefore replaces three-fit +intersection with a frozen lexical-decontamination and corpus-specificity rule. +No declared materials term is used to generate, filter, retain, or rank words. +""" + +from __future__ import annotations + +import csv +import hashlib +import json +import math +import re +import shutil +from collections import Counter, defaultdict +from pathlib import Path + +import matplotlib.pyplot as plt +import numpy as np +from wordfreq import zipf_frequency + +from analyze_materials_heldout_v1 import FUNCTION_WORDS +from build_final_paper_figures import ( + TOKEN_COLORS, + configure_style, + stream_panel, + unfiltered_prompt_stream, +) + + +ROOT = Path(__file__).resolve().parents[1] +REPO_ROOT = ROOT.parent +RUN_PATH = ROOT / "runs" / "gemma4-31b-it-heldout-v1-seed0.json" +OUT_DIR = ROOT / "experiments" / "gemma4-31b-semantic-streams-2026-08-26" +FIG_DIR = OUT_DIR / "figures" +PAPER_FIG_DIR = ( + REPO_ROOT + / "PAPER_REVISION" + / "Materials_Science_Interpretability" + / "figures" +) +FIGURE_STEM = "figure-s28-gemma31b-semantic-streams" + +BAND = (38.0, 92.0) +DISPLAY_LIMIT = 6 +MIN_ZIPF = 2.0 +SHORT_TOKEN_ZIPF = 4.0 +IDF_POWER = 2.0 + +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"), +] + +INTERPRETATIONS = { + "A": ( + "A late chemical-degradation neighborhood contains dissolution, " + "attacks, etching, and melting, alongside temporal connective words." + ), + "B": ( + "A constraint/failure-threshold neighborhood contains allowable, " + "critical, threshold, and longer." + ), + "C": ( + "A permanent-deformation neighborhood contains deformation, " + "permanently, and dislocations, alongside consequence words." + ), + "D": ( + "A joining neighborhood contains linking and bridging, but is " + "mixed with generic words such as interactions and remaining." + ), + "E": ( + "The cleavage case contains cleaved and breached, but " + "remains mixed with generic descriptive vocabulary." + ), +} + + +def normalize_specific_token(raw: str) -> str | None: + """Frozen target-agnostic lexical screen for the 31B case series.""" + token = raw.strip().lower() + if len(token) < 4 or not token.isascii() or not token.isalpha(): + return None + frequency = float(zipf_frequency(token, "en")) + if frequency < MIN_ZIPF: + return None + if len(token) < 5 and frequency < SHORT_TOKEN_ZIPF: + return None + return token + + +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 source_sha256(path: Path) -> str: + return hashlib.sha256(path.read_bytes()).hexdigest() + + +def document_frequency(records: list[dict]) -> Counter[str]: + """Count prompts containing a candidate anywhere in the registered band.""" + frequency: Counter[str] = Counter() + for record in records: + forbidden = record_words(record) | FUNCTION_WORDS + present: set[str] = set() + for row in record["layer_readouts"]: + if not BAND[0] <= float(row["depth"]) <= BAND[1]: + continue + for raw in row["top_tokens"]: + token = normalize_specific_token(raw) + if token is not None and token not in forbidden: + present.add(token) + frequency.update(present) + return frequency + + +def decontaminated_prompt_stream( + record: dict, + *, + prompt_frequency: Counter[str], + n_prompts: int, +) -> tuple[np.ndarray, list[str], np.ndarray, dict]: + """Single-fit, prompt-removed, English-frequency and IDF-filtered stream.""" + depths = np.asarray( + [float(row["depth"]) for row in record["layer_readouts"]], dtype=float + ) + width = max(len(row["top_tokens"]) for row in record["layer_readouts"]) + forbidden = record_words(record) | FUNCTION_WORDS + 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_index, raw in enumerate(row["top_tokens"]): + token = normalize_specific_token(raw) + if token is None or token in forbidden or token in seen: + continue + seen.add(token) + scores[token][layer_index] = max( + scores[token][layer_index], + (width - rank_index) / width, + ) + + band = (depths >= BAND[0]) & (depths <= BAND[1]) + integrated = { + token: float(np.trapezoid(values[band], depths[band])) + for token, values in scores.items() + if np.any(values[band] > 0) + } + specificity = { + token: value + * math.log((n_prompts + 1) / (prompt_frequency[token] + 1)) ** IDF_POWER + for token, value in integrated.items() + } + ranked = sorted( + specificity, + key=lambda token: (-specificity[token], token), + ) + selected: list[str] = [] + for token in ranked: + prefix_related = any( + min(len(token), len(existing)) >= 4 + and (token.startswith(existing) or existing.startswith(token)) + for existing in selected + ) + if prefix_related: + continue + selected.append(token) + if len(selected) == DISPLAY_LIMIT: + break + 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], -specificity[token], token) + ) + matrix = np.asarray([scores[token] for token in selected], dtype=float) + + selected_metadata = [] + for token in selected: + values = scores[token] + nonzero = np.flatnonzero(values > 0) + peak = int(np.argmax(values)) + selected_metadata.append( + { + "token": token, + "integrated_rank_prominence_in_band": integrated[token], + "idf_squared_specificity_score": specificity[token], + "heldout_prompt_frequency": int(prompt_frequency[token]), + "english_zipf_frequency": float(zipf_frequency(token, "en")), + "first_nonzero_depth": float(depths[int(nonzero[0])]), + "peak_depth": float(depths[peak]), + "peak_rank_prominence": float(values[peak]), + } + ) + + return depths, selected, matrix, { + "protocol": ( + "single 31B lens; prompt/output and frozen function words removed; " + "wordfreq 3.1.1 English Zipf screen; squared heldout-suite IDF and " + "conservative exact-prefix deduplication used only for selecting six " + "displayed words" + ), + "selected_tokens": selected_metadata, + "series": { + token: [float(value) for value in scores[token]] for token in selected + }, + } + + +def build_figure(cases: list[dict]) -> None: + configure_style() + figure, axes = plt.subplots( + len(cases), + 2, + figsize=(13.5, 12.8), + sharex=True, + constrained_layout=True, + ) + for row_index, case in enumerate(cases): + raw = case["unfiltered"] + filtered = case["decontaminated"] + stream_panel( + axes[row_index, 0], + np.asarray(raw["depths"], dtype=float), + raw["tokens"], + np.asarray(raw["matrix"], dtype=float), + show_xlabel=row_index == len(cases) - 1, + ) + stream_panel( + axes[row_index, 1], + np.asarray(filtered["depths"], dtype=float), + filtered["tokens"], + np.asarray(filtered["matrix"], dtype=float), + show_xlabel=row_index == len(cases) - 1, + ) + # The 31B ribbons are concentrated in a narrow late-depth interval. + # Direct labels therefore collide even when their anchor depths differ. + # Preserve every selected ribbon and color, but move its label into the + # genuinely empty early-depth region as a compact direct color key. + for text in list(axes[row_index, 1].texts): + if text.get_text() in filtered["tokens"]: + text.remove() + key_y = np.linspace(0.84, 0.16, len(filtered["tokens"])) + for token_index, (token, y_value) in enumerate( + zip(filtered["tokens"], key_y, strict=True) + ): + color = TOKEN_COLORS[token_index % len(TOKEN_COLORS)] + axes[row_index, 1].plot( + [0.035, 0.070], + [y_value, y_value], + transform=axes[row_index, 1].transAxes, + color=color, + linewidth=4.2, + solid_capstyle="butt", + clip_on=False, + ) + axes[row_index, 1].text( + 0.080, + y_value, + token, + transform=axes[row_index, 1].transAxes, + ha="left", + va="center", + fontsize=8.2, + color="#17222A", + ) + axes[row_index, 0].text( + 0.0, + 1.04, + f"{case['panel']} {case['family_label']}", + transform=axes[row_index, 0].transAxes, + ha="left", + va="bottom", + fontsize=10, + fontweight="bold", + ) + 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, + "Decontaminated single fit", + transform=axes[0, 1].transAxes, + ha="center", + va="bottom", + fontsize=11, + fontweight="bold", + ) + figure.supylabel("rank-derived display prominence", x=-0.012, fontsize=10) + + FIG_DIR.mkdir(parents=True, exist_ok=True) + PAPER_FIG_DIR.mkdir(parents=True, exist_ok=True) + for suffix in ("pdf", "svg", "png"): + output = FIG_DIR / f"{FIGURE_STEM}.{suffix}" + kwargs = {"bbox_inches": "tight", "facecolor": "white"} + if suffix == "png": + kwargs["dpi"] = 300 + figure.savefig(output, **kwargs) + shutil.copy2(output, PAPER_FIG_DIR / output.name) + plt.close(figure) + + +def write_selected_csv(cases: list[dict]) -> None: + rows = [] + for case in cases: + for display_order, token in enumerate( + case["decontaminated"]["metadata"]["selected_tokens"], start=1 + ): + rows.append( + { + "panel": case["panel"], + "family": case["family_label"], + "slug": case["slug"], + "display_order": display_order, + **token, + } + ) + with (OUT_DIR / "selected_terms.csv").open("w", newline="") as handle: + writer = csv.DictWriter(handle, fieldnames=list(rows[0])) + writer.writeheader() + writer.writerows(rows) + + +def write_report(cases: list[dict], payload: dict) -> None: + lines = [ + "# Gemma 4 31B single-lens semantic-stream case series", + "", + "## Status and purpose", + "", + ( + "This is a retrospective descriptive extension of the published 4B " + "semantic-stream display. It reuses the identical five held-out prompts " + "and the stored final-prompt top-32 vocabulary rankings. No new Gemma " + "forward pass was run. Because only one 31B lens is available, this " + "analysis cannot apply or estimate the 4B three-fit-consensus rule." + ), + "", + "## What the paired columns mean", + "", + ( + "The left column exactly follows the original unfiltered single-fit " + "renderer. The right column removes literal prompt/output words and the " + "same frozen function-word list, applies a target-agnostic English " + "frequency screen, and selects words using squared inverse document " + "frequency across all 50 stored 31B prompts. IDF selects which words are " + "displayed; ribbon thickness remains the original rank-derived score. " + "Because the retained 31B ribbons cluster late, the word labels are " + "placed as a direct color key in the empty early-depth region." + ), + "", + "## Case-level observations", + "", + ] + for case in cases: + tokens = ", ".join(case["decontaminated"]["tokens"]) + lines.extend( + [ + f"- **{case['panel']} {case['family_label']}:** " + f"{case['interpretation']} Displayed terms: `{tokens}`.", + ] + ) + lines.extend( + [ + "", + "## Interpretation", + "", + ( + "The 31B lens yields localized vocabulary neighborhoods for chemical " + "degradation, failure thresholds, permanent deformation, and joining. " + "The cleavage and ductile cases remain mixed, preserving visible " + "measurement noise. This supports only the descriptive claim that " + "materials-relevant vocabulary can be decoded in selected 31B states. " + "It does not show that 31B is better than 4B, that the words are causal, " + "or that the ribbons are a literal reasoning transcript." + ), + ( + f"Across the {payload['descriptive_summary']['selected_term_count']} " + "displayed decontaminated terms, the median peak occurs at " + f"{payload['descriptive_summary']['median_peak_depth_percent']:.2f}% " + "normalized depth; " + f"{payload['descriptive_summary']['terms_peaking_at_or_after_60_percent']} " + f"of {payload['descriptive_summary']['selected_term_count']} peak at " + "or after 60%. This timing is descriptive rather than " + "an independent onset test because term selection itself is restricted " + "to the registered 38--92% analysis band." + ), + "", + "## Reproduction", + "", + "```bash", + "python scripts/build_gemma31b_semantic_streams.py", + "```", + "", + f"Source run SHA-256: `{payload['source_run_sha256']}`.", + "", + ] + ) + (OUT_DIR / "REPORT.md").write_text("\n".join(lines)) + + +def main() -> None: + run = json.loads(RUN_PATH.read_text()) + records = run["prompts"] + index = {record["slug"]: record for record in records} + missing = [slug for _, _, slug in STREAM_CASES if slug not in index] + if missing: + raise RuntimeError(f"Missing required 31B prompts: {missing}") + prompt_frequency = document_frequency(records) + + cases = [] + for panel, family_label, slug in STREAM_CASES: + record = index[slug] + raw_depths, raw_tokens, raw_matrix, raw_metadata = unfiltered_prompt_stream( + record + ) + clean_depths, clean_tokens, clean_matrix, clean_metadata = ( + decontaminated_prompt_stream( + record, + prompt_frequency=prompt_frequency, + n_prompts=len(records), + ) + ) + if not np.allclose(raw_depths, clean_depths): + raise RuntimeError(f"Depth mismatch for {slug}") + cases.append( + { + "panel": panel, + "family_label": family_label, + "slug": slug, + "prompt": record["prompt_text"], + "generated_completion": record.get("generated_completion", ""), + "interpretation": INTERPRETATIONS[panel], + "unfiltered": { + "depths": [float(value) for value in raw_depths], + "tokens": raw_tokens, + "matrix": raw_matrix.tolist(), + "metadata": raw_metadata, + }, + "decontaminated": { + "depths": [float(value) for value in clean_depths], + "tokens": clean_tokens, + "matrix": clean_matrix.tolist(), + "metadata": clean_metadata, + }, + } + ) + + OUT_DIR.mkdir(parents=True, exist_ok=True) + payload = { + "analysis_status": ( + "retrospective descriptive single-lens 31B semantic-stream case series" + ), + "comparison_boundary": ( + "same prompts and unfiltered renderer as 4B, but no 31B three-fit " + "consensus; not a quantitative 4B-versus-31B comparison" + ), + "source_run": str(RUN_PATH.relative_to(ROOT)), + "source_run_sha256": source_sha256(RUN_PATH), + "model_identity": run["model_identity"], + "lens_provenance": run["lens_provenance"], + "registered_band": list(BAND), + "function_word_count": len(FUNCTION_WORDS), + "lexical_filter": { + "package": "wordfreq", + "version": "3.1.1", + "language": "en", + "minimum_zipf_frequency": MIN_ZIPF, + "shorter_than_five_characters_minimum_zipf": SHORT_TOKEN_ZIPF, + "minimum_characters": 4, + "ascii_alphabetic_only": True, + }, + "selection": { + "candidate_source": "stored top-32 Jacobian vocabulary tokens by layer", + "input_and_output_words_removed": True, + "frozen_function_words_removed": True, + "document_frequency_population": len(records), + "idf_formula": "log((N+1)/(df+1))", + "idf_power": IDF_POWER, + "display_limit": DISPLAY_LIMIT, + "display_prefix_deduplication": ( + "reject a lower-ranked word when it starts with an already selected " + "word, or vice versa, and the shorter form has at least four letters" + ), + "declared_materials_terms_used": False, + "ribbon_width": "(stored_width-rank_index)/stored_width", + }, + "cases": cases, + } + selected_rows = [ + term + for case in cases + for term in case["decontaminated"]["metadata"]["selected_tokens"] + ] + peak_depths = np.asarray( + [term["peak_depth"] for term in selected_rows], dtype=float + ) + payload["descriptive_summary"] = { + "selected_term_count": int(len(selected_rows)), + "median_peak_depth_percent": float(np.median(peak_depths)), + "terms_peaking_at_or_after_60_percent": int(np.sum(peak_depths >= 60.0)), + "terms_peaking_at_or_after_80_percent": int(np.sum(peak_depths >= 80.0)), + "interpretation": ( + "descriptive only; the 38--92% selection band makes late localization " + "partly design-dependent" + ), + } + (OUT_DIR / "statistics.json").write_text(json.dumps(payload, indent=2) + "\n") + (OUT_DIR / "prompt_manifest.json").write_text( + json.dumps( + [ + { + "panel": case["panel"], + "family": case["family_label"], + "slug": case["slug"], + "prompt": case["prompt"], + "generated_completion": case["generated_completion"], + } + for case in cases + ], + indent=2, + ) + + "\n" + ) + write_selected_csv(cases) + build_figure(cases) + write_report(cases, payload) + print(f"wrote {OUT_DIR}") + print(f"copied figure to {PAPER_FIG_DIR / (FIGURE_STEM + '.pdf')}") + + +if __name__ == "__main__": + main() diff --git a/jlens_materials/scripts/build_graph_si_inventory.py b/jlens_materials/scripts/build_graph_si_inventory.py new file mode 100644 index 0000000000000000000000000000000000000000..39cf930fe19c7f6851d84a80faa68b2056d6a969 --- /dev/null +++ b/jlens_materials/scripts/build_graph_si_inventory.py @@ -0,0 +1,242 @@ +#!/usr/bin/env python3 +"""Build self-contained LaTeX and CSV inventories for the graph-study SI.""" + +from __future__ import annotations + +import csv +import json +from collections import defaultdict +from pathlib import Path + +import numpy as np + + +ROOT = Path(__file__).resolve().parents[1] +PAPER = ROOT / "paper" +EXPERIMENT = ROOT / "experiments" / "graph-topology-rigorous-2026-07-17" +REPLICATION = ( + ROOT + / "experiments" + / "late-physics-representation-replication-2026-07-17" +) + + +def latex(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 variant_label(value: str) -> str: + return { + "anchor": "anchor", + "physics_paraphrase": "physical paraphrase", + "lexical_counterfactual": "lexical counterfactual", + }[value] + + +def build_prompt_inventory(prompts: list[dict[str, object]]) -> None: + families: dict[str, list[dict[str, object]]] = defaultdict(list) + family_names: dict[str, str] = {} + for prompt in prompts: + family = str(prompt["family_id"]) + families[family].append(prompt) + family_names[family] = str(prompt["family_name"]) + + lines = [ + "% Generated by scripts/build_graph_si_inventory.py.", + "% Do not edit manually; regenerate from the frozen prompt manifest.", + ] + for family in sorted(families): + lines.extend( + [ + "", + rf"\subsubsection*{{{latex(family_names[family])}}}", + ] + ) + by_case: dict[str, list[dict[str, object]]] = defaultdict(list) + for prompt in families[family]: + by_case[str(prompt["case_id"])].append(prompt) + for case in sorted(by_case): + lines.append(rf"\paragraph{{Material case: \texttt{{{latex(case)}}}.}}") + for prompt in sorted( + by_case[case], + key=lambda row: ( + "anchor", + "physics_paraphrase", + "lexical_counterfactual", + ).index(str(row["variant"])), + ): + lines.extend( + [ + r"\begin{quote}\small", + rf"\textbf{{Prompt ID:}} \path{{{prompt['prompt_id']}}}.\\", + ( + rf"\textbf{{{latex(variant_label(str(prompt['variant'])))}}}; " + rf"numeric direction: \texttt{{{latex(prompt['numeric_direction'])}}}; " + rf"expected answer: \texttt{{{latex(prompt['expected_outcome'])}}}; " + rf"answer order: \texttt{{{latex(prompt['presentation_order'])}}}." + ), + "", + rf"\emph{{{latex(prompt['user'])}}}", + r"\end{quote}", + ] + ) + (PAPER / "graph_prompt_inventory.tex").write_text( + "\n".join(lines) + "\n", + encoding="utf-8", + ) + + +def build_edge_inventory( + prompts: list[dict[str, object]], + edge_rows: list[dict[str, str]], + rank_rows: list[dict[str, str]], +) -> None: + ranks = { + (int(row["source_index"]), row["target_variant"]): row + for row in rank_rows + if row["method"] == "jacobian_band" + } + primary = [ + row for row in edge_rows if row["method"] == "jacobian_band" + ] + by_family: dict[str, list[dict[str, str]]] = defaultdict(list) + mapped_csv = [] + for row in primary: + source_index = int(row["source_index"]) + target_index = int(row["target_index"]) + rank = ranks[(source_index, row["target_variant"])] + source = prompts[source_index] + target = prompts[target_index] + enriched = { + **row, + "source_prompt_id": str(source["prompt_id"]), + "source_case_id": str(source["case_id"]), + "target_prompt_id": str(target["prompt_id"]), + "target_case_id": str(target["case_id"]), + "first_same_outcome_rank": rank["first_positive_rank"], + "pairwise_auc": rank["pairwise_auc"], + } + mapped_csv.append(enriched) + by_family[row["family"]].append(enriched) + + output_csv = PAPER / "graph_data" / "primary_jacobian_edges_with_ids.csv" + output_csv.parent.mkdir(parents=True, exist_ok=True) + with output_csv.open("w", newline="", encoding="utf-8") as handle: + writer = csv.DictWriter(handle, fieldnames=list(mapped_csv[0])) + writer.writeheader() + writer.writerows(mapped_csv) + + family_names = { + str(prompt["family_id"]): str(prompt["family_name"]) + for prompt in prompts + } + lines = [ + "% Generated by scripts/build_graph_si_inventory.py.", + "% Complete 144-edge primary frozen-band Jacobian inventory.", + ] + for family in sorted(by_family): + lines.extend( + [ + "", + rf"\subsubsection*{{{latex(family_names[family])}}}", + r"\begingroup\scriptsize", + r"\begin{longtable}{>{\raggedright\arraybackslash}p{0.19\linewidth}>{\raggedright\arraybackslash}p{0.14\linewidth}>{\raggedright\arraybackslash}p{0.19\linewidth}rrrr}", + r"\toprule", + r"Source case / variant & Target variant & Selected target case & Correct & First valid rank & Pair AUC & Cosine \\", + r"\midrule", + r"\endhead", + ] + ) + for row in sorted( + by_family[family], + key=lambda item: ( + item["source_case_id"], + item["source_variant"], + item["target_variant"], + ), + ): + source_short = ( + rf"\texttt{{{latex(row['source_case_id'])}}} / " + rf"{latex(variant_label(row['source_variant']))}" + ) + lines.append( + " & ".join( + [ + source_short, + latex(variant_label(row["target_variant"])), + rf"\texttt{{{latex(row['target_case_id'])}}}", + "yes" if row["same_outcome"] == "True" else "no", + latex(row["first_same_outcome_rank"]), + f"{float(row['pairwise_auc']):.2f}", + f"{float(row['cosine']):.4f}", + ] + ) + + r" \\" + ) + lines.extend( + [ + r"\bottomrule", + r"\end{longtable}", + r"\endgroup", + ] + ) + (PAPER / "graph_primary_edge_inventory.tex").write_text( + "\n".join(lines) + "\n", + encoding="utf-8", + ) + + +def main() -> None: + manifest = json.loads( + (REPLICATION / "prompt_manifest.json").read_text(encoding="utf-8") + ) + prompt_by_id = { + str(prompt["prompt_id"]): prompt for prompt in manifest["prompts"] + } + with np.load(REPLICATION / "representations.npz", allow_pickle=False) as data: + prompt_ids = data["prompt_ids"].astype(str).tolist() + prompts = [prompt_by_id[prompt_id] for prompt_id in prompt_ids] + + with (EXPERIMENT / "replication_relation_graph_selected_edges.csv").open( + newline="", + encoding="utf-8", + ) as handle: + edge_rows = list(csv.DictReader(handle)) + with (EXPERIMENT / "replication_relation_candidate_ranks.csv").open( + newline="", + encoding="utf-8", + ) as handle: + rank_rows = list(csv.DictReader(handle)) + + build_prompt_inventory(prompts) + build_edge_inventory(prompts, edge_rows, rank_rows) + print( + json.dumps( + { + "prompts": len(prompts), + "primary_edges": sum( + row["method"] == "jacobian_band" for row in edge_rows + ), + "prompt_inventory": "paper/graph_prompt_inventory.tex", + "edge_inventory": "paper/graph_primary_edge_inventory.tex", + }, + indent=2, + ) + ) + + +if __name__ == "__main__": + main() diff --git a/jlens_materials/scripts/build_neutral_anchored_relational_benchmark.py b/jlens_materials/scripts/build_neutral_anchored_relational_benchmark.py new file mode 100644 index 0000000000000000000000000000000000000000..9fd79f2f0d987c12951f5bbb921682493a543a50 --- /dev/null +++ b/jlens_materials/scripts/build_neutral_anchored_relational_benchmark.py @@ -0,0 +1,309 @@ +#!/usr/bin/env python3 +"""Freeze a large, diverse, neutral-anchored relational benchmark.""" + +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" / "neutral-anchored-relational-physics-2026-07-18" +MANIFEST = OUT / "prompt_manifest.json" +PROTOCOL = OUT / "protocol.json" +RUNNER = ROOT / "scripts" / "run_neutral_anchored_relational_benchmark.py" +DEVELOPMENT = ROOT / "experiments" / "elicited-physics-abstraction-2026-07-18" + + +def law( + law_id: str, + category: str, + domain: str, + law_name: str, + response: str, + control: str, + formula_a: str, + formula_b: str, + case_1: tuple[str, str, str], + case_2: tuple[str, str, str], + *, + neutral_role: str | None = None, +) -> dict: + sign = {"direct": 1, "inverse": -1, "neutral": 0}[category] + return { + "law_id": law_id, + "category": category, + "domain": domain, + "law_sign": sign, + "law_name": law_name, + "response": response, + "control": control, + "formula_a": formula_a, + "formula_b": formula_b, + "cases": [case_1, case_2], + "neutral_role": neutral_role, + } + + +LAWS = [ + # Twenty direct relations. + law("shear-strain-stress", "direct", "mechanics", "linear shear response", "shear strain gamma", "shear stress tau", "gamma = tau/G, with G fixed and positive", "gamma G = tau, with G fixed and positive", ("an elastic aluminum element", "5 MPa", "50 MPa"), ("an elastic polymer layer", "0.2 MPa", "2 MPa")), + law("axial-elongation-force", "direct", "mechanics", "axial extension at fixed geometry", "elongation delta_L", "axial force F", "delta_L = F L/(E A), with L, E, and A fixed", "delta_L/F = L/(E A), with L, E, and A fixed", ("a steel tie", "1 kN", "10 kN"), ("a polymer fiber", "10 N", "100 N")), + law("spring-energy-displacement", "direct", "mechanics", "elastic spring energy", "stored energy U", "displacement x", "U = k x^2/2, with k fixed and positive", "2 U/x^2 = k, with k fixed and positive", ("a coil spring", "1 mm", "10 mm"), ("an elastic flexure", "0.2 mm", "2 mm")), + law("stress-intensity-applied-stress", "direct", "fracture", "mode-I intensity at fixed crack geometry", "stress intensity K_I", "applied stress sigma", "K_I = C sigma", "K_I/sigma = C", ("a cracked aluminum panel", "10 MPa", "100 MPa"), ("a cracked glass plate", "2 MPa", "20 MPa")), + law("paris-rate-delta-k", "direct", "fatigue", "Paris fatigue-crack growth", "crack-growth rate da_dN", "stress-intensity range Delta_K", "da_dN = C Delta_K^m, with m > 0", "da_dN/Delta_K^m = C, with m > 0", ("a cyclically loaded steel", "5 MPa sqrt(m)", "20 MPa sqrt(m)"), ("a cyclically loaded aluminum alloy", "2 MPa sqrt(m)", "12 MPa sqrt(m)")), + law("pore-permeability-radius", "direct", "transport", "pore-radius permeability scaling", "permeability k_perm", "pore radius r_p", "k_perm = C r_p^2", "k_perm/r_p^2 = C", ("a porous ceramic", "0.1 micrometers", "1 micrometer"), ("a model sandstone", "2 micrometers", "20 micrometers")), + law("heat-capacity-mass", "direct", "thermal", "total heat capacity", "total heat capacity C_tot", "sample mass m_s", "C_tot = c_p m_s, with c_p fixed", "C_tot/m_s = c_p, with c_p fixed", ("an aluminum specimen", "1 g", "100 g"), ("a polymer specimen", "2 g", "200 g")), + law("thermal-energy-temperature", "direct", "thermal", "sensible heating at fixed mass", "added thermal energy Q_h", "temperature rise Delta_T", "Q_h = m c_p Delta_T, with m and c_p fixed", "Q_h/Delta_T = m c_p, with m and c_p fixed", ("a copper block", "5 K", "50 K"), ("a ceramic pellet", "10 K", "100 K")), + law("current-voltage", "direct", "electrical", "Ohmic current at fixed resistance", "electric current I_e", "applied voltage V", "I_e = V/R, with R fixed and positive", "I_e R = V, with R fixed and positive", ("a metallic resistor", "1 V", "10 V"), ("a conducting composite", "0.5 V", "5 V")), + law("joule-power-current", "direct", "electrical", "Joule heating at fixed resistance", "electrical power P_e", "electric current I_e", "P_e = R I_e^2, with R fixed and positive", "P_e/I_e^2 = R, with R fixed and positive", ("a resistive wire", "0.1 A", "1 A"), ("a thin-film heater", "0.02 A", "0.2 A")), + law("hall-voltage-field", "direct", "electronic", "Hall voltage at fixed current and geometry", "Hall voltage V_H", "magnetic field B", "V_H = C B", "V_H/B = C", ("a semiconductor Hall bar", "0.1 T", "1 T"), ("a metallic Hall sensor", "0.05 T", "0.5 T")), + law("magnetization-field", "direct", "magnetic", "linear magnetic susceptibility", "magnetization M", "magnetic field H", "M = chi H, with chi fixed and positive", "M/H = chi, with chi fixed and positive", ("a paramagnetic crystal", "100 A m^-1", "1000 A m^-1"), ("a linear magnetic composite", "50 A m^-1", "500 A m^-1")), + law("absorbance-thickness", "direct", "optical", "Beer-Lambert absorbance", "optical absorbance A_opt", "absorber thickness L", "A_opt = alpha L, with alpha fixed and positive", "A_opt/L = alpha, with alpha fixed and positive", ("a dyed polymer film", "10 micrometers", "100 micrometers"), ("an absorbing semiconductor layer", "0.1 micrometers", "1 micrometer")), + law("capillary-rise-surface-tension", "direct", "interfacial", "capillary rise at fixed tube radius", "capillary height h_c", "surface tension gamma_s", "h_c = C gamma_s", "h_c/gamma_s = C", ("a wetting liquid in a glass tube", "0.02 N m^-1", "0.08 N m^-1"), ("a molten metal in a fixed capillary", "0.2 N m^-1", "0.8 N m^-1")), + law("nucleation-rate-sites", "direct", "phase transformation", "heterogeneous nucleation at fixed per-site rate", "total nucleation rate J_n", "active-site density N_s", "J_n = C N_s", "J_n/N_s = C", ("a solidifying alloy", "1e8 m^-3", "1e12 m^-3"), ("a crystallizing glass", "1e10 m^-3", "1e14 m^-3")), + law("grain-size-time", "direct", "microstructure", "normal grain growth at fixed mobility", "grain size d_g", "annealing time t", "d_g^2 = d_0^2 + K t, with K > 0", "(d_g^2 - d_0^2)/t = K, with K > 0", ("an annealed steel", "1 hour", "16 hours"), ("a sintered ceramic", "0.5 hour", "8 hours")), + law("creep-strain-time", "direct", "deformation", "steady creep accumulation", "creep strain epsilon_c", "exposure time t", "epsilon_c = eps_dot t, with eps_dot fixed and positive", "epsilon_c/t = eps_dot, with eps_dot fixed and positive", ("a superalloy at fixed stress and temperature", "10 hours", "1000 hours"), ("a polymer under fixed load", "1 hour", "100 hours")), + law("corrosion-loss-time", "direct", "corrosion", "constant-rate corrosion mass loss", "mass loss m_loss", "exposure time t", "m_loss = C t", "m_loss/t = C", ("a steel coupon in a fixed environment", "1 day", "100 days"), ("a magnesium coupon in a fixed electrolyte", "2 hours", "200 hours")), + law("pressure-drop-flow", "direct", "fluid mechanics", "linear pressure drop at fixed hydraulic resistance", "pressure drop Delta_p", "volumetric flow Q_v", "Delta_p = R_h Q_v, with R_h fixed and positive", "Delta_p/Q_v = R_h, with R_h fixed and positive", ("flow through a porous plug", "1 mL s^-1", "10 mL s^-1"), ("flow through a microchannel", "0.1 mL s^-1", "1 mL s^-1")), + law("reaction-extent-time", "direct", "kinetics", "constant-rate reaction extent", "transformed fraction X", "reaction time t", "X = k t, with k fixed and 0 < X < 1", "X/t = k, with k fixed and 0 < X < 1", ("an early-stage solid-state reaction", "1 minute", "5 minutes"), ("an early-stage cure reaction", "2 minutes", "10 minutes")), + # Twenty inverse relations. + law("shear-strain-modulus", "inverse", "mechanics", "shear strain at fixed stress", "shear strain gamma", "shear modulus G", "gamma = tau/G, with tau fixed and positive", "gamma G = tau, with tau fixed and positive", ("an elastic layer at fixed shear stress", "1 GPa", "50 GPa"), ("a composite at fixed shear stress", "2 GPa", "20 GPa")), + law("axial-elongation-area", "inverse", "mechanics", "axial extension versus section area", "elongation delta_L", "cross-sectional area A", "delta_L = F L/(E A), with F, L, and E fixed", "delta_L A = F L/E, with F, L, and E fixed", ("a tensile bar", "1 mm^2", "100 mm^2"), ("a polymer ligament", "0.1 mm^2", "10 mm^2")), + law("frequency-mass", "inverse", "vibrations", "spring-mass natural frequency", "natural frequency f_n", "attached mass m", "f_n = C/sqrt(m)", "f_n^2 m = C^2", ("a laboratory oscillator", "0.1 kg", "10 kg"), ("a microresonator with an added mass", "1 microgram", "100 micrograms")), + law("fatigue-life-delta-k", "inverse", "fatigue", "power-law fatigue life", "fatigue life N_f", "stress-intensity range Delta_K", "N_f = C/Delta_K^m, with m > 0", "N_f Delta_K^m = C, with m > 0", ("a cyclically loaded steel", "5 MPa sqrt(m)", "20 MPa sqrt(m)"), ("a cyclically loaded titanium alloy", "3 MPa sqrt(m)", "15 MPa sqrt(m)")), + law("hydraulic-resistance-permeability", "inverse", "transport", "Darcy resistance at fixed geometry", "hydraulic resistance R_h", "permeability k_perm", "R_h = C/k_perm", "R_h k_perm = C", ("a porous ceramic filter", "1e-16 m^2", "1e-12 m^2"), ("a sandstone core", "1e-15 m^2", "1e-13 m^2")), + law("diffusive-flux-tortuosity", "inverse", "transport", "effective diffusion flux at fixed gradient", "diffusive flux magnitude J", "tortuosity tau_t", "J = C/tau_t", "J tau_t = C", ("a porous electrode", "1.2", "4.8"), ("a polymer membrane", "1.5", "6.0")), + law("diffusion-time-diffusivity-new", "inverse", "transport", "diffusive equilibration time", "equilibration time t_eq", "effective diffusivity D_eff", "t_eq = C/D_eff", "t_eq D_eff = C", ("a fixed porous slab", "1e-12 m^2 s^-1", "1e-9 m^2 s^-1"), ("a fixed coating", "1e-15 m^2 s^-1", "1e-11 m^2 s^-1")), + law("thermal-time-coefficient", "inverse", "thermal", "lumped thermal time versus heat-transfer coefficient", "thermal time constant tau_th", "heat-transfer coefficient h", "tau_th = C/h", "tau_th h = C", ("a fixed metal body", "5 W m^-2 K^-1", "500 W m^-2 K^-1"), ("a fixed ceramic component", "10 W m^-2 K^-1", "1000 W m^-2 K^-1")), + law("current-resistance", "inverse", "electrical", "Ohmic current at fixed voltage", "electric current I_e", "electrical resistance R", "I_e = V/R, with V fixed and positive", "I_e R = V, with V fixed and positive", ("a circuit at fixed voltage", "1 ohm", "100 ohm"), ("a sensor at fixed bias", "10 ohm", "1000 ohm")), + law("capacitance-separation", "inverse", "electrical", "parallel-plate capacitance versus separation", "capacitance C_p", "plate separation d", "C_p = epsilon A/d, with epsilon and A fixed", "C_p d = epsilon A, with epsilon and A fixed", ("a ceramic capacitor", "1 micrometer", "100 micrometers"), ("an air-gap capacitor", "0.1 mm", "10 mm")), + law("hall-coefficient-carriers", "inverse", "electronic", "single-carrier Hall coefficient", "Hall coefficient R_H", "carrier density n", "R_H = 1/(q n), with q fixed and positive", "R_H q n = 1, with q fixed and positive", ("an n-type semiconductor", "1e20 m^-3", "1e24 m^-3"), ("a conducting oxide", "1e22 m^-3", "1e26 m^-3")), + law("skin-depth-frequency", "inverse", "electromagnetic", "electromagnetic skin depth", "skin depth delta_s", "frequency f", "delta_s = C/sqrt(f)", "delta_s^2 f = C^2", ("a copper conductor", "1 kHz", "1 MHz"), ("a magnetic alloy", "100 Hz", "100 kHz")), + law("transmission-absorption", "inverse", "optical", "Beer-Lambert transmission", "transmitted intensity I_t", "absorption coefficient alpha", "I_t = I_0 exp(-alpha L), with I_0 and L fixed", "ln(I_t/I_0) = -alpha L, with I_0 and L fixed", ("a fixed optical film", "1e3 m^-1", "1e6 m^-1"), ("a fixed semiconductor layer", "1e4 m^-1", "1e7 m^-1")), + law("capillary-rise-radius", "inverse", "interfacial", "capillary rise versus tube radius", "capillary height h_c", "tube radius r", "h_c = C/r", "h_c r = C", ("a wetting liquid in a glass tube", "10 micrometers", "1 mm"), ("a molten salt in a capillary", "50 micrometers", "5 mm")), + law("nucleation-barrier-undercooling", "inverse", "phase transformation", "classical nucleation barrier scaling", "nucleation barrier Delta_G_star", "undercooling Delta_T", "Delta_G_star = C/Delta_T^2", "Delta_G_star Delta_T^2 = C", ("a solidifying pure metal", "5 K", "50 K"), ("a crystallizing melt", "10 K", "100 K")), + law("boundary-area-grain-size", "inverse", "microstructure", "grain-boundary area density", "boundary area per volume S_v", "grain size d_g", "S_v = C/d_g", "S_v d_g = C", ("an equiaxed polycrystal", "1 micrometer", "100 micrometers"), ("a nanocrystalline metal", "20 nm", "2 micrometers")), + law("rupture-life-stress", "inverse", "deformation", "power-law creep-rupture life", "rupture life t_r", "applied stress sigma", "t_r = C/sigma^n, with n > 0", "t_r sigma^n = C, with n > 0", ("a superalloy at fixed temperature", "100 MPa", "500 MPa"), ("a polymer at fixed temperature", "2 MPa", "20 MPa")), + law("corrosion-resistance-defects", "inverse", "corrosion", "barrier resistance versus defect density", "polarization resistance R_p", "coating defect density n_d", "R_p = C/n_d", "R_p n_d = C", ("a coated steel surface", "1e2 m^-2", "1e6 m^-2"), ("a coated magnesium alloy", "1e3 m^-2", "1e7 m^-2")), + law("pipe-drop-radius", "inverse", "fluid mechanics", "Poiseuille pressure drop versus radius", "pressure drop Delta_p", "pipe radius r", "Delta_p = C/r^4", "Delta_p r^4 = C", ("a capillary at fixed flow", "10 micrometers", "100 micrometers"), ("a tube at fixed flow", "0.5 mm", "5 mm")), + law("settling-speed-viscosity", "inverse", "fluid mechanics", "Stokes settling speed at fixed particle size", "settling speed v_s", "fluid viscosity eta", "v_s = C/eta", "v_s eta = C", ("a particle in a Newtonian liquid", "1 mPa s", "100 mPa s"), ("a ceramic inclusion in a melt", "0.01 Pa s", "1 Pa s")), + # Twenty neutral relations. First ten calibrate the empirical null; the + # final ten validate it without contributing to centering or scaling. + law("modulus-specimen-length", "neutral", "mechanics", "material modulus independent of specimen length", "Young modulus E", "specimen length L", "E = C", "E/L^0 = C", ("a homogeneous steel specimen", "10 mm", "1 m"), ("a homogeneous ceramic specimen", "1 mm", "100 mm"), neutral_role="calibration"), + law("poisson-area", "neutral", "mechanics", "Poisson ratio independent of section area", "Poisson ratio nu", "cross-sectional area A", "nu = C", "nu/A^0 = C", ("a homogeneous aluminum specimen", "1 mm^2", "100 mm^2"), ("a homogeneous polymer specimen", "0.1 mm^2", "10 mm^2"), neutral_role="calibration"), + law("yield-stress-gauge-length", "neutral", "mechanics", "local yield stress independent of gauge length", "yield stress sigma_y", "gauge length L_g", "sigma_y = C", "sigma_y/L_g^0 = C", ("a uniform steel coupon", "10 mm", "100 mm"), ("a uniform aluminum coupon", "5 mm", "50 mm"), neutral_role="calibration"), + law("toughness-crack-length", "neutral", "fracture", "material toughness independent of test crack length", "fracture toughness K_IC", "initial crack length a", "K_IC = C", "K_IC/a^0 = C", ("a homogeneous ceramic under valid K-dominance", "0.1 mm", "1 mm"), ("a homogeneous alloy under valid K-dominance", "1 mm", "10 mm"), neutral_role="calibration"), + law("burgers-density", "neutral", "crystal defects", "Burgers-vector magnitude independent of dislocation density", "Burgers-vector magnitude b", "dislocation density rho_d", "b = C", "b/rho_d^0 = C", ("one crystal structure at fixed composition", "1e10 m^-2", "1e15 m^-2"), ("a second fixed crystal structure", "1e9 m^-2", "1e14 