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235c71ce064d329e69884986d1397f626a3f9e14..467c3ed24b89aab4b513bb24810ef3148169c85f 100644 --- a/RELEASE_METADATA.json +++ b/RELEASE_METADATA.json @@ -2,12 +2,17 @@ "contains_benchmark_material": true, "contains_candidate_text": true, "contains_rationales": true, - "created_utc": "2026-07-15T13:58:11+00:00", + "created_utc": "2026-08-24T09:39:37+00:00", "dataset_repository": "Biogod/mas_reasoning", "excluded_file_count": 2, "expected_repository_visibility": "private", - "file_count": 1407, - "payload_bytes": 12816725415, + "file_count": 1568, + "medical_mas_increment": { + "base_revision": "963debcaed4fc46cc7de50f0507ecb296d404a79", + "remote_prefix": "data/external/results/formal/early_mas_architecture_motivation", + "uploaded_payload_files": 170 + }, + "payload_bytes": 12953259548, "release_scope": "full_private", "source_symlink_count": 242 } diff --git a/data/external/results/formal/early_mas_architecture_motivation/README.md b/data/external/results/formal/early_mas_architecture_motivation/README.md index c9e2f8247af7f6397974a01dd854c6b528eb59eb..9becf376f8d8cba23f740845001797fdcbaa58f9 100644 --- a/data/external/results/formal/early_mas_architecture_motivation/README.md +++ b/data/external/results/formal/early_mas_architecture_motivation/README.md @@ -7,21 +7,57 @@ from black-box communication architectures to a measurable selection-bottleneck ## Scope - `med-agent` topology experiments compare star, complete, ring and hierarchical communication topologies on MedXpertQA-style medical diagnosis tasks. -- `med_board` protocol experiments compare single-agent, majority vote, open committee and anonymous evidence board protocols, plus mechanism variants. +- `med_board` protocol experiments compare single-agent, majority vote, open committee and anonymous evidence board protocols. - The central interpretation is historical motivation: more communication and richer architectures produced modest gains at substantial token cost, while oracle readouts showed that correct traces were often already present but not reliably selected. +## Reproduction + +The formal analysis runs only from repository-local, question-text-free inputs: + +```bash +python scripts/run_analysis.py --config configs/analyses/early_mas_architecture_motivation.yaml +``` + +The one-time source freeze and analysis-ready import require the protected archive root explicitly: + +```bash +MEDICAL_MAS_SOURCE_ROOT=/path/to/med_repos_20260604 \ + python scripts/run_analysis.py --config configs/analyses/medical_mas_source_inventory.yaml +MEDICAL_MAS_SOURCE_ROOT=/path/to/med_repos_20260604 \ + python scripts/run_