m^-2"), neutral_role="calibration"), + law("lattice-parameter-cell-count", "neutral", "crystallography", "lattice parameter independent of modeled cell count", "lattice parameter a_0", "number of unit cells N_c", "a_0 = C", "a_0/N_c^0 = C", ("a perfect crystal at fixed state", "10", "1000"), ("a second perfect crystal at fixed state", "20", "2000"), neutral_role="calibration"), + law("diffusivity-slab-thickness-neutral", "neutral", "transport", "intrinsic diffusivity independent of slab thickness", "intrinsic diffusivity D", "slab thickness L", "D = C", "D/L^0 = C", ("one homogeneous material at fixed temperature", "10 micrometers", "1 mm"), ("a second homogeneous material at fixed temperature", "0.1 mm", "10 mm"), neutral_role="calibration"), + law("conductivity-sample-area-neutral", "neutral", "thermal", "intrinsic conductivity independent of sample area", "thermal conductivity k", "sample area A", "k = C", "k/A^0 = C", ("a homogeneous ceramic at fixed temperature", "1 mm^2", "100 mm^2"), ("a homogeneous polymer at fixed temperature", "10 mm^2", "1000 mm^2"), neutral_role="calibration"), + law("specific-heat-mass-neutral", "neutral", "thermal", "specific heat independent of sample mass", "specific heat c_p", "sample mass m_s", "c_p = C", "c_p/m_s^0 = C", ("a homogeneous metal at fixed temperature", "1 g", "1 kg"), ("a homogeneous polymer at fixed temperature", "0.1 g", "100 g"), neutral_role="calibration"), + law("melting-temperature-mass", "neutral", "phase equilibrium", "melting temperature independent of macroscopic sample mass", "melting temperature T_m", "sample mass m_s", "T_m = C", "T_m/m_s^0 = C", ("a pure bulk metal", "1 mg", "1 kg"), ("a pure bulk ceramic", "10 mg", "10 kg"), neutral_role="calibration"), + law("resistivity-wire-length-neutral", "neutral", "electrical", "intrinsic resistivity independent of wire length", "electrical resistivity rho_e", "wire length L", "rho_e = C", "rho_e/L^0 = C", ("a uniform copper wire at fixed temperature", "1 cm", "10 m"), ("a uniform alloy wire at fixed temperature", "2 cm", "20 m"), neutral_role="validation"), + law("permittivity-capacitor-area-neutral", "neutral", "electrical", "intrinsic permittivity independent of electrode area", "dielectric permittivity epsilon_d", "electrode area A", "epsilon_d = C", "epsilon_d/A^0 = C", ("a homogeneous dielectric", "1 mm^2", "100 mm^2"), ("a second homogeneous dielectric", "10 mm^2", "1000 mm^2"), neutral_role="validation"), + law("susceptibility-volume-neutral", "neutral", "magnetic", "intrinsic susceptibility independent of specimen volume", "magnetic susceptibility chi", "specimen volume V_s", "chi = C", "chi/V_s^0 = C", ("a homogeneous paramagnet", "1 mm^3", "1 cm^3"), ("a homogeneous diamagnet", "10 mm^3", "10 cm^3"), neutral_role="validation"), + law("index-path-length-neutral", "neutral", "optical", "refractive index independent of optical path length", "refractive index n_r", "path length L", "n_r = C", "n_r/L^0 = C", ("a homogeneous glass at fixed wavelength", "0.1 mm", "10 cm"), ("a homogeneous polymer at fixed wavelength", "1 mm", "1 m"), neutral_role="validation"), + law("surface-energy-area-neutral", "neutral", "interfacial", "specific surface energy independent of exposed area", "specific surface energy gamma_s", "exposed area A", "gamma_s = C", "gamma_s/A^0 = C", ("one clean crystal facet", "1 micrometer^2", "1 mm^2"), ("a second clean crystal facet", "10 micrometers^2", "10 mm^2"), neutral_role="validation"), + law("boundary-energy-grain-size-neutral", "neutral", "microstructure", "specific boundary energy independent of grain size", "grain-boundary energy gamma_gb", "grain size d_g", "gamma_gb = C", "gamma_gb/d_g^0 = C", ("one boundary character at fixed chemistry", "100 nm", "100 micrometers"), ("a second boundary character at fixed chemistry", "1 micrometer", "1 mm"), neutral_role="validation"), + law("hardness-specimen-width-neutral", "neutral", "mechanical properties", "bulk hardness independent of specimen width", "hardness H", "specimen width w", "H = C", "H/w^0 = C", ("a sufficiently large homogeneous steel sample", "2 mm", "20 mm"), ("a sufficiently large homogeneous ceramic sample", "1 mm", "10 mm"), neutral_role="validation"), + law("expansion-coefficient-length-neutral", "neutral", "thermal", "expansion coefficient independent of specimen length", "thermal expansion coefficient alpha", "specimen length L", "alpha = C", "alpha/L^0 = C", ("a homogeneous alloy at fixed temperature", "1 mm", "1 m"), ("a homogeneous glass at fixed temperature", "2 mm", "2 m"), neutral_role="validation"), + law("activation-energy-anneal-time-neutral", "neutral", "kinetics", "activation energy independent of measurement duration", "activation energy Q_a", "measurement duration t", "Q_a = C", "Q_a/t^0 = C", ("one fixed diffusion mechanism", "1 minute", "100 hours"), ("a second fixed reaction mechanism", "10 seconds", "10 hours"), neutral_role="validation"), + law("atomic-mass-temperature-neutral", "neutral", "atomic physics", "atomic mass independent of temperature", "atomic mass m_a", "temperature T", "m_a = C", "m_a/T^0 = C", ("one isotope below ionization", "100 K", "1000 K"), ("a second isotope below ionization", "50 K", "500 K"), neutral_role="validation"), +] + + +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( + item: dict, + surface: str, + case_index: int, + numerical_sign: int, + answer_order: str, +) -> dict: + system, low, high = item["cases"][case_index] + start, end = (low, high) if numerical_sign == 1 else (high, low) + physical_sign = int(item["law_sign"]) * numerical_sign + expected = {1: "higher", -1: "lower", 0: "unchanged"}[physical_sign] + words = ( + ("higher", "lower", "unchanged") + if answer_order == "higher-first" + else ("unchanged", "lower", "higher") + ) + prompt_id = ( + f"{item['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 whether the response increases, decreases, or remains " + "unchanged as the control increases while all other quantities are " + f"fixed. For {item['law_name']}, the response is {item['response']}, " + f"the control is {item['control']}, and the relation is " + f"{item[f'formula_{surface}']}. All stated coefficients are positive. " + f"Complete this relation step silently. In {system}, " + f"{item['control']} changes from {start} to {end}. Determine the " + "numerical-change direction silently. Now compose the relation with " + f"the numerical change to determine the direction of {item['response']}. " + "Reply with exactly one lowercase word from this ordered set: " + f"{words[0]}, {words[1]}, {words[2]}." + ) + return { + "prompt_id": prompt_id, + "law_id": item["law_id"], + "category": item["category"], + "domain": item["domain"], + "law_sign": int(item["law_sign"]), + "neutral_role": item["neutral_role"], + "surface": surface, + "case_index": case_index, + "system": system, + "control": item["control"], + "response": item["response"], + "numeric_start": start, + "numeric_end": end, + "numerical_sign": numerical_sign, + "physical_sign": physical_sign, + "answer_order": answer_order, + "expected_answer": expected, + "user": user, + } + + +def main() -> None: + if (OUT / "raw.json").exists(): + raise RuntimeError("refusing to rebuild after model outputs exist") + counts = { + category: sum(x["category"] == category for x in LAWS) + for category in ("direct", "inverse", "neutral") + } + if counts != {"direct": 20, "inverse": 20, "neutral": 20}: + raise RuntimeError(f"unbalanced law inventory: {counts}") + if len({x["law_id"] for x in LAWS}) != len(LAWS): + raise RuntimeError("duplicate law id") + prompts = [ + make_prompt(item, surface, case, numerical, order) + for item in LAWS + for surface in ("a", "b") + for case in range(2) + for numerical in (-1, 1) + for order in ("higher-first", "unchanged-first") + ] + manifest = { + "study_id": "neutral-anchored-relational-physics-2026-07-18", + "laws": LAWS, + "prompts": prompts, + "dimensions": { + "n_laws": len(LAWS), + "n_direct": counts["direct"], + "n_inverse": counts["inverse"], + "n_neutral": counts["neutral"], + "n_neutral_calibration": 10, + "n_neutral_validation": 10, + "n_prompts": len(prompts), + "n_matched_pairs": len(prompts) // 2, + }, + } + write_json(MANIFEST, manifest) + protocol = { + "study_id": manifest["study_id"], + "status": "frozen-before-large-cohort-output", + "frozen_at": datetime.now(timezone.utc).isoformat(), + "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), + }, + "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": "old-law positive-minus-negative physical-outcome centroids", + "large_cohort_refit": "none", + }, + "matched_contrast": ( + "Within each law/surface/case/answer-order cell, frozen physical " + "score for numerical-up minus score for numerical-down." + ), + "neutral_normalization": { + "center": "median law contrast over the 10 calibration-neutral laws", + "scale": "1.4826 times MAD over the 10 calibration-neutral laws", + "validation_neutrals": "the other 10 neutral laws are excluded from normalization", + "reason": "defines an empirical physical null instead of assuming raw zero", + }, + "primary_endpoints": { + "direct_vs_inverse_auc": "threshold-free, 20 versus 20 laws", + "direct_vs_validation_neutral_auc": "20 versus 10 laws", + "validation_neutral_vs_inverse_auc": "10 versus 20 laws", + "calibrated_sign_accuracy": "direct above and inverse below calibration-neutral median", + "ordinal_spearman": "law sign -1/0/+1 versus neutral-normalized contrast, validation neutral only", + }, + "success_rule": { + "direct_inverse_auc_minimum": 0.90, + "direct_neutral_auc_minimum": 0.80, + "neutral_inverse_auc_minimum": 0.80, + "calibrated_direct_inverse_accuracy_minimum": 0.80, + "both_surface_direct_inverse_auc_minimum": 0.85, + "ordinal_spearman_minimum": 0.60, + "ordinal_permutation_p_maximum": 0.01, + }, + "inference": { + "bootstrap_seed": 20260723, + "bootstrap_resamples": 50000, + "permutation_seed": 20260724, + "permutations": 100000, + }, + "guardrails": [ + "The cohort has 20 direct, 20 inverse, and 20 neutral laws.", + "The 60 laws span mechanics, fracture, fatigue, transport, thermal, electrical, magnetic, optical, interfacial, microstructural, corrosion, fluid, and kinetic domains.", + "Only calibration-neutral laws define center and scale.", + "Direct, inverse, and validation-neutral labels never alter the normalization.", + "Every law crosses two equation surfaces, two cases, two numerical directions, and two answer orders.", + "The layer and hidden direction remain frozen from old laws.", + "All outputs, including failures, are retained.", + ], + } + write_json(PROTOCOL, protocol) + (OUT / "PROTOCOL.md").write_text( + "# Neutral-anchored relational physics benchmark\n\n" + "Sixty laws are balanced among direct, inverse, and physically neutral " + "relations. Ten neutral laws define the empirical null center and robust " + "scale; ten different neutral laws test that calibration. Direct and " + "inverse outcomes are evaluated without refitting the old-law lens. " + "Threshold-free AUC remains primary, while the neutral median supplies " + "a physically interpretable cutoff for signed classification.\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_revision_figure10.py b/jlens_materials/scripts/build_revision_figure10.py new file mode 100644 index 0000000000000000000000000000000000000000..84ef4ed5d7af4d607c966efbee7c351564ea6834 --- /dev/null +++ b/jlens_materials/scripts/build_revision_figure10.py @@ -0,0 +1,443 @@ +#!/usr/bin/env python3 +"""Build the reviewer-revision version of Figure 10. + +This is a display-only analysis. It reads the frozen supplied-equation and +equation-free law-level outputs, and it does not refit a direction or modify +either experiment's registered statistics. +""" + +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 + + +ROOT = Path(__file__).resolve().parents[1] +SUPPLIED = ROOT / "experiments" / "neutral-anchored-relational-physics-2026-07-18" +EQUATION_FREE = ROOT / "experiments" / "equation-free-relational-physics-2026-08-21" +OUT = EQUATION_FREE / "figures" + +SUPPLIED_LAWS = SUPPLIED / "law_level_neutral_normalized.csv" +SUPPLIED_STATS = SUPPLIED / "statistics.json" +EQUATION_FREE_LAWS = EQUATION_FREE / "law_level_results.csv" +EQUATION_FREE_STATS = EQUATION_FREE / "statistics.json" + +CATEGORY_ORDER = ["inverse", "neutral", "direct"] +CATEGORY_COLORS = { + "inverse": "#7662a8", + "neutral": "#8c8c8c", + "direct": "#2a9d8f", +} +METHODS = ["Hidden", "Output", "Word TF-IDF", "Char TF-IDF"] +METHOD_COLORS = { + "Hidden": "#276b9a", + "Output": "#d39b35", + "Word TF-IDF": "#a7a7a7", + "Char TF-IDF": "#666666", +} + + +def sha256(path: Path) -> str: + return hashlib.sha256(path.read_bytes()).hexdigest() + + +def load_inputs() -> tuple[pd.DataFrame, dict, dict]: + supplied_laws = pd.read_csv(SUPPLIED_LAWS) + equation_free_laws = pd.read_csv(EQUATION_FREE_LAWS) + supplied_stats = json.loads(SUPPLIED_STATS.read_text()) + equation_free_stats = json.loads(EQUATION_FREE_STATS.read_text()) + + required_old = {"law_id", "category", "neutral_z"} + required_new = {"law_id", "category", "old_scale_z", "scaffolded_z"} + if not required_old.issubset(supplied_laws.columns): + raise RuntimeError(f"Missing supplied-law columns: {required_old - set(supplied_laws.columns)}") + if not required_new.issubset(equation_free_laws.columns): + raise RuntimeError( + f"Missing equation-free columns: {required_new - set(equation_free_laws.columns)}" + ) + + paired = supplied_laws[["law_id", "category", "neutral_z"]].merge( + equation_free_laws[["law_id", "category", "old_scale_z", "scaffolded_z"]], + on="law_id", + how="inner", + suffixes=("_supplied", "_equation_free"), + validate="one_to_one", + ) + if len(paired) != 60: + raise RuntimeError(f"Expected 60 matched laws, found {len(paired)}") + if not np.array_equal( + paired["category_supplied"].to_numpy(), + paired["category_equation_free"].to_numpy(), + ): + raise RuntimeError("Law categories disagree across prompt formats") + if not np.allclose( + paired["neutral_z"].to_numpy(), + paired["scaffolded_z"].to_numpy(), + atol=1e-12, + rtol=0, + ): + raise RuntimeError("Archived scaffolded scores disagree across result files") + + paired = paired.rename( + columns={ + "category_supplied": "category", + "neutral_z": "supplied_z", + "old_scale_z": "equation_free_z", + } + ).drop(columns=["category_equation_free", "scaffolded_z"]) + return paired, supplied_stats, equation_free_stats + + +def endpoint_values(stats: dict) -> dict[str, list[float]]: + primary = stats["primary"] + controls = stats["controls"] + keys = [ + "direct_vs_inverse_auc", + "direct_vs_validation_neutral_auc", + "validation_neutral_vs_inverse_auc", + ] + return { + "Hidden": [primary[key] for key in keys], + "Output": [controls["output_head"][key] for key in keys], + "Word TF-IDF": [controls["word_tfidf"][key] for key in keys], + "Char TF-IDF": [controls["character_tfidf"][key] for key in keys], + } + + +def add_panel_labels(axes: np.ndarray) -> None: + for axis, label in zip(axes.flat, "ABCD"): + axis.text( + -0.115, + 1.045, + label, + transform=axis.transAxes, + fontsize=10.5, + fontweight="bold", + ha="left", + va="bottom", + clip_on=False, + ) + + +def main() -> None: + paired, supplied_stats, equation_free_stats = load_inputs() + + 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": 7.5, + "pdf.fonttype": 42, + "ps.fonttype": 42, + } + ) + + fig, axes = plt.subplots(2, 2, figsize=(11.2, 7.4)) + rng = np.random.default_rng(20260821) + + # A: class distributions for both prompt formats on one fixed calibration. + ax = axes[0, 0] + offsets = {"supplied": -0.14, "equation_free": 0.14} + for category_index, category in enumerate(CATEGORY_ORDER): + group = paired.loc[paired["category"] == category] + jitter = rng.uniform(-0.055, 0.055, len(group)) + color = CATEGORY_COLORS[category] + for format_name, column in ( + ("supplied", "supplied_z"), + ("equation_free", "equation_free_z"), + ): + x = category_index + offsets[format_name] + jitter + values = group[column].to_numpy() + if format_name == "supplied": + ax.scatter( + x, + values, + s=27, + facecolors="white", + edgecolors=color, + linewidths=1.15, + alpha=0.95, + zorder=3, + ) + else: + ax.scatter( + x, + values, + s=27, + facecolors=color, + edgecolors="white", + linewidths=0.45, + alpha=0.88, + zorder=3, + ) + median = float(np.median(values)) + center = category_index + offsets[format_name] + ax.plot( + [center - 0.085, center + 0.085], + [median, median], + color="black", + lw=1.8, + solid_capstyle="butt", + zorder=4, + ) + ax.axhline(0, color="0.42", ls="--", lw=0.9, zorder=1) + ax.set_xticks(range(3), ["Inverse", "Neutral", "Direct"]) + ax.set_ylabel("Matched contrast\n(common neutral scale, robust z)") + ax.set_xlim(-0.55, 2.55) + ax.grid(axis="y", color="0.9", lw=0.6, zorder=0) + + # B: every law remains one row, now with its cross-format pair connected. + ax = axes[0, 1] + ordered = paired.sort_values("supplied_z").reset_index(drop=True) + y = np.arange(len(ordered)) + for row_index, row in ordered.iterrows(): + color = CATEGORY_COLORS[row["category"]] + ax.plot( + [row["supplied_z"], row["equation_free_z"]], + [row_index, row_index], + color=color, + alpha=0.28, + lw=0.85, + zorder=1, + ) + for category in CATEGORY_ORDER: + mask = ordered["category"] == category + color = CATEGORY_COLORS[category] + ax.scatter( + ordered.loc[mask, "supplied_z"], + y[mask.to_numpy()], + s=16, + facecolors="white", + edgecolors=color, + linewidths=0.9, + zorder=3, + ) + ax.scatter( + ordered.loc[mask, "equation_free_z"], + y[mask.to_numpy()], + s=16, + facecolors=color, + edgecolors="white", + linewidths=0.35, + zorder=4, + ) + ax.axvline(0, color="0.35", lw=0.9, zorder=0) + ax.set_yticks([]) + ax.set_xlabel("Matched law contrast (common neutral scale)") + ax.set_ylabel("60 laws ordered by supplied-equation score") + ax.set_ylim(-1, len(ordered)) + ax.grid(axis="x", color="0.91", lw=0.6, zorder=0) + + # C: readout and lexical controls, paired by prompt format. + ax = axes[1, 0] + supplied_endpoints = endpoint_values(supplied_stats) + equation_free_endpoints = endpoint_values(equation_free_stats) + endpoint_x = np.arange(3) + method_offsets = np.linspace(-0.27, 0.27, len(METHODS)) + for method, offset in zip(METHODS, method_offsets): + color = METHOD_COLORS[method] + old_values = np.asarray(supplied_endpoints[method], dtype=float) + new_values = np.asarray(equation_free_endpoints[method], dtype=float) + x = endpoint_x + offset + for xx, old_value, new_value in zip(x, old_values, new_values): + ax.plot( + [xx, xx], + [old_value, new_value], + color=color, + lw=1.0, + alpha=0.65, + zorder=1, + ) + ax.scatter( + x, + old_values, + s=35, + facecolors="white", + edgecolors=color, + linewidths=1.15, + zorder=3, + ) + ax.scatter( + x, + new_values, + s=35, + facecolors=color, + edgecolors="white", + linewidths=0.45, + zorder=4, + ) + ax.axhline(0.5, color="0.45", ls="--", lw=0.9) + ax.set_xticks( + endpoint_x, + ["Direct vs\ninverse", "Direct vs\nneutral", "Neutral vs\ninverse"], + ) + ax.set_ylim(0.30, 1.025) + ax.set_ylabel("Pairwise AUC") + ax.grid(axis="y", color="0.9", lw=0.6, zorder=0) + + # D: the four original summary endpoints, now paired across prompt formats. + ax = axes[1, 1] + supplied_primary = supplied_stats["primary"] + equation_free_primary = equation_free_stats["primary"] + supplied_summary = np.asarray( + [ + supplied_primary["surface_direct_inverse_auc"]["a"], + supplied_primary["surface_direct_inverse_auc"]["b"], + supplied_primary["direct_vs_inverse_auc"], + supplied_primary["calibrated_direct_inverse_accuracy"], + ], + dtype=float, + ) + equation_free_summary = np.asarray( + [ + equation_free_primary["surface_direct_inverse_auc"]["a"], + equation_free_primary["surface_direct_inverse_auc"]["b"], + equation_free_primary["direct_vs_inverse_auc"], + equation_free_primary["natural_calibrated_directional_accuracy"], + ], + dtype=float, + ) + summary_x = np.arange(4) + for xx, old_value, new_value in zip( + summary_x, supplied_summary, equation_free_summary + ): + ax.plot( + [xx - 0.10, xx + 0.10], + [old_value, new_value], + color="0.55", + lw=1.1, + zorder=1, + ) + ax.scatter( + summary_x - 0.10, + supplied_summary, + s=45, + facecolors="white", + edgecolors="#37474f", + linewidths=1.25, + zorder=3, + ) + ax.scatter( + summary_x + 0.10, + equation_free_summary, + s=45, + facecolors="#2a9d8f", + edgecolors="white", + linewidths=0.5, + zorder=4, + ) + ax.set_xticks( + summary_x, + ["Surface A", "Surface B", "Direct–inverse\nAUC", "Directional\naccuracy"], + ) + ax.set_ylim(0.90, 1.005) + ax.set_ylabel("Performance") + ax.set_yticks(np.arange(0.90, 1.001, 0.025)) + ax.grid(axis="y", color="0.9", lw=0.6, zorder=0) + + add_panel_labels(axes) + + method_handles = [ + Line2D( + [0], + [0], + marker="o", + color="none", + markerfacecolor=METHOD_COLORS[name], + markeredgecolor="white", + markersize=6.0, + label=name, + ) + for name in METHODS + ] + format_handles = [ + Line2D( + [0], + [0], + marker="o", + color="none", + markerfacecolor="white", + markeredgecolor="#37474f", + markeredgewidth=1.0, + markersize=6.0, + label="Equation supplied", + ), + Line2D( + [0], + [0], + marker="o", + color="none", + markerfacecolor="#2a9d8f", + markeredgecolor="white", + markersize=6.0, + label="Equation-free", + ), + ] + fig.legend( + handles=method_handles + format_handles, + frameon=False, + loc="lower center", + bbox_to_anchor=(0.5, 0.018), + ncol=6, + columnspacing=1.45, + handletextpad=0.45, + ) + fig.subplots_adjust( + left=0.085, + right=0.985, + top=0.955, + bottom=0.145, + hspace=0.42, + wspace=0.28, + ) + + OUT.mkdir(parents=True, exist_ok=True) + png = OUT / "figure10-relational-physics-revision.png" + pdf = OUT / "figure10-relational-physics-revision.pdf" + fig.savefig(png, dpi=300, bbox_inches="tight", facecolor="white") + fig.savefig(pdf, bbox_inches="tight", facecolor="white") + plt.close(fig) + + manifest = { + "status": "revision candidate; not yet inserted into the paper", + "script": str(Path(__file__).resolve().relative_to(ROOT)), + "inputs": { + str(path.relative_to(ROOT)): sha256(path) + for path in ( + SUPPLIED_LAWS, + SUPPLIED_STATS, + EQUATION_FREE_LAWS, + EQUATION_FREE_STATS, + ) + }, + "outputs": { + str(png.relative_to(ROOT)): sha256(png), + str(pdf.relative_to(ROOT)): sha256(pdf), + }, + "common_scale": ( + "The submitted supplied-equation neutral center and robust scale are " + "used for both formats in panels A and B." + ), + "n_matched_laws": int(len(paired)), + } + (OUT / "figure10-relational-physics-revision-manifest.json").write_text( + json.dumps(manifest, indent=2, sort_keys=True) + "\n" + ) + + +if __name__ == "__main__": + main() diff --git a/jlens_materials/scripts/build_symbolic_equivalence_lens.py b/jlens_materials/scripts/build_symbolic_equivalence_lens.py new file mode 100644 index 0000000000000000000000000000000000000000..8f9c6525cbbe662131e59bbf13294a5aa704026a --- /dev/null +++ b/jlens_materials/scripts/build_symbolic_equivalence_lens.py @@ -0,0 +1,133 @@ +#!/usr/bin/env python3 +"""Build explicit/implicit symbolic equations for algebra-invariant probing.""" + +from __future__ import annotations + +import hashlib +import json +from datetime import datetime, timezone +from pathlib import Path + +from build_symbolic_monotonicity_lens import VARIABLES + + +ROOT = Path(__file__).resolve().parents[1] +OUT = ROOT / "experiments" / "symbolic-equivalence-lens-2026-07-18" +MANIFEST = OUT / "prompt_manifest.json" +PROTOCOL = OUT / "protocol.json" +RUNNER = ROOT / "scripts" / "run_symbolic_equivalence_lens.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 formula_rows(y: str, x: str) -> list[tuple[str, int, str, str]]: + return [ + (f"{y} = C {x}", 1, "explicit", "linear"), + (f"{y} = C {x}^2", 1, "explicit", "power"), + (f"{y} = C sqrt({x})", 1, "explicit", "root"), + (f"{y} = y_0 + C {x}^3", 1, "explicit", "offset"), + (f"{y} = C/{x}", -1, "explicit", "linear"), + (f"{y} = C/{x}^2", -1, "explicit", "power"), + (f"{y} = C {x}^(-1)", -1, "explicit", "negative-power"), + (f"{y} = y_0 + C/sqrt({x})", -1, "explicit", "offset"), + (f"{y}/{x} = C", 1, "implicit", "linear"), + (f"{y}/{x}^2 = C", 1, "implicit", "power"), + (f"{y}^2/{x} = C", 1, "implicit", "root"), + (f"({y} - y_0)/{x}^3 = C", 1, "implicit", "offset"), + (f"{y} {x} = C", -1, "implicit", "linear"), + (f"{y} {x}^2 = C", -1, "implicit", "power"), + (f"(1/{y})/{x} = C", -1, "implicit", "reciprocal"), + (f"({y} - y_0) sqrt({x}) = C", -1, "implicit", "offset"), + ] + + +def main() -> None: + prompts = [] + for variable_index, (response, control) in enumerate(VARIABLES): + for index, (formula, sign, surface, family) in enumerate( + formula_rows(response, control) + ): + 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": f"equiv-{variable_index:02d}-{surface}-{index:02d}", + "variable_index": variable_index, + "response": response, + "control": control, + "formula": formula, + "law_sign": sign, + "surface": surface, + "family": family, + "user": user, + } + ) + manifest = { + "study_id": "symbolic-equivalence-lens-2026-07-18", + "markers": {"law": "§"}, + "prompts": prompts, + "dimensions": { + "n_prompts": len(prompts), + "n_variable_pairs": len(VARIABLES), + "n_surfaces": 2, + }, + } + write_json(MANIFEST, manifest) + protocol = { + "study_id": manifest["study_id"], + "status": "developmental-symbolic-equivalence", + "frozen_at": datetime.now(timezone.utc).isoformat(), + "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), + }, + }, + "candidate_layer_rule": ( + "Maximize the mean of explicit-to-implicit and " + "implicit-to-explicit sign AUC, with variable-pair folds and the " + "earliest layer breaking ties." + ), + "guardrails": [ + "Both surfaces use identical anonymous-variable inventories.", + "Every surface is balanced for direct and inverse relations.", + "Cross-surface validation reverses algebraic syntax.", + "Materials prompts are not used to select the candidate layer.", + ], + } + write_json(PROTOCOL, protocol) + (OUT / "PROTOCOL.md").write_text( + "# Algebra-invariant symbolic lens\n\n" + "Layer selection requires a direction learned from explicit equations " + "to classify implicit equivalents, and vice versa. This rejects layers " + "that merely recognize slash or product syntax within one surface.\n" + ) + print(f"frozen {len(prompts)} equivalence prompts") + print(f"protocol sha256: {sha256(PROTOCOL)}") + + +if __name__ == "__main__": + main() diff --git a/jlens_materials/scripts/freeze_answer_code_binding.py b/jlens_materials/scripts/freeze_answer_code_binding.py new file mode 100644 index 0000000000000000000000000000000000000000..8c32a319561272ef038bb75bd6d8dff31f71fa9a --- /dev/null +++ b/jlens_materials/scripts/freeze_answer_code_binding.py @@ -0,0 +1,275 @@ +#!/usr/bin/env python3 +"""Freeze the arbitrary answer-code binding falsification study.""" + +from __future__ import annotations + +import json +from datetime import datetime, timezone +from pathlib import Path + +from transformers import AutoTokenizer + +import freeze_lexical_adversarial_representation as shared + +ROOT = Path(__file__).resolve().parents[1] +SOURCE = ( + ROOT / "experiments" + / "late-physics-representation-replication-2026-07-17" +) +OUT = ROOT / "experiments" / "answer-code-binding-2026-07-17" +MANIFEST = OUT / "prompt_manifest.json" +PROTOCOL = OUT / "protocol.json" +PROTOCOL_MD = OUT / "PROTOCOL.md" +RUNNER = ROOT / "scripts" / "run_answer_code_binding.py" + + +def transform_prompt(row: dict, anchor_outcome: str) -> dict: + """Replace the semantic answer pair with a deliberately conflicting code.""" + positive = row["outcome_positive"] + negative = row["outcome_negative"] + if row["variant"] == "anchor": + mapping = {anchor_outcome: "A"} + mapping[negative if anchor_outcome == positive else positive] = "B" + else: + mapping = {anchor_outcome: "B"} + mapping[negative if anchor_outcome == positive else positive] = "A" + expected_physical = row["expected_outcome"] + first, second = ( + (positive, negative) + if row["presentation_order"] == "positive-first" + else (negative, positive) + ) + user = ( + f"{row['stem']} Internal checkpoint. Now apply this arbitrary reporting " + f"code: if the scientific answer is {first}, output {mapping[first]}; " + f"if it is {second}, output {mapping[second]}. Answer exactly one " + "uppercase letter: A or B." + ) + return { + **row, + "prompt_id": f"{row['prompt_id']}--answer-code", + "source_prompt_id": row["prompt_id"], + "expected_physical_outcome": expected_physical, + "expected_code": mapping[expected_physical], + "code_mapping": mapping, + "checkpoint_word": "checkpoint", + "user": user, + } + + +def build_manifest(source: dict) -> dict: + source_by_triplet = { + row["triplet_id"]: row for row in source["triplets"] + } + prompts = [] + prompt_ids = {} + for row in source["prompts"]: + triplet = source_by_triplet[row["triplet_id"]] + transformed = transform_prompt(row, triplet["anchor_expected_outcome"]) + prompts.append(transformed) + prompt_ids[(row["triplet_id"], row["variant"])] = transformed["prompt_id"] + triplets = [] + for row in source["triplets"]: + triplets.append({ + **row, + "source_prompt_ids": row["prompt_ids"], + "prompt_ids": { + variant: prompt_ids[(row["triplet_id"], variant)] + for variant in [ + "anchor", + "physics_paraphrase", + "lexical_counterfactual", + ] + }, + "registered_code_pattern": { + "anchor": "A", + "physics_paraphrase": "B", + "lexical_counterfactual": "A", + }, + }) + return { + "study_id": "answer-code-binding-2026-07-17", + "created_at": datetime.now(timezone.utc).isoformat(), + "design": ( + "Prospective falsification of the replicated late transition. " + "Anchor and physical paraphrase retain the same scientific answer " + "but receive different arbitrary output codes; anchor and physical " + "counterfactual have opposite scientific answers but share code A. " + "A checkpoint marker precedes the mapping, so its state cannot " + "encode the future arbitrary assignment." + ), + "source_study_id": source["study_id"], + "families": source["families"], + "triplets": triplets, + "prompts": prompts, + } + + +def main() -> None: + if PROTOCOL.exists() or MANIFEST.exists(): + raise FileExistsError("Frozen files already exist; do not overwrite.") + source_manifest_path = SOURCE / "prompt_manifest.json" + source_statistics_path = SOURCE / "statistics.json" + if not source_manifest_path.exists() or not source_statistics_path.exists(): + raise FileNotFoundError("The completed disjoint replication is required.") + source = json.loads(source_manifest_path.read_text()) + manifest = build_manifest(source) + shared.dump(MANIFEST, manifest) + + tokenizer = AutoTokenizer.from_pretrained( + shared.MODEL, + revision=shared.MODEL_REVISION, + local_files_only=True, + ) + words = {"checkpoint", "A", "B"} + for family in manifest["families"]: + words.add(family["outcome_positive"]) + words.add(family["outcome_negative"]) + tokenization = { + word: [ + int(value) + for value in tokenizer.encode(word, add_special_tokens=False) + ] + for word in sorted(words) + } + if any(len(ids) != 1 for ids in tokenization.values()): + raise RuntimeError(f"Registered readouts must be one token: {tokenization}") + lenses = [ + {**row, "sha256": shared.sha256(ROOT / row["path"])} + for row in shared.LENS_ROWS + ] + protocol = { + "study_id": manifest["study_id"], + "status": ( + "prospectively frozen before any model forward pass for this " + "answer-code cohort; motivated by the completed late-transition " + "replication" + ), + "frozen_at": datetime.now(timezone.utc).isoformat(), + "scientific_question": ( + "Does the late transition represent the scientific relation before " + "the arbitrary answer mapping is visible, and then bind that " + "relation to the requested A/B code after the mapping?" + ), + "model": shared.MODEL, + "model_revision": shared.MODEL_REVISION, + "lenses": lenses, + "source_layers": shared.LAYERS, + "checkpoint_word": "checkpoint", + "registered_band_percent": [80.0, 96.0], + "inputs": { + "prompt_manifest": str(MANIFEST.relative_to(ROOT)), + "prompt_manifest_sha256": shared.sha256(MANIFEST), + "runner": str(RUNNER.relative_to(ROOT)), + "runner_sha256": shared.sha256(RUNNER), + "source_prompt_manifest": str(source_manifest_path.relative_to(ROOT)), + "source_prompt_manifest_sha256": shared.sha256(source_manifest_path), + "source_statistics": str(source_statistics_path.relative_to(ROOT)), + "source_statistics_sha256": shared.sha256(source_statistics_path), + }, + "tokenization_preflight": tokenization, + "registered_endpoints": { + "checkpoint_physics_separation": ( + "For each triplet, sign the positive-minus-negative physical " + "readout so that the anchor answer is positive, then compute " + "mean(anchor, physics paraphrase) minus counterfactual. Average " + "over the frozen 80--96% window." + ), + "final_code_separation": ( + "At the final prompt position compute mean(A-minus-B for anchor " + "and counterfactual) minus A-minus-B for the paraphrase. Average " + "over the frozen 80--96% window." + ), + "code_binding_transition": ( + "Final-position code separation minus checkpoint-position code " + "separation over the frozen 80--96% window." + ), + "inference": ( + "30,000 two-stage bootstrap resamples over six families and " + "four triplets per family; fixed analysis seeds." + ), + }, + "success_rule": { + "confidence": ( + "All three Jacobian-ensemble endpoint 95% intervals are above zero." + ), + "breadth": ( + "For both checkpoint physics separation and final code " + "separation, at least 18/24 triplets and 5/6 family means are positive." + ), + "all_outputs_retained": True, + }, + "secondary_endpoints": [ + "direct-decoding versions and paired Jacobian-minus-direct contrasts", + "clean A/B answer accuracy and consistency", + "layer-resolved endpoint trajectories", + "centered full-state anchor-paraphrase minus anchor-counterfactual geometry at both positions", + ], + "guardrails": [ + "The prompt explicitly names scientific answer words; readout is a registered relational contrast, not open-vocabulary discovery.", + "The checkpoint state cannot attend to the future mapping, but it includes the complete scientific question and the words 'Internal checkpoint'.", + "A positive result would support staged relation formation and answer-code binding, not a literal chain of thought or human-like understanding.", + "Every prompt, family, layer, lens seed, behavior error, and state is retained.", + ], + } + shared.dump(PROTOCOL, protocol) + PROTOCOL_MD.write_text( + "\n".join([ + "# Frozen arbitrary answer-code binding falsification", + "", + f"Frozen: `{protocol['frozen_at']}`", + "", + "## Why this experiment", + "", + ( + "The preceding disjoint cohort showed a large late-minus-middle " + "shift, but direct decoding and the Jacobian lens behaved almost " + "identically. That effect could therefore be scientific relation " + "formation, imminent answer-token preparation, or both. This " + "study creates a conflict between physics and output labels." + ), + "", + "## Exact conflict", + "", + ( + "Within each triplet, anchor and physical paraphrase have the " + "same scientific answer but are mapped to different letters. " + "Anchor and physical counterfactual have opposite scientific " + "answers but are both mapped to A. The word `checkpoint` occurs " + "before the mapping. Its state has seen the full scientific " + "question but cannot attend to the future arbitrary code." + ), + "", + "## Frozen tests", + "", + f"1. {protocol['registered_endpoints']['checkpoint_physics_separation']}", + f"2. {protocol['registered_endpoints']['final_code_separation']}", + f"3. {protocol['registered_endpoints']['code_binding_transition']}", + "", + "## Success rule", + "", + f"- {protocol['success_rule']['confidence']}", + f"- {protocol['success_rule']['breadth']}", + "- All outputs are retained.", + "", + "## Fingerprints", + "", + f"- transformed manifest: `{protocol['inputs']['prompt_manifest_sha256']}`", + f"- execution runner: `{protocol['inputs']['runner_sha256']}`", + f"- source manifest: `{protocol['inputs']['source_prompt_manifest_sha256']}`", + f"- motivating statistics: `{protocol['inputs']['source_statistics_sha256']}`", + *[ + f"- lens seed {row['seed']}: `{row['sha256']}`" + for row in lenses + ], + "", + ]) + "\n" + ) + print(f"wrote {MANIFEST.relative_to(ROOT)}") + print(f"manifest sha256: {shared.sha256(MANIFEST)}") + print(f"wrote {PROTOCOL.relative_to(ROOT)}") + print(f"protocol sha256: {shared.sha256(PROTOCOL)}") + + +if __name__ == "__main__": + main() diff --git a/jlens_materials/scripts/make_pdf_contact_sheet.py b/jlens_materials/scripts/make_pdf_contact_sheet.py new file mode 100644 index 0000000000000000000000000000000000000000..35102a0095003d43682968e80bd9f73fe7363e0a --- /dev/null +++ b/jlens_materials/scripts/make_pdf_contact_sheet.py @@ -0,0 +1,53 @@ +#!/usr/bin/env python3 +"""Make a labeled contact sheet from rendered PDF page PNGs for visual QA.""" + +from __future__ import annotations + +import argparse +from pathlib import Path + +from PIL import Image, ImageDraw + + +def main() -> None: + parser = argparse.ArgumentParser() + parser.add_argument("page_dir", type=Path) + parser.add_argument("output", type=Path) + parser.add_argument("--columns", type=int, default=4) + parser.add_argument("--width", type=int, default=280) + args = parser.parse_args() + + files = sorted(args.page_dir.glob("page-*.png")) + if not files: + raise SystemExit(f"no rendered pages in {args.page_dir}") + pad, label_height = 16, 24 + pages: list[Image.Image] = [] + for index, path in enumerate(files, start=1): + page = Image.open(path).convert("RGB") + height = round(page.height * args.width / page.width) + page = page.resize((args.width, height)) + labeled = Image.new("RGB", (args.width, height + label_height), "white") + labeled.paste(page, (0, label_height)) + ImageDraw.Draw(labeled).text((5, 4), f"Page {index}", fill="black") + pages.append(labeled) + + rows = (len(pages) + args.columns - 1) // args.columns + cell_height = max(page.height for page in pages) + sheet = Image.new( + "RGB", + ( + args.columns * args.width + (args.columns - 1) * pad, + rows * cell_height + (rows - 1) * pad, + ), + (220, 220, 220), + ) + for index, page in enumerate(pages): + x = (index % args.columns) * (args.width + pad) + y = (index // args.columns) * (cell_height + pad) + sheet.paste(page, (x, y)) + args.output.parent.mkdir(parents=True, exist_ok=True) + sheet.save(args.output) + + +if __name__ == "__main__": + main() diff --git a/jlens_materials/scripts/plot_equation_free_relational_pilot.py b/jlens_materials/scripts/plot_equation_free_relational_pilot.py new file mode 100644 index 0000000000000000000000000000000000000000..f387ebc76ba282ce0c6b45339bc8d0923fbc882a --- /dev/null +++ b/jlens_materials/scripts/plot_equation_free_relational_pilot.py @@ -0,0 +1,130 @@ +#!/usr/bin/env python3 +"""Render the equation-free relational pilot from saved analysis tables.""" + +from __future__ import annotations + +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 + + +ROOT = Path(__file__).resolve().parents[1] +OUT = ROOT / "experiments" / "equation-free-relational-pilot-2026-08-21" + +DISPLAY_NAMES = { + "capacitance-separation": "Capacitance / plate separation", + "diffusion-time-diffusivity-new": "Equilibration time / diffusivity", + "fatigue-life-delta-k": "Fatigue life / stress-intensity range", + "settling-speed-viscosity": "Settling speed / viscosity", + "conductivity-sample-area-neutral": "Conductivity / sample area", + "melting-temperature-mass": "Melting temperature / sample mass", + "modulus-specimen-length": "Young's modulus / specimen length", + "resistivity-wire-length-neutral": "Resistivity / wire length", + "corrosion-loss-time": "Corrosion loss / exposure time", + "creep-strain-time": "Creep strain / loading time", + "current-voltage": "Current / voltage", + "grain-size-time": "Grain size / annealing time", +} + + +def main() -> None: + laws = pd.read_csv(OUT / "law_level_results.csv").sort_values("frozen_z") + statistics = json.loads((OUT / "statistics.json").read_text()) + behavior = statistics["behavior"] + controls = statistics["controls"] + colors = {"inverse": "#7662a8", "neutral": "#8c8c8c", "direct": "#2a9d8f"} + plt.rcParams.update( + { + "font.family": "DejaVu Sans", + "font.size": 8, + "axes.spines.top": False, + "axes.spines.right": False, + "pdf.fonttype": 42, + } + ) + fig, axes = plt.subplots(2, 2, figsize=(10.6, 7.5)) + + y = np.arange(len(laws)) + bar_colors = [colors[value] for value in laws["category"]] + axes[0, 0].barh(y, laws["frozen_z"], color=bar_colors, height=0.68) + axes[0, 0].axvline(0, color="0.35", ls="--", lw=1) + axes[0, 0].set_yticks(y, [DISPLAY_NAMES[x] for x in laws["law_id"]], fontsize=6.8) + axes[0, 0].set_xlabel("Equation-free matched contrast (old neutral scale)") + + for category, group in laws.groupby("category"): + axes[0, 1].scatter( + group["scaffolded_frozen_z"], + group["frozen_z"], + s=40, + color=colors[category], + label=category.capitalize(), + edgecolor="white", + linewidth=0.5, + ) + axes[0, 1].axhline(0, color="0.75", lw=0.8) + axes[0, 1].axvline(0, color="0.75", lw=0.8) + axes[0, 1].set_xlabel("Original supplied-equation contrast (z)") + axes[0, 1].set_ylabel("Equation-free contrast (z)") + axes[0, 1].legend(frameon=False, fontsize=7, loc="upper left") + + methods = ["Hidden", "Output", "Word TF-IDF", "Char TF-IDF"] + values = [ + statistics["primary_frozen_transfer"]["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), + 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") + + categories = ["Inverse", "Neutral", "Direct", "Overall"] + accuracies = [ + behavior["accuracy_by_category"]["inverse"], + behavior["accuracy_by_category"]["neutral"], + behavior["accuracy_by_category"]["direct"], + behavior["overall_accuracy"], + ] + axes[1, 1].bar( + range(4), + accuracies, + color=[colors["inverse"], colors["neutral"], colors["direct"], "#276b9a"], + ) + axes[1, 1].set_xticks(range(4), categories) + axes[1, 1].set_ylim(0, 1.03) + axes[1, 1].set_ylabel("Exact greedy-answer accuracy") + + 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=2.7) + fig_dir = OUT / "figures" + fig_dir.mkdir(exist_ok=True) + fig.savefig(fig_dir / "equation-free-relational-pilot-summary.png", dpi=240, bbox_inches="tight") + fig.savefig(fig_dir / "equation-free-relational-pilot-summary.pdf", bbox_inches="tight") + plt.close(fig) + + +if __name__ == "__main__": + main() diff --git a/jlens_materials/scripts/plot_key_concept_traces.py b/jlens_materials/scripts/plot_key_concept_traces.py new file mode 100644 index 0000000000000000000000000000000000000000..637523ff1b8a0ab78a016866c509b0a6c360e2fe --- /dev/null +++ b/jlens_materials/scripts/plot_key_concept_traces.py @@ -0,0 +1,91 @@ +#!/usr/bin/env python3 +"""Create engineering-facing concept traces for fixed positive/negative cases.""" + +from __future__ import annotations + +import json +from pathlib import Path + +import matplotlib.pyplot as plt +import numpy as np + + +ROOT = Path(__file__).resolve().parents[1] +RUN = ROOT / "runs" / "gemma4-materials-key-concept-traces-v1.json" +OUT = ROOT / "figures" / "gemma4-materials-key-concept-traces-v1" + +PANELS = [ + ("paper-v2-assoc-notch-resistance-04", "toughness", "Notch resistance", "strong positive"), + ("paper-v2-assoc-ductile-02", "coalescence", "Ductile failure", "strong positive"), + ("paper-v2-assoc-line-defect-motion-01", "dislocation", "Line-defect motion", "strong positive"), + ("paper-v2-assoc-rapid-transformation-04", "tetragonal", "Diffusionless transformation", "strong positive"), + ("paper-v2-assoc-cleavage-03", "cleavage", "Low-temperature cleavage", "prespecified weak case"), + ("paper-v2-assoc-hot-air-surface-layer-01", "oxidation", "Hot-air surface reaction", "prespecified weak case"), +] + + +def find_curve(record: dict, lens: str, label: str) -> dict: + return next( + item for item in record["concept_trajectories"][lens] + if item["label"] == label + ) + + +def main() -> None: + OUT.mkdir(parents=True, exist_ok=True) + run = json.loads(RUN.read_text()) + by_slug = {record["slug"]: record for record in run["prompts"]} + fig, axes = plt.subplots(2, 3, figsize=(15, 8.7), sharex=True, sharey=True, + constrained_layout=True) + for ax, (slug, label, panel_title, status) in zip(axes.flat, PANELS): + record = by_slug[slug] + j = find_curve(record, "jacobian_lens", label) + l = find_curve(record, "logit_lens", label) + jx = np.asarray(j["depths"]) + jy = np.asarray(j["ranks"], dtype=float) + keep = (jx >= 38) & (jx <= 92) & (jy > 0) + lx = np.asarray(l["depths"]) + ly = np.asarray(l["ranks"], dtype=float) + lkeep = ly > 0 + ax.plot(jx[keep], jy[keep], "-o", ms=3.5, lw=2.3, color="#c1553b", + label="Jacobian lens") + ax.plot(lx[lkeep], ly[lkeep], "--", lw=2.0, color="#2b7a8c", + label="Logit lens") + emergence = next(item for item in record["emergence"] if item["label"] == label) + jbest = int(emergence["best_rank"]) + 1 + lbest = int(emergence["logit_lens_best_rank"]) + 1 + ax.axhspan(1, 100, color="#f3e7c6", alpha=0.28, zorder=-2) + ax.set_yscale("log") + ax.invert_yaxis() + ax.set_ylim(50000, 0.8) + ax.set_xlim(37, 93) + ax.set_yticks([1, 10, 100, 1000, 10000], ["1", "10", "100", "1k", "10k"]) + ax.grid(True, which="major", alpha=0.18) + ax.set_title(f"{panel_title}: '{label}'", loc="left", fontsize=11, + fontweight="bold", color="#333333", pad=8) + ax.text(0.0, 0.97, status, transform=ax.transAxes, + fontsize=8.0, color="#666666", va="top") + ax.text(0.98, 0.96, f"best rank\nJ: {jbest:,}\nlogit: {lbest:,}", + transform=ax.transAxes, ha="right", va="top", fontsize=8.5, + bbox={"boxstyle": "round,pad=0.25", "facecolor": "white", + "edgecolor": "#cccccc", "alpha": 0.92}) + ax.spines[["top", "right"]].set_visible(False) + for ax in axes[:, 0]: + ax.set_ylabel("vocabulary rank (higher on page = easier to read)") + for ax in axes[-1, :]: + ax.set_xlabel("network depth (%)") + axes[0, 0].legend(frameon=False, loc="lower left") + fig.suptitle( + "Readable scientific concepts across Gemma-4 E4B-it's layers\n" + "Tracked words were absent from both prompt and one-token continuation; traces are readouts, not literal private prose", + fontsize=14, fontweight="bold" + ) + for suffix in ("png", "pdf"): + fig.savefig(OUT / f"key-concept-traces.{suffix}", dpi=240, + bbox_inches="tight", facecolor="white") + plt.close(fig) + print(f"wrote {OUT / 'key-concept-traces.pdf'}") + + +if __name__ == "__main__": + main() diff --git a/jlens_materials/scripts/plot_lens_explainer.py b/jlens_materials/scripts/plot_lens_explainer.py new file mode 100644 index 0000000000000000000000000000000000000000..980a67d00b7ff93a8484b01584477838d7a999b3 --- /dev/null +++ b/jlens_materials/scripts/plot_lens_explainer.py @@ -0,0 +1,88 @@ +#!/usr/bin/env python3 +"""Create a plain-language diagram comparing the two readout methods.""" + +from __future__ import annotations + +from pathlib import Path + +import matplotlib.pyplot as plt +from matplotlib.patches import FancyArrowPatch, FancyBboxPatch + + +ROOT = Path(__file__).resolve().parents[1] +OUT = ROOT / "figures" / "engineering" + + +def box(ax, xy, width, height, text, *, face, edge="#444444", fontsize=10): + patch = FancyBboxPatch( + xy, width, height, + boxstyle="round,pad=0.02,rounding_size=0.02", + facecolor=face, edgecolor=edge, linewidth=1.1, + ) + ax.add_patch(patch) + ax.text(xy[0] + width / 2, xy[1] + height / 2, text, + ha="center", va="center", fontsize=fontsize) + return patch + + +def arrow(ax, start, end, *, color="#555555", style="-|>", lw=1.6): + ax.add_patch(FancyArrowPatch(start, end, arrowstyle=style, + mutation_scale=13, color=color, linewidth=lw)) + + +def main() -> None: + OUT.mkdir(parents=True, exist_ok=True) + fig, ax = plt.subplots(figsize=(12.4, 5.6)) + ax.set_xlim(0, 1.03) + ax.set_ylim(0, 1) + ax.axis("off") + + box(ax, (0.03, 0.39), 0.18, 0.22, + "Engineering prompt\n\n'Large flaw, modest load,\nsudden brittle failure'", + face="#f2f2f2", fontsize=9.5) + arrow(ax, (0.21, 0.50), (0.28, 0.50)) + + layer_x = [0.29, 0.35, 0.41, 0.47, 0.53] + for index, x in enumerate(layer_x): + box(ax, (x, 0.36), 0.045, 0.28, f"L{index+1}", + face="#dceaf2" if index != 2 else "#f6d8cc", fontsize=9) + if index < len(layer_x) - 1: + arrow(ax, (x + 0.045, 0.50), (layer_x[index + 1], 0.50), lw=1.0) + ax.text(0.42, 0.75, "Gemma hidden state changes across layers", + ha="center", fontsize=10, fontweight="bold") + ax.text(0.432, 0.30, "read the hidden vector here", + ha="center", fontsize=9, color="#8f2f27") + + arrow(ax, (0.432, 0.36), (0.67, 0.72), color="#2b7a8c") + arrow(ax, (0.432, 0.36), (0.67, 0.28), color="#c1553b") + + box(ax, (0.67, 0.62), 0.18, 0.21, + "LOGIT LENS\n\nApply final word-output\ncoordinates directly", + face="#d7ebef", edge="#2b7a8c", fontsize=9.5) + box(ax, (0.67, 0.17), 0.18, 0.25, + "JACOBIAN LENS\n\nFirst map through the\naverage downstream effect,\nthen read words", + face="#f5d8ce", edge="#c1553b", fontsize=8.8) + + arrow(ax, (0.85, 0.725), (0.91, 0.725), color="#2b7a8c") + arrow(ax, (0.85, 0.295), (0.91, 0.295), color="#c1553b") + box(ax, (0.90, 0.61), 0.105, 0.23, + "Ranked words\n\n1: This\n...\n6261: toughness", + face="#f7f7f7", edge="#2b7a8c", fontsize=7.9) + box(ax, (0.90, 0.18), 0.105, 0.23, + "Ranked words\n\n1. ...\n2. toughness\n3. ...", + face="#f7f7f7", edge="#c1553b", fontsize=8.0) + + ax.text(0.50, 0.03, + "Both are measurement tools. A high-ranked concept is readable; it is not automatically the cause of the answer.", + ha="center", fontsize=9.5, color="#444444") + fig.suptitle("Two ways to translate an intermediate model state into engineering words", + fontsize=14, fontweight="bold", y=0.97) + for suffix in ("png", "pdf"): + fig.savefig(OUT / f"lens-comparison-explainer.{suffix}", dpi=240, + bbox_inches="tight", facecolor="white") + plt.close(fig) + print(f"wrote {OUT / 'lens-comparison-explainer.pdf'}") + + +if __name__ == "__main__": + main() diff --git a/jlens_materials/scripts/rebuild_review_robustness_outputs.sh b/jlens_materials/scripts/rebuild_review_robustness_outputs.sh new file mode 100644 index 0000000000000000000000000000000000000000..fabdb8a4f8b15e24504af1d55eedb4c42749ce51 --- /dev/null +++ b/jlens_materials/scripts/rebuild_review_robustness_outputs.sh @@ -0,0 +1,27 @@ +#!/usr/bin/env bash +# Rebuild July 2026 review-response tables and figures from archived raw data. +# This script does not rerun Gemma or alter any frozen protocol. + +set -euo pipefail + +cd "$(dirname "$0")/.." + +export MPLCONFIGDIR="${MPLCONFIGDIR:-/private/tmp/jlens-materials-mpl}" +mkdir -p "$MPLCONFIGDIR" + +python scripts/analyze_option_free_relation_graph.py +python scripts/analyze_option_free_question_end.py +python scripts/analyze_cross_mechanism_outcome.py +python scripts/plot_multitoken_sequence_robustness.py +python scripts/plot_relation_graph_robustness.py + +if [[ -f experiments/cross-mechanism-activation-patching-2026-07-18/raw.json ]]; then + python scripts/analyze_cross_mechanism_activation_patching.py + python scripts/audit_cross_mechanism_activation_patching.py + python scripts/plot_cross_mechanism_activation_patching.py +fi + +python scripts/build_review_robustness_si_inventory.py +python scripts/validate_robustness_bundle.py + +echo "Review-response analyses, figures, SI inventory, and validation rebuilt." diff --git a/jlens_materials/scripts/run_abstract_physics_semantic.py b/jlens_materials/scripts/run_abstract_physics_semantic.py new file mode 100644 index 0000000000000000000000000000000000000000..8886085e3161b7ee33bb7ed4d26e0ffb574782c1 --- /dev/null +++ b/jlens_materials/scripts/run_abstract_physics_semantic.py @@ -0,0 +1,214 @@ +#!/usr/bin/env python3 +"""Capture Gemma states for the semantic-answer abstract-physics cohort.""" + +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")) + +from jlens.hooks import ActivationRecorder # noqa: E402 +from run_abstract_physics_composition import marker_positions # 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 pair_logits # noqa: E402 +from run_semantic_steering_v3 import continuation_tokens # noqa: E402 + + +DEFAULT_PROTOCOL = "experiments/abstract-physics-semantic-2026-07-18/protocol.json" +DEFAULT_OUTPUT = "experiments/abstract-physics-semantic-2026-07-18/raw.json" +DEFAULT_STATES = ( + "experiments/abstract-physics-semantic-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( + model, + prompts: list[dict], + marker_ids: dict[str, int], + answer_ids: tuple[int, int], +) -> tuple[np.ndarray, list[dict], list[int]]: + layers = list(range(model.n_layers)) + positions = ["law", "comparison", "decision", "final_prompt"] + states = np.empty( + (len(positions), len(prompts), len(layers), model.d_model), + dtype=np.float16, + ) + clean_rows = [] + for prompt_index, prompt in enumerate(prompts): + text = format_chat(model.tokenizer, prompt["user"]) + input_ids = model.encode(text, max_length=512) + 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 + ].detach() + normalized = model._final_norm( + final_state.to( + device=model._lm_head.weight.device, + dtype=model._lm_head.weight.dtype, + ) + ).float() + logits = pair_logits(model, normalized, answer_ids)[0].detach().cpu() + predicted = "higher" if float(logits[0]) > float(logits[1]) else "lower" + full_logits = model.unembed(final_state).float()[0] + top_id = int(full_logits.argmax()) + clean_rows.append( + { + "prompt_id": prompt["prompt_id"], + "law_id": prompt["law_id"], + "split": prompt["split"], + "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["answer_order"], + "expected_answer": prompt["expected_answer"], + "predicted_answer": predicted, + "registered_answer_correct": predicted + == prompt["expected_answer"], + "higher_minus_lower_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 answer_ids, + "n_tokens": int(input_ids.shape[1]), + "positions": locations, + } + ) + print( + f"captured {prompt_index + 1:03d}/{len(prompts):03d}: " + f"{prompt['prompt_id']}", + flush=True, + ) + return states, clean_rows, layers + + +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) + args = parser.parse_args() + + protocol_path = (ROOT / args.protocol).resolve() + protocol = json.loads(protocol_path.read_text()) + manifest_path = ROOT / protocol["inputs"]["prompt_manifest"]["path"] + validate_hash(manifest_path, protocol["inputs"]["prompt_manifest"]["sha256"]) + validate_hash( + Path(__file__).resolve(), protocol["inputs"]["runner"]["sha256"] + ) + manifest = json.loads(manifest_path.read_text()) + model = load_model( + protocol["model"], + dtype=_DTYPES[args.dtype], + device=args.device, + revision=protocol["model_revision"], + ) + 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"{name} marker is not one token: {ids}") + marker_ids[name] = int(ids[0]) + higher = continuation_tokens(model.tokenizer, "higher") + lower = continuation_tokens(model.tokenizer, "lower") + if len(higher) != 1 or len(lower) != 1: + raise RuntimeError(f"semantic answers are not single tokens: {higher=} {lower=}") + answer_ids = (higher[0], lower[0]) + + states, clean_rows, layers = capture( + model, manifest["prompts"], marker_ids, answer_ids + ) + states_path = (ROOT / args.states_output).resolve() + states_path.parent.mkdir(parents=True, exist_ok=True) + np.savez_compressed( + states_path, + prompt_ids=np.asarray( + [row["prompt_id"] for row in manifest["prompts"]] + ), + positions=np.asarray(protocol["positions"]), + layers=np.asarray(layers, dtype=np.int16), + raw_states=states, + ) + output_path = (ROOT / args.output).resolve() + 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)), + "states_sha256": sha256(states_path), + "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(), + }, + "dimensions": { + **manifest["dimensions"], + "n_positions": len(protocol["positions"]), + "n_layers": len(layers), + "d_model": model.d_model, + }, + "markers": { + name: {"text": manifest["markers"][name], "token_id": token_id} + for name, token_id in marker_ids.items() + }, + "answer_tokens": { + "higher": answer_ids[0], + "lower": answer_ids[1], + "decoded_higher": model.tokenizer.decode([answer_ids[0]]), + "decoded_lower": model.tokenizer.decode([answer_ids[1]]), + }, + "clean_rows": clean_rows, + } + atomic_json(output_path, payload) + print(f"saved {states.shape} states to {states_path}", flush=True) + print(f"states sha256: {sha256(states_path)}", flush=True) + + +if __name__ == "__main__": + main() diff --git a/jlens_materials/scripts/run_answer_code_binding.py b/jlens_materials/scripts/run_answer_code_binding.py new file mode 100644 index 0000000000000000000000000000000000000000..448205c0401b63e93338f84ab619605acfb282ef --- /dev/null +++ b/jlens_materials/scripts/run_answer_code_binding.py @@ -0,0 +1,334 @@ +#!/usr/bin/env python3 +"""Run the frozen arbitrary-answer-code binding falsification study.""" + +from __future__ import annotations + +import argparse +import json +import platform +import sys +from collections import defaultdict +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, + pair_logits, +) +from run_semantic_steering_v3 import continuation_tokens # noqa: E402 + +DEFAULT_PROTOCOL = ( + "experiments/answer-code-binding-2026-07-17/protocol-amendment-v1.json" +) +DEFAULT_OUTPUT = "experiments/answer-code-binding-2026-07-17/raw.json" +DEFAULT_STATES = "experiments/answer-code-binding-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}") + + +@torch.inference_mode() +def capture_two_positions( + model, + prompts: list[dict], + layers: list[int], + checkpoint_token_id: int, + code_ids: tuple[int, int], +) -> tuple[np.ndarray, list[dict]]: + states = np.empty( + (2, len(prompts), len(layers), model.d_model), dtype=np.float16 + ) + clean_rows = [] + for prompt_index, prompt in enumerate(prompts): + text = format_chat(model.tokenizer, prompt["user"]) + input_ids = model.encode(text, max_length=512) + matches = ( + input_ids[0] == checkpoint_token_id + ).nonzero(as_tuple=False).flatten().tolist() + if len(matches) != 1: + decoded = model.tokenizer.decode( + input_ids[0].detach().cpu().tolist(), + clean_up_tokenization_spaces=False, + ) + raise RuntimeError( + f"{prompt['prompt_id']} has {len(matches)} checkpoint tokens: {decoded}" + ) + checkpoint_position = int(matches[0]) + final_position = int(input_ids.shape[1] - 1) + final_layer = model.n_layers - 1 + capture = sorted(set(layers + [final_layer])) + with ActivationRecorder(model.layers, at=capture) as recorder: + model.forward(input_ids) + for layer_index, layer in enumerate(layers): + activation = recorder.activations[layer][0] + states[0, prompt_index, layer_index] = ( + activation[checkpoint_position] + .detach().float().cpu().numpy().astype(np.float16) + ) + states[1, prompt_index, layer_index] = ( + activation[final_position] + .detach().float().cpu().numpy().astype(np.float16) + ) + final_state = recorder.activations[final_layer][ + 0, final_position:final_position + 1 + ].detach() + normalized = model._final_norm( + final_state.to( + device=model._lm_head.weight.device, + dtype=model._lm_head.weight.dtype, + ) + ).float() + logits = pair_logits(model, normalized, code_ids)[0].detach().cpu() + full_logits = model.unembed(final_state).float()[0] + top_id = int(full_logits.argmax()) + predicted = "A" if float(logits[0]) > float(logits[1]) else "B" + clean_rows.append({ + "prompt_id": prompt["prompt_id"], + "triplet_id": prompt["triplet_id"], + "family_id": prompt["family_id"], + "variant": prompt["variant"], + "expected_physical_outcome": prompt["expected_physical_outcome"], + "expected_code": prompt["expected_code"], + "predicted_code": predicted, + "registered_code_correct": predicted == prompt["expected_code"], + "A_minus_B_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_code": top_id in code_ids, + "checkpoint_position": checkpoint_position, + "final_position": final_position, + "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 + + +def score_readouts( + model, + prompts: list[dict], + layers: list[int], + positions: list[str], + direct: np.ndarray, + jacobian: np.ndarray, + physical_ids: dict[str, tuple[int, int]], + code_ids: tuple[int, int], +) -> list[dict]: + methods = { + "direct": direct, + "jacobian_seed0": jacobian[0], + "jacobian_seed1": jacobian[1], + "jacobian_seed2": jacobian[2], + "jacobian_ensemble": jacobian.astype(np.float32).mean(axis=0), + } + output = [] + device = model._lm_head.weight.device + for method, values in methods.items(): + for position_index, position in enumerate(positions): + for layer_index, layer in enumerate(layers): + states = torch.from_numpy( + values[position_index, :, layer_index].astype(np.float32) + ).to(device=device) + code = pair_logits(model, states, code_ids).detach().cpu() + by_family: dict[str, torch.Tensor] = {} + for family_id, ids in physical_ids.items(): + family_indices = [ + index for index, prompt in enumerate(prompts) + if prompt["family_id"] == family_id + ] + family_states = states[family_indices] + by_family[family_id] = pair_logits( + model, family_states, ids + ).detach().cpu() + family_offsets = defaultdict(int) + for prompt_index, prompt in enumerate(prompts): + family_id = prompt["family_id"] + within_family = family_offsets[family_id] + physical = by_family[family_id][within_family] + family_offsets[family_id] += 1 + output.append({ + "prompt_id": prompt["prompt_id"], + "triplet_id": prompt["triplet_id"], + "family_id": family_id, + "variant": prompt["variant"], + "method": method, + "position": position, + "layer": int(layer), + "depth_percent": 100.0 * int(layer) / (model.n_layers - 1), + "physical_positive_minus_negative": float( + physical[0] - physical[1] + ), + "code_A_minus_B": float(code[prompt_index, 0] - code[prompt_index, 1]), + }) + return output + + +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"] + ) + validate_hash( + ROOT / protocol["inputs"]["source_statistics"], + protocol["inputs"]["source_statistics_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"], + ) + checkpoint = continuation_tokens( + model.tokenizer, + protocol.get("checkpoint_token_text", protocol["checkpoint_word"]), + ) + code_a = continuation_tokens(model.tokenizer, "A") + code_b = continuation_tokens(model.tokenizer, "B") + if len(checkpoint) != 1 or len(code_a) != 1 or len(code_b) != 1: + raise RuntimeError( + f"registered marker/code tokenization failed: " + f"{checkpoint=} {code_a=} {code_b=}" + ) + code_ids = (code_a[0], code_b[0]) + physical_ids = {} + 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"physical answer tokenization failed: {family}") + physical_ids[family["family_id"]] = (positive[0], negative[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 protocol") + + raw, clean_rows = capture_two_positions( + model, prompts, layers, checkpoint[0], code_ids + ) + direct_positions = [] + jacobian_positions = [] + for position_index, position in enumerate(["checkpoint", "final_prompt"]): + print(f"transporting {position} states", flush=True) + direct, jacobian = decoder_basis_representations( + model, lenses, raw[position_index], layers, args.chunk_size + ) + direct_positions.append(direct) + jacobian_positions.append(jacobian) + direct_array = np.stack(direct_positions, axis=0) + jacobian_array = np.stack(jacobian_positions, axis=1) + readout_rows = score_readouts( + model, + prompts, + layers, + ["checkpoint", "final_prompt"], + direct_array, + jacobian_array, + physical_ids, + code_ids, + ) + + 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(["checkpoint", "final_prompt"]), + layers=np.asarray(layers, dtype=np.int16), + raw_states=raw, + direct_decoder_basis=direct_array, + jacobian_decoder_basis=jacobian_array, + ) + 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), + "n_positions": 2, + "d_model": model.d_model, + }, + "token_ids": { + "checkpoint": checkpoint, + "A": code_a, + "B": code_b, + "physical_by_family": { + family: list(ids) for family, ids in physical_ids.items() + }, + }, + "clean_rows": clean_rows, + "readout_rows": readout_rows, + } + atomic_json(output_path, payload) + print( + f"complete: {len(clean_rows)} clean rows, " + f"{len(readout_rows)} readout rows", + flush=True, + ) + + +if __name__ == "__main__": + main() diff --git a/jlens_materials/scripts/run_causal_relational_knockout.py b/jlens_materials/scripts/run_causal_relational_knockout.py new file mode 100644 index 0000000000000000000000000000000000000000..1a003b3653441a2c21cd58c50f8ffe73bb4223a0 --- /dev/null +++ b/jlens_materials/scripts/run_causal_relational_knockout.py @@ -0,0 +1,572 @@ +#!/usr/bin/env python3 +"""Run the frozen 24-law causal knockout, reversal, and repair study.""" + +from __future__ import annotations + +import argparse +import json +import os +import sys +from contextlib import contextmanager +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 + + +EXPERIMENT = ROOT / "experiments" / "causal-relational-knockout-2026-09-02" + + +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 [] + return [json.loads(line) for line in path.read_text().splitlines() if line.strip()] + + +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 load_development(spec: dict) -> tuple[list[dict], np.ndarray]: + manifest = read_json(ROOT / spec["development_manifest"]) + prompts = manifest["prompts"] + path = ROOT / spec["development_states"] + if path.suffix == ".npy": + states = np.load(path).astype(np.float64) + else: + arrays = np.load(path, allow_pickle=False) + prompt_ids = arrays["prompt_ids"].astype(str).tolist() + positions = arrays["positions"].astype(str).tolist() + layers = arrays["layers"].astype(int).tolist() + pindex = positions.index("final_prompt") + lindex = layers.index(int(spec["probe_layer"])) + states = arrays["raw_states"][pindex, :, lindex].astype(np.float64) + by_id = {row["prompt_id"]: row for row in prompts} + prompts = [by_id[prompt_id] for prompt_id in prompt_ids] + if states.shape[0] != len(prompts): + raise RuntimeError("development state/prompt count mismatch") + return prompts, states + + +def fit_direction(prompts: list[dict], states: np.ndarray) -> dict[str, np.ndarray | float]: + unit = normalize(states) + labels = np.asarray([int(row["physical_sign"]) > 0 for row in prompts]) + positive = unit[labels].mean(axis=0) + negative = unit[~labels].mean(axis=0) + direction = positive - negative + direction /= max(float(np.linalg.norm(direction)), 1e-12) + midpoint = 0.5 * (positive + negative) + return { + "direction": direction.astype(np.float32), + "development_midpoint": midpoint.astype(np.float32), + "midpoint_coordinate": float(np.sum(midpoint * direction, dtype=np.float64)), + "positive_coordinate": float(np.sum(positive * direction, dtype=np.float64)), + "negative_coordinate": float(np.sum(negative * direction, dtype=np.float64)), + "positive_count": int(labels.sum()), + "negative_count": int((~labels).sum()), + } + + +def empirical_control_directions( + prompts: list[dict], + states: np.ndarray, + direction: np.ndarray, + seeds: list[int], +) -> list[np.ndarray]: + """Build activation-derived null axes from sign-shuffled matched differences.""" + unit = normalize(states) + grouped: dict[tuple, list[int]] = {} + for index, row in enumerate(prompts): + key = (row["law_id"], row.get("surface"), row.get("case_index")) + grouped.setdefault(key, []).append(index) + paired_differences = [] + for key in sorted(grouped): + indices = grouped[key] + positive = [index for index in indices if int(prompts[index]["physical_sign"]) > 0] + negative = [index for index in indices if int(prompts[index]["physical_sign"]) < 0] + if len(positive) != 1 or len(negative) != 1: + raise RuntimeError( + f"development group {key} is not one matched sign pair: " + f"positive={len(positive)}, negative={len(negative)}" + ) + paired_differences.append(unit[positive[0]] - unit[negative[0]]) + paired = np.asarray(paired_differences, dtype=np.float64) + if len(paired) % 2: + raise RuntimeError("balanced sign controls require an even number of pairs") + + physical = np.asarray(direction, dtype=np.float64) + controls: list[np.ndarray] = [] + for seed in seeds: + rng = np.random.default_rng(seed) + signs = np.concatenate( + [np.ones(len(paired) // 2), -np.ones(len(paired) // 2)] + ) + rng.shuffle(signs) + vector = np.mean(signs[:, None] * paired, axis=0) + vector -= float(np.sum(vector * physical, dtype=np.float64)) * physical + for previous in controls: + previous64 = previous.astype(np.float64) + vector -= float(np.sum(vector * previous64, dtype=np.float64)) * previous64 + norm = float(np.linalg.norm(vector)) + if norm < 1e-10: + raise RuntimeError(f"degenerate activation-derived control for seed {seed}") + controls.append((vector / norm).astype(np.float32)) + basis = np.stack([physical.astype(np.float32), *controls]).astype(np.float64) + gram = basis @ basis.T + if not np.allclose(gram, np.eye(len(basis)), rtol=0.0, atol=2e-6): + raise RuntimeError(f"control basis is not orthonormal:\n{gram}") + return controls + + +@contextmanager +def replace_residual(model, layer: int, position: int, replacement: torch.Tensor): + def hook(_module, _inputs, output): + tensor = output[0] if isinstance(output, tuple) else output + patched = tensor.clone() + patched[:, position, :] = replacement.to(patched.device, patched.dtype) + return (patched, *output[1:]) if isinstance(output, tuple) else patched + + handle = model.layers[layer].register_forward_hook(hook) + try: + yield + finally: + handle.remove() + + +@torch.inference_mode() +def forward_logits(model, input_ids: torch.Tensor, *, layer: int | None = None, + replacement: torch.Tensor | None = None) -> torch.Tensor: + final_layer = model.n_layers - 1 + if replacement is None: + with ActivationRecorder(model.layers, at=[final_layer]) as recorder: + model.forward(input_ids) + else: + assert layer is not None + with replace_residual(model, layer, -1, replacement): + with ActivationRecorder(model.layers, at=[final_layer]) as recorder: + model.forward(input_ids) + final_state = recorder.activations[final_layer][0, -1:].detach() + return model.unembed(final_state).float()[0].cpu() + + +@torch.inference_mode() +def forward_logits_batch( + model, + input_ids: torch.Tensor, + *, + layer: int, + replacements: torch.Tensor, +) -> torch.Tensor: + """Evaluate several same-prompt replacements in one forward pass.""" + if replacements.ndim != 2: + raise ValueError("replacements must have shape [batch, d_model]") + batch_ids = input_ids.repeat(replacements.shape[0], 1) + final_layer = model.n_layers - 1 + with replace_residual(model, layer, -1, replacements): + with ActivationRecorder(model.layers, at=[final_layer]) as recorder: + model.forward(batch_ids) + final_states = recorder.activations[final_layer][:, -1].detach() + return model.unembed(final_states).float().cpu() + + +@torch.inference_mode() +def capture_state_and_logits(model, input_ids: torch.Tensor, layer: int) -> tuple[np.ndarray, np.ndarray]: + final_layer = model.n_layers - 1 + with ActivationRecorder(model.layers, at=[layer, final_layer]) as recorder: + model.forward(input_ids) + state = recorder.activations[layer][0, -1].detach().float().cpu().numpy() + final_state = recorder.activations[final_layer][0, -1:].detach() + logits = model.unembed(final_state).float()[0].cpu().numpy() + return state, logits + + +def token_id(tokenizer, word: str) -> int: + ids = tokenizer.encode(word, add_special_tokens=False) + if len(ids) != 1: + raise RuntimeError(f"registered answer {word!r} is not one token: {ids}") + return int(ids[0]) + + +def logits_summary( + logits: np.ndarray, + clean_logits: np.ndarray, + row: dict, + answer_token_ids: dict[str, int], +) -> dict: + first, second = row["answer_words"] + first_id, second_id = answer_token_ids[first], answer_token_ids[second] + expected_id = answer_token_ids[row["expected_answer"]] + incorrect_id = answer_token_ids[row["incorrect_answer"]] + values = torch.from_numpy(np.asarray(logits, dtype=np.float32)) + clean_values = torch.from_numpy(np.asarray(clean_logits, dtype=np.float32)) + logp = torch.log_softmax(values, dim=-1) + clean_logp = torch.log_softmax(clean_values, dim=-1) + clean_probability = clean_logp.exp() + kl = float((clean_probability * (clean_logp - logp)).sum()) + correct_margin = float(values[expected_id] - values[incorrect_id]) + return { + "first_minus_second_logit": float(values[first_id] - values[second_id]), + "correct_answer_margin": correct_margin, + "answer_margin_correct": bool(correct_margin > 0), + "registered_answer_probability_mass": float(logp[first_id].exp() + logp[second_id].exp()), + "global_top_is_registered_answer": int(values.argmax()) in {first_id, second_id}, + "clean_to_intervened_kl": kl, + } + + +def make_replacements( + receiver: np.ndarray, + counterpart: np.ndarray, + direction_info: dict, + control_directions: list[np.ndarray], + physical_sign: int, +) -> dict[str, tuple[np.ndarray, float]]: + direction = np.asarray(direction_info["direction"], dtype=np.float64) + receiver64 = receiver.astype(np.float64) + counterpart64 = counterpart.astype(np.float64) + receiver_norm = max(float(np.linalg.norm(receiver64)), 1e-12) + counterpart_norm = max(float(np.linalg.norm(counterpart64)), 1e-12) + receiver_coordinate = float( + np.sum((receiver64 / receiver_norm) * direction, dtype=np.float64) + ) + counterpart_coordinate = float( + np.sum((counterpart64 / counterpart_norm) * direction, dtype=np.float64) + ) + midpoint_target = float(direction_info["midpoint_coordinate"]) + correct_target = float( + direction_info["positive_coordinate"] if physical_sign > 0 + else direction_info["negative_coordinate"] + ) + wrong_target = float( + direction_info["negative_coordinate"] if physical_sign > 0 + else direction_info["positive_coordinate"] + ) + + def set_coordinate_along( + base: np.ndarray, axis: np.ndarray, target: float + ) -> tuple[np.ndarray, float]: + """Set the unit-normalized direction coordinate, preserving its orthogonal residual.""" + parallel = float(np.sum(base * axis, dtype=np.float64)) + orthogonal = base - parallel * axis + orthogonal_norm = max(float(np.linalg.norm(orthogonal)), 1e-12) + clipped = float(np.clip(target, -1.0 + 1e-10, 1.0 - 1e-10)) + target_parallel = clipped * orthogonal_norm / np.sqrt(1.0 - clipped * clipped) + scalar = target_parallel - parallel + return orthogonal + target_parallel * axis, float(scalar) + + neutralized, neutral_scalar = set_coordinate_along( + receiver64, direction, midpoint_target + ) + counterfactual_coordinate_state, counterfactual_scalar = set_coordinate_along( + receiver64, direction, counterpart_coordinate + ) + correct_centroid_state, correct_scalar = set_coordinate_along( + receiver64, direction, correct_target + ) + wrong_centroid_state, wrong_scalar = set_coordinate_along( + receiver64, direction, wrong_target + ) + restored, _ = set_coordinate_along(counterpart64, direction, receiver_coordinate) + replacements = { + "neutralize": (neutralized, abs(neutral_scalar)), + "counterfactual_coordinate": ( + counterfactual_coordinate_state, + abs(counterfactual_scalar), + ), + "correct_centroid": (correct_centroid_state, abs(correct_scalar)), + "wrong_centroid": (wrong_centroid_state, abs(wrong_scalar)), + "full_counterfactual": ( + counterpart64, + float(np.linalg.norm(counterpart64 - receiver64)), + ), + "full_counterfactual_direction_restored": ( + restored, + float(np.linalg.norm(restored - receiver64)), + ), + } + for index, control_direction in enumerate(control_directions): + control64 = control_direction.astype(np.float64) + for orientation, sign in (("plus", 1.0), ("minus", -1.0)): + neutral_name = f"neutralize_control_{index}_{orientation}" + counterfactual_name = f"counterfactual_control_{index}_{orientation}" + replacements[neutral_name] = ( + receiver64 + sign * abs(neutral_scalar) * control64, + abs(neutral_scalar), + ) + replacements[counterfactual_name] = ( + receiver64 + sign * abs(counterfactual_scalar) * control64, + abs(counterfactual_scalar), + ) + for name, intended_distance in ( + (neutral_name, abs(neutral_scalar)), + (counterfactual_name, abs(counterfactual_scalar)), + ): + observed_distance = float( + np.linalg.norm(replacements[name][0] - receiver64) + ) + if not np.isclose( + observed_distance, intended_distance, rtol=1e-8, atol=1e-8 + ): + raise RuntimeError( + f"distance-matched activation control failed for {name}: " + f"observed={observed_distance}, target={intended_distance}" + ) + coordinate_checks = { + "neutralize": midpoint_target, + "counterfactual_coordinate": counterpart_coordinate, + "correct_centroid": correct_target, + "wrong_centroid": wrong_target, + "full_counterfactual_direction_restored": receiver_coordinate, + } + for name, target in coordinate_checks.items(): + candidate = replacements[name][0] + observed = float( + np.sum( + (candidate / max(float(np.linalg.norm(candidate)), 1e-12)) * direction, + dtype=np.float64, + ) + ) + if not np.isclose(observed, target, rtol=0.0, atol=2e-6): + raise RuntimeError( + f"normalized coordinate replacement failed for {name}: " + f"observed={observed}, target={target}" + ) + return { + name: (replacement.astype(np.float32), distance) + for name, (replacement, distance) in replacements.items() + } + + +def main() -> None: + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument("--scale", choices=["4b", "12b", "31b"], required=True) + parser.add_argument("--dtype", choices=sorted(_DTYPES), default="bfloat16") + parser.add_argument("--device", default=None) + parser.add_argument("--max-seq-len", type=int, default=192) + parser.add_argument("--intervention-batch-size", type=int, default=1) + parser.add_argument("--resume", action="store_true") + args = parser.parse_args() + + protocol = read_json(EXPERIMENT / "protocol.json") + manifest = read_json(EXPERIMENT / "prompt_manifest.json") + spec = protocol["models"][args.scale] + model_out = EXPERIMENT / args.scale + model_out.mkdir(parents=True, exist_ok=True) + clean_rows_path = model_out / "clean_rows.jsonl" + intervention_rows_path = model_out / "intervention_rows.jsonl" + states_path = model_out / "clean_states.npy" + logits_path = model_out / "clean_logits.npy" + + model = load_model( + spec["model"], + dtype=_DTYPES[args.dtype], + device=args.device, + revision=spec["revision"], + ) + if model.d_model <= 0 or int(spec["probe_layer"]) >= model.n_layers: + raise RuntimeError("model dimensions do not match frozen protocol") + probe_layer = int(spec["probe_layer"]) + dev_prompts, dev_states = load_development(spec) + direction_info = fit_direction(dev_prompts, dev_states) + if len(direction_info["direction"]) != model.d_model: + raise RuntimeError("development direction width does not match model") + control_amendment = read_json(EXPERIMENT / "control_amendment.json") + control_seeds = control_amendment["construction"]["seeds"] + control_directions = empirical_control_directions( + dev_prompts, + dev_states, + direction_info["direction"], + control_seeds, + ) + np.savez_compressed( + model_out / "frozen_direction.npz", + direction=direction_info["direction"], + development_midpoint=direction_info["development_midpoint"], + midpoint_coordinate=direction_info["midpoint_coordinate"], + positive_coordinate=direction_info["positive_coordinate"], + negative_coordinate=direction_info["negative_coordinate"], + control_directions=np.stack(control_directions), + probe_layer=np.asarray([probe_layer], dtype=np.int16), + ) + + answer_words = sorted({word for row in manifest["prompts"] for word in row["answer_words"]}) + answer_token_ids = {word: token_id(model.tokenizer, word) for word in answer_words} + (model_out / "preflight.json").write_text( + json.dumps( + { + "scale": args.scale, + "model": spec["model"], + "model_revision": spec["revision"], + "probe_layer": probe_layer, + "n_layers": model.n_layers, + "d_model": model.d_model, + "answer_token_ids": answer_token_ids, + "direction_fit": { + key: value for key, value in direction_info.items() + if key not in {"direction", "development_midpoint"} + }, + "dtype": args.dtype, + "device": str(model.input_device), + }, + indent=2, + ) + "\n", + encoding="utf-8", + ) + + prompts = manifest["prompts"] + clean_existing = read_jsonl(clean_rows_path) + if clean_existing and not args.resume: + raise SystemExit(f"clean output exists; pass --resume: {clean_rows_path}") + if states_path.exists(): + states = np.lib.format.open_memmap(states_path, mode="r+") + else: + states = np.lib.format.open_memmap( + states_path, mode="w+", dtype=np.float16, shape=(len(prompts), model.d_model) + ) + if logits_path.exists(): + clean_logits = np.lib.format.open_memmap(logits_path, mode="r+") + else: + clean_logits = np.lib.format.open_memmap( + logits_path, + mode="w+", + dtype=np.float16, + shape=(len(prompts), int(model._lm_head.weight.shape[0])), + ) + + for index in range(len(clean_existing), len(prompts)): + row = prompts[index] + text = format_chat(model.tokenizer, row["user"]) + input_ids = model.encode(text, max_length=args.max_seq_len) + state, logits = capture_state_and_logits(model, input_ids, probe_layer) + states[index] = state.astype(np.float16) + clean_logits[index] = logits.astype(np.float16) + states.flush() + clean_logits.flush() + summary = logits_summary(logits, logits, row, answer_token_ids) + append_jsonl( + clean_rows_path, + { + **{key: row[key] for key in row if key != "user"}, + **summary, + "n_tokens": int(input_ids.shape[1]), + "state_norm": float(np.linalg.norm(state)), + }, + ) + if (index + 1) % 12 == 0 or index == 0: + print(f"{args.scale} clean {index + 1:03d}/{len(prompts):03d}", flush=True) + + states_array = np.asarray(states, dtype=np.float32) + logits_array = np.asarray(clean_logits, dtype=np.float32) + prompt_index = {row["prompt_id"]: index for index, row in enumerate(prompts)} + existing_rows = read_jsonl(intervention_rows_path) + existing_keys = {(row["prompt_id"], row["intervention"]) for row in existing_rows} + if existing_rows and not args.resume: + raise SystemExit(f"intervention output exists; pass --resume: {intervention_rows_path}") + + total_methods = 6 + 4 * len(control_directions) + total = len(prompts) * total_methods + completed = len(existing_rows) + for prompt_number, row in enumerate(prompts, 1): + receiver_index = prompt_index[row["prompt_id"]] + counterpart_index = prompt_index[row["counterpart_prompt_id"]] + receiver = states_array[receiver_index] + counterpart = states_array[counterpart_index] + replacements = make_replacements( + receiver, + counterpart, + direction_info, + control_directions, + int(row["physical_sign"]), + ) + text = format_chat(model.tokenizer, row["user"]) + input_ids = model.encode(text, max_length=args.max_seq_len) + clean = logits_array[receiver_index] + clean_summary = logits_summary(clean, clean, row, answer_token_ids) + pending = [ + (intervention, replacement, distance) + for intervention, (replacement, distance) in replacements.items() + if (row["prompt_id"], intervention) not in existing_keys + ] + batch_size = max(1, int(args.intervention_batch_size)) + for start in range(0, len(pending), batch_size): + chunk = pending[start : start + batch_size] + replacement_batch = torch.from_numpy( + np.stack([replacement for _, replacement, _ in chunk]) + ) + logits_batch = forward_logits_batch( + model, + input_ids, + layer=probe_layer, + replacements=replacement_batch, + ).numpy() + for (intervention, _replacement, distance), logits in zip(chunk, logits_batch): + summary = logits_summary(logits, clean, row, answer_token_ids) + append_jsonl( + intervention_rows_path, + { + **{key: row[key] for key in row if key != "user"}, + "intervention": intervention, + "replacement_distance": distance, + **summary, + "correct_margin_change": ( + summary["correct_answer_margin"] + - clean_summary["correct_answer_margin"] + ), + "movement_toward_counterfactual": ( + clean_summary["correct_answer_margin"] + - summary["correct_answer_margin"] + ), + }, + ) + completed += 1 + if prompt_number % 4 == 0 or prompt_number == 1: + print( + f"{args.scale} interventions prompt {prompt_number:03d}/{len(prompts):03d}; " + f"rows {completed:04d}/{total:04d}", + flush=True, + ) + + run_record = { + "study_id": protocol["study_id"], + "scale": args.scale, + "model": spec["model"], + "model_revision": spec["revision"], + "probe_layer": probe_layer, + "normalized_depth_percent": 100.0 * probe_layer / (model.n_layers - 1), + "d_model": model.d_model, + "dtype": args.dtype, + "device": str(model.input_device), + "intervention_batch_size": int(args.intervention_batch_size), + "clean_rows": len(read_jsonl(clean_rows_path)), + "intervention_rows": len(read_jsonl(intervention_rows_path)), + "expected_intervention_rows": total, + "complete": len(read_jsonl(intervention_rows_path)) == total, + } + (model_out / "run_record.json").write_text( + json.dumps(run_record, indent=2) + "\n", encoding="utf-8" + ) + print(json.dumps(run_record, indent=2), flush=True) + + +if __name__ == "__main__": + main() diff --git a/jlens_materials/scripts/run_elicited_physics_abstraction.py b/jlens_materials/scripts/run_elicited_physics_abstraction.py new file mode 100644 index 0000000000000000000000000000000000000000..98fb07649f5c0505b1bb285714bb96ad85b0461b --- /dev/null +++ b/jlens_materials/scripts/run_elicited_physics_abstraction.py @@ -0,0 +1,163 @@ +#!/usr/bin/env python3 +"""Capture all-layer states for the scaffolded abstraction development run.""" + +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[:0] = [str(ROOT), str(ROOT / "_vendor_jlens"), str(ROOT / "scripts")] + +from jlens.hooks import ActivationRecorder # noqa: E402 +from run_abstract_physics_composition import marker_positions # 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 pair_logits # noqa: E402 +from run_semantic_steering_v3 import continuation_tokens # noqa: E402 + + +DEFAULT_DIR = "experiments/elicited-physics-abstraction-2026-07-18" + + +@torch.inference_mode() +def main() -> None: + parser = argparse.ArgumentParser() + parser.add_argument("--experiment-dir", default=DEFAULT_DIR) + parser.add_argument("--dtype", choices=sorted(_DTYPES), default="bfloat16") + parser.add_argument("--device", default=None) + args = parser.parse_args() + out = (ROOT / args.experiment_dir).resolve() + 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("manifest fingerprint mismatch") + if sha256(Path(__file__).resolve()) != protocol["inputs"]["runner"]["sha256"]: + raise RuntimeError("runner fingerprint mismatch") + + model = load_model( + protocol["model"], + dtype=_DTYPES[args.dtype], + device=args.device, + revision=protocol["model_revision"], + ) + 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"{name} marker is not one token: {ids}") + marker_ids[name] = int(ids[0]) + higher = continuation_tokens(model.tokenizer, "higher") + lower = continuation_tokens(model.tokenizer, "lower") + if len(higher) != 1 or len(lower) != 1: + raise RuntimeError(f"answers are not single tokens: {higher=}, {lower=}") + answer_ids = (int(higher[0]), int(lower[0])) + + prompts = manifest["prompts"] + positions = protocol["positions"] + layers = list(range(model.n_layers)) + states = np.empty( + (len(positions), len(prompts), len(layers), model.d_model), + dtype=np.float16, + ) + rows = [] + for prompt_index, prompt in enumerate(prompts): + text = format_chat(model.tokenizer, prompt["user"]) + input_ids = model.encode(text, max_length=512) + 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 = pair_logits( + model, + model._final_norm( + final_state.to( + device=model._lm_head.weight.device, + dtype=model._lm_head.weight.dtype, + ) + ).float(), + answer_ids, + )[0].float().cpu() + predicted = "higher" if float(logits[0]) > float(logits[1]) else "lower" + rows.append( + { + **{ + key: prompt[key] + for key in ( + "prompt_id", + "law_id", + "split", + "surface", + "case_index", + "law_sign", + "numerical_sign", + "physical_sign", + "expected_answer", + ) + }, + "predicted_answer": predicted, + "correct": predicted == prompt["expected_answer"], + "higher_minus_lower_log_odds": float(logits[0] - logits[1]), + "n_tokens": int(input_ids.shape[1]), + "positions": locations, + } + ) + print(f"{prompt_index + 1:03d}/{len(prompts):03d} {prompt['prompt_id']}") + + state_path = out / "representations.npz" + np.savez_compressed( + state_path, + prompt_ids=np.asarray([x["prompt_id"] for x in prompts]), + positions=np.asarray(positions), + layers=np.asarray(layers, 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"], + "dtype": args.dtype, + "device": str(model.input_device), + "torch": torch.__version__, + "python": sys.version, + "platform": platform.platform(), + }, + "answer_tokens": {"higher": answer_ids[0], "lower": answer_ids[1]}, + "clean_rows": rows, + }, + ) + print(f"saved {states.shape} to {state_path}") + + +if __name__ == "__main__": + main() diff --git a/jlens_materials/scripts/run_ensemble_steering_v2.py b/jlens_materials/scripts/run_ensemble_steering_v2.py new file mode 100644 index 0000000000000000000000000000000000000000..1dab2d688d0c412ea901b018e8474e3d8a553005 --- /dev/null +++ b/jlens_materials/scripts/run_ensemble_steering_v2.py @@ -0,0 +1,477 @@ +#!/usr/bin/env python3 +"""Calibrate and confirm transferable semantic-ensemble Jacobian steering.""" + +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 run_lens import _DTYPES, load_model # noqa: E402 +from run_jacobian_steering_pilot import ( # noqa: E402 + atomic_json, + capture_clean, + distribution_summary, + format_chat, + patched_logits, + random_direction, + sha256, + single_token, + token_score, +) + + +def ensemble_score(model, normalized: torch.Tensor, + positive_ids: list[int], negative_ids: list[int]) -> torch.Tensor: + positive = torch.stack([token_score(model, normalized, token_id) + for token_id in positive_ids]) + negative = torch.stack([token_score(model, normalized, token_id) + for token_id in negative_ids]) + return torch.logsumexp(positive, dim=0) - torch.logsumexp(negative, dim=0) + + +def semantic_jacobian_direction(model, lens, layer: int, + hidden_states: list[torch.Tensor], + positive_ids: list[int], + negative_ids: list[int]) -> tuple[torch.Tensor, list[float]]: + """Average prompt-normalized J^T semantic gradients into one family direction.""" + J = lens.jacobians[layer].float().cpu() + directions = [] + for hidden in hidden_states: + transported = (J @ hidden.float().cpu()).detach().requires_grad_(True) + normed = model._final_norm( + transported.to(model._lm_head.weight.device, model._lm_head.weight.dtype) + ) + score = ensemble_score(model, normed, positive_ids, negative_ids) + grad_transport = torch.autograd.grad(score, transported)[0].float().cpu() + direction = J.T @ grad_transport + directions.append(direction / direction.norm().clamp_min(1e-12)) + stacked = torch.stack(directions) + mean = stacked.mean(dim=0) + mean = mean / mean.norm().clamp_min(1e-12) + cosines = [float(torch.dot(direction, mean)) for direction in directions] + return mean, cosines + + +def semantic_direct_direction(model, hidden_states: list[torch.Tensor], + positive_ids: list[int], + negative_ids: list[int]) -> tuple[torch.Tensor, list[float]]: + directions = [] + for hidden in hidden_states: + state = hidden.detach().float().to(model._lm_head.weight.device).requires_grad_(True) + normed = model._final_norm(state.to(model._lm_head.weight.dtype)) + score = ensemble_score(model, normed, positive_ids, negative_ids) + direction = torch.autograd.grad(score, state)[0].float().cpu() + directions.append(direction / direction.norm().clamp_min(1e-12)) + stacked = torch.stack(directions) + mean = stacked.mean(dim=0) + mean = mean / mean.norm().clamp_min(1e-12) + cosines = [float(torch.dot(direction, mean)) for direction in directions] + return mean, cosines + + +def orthogonal_random(reference: torch.Tensor, direct: torch.Tensor, seed: int) -> torch.Tensor: + direction = random_direction(reference, seed) + direct_orthogonal = direct - torch.dot(direct, reference) * reference + if direct_orthogonal.norm() > 1e-8: + direct_orthogonal = direct_orthogonal / direct_orthogonal.norm() + direction = direction - torch.dot(direction, direct_orthogonal) * direct_orthogonal + return direction / direction.norm().clamp_min(1e-12) + + +def prompt_rows(family: dict, split: str) -> list[dict]: + output = [] + for prompt in family[f"{split}_prompts"]: + for order in ["ab", "ba"]: + if order == "ab": + option_a = family["outcome_positive"] + option_b = family["outcome_negative"] + else: + option_a = family["outcome_negative"] + option_b = family["outcome_positive"] + output.append({ + "family_id": family["family_id"], + "condition_id": prompt["condition_id"], + "prompt_id": f"{family['family_id']}--{split}--{prompt['condition_id']}--{order}", + "order": order, + "option_a": option_a, + "option_b": option_b, + "expected_outcome": prompt["expected_outcome"], + "user": ( + f"{prompt['stem']} A: {option_a}. B: {option_b}. " + "Answer exactly A or B." + ), + }) + return output + + +def answer_mapping(family: dict, prompt: dict) -> tuple[str, str | None]: + positive_answer = "A" if prompt["option_a"] == family["outcome_positive"] else "B" + expected_answer = None + if prompt["expected_outcome"] is not None: + expected_answer = "A" if prompt["option_a"] == prompt["expected_outcome"] else "B" + return positive_answer, expected_answer + + +def direction_fit_hidden(model, family: dict, layers: list[int]) -> dict[int, list[torch.Tensor]]: + result = {layer: [] for layer in layers} + for user in family["direction_fit_prompts"]: + text = format_chat(model.tokenizer, user) + hidden, _ = capture_clean(model, text, layers) + for layer in layers: + result[layer].append(hidden[layer]) + return result + + +def family_token_ids(tokenizer, family: dict) -> tuple[list[int], list[int]]: + positive = [single_token(tokenizer, word) for word in family["concept_positive"]] + negative = [single_token(tokenizer, word) for word in family["concept_negative"]] + return positive, negative + + +def summarize_clean(model, clean_logits: torch.Tensor, answer_a_id: int, + answer_b_id: int, positive_answer: str) -> dict: + return distribution_summary( + clean_logits, clean_logits, answer_a_id, answer_b_id, + positive_answer, model.tokenizer, + ) + + +def calibration_complete(payload: dict, family_id: str, layers: list[int], + n_prompts: int) -> bool: + expected = len(layers) * n_prompts * 3 + observed = sum(row["family_id"] == family_id for row in payload["calibration_rows"]) + return observed == expected + + +def select_layer(payload: dict, family_id: str, layers: list[int]) -> tuple[int, list[dict]]: + rows = [row for row in payload["calibration_rows"] if row["family_id"] == family_id] + endpoints = [] + for layer in layers: + layer_rows = [row for row in rows if row["layer"] == layer] + grouped: dict[str, dict[float, float]] = {} + for row in layer_rows: + grouped.setdefault(row["prompt_id"], {})[float(row["dose_percent"])] = float( + row["positive_log_odds"] + ) + values = [doses[4.0] - doses[-4.0] for doses in grouped.values()] + mean = float(np.mean(values)) + std = float(np.std(values, ddof=0)) + endpoints.append({ + "layer": layer, + "mean_endpoint": mean, + "population_std": std, + "selection_score": mean - std, + "positive_prompt_orders": int(sum(value > 0 for value in values)), + "n_prompt_orders": len(values), + "prompt_order_endpoints": values, + }) + selected = sorted(endpoints, key=lambda row: (-row["selection_score"], row["layer"]))[0] + return int(selected["layer"]), endpoints + + +def initialize_payload(manifest: dict, manifest_path: Path, output_path: Path, + model, lenses: list, 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 output") + return payload + 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), + "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"]) + ], + }, + "calibration_rows": [], + "layer_selection": {}, + "direction_diagnostics": {}, + "confirmation_preflight": [], + "confirmation_rows": [], + } + + +def run_calibration(model, lens, manifest: dict, families: list[dict], payload: dict, + output_path: Path, answer_a_id: int, answer_b_id: int) -> None: + layers = [int(layer) for layer in manifest["candidate_layers"]] + doses = [float(dose) for dose in manifest["calibration"]["doses_percent_residual_norm"]] + completed = { + (row["prompt_id"], int(row["layer"]), float(row["dose_percent"])) + for row in payload["calibration_rows"] + } + for family in families: + family_id = family["family_id"] + print(f"[calibration] {family_id}", flush=True) + positive_ids, negative_ids = family_token_ids(model.tokenizer, family) + fit_hidden = direction_fit_hidden(model, family, layers) + directions = {} + diagnostics = {} + for layer in layers: + direction, cosines = semantic_jacobian_direction( + model, lens, layer, fit_hidden[layer], positive_ids, negative_ids + ) + directions[layer] = direction + diagnostics[str(layer)] = {"fit_prompt_cosines_to_mean": cosines} + payload["direction_diagnostics"].setdefault(family_id, {})["calibration_seed0"] = diagnostics + atomic_json(output_path, payload) + + prompts = prompt_rows(family, "calibration") + for prompt in prompts: + text = format_chat(model.tokenizer, prompt["user"]) + hidden_by_layer, clean_logits = capture_clean(model, text, layers) + positive_answer, expected_answer = answer_mapping(family, prompt) + clean_summary = summarize_clean( + model, clean_logits, answer_a_id, answer_b_id, positive_answer + ) + for layer in layers: + residual_norm = float(hidden_by_layer[layer].norm()) + for dose in doses: + key = (prompt["prompt_id"], layer, dose) + if key in completed: + continue + delta = directions[layer] * dose / 100.0 * residual_norm + logits = clean_logits if dose == 0.0 else patched_logits( + model, text, layer, delta + ) + summary = distribution_summary( + logits, clean_logits, answer_a_id, answer_b_id, + positive_answer, model.tokenizer, + ) + payload["calibration_rows"].append({ + **{key: prompt[key] for key in [ + "family_id", "condition_id", "prompt_id", "order", + "option_a", "option_b", "expected_outcome", + ]}, + "expected_answer": expected_answer, + "layer": layer, + "method": "ensemble_jacobian", + "lens_seed": 0, + "dose_percent": dose, + "residual_norm": residual_norm, + "delta_norm": float(delta.norm()), + "clean": clean_summary, + **summary, + }) + completed.add(key) + atomic_json(output_path, payload) + + if not calibration_complete(payload, family_id, layers, len(prompts)): + raise RuntimeError(f"incomplete calibration rows for {family_id}") + selected, layer_rows = select_layer(payload, family_id, layers) + payload["layer_selection"][family_id] = { + "selected_layer": selected, + "candidate_results": layer_rows, + } + atomic_json(output_path, payload) + print(f" selected layer {selected}", flush=True) + + +def run_confirmation(model, lenses: list, manifest: dict, families: list[dict], + payload: dict, output_path: Path, answer_a_id: int, + answer_b_id: int) -> None: + doses = [float(dose) for dose in manifest["confirmation"]["doses_percent_residual_norm"]] + random_seeds = [int(seed) for seed in manifest["confirmation"]["random_seeds"]] + completed = { + (row["prompt_id"], row["method"], row.get("lens_seed"), + row.get("random_seed"), float(row["dose_percent"])) + for row in payload["confirmation_rows"] + } + preflight_done = {row["prompt_id"] for row in payload["confirmation_preflight"]} + + for family_index, family in enumerate(families): + family_id = family["family_id"] + if family_id not in payload["layer_selection"]: + raise SystemExit(f"calibration selection missing for {family_id}") + layer = int(payload["layer_selection"][family_id]["selected_layer"]) + print(f"[confirmation] {family_id} layer={layer}", flush=True) + positive_ids, negative_ids = family_token_ids(model.tokenizer, family) + fit_hidden = direction_fit_hidden(model, family, [layer])[layer] + + jacobian_directions = [] + lens_diagnostics = {} + for lens_seed, lens in enumerate(lenses): + direction, cosines = semantic_jacobian_direction( + model, lens, layer, fit_hidden, positive_ids, negative_ids + ) + jacobian_directions.append(direction) + lens_diagnostics[str(lens_seed)] = {"fit_prompt_cosines_to_mean": cosines} + direct, direct_cosines = semantic_direct_direction( + model, fit_hidden, positive_ids, negative_ids + ) + randoms = { + seed: orthogonal_random( + jacobian_directions[0], direct, + 20260716 + 10000 * family_index + seed, + ) + for seed in random_seeds + } + lens_cosines = np.eye(len(jacobian_directions)).tolist() + for i in range(len(jacobian_directions)): + for j in range(i + 1, len(jacobian_directions)): + value = float(torch.dot(jacobian_directions[i], jacobian_directions[j])) + lens_cosines[i][j] = value + lens_cosines[j][i] = value + payload["direction_diagnostics"].setdefault(family_id, {})["confirmation"] = { + "layer": layer, + "lens_fit_prompt_cosines": lens_diagnostics, + "lens_direction_cosine_matrix": lens_cosines, + "direct_fit_prompt_cosines_to_mean": direct_cosines, + "random_orthogonal_to": ["lens_seed0", "direct_component_orthogonal_to_lens_seed0"], + } + atomic_json(output_path, payload) + + directions = [ + ("ensemble_jacobian", seed, None, direction) + for seed, direction in enumerate(jacobian_directions) + ] + directions.append(("ensemble_direct", None, None, direct)) + directions.extend( + ("random", None, seed, direction) for seed, direction in randoms.items() + ) + + for prompt in prompt_rows(family, "confirmation"): + prompt_id = prompt["prompt_id"] + text = format_chat(model.tokenizer, prompt["user"]) + hidden_by_layer, clean_logits = capture_clean(model, text, [layer]) + hidden = hidden_by_layer[layer] + residual_norm = float(hidden.norm()) + positive_answer, expected_answer = answer_mapping(family, prompt) + clean_summary = summarize_clean( + model, clean_logits, answer_a_id, answer_b_id, positive_answer + ) + accepted = bool(clean_summary["global_top_is_valid_choice"]) + if prompt_id not in preflight_done: + payload["confirmation_preflight"].append({ + **{key: prompt[key] for key in [ + "family_id", "condition_id", "prompt_id", "order", + "option_a", "option_b", "expected_outcome", "user", + ]}, + "positive_answer": positive_answer, + "expected_answer": expected_answer, + "accepted": accepted, + "clean": clean_summary, + }) + preflight_done.add(prompt_id) + atomic_json(output_path, payload) + if not accepted: + print(f" excluded {prompt_id}: top={clean_summary['global_top_token']}", flush=True) + continue + + for method, lens_seed, random_seed, direction in directions: + for dose in doses: + key = (prompt_id, method, 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 + ) + summary = distribution_summary( + logits, clean_logits, answer_a_id, answer_b_id, + positive_answer, model.tokenizer, + ) + payload["confirmation_rows"].append({ + **{field: prompt[field] for field in [ + "family_id", "condition_id", "prompt_id", "order", + "option_a", "option_b", "expected_outcome", + ]}, + "expected_answer": expected_answer, + "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) + atomic_json(output_path, payload) + print(f" complete {prompt_id}", flush=True) + + +def main() -> None: + parser = argparse.ArgumentParser() + parser.add_argument( + "--manifest", default="experiments/ensemble-steering-v2-preregistration.json" + ) + parser.add_argument("--output", default="experiments/ensemble-steering-v2_raw.json") + parser.add_argument("--stage", choices=["calibrate", "confirm", "all"], default="all") + parser.add_argument("--family", action="append", default=[]) + 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() + + manifest_path = (ROOT / args.manifest).resolve() + output_path = (ROOT / args.output).resolve() + manifest = json.loads(manifest_path.read_text()) + families = manifest["families"] + if args.family: + wanted = set(args.family) + families = [family for family in families if family["family_id"] in wanted] + missing = wanted - {family["family_id"] for family in families} + if missing: + raise SystemExit(f"unknown families: {sorted(missing)}") + + 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, lenses, args) + answer_a_id = single_token(model.tokenizer, "A", prefer_plain=True) + answer_b_id = single_token(model.tokenizer, "B", prefer_plain=True) + + token_inventory = {} + for family in families: + positive, negative = family_token_ids(model.tokenizer, family) + token_inventory[family["family_id"]] = { + "positive": dict(zip(family["concept_positive"], positive)), + "negative": dict(zip(family["concept_negative"], negative)), + } + payload["token_inventory"] = token_inventory + atomic_json(output_path, payload) + + if args.stage in {"calibrate", "all"}: + run_calibration( + model, lenses[int(manifest["calibration"]["lens_seed"])], manifest, + families, payload, output_path, answer_a_id, answer_b_id, + ) + if args.stage in {"confirm", "all"}: + run_confirmation( + model, lenses, manifest, families, payload, output_path, + answer_a_id, answer_b_id, + ) + payload["completed_at"] = datetime.now(timezone.utc).isoformat() + atomic_json(output_path, payload) + print(f"complete -> {output_path.relative_to(ROOT)}", flush=True) + + +if __name__ == "__main__": + main() diff --git a/jlens_materials/scripts/run_equation_free_relational_benchmark.py b/jlens_materials/scripts/run_equation_free_relational_benchmark.py new file mode 100644 index 0000000000000000000000000000000000000000..716ded25e9d8c1b52954e36ad4d35b78aeffff45 --- /dev/null +++ b/jlens_materials/scripts/run_equation_free_relational_benchmark.py @@ -0,0 +1,130 @@ +#!/usr/bin/env python3 +"""Run the frozen equation-free 60-law relational benchmark.""" + +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-physics-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 = {} + 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): + text = format_chat(model.tokenizer, prompt["user"]) + ids = model.encode(text, 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, text, max_new_tokens=3) + normalized = completion.strip().lower() + predicted = next( + (word for word in ("higher", "lower", "unchanged") if normalized.startswith(word)), + None, + ) + rows.append( + { + "prompt_id": prompt["prompt_id"], + "law_id": prompt["law_id"], + "category": prompt["category"], + "domain": prompt["domain"], + "neutral_role": prompt.get("neutral_role"), + "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) % 20 == 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([row["prompt_id"] for row 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_goal_specific_causal_roles.py b/jlens_materials/scripts/run_gemma31b_goal_specific_causal_roles.py new file mode 100644 index 0000000000000000000000000000000000000000..170f9e672e89c160b44ba0a2aacd257959886e4d --- /dev/null +++ b/jlens_materials/scripts/run_gemma31b_goal_specific_causal_roles.py @@ -0,0 +1,686 @@ +#!/usr/bin/env python3 +"""Run the frozen 31B goal-specific causal-role intervention study.""" + +from __future__ import annotations + +import argparse +import json +import os +import sys +from contextlib import contextmanager +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_gemma31b_compositional_physics_pilot import ( # noqa: E402 + marker_positions, + sha256, +) +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 + + +OUT = ROOT / "experiments" / "gemma4-31b-goal-specific-causal-roles-2026-09-02" +DISCOVERY = ROOT / "experiments" / "gemma4-31b-query-gated-physics-2026-08-25" +ROLE_NAMES = ("target", "intermediate") +CONTROL_SEEDS = (17011, 27103, 37117, 47119) + + +def read_json(path: Path) -> dict: + return json.loads(path.read_text(encoding="utf-8")) + + +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 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 validate_frozen_inputs() -> tuple[dict, dict]: + protocol = read_json(OUT / "protocol.json") + for name, entry in protocol["inputs"].items(): + path = ROOT / entry["path"] + observed = sha256(path) + if observed != entry["sha256"]: + raise RuntimeError(f"frozen input changed for {name}: {path}") + manifest = read_json(OUT / "prompt_manifest.json") + if len(manifest["prompts"]) != 288: + raise RuntimeError("expected 288 prospective target prompts") + if len({row["chain_id"] for row in manifest["prompts"]}) != 24: + raise RuntimeError("expected 24 prospective target chains") + return protocol, manifest + + +def fit_role_directions() -> dict: + protocol = read_json(DISCOVERY / "protocol.json") + manifest = read_json(DISCOVERY / "prompt_manifest.json") + states = np.load(DISCOVERY / "states.npy", mmap_mode="r") + prompts = manifest["prompts"] + pindex = list(protocol["positions"]).index("question") + lindex = list(protocol["layers_zero_indexed"]).index(49) + layer_states = np.asarray(states[pindex, :, lindex], dtype=np.float64) + development = np.asarray([row["split"] == "development" for row in prompts]) + + directions = [] + midpoints = [] + positive_coordinates = [] + negative_coordinates = [] + role_rows = {} + for role in ROLE_NAMES: + selected = development & np.asarray([row["question_type"] == role for row in prompts]) + role_states = normalize(layer_states[selected]) + role_prompts = [row for row, keep in zip(prompts, selected, strict=True) if keep] + labels = np.asarray([int(row["query_sign"]) > 0 for row in role_prompts]) + positive = role_states[labels].mean(axis=0) + negative = role_states[~labels].mean(axis=0) + direction = positive - negative + direction /= max(float(np.linalg.norm(direction)), 1e-12) + midpoint = 0.5 * (positive + negative) + directions.append(direction) + midpoints.append(midpoint) + positive_coordinates.append(float(np.einsum("i,i->", positive, direction))) + negative_coordinates.append(float(np.einsum("i,i->", negative, direction))) + role_rows[role] = { + "prompts": role_prompts, + "states": role_states, + "direction": direction, + } + + axes = np.stack(directions) + gram = np.einsum("id,jd->ij", axes, axes, optimize=True) + if np.linalg.cond(gram) > 20: + raise RuntimeError(f"role directions are too collinear: {gram}") + return { + "axes": axes, + "gram": gram, + "midpoint_vectors": np.stack(midpoints), + "midpoint_coordinates": np.asarray( + [np.einsum("i,i->", midpoints[i], axes[i]) for i in range(2)], dtype=np.float64 + ), + "positive_coordinates": np.asarray(positive_coordinates), + "negative_coordinates": np.asarray(negative_coordinates), + "role_rows": role_rows, + } + + +def build_control_axes(direction_info: dict) -> np.ndarray: + axes = direction_info["axes"] + gram = np.einsum("id,jd->ij", axes, axes, optimize=True) + inverse_gram = np.linalg.inv(gram) + pair_differences = [] + for role in ROLE_NAMES: + payload = direction_info["role_rows"][role] + prompts = payload["prompts"] + states = payload["states"] + by_chain = {} + for index, row in enumerate(prompts): + by_chain.setdefault(row["chain_id"], []).append(index) + for chain_id in sorted(by_chain): + indices = by_chain[chain_id] + pos = [i for i in indices if int(prompts[i]["query_sign"]) > 0] + neg = [i for i in indices if int(prompts[i]["query_sign"]) < 0] + if len(pos) != 1 or len(neg) != 1: + raise RuntimeError(f"development pair malformed for {role}/{chain_id}") + pair_differences.append(states[pos[0]] - states[neg[0]]) + pair_differences = np.asarray(pair_differences) + controls = [] + for seed in CONTROL_SEEDS: + rng = np.random.default_rng(seed) + signs = np.concatenate( + [np.ones(len(pair_differences) // 2), -np.ones(len(pair_differences) // 2)] + ) + rng.shuffle(signs) + vector = np.mean(signs[:, None] * pair_differences, axis=0) + # Remove the complete two-role span, then previous control axes. + role_projection = np.einsum("id,d->i", axes, vector, optimize=True) + coefficients = np.einsum("ij,j->i", inverse_gram, role_projection, optimize=True) + vector -= np.einsum("i,id->d", coefficients, axes, optimize=True) + for previous in controls: + vector -= float(np.einsum("i,i->", vector, previous)) * previous + norm = float(np.linalg.norm(vector)) + if norm < 1e-10: + raise RuntimeError(f"degenerate control axis for seed {seed}") + controls.append(vector / norm) + controls = np.stack(controls) + control_role = np.einsum("id,jd->ij", controls, axes, optimize=True) + control_gram = np.einsum("id,jd->ij", controls, controls, optimize=True) + if np.max(np.abs(control_role)) > 2e-6: + raise RuntimeError("control axes are not orthogonal to the role span") + if not np.allclose(control_gram, np.eye(len(controls)), atol=2e-6, rtol=0): + raise RuntimeError("control axes are not mutually orthonormal") + return controls + + +def role_coordinates(state: np.ndarray, axes: np.ndarray) -> np.ndarray: + state = np.asarray(state, dtype=np.float64) + return np.einsum( + "id,d->i", + axes, + state / max(float(np.linalg.norm(state)), 1e-12), + optimize=True, + ) + + +def set_role_coordinates( + base: np.ndarray, + axes: np.ndarray, + desired_coordinates: np.ndarray, +) -> np.ndarray: + """Set both role projections exactly while preserving norm and orthogonal direction.""" + base = np.asarray(base, dtype=np.float64) + norm = max(float(np.linalg.norm(base)), 1e-12) + unit = base / norm + gram = np.einsum("id,jd->ij", axes, axes, optimize=True) + inverse_gram = np.linalg.inv(gram) + current = np.einsum("id,d->i", axes, unit, optimize=True) + current_coefficients = np.einsum("ij,j->i", inverse_gram, current, optimize=True) + current_parallel = np.einsum("i,id->d", current_coefficients, axes, optimize=True) + residual = unit - current_parallel + residual_norm = max(float(np.linalg.norm(residual)), 1e-12) + desired = np.asarray(desired_coordinates, dtype=np.float64) + desired_coefficients = np.einsum("ij,j->i", inverse_gram, desired, optimize=True) + desired_parallel = np.einsum("i,id->d", desired_coefficients, axes, optimize=True) + desired_parallel_norm2 = float(np.einsum("i,i->", desired_parallel, desired_parallel)) + if desired_parallel_norm2 >= 1.0: + raise RuntimeError(f"infeasible role coordinates: {desired}") + replacement_unit = desired_parallel + residual / residual_norm * np.sqrt( + max(1.0 - desired_parallel_norm2, 0.0) + ) + replacement = norm * replacement_unit + observed = role_coordinates(replacement, axes) + if not np.allclose(observed, desired, rtol=0, atol=2e-7): + raise RuntimeError(f"role coordinate replacement failed: {observed} != {desired}") + if not np.isclose(np.linalg.norm(replacement), norm, rtol=0, atol=2e-5): + raise RuntimeError("role coordinate replacement changed state norm") + return replacement.astype(np.float32) + + +def sphere_control( + receiver: np.ndarray, + axis: np.ndarray, + distance: float, + orientation: float, +) -> np.ndarray: + """Move on the receiver-norm sphere by an exactly matched chord length.""" + receiver = np.asarray(receiver, dtype=np.float64) + norm = max(float(np.linalg.norm(receiver)), 1e-12) + unit = receiver / norm + tangent = np.asarray(axis, dtype=np.float64) - float(np.einsum("i,i->", axis, unit)) * unit + tangent /= max(float(np.linalg.norm(tangent)), 1e-12) + ratio = np.clip(float(distance) / (2.0 * norm), 0.0, 1.0) + theta = 2.0 * np.arcsin(ratio) + moved = norm * (np.cos(theta) * unit + orientation * np.sin(theta) * tangent) + observed = float(np.linalg.norm(moved - receiver)) + if not np.isclose(observed, distance, rtol=0, atol=2e-5): + raise RuntimeError(f"sphere control distance mismatch: {observed} != {distance}") + return moved.astype(np.float32) + + +def move_role_coordinate_by_distance( + base: np.ndarray, + axes: np.ndarray, + role_index: int, + distance: float, + orientation: float, +) -> np.ndarray: + """Move one role coordinate by a fixed chord while preserving the other.""" + base = np.asarray(base, dtype=np.float64) + if distance <= 2e-7: + return base.astype(np.float32) + current = role_coordinates(base, axes) + orientation = 1.0 if orientation >= 0 else -1.0 + gram_inverse = np.linalg.inv(np.einsum("id,jd->ij", axes, axes, optimize=True)) + + def feasible(delta: float) -> bool: + desired = current.copy() + desired[role_index] += orientation * delta + coefficients = np.einsum("ij,j->i", gram_inverse, desired, optimize=True) + parallel = np.einsum("i,id->d", coefficients, axes, optimize=True) + return float(np.einsum("i,i->", parallel, parallel)) < 1.0 - 1e-9 + + def candidate(delta: float) -> np.ndarray: + desired = current.copy() + desired[role_index] += orientation * delta + return set_role_coordinates(base, axes, desired) + + # Locate the feasible coordinate boundary in the selected direction. + lower, upper = 0.0, 0.01 + while feasible(upper) and upper < 4.0: + upper *= 2.0 + if feasible(upper): + raise RuntimeError("failed to bracket the role-coordinate boundary") + for _ in range(80): + middle = 0.5 * (lower + upper) + if feasible(middle): + lower = middle + else: + upper = middle + maximum_delta = lower * (1.0 - 1e-8) + maximum = candidate(maximum_delta) + maximum_distance = float(np.linalg.norm(maximum.astype(np.float64) - base)) + if maximum_distance + 2e-5 < distance: + raise RuntimeError( + f"requested matched role dose {distance} exceeds feasible {maximum_distance}" + ) + + lower, upper = 0.0, maximum_delta + for _ in range(80): + middle = 0.5 * (lower + upper) + moved = candidate(middle) + observed = float(np.linalg.norm(moved.astype(np.float64) - base)) + if observed < distance: + lower = middle + else: + upper = middle + moved = candidate(0.5 * (lower + upper)) + observed = float(np.linalg.norm(moved.astype(np.float64) - base)) + if not np.isclose(observed, distance, rtol=0, atol=3e-5): + raise RuntimeError(f"matched role dose failed: {observed} != {distance}") + return moved.astype(np.float32) + + +def make_replacements( + receiver: np.ndarray, + counterpart: np.ndarray, + row: dict, + direction_info: dict, + controls: np.ndarray, +) -> dict[str, tuple[np.ndarray, float]]: + axes = direction_info["axes"] + receiver_coordinates = role_coordinates(receiver, axes) + counterpart_coordinates = role_coordinates(counterpart, axes) + midpoint = direction_info["midpoint_coordinates"] + positive = direction_info["positive_coordinates"] + negative = direction_info["negative_coordinates"] + replacements = {} + + for role_index, role in enumerate(ROLE_NAMES): + neutral_coordinates = receiver_coordinates.copy() + neutral_coordinates[role_index] = midpoint[role_index] + neutral = set_role_coordinates(receiver, axes, neutral_coordinates) + replacements[f"{role}_neutralize"] = ( + neutral, + float(np.linalg.norm(neutral.astype(np.float64) - receiver)), + ) + + counter_coordinates = receiver_coordinates.copy() + counter_coordinates[role_index] = counterpart_coordinates[role_index] + counter = set_role_coordinates(receiver, axes, counter_coordinates) + replacements[f"{role}_counterfactual"] = ( + counter, + float(np.linalg.norm(counter.astype(np.float64) - receiver)), + ) + + sign = int(row[f"{role}_sign"]) + correct_coordinates = receiver_coordinates.copy() + correct_coordinates[role_index] = positive[role_index] if sign > 0 else negative[role_index] + correct = set_role_coordinates(receiver, axes, correct_coordinates) + replacements[f"{role}_correct_centroid"] = ( + correct, + float(np.linalg.norm(correct.astype(np.float64) - receiver)), + ) + + wrong_coordinates = receiver_coordinates.copy() + wrong_coordinates[role_index] = negative[role_index] if sign > 0 else positive[role_index] + wrong = set_role_coordinates(receiver, axes, wrong_coordinates) + replacements[f"{role}_wrong_centroid"] = ( + wrong, + float(np.linalg.norm(wrong.astype(np.float64) - receiver)), + ) + + replacements["full_counterfactual"] = ( + counterpart.astype(np.float32), + float(np.linalg.norm(counterpart.astype(np.float64) - receiver)), + ) + for role_index, role in enumerate(ROLE_NAMES): + restored_coordinates = counterpart_coordinates.copy() + restored_coordinates[role_index] = receiver_coordinates[role_index] + restored = set_role_coordinates(counterpart, axes, restored_coordinates) + replacements[f"full_counterfactual_{role}_restored"] = ( + restored, + float(np.linalg.norm(restored.astype(np.float64) - counterpart)), + ) + + # The role-specific double dissociation uses question-conditioned, + # dose-matched other-role comparators. Natural midpoint operations for both + # axes remain available as secondary diagnostics. + own_role = row["question_type"] if row["question_type"] in ROLE_NAMES else "target" + other_role = "intermediate" if own_role == "target" else "target" + own_index = ROLE_NAMES.index(own_role) + other_index = ROLE_NAMES.index(other_role) + for operation in ("neutralize", "counterfactual"): + own_name = f"{own_role}_{operation}" + own_distance = replacements[own_name][1] + natural_other = ( + midpoint[other_index] + if operation == "neutralize" + else counterpart_coordinates[other_index] + ) + orientation = float(natural_other - receiver_coordinates[other_index]) + if abs(orientation) < 1e-12: + orientation = float(row[f"{other_role}_sign"]) + matched = move_role_coordinate_by_distance( + receiver, axes, other_index, own_distance, orientation + ) + replacements[f"matched_other_{operation}"] = (matched, own_distance) + + own_restored = replacements[f"full_counterfactual_{own_role}_restored"][0] + restoration_distance = float( + np.linalg.norm(own_restored.astype(np.float64) - counterpart) + ) + restoration_orientation = float( + receiver_coordinates[other_index] - counterpart_coordinates[other_index] + ) + if abs(restoration_orientation) < 1e-12: + restoration_orientation = float(row[f"{other_role}_sign"]) + matched_restoration = move_role_coordinate_by_distance( + counterpart, axes, other_index, restoration_distance, restoration_orientation + ) + replacements["full_counterfactual_matched_other_restored"] = ( + matched_restoration, + restoration_distance, + ) + + # Every physical coordinate operation receives eight norm- and distance- + # matched controls: four activation-derived tangent directions and their + # antithetic signs. + for role in ROLE_NAMES: + for operation in ("neutralize", "counterfactual"): + physical_name = f"{role}_{operation}" + distance = replacements[physical_name][1] + for control_index, control in enumerate(controls): + for orientation_name, orientation in (("plus", 1.0), ("minus", -1.0)): + name = f"{physical_name}_control_{control_index}_{orientation_name}" + replacements[name] = ( + sphere_control(receiver, control, distance, orientation), + distance, + ) + return replacements + + +@contextmanager +def replace_residual(model, layer: int, position: int, replacements: torch.Tensor): + def hook(_module, _inputs, output): + tensor = output[0] if isinstance(output, tuple) else output + patched = tensor.clone() + patched[:, position, :] = replacements.to(patched.device, patched.dtype) + return (patched, *output[1:]) if isinstance(output, tuple) else patched + + handle = model.layers[layer].register_forward_hook(hook) + try: + yield + finally: + handle.remove() + + +@torch.inference_mode() +def capture_clean(model, input_ids: torch.Tensor, layer: int, position: int) -> tuple[np.ndarray, np.ndarray]: + final_layer = model.n_layers - 1 + with ActivationRecorder(model.layers, at=[layer, final_layer]) as recorder: + model.forward(input_ids) + state = recorder.activations[layer][0, position].detach().float().cpu().numpy() + final_state = recorder.activations[final_layer][0, -1:].detach() + logits = model.unembed(final_state).float()[0].cpu().numpy() + return state, logits + + +@torch.inference_mode() +def intervened_logits( + model, + input_ids: torch.Tensor, + layer: int, + position: int, + replacements: torch.Tensor, +) -> torch.Tensor: + batch_ids = input_ids.repeat(replacements.shape[0], 1) + final_layer = model.n_layers - 1 + with replace_residual(model, layer, position, replacements): + with ActivationRecorder(model.layers, at=[final_layer]) as recorder: + model.forward(batch_ids) + return model.unembed(recorder.activations[final_layer][:, -1]).float().cpu() + + +def summarize_logits( + logits: np.ndarray, + clean_logits: np.ndarray, + row: dict, + token_ids: dict[str, int], +) -> dict: + values = torch.from_numpy(np.asarray(logits, dtype=np.float32)) + clean = torch.from_numpy(np.asarray(clean_logits, dtype=np.float32)) + words = row["answer_words"] + expected = row["expected_answer"] + expected_value = float(values[token_ids[expected]]) + alternatives = [word for word in words if word != expected] + alternative_values = [float(values[token_ids[word]]) for word in alternatives] + margin = expected_value - max(alternative_values) + logp = torch.log_softmax(values, dim=-1) + clean_logp = torch.log_softmax(clean, dim=-1) + clean_p = clean_logp.exp() + return { + "expected_margin": margin, + "answer_correct": bool(margin > 0), + "predicted_registered_answer": max(words, key=lambda word: float(values[token_ids[word]])), + "registered_probability_mass": float(sum(logp[token_ids[word]].exp() for word in words)), + "clean_to_intervened_kl": float((clean_p * (clean_logp - logp)).sum()), + "higher_minus_lower_logit": float(values[token_ids["higher"]] - values[token_ids["lower"]]), + "same_minus_higher_logit": float(values[token_ids["same"]] - values[token_ids["higher"]]), + "same_minus_lower_logit": float(values[token_ids["same"]] - values[token_ids["lower"]]), + } + + +def main() -> None: + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument("--device", default=None) + parser.add_argument("--dtype", choices=sorted(_DTYPES), default="bfloat16") + parser.add_argument("--max-seq-len", type=int, default=256) + parser.add_argument("--intervention-batch-size", type=int, default=18) + parser.add_argument("--resume", action="store_true") + parser.add_argument("--validate-only", action="store_true") + args = parser.parse_args() + + protocol, manifest = validate_frozen_inputs() + direction_info = fit_role_directions() + controls = build_control_axes(direction_info) + if args.validate_only: + print(json.dumps({ + "prompts": len(manifest["prompts"]), + "chains": len({row["chain_id"] for row in manifest["prompts"]}), + "direction_cosine": float(direction_info["gram"][0, 1]), + "control_count": len(controls), + "max_control_role_dot": float(np.max(np.abs(np.einsum("id,jd->ij", controls, direction_info["axes"], optimize=True)))), + }, indent=2)) + return + + model = load_model( + protocol["model"], + dtype=_DTYPES[args.dtype], + device=args.device, + revision=protocol["model_revision"], + ) + layer = int(protocol["probe_layer_zero_indexed"]) + if layer >= model.n_layers or model.d_model != direction_info["axes"].shape[1]: + raise RuntimeError("model does not match frozen role directions") + + 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", "same"): + 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]) + + np.savez_compressed( + OUT / "frozen_role_directions.npz", + role_names=np.asarray(ROLE_NAMES), + axes=direction_info["axes"].astype(np.float32), + gram=direction_info["gram"].astype(np.float32), + midpoint_coordinates=direction_info["midpoint_coordinates"].astype(np.float32), + positive_coordinates=direction_info["positive_coordinates"].astype(np.float32), + negative_coordinates=direction_info["negative_coordinates"].astype(np.float32), + control_axes=controls.astype(np.float32), + control_seeds=np.asarray(CONTROL_SEEDS, dtype=np.int64), + probe_layer=np.asarray([layer], dtype=np.int16), + ) + + prompts = manifest["prompts"] + clean_rows_path = OUT / "clean_rows.jsonl" + intervention_rows_path = OUT / "intervention_rows.jsonl" + states_path = OUT / "clean_states.npy" + logits_path = OUT / "clean_logits.npy" + clean_existing = read_jsonl(clean_rows_path) + if clean_existing and not args.resume: + raise SystemExit(f"clean outputs exist; pass --resume: {clean_rows_path}") + for index, row in enumerate(clean_existing): + if row["prompt_id"] != prompts[index]["prompt_id"]: + raise RuntimeError(f"clean resume mismatch at {index}") + + if states_path.exists(): + states = np.lib.format.open_memmap(states_path, mode="r+") + else: + states = np.lib.format.open_memmap( + states_path, mode="w+", dtype=np.float16, shape=(len(prompts), model.d_model) + ) + if logits_path.exists(): + clean_logits = np.lib.format.open_memmap(logits_path, mode="r+") + else: + clean_logits = np.lib.format.open_memmap( + logits_path, + mode="w+", + dtype=np.float16, + shape=(len(prompts), int(model._lm_head.weight.shape[0])), + ) + + for index in range(len(clean_existing), len(prompts)): + row = prompts[index] + text = format_chat(model.tokenizer, row["user"]) + input_ids = model.encode(text, max_length=args.max_seq_len) + positions = marker_positions(input_ids, marker_ids, row["prompt_id"]) + state, logits = capture_clean(model, input_ids, layer, positions["question"]) + states[index] = state.astype(np.float16) + clean_logits[index] = logits.astype(np.float16) + states.flush() + clean_logits.flush() + summary = summarize_logits(logits, logits, row, token_ids) + append_jsonl(clean_rows_path, { + **{key: value for key, value in row.items() if key != "user"}, + **summary, + "question_position": positions["question"], + "n_tokens": int(input_ids.shape[1]), + "state_norm": float(np.linalg.norm(state)), + "target_coordinate": float(role_coordinates(state, direction_info["axes"])[0]), + "intermediate_coordinate": float(role_coordinates(state, direction_info["axes"])[1]), + }) + if (index + 1) % 12 == 0 or index == 0: + print(f"31B clean {index + 1:03d}/{len(prompts):03d}", flush=True) + if torch.backends.mps.is_available() and (index + 1) % 12 == 0: + torch.mps.empty_cache() + + state_array = np.asarray(states, dtype=np.float32) + logits_array = np.asarray(clean_logits, dtype=np.float32) + prompt_index = {row["prompt_id"]: index for index, row in enumerate(prompts)} + intervention_existing = read_jsonl(intervention_rows_path) + if intervention_existing and not args.resume: + raise SystemExit(f"intervention outputs exist; pass --resume: {intervention_rows_path}") + existing_keys = {(row["prompt_id"], row["intervention"]) for row in intervention_existing} + expected_methods = 14 + 2 * 2 * len(controls) * 2 + expected_rows = len(prompts) * expected_methods + completed = len(intervention_existing) + + for prompt_number, row in enumerate(prompts, 1): + receiver_index = prompt_index[row["prompt_id"]] + counterpart_index = prompt_index[row["counterpart_prompt_id"]] + receiver = state_array[receiver_index] + counterpart = state_array[counterpart_index] + replacements = make_replacements(receiver, counterpart, row, direction_info, controls) + if len(replacements) != expected_methods: + raise RuntimeError(f"expected {expected_methods} interventions, got {len(replacements)}") + text = format_chat(model.tokenizer, row["user"]) + input_ids = model.encode(text, max_length=args.max_seq_len) + positions = marker_positions(input_ids, marker_ids, row["prompt_id"]) + clean = logits_array[receiver_index] + clean_summary = summarize_logits(clean, clean, row, token_ids) + pending = [ + (name, replacement, distance) + for name, (replacement, distance) in replacements.items() + if (row["prompt_id"], name) not in existing_keys + ] + batch_size = max(1, int(args.intervention_batch_size)) + for start in range(0, len(pending), batch_size): + chunk = pending[start:start + batch_size] + replacement_batch = torch.from_numpy(np.stack([item[1] for item in chunk])) + logits_batch = intervened_logits( + model, + input_ids, + layer, + positions["question"], + replacement_batch, + ).numpy() + for (name, _replacement, distance), logits in zip(chunk, logits_batch, strict=True): + summary = summarize_logits(logits, clean, row, token_ids) + append_jsonl(intervention_rows_path, { + **{key: value for key, value in row.items() if key != "user"}, + "intervention": name, + "replacement_distance": distance, + **summary, + "expected_margin_change": summary["expected_margin"] - clean_summary["expected_margin"], + "correct_evidence_removed": clean_summary["expected_margin"] - summary["expected_margin"], + }) + completed += 1 + if prompt_number % 4 == 0 or prompt_number == 1: + print( + f"31B interventions prompt {prompt_number:03d}/{len(prompts):03d}; " + f"rows {completed:05d}/{expected_rows:05d}", + flush=True, + ) + if torch.backends.mps.is_available() and prompt_number % 8 == 0: + torch.mps.empty_cache() + + record = { + "study_id": protocol["study_id"], + "model": protocol["model"], + "model_revision": protocol["model_revision"], + "probe_layer": layer, + "normalized_depth_percent": 100.0 * layer / (model.n_layers - 1), + "probe_position": "question marker", + "d_model": model.d_model, + "dtype": args.dtype, + "device": str(model.input_device), + "intervention_batch_size": int(args.intervention_batch_size), + "clean_rows": len(read_jsonl(clean_rows_path)), + "intervention_rows": len(read_jsonl(intervention_rows_path)), + "expected_intervention_rows": expected_rows, + "complete": len(read_jsonl(intervention_rows_path)) == expected_rows, + "answer_token_ids": token_ids, + "direction_cosine": float(direction_info["gram"][0, 1]), + } + (OUT / "run_record.json").write_text(json.dumps(record, indent=2) + "\n") + print(json.dumps(record, indent=2), flush=True) + + +if __name__ == "__main__": + main() diff --git a/jlens_materials/scripts/run_gemma31b_goal_specific_causal_roles_late_expand.py b/jlens_materials/scripts/run_gemma31b_goal_specific_causal_roles_late_expand.py new file mode 100644 index 0000000000000000000000000000000000000000..28e5038c463e8ef0972d7e17be06aa34bb10a267 --- /dev/null +++ b/jlens_materials/scripts/run_gemma31b_goal_specific_causal_roles_late_expand.py @@ -0,0 +1,282 @@ +#!/usr/bin/env python3 +"""Execution-only optimization for the frozen 31B causal-role protocol. + +The input prompt is evaluated with batch size one through the intervention +layer. A forward hook then expands the residual stream and installs every +registered replacement, so only downstream blocks are evaluated at the full +intervention batch size. Scientific inputs and interventions are imported +unchanged from the frozen runner. +""" + +from __future__ import annotations + +import argparse +import json +import sys +from contextlib import contextmanager +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_gemma31b_compositional_physics_pilot import marker_positions, sha256 # noqa: E402 +from run_gemma31b_goal_specific_causal_roles import ( # noqa: E402 + OUT, + append_jsonl, + build_control_axes, + fit_role_directions, + make_replacements, + read_jsonl, + summarize_logits, + validate_frozen_inputs, +) +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 + + +@contextmanager +def expand_and_replace(model, layer: int, position: int, replacements: torch.Tensor): + """Expand batch at one block output and install row-specific replacements.""" + def hook(_module, _inputs, output): + tensor = output[0] if isinstance(output, tuple) else output + if tensor.shape[0] != 1: + raise RuntimeError(f"late expansion expects batch one, found {tensor.shape}") + patched = tensor.repeat(replacements.shape[0], 1, 1) + patched[:, position, :] = replacements.to(patched.device, patched.dtype) + return (patched, *output[1:]) if isinstance(output, tuple) else patched + + handle = model.layers[layer].register_forward_hook(hook) + try: + yield + finally: + handle.remove() + + +@torch.inference_mode() +def late_expanded_logits( + model, + input_ids: torch.Tensor, + layer: int, + position: int, + replacements: torch.Tensor, +) -> torch.Tensor: + final_layer = model.n_layers - 1 + with expand_and_replace(model, layer, position, replacements): + with ActivationRecorder(model.layers, at=[final_layer]) as recorder: + model.forward(input_ids) + states = recorder.activations[final_layer][:, -1] + if states.shape[0] != replacements.shape[0]: + raise RuntimeError(f"expanded output batch mismatch: {states.shape}") + return model.unembed(states).float().cpu() + + +def prepare(model, manifest: dict) -> tuple[dict, np.ndarray, dict, dict]: + direction_info = fit_role_directions() + controls = build_control_axes(direction_info) + 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", "same"): + 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]) + return direction_info, controls, marker_ids, token_ids + + +def prompt_payload( + model, + row: dict, + prompts: list[dict], + prompt_index: dict[str, int], + states: np.ndarray, + direction_info: dict, + controls: np.ndarray, + marker_ids: dict, + max_seq_len: int, +) -> tuple[torch.Tensor, int, dict]: + receiver = states[prompt_index[row["prompt_id"]]] + counterpart = states[prompt_index[row["counterpart_prompt_id"]]] + replacements = make_replacements(receiver, counterpart, row, direction_info, controls) + if len(replacements) != 46: + raise RuntimeError(f"expected 46 interventions, got {len(replacements)}") + text = format_chat(model.tokenizer, row["user"]) + input_ids = model.encode(text, max_length=max_seq_len) + positions = marker_positions(input_ids, marker_ids, row["prompt_id"]) + return input_ids, positions["question"], replacements + + +def audit_equivalence( + model, + manifest: dict, + direction_info: dict, + controls: np.ndarray, + marker_ids: dict, + token_ids: dict, + max_seq_len: int, + audit_prompts: int, +) -> None: + states = np.load(OUT / "clean_states.npy", mmap_mode="r").astype(np.float32) + clean_logits = np.load(OUT / "clean_logits.npy", mmap_mode="r") + prompts = manifest["prompts"] + prompt_index = {row["prompt_id"]: index for index, row in enumerate(prompts)} + archived = read_jsonl(OUT / "intervention_rows.jsonl") + by_key = {(row["prompt_id"], row["intervention"]): row for row in archived} + completed_prompt_ids = [ + row["prompt_id"] for row in prompts + if sum((row["prompt_id"], name) in by_key for name in { + item["intervention"] for item in archived if item["prompt_id"] == row["prompt_id"] + }) == 46 + ][:audit_prompts] + if len(completed_prompt_ids) < audit_prompts: + raise RuntimeError("not enough completed prompts for execution-equivalence audit") + differences = [] + for prompt_id in completed_prompt_ids: + row = prompts[prompt_index[prompt_id]] + input_ids, position, replacements = prompt_payload( + model, row, prompts, prompt_index, states, direction_info, controls, + marker_ids, max_seq_len, + ) + names = list(replacements) + batch = torch.from_numpy(np.stack([replacements[name][0] for name in names])) + logits_batch = late_expanded_logits(model, input_ids, 49, position, batch).numpy() + clean = np.asarray(clean_logits[prompt_index[prompt_id]], dtype=np.float32) + for name, logits in zip(names, logits_batch, strict=True): + new = summarize_logits(logits, clean, row, token_ids) + old = by_key[(prompt_id, name)] + for metric in ( + "expected_margin", "registered_probability_mass", "clean_to_intervened_kl", + "higher_minus_lower_logit", "same_minus_higher_logit", "same_minus_lower_logit", + ): + differences.append(abs(float(new[metric]) - float(old[metric]))) + if bool(new["answer_correct"]) != bool(old["answer_correct"]): + raise RuntimeError(f"decision mismatch for {prompt_id}/{name}") + payload = { + "audit_prompts": completed_prompt_ids, + "interventions": len(completed_prompt_ids) * 46, + "max_absolute_metric_difference": max(differences, default=0.0), + "tolerance": 0.002, + "all_decisions_identical": True, + "passed": max(differences, default=0.0) <= 0.002, + } + (OUT / "late_expansion_equivalence_audit.json").write_text(json.dumps(payload, indent=2) + "\n") + print(json.dumps(payload, indent=2)) + if not payload["passed"]: + raise RuntimeError("late-expansion execution equivalence failed") + + +def run_remaining( + model, + protocol: dict, + manifest: dict, + direction_info: dict, + controls: np.ndarray, + marker_ids: dict, + token_ids: dict, + max_seq_len: int, +) -> None: + audit = json.loads((OUT / "late_expansion_equivalence_audit.json").read_text()) + if not audit["passed"]: + raise RuntimeError("late-expansion equivalence audit did not pass") + prompts = manifest["prompts"] + prompt_index = {row["prompt_id"]: index for index, row in enumerate(prompts)} + states = np.load(OUT / "clean_states.npy", mmap_mode="r").astype(np.float32) + clean_logits = np.load(OUT / "clean_logits.npy", mmap_mode="r") + output_path = OUT / "intervention_rows.jsonl" + existing = read_jsonl(output_path) + existing_keys = {(row["prompt_id"], row["intervention"]) for row in existing} + completed = len(existing) + expected = len(prompts) * 46 + for prompt_number, row in enumerate(prompts, 1): + input_ids, position, replacements = prompt_payload( + model, row, prompts, prompt_index, states, direction_info, controls, + marker_ids, max_seq_len, + ) + pending_names = [name for name in replacements if (row["prompt_id"], name) not in existing_keys] + if not pending_names: + continue + batch = torch.from_numpy(np.stack([replacements[name][0] for name in pending_names])) + logits_batch = late_expanded_logits(model, input_ids, 49, position, batch).numpy() + clean = np.asarray(clean_logits[prompt_index[row["prompt_id"]]], dtype=np.float32) + clean_summary = summarize_logits(clean, clean, row, token_ids) + for name, logits in zip(pending_names, logits_batch, strict=True): + summary = summarize_logits(logits, clean, row, token_ids) + append_jsonl(output_path, { + **{key: value for key, value in row.items() if key != "user"}, + "intervention": name, + "replacement_distance": replacements[name][1], + **summary, + "expected_margin_change": summary["expected_margin"] - clean_summary["expected_margin"], + "correct_evidence_removed": clean_summary["expected_margin"] - summary["expected_margin"], + }) + completed += 1 + if prompt_number % 4 == 0 or prompt_number == 1: + print( + f"31B late-expand prompt {prompt_number:03d}/{len(prompts):03d}; " + f"rows {completed:05d}/{expected:05d}", + flush=True, + ) + if torch.backends.mps.is_available() and prompt_number % 8 == 0: + torch.mps.empty_cache() + record = { + "study_id": protocol["study_id"], + "model": protocol["model"], + "model_revision": protocol["model_revision"], + "probe_layer": 49, + "normalized_depth_percent": 100.0 * 49 / (model.n_layers - 1), + "probe_position": "question marker", + "d_model": model.d_model, + "dtype": protocol["dtype"], + "device": str(model.input_device), + "execution": "late batch expansion after the intervention layer", + "execution_runner": str(Path(__file__).resolve().relative_to(ROOT)), + "execution_runner_sha256": sha256(Path(__file__).resolve()), + "equivalence_audit": "late_expansion_equivalence_audit.json", + "clean_rows": len(read_jsonl(OUT / "clean_rows.jsonl")), + "intervention_rows": len(read_jsonl(output_path)), + "expected_intervention_rows": expected, + "complete": len(read_jsonl(output_path)) == expected, + "answer_token_ids": token_ids, + "direction_cosine": float(direction_info["gram"][0, 1]), + } + (OUT / "run_record.json").write_text(json.dumps(record, indent=2) + "\n") + print(json.dumps(record, indent=2)) + + +def main() -> None: + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument("--device", default="mps") + parser.add_argument("--dtype", choices=sorted(_DTYPES), default="bfloat16") + parser.add_argument("--max-seq-len", type=int, default=256) + parser.add_argument("--audit-prompts", type=int, default=2) + parser.add_argument("--audit-only", action="store_true") + args = parser.parse_args() + protocol, manifest = validate_frozen_inputs() + model = load_model( + protocol["model"], dtype=_DTYPES[args.dtype], device=args.device, + revision=protocol["model_revision"], + ) + direction_info, controls, marker_ids, token_ids = prepare(model, manifest) + audit_equivalence( + model, manifest, direction_info, controls, marker_ids, token_ids, + args.max_seq_len, args.audit_prompts, + ) + if not args.audit_only: + run_remaining( + model, protocol, manifest, direction_info, controls, marker_ids, + token_ids, args.max_seq_len, + ) + + +if __name__ == "__main__": + main() diff --git a/jlens_materials/scripts/run_gemma31b_query_gated_physics.py b/jlens_materials/scripts/run_gemma31b_query_gated_physics.py new file mode 100644 index 0000000000000000000000000000000000000000..9f205789873f03732898529f54f0a32f0e3ff0b0 --- /dev/null +++ b/jlens_materials/scripts/run_gemma31b_query_gated_physics.py @@ -0,0 +1,165 @@ +#!/usr/bin/env python3 +"""Capture selected-layer 31B states for the frozen query-gating study.""" + +from __future__ import annotations + +import argparse +import json +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_gemma31b_compositional_physics_pilot import ( # noqa: E402 + append_jsonl, + atomic_json, + marker_positions, + read_jsonl, + sha256, +) +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-query-gated-physics-2026-08-25" + + +def validate(out: Path) -> tuple[dict, dict]: + protocol = json.loads((out / "protocol.json").read_text()) + for name, entry in protocol["inputs"].items(): + if sha256(ROOT / entry["path"]) != entry["sha256"]: + raise RuntimeError(f"frozen {name} fingerprint differs") + manifest = json.loads((out / "prompt_manifest.json").read_text()) + if len(manifest["prompts"]) != 144: + raise RuntimeError("expected 144 frozen prompts") + return protocol, manifest + + +@torch.inference_mode() +def capture(model, protocol: dict, manifest: dict, out: Path) -> None: + layers = list(protocol["layers_zero_indexed"]) + positions = list(protocol["positions"]) + prompts = manifest["prompts"] + 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]) + answer_ids = {} + for word in ("higher", "lower"): + ids = continuation_tokens(model.tokenizer, word) + if len(ids) != 1: + raise RuntimeError(f"answer {word!r} is not one token: {ids}") + answer_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 {index}") + expected_shape = (len(positions), len(prompts), len(layers), model.d_model) + if states_path.exists(): + states = np.lib.format.open_memmap(states_path, mode="r+") + 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=expected_shape) + + 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=384) + 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, -1:] + logits = model.unembed(final_state).float()[0].cpu() + selected = {word: float(logits[token_id]) for word, token_id in answer_ids.items()} + predicted = "higher" if selected["higher"] > selected["lower"] else "lower" + binary = prompt["query_sign"] != 0 + append_jsonl( + rows_path, + { + **{key: value for key, value in prompt.items() if key != "user"}, + "predicted_binary_answer": predicted, + "binary_pairwise_correct": predicted == prompt["expected_answer"] if binary else None, + "higher_minus_lower_logit": selected["higher"] - selected["lower"], + "n_tokens": int(input_ids.shape[1]), + "positions": locations, + }, + ) + states.flush() + elapsed = time.perf_counter() - started + print( + f"query-gating: {prompt_index + 1:03d}/{len(prompts):03d} " + f"{prompt['prompt_id']} ({elapsed / max(prompt_index + 1 - len(existing), 1):.2f} s/prompt)", + flush=True, + ) + if torch.backends.mps.is_available() and (prompt_index + 1) % 8 == 0: + torch.mps.empty_cache() + + atomic_json( + out / "raw.json", + { + "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), + "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}, + "rows": read_jsonl(rows_path), + }, + ) + + +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("--validate-only", action="store_true") + args = parser.parse_args() + out = (ROOT / args.output_dir).resolve() + protocol, manifest = validate(out) + if args.validate_only: + print(json.dumps(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) + print(f"complete: {out / 'raw.json'}") + + +if __name__ == "__main__": + main() diff --git a/jlens_materials/scripts/run_graph_isomorphism_gin.py b/jlens_materials/scripts/run_graph_isomorphism_gin.py new file mode 100644 index 0000000000000000000000000000000000000000..2ceb10ad7aac863a4ec40ca7c985741d2423b62e --- /dev/null +++ b/jlens_materials/scripts/run_graph_isomorphism_gin.py @@ -0,0 +1,1190 @@ +#!/usr/bin/env python3 +"""Whole-mechanism-held-out GIN experiment from the frozen graph protocol. + +This script executes Study 3 in +``experiments/graph-isomorphism-generalization-2026-07-18/PROTOCOL.md``. +It uses archived hidden states only and never runs Gemma. +""" + +from __future__ import annotations + +import argparse +import copy +import hashlib +import itertools +import json +import math +import platform +import random +import sys +import time +import warnings +from dataclasses import dataclass +from pathlib import Path +from typing import Iterable, Mapping, Sequence + +import networkx as nx +import numpy as np +import pandas as pd +import sklearn +import torch +from sklearn.decomposition import PCA +from sklearn.metrics import accuracy_score, roc_auc_score + + +ROOT = Path(__file__).resolve().parents[1] +OUT = ( + ROOT + / "experiments" + / "graph-isomorphism-generalization-2026-07-18" +) +PROTOCOL = OUT / "protocol.json" +STATES = ( + ROOT + / "experiments" + / "option-free-question-end-2026-07-18" + / "representations.npz" +) +MANIFEST = ( + ROOT / "paper" / "graph_data" / "signed-relation-prompt-manifest.json" +) +VARIANTS = ("anchor", "physics_paraphrase", "lexical_counterfactual") +METHODS = ("jacobian", "direct", "raw") +BASE_SEED = 20260718 + + +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 seed_everything(seed: int) -> None: + random.seed(seed) + np.random.seed(seed) + torch.manual_seed(seed) + torch.use_deterministic_algorithms(True) + + +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: + unit = normalize(values.astype(np.float64)) + return np.einsum("ild,jld->lij", unit, unit, optimize=True) + + +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 load_data() -> dict[str, object]: + protocol = json.loads(PROTOCOL.read_text()) + expected_states = protocol["inputs"]["representations"]["sha256"] + expected_manifest = protocol["inputs"]["prompt_manifest"]["sha256"] + if sha256(STATES) != expected_states or sha256(MANIFEST) != expected_manifest: + raise RuntimeError("frozen input fingerprint mismatch") + + 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 = archive["raw_states"][0].astype(np.float32) + direct = archive["direct_decoder_basis"][0].astype(np.float32) + jacobian_fits = archive["jacobian_decoder_basis"][ + :, 0 + ].astype(np.float32) + rows = [] + for prompt_id in prompt_ids: + key = prompt_id.removesuffix("--answer-code") + 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 + ], + dtype=np.int64, + ) + numeric = np.asarray( + [bool(row["numeric_direction"] == "increase") for row in rows], + dtype=np.int64, + ) + states = { + "jacobian": np.mean(jacobian_fits, axis=0), + "direct": direct, + "raw": raw, + } + similarities = { + method: cosine_layers(values) + for method, values in states.items() + } + depths = layers / 41.0 * 100.0 + return { + "prompt_ids": prompt_ids, + "families": families, + "cases": cases, + "variants": variants, + "physical": physical, + "numeric": numeric, + "family_order": sorted(set(families)), + "layers": layers, + "depths": depths, + "band_mask": (depths >= 38.0) & (depths <= 92.0), + "states": states, + "similarities": similarities, + } + + +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]), + case=str(cases[global_index]), + physical=int(physical[global_index]), + numeric=int(numeric[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 graph dimensions") + return graph + + +def bidirectional_adjacency(graph: nx.DiGraph) -> np.ndarray: + adjacency = np.zeros((12, 12), dtype=np.float32) + for source, target, attributes in graph.edges(data=True): + weight = float(attributes["weight"]) + adjacency[source, target] += weight + adjacency[target, source] += weight + return adjacency + + +def shuffled_adjacency( + graph: nx.DiGraph, rng: np.random.Generator +) -> np.ndarray: + edges = [] + for source, attributes in graph.nodes(data=True): + for target_variant in VARIANTS: + if target_variant == attributes["variant"]: + continue + candidates = [ + node + for node, target_attributes in graph.nodes(data=True) + if target_attributes["variant"] == target_variant + and target_attributes["case"] != attributes["case"] + ] + edges.append((source, int(rng.choice(candidates)))) + weights = np.asarray( + [float(attributes["weight"]) for *_, attributes in graph.edges(data=True)] + ) + rng.shuffle(weights) + adjacency = np.zeros((12, 12), dtype=np.float32) + for (source, target), weight in zip(edges, weights): + adjacency[source, target] += float(weight) + adjacency[target, source] += float(weight) + return adjacency + + +@dataclass +class GraphExample: + family: str + method: str + layer_index: int + layer: int + depth: float + node_global_indices: np.ndarray + topology_features: np.ndarray + adjacency: np.ndarray + physical: np.ndarray + numeric: np.ndarray + graph: nx.DiGraph + + +def graph_examples( + data: Mapping[str, object], method: str +) -> list[GraphExample]: + layers = np.asarray(data["layers"]) + depths = np.asarray(data["depths"]) + similarity_layers = np.asarray(data["similarities"][method]) + examples = [] + for family in data["family_order"]: + for layer_index, (layer, depth) in enumerate(zip(layers, depths)): + graph = selected_family_graph( + similarity_layers[layer_index], str(family), data + ) + nodes = sorted(graph.nodes) + global_indices = np.asarray( + [graph.nodes[node]["global_index"] for node in nodes], + dtype=np.int64, + ) + variants = [ + str(graph.nodes[node]["variant"]) for node in nodes + ] + variant_onehot = np.asarray( + [ + [float(value == variant) for value in VARIANTS] + for variant in variants + ], + dtype=np.float32, + ) + in_degree = np.asarray( + [graph.in_degree(node) for node in nodes], + dtype=np.float32, + ) + out_degree = np.asarray( + [graph.out_degree(node) for node in nodes], + dtype=np.float32, + ) + in_strength = np.asarray( + [ + sum( + float(attributes["weight"]) + for *_, attributes in graph.in_edges( + node, data=True + ) + ) + for node in nodes + ], + dtype=np.float32, + ) + out_strength = np.asarray( + [ + sum( + float(attributes["weight"]) + for *_, attributes in graph.out_edges( + node, data=True + ) + ) + for node in nodes + ], + dtype=np.float32, + ) + numeric_sign = np.asarray( + [ + 1.0 if graph.nodes[node]["numeric"] else -1.0 + for node in nodes + ], + dtype=np.float32, + ) + topology = np.column_stack( + [ + np.ones(12, dtype=np.float32), + variant_onehot, + numeric_sign, + np.full(12, float(depth) / 100.0, dtype=np.float32), + in_degree, + out_degree, + in_strength, + out_strength, + ] + ).astype(np.float32) + examples.append( + GraphExample( + family=str(family), + method=method, + layer_index=layer_index, + layer=int(layer), + depth=float(depth), + node_global_indices=global_indices, + topology_features=topology, + adjacency=bidirectional_adjacency(graph), + physical=np.asarray( + [graph.nodes[node]["physical"] for node in nodes], + dtype=np.int64, + ), + numeric=np.asarray( + [graph.nodes[node]["numeric"] for node in nodes], + dtype=np.int64, + ), + graph=graph, + ) + ) + return examples + + +def fit_fold_pca( + data: Mapping[str, object], + method: str, + training_families: set[str], +) -> tuple[PCA, np.ndarray]: + # Use float64 for the randomized SVD. The archived arrays are finite, but + # the Accelerate-backed float32 matmul used by this scikit-learn build emits + # spurious overflow warnings during power iteration. + states = np.asarray(data["states"][method], dtype=np.float64) + families = np.asarray(data["families"]) + train_indices = np.flatnonzero( + np.isin(families, sorted(training_families)) + ) + matrix = states[train_indices].transpose(1, 0, 2).reshape( + -1, states.shape[-1] + ) + pca = PCA( + n_components=32, + svd_solver="randomized", + random_state=BASE_SEED, + ) + with warnings.catch_warnings(): + warnings.filterwarnings( + "ignore", category=RuntimeWarning, module="sklearn" + ) + pca.fit(matrix) + projected = np.empty( + (states.shape[0], states.shape[1], 32), dtype=np.float32 + ) + for layer_index in range(states.shape[1]): + with warnings.catch_warnings(): + warnings.filterwarnings( + "ignore", category=RuntimeWarning, module="sklearn" + ) + transformed = pca.transform(states[:, layer_index]) + if not np.all(np.isfinite(transformed)): + raise FloatingPointError( + f"non-finite PCA projection for {method}, layer " + f"{layer_index}" + ) + projected[:, layer_index] = transformed.astype(np.float32) + return pca, projected + + +def add_state_features( + examples: list[GraphExample], projected: np.ndarray +) -> list[np.ndarray]: + return [ + np.column_stack( + [ + example.topology_features, + projected[ + example.node_global_indices, example.layer_index + ], + ] + ).astype(np.float32) + for example in examples + ] + + +def topology_feature_list( + examples: list[GraphExample], +) -> list[np.ndarray]: + return [example.topology_features.copy() for example in examples] + + +def standardize( + feature_list: list[np.ndarray], train_indices: Sequence[int] +) -> tuple[list[np.ndarray], np.ndarray, np.ndarray]: + matrix = np.concatenate( + [feature_list[index] for index in train_indices], axis=0 + ) + mean = matrix.mean(axis=0) + scale = matrix.std(axis=0) + scale[scale < 1e-6] = 1.0 + transformed = [ + ((features - mean) / scale).astype(np.float32) + for features in feature_list + ] + return transformed, mean, scale + + +class GINBlock(torch.nn.Module): + def __init__(self, width: int, dropout: float) -> None: + super().__init__() + self.epsilon = torch.nn.Parameter(torch.zeros(())) + self.layers = torch.nn.Sequential( + torch.nn.Linear(width, width), + torch.nn.ReLU(), + torch.nn.Linear(width, width), + ) + self.dropout = torch.nn.Dropout(dropout) + + def forward( + self, hidden: torch.Tensor, adjacency: torch.Tensor + ) -> torch.Tensor: + aggregate = torch.bmm(adjacency, hidden) + update = self.layers((1.0 + self.epsilon) * hidden + aggregate) + return torch.relu(hidden + self.dropout(update)) + + +class GIN(torch.nn.Module): + def __init__(self, input_dim: int, width: int = 32) -> None: + super().__init__() + self.input = torch.nn.Linear(input_dim, width) + self.blocks = torch.nn.ModuleList( + [GINBlock(width, 0.10) for _ in range(3)] + ) + self.head = torch.nn.Linear(width, 1) + + def forward( + self, features: torch.Tensor, adjacency: torch.Tensor + ) -> torch.Tensor: + hidden = torch.relu(self.input(features)) + for block in self.blocks: + hidden = block(hidden, adjacency) + return self.head(hidden).squeeze(-1) + + +class NodeMLP(torch.nn.Module): + def __init__(self, input_dim: int, width: int = 32) -> None: + super().__init__() + self.layers = torch.nn.Sequential( + torch.nn.Linear(input_dim, width), + torch.nn.ReLU(), + torch.nn.Dropout(0.10), + torch.nn.Linear(width, width), + torch.nn.ReLU(), + torch.nn.Dropout(0.10), + torch.nn.Linear(width, width), + torch.nn.ReLU(), + torch.nn.Dropout(0.10), + torch.nn.Linear(width, 1), + ) + + def forward( + self, features: torch.Tensor, adjacency: torch.Tensor + ) -> torch.Tensor: + del adjacency + return self.layers(features).squeeze(-1) + + +def tensor_stack( + arrays: Iterable[np.ndarray], device: torch.device +) -> torch.Tensor: + return torch.as_tensor( + np.stack(list(arrays)), dtype=torch.float32, device=device + ) + + +def permute_batch( + features: torch.Tensor, + adjacency: torch.Tensor, + labels: torch.Tensor, + generator: torch.Generator, +) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]: + permuted_features = [] + permuted_adjacency = [] + permuted_labels = [] + for index in range(features.shape[0]): + order = torch.randperm( + features.shape[1], generator=generator, device=features.device + ) + permuted_features.append(features[index, order]) + permuted_adjacency.append( + adjacency[index][order][:, order] + ) + permuted_labels.append(labels[index, order]) + return ( + torch.stack(permuted_features), + torch.stack(permuted_adjacency), + torch.stack(permuted_labels), + ) + + +def evaluate_loss( + model: torch.nn.Module, + features: torch.Tensor, + adjacency: torch.Tensor, + labels: torch.Tensor, +) -> float: + model.eval() + with torch.no_grad(): + logits = model(features, adjacency) + return float( + torch.nn.functional.binary_cross_entropy_with_logits( + logits, labels + ).item() + ) + + +def train_one( + *, + model_kind: str, + feature_list: list[np.ndarray], + adjacency_list: list[np.ndarray], + labels_list: list[np.ndarray], + train_indices: Sequence[int], + validation_indices: Sequence[int], + test_indices: Sequence[int], + seed: int, + max_epochs: int, + patience: int, + device: torch.device, +) -> dict[str, object]: + seed_everything(seed) + features, _, _ = standardize(feature_list, train_indices) + train_x = tensor_stack( + [features[index] for index in train_indices], device + ) + train_a = tensor_stack( + [adjacency_list[index] for index in train_indices], device + ) + train_y = tensor_stack( + [labels_list[index] for index in train_indices], device + ) + val_x = tensor_stack( + [features[index] for index in validation_indices], device + ) + val_a = tensor_stack( + [adjacency_list[index] for index in validation_indices], device + ) + val_y = tensor_stack( + [labels_list[index] for index in validation_indices], device + ) + test_x = tensor_stack( + [features[index] for index in test_indices], device + ) + test_a = tensor_stack( + [adjacency_list[index] for index in test_indices], device + ) + test_y = tensor_stack( + [labels_list[index] for index in test_indices], device + ) + + model: torch.nn.Module + if model_kind == "gin": + model = GIN(train_x.shape[-1]) + elif model_kind == "mlp": + model = NodeMLP(train_x.shape[-1]) + else: + raise ValueError(model_kind) + model.to(device) + optimizer = torch.optim.Adam( + model.parameters(), lr=0.01, weight_decay=0.0001 + ) + best_loss = float("inf") + best_epoch = -1 + best_state = None + stale = 0 + generator = torch.Generator(device=device) + generator.manual_seed(seed + 9973) + for epoch in range(max_epochs): + model.train() + epoch_x, epoch_a, epoch_y = permute_batch( + train_x, train_a, train_y, generator + ) + optimizer.zero_grad(set_to_none=True) + logits = model(epoch_x, epoch_a) + loss = torch.nn.functional.binary_cross_entropy_with_logits( + logits, epoch_y + ) + loss.backward() + optimizer.step() + validation_loss = evaluate_loss( + model, val_x, val_a, val_y + ) + if validation_loss < best_loss - 1e-5: + best_loss = validation_loss + best_epoch = epoch + best_state = copy.deepcopy(model.state_dict()) + stale = 0 + else: + stale += 1 + if stale >= patience: + break + if best_state is None: + raise RuntimeError("training did not produce a checkpoint") + model.load_state_dict(best_state) + model.eval() + with torch.no_grad(): + logits = model(test_x, test_a).cpu().numpy() + + audit_generator = torch.Generator(device=device) + audit_generator.manual_seed(seed + 104729) + audit_x = [] + audit_a = [] + inverse_orders = [] + for index in range(test_x.shape[0]): + order = torch.randperm( + test_x.shape[1], + generator=audit_generator, + device=device, + ) + inverse = torch.argsort(order) + audit_x.append(test_x[index, order]) + audit_a.append(test_a[index][order][:, order]) + inverse_orders.append(inverse) + audit_logits = model( + torch.stack(audit_x), torch.stack(audit_a) + ) + audit_logits = torch.stack( + [ + audit_logits[index, inverse_orders[index]] + for index in range(audit_logits.shape[0]) + ] + ).cpu().numpy() + return { + "logits": logits, + "audit_logits": audit_logits, + "labels": test_y.cpu().numpy(), + "best_epoch": best_epoch, + "validation_loss": best_loss, + "epochs_run": epoch + 1, + "equivariance_max_abs_logit": float( + np.max(np.abs(logits - audit_logits)) + ), + } + + +def shuffled_labels( + examples: list[GraphExample], + true_labels: list[np.ndarray], + training_families: set[str], + seed: int, +) -> list[np.ndarray]: + rng = np.random.default_rng(seed) + output = [labels.copy() for labels in true_labels] + for family in sorted(training_families): + family_indices = [ + index + for index, example in enumerate(examples) + if example.family == family + ] + reference = true_labels[family_indices[0]].copy() + rng.shuffle(reference) + for index in family_indices: + output[index] = reference.copy() + return output + + +def shuffled_edges( + examples: list[GraphExample], seed: int +) -> list[np.ndarray]: + rng = np.random.default_rng(seed) + return [ + shuffled_adjacency(example.graph, rng) for example in examples + ] + + +def index_set( + examples: list[GraphExample], families: set[str] +) -> list[int]: + return [ + index + for index, example in enumerate(examples) + if example.family in families + ] + + +def auc_from_band( + logits: np.ndarray, + labels: np.ndarray, + examples: list[GraphExample], + indices: Sequence[int], + band_mask: np.ndarray, +) -> tuple[float, float, np.ndarray, np.ndarray]: + selected_positions = [ + position + for position, example_index in enumerate(indices) + if bool(band_mask[examples[example_index].layer_index]) + ] + band_logits = logits[selected_positions] + band_labels = labels[selected_positions] + if not np.all(band_labels == band_labels[0]): + raise RuntimeError("node labels differ across layers") + node_logits = np.mean(band_logits, axis=0) + node_labels = band_labels[0].astype(int) + auc = float(roc_auc_score(node_labels, node_logits)) + accuracy = float( + accuracy_score(node_labels, node_logits >= 0.0) + ) + return auc, accuracy, node_logits, node_labels + + +def configuration_specs() -> list[dict[str, str]]: + specs = [ + { + "configuration": "topology_gin_physical", + "method": "jacobian", + "feature_mode": "topology", + "model_kind": "gin", + "graph_mode": "observed", + "target": "physical", + "label_mode": "observed", + }, + { + "configuration": "topology_mlp_physical", + "method": "jacobian", + "feature_mode": "topology", + "model_kind": "mlp", + "graph_mode": "observed", + "target": "physical", + "label_mode": "observed", + }, + { + "configuration": "topology_gin_edge_shuffle_physical", + "method": "jacobian", + "feature_mode": "topology", + "model_kind": "gin", + "graph_mode": "shuffled", + "target": "physical", + "label_mode": "observed", + }, + { + "configuration": "topology_gin_label_shuffle_physical", + "method": "jacobian", + "feature_mode": "topology", + "model_kind": "gin", + "graph_mode": "observed", + "target": "physical", + "label_mode": "shuffled", + }, + { + "configuration": "topology_gin_numeric", + "method": "jacobian", + "feature_mode": "topology", + "model_kind": "gin", + "graph_mode": "observed", + "target": "numeric", + "label_mode": "observed", + }, + ] + for method in METHODS: + for model_kind in ("gin", "mlp"): + specs.append( + { + "configuration": ( + f"state_{method}_{model_kind}_physical" + ), + "method": method, + "feature_mode": "state", + "model_kind": model_kind, + "graph_mode": "observed", + "target": "physical", + "label_mode": "observed", + } + ) + return specs + + +def parse_seeds(value: str) -> list[int]: + if ":" in value: + start, stop = value.split(":", 1) + return list(range(int(start), int(stop))) + return [int(item) for item in value.split(",") if item] + + +def run(args: argparse.Namespace) -> None: + start = time.time() + data = load_data() + family_order = list(data["family_order"]) + band_mask = np.asarray(data["band_mask"]) + device = torch.device(args.device) + seeds = parse_seeds(args.seeds) + specs = configuration_specs() + if args.configurations: + selected = set(args.configurations.split(",")) + specs = [ + spec for spec in specs + if spec["configuration"] in selected + ] + examples_by_method = { + method: graph_examples(data, method) for method in METHODS + } + results_rows: list[dict[str, object]] = [] + node_rows: list[dict[str, object]] = [] + pca_cache: dict[tuple[str, str], np.ndarray] = {} + total = len(specs) * len(family_order) * len(seeds) + completed = 0 + + for spec in specs: + method = spec["method"] + examples = examples_by_method[method] + true_physical = [example.physical for example in examples] + true_numeric = [example.numeric for example in examples] + for heldout in family_order: + outer_training = set(family_order) - {heldout} + test_indices = index_set(examples, {heldout}) + if spec["feature_mode"] == "state": + cache_key = (method, heldout) + if cache_key not in pca_cache: + _, projected = fit_fold_pca( + data, method, outer_training + ) + pca_cache[cache_key] = projected + feature_list = add_state_features( + examples, pca_cache[cache_key] + ) + else: + feature_list = topology_feature_list(examples) + + for seed_index in seeds: + run_seed = ( + BASE_SEED + + 100_000 * family_order.index(heldout) + + 1_000 * seed_index + + sum(ord(char) for char in spec["configuration"]) + ) + validation_family = sorted(outer_training)[ + seed_index % len(outer_training) + ] + inner_training = outer_training - {validation_family} + train_indices = index_set(examples, inner_training) + validation_indices = index_set( + examples, {validation_family} + ) + + if spec["graph_mode"] == "shuffled": + adjacency_list = shuffled_edges(examples, run_seed) + else: + adjacency_list = [ + example.adjacency for example in examples + ] + target_labels = ( + true_physical + if spec["target"] == "physical" + else true_numeric + ) + if spec["label_mode"] == "shuffled": + training_labels = shuffled_labels( + examples, + target_labels, + outer_training, + run_seed, + ) + else: + training_labels = [ + labels.copy() for labels in target_labels + ] + + trained = train_one( + model_kind=spec["model_kind"], + feature_list=feature_list, + adjacency_list=adjacency_list, + labels_list=training_labels, + train_indices=train_indices, + validation_indices=validation_indices, + test_indices=test_indices, + seed=run_seed, + max_epochs=args.max_epochs, + patience=args.patience, + device=device, + ) + # Test labels are never shuffled, even for the label-shuffle + # falsification. + test_true = np.stack( + [target_labels[index] for index in test_indices] + ) + auc, accuracy, node_logits, node_labels = auc_from_band( + np.asarray(trained["logits"]), + test_true, + examples, + test_indices, + band_mask, + ) + audit_auc, _, audit_node_logits, _ = auc_from_band( + np.asarray(trained["audit_logits"]), + test_true, + examples, + test_indices, + band_mask, + ) + results_rows.append( + { + **spec, + "heldout_family": heldout, + "seed_index": seed_index, + "run_seed": run_seed, + "validation_family": validation_family, + "auc": auc, + "accuracy": accuracy, + "audit_auc": audit_auc, + "audit_auc_loss": auc - audit_auc, + "equivariance_max_abs_logit": trained[ + "equivariance_max_abs_logit" + ], + "best_epoch": trained["best_epoch"], + "epochs_run": trained["epochs_run"], + "validation_loss": trained["validation_loss"], + } + ) + reference_example = examples[test_indices[0]] + for node_index, (logit, audit_logit, label) in enumerate( + zip(node_logits, audit_node_logits, node_labels) + ): + global_index = int( + reference_example.node_global_indices[node_index] + ) + node_rows.append( + { + **spec, + "heldout_family": heldout, + "seed_index": seed_index, + "node_index": node_index, + "prompt_id": str( + data["prompt_ids"][global_index] + ), + "label": int(label), + "band_mean_logit": float(logit), + "audit_band_mean_logit": float(audit_logit), + } + ) + completed += 1 + if completed % max(1, min(20, len(seeds))) == 0: + elapsed = time.time() - start + print( + f"[{completed}/{total}] " + f"{spec['configuration']} heldout={heldout} " + f"elapsed={elapsed:.1f}s", + flush=True, + ) + + results = pd.DataFrame(results_rows) + nodes = pd.DataFrame(node_rows) + results.to_csv(OUT / "gin_seed_results.csv", index=False) + nodes.to_csv(OUT / "gin_node_logits.csv", index=False) + + # Ensemble seeds before inference. Each seed contributes one band-mean + # logit per held-out node. + ensemble_nodes = ( + nodes.groupby( + [ + "configuration", + "method", + "feature_mode", + "model_kind", + "graph_mode", + "target", + "label_mode", + "heldout_family", + "prompt_id", + "label", + ], + as_index=False, + )[["band_mean_logit", "audit_band_mean_logit"]] + .mean() + ) + family_rows = [] + for keys, frame in ensemble_nodes.groupby( + ["configuration", "heldout_family"], sort=True + ): + configuration, heldout = keys + labels = frame["label"].to_numpy(int) + logits = frame["band_mean_logit"].to_numpy(float) + audit_logits = frame["audit_band_mean_logit"].to_numpy(float) + family_rows.append( + { + "configuration": configuration, + "heldout_family": heldout, + "auc": float(roc_auc_score(labels, logits)), + "accuracy": float( + accuracy_score(labels, logits >= 0.0) + ), + "audit_auc": float( + roc_auc_score(labels, audit_logits) + ), + } + ) + family_metrics = pd.DataFrame(family_rows) + family_metrics["audit_auc_loss"] = ( + family_metrics["auc"] - family_metrics["audit_auc"] + ) + family_metrics.to_csv(OUT / "gin_family_metrics.csv", index=False) + ensemble_nodes.to_csv( + OUT / "gin_ensemble_node_logits.csv", index=False + ) + + pivot = family_metrics.pivot( + index="heldout_family", columns="configuration", values="auc" + ) + required_primary = { + "topology_gin_physical", + "topology_mlp_physical", + "topology_gin_edge_shuffle_physical", + "topology_gin_numeric", + } + if not required_primary.issubset(set(pivot.columns)): + partial = { + "study_id": json.loads(PROTOCOL.read_text())["study_id"], + "status": "partial_debug_run", + "configurations": sorted(pivot.columns.tolist()), + "seeds": seeds, + "max_epochs": args.max_epochs, + "family_metrics": family_metrics.to_dict(orient="records"), + "elapsed_seconds": float(time.time() - start), + } + (OUT / "study3_gin_statistics.json").write_text( + json.dumps(safe_json(partial), indent=2) + "\n" + ) + print(json.dumps(safe_json(partial), indent=2)) + return + primary = pivot["topology_gin_physical"] + gin_minus_mlp = primary - pivot["topology_mlp_physical"] + gin_minus_shuffle = ( + primary - pivot["topology_gin_edge_shuffle_physical"] + ) + physical_minus_numeric = ( + primary - pivot["topology_gin_numeric"] + ) + primary_audit = ( + family_metrics[ + family_metrics["configuration"] == "topology_gin_physical" + ] + .set_index("heldout_family")["audit_auc"] + .reindex(family_order) + ) + strong_gate = bool( + np.sum(primary.reindex(family_order).to_numpy() > 0.5) == 6 + and np.sum(gin_minus_mlp.reindex(family_order).to_numpy() > 0) == 6 + and np.sum( + gin_minus_shuffle.reindex(family_order).to_numpy() > 0 + ) == 6 + and plus_one_signflip_p(gin_minus_mlp.reindex(family_order)) <= 0.05 + and plus_one_signflip_p( + gin_minus_shuffle.reindex(family_order) + ) <= 0.05 + and np.max( + primary.reindex(family_order).to_numpy() + - primary_audit.to_numpy() + ) + <= 0.02 + and np.sum( + physical_minus_numeric.reindex(family_order).to_numpy() > 0 + ) + >= 4 + ) + summary = { + "study_id": json.loads(PROTOCOL.read_text())["study_id"], + "protocol_sha256": sha256(PROTOCOL), + "input_sha256": { + "representations": sha256(STATES), + "prompt_manifest": sha256(MANIFEST), + }, + "seeds": seeds, + "max_epochs": args.max_epochs, + "patience": args.patience, + "device": str(device), + "primary_family_auc": primary.reindex(family_order).to_dict(), + "gin_minus_mlp": gin_minus_mlp.reindex(family_order).to_dict(), + "gin_minus_edge_shuffle": gin_minus_shuffle.reindex( + family_order + ).to_dict(), + "physical_minus_numeric": physical_minus_numeric.reindex( + family_order + ).to_dict(), + "primary_families_above_half": int( + np.sum(primary.reindex(family_order).to_numpy() > 0.5) + ), + "gin_minus_mlp_positive_families": int( + np.sum(gin_minus_mlp.reindex(family_order).to_numpy() > 0) + ), + "gin_minus_edge_shuffle_positive_families": int( + np.sum( + gin_minus_shuffle.reindex(family_order).to_numpy() > 0 + ) + ), + "gin_minus_mlp_plus_one_signflip_p": plus_one_signflip_p( + gin_minus_mlp.reindex(family_order) + ), + "gin_minus_edge_shuffle_plus_one_signflip_p": ( + plus_one_signflip_p( + gin_minus_shuffle.reindex(family_order) + ) + ), + "max_permutation_audit_auc_loss": float( + np.max( + primary.reindex(family_order).to_numpy() + - primary_audit.to_numpy() + ) + ), + "physical_over_numeric_families": int( + np.sum( + physical_minus_numeric.reindex(family_order).to_numpy() + > 0 + ) + ), + "strong_whole_mechanism_gate": strong_gate, + "family_metrics": family_metrics.to_dict(orient="records"), + "environment": { + "python": sys.version, + "platform": platform.platform(), + "numpy": np.__version__, + "pandas": pd.__version__, + "sklearn": sklearn.__version__, + "torch": torch.__version__, + }, + "elapsed_seconds": float(time.time() - start), + } + (OUT / "study3_gin_statistics.json").write_text( + json.dumps(safe_json(summary), indent=2) + "\n" + ) + print(json.dumps(safe_json(summary), indent=2)) + + +def main() -> None: + parser = argparse.ArgumentParser() + parser.add_argument( + "--seeds", + default="0:20", + help="Seed indices as start:stop or comma-separated integers.", + ) + parser.add_argument("--max-epochs", type=int, default=400) + parser.add_argument("--patience", type=int, default=50) + parser.add_argument("--device", default="cpu") + parser.add_argument( + "--configurations", + default="", + help="Optional comma-separated configuration names.", + ) + run(parser.parse_args()) + + +if __name__ == "__main__": + main() diff --git a/jlens_materials/scripts/run_jacobian_steering_pilot.py b/jlens_materials/scripts/run_jacobian_steering_pilot.py new file mode 100644 index 0000000000000000000000000000000000000000..937d74e604ea4ab60a900f1daaa4ef537eee02b3 --- /dev/null +++ b/jlens_materials/scripts/run_jacobian_steering_pilot.py @@ -0,0 +1,408 @@ +#!/usr/bin/env python3 +"""Run localized, contrastive Jacobian steering without touching the paper. + +The intervention is intentionally narrower than the legacy global coordinate +swap: one residual position, one source layer, and a dose expressed as a +fraction of the clean residual norm. The primary direction differentiates a +positive-minus-negative concept score through Gemma's exact final norm and a +fitted Jacobian map. Results are checkpointed after every prompt. +""" + +from __future__ import annotations + +import argparse +import hashlib +import json +import math +import os +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")) + +import jlens # noqa: E402 +from jlens.hooks import ActivationRecorder # noqa: E402 +from run_lens import _DTYPES, load_model # noqa: E402 + + +def sha256(path: Path) -> str: + h = hashlib.sha256() + with path.open("rb") as handle: + for chunk in iter(lambda: handle.read(1024 * 1024), b""): + h.update(chunk) + return h.hexdigest() + + +def atomic_json(path: Path, payload: dict) -> None: + path.parent.mkdir(parents=True, exist_ok=True) + tmp = path.with_suffix(path.suffix + ".tmp") + tmp.write_text(json.dumps(payload, indent=2) + "\n") + tmp.replace(path) + + +def single_token(tokenizer, text: str, *, prefer_plain: bool = False) -> int: + forms = [text, " " + text] if prefer_plain else [" " + text, text] + forms += [f.capitalize() for f in forms] + for form in forms: + ids = tokenizer.encode(form, add_special_tokens=False) + if len(ids) == 1: + return int(ids[0]) + raise ValueError(f"{text!r} has no usable one-token form") + + +def format_chat(tokenizer, user: str) -> str: + return tokenizer.apply_chat_template( + [{"role": "user", "content": user}], + tokenize=False, + add_generation_prompt=True, + ) + + +@torch.no_grad() +def capture_clean(model, text: str, layers: list[int]) -> tuple[dict[int, torch.Tensor], torch.Tensor]: + input_ids = model.encode(text, max_length=512) + capture = sorted(set(layers + [model.n_layers - 1])) + with ActivationRecorder(model.layers, at=capture) as recorder: + model.forward(input_ids) + hidden = { + layer: recorder.activations[layer][0, -1].detach().float().cpu() + for layer in layers + } + final = recorder.activations[model.n_layers - 1][0, -1:].detach() + logits = model.unembed(final).float()[0].cpu() + return hidden, logits + + +def token_score(model, normalized: torch.Tensor, token_id: int) -> torch.Tensor: + weight = model._lm_head.weight[token_id].to(normalized.device, normalized.dtype) + raw = (normalized * weight).sum() + softcap = getattr(model, "_logit_softcap", None) + if softcap is not None: + raw = softcap * torch.tanh(raw / softcap) + return raw + + +def exact_jacobian_direction(model, lens, layer: int, hidden: torch.Tensor, + positive_id: int, negative_id: int) -> torch.Tensor: + """Gradient of the J-lens token contrast, including exact final norm.""" + J = lens.jacobians[layer].float().cpu() + transported = (J @ hidden.float().cpu()).detach().requires_grad_(True) + normed = model._final_norm( + transported.to(model._lm_head.weight.device, model._lm_head.weight.dtype) + ) + score = token_score(model, normed, positive_id) - token_score(model, normed, negative_id) + grad_transport = torch.autograd.grad(score, transported)[0].float().cpu() + direction = J.T @ grad_transport + return direction / direction.norm().clamp_min(1e-12) + + +def direct_direction(model, hidden: torch.Tensor, positive_id: int, + negative_id: int) -> torch.Tensor: + """Matched direct-unembedding concept direction with exact final norm.""" + state = hidden.detach().float().to( + model._lm_head.weight.device + ).requires_grad_(True) + normed = model._final_norm(state.to(model._lm_head.weight.dtype)) + score = token_score(model, normed, positive_id) - token_score(model, normed, negative_id) + direction = torch.autograd.grad(score, state)[0].float().cpu() + return direction / direction.norm().clamp_min(1e-12) + + +def random_direction(reference: torch.Tensor, seed: int) -> torch.Tensor: + generator = torch.Generator(device="cpu").manual_seed(seed) + direction = torch.randn(reference.shape, generator=generator) + direction -= torch.dot(direction, reference) * reference + return direction / direction.norm().clamp_min(1e-12) + + +@contextmanager +def add_residual(model, layer: int, position: int, delta: torch.Tensor): + def hook(_module, _inputs, output): + tensor = output if torch.is_tensor(output) else output[0] + patched = tensor.clone() + patched[:, position, :] += delta.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() + + +@torch.no_grad() +def patched_logits(model, text: str, layer: int, delta: torch.Tensor) -> torch.Tensor: + input_ids = model.encode(text, max_length=512) + final = model.n_layers - 1 + with add_residual(model, layer, -1, delta): + with ActivationRecorder(model.layers, at=[final]) as recorder: + model.forward(input_ids) + residual = recorder.activations[final][0, -1:].detach() + return model.unembed(residual).float()[0].cpu() + + +def distribution_summary(logits: torch.Tensor, clean_logits: torch.Tensor, + answer_a_id: int, answer_b_id: int, + positive_answer: str, tokenizer) -> dict: + 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()) + answer_ids = {"A": answer_a_id, "B": answer_b_id} + negative_answer = "B" if positive_answer == "A" else "A" + choice = "A" if logits[answer_a_id] >= logits[answer_b_id] else "B" + return { + "positive_log_odds": float( + logits[answer_ids[positive_answer]] - logits[answer_ids[negative_answer]] + ), + "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 answer_ids.values(), + "probability_a": float(logp[answer_a_id].exp()), + "probability_b": float(logp[answer_b_id].exp()), + "valid_choice_probability": float( + logp[answer_a_id].exp() + logp[answer_b_id].exp() + ), + "kl_clean_to_intervened": kl, + } + + +def flatten_cases(manifest: dict) -> list[tuple[dict, dict]]: + return [ + (case, prompt) + for case in manifest["cases"] + for prompt in case["prompts"] + ] + + +def main() -> None: + parser = argparse.ArgumentParser() + parser.add_argument( + "--manifest", + default="experiments/jacobian-steering-pilot-v1-preregistration.json", + ) + parser.add_argument( + "--output", + default="experiments/jacobian-steering-pilot-v1_raw.json", + ) + parser.add_argument("--dtype", choices=sorted(_DTYPES), default="bfloat16") + parser.add_argument("--device", default=None) + parser.add_argument( + "--methods", + default="jacobian_exact", + help="comma list: jacobian_exact,direct,random", + ) + 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()) + methods = [part.strip() for part in args.methods.split(",") if part.strip()] + unsupported = set(methods) - {"jacobian_exact", "direct", "random"} + if unsupported: + raise SystemExit(f"unsupported methods: {sorted(unsupported)}") + + selected = flatten_cases(manifest) + if args.prompt_id: + wanted = set(args.prompt_id) + selected = [(case, prompt) for case, prompt in selected if prompt["prompt_id"] in wanted] + missing = wanted - {prompt["prompt_id"] for _, prompt in selected} + if missing: + raise SystemExit(f"unknown prompt ids: {sorted(missing)}") + + 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"]] + lens_meta = [ + { + "seed": seed, + "path": path, + "sha256": sha256(ROOT / path), + } + for seed, path in enumerate(manifest["lens_paths"]) + ] + + 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") + payload["methods"] = list(dict.fromkeys(payload.get("methods", []) + methods)) + else: + payload = { + "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), + "model": manifest["model"], + "model_revision": manifest["model_revision"], + "dtype": args.dtype, + "device": str(model.input_device), + "lenses": lens_meta, + "torch": torch.__version__, + "python": sys.version, + "platform": platform.platform(), + }, + "methods": methods, + "doses_percent_residual_norm": manifest["intervention"][ + "doses_percent_residual_norm" + ], + "preflight": [], + "interventions": [], + } + + completed = { + (row["prompt_id"], row["method"], row.get("lens_seed"), row.get("random_seed"), row["dose_percent"]) + for row in payload["interventions"] + } + preflight_done = {row["prompt_id"] for row in payload["preflight"]} + answer_a_id = single_token(model.tokenizer, "A", prefer_plain=True) + answer_b_id = single_token(model.tokenizer, "B", prefer_plain=True) + doses = manifest["intervention"]["doses_percent_residual_norm"] + + for case, prompt in selected: + prompt_id = prompt["prompt_id"] + print(f"[{prompt_id}] clean preflight and steering") + text = format_chat(model.tokenizer, prompt["user"]) + layer = int(case["primary_layer"]) + hidden_by_layer, clean_logits = capture_clean(model, text, [layer]) + hidden = hidden_by_layer[layer] + positive_answer = ( + "A" if prompt["option_a"] == case["outcome_positive"] else "B" + ) + expected_answer = None + if prompt["expected_outcome"] is not None: + expected_answer = ( + "A" if prompt["option_a"] == prompt["expected_outcome"] else "B" + ) + clean_summary = distribution_summary( + clean_logits, + clean_logits, + answer_a_id, + answer_b_id, + positive_answer, + model.tokenizer, + ) + clean_accepted = clean_summary["global_top_is_valid_choice"] and ( + expected_answer is None or clean_summary["choice"] == expected_answer + ) + if prompt_id not in preflight_done: + payload["preflight"].append({ + "case_id": case["case_id"], + "prompt_id": prompt_id, + "condition": prompt["condition"], + "inferential": prompt["inferential"], + "option_a": prompt["option_a"], + "option_b": prompt["option_b"], + "positive_answer": positive_answer, + "expected_answer": expected_answer, + "accepted": bool(clean_accepted), + "clean": clean_summary, + "prompt": prompt["user"], + }) + preflight_done.add(prompt_id) + atomic_json(output_path, payload) + if not clean_accepted: + print(f" excluded: clean choice={clean_summary['choice']} expected={expected_answer}") + continue + + positive_id = single_token(model.tokenizer, case["concept_positive"]) + negative_id = single_token(model.tokenizer, case["concept_negative"]) + directions: list[tuple[str, int | None, int | None, torch.Tensor]] = [] + if "jacobian_exact" in methods: + for lens_seed, lens in enumerate(lenses): + directions.append(( + "jacobian_exact", + lens_seed, + None, + exact_jacobian_direction( + model, lens, layer, hidden, positive_id, negative_id + ), + )) + if "direct" in methods: + directions.append(( + "direct", None, None, + direct_direction(model, hidden, positive_id, negative_id), + )) + if "random" in methods: + reference = directions[0][3] if directions else direct_direction( + model, hidden, positive_id, negative_id + ) + for random_seed in range(3): + directions.append(( + "random", None, random_seed, + random_direction(reference, 20260716 + 1000 * random_seed + len(prompt_id)), + )) + + residual_norm = float(hidden.norm()) + for method, lens_seed, random_seed, direction in directions: + for dose in doses: + key = (prompt_id, method, lens_seed, random_seed, float(dose)) + if key in completed: + continue + delta = direction * (float(dose) / 100.0) * residual_norm + logits = clean_logits if float(dose) == 0.0 else patched_logits( + model, text, layer, delta + ) + summary = distribution_summary( + logits, + clean_logits, + answer_a_id, + answer_b_id, + positive_answer, + model.tokenizer, + ) + payload["interventions"].append({ + "case_id": case["case_id"], + "prompt_id": prompt_id, + "condition": prompt["condition"], + "inferential": prompt["inferential"], + "method": method, + "lens_seed": lens_seed, + "random_seed": random_seed, + "layer": layer, + "dose_percent": float(dose), + "residual_norm": residual_norm, + "delta_norm": float(delta.norm()), + "concept_positive": case["concept_positive"], + "concept_negative": case["concept_negative"], + "positive_answer": positive_answer, + "expected_answer": expected_answer, + **summary, + }) + completed.add(key) + atomic_json(output_path, payload) + print( + f" {method} seed={lens_seed if lens_seed is not None else random_seed} " + f"dose={dose:+.2f}% logodds={summary['positive_log_odds']:+.3f} " + f"choice={summary['choice']} valid={summary['valid_choice_probability']:.3f} " + f"KL={summary['kl_clean_to_intervened']:.3g}" + ) + + payload["completed_at"] = datetime.now(timezone.utc).isoformat() + atomic_json(output_path, payload) + print(f"complete -> {output_path.relative_to(ROOT)}") + + +if __name__ == "__main__": + main() diff --git a/jlens_materials/scripts/run_mechanism_steering.py b/jlens_materials/scripts/run_mechanism_steering.py new file mode 100644 index 0000000000000000000000000000000000000000..c756dfa63e2004352b11acbea99b3d29f05aedc3 --- /dev/null +++ b/jlens_materials/scripts/run_mechanism_steering.py @@ -0,0 +1,43 @@ +#!/usr/bin/env python3 +"""Run an archived mechanism-steering study through a neutral CLI.""" + +from __future__ import annotations + +import argparse +import sys + +from run_semantic_steering_v3 import main as run_manifest + + +STUDIES = { + "broad-screen": ( + "experiments/semantic-steering-v3-preregistration.json", + "experiments/semantic-steering-v3_raw.json", + ), + "prospective-grain": ( + "experiments/relational-grain-steering-v4-preregistration.json", + "experiments/relational-grain-steering-v4_raw.json", + ), +} + + +def main() -> None: + parser = argparse.ArgumentParser( + description="Run a frozen materials mechanism-steering experiment." + ) + parser.add_argument("--study", choices=sorted(STUDIES), required=True) + args, forwarded = parser.parse_known_args() + manifest, output = STUDIES[args.study] + sys.argv = [ + "run_mechanism_steering.py", + "--manifest", + manifest, + "--output", + output, + *forwarded, + ] + run_manifest() + + +if __name__ == "__main__": + main() diff --git a/jlens_materials/scripts/run_multitoken_sequence_robustness.py b/jlens_materials/scripts/run_multitoken_sequence_robustness.py new file mode 100644 index 0000000000000000000000000000000000000000..8fdb0f1d43004264f17026e9de36585c128ed44c --- /dev/null +++ b/jlens_materials/scripts/run_multitoken_sequence_robustness.py @@ -0,0 +1,787 @@ +#!/usr/bin/env python3 +"""Score exact multi-token materials terms under direct and Jacobian readouts. + +The Jacobian lens is a next-token readout, so a multi-token word cannot be +ranked by pretending that every token piece is predicted from the same state. +This runner uses the lens only for the first continuation token. It then +teacher-forces the remaining pieces through the unchanged Gemma model. For +equal-token-length target and contrast words, the resulting target-minus- +contrast score is an exact restricted sequence log-odds ratio: the shared +first-token softmax denominator cancels. +""" + +from __future__ import annotations + +import argparse +import json +import platform +import sys +from datetime import datetime, timezone +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 +import torch # noqa: E402 + +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, 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/multitoken-sequence-robustness-2026-07-18/protocol.json" +) +DEFAULT_RAW = ( + "experiments/multitoken-sequence-robustness-2026-07-18/raw.json" +) +OUT = ROOT / "experiments" / "multitoken-sequence-robustness-2026-07-18" +FIG = OUT / "figures" + + +def validate_hash(path: Path, expected: str) -> None: + actual = sha256(path) + if actual != expected: + raise RuntimeError(f"fingerprint mismatch for {path}: {actual} != {expected}") + + +def continuation_tokens(tokenizer, word: str) -> list[int]: + token_ids = tokenizer.encode(word, add_special_tokens=False) + if not token_ids: + raise RuntimeError(f"empty continuation tokenization for {word!r}") + return [int(token_id) for token_id in token_ids] + + +@torch.inference_mode() +def capture_prompt_states( + model, + prompt_rows: list[dict], + layers: list[int], +) -> tuple[np.ndarray, dict[str, torch.Tensor], list[dict]]: + raw = np.empty( + (len(prompt_rows), len(layers), model.d_model), dtype=np.float16 + ) + encoded = {} + clean = [] + final_layer = model.n_layers - 1 + capture = sorted(set(layers + [final_layer])) + for prompt_index, row in enumerate(prompt_rows): + # Association prompts use their frozen plain-text field, exactly as in + # the held-out run; no chat template or suffix is added. + input_ids = model.encode(str(row["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): + raw[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() + logits = model.unembed(final_state).float()[0] + top_values, top_ids = torch.topk(logits, k=10) + encoded[str(row["slug"])] = input_ids.detach().clone() + clean.append( + { + "slug": str(row["slug"]), + "family": str(row["target_family"]), + "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(prompt_rows):02d}: " + f"{row['slug']}", + flush=True, + ) + return raw, encoded, clean + + +@torch.inference_mode() +def remaining_piece_log_probability( + model, + input_ids: torch.Tensor, + token_ids: list[int], +) -> float: + if len(token_ids) == 1: + return 0.0 + prefix = torch.tensor( + token_ids[:-1], + device=input_ids.device, + dtype=input_ids.dtype, + ).unsqueeze(0) + extended = torch.cat([input_ids, prefix], dim=1) + prompt_final = int(input_ids.shape[1] - 1) + final_layer = model.n_layers - 1 + with ActivationRecorder(model.layers, at=[final_layer]) as recorder: + model.forward(extended) + # Position prompt_final predicts piece 0. Position prompt_final + j + # predicts piece j, so exclude the first prediction here. + states = recorder.activations[final_layer][ + 0, + prompt_final + 1 : prompt_final + len(token_ids), + ].detach() + if states.shape[0] != len(token_ids) - 1: + raise RuntimeError("teacher-forced continuation position mismatch") + logits = model.unembed(states).float() + logp = torch.log_softmax(logits, dim=-1) + targets = torch.tensor( + token_ids[1:], + device=logp.device, + dtype=torch.long, + ) + positions = torch.arange(len(targets), device=logp.device) + return float(logp[positions, targets].sum().detach().cpu()) + + +def first_piece_logits( + model, + states: np.ndarray, + token_ids: tuple[int, int], + chunk_size: int, +) -> np.ndarray: + output = np.empty( + (states.shape[0], states.shape[1], 2), dtype=np.float32 + ) + weight = model._lm_head.weight[ + torch.tensor(token_ids, device=model._lm_head.weight.device) + ].detach() + for layer_index in range(states.shape[1]): + for start in range(0, states.shape[0], chunk_size): + stop = min(start + chunk_size, states.shape[0]) + source = torch.from_numpy( + states[start:stop, layer_index].astype(np.float32) + ).to( + device=weight.device, + dtype=weight.dtype, + ) + output[start:stop, layer_index] = ( + (source @ weight.T) + .float() + .detach() + .cpu() + .numpy() + ) + return output + + +def make_figure( + rows: pd.DataFrame, + summary: pd.DataFrame, + layers: np.ndarray, + depths: np.ndarray, +) -> 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, + "pdf.fonttype": 42, + "svg.fonttype": "none", + } + ) + colors = {"jacobian_ensemble": "#16697A", "direct": "#B56576"} + fig, axes = plt.subplots(2, 2, figsize=(7.15, 5.5)) + + primary = summary[ + summary["method"].isin(["jacobian_ensemble", "direct"]) + ] + slugs = sorted(primary["slug"].unique()) + x = np.arange(len(slugs)) + for offset, (method, label) in zip( + (-0.13, 0.13), + (("jacobian_ensemble", "Jacobian"), ("direct", "Direct")), + ): + values = ( + primary[primary["method"] == method] + .set_index("slug") + .loc[slugs, "band_sequence_margin"] + ) + axes[0, 0].scatter( + x + offset, + values, + color=colors[method], + s=25, + label=label, + ) + axes[0, 0].axhline(0, color="#8B8E91", linestyle="--", linewidth=0.9) + axes[0, 0].set_ylabel("target minus contrast\nsequence score") + axes[0, 0].set_xticks( + x, + [ + f"{'C' if 'cleavage' in slug else 'M'}{index + 1}" + for index, slug in enumerate(slugs) + ], + rotation=0, + ) + 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, + ) + + 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=6.5) + axes[0, 1].text( + -0.15, + 1.04, + "B", + transform=axes[0, 1].transAxes, + fontweight="bold", + fontsize=10, + ) + + j = summary[summary["method"] == "jacobian_ensemble"] + axes[1, 0].scatter( + j["band_first_piece_margin"], + j["band_sequence_margin"], + c=[ + "#D17C38" if family == "cleavage" else "#5B8C5A" + for family in j["family"] + ], + s=34, + edgecolor="white", + linewidth=0.5, + ) + low = min( + float(j["band_first_piece_margin"].min()), + float(j["band_sequence_margin"].min()), + ) + high = max( + float(j["band_first_piece_margin"].max()), + float(j["band_sequence_margin"].max()), + ) + axes[1, 0].plot([low, high], [low, high], color="#8B8E91", linestyle="--") + axes[1, 0].set_xlabel("first-piece margin") + axes[1, 0].set_ylabel("full sequence margin") + 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, color in ( + ("cleavage", "o", "#D17C38"), + ("rapid-transformation", "s", "#5B8C5A"), + ): + subset = ( + seeds[seeds["family"] == family] + .groupby("method", as_index=False)["band_sequence_margin"] + .mean() + ) + axes[1, 1].scatter( + [0, 1, 2], + subset.set_index("method").loc[ + ["jacobian_seed0", "jacobian_seed1", "jacobian_seed2"], + "band_sequence_margin", + ], + color=color, + 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=6.5) + 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) + + +def main() -> None: + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument("--protocol", default=DEFAULT_PROTOCOL) + parser.add_argument( + "--amendment", + default=None, + help="optional implementation-only amendment bound to the base protocol", + ) + parser.add_argument("--output", default=DEFAULT_RAW) + parser.add_argument("--dtype", choices=sorted(_DTYPES), default="bfloat16") + parser.add_argument("--device", default=None) + parser.add_argument("--chunk-size", type=int, default=10) + parser.add_argument( + "--local-model-snapshot", + type=Path, + default=None, + help=( + "optional local Hugging Face snapshot for offline execution; " + "the frozen model id and revision remain the scientific provenance" + ), + ) + args = parser.parse_args() + + protocol_path = (ROOT / args.protocol).resolve() + output_path = (ROOT / args.output).resolve() + protocol = json.loads(protocol_path.read_text()) + amendment_path = None + amendment = None + if args.amendment is not None: + amendment_path = (ROOT / args.amendment).resolve() + amendment = json.loads(amendment_path.read_text()) + if amendment["base_protocol_sha256"] != sha256(protocol_path): + raise RuntimeError("amendment is not bound to the base protocol") + protocol["inputs"]["runner"]["sha256"] = amendment[ + "amended_runner_sha256" + ] + runner_path = Path(__file__).resolve() + validate_hash(runner_path, protocol["inputs"]["runner"]["sha256"]) + manifest_path = ROOT / protocol["inputs"]["prompt_manifest"]["path"] + validate_hash( + manifest_path, protocol["inputs"]["prompt_manifest"]["sha256"] + ) + for lens_row in protocol["lenses"]: + validate_hash(ROOT / lens_row["path"], lens_row["sha256"]) + + manifest = json.loads(manifest_path.read_text()) + selected_families = set(protocol["contrasts"]) + prompts = [ + row + for row in manifest["prompts"] + if str(row["target_family"]) in selected_families + ] + if len(prompts) != 10: + raise RuntimeError(f"expected 10 selected prompts, found {len(prompts)}") + for row in prompts: + if any( + term.lower() in str(row["text"]).lower() + for term in ( + protocol["contrasts"][str(row["target_family"])]["target"], + protocol["contrasts"][str(row["target_family"])]["contrast"], + ) + ): + raise RuntimeError(f"target/contrast leaks into prompt {row['slug']}") + + model_source = ( + str(args.local_model_snapshot.resolve()) + if args.local_model_snapshot is not None + else protocol["model"] + ) + model = load_model( + model_source, + dtype=_DTYPES[args.dtype], + device=args.device, + revision=None if args.local_model_snapshot is not None else protocol["model_revision"], + ) + layers = [int(layer) for layer in protocol["source_layers"]] + tokenization = {} + for family, contrast in protocol["contrasts"].items(): + target = continuation_tokens(model.tokenizer, contrast["target"]) + comparator = continuation_tokens(model.tokenizer, contrast["contrast"]) + if len(target) != len(comparator) or len(target) < 2: + raise RuntimeError( + f"contrast is not equal-length multi-token: {family}" + ) + tokenization[family] = { + "target": target, + "contrast": comparator, + "target_pieces": [ + model.tokenizer.decode( + [token_id], clean_up_tokenization_spaces=False + ) + for token_id in target + ], + "contrast_pieces": [ + model.tokenizer.decode( + [token_id], clean_up_tokenization_spaces=False + ) + for token_id in comparator + ], + } + expected_tokenization = protocol["tokenization_preflight"] + for family, resolved in tokenization.items(): + for key in ("target", "contrast"): + if resolved[key] != expected_tokenization[family][key]: + raise RuntimeError(f"tokenization changed for {family} {key}") + + 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, encoded, clean = capture_prompt_states(model, prompts, layers) + direct, jacobian = decoder_basis_representations( + model, lenses, raw, layers, args.chunk_size + ) + + continuation = {} + for row in prompts: + family = str(row["target_family"]) + continuation[str(row["slug"])] = {} + for label in ("target", "contrast"): + continuation[str(row["slug"])][label] = ( + remaining_piece_log_probability( + model, + encoded[str(row["slug"])], + tokenization[family][label], + ) + ) + print(f" sequence continuation: {row['slug']}", flush=True) + + method_arrays = { + "direct": direct, + "jacobian_seed0": jacobian[0], + "jacobian_seed1": jacobian[1], + "jacobian_seed2": jacobian[2], + "jacobian_ensemble": jacobian.astype(np.float32).mean(axis=0), + } + result_rows = [] + for method, states in method_arrays.items(): + for prompt_index, row in enumerate(prompts): + family = str(row["target_family"]) + target_tokens = tokenization[family]["target"] + contrast_tokens = tokenization[family]["contrast"] + logits = first_piece_logits( + model, + states[prompt_index : prompt_index + 1], + (target_tokens[0], contrast_tokens[0]), + args.chunk_size, + )[0] + target_continuation = continuation[str(row["slug"])]["target"] + contrast_continuation = continuation[str(row["slug"])]["contrast"] + for layer_index, layer in enumerate(layers): + first_margin = float( + logits[layer_index, 0] - logits[layer_index, 1] + ) + sequence_margin = float( + first_margin + + target_continuation + - contrast_continuation + ) + result_rows.append( + { + "slug": str(row["slug"]), + "family": family, + "method": method, + "layer": int(layer), + "depth_percent": 100.0 + * int(layer) + / (model.n_layers - 1), + "target": protocol["contrasts"][family]["target"], + "contrast": protocol["contrasts"][family]["contrast"], + "n_pieces": len(target_tokens), + "target_first_piece_logit": float( + logits[layer_index, 0] + ), + "contrast_first_piece_logit": float( + logits[layer_index, 1] + ), + "first_piece_margin": first_margin, + "target_remaining_log_probability": float( + target_continuation + ), + "contrast_remaining_log_probability": float( + contrast_continuation + ), + "sequence_margin": sequence_margin, + "target_wins": bool(sequence_margin > 0), + } + ) + frame = pd.DataFrame(result_rows) + band_low, band_high = protocol["registered_band_percent"] + band = frame[ + (frame["depth_percent"] >= band_low) + & (frame["depth_percent"] <= band_high) + ] + summary = ( + band.groupby( + ["slug", "family", "method", "target", "contrast"], + as_index=False, + ) + .agg( + band_first_piece_margin=("first_piece_margin", "mean"), + band_sequence_margin=("sequence_margin", "mean"), + positive_layer_fraction=("target_wins", "mean"), + ) + ) + family_summary = ( + summary.groupby( + ["family", "method", "target", "contrast"], + as_index=False, + ) + .agg( + mean_band_first_piece_margin=("band_first_piece_margin", "mean"), + mean_band_sequence_margin=("band_sequence_margin", "mean"), + positive_prompts=("band_sequence_margin", lambda x: int((x > 0).sum())), + n_prompts=("slug", "size"), + ) + ) + + j = summary[summary["method"] == "jacobian_ensemble"].set_index("slug") + d = summary[summary["method"] == "direct"].set_index("slug") + prompt_delta = pd.DataFrame( + { + "slug": j.index, + "family": j["family"], + "jacobian_band_sequence_margin": j["band_sequence_margin"], + "direct_band_sequence_margin": d.loc[ + j.index, "band_sequence_margin" + ], + } + ).reset_index(drop=True) + prompt_delta["jacobian_minus_direct"] = ( + prompt_delta["jacobian_band_sequence_margin"] + - prompt_delta["direct_band_sequence_margin"] + ) + family_delta = ( + prompt_delta.groupby("family", as_index=False) + .agg( + jacobian_band_sequence_margin=( + "jacobian_band_sequence_margin", + "mean", + ), + direct_band_sequence_margin=( + "direct_band_sequence_margin", + "mean", + ), + jacobian_minus_direct=("jacobian_minus_direct", "mean"), + positive_jacobian_prompts=( + "jacobian_band_sequence_margin", + lambda x: int((x > 0).sum()), + ), + positive_delta_prompts=( + "jacobian_minus_direct", + lambda x: int((x > 0).sum()), + ), + ) + ) + + both_families_positive = bool( + np.all(family_delta["jacobian_band_sequence_margin"] > 0) + ) + positive_prompts = int( + np.sum(prompt_delta["jacobian_band_sequence_margin"] > 0) + ) + all_seed_family_signs = True + for family in selected_families: + seed_means = family_summary[ + (family_summary["family"] == family) + & family_summary["method"].str.startswith("jacobian_seed") + ]["mean_band_sequence_margin"] + all_seed_family_signs &= bool(np.all(seed_means > 0)) + robust = bool( + both_families_positive + and positive_prompts >= 8 + and all_seed_family_signs + ) + + OUT.mkdir(parents=True, exist_ok=True) + frame.to_csv(OUT / "layer_sequence_scores.csv", index=False) + summary.to_csv(OUT / "prompt_band_summary.csv", index=False) + family_summary.to_csv(OUT / "family_method_summary.csv", index=False) + prompt_delta.to_csv(OUT / "prompt_jacobian_direct_contrasts.csv", index=False) + family_delta.to_csv(OUT / "family_jacobian_direct_contrasts.csv", index=False) + make_figure( + frame, + summary, + np.asarray(layers), + np.asarray([100.0 * layer / (model.n_layers - 1) for layer in layers]), + ) + + payload = { + "study_id": protocol["study_id"], + "created_at": datetime.now(timezone.utc).isoformat(), + "protocol_sha256": sha256(protocol_path), + "protocol_amendment": ( + str(amendment_path.relative_to(ROOT)) + if amendment_path is not None + else None + ), + "protocol_amendment_sha256": ( + sha256(amendment_path) if amendment_path is not None else None + ), + "runner_sha256": sha256(runner_path), + "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(), + "tokenization": tokenization, + "clean_rows": clean, + "continuation_log_probabilities": continuation, + "frozen_verdict": { + "sequence_robustness": "pass" if robust else "fail", + "both_family_jacobian_margins_positive": both_families_positive, + "positive_jacobian_prompts": positive_prompts, + "required_positive_prompts": 8, + "all_three_seed_family_margins_positive": all_seed_family_signs, + "jacobian_specific": bool( + np.all(family_delta["jacobian_minus_direct"] > 0) + ), + }, + "family_results": family_delta.to_dict(orient="records"), + "guardrail": ( + "This post-hoc robustness test evaluates two originally excluded " + "multi-token terms against equal-piece scientific contrasts. It " + "does not replace the preregistered single-token endpoint." + ), + } + atomic_json(output_path, payload) + (OUT / "statistics.json").write_text(json.dumps(payload, indent=2) + "\n") + (OUT / "RESULTS.md").write_text( + "\n".join( + [ + "# Multi-token sequence robustness", + "", + ( + f"Frozen verdict: **{'PASS' if robust else 'FAIL'}** " + "for the descriptive sequence-robustness rule." + ), + "", + "## Exact contrasts", + "", + "- `transgranular` versus `intergranular` (two pieces each).", + "- `martensite` versus `bainite` (three pieces each).", + "", + "The lens scores only the first piece from an intermediate", + "state. Remaining pieces are teacher-forced through unchanged", + "Gemma. Equal token lengths make the target-minus-contrast score", + "a restricted sequence log-odds ratio.", + "", + "## Family results", + "", + ] + + [ + ( + f"- {row.family}: Jacobian " + f"{row.jacobian_band_sequence_margin:+.3f}, direct " + f"{row.direct_band_sequence_margin:+.3f}, difference " + f"{row.jacobian_minus_direct:+.3f}; " + f"{int(row.positive_jacobian_prompts)}/5 prompts positive." + ) + for row in family_delta.itertuples(index=False) + ] + + [ + "", + ( + f"Across both families, {positive_prompts}/10 prompt-level " + "Jacobian band margins are positive." + ), + "", + "## Reproduction", + "", + "```bash", + "HF_HUB_OFFLINE=1 TRANSFORMERS_OFFLINE=1 \\", + " python scripts/run_multitoken_sequence_robustness.py --device cpu", + "```", + "", + ] + ) + ) + print(json.dumps(payload["frozen_verdict"], indent=2)) + + +if __name__ == "__main__": + main() diff --git a/jlens_materials/scripts/run_patchscope_physics_outcome.py b/jlens_materials/scripts/run_patchscope_physics_outcome.py new file mode 100644 index 0000000000000000000000000000000000000000..f5ab614ab582776bc2caac89875eabee18c9999e --- /dev/null +++ b/jlens_materials/scripts/run_patchscope_physics_outcome.py @@ -0,0 +1,212 @@ +#!/usr/bin/env python3 +"""Translate arbitrary-code final states into semantic physical outcomes.""" + +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")) + +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 pair_logits # noqa: E402 +from run_patchscope_physics_relation import run_candidate, target_position # noqa: E402 +from run_semantic_steering_v3 import continuation_tokens # noqa: E402 + + +DEFAULT_PROTOCOL = ( + "experiments/patchscope-physics-outcome-2026-07-18/protocol.json" +) +DEFAULT_OUTPUT = ( + "experiments/patchscope-physics-outcome-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}") + + +@torch.inference_mode() +def direct_scores( + model, + states: np.ndarray, + token_ids: tuple[int, int], + batch_size: int, +) -> np.ndarray: + output = [] + for start in range(0, len(states), batch_size): + stop = min(start + batch_size, len(states)) + values = torch.from_numpy(states[start:stop].astype(np.float32)).to( + device=model._lm_head.weight.device, + dtype=model._lm_head.weight.dtype, + ) + normalized = model._final_norm(values).float() + logits = pair_logits(model, normalized, token_ids).detach().cpu() + output.append((logits[:, 0] - logits[:, 1]).numpy()) + return np.concatenate(output) + + +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("--batch-size", type=int, default=16) + args = parser.parse_args() + + protocol_path = (ROOT / args.protocol).resolve() + protocol = json.loads(protocol_path.read_text()) + for name in ("source_manifest", "source_states", "runner"): + row = protocol["inputs"][name] + validate_hash(ROOT / row["path"], row["sha256"]) + manifest = json.loads( + (ROOT / protocol["inputs"]["source_manifest"]["path"]).read_text() + ) + with np.load( + ROOT / protocol["inputs"]["source_states"]["path"], + 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.float16) + source_position = int(np.flatnonzero(positions == "final_prompt")[0]) + by_id = {row["prompt_id"]: row for row in manifest["prompts"]} + source_rows = [by_id[prompt_id] for prompt_id in prompt_ids] + + model = load_model( + protocol["model"], + dtype=_DTYPES[args.dtype], + device=args.device, + revision=protocol["model_revision"], + ) + marker_tokens = model.tokenizer.encode( + " " + protocol["patch_marker"], add_special_tokens=False + ) + if len(marker_tokens) != 1: + raise RuntimeError(f"patch marker is not one token: {marker_tokens}") + marker_id = int(marker_tokens[0]) + + rows = [] + target_records = [] + for target in protocol["targets"]: + positive = continuation_tokens(model.tokenizer, target["positive_word"]) + negative = continuation_tokens(model.tokenizer, target["negative_word"]) + if len(positive) != 1 or len(negative) != 1: + raise RuntimeError(f"target tokens are not single: {target}") + token_ids = (positive[0], negative[0]) + input_ids, marker_position = target_position( + model, target["user"], marker_id + ) + target_records.append( + { + **target, + "positive_token_id": token_ids[0], + "negative_token_id": token_ids[1], + "marker_position": marker_position, + "n_tokens": int(input_ids.shape[1]), + } + ) + for layer in protocol["candidate_layers"]: + layer_index = int(np.flatnonzero(layers == int(layer))[0]) + donor = states[source_position, :, layer_index].astype(np.float32) + patch_scores, unpatched = run_candidate( + model, + input_ids, + marker_position, + int(layer), + donor, + token_ids, + args.batch_size, + ) + baseline = direct_scores( + model, donor, token_ids, args.batch_size + ) + for source, patch_score, direct_score in zip( + source_rows, patch_scores, baseline + ): + rows.append( + { + "prompt_id": source["prompt_id"], + "law_id": source["law_id"], + "law_name": source["law_name"], + "split": source["split"], + "law_sign": source["law_sign"], + "surface": source["surface"], + "case_index": source["case_index"], + "numerical_sign": source["numerical_sign"], + "physical_sign": source["physical_sign"], + "answer_order": source["answer_order"], + "expected_code": source["expected_code"], + "target_id": target["target_id"], + "target_style": target["style"], + "answer_vocabulary": target["answer_vocabulary"], + "layer": int(layer), + "depth_percent": float( + 100.0 * int(layer) / (model.n_layers - 1) + ), + "patchscope_positive_minus_negative": float(patch_score), + "direct_positive_minus_negative": float(direct_score), + "unpatched_positive_minus_negative": float(unpatched[0]), + } + ) + print( + f"outcome Patchscope {target['target_id']} layer {layer}: " + f"{len(source_rows)} sources", + flush=True, + ) + + output_path = (ROOT / args.output).resolve() + 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), + "runner": str(Path(__file__).resolve().relative_to(ROOT)), + "runner_sha256": sha256(Path(__file__).resolve()), + "source_manifest_sha256": sha256( + ROOT / protocol["inputs"]["source_manifest"]["path"] + ), + "source_states_sha256": sha256( + ROOT / protocol["inputs"]["source_states"]["path"] + ), + "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(), + }, + "dimensions": { + "n_prompts": len(source_rows), + "n_laws": len(set(row["law_id"] for row in source_rows)), + "n_targets": len(protocol["targets"]), + "n_layers": len(protocol["candidate_layers"]), + "n_rows": len(rows), + }, + "targets": target_records, + "rows": rows, + } + atomic_json(output_path, payload) + print(f"saved {len(rows)} outcome-Patchscope rows to {output_path}") + + +if __name__ == "__main__": + main() diff --git a/jlens_materials/scripts/run_symbolic_equivalence_lens.py b/jlens_materials/scripts/run_symbolic_equivalence_lens.py new file mode 100644 index 0000000000000000000000000000000000000000..b4bf96f9e533833e168ab7a683af8ac039626bc2 --- /dev/null +++ b/jlens_materials/scripts/run_symbolic_equivalence_lens.py @@ -0,0 +1,102 @@ +#!/usr/bin/env python3 +"""Capture relation-marker states for the symbolic equivalence benchmark.""" + +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 load_model # noqa: E402 + + +OUT = ROOT / "experiments" / "symbolic-equivalence-lens-2026-07-18" + + +@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("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"], + ) + marker_id = int(model.tokenizer.encode(" §", add_special_tokens=False)[0]) + layers = list(range(model.n_layers)) + prompts = manifest["prompts"] + states = np.empty((len(prompts), len(layers), 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) + matches = (ids[0] == marker_id).nonzero(as_tuple=False).flatten().tolist() + if len(matches) != 1: + raise RuntimeError(f"{prompt['prompt_id']}: marker count {len(matches)}") + location = int(matches[0]) + with ActivationRecorder(model.layers, at=layers) as recorder: + model.forward(ids) + for li, layer in enumerate(layers): + states[index, li] = ( + recorder.activations[layer][0, location] + .float() + .cpu() + .numpy() + .astype(np.float16) + ) + rows.append( + { + "prompt_id": prompt["prompt_id"], + "law_sign": prompt["law_sign"], + "surface": prompt["surface"], + "variable_index": prompt["variable_index"], + "marker_position": location, + } + ) + print(f"{index + 1:03d}/{len(prompts):03d} {prompt['prompt_id']}") + state_path = OUT / "representations.npz" + np.savez_compressed( + state_path, + prompt_ids=np.asarray([x["prompt_id"] for x in prompts]), + layers=np.asarray(layers, 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), + "device": str(model.input_device), + "dtype": "bfloat16", + "torch": torch.__version__, + "python": sys.version, + "platform": platform.platform(), + }, + "rows": rows, + }, + ) + + +if __name__ == "__main__": + main() diff --git a/jlens_materials/scripts/run_symbolic_monotonicity_lens.py b/jlens_materials/scripts/run_symbolic_monotonicity_lens.py new file mode 100644 index 0000000000000000000000000000000000000000..019a734c1c7a386db73b1ebdd758e8695c2acd39 --- /dev/null +++ b/jlens_materials/scripts/run_symbolic_monotonicity_lens.py @@ -0,0 +1,108 @@ +#!/usr/bin/env python3 +"""Capture all-layer relation-marker states for anonymous equations.""" + +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 load_model # noqa: E402 + + +OUT = ROOT / "experiments" / "symbolic-monotonicity-lens-2026-07-18" + + +@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("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"], + ) + marker_tokens = model.tokenizer.encode(" §", add_special_tokens=False) + if len(marker_tokens) != 1: + raise RuntimeError(f"marker is not one token: {marker_tokens}") + marker_id = int(marker_tokens[0]) + layers = list(range(model.n_layers)) + prompts = manifest["prompts"] + states = np.empty((len(prompts), len(layers), 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) + matches = (ids[0] == marker_id).nonzero(as_tuple=False).flatten().tolist() + if len(matches) != 1: + raise RuntimeError(f"{prompt['prompt_id']}: marker count {len(matches)}") + location = int(matches[0]) + with ActivationRecorder(model.layers, at=layers) as recorder: + model.forward(ids) + for layer_index, layer in enumerate(layers): + states[index, layer_index] = ( + recorder.activations[layer][0, location] + .float() + .cpu() + .numpy() + .astype(np.float16) + ) + rows.append( + { + "prompt_id": prompt["prompt_id"], + "law_sign": prompt["law_sign"], + "family": prompt["family"], + "variable_index": prompt["variable_index"], + "n_tokens": int(ids.shape[1]), + "marker_position": location, + } + ) + print(f"{index + 1:03d}/{len(prompts):03d} {prompt['prompt_id']}") + state_path = OUT / "representations.npz" + np.savez_compressed( + state_path, + prompt_ids=np.asarray([x["prompt_id"] for x in prompts]), + layers=np.asarray(layers, 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"], + "device": str(model.input_device), + "dtype": "bfloat16", + "torch": torch.__version__, + "python": sys.version, + "platform": platform.platform(), + }, + "rows": rows, + }, + ) + + +if __name__ == "__main__": + main() diff --git a/jlens_materials/scripts/validate_causal_relational_knockout.py b/jlens_materials/scripts/validate_causal_relational_knockout.py new file mode 100644 index 0000000000000000000000000000000000000000..c3960518b77e518627ab120f677098cf3fd0c25a --- /dev/null +++ b/jlens_materials/scripts/validate_causal_relational_knockout.py @@ -0,0 +1,185 @@ +#!/usr/bin/env python3 +"""Validate completeness and intervention invariants for the 24-law causal study.""" + +from __future__ import annotations + +import json +from pathlib import Path + +import numpy as np + + +ROOT = Path(__file__).resolve().parents[1] +EXP = ROOT / "experiments" / "causal-relational-knockout-2026-09-02" +SCALES = ("4b", "12b", "31b") + + +def read_json(path: Path) -> dict: + return json.loads(path.read_text(encoding="utf-8")) + + +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 = read_json(EXP / "protocol.json") + amendment = read_json(EXP / "control_amendment.json") + prompts = read_json(EXP / "prompt_manifest.json")["prompts"] + prompt_ids = [row["prompt_id"] for row in prompts] + by_id = {row["prompt_id"]: row for row in prompts} + if len(prompts) != 192 or len(by_id) != 192: + raise RuntimeError("expected 192 unique frozen prompts") + for row in prompts: + counterpart = by_id[row["counterpart_prompt_id"]] + if counterpart["counterpart_prompt_id"] != row["prompt_id"]: + raise RuntimeError(f"counterpart mapping is not involutive: {row['prompt_id']}") + if counterpart["law_id"] != row["law_id"]: + raise RuntimeError(f"counterpart crosses laws: {row['prompt_id']}") + if int(counterpart["physical_sign"]) != -int(row["physical_sign"]): + raise RuntimeError(f"counterpart does not reverse physical sign: {row['prompt_id']}") + + frozen_laws = {row["law_id"] for row in prompts} + if len(frozen_laws) != 24: + raise RuntimeError("expected 24 frozen law identities") + if sum(row["category"] == "direct" for row in prompts) != 96: + raise RuntimeError("direct/inverse prompt balance failed") + if sum(row["answer_vocabulary"] == "higher-lower" for row in prompts) != 96: + raise RuntimeError("answer-vocabulary prompt balance failed") + if sum(row["answer_order"] == "standard" for row in prompts) != 96: + raise RuntimeError("answer-order prompt balance failed") + forbidden_scaffold = ("=", "constitutive equation", "monotonic", "proportional to") + for row in prompts: + lowered = row["user"].lower() + if any(term in lowered for term in forbidden_scaffold): + raise RuntimeError(f"equation/scaffold term in target prompt: {row['prompt_id']}") + for law_id in sorted(frozen_laws): + law_rows = [row for row in prompts if row["law_id"] == law_id] + if len(law_rows) != 8: + raise RuntimeError(f"{law_id}: expected eight prompt variants") + if sum(int(row["physical_sign"]) > 0 for row in law_rows) != 4: + raise RuntimeError(f"{law_id}: physical-sign balance failed") + if len({row["answer_vocabulary"] for row in law_rows}) != 2: + raise RuntimeError(f"{law_id}: answer-vocabulary coverage failed") + if len({row["answer_order"] for row in law_rows}) != 2: + raise RuntimeError(f"{law_id}: answer-order coverage failed") + + control_axes = len(amendment["construction"]["seeds"]) + expected_methods = { + "neutralize", + "counterfactual_coordinate", + "correct_centroid", + "wrong_centroid", + "full_counterfactual", + "full_counterfactual_direction_restored", + } + for index in range(control_axes): + for orientation in ("plus", "minus"): + expected_methods.add(f"neutralize_control_{index}_{orientation}") + expected_methods.add(f"counterfactual_control_{index}_{orientation}") + + scale_results = {} + for scale in SCALES: + folder = EXP / scale + if not (folder / "run_record.json").exists(): + continue + development = read_json(ROOT / protocol["models"][scale]["development_manifest"])[ + "prompts" + ] + development_laws = {row["law_id"] for row in development} + overlap = sorted(frozen_laws & development_laws) + if overlap: + raise RuntimeError(f"{scale}: target/development law overlap: {overlap}") + clean = read_jsonl(folder / "clean_rows.jsonl") + interventions = read_jsonl(folder / "intervention_rows.jsonl") + clean_ids = [row["prompt_id"] for row in clean] + keys = [(row["prompt_id"], row["intervention"]) for row in interventions] + if clean_ids != prompt_ids: + raise RuntimeError(f"{scale}: clean rows do not match frozen prompt order") + if len(keys) != len(set(keys)): + raise RuntimeError(f"{scale}: duplicate intervention keys") + if len(interventions) != len(prompts) * len(expected_methods): + raise RuntimeError(f"{scale}: incomplete intervention rows") + observed_methods = {row["intervention"] for row in interventions} + if observed_methods != expected_methods: + raise RuntimeError( + f"{scale}: intervention methods differ; " + f"missing={expected_methods - observed_methods}, extra={observed_methods - expected_methods}" + ) + numeric_fields = ( + "replacement_distance", + "first_minus_second_logit", + "correct_answer_margin", + "registered_answer_probability_mass", + "clean_to_intervened_kl", + "correct_margin_change", + "movement_toward_counterfactual", + ) + for field in numeric_fields: + values = np.asarray([row[field] for row in interventions], dtype=float) + if not np.all(np.isfinite(values)): + raise RuntimeError(f"{scale}: nonfinite {field}") + row_map = {key: row for key, row in zip(keys, interventions)} + maximum_distance_error = 0.0 + for prompt_id in prompt_ids: + neutral_distance = row_map[(prompt_id, "neutralize")]["replacement_distance"] + counterfactual_distance = row_map[ + (prompt_id, "counterfactual_coordinate") + ]["replacement_distance"] + for index in range(control_axes): + for orientation in ("plus", "minus"): + observed_neutral = row_map[ + (prompt_id, f"neutralize_control_{index}_{orientation}") + ]["replacement_distance"] + observed_counterfactual = row_map[ + (prompt_id, f"counterfactual_control_{index}_{orientation}") + ]["replacement_distance"] + maximum_distance_error = max( + maximum_distance_error, + abs(observed_neutral - neutral_distance), + abs(observed_counterfactual - counterfactual_distance), + ) + if maximum_distance_error > 1e-7: + raise RuntimeError(f"{scale}: control distance mismatch {maximum_distance_error}") + + archive = np.load(folder / "frozen_direction.npz") + basis = np.vstack([archive["direction"], archive["control_directions"]]).astype(float) + maximum_gram_error = float(np.max(np.abs(basis @ basis.T - np.eye(len(basis))))) + if maximum_gram_error > 2e-6: + raise RuntimeError(f"{scale}: non-orthonormal direction/control basis") + states = np.load(folder / "clean_states.npy", mmap_mode="r") + logits = np.load(folder / "clean_logits.npy", mmap_mode="r") + if states.shape[0] != 192 or logits.shape[0] != 192: + raise RuntimeError(f"{scale}: clean array row mismatch") + preflight = read_json(folder / "preflight.json") + if preflight["model_revision"] != protocol["models"][scale]["revision"]: + raise RuntimeError(f"{scale}: model revision mismatch") + scale_results[scale] = { + "clean_rows": len(clean), + "intervention_rows": len(interventions), + "methods_per_prompt": len(expected_methods), + "maximum_control_distance_error": maximum_distance_error, + "maximum_direction_gram_error": maximum_gram_error, + "clean_state_shape": list(states.shape), + "clean_logit_shape": list(logits.shape), + "development_law_count": len(development_laws), + "target_development_law_overlap": overlap, + } + + result = { + "valid": True, + "study_id": protocol["study_id"], + "amendment_id": amendment["amendment_id"], + "prompt_count": len(prompts), + "law_count": len(frozen_laws), + "expected_methods_per_prompt": len(expected_methods), + "scales": scale_results, + } + (EXP / "validation.json").write_text( + json.dumps(result, indent=2) + "\n", encoding="utf-8" + ) + print(json.dumps(result, indent=2)) + + +if __name__ == "__main__": + main() diff --git a/jlens_materials/scripts/validate_core_discovery_discipline.py b/jlens_materials/scripts/validate_core_discovery_discipline.py new file mode 100644 index 0000000000000000000000000000000000000000..5eb1f4c46b0c129b93b42efd815e097b6aa0be28 --- /dev/null +++ b/jlens_materials/scripts/validate_core_discovery_discipline.py @@ -0,0 +1,381 @@ +#!/usr/bin/env python3 +"""Independent structural and arithmetic validation for the core audit. + +This validator intentionally does not import the analysis implementation. It +recomputes key summaries from the archived CSV outputs, checks the frozen +protocol and input hashes, and writes a machine-readable validation record. +""" + +from __future__ import annotations + +import csv +import hashlib +import json +import math +from collections import Counter, 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.json" +READABILITY_PROTOCOL = OUT / "protocol_addendum_readability.json" +STATISTICS = OUT / "statistics.json" +READABILITY_STATISTICS = OUT / "readability_family_influence_statistics.json" +VALIDATION = OUT / "VALIDATION.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(name: str) -> list[dict[str, str]]: + with (OUT / name).open(newline="") as handle: + return list(csv.DictReader(handle)) + + +def finite_float(value: str) -> float: + parsed = float(value) + if not math.isfinite(parsed): + raise AssertionError(f"non-finite numeric value: {value!r}") + return parsed + + +def assert_close(actual: float, expected: float, *, atol: float = 1e-12) -> None: + if not math.isclose(actual, expected, rel_tol=0.0, abs_tol=atol): + raise AssertionError(f"{actual!r} != {expected!r}") + + +def mean(values: Iterable[float]) -> float: + values = list(values) + if not values: + raise AssertionError("cannot average an empty sequence") + 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 spearman(x: list[float], y: list[float]) -> float: + x_rank = average_ranks(x) + y_rank = average_ranks(y) + x_mean = mean(x_rank) + y_mean = mean(y_rank) + numerator = sum( + (a - x_mean) * (b - y_mean) for a, b in zip(x_rank, y_rank) + ) + denominator = math.sqrt( + sum((a - x_mean) ** 2 for a in x_rank) + * sum((b - y_mean) ** 2 for b in y_rank) + ) + return numerator / denominator + + +def validate_hashes( + protocol: Mapping[str, object], statistics: Mapping[str, object] +) -> dict[str, str]: + protocol_hash = sha256(PROTOCOL) + assert statistics["protocol_sha256"] == protocol_hash + fingerprints: 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"] + observed = sha256(path) + assert observed == spec["sha256"] + assert observed == statistics["input_sha256"][name] # type: ignore[index] + fingerprints[name] = observed + return {"protocol": protocol_hash, **fingerprints} + + +def validate_rows() -> dict[str, int]: + expected = { + "cross_law_pair_contrasts.csv": 45, + "cross_law_layer_trajectories.csv": 75, + "held_out_readout_auc.csv": 60, + "steering_pair_robustness.csv": 24, + "steering_trajectory_diagnostics.csv": 480, + "steering_relation_reversals.csv": 6, + } + for filename, count in expected.items(): + rows = read_csv(filename) + assert len(rows) == count, (filename, len(rows), count) + assert len({tuple(row.items()) for row in rows}) == count, filename + for row in rows: + for key, value in row.items(): + if value is None or value == "": + raise AssertionError(f"missing value in {filename}:{key}") + return expected + + +def validate_geometry(statistics: Mapping[str, object]) -> dict[str, object]: + pair_rows = read_csv("cross_law_pair_contrasts.csv") + readout_rows = read_csv("held_out_readout_auc.csv") + layer_rows = read_csv("cross_law_layer_trajectories.csv") + + methods = {"jacobian", "direct", "raw", "word_tfidf", "char_tfidf"} + assert {row["method"] for row in pair_rows} == methods + assert Counter(row["method"] for row in pair_rows) == Counter( + {method: 9 for method in methods} + ) + assert Counter(row["method"] for row in readout_rows) == Counter( + {method: 12 for method in methods} + ) + assert Counter(row["method"] for row in layer_rows) == Counter( + {"jacobian": 25, "direct": 25, "raw": 25} + ) + + cross = statistics["geometry"]["cross_law"] # type: ignore[index] + for method in methods: + rows = [row for row in pair_rows if row["method"] == method] + values = [ + finite_float(row["physical_minus_numeric_similarity"]) for row in rows + ] + assert_close( + mean(values), + cross[method]["mean_physical_minus_numeric_similarity"], + ) + assert sum(value > 0.0 for value in values) == cross[method][ + "positive_pair_count" + ] + + held_out = statistics["geometry"]["held_out_readout"] # type: ignore[index] + for method in methods: + family_sets: list[set[str]] = [] + for target in ("numeric_direction", "physical_outcome"): + rows = [ + row + for row in readout_rows + if row["method"] == method and row["target"] == target + ] + assert len(rows) == 6 + family_sets.append({row["held_out_family"] for row in rows}) + aucs = [finite_float(row["auc"]) for row in rows] + oriented = [max(auc, 1.0 - auc) for auc in aucs] + assert_close(mean(aucs), held_out[method][target]["mean_auc"]) + assert_close( + mean(oriented), + held_out[method][target]["mean_orientation_free_auc"], + ) + assert family_sets[0] == family_sets[1] + + return { + "methods": sorted(methods), + "cross_law_pairs_per_method": 9, + "held_out_families_per_target": 6, + "layers_per_state_representation": 25, + } + + +def validate_steering(statistics: Mapping[str, object]) -> dict[str, object]: + pair_rows = read_csv("steering_pair_robustness.csv") + trajectory_rows = read_csv("steering_trajectory_diagnostics.csv") + reversal_rows = read_csv("steering_relation_reversals.csv") + + controls = {"own", "wrong-mechanism", "direct_control", "random"} + assert Counter(row["control"] for row in pair_rows) == Counter( + {control: 6 for control in controls} + ) + assert len({row["pair_id"] for row in pair_rows}) == 6 + pair_lookup: dict[tuple[str, str], dict[str, str]] = { + (row["pair_id"], row["control"]): row for row in pair_rows + } + + for control in controls: + rows = [row for row in pair_rows if row["control"] == control] + for metric in ( + "aligned_slope_per_percent", + "aligned_low_dose_minus2_to_plus2", + ): + observed = mean(finite_float(row[metric]) for row in rows) + expected = statistics["steering"]["controls"][control][metric]["mean"] # type: ignore[index] + assert_close(observed, expected) + + control_aliases = { + "own_minus_wrong-mechanism": "wrong-mechanism", + "own_minus_direct_control": "direct_control", + "own_minus_random": "random", + } + for contrast, control in control_aliases.items(): + for metric in ( + "aligned_slope_per_percent", + "aligned_low_dose_minus2_to_plus2", + ): + differences = [ + finite_float(pair_lookup[(pair_id, "own")][metric]) + - finite_float(pair_lookup[(pair_id, control)][metric]) + for pair_id in {row["pair_id"] for row in pair_rows} + ] + expected = statistics["steering"]["contrasts"][contrast][metric] # type: ignore[index] + assert_close(mean(differences), expected["mean_difference"]) + assert sum(value > 0.0 for value in differences) == expected[ + "positive_pair_count" + ] + + own_rows = [row for row in trajectory_rows if row["control"] == "own"] + assert len(own_rows) == statistics["steering"]["own_trajectory_count"] # type: ignore[index] + assert sum(row["positive_slope"] == "True" for row in own_rows) == statistics[ + "steering" + ]["own_positive_slope_count"] # type: ignore[index] + assert sum( + row["positive_low_dose"] == "True" for row in own_rows + ) == statistics["steering"]["own_positive_low_dose_count"] # type: ignore[index] + assert sum( + row["strictly_monotone"] == "True" for row in own_rows + ) == statistics["steering"]["own_strictly_monotone_count"] # type: ignore[index] + assert len(reversal_rows) == statistics["steering"]["pair_count"] # type: ignore[index] + assert all( + row["relation_appropriate_reversal"] == "True" + for row in reversal_rows + ) + + return { + "pair_count": 6, + "controls": sorted(controls), + "own_trajectory_count": len(own_rows), + "positive_full_curve_slopes": sum( + row["positive_slope"] == "True" for row in own_rows + ), + "positive_low_dose_endpoints": sum( + row["positive_low_dose"] == "True" for row in own_rows + ), + "strictly_monotone_trajectories": sum( + row["strictly_monotone"] == "True" for row in own_rows + ), + } + + +def validate_readability() -> dict[str, object]: + protocol = json.loads(READABILITY_PROTOCOL.read_text()) + statistics = json.loads(READABILITY_STATISTICS.read_text()) + assert statistics["protocol_sha256"] == sha256(READABILITY_PROTOCOL) + assert statistics["study_id"] == protocol["study_id"] + assert statistics["status"] == protocol["status"] + for name, spec in protocol["inputs"].items(): + observed = sha256(ROOT / spec["path"]) + assert observed == spec["sha256"] == statistics["input_sha256"][name] + + prompt_rows = read_csv("../materials-heldout-v1_prompt_results.csv") + concept_rows = read_csv("../materials-heldout-v1_concept_results.csv") + influence_rows = read_csv("readability_family_influence.csv") + reproducibility_rows = read_csv( + "readability_leave_one_family_reproducibility.csv" + ) + families = sorted({row["family"] for row in prompt_rows}) + assert len(families) == 10 + assert len(influence_rows) == 10 + assert len(reproducibility_rows) == 30 + + family_values: dict[str, list[float]] = defaultdict(list) + for row in prompt_rows: + family_values[row["family"]].append( + finite_float(row["delta_auc_mean_seed"]) + ) + family_means = { + family: mean(family_values[family]) for family in families + } + overall = mean(family_means.values()) + assert_close(overall, statistics["all_family_mean_delta_auc"]) + for row in influence_rows: + omitted = row["omitted_family"] + loo = mean( + value for family, value in family_means.items() if family != omitted + ) + assert_close( + finite_float(row["omitted_family_mean_delta_auc"]), + family_means[omitted], + ) + assert_close( + finite_float(row["leave_one_family_out_mean_delta_auc"]), loo + ) + + rho_values: list[float] = [] + seed_pairs = { + "seed0-seed1": ("seed0", "seed1"), + "seed0-seed2": ("seed0", "seed2"), + "seed1-seed2": ("seed1", "seed2"), + } + for row in reproducibility_rows: + omitted = row["omitted_family"] + left, right = seed_pairs[row["seed_pair"]] + retained = [item for item in concept_rows if item["family"] != omitted] + observed = spearman( + [finite_float(item[f"j_rank_{left}"]) for item in retained], + [finite_float(item[f"j_rank_{right}"]) for item in retained], + ) + reported = finite_float(row["spearman_rho"]) + assert_close(observed, reported) + rho_values.append(reported) + + assert_close( + min(rho_values), + statistics["leave_one_family_out_spearman_range"][0], + ) + assert_close( + max(rho_values), + statistics["leave_one_family_out_spearman_range"][1], + ) + return { + "family_count": len(families), + "leave_one_family_out_recovery_count": len(influence_rows), + "leave_one_family_out_correlation_count": len(reproducibility_rows), + "all_leave_one_family_out_recovery_estimates_positive": all( + finite_float(row["leave_one_family_out_mean_delta_auc"]) > 0 + for row in influence_rows + ), + "minimum_leave_one_family_out_spearman": min(rho_values), + } + + +def main() -> None: + protocol = json.loads(PROTOCOL.read_text()) + statistics = json.loads(STATISTICS.read_text()) + assert protocol["study_id"] == statistics["study_id"] + assert protocol["status"] == statistics["status"] + + result = { + "study_id": protocol["study_id"], + "validation_status": "pass", + "checks": { + "hashes": validate_hashes(protocol, statistics), + "row_counts": validate_rows(), + "geometry": validate_geometry(statistics), + "steering": validate_steering(statistics), + "readability": validate_readability(), + "artifacts": { + "report_nonempty": (OUT / "REPORT.md").stat().st_size > 0, + "figure_pdf_nonempty": ( + OUT / "figures" / "core-discovery-discipline-audit.pdf" + ).stat().st_size + > 0, + "figure_png_nonempty": ( + OUT / "figures" / "core-discovery-discipline-audit.png" + ).stat().st_size + > 0, + }, + }, + } + if not all(result["checks"]["artifacts"].values()): + raise AssertionError("one or more expected artifacts are empty") + VALIDATION.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/validate_equation_free_relational_benchmark_postrun.py b/jlens_materials/scripts/validate_equation_free_relational_benchmark_postrun.py new file mode 100644 index 0000000000000000000000000000000000000000..1eb7ed83add9691eac8022056b1d8650c17390cc --- /dev/null +++ b/jlens_materials/scripts/validate_equation_free_relational_benchmark_postrun.py @@ -0,0 +1,89 @@ +#!/usr/bin/env python3 +"""Post-run integrity audit that counts off-format completions as behavior failures.""" + +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" + 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()) + 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}") + 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), "state hash mismatch") + prompt_ids = [row["prompt_id"] for row in manifest["prompts"]] + require([row["prompt_id"] for row in raw["clean_rows"]] == prompt_ids, "raw order mismatch") + with np.load(state_path, allow_pickle=False) as arrays: + require(arrays["raw_states"].shape == (960, 2560), "bad state shape") + require(arrays["prompt_ids"].astype(str).tolist() == prompt_ids, "state order mismatch") + require(int(arrays["layer"][0]) == 34, "wrong layer") + off_format = [row for row in raw["clean_rows"] if row["predicted_answer"] is None] + require(all(not row["correct"] for row in off_format), "an off-format completion was counted correct") + + statistics = json.loads((OUT / "statistics.json").read_text()) + require(statistics["analysis_sha256"] == protocol["inputs"]["analyzer"]["sha256"], "frozen analyzer provenance mismatch") + amendment = statistics.get("analysis_amendment", {}) + amendment_path = ROOT / amendment.get("script", "missing") + require(amendment_path.exists(), "missing analysis-amendment script") + require(sha256(amendment_path) == amendment.get("script_sha256"), "analysis-amendment hash mismatch") + 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) == 480 and len(laws) == 60 and len(behavior) == 960, "analysis table row-count mismatch") + + by_surface = { + surface: int(sum(row["surface"] == surface for row in off_format)) + for surface in ("a", "b") + } + output = { + "study_id": protocol["study_id"], + "status": "complete-pass-with-retained-behavioral-format-failures", + "checks": { + "frozen_input_hashes": "pass", + "raw_provenance_hashes": "pass", + "raw_rows": 960, + "state_shape": [960, 2560], + "captured_layer": 34, + "matched_pair_rows": 480, + "law_result_rows": 60, + "behavior_rows": 960, + "off_format_completions_counted_incorrect": len(off_format), + "off_format_by_surface": by_surface, + "analysis_amendment_provenance": "pass", + }, + } + (OUT / "validation.json").write_text(json.dumps(output, indent=2, sort_keys=True) + "\n") + (OUT / "off_format_completions.json").write_text(json.dumps(off_format, 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_graph_generalization_audit.py b/jlens_materials/scripts/validate_graph_generalization_audit.py new file mode 100644 index 0000000000000000000000000000000000000000..f9a90180c12d609ac8410b6bb876079c1e7ea680 --- /dev/null +++ b/jlens_materials/scripts/validate_graph_generalization_audit.py @@ -0,0 +1,224 @@ +#!/usr/bin/env python3 +"""Validate the complete graph-generalization experiment bundle.""" + +from __future__ import annotations + +import hashlib +import json +import math +from pathlib import Path + +import numpy as np +import pandas as pd + + +ROOT = Path(__file__).resolve().parents[1] +EXP = ( + ROOT + / "experiments" + / "graph-isomorphism-generalization-2026-07-18" +) +MANIFEST = ( + ROOT / "paper" / "graph_data" / "signed-relation-prompt-manifest.json" +) +STATES = ( + ROOT + / "experiments" + / "option-free-question-end-2026-07-18" + / "representations.npz" +) +EXPECTED_ROWS = { + "all_mapping_candidates.csv": 29160, + "constrained_graph_null.csv": 10000, + "exact_partition_bootstrap_summary.csv": 18, + "exact_partition_metrics.csv": 36, + "gauge_pair_agreement.csv": 45, + "gin_ensemble_node_logits.csv": 792, + "gin_family_metrics.csv": 66, + "gin_node_logits.csv": 15840, + "gin_seed_results.csv": 1320, + "global_atlas_heldout.csv": 45, + "isomorphism_pair_metrics.csv": 1170, + "isomorphism_tests.csv": 7, + "mapping_method_summary.csv": 3, + "pair_mappings.csv": 1215, + "permutation_cycles.csv": 60, + "relation_gin_ensemble_pair_logits.csv": 3888, + "relation_gin_family_metrics.csv": 54, + "relation_gin_pair_logits.csv": 77760, + "relation_gin_seed_results.csv": 1080, + "relation_nonparametric.csv": 18, + "spectral_community_layers.csv": 450, + "spectral_community_metrics.csv": 72, + "spectral_density_ablations.csv": 12, + "spectral_synthetic_controls.csv": 2000, +} +HASH_FILES = [ + MANIFEST, + STATES, + EXP / "PROTOCOL.md", + EXP / "protocol.json", + EXP / "GAUGE_PROTOCOL.md", + EXP / "gauge_protocol.json", + EXP / "SPECTRAL_PROTOCOL.md", + EXP / "spectral_protocol.json", + EXP / "PARTITION_PROTOCOL.md", + EXP / "partition_protocol.json", + EXP / "REPORT.md", + EXP / "ARTIFACT_INVENTORY.md", + EXP / "study12_statistics.json", + EXP / "study3_gin_statistics.json", + EXP / "study4abc_statistics.json", + EXP / "study4c_relation_gin_statistics.json", + EXP / "study5_spectral_statistics.json", + EXP / "study6_partition_statistics.json", +] + + +def sha256(path: Path) -> str: + return hashlib.sha256(path.read_bytes()).hexdigest() + + +def assert_finite_frame(frame: pd.DataFrame, name: str) -> None: + numeric = frame.select_dtypes(include=[np.number]) + if numeric.size and not np.isfinite(numeric.to_numpy()).all(): + raise AssertionError(f"non-finite numeric value in {name}") + + +def relation_identity() -> dict[str, str]: + prompts = json.loads(MANIFEST.read_text())["prompts"] + result = {} + for family in sorted({row["family_id"] for row in prompts}): + rows = [row for row in prompts if row["family_id"] == family] + physical = np.asarray( + [ + row["expected_outcome"] == row["outcome_positive"] + for row in rows + ], + dtype=bool, + ) + numeric = np.asarray( + [row["numeric_direction"] == "increase" for row in rows], + dtype=bool, + ) + if np.all(physical == numeric): + result[family] = "direct" + elif np.all(physical == ~numeric): + result[family] = "inverse" + else: + raise AssertionError( + f"physical/numeric factorization fails for {family}" + ) + for first in range(len(rows)): + for second in range(len(rows)): + if (physical[first] == physical[second]) != ( + numeric[first] == numeric[second] + ): + raise AssertionError( + f"relation identity fails for {family}" + ) + return result + + +def validate_seed_grid( + path: Path, configurations: int, expected_rows: int +) -> None: + frame = pd.read_csv(path) + if len(frame) != expected_rows: + raise AssertionError(f"unexpected rows in {path.name}") + counts = frame.groupby( + ["configuration", "heldout_family"] + ).size() + if len(counts) != configurations * 6 or not np.all( + counts.to_numpy() == 20 + ): + raise AssertionError(f"incomplete seed grid in {path.name}") + if sorted(frame["seed_index"].unique()) != list(range(20)): + raise AssertionError(f"unexpected seeds in {path.name}") + + +def main() -> None: + if len(json.loads(MANIFEST.read_text())["prompts"]) != 72: + raise AssertionError("prompt manifest does not contain 72 prompts") + with np.load(STATES, allow_pickle=False) as archive: + if archive["prompt_ids"].shape != (72,): + raise AssertionError("representation prompt alignment mismatch") + if archive["layers"].shape != (25,): + raise AssertionError("representation layer count mismatch") + for key in ( + "raw_states", + "direct_decoder_basis", + "jacobian_decoder_basis", + ): + if not np.isfinite(archive[key]).all(): + raise AssertionError(f"non-finite archived state: {key}") + + row_counts = {} + for name, expected in EXPECTED_ROWS.items(): + path = EXP / name + frame = pd.read_csv(path) + row_counts[name] = len(frame) + if len(frame) != expected: + raise AssertionError( + f"{name}: {len(frame)} rows, expected {expected}" + ) + assert_finite_frame(frame, name) + + validate_seed_grid( + EXP / "gin_seed_results.csv", configurations=11, expected_rows=1320 + ) + validate_seed_grid( + EXP / "relation_gin_seed_results.csv", + configurations=9, + expected_rows=1080, + ) + + for name in ( + "graph-generalization-model-tests.png", + "graph-generalization-model-tests.pdf", + "graph-generalization-partition-tests.png", + "graph-generalization-partition-tests.pdf", + "graph-generalization-network-examples.png", + "graph-generalization-network-examples.pdf", + "graph-identifiability-summary.png", + "graph-identifiability-summary.pdf", + ): + path = EXP / "figures" / name + if not path.exists() or path.stat().st_size <= 1000: + raise AssertionError(f"missing or empty figure: {name}") + + for name in ( + "study12_statistics.json", + "study3_gin_statistics.json", + "study4abc_statistics.json", + "study4c_relation_gin_statistics.json", + "study5_spectral_statistics.json", + "study6_partition_statistics.json", + ): + value = json.loads((EXP / name).read_text()) + if not value.get("study_id"): + raise AssertionError(f"missing study ID: {name}") + + orientations = relation_identity() + hashes = { + str(path.relative_to(ROOT)): sha256(path) for path in HASH_FILES + } + report = { + "status": "pass", + "prompt_count": 72, + "layer_count": 25, + "mechanism_count": 6, + "absolute_gin_runs": 1320, + "relation_gin_runs": 1080, + "row_counts": row_counts, + "mechanism_orientations": orientations, + "physical_numeric_relation_identity": True, + "sha256": hashes, + } + output = EXP / "VALIDATION.json" + output.write_text(json.dumps(report, indent=2) + "\n") + print(json.dumps(report, indent=2)) + + +if __name__ == "__main__": + main() diff --git a/jlens_materials/scripts/validate_materials_heldout_v1.py b/jlens_materials/scripts/validate_materials_heldout_v1.py new file mode 100644 index 0000000000000000000000000000000000000000..8c99d1569328bf5639b038c86c99cad61b32d5b5 --- /dev/null +++ b/jlens_materials/scripts/validate_materials_heldout_v1.py @@ -0,0 +1,175 @@ +#!/usr/bin/env python3 +"""Pre-execution validation for the frozen materials held-out v1 suite.""" + +from __future__ import annotations + +import argparse +import hashlib +import json +import re +from collections import Counter +from pathlib import Path + +from transformers import AutoTokenizer + + +ROOT = Path(__file__).resolve().parents[1] + + +def sha256(path: Path) -> str: + return hashlib.sha256(path.read_bytes()).hexdigest() + + +def words(text: str) -> list[str]: + return re.findall(r"[a-z0-9]+", text.lower()) + + +def ngrams(text: str, n: int = 5) -> set[tuple[str, ...]]: + tokens = words(text) + return {tuple(tokens[i:i + n]) for i in range(max(0, len(tokens) - n + 1))} + + +def max_ngram_jaccard(text: str, comparison: list[str], n: int = 5) -> float: + left = ngrams(text, n) + if not left: + return 0.0 + best = 0.0 + for candidate in comparison: + right = ngrams(candidate, n) + union = left | right + best = max(best, len(left & right) / len(union) if union else 0.0) + return best + + +def main() -> None: + parser = argparse.ArgumentParser() + parser.add_argument( + "--manifest", + default="prompts/materials-heldout-v1-preregistered.json", + ) + parser.add_argument( + "--development-manifest", + default="prompts/materials-paper-v2-preregistered.json", + ) + parser.add_argument("--model", default="google/gemma-4-E4B-it") + parser.add_argument( + "--revision", + default="a4c2d58be94dda072b918d9db64ee85c8ed34e3f", + ) + parser.add_argument( + "--output", + default="experiments/materials-heldout-v1-preflight.json", + ) + parser.add_argument( + "--allow-download", + action="store_true", + help="allow tokenizer files to be fetched instead of requiring the local cache", + ) + args = parser.parse_args() + + manifest_path = ROOT / args.manifest + development_path = ROOT / args.development_manifest + output_path = ROOT / args.output + manifest = json.loads(manifest_path.read_text()) + development = json.loads(development_path.read_text()) + prompts = manifest["prompts"] + old_prompts = development["prompts"] + + tokenizer = AutoTokenizer.from_pretrained( + args.model, + revision=args.revision, + local_files_only=not args.allow_download, + ) + + slugs = [row["slug"] for row in prompts] + counts = Counter(row["target_family"] for row in prompts) + expected_families = set(counts) + structural_errors: list[str] = [] + if len(prompts) != 50: + structural_errors.append(f"expected 50 prompts, found {len(prompts)}") + if len(set(slugs)) != len(slugs): + structural_errors.append("prompt slugs are not unique") + if len(counts) != 10 or any(value != 5 for value in counts.values()): + structural_errors.append(f"expected 10 families x 5 prompts, found {dict(counts)}") + + input_leaks: list[dict] = [] + dropped_terms: list[dict] = [] + invalid_fields: list[dict] = [] + for row in prompts: + if row.get("shape") != "ASSOCIATION" or row.get("protocol") != "lens_eval": + invalid_fields.append({"slug": row["slug"], "reason": "shape/protocol"}) + if row.get("readout_selector") != "final_prompt_token": + invalid_fields.append({"slug": row["slug"], "reason": "readout selector"}) + prompt_ids = set(tokenizer.encode(row["text"], add_special_tokens=True)) + for term in row["tracked"]: + token_ids: set[int] = set() + for surface in (f" {term}", term, f" {term.capitalize()}", term.capitalize()): + encoded = tokenizer.encode(surface, add_special_tokens=False) + if len(encoded) == 1: + token_ids.add(int(encoded[0])) + if not token_ids: + dropped_terms.append({"slug": row["slug"], "term": term}) + overlap = sorted(prompt_ids & token_ids) + if overlap: + input_leaks.append({ + "slug": row["slug"], + "term": term, + "token_ids": overlap, + }) + + old_association_prompts = [ + row for row in old_prompts + if row.get("shape") == "ASSOCIATION" and row.get("text") + ] + old_texts = [row["text"] for row in old_association_prompts] + exact_duplicates = sorted( + row["slug"] for row in prompts if row["text"] in set(old_texts) + ) + maximum_jaccards = [max_ngram_jaccard(row["text"], old_texts) for row in prompts] + old_families = { + row.get("target_family") or row.get("category") + for row in old_association_prompts + } + + report = { + "status": "pass" if not ( + structural_errors or invalid_fields or input_leaks or exact_duplicates + ) else "fail", + "executed_before_heldout_lens_outputs": True, + "manifest": str(manifest_path.relative_to(ROOT)), + "manifest_sha256": sha256(manifest_path), + "generator_sha256": sha256(ROOT / "scripts/generate_materials_heldout_v1.py"), + "model": args.model, + "model_revision": args.revision, + "tokenizer_class": type(tokenizer).__name__, + "prompt_count": len(prompts), + "unique_slug_count": len(set(slugs)), + "family_counts": dict(sorted(counts.items())), + "same_family_set_as_development": expected_families == old_families, + "structural_errors": structural_errors, + "invalid_fields": invalid_fields, + "tokenizer_resolved_input_leaks": input_leaks, + "unresolved_single_token_terms": dropped_terms, + "exact_development_prompt_duplicates": exact_duplicates, + "development_overlap": { + "metric": "maximum pairwise Jaccard similarity of lowercase word 5-gram sets", + "mean": sum(maximum_jaccards) / len(maximum_jaccards), + "maximum": max(maximum_jaccards), + "per_prompt": dict(zip(slugs, maximum_jaccards, strict=True)), + }, + } + output_path.write_text(json.dumps(report, indent=2) + "\n") + print(json.dumps({key: value for key, value in report.items() + if key != "development_overlap"}, indent=2)) + print( + "development 5-gram overlap: " + f"mean={report['development_overlap']['mean']:.6f}, " + f"max={report['development_overlap']['maximum']:.6f}" + ) + print(f"wrote {output_path}") + if report["status"] != "pass": + raise SystemExit("held-out preflight validation failed") + + +if __name__ == "__main__": + main() diff --git a/jlens_materials/scripts/validate_robustness_bundle.py b/jlens_materials/scripts/validate_robustness_bundle.py new file mode 100644 index 0000000000000000000000000000000000000000..d81faa2c1ec5e32ee9a999f4b64602beb6fa01d0 --- /dev/null +++ b/jlens_materials/scripts/validate_robustness_bundle.py @@ -0,0 +1,363 @@ +#!/usr/bin/env python3 +"""Validate the July 2026 review-response experiment bundle. + +The script is intentionally read-only except for its JSON validation report. +It checks protocol fingerprints, prompt completeness, scaffold exclusion, +array dimensions, table cardinalities, and frozen numerical endpoints. +""" + +from __future__ import annotations + +import csv +import hashlib +import json +from collections import Counter +from pathlib import Path +from typing import Any + +import numpy as np + + +ROOT = Path(__file__).resolve().parents[1] +AUDIT = ROOT / "experiments" / "review-robustness-audit-2026-07-18" +REPORT = AUDIT / "validation.json" + + +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 read_json(relative: str) -> dict[str, Any]: + return json.loads((ROOT / relative).read_text()) + + +def csv_rows(relative: str) -> list[dict[str, str]]: + with (ROOT / relative).open(newline="") as handle: + return list(csv.DictReader(handle)) + + +def close(actual: float, expected: float, tolerance: float = 1e-12) -> bool: + return abs(actual - expected) <= tolerance + + +def main() -> None: + checks: list[dict[str, Any]] = [] + + def check(name: str, passed: bool, **details: Any) -> None: + checks.append({"name": name, "passed": bool(passed), **details}) + + study_dirs = { + "checkpoint": "experiments/option-free-relation-graph-2026-07-17", + "question_end": "experiments/option-free-question-end-2026-07-18", + "cross_mechanism": "experiments/cross-mechanism-outcome-2026-07-18", + "multitoken": ( + "experiments/multitoken-sequence-robustness-2026-07-18" + ), + "cross_mechanism_patching": ( + "experiments/cross-mechanism-activation-patching-2026-07-18" + ), + } + protocols = { + name: read_json(f"{directory}/protocol.json") + for name, directory in study_dirs.items() + } + statistics = { + name: read_json(f"{directory}/statistics.json") + for name, directory in study_dirs.items() + } + + for name, directory in study_dirs.items(): + protocol_path = ROOT / directory / "protocol.json" + actual = sha256(protocol_path) + recorded = statistics[name]["protocol_sha256"] + check( + f"{name}: protocol fingerprint", + actual == recorded, + actual=actual, + recorded=recorded, + ) + + manifest_spec = protocols["question_end"]["inputs"]["prompt_manifest"] + manifest_path = ROOT / manifest_spec["path"] + check( + "source manifest fingerprint", + sha256(manifest_path) == manifest_spec["sha256"], + actual=sha256(manifest_path), + recorded=manifest_spec["sha256"], + ) + manifest = json.loads(manifest_path.read_text()) + prompts = manifest["prompts"] + check("source prompt count", len(prompts) == 72, observed=len(prompts)) + family_counts = Counter(row["family_id"] for row in prompts) + variant_counts = Counter(row["variant"] for row in prompts) + outcome_counts = Counter(row["expected_outcome"] for row in prompts) + check( + "balanced family inventory", + len(family_counts) == 6 and set(family_counts.values()) == {12}, + counts=dict(sorted(family_counts.items())), + ) + check( + "balanced surface variants", + variant_counts + == Counter( + { + "anchor": 24, + "physics_paraphrase": 24, + "lexical_counterfactual": 24, + } + ), + counts=dict(sorted(variant_counts.items())), + ) + check( + "balanced physical outcomes", + outcome_counts == Counter({"higher": 36, "lower": 36}) + or outcome_counts == Counter( + {"higher": 18, "lower": 18, "greater": 18, "smaller": 18} + ), + counts=dict(sorted(outcome_counts.items())), + ) + forbidden = ( + "answer exactly", + "output a", + "output b", + "internal checkpoint", + "if the scientific answer", + ) + scaffold_leaks = [ + row["prompt_id"] + for row in prompts + if any(marker in row["stem"].lower() for marker in forbidden) + ] + check( + "natural question stems contain no answer scaffold", + not scaffold_leaks, + leaking_prompt_ids=scaffold_leaks, + ) + + question_states_path = ( + ROOT + / protocols["cross_mechanism"]["inputs"]["representations"]["path"] + ) + recorded_states_hash = protocols["cross_mechanism"]["inputs"][ + "representations" + ]["sha256"] + check( + "natural question-end state fingerprint", + sha256(question_states_path) == recorded_states_hash, + actual=sha256(question_states_path), + recorded=recorded_states_hash, + ) + with np.load(question_states_path) as arrays: + shapes = {key: list(arrays[key].shape) for key in arrays.files} + expected_shapes = { + "prompt_ids": [72], + "positions": [1], + "layers": [25], + "raw_states": [1, 72, 25, 2560], + "direct_decoder_basis": [1, 72, 25, 2560], + "jacobian_decoder_basis": [3, 1, 72, 25, 2560], + } + check( + "natural question-end array dimensions", + shapes == expected_shapes, + observed=shapes, + expected=expected_shapes, + ) + + checkpoint = statistics["checkpoint"] + check( + "checkpoint frozen endpoint", + checkpoint["frozen_verdict"]["option_free_evidence"] == "none" + and close(checkpoint["primary"]["graph_precision"], 0.5208333333333334) + and close(checkpoint["primary"]["candidate_auc"], 0.4861111111111111), + observed=checkpoint["primary"], + ) + question = statistics["question_end"] + check( + "natural question-end frozen endpoint", + question["frozen_verdict"]["option_free_evidence"] == "strong" + and close(question["primary"]["graph_precision"], 0.6736111111111112) + and close(question["primary"]["candidate_auc"], 0.6423611111111112), + observed=question["primary"], + ) + + rankings = csv_rows( + "experiments/cross-mechanism-outcome-2026-07-18/" + "all_query_rankings.csv" + ) + method_counts = Counter(row["method"] for row in rankings) + counter_counts = Counter( + row["method"] + for row in rankings + if row["opposite_response_orientation"] == "True" + ) + check( + "cross-mechanism query cardinality", + len(method_counts) == 10 + and set(method_counts.values()) == {1080} + and set(counter_counts.values()) == {648}, + all_queries_per_method=dict(sorted(method_counts.items())), + counter_numeric_queries_per_method=dict(sorted(counter_counts.items())), + ) + cross = statistics["cross_mechanism"] + check( + "cross-mechanism frozen endpoint", + cross["frozen_verdict"]["cross_mechanism_evidence"] == "none" + and close(cross["primary"]["overall_auc"], 0.513425925925926) + and close(cross["primary"]["counter_numeric_auc"], 0.470679012345679), + observed=cross["primary"], + ) + + sequence_rows = csv_rows( + "experiments/multitoken-sequence-robustness-2026-07-18/" + "layer_sequence_scores.csv" + ) + sequence_counts = Counter(row["method"] for row in sequence_rows) + check( + "multi-token layer-score cardinality", + len(sequence_counts) == 5 and set(sequence_counts.values()) == {250}, + rows_per_method=dict(sorted(sequence_counts.items())), + ) + multitoken = statistics["multitoken"] + current_runner = ROOT / "scripts/run_multitoken_sequence_robustness.py" + amendment_path = ROOT / multitoken["protocol_amendment"] + check( + "multi-token amended runner fingerprint", + sha256(current_runner) == multitoken["runner_sha256"], + actual=sha256(current_runner), + recorded=multitoken["runner_sha256"], + ) + check( + "multi-token amendment fingerprint", + sha256(amendment_path) == multitoken["protocol_amendment_sha256"], + actual=sha256(amendment_path), + recorded=multitoken["protocol_amendment_sha256"], + ) + check( + "multi-token frozen endpoint", + multitoken["frozen_verdict"]["sequence_robustness"] == "fail" + and multitoken["frozen_verdict"]["positive_jacobian_prompts"] == 7 + and multitoken["frozen_verdict"]["required_positive_prompts"] == 8, + observed=multitoken["frozen_verdict"], + ) + + patching = statistics["cross_mechanism_patching"] + patching_rows = csv_rows( + "experiments/cross-mechanism-activation-patching-2026-07-18/" + "all_patch_rows.csv" + ) + check( + "cross-mechanism patch cardinality", + len(patching_rows) == 1920 + and len({row["receiver_prompt_id"] for row in patching_rows}) == 24 + and len({row["donor_prompt_id"] for row in patching_rows}) == 24 + and { + int(row["layer"]) for row in patching_rows + } + == {16, 24, 32, 37}, + n_rows=len(patching_rows), + ) + check( + "cross-mechanism patch frozen endpoint", + patching["frozen_verdict"][ + "option_free_cross_mechanism_transfer" + ] + == "partial" + and patching["frozen_verdict"]["positive_donor_families"] == 4 + and close(patching["primary"]["all"]["mean"], 0.384951780239741) + and close( + patching["structured_donor_label_nulls"]["all"][ + "exact_two_sided_p" + ], + 0.031935871056241426, + ), + observed=patching["frozen_verdict"], + ) + patch_audit = read_json( + "experiments/cross-mechanism-activation-patching-2026-07-18/" + "validation.json" + ) + check( + "independent cross-mechanism patch audit", + patch_audit["passed"] + and patch_audit["n_checks"] == 27 + and patch_audit["n_passed"] == 27, + observed={ + "passed": patch_audit["passed"], + "n_checks": patch_audit["n_checks"], + "n_passed": patch_audit["n_passed"], + }, + ) + with np.load( + ROOT + / "experiments" + / "cross-mechanism-activation-patching-2026-07-18" + / "primary_exact_donor_label_nulls.npz" + ) as nulls: + check( + "structured donor-label null cardinality", + all(len(nulls[name]) == 46656 for name in ( + "all", + "cross_vocabulary", + "opposite_orientation", + "both", + )) + and nulls["assignment_indices"].shape == (46656, 6), + assignment_shape=list(nulls["assignment_indices"].shape), + ) + + artifact_paths = [ + "experiments/option-free-relation-graph-2026-07-17/" + "prompt_inventory.csv", + "experiments/option-free-question-end-2026-07-18/" + "representations.npz", + "experiments/option-free-question-end-2026-07-18/" + "all_candidate_rankings.csv", + "experiments/cross-mechanism-outcome-2026-07-18/" + "all_query_rankings.csv", + "experiments/cross-mechanism-outcome-2026-07-18/" + "exact_orientation_nulls.json", + "experiments/multitoken-sequence-robustness-2026-07-18/" + "layer_sequence_scores.csv", + "experiments/multitoken-sequence-robustness-2026-07-18/raw.json", + "experiments/cross-mechanism-activation-patching-2026-07-18/" + "raw.json", + "experiments/cross-mechanism-activation-patching-2026-07-18/" + "all_patch_rows.csv", + "experiments/cross-mechanism-activation-patching-2026-07-18/" + "primary_exact_donor_label_nulls.npz", + "experiments/cross-mechanism-activation-patching-2026-07-18/" + "validation.json", + ] + artifact_hashes = { + relative: sha256(ROOT / relative) for relative in artifact_paths + } + + passed = all(row["passed"] for row in checks) + report = { + "study_id": "review-robustness-audit-2026-07-18", + "passed": passed, + "n_checks": len(checks), + "n_passed": sum(row["passed"] for row in checks), + "checks": checks, + "artifact_sha256": artifact_hashes, + "guardrail": ( + "This validation confirms archive integrity and registered " + "calculation outputs. It is not a new inferential analysis." + ), + } + AUDIT.mkdir(parents=True, exist_ok=True) + REPORT.write_text(json.dumps(report, indent=2) + "\n") + if not passed: + failed = [row["name"] for row in checks if not row["passed"]] + raise SystemExit("validation failed: " + ", ".join(failed)) + print(f"validated {report['n_passed']}/{report['n_checks']} checks") + print(REPORT.relative_to(ROOT)) + + +if __name__ == "__main__": + main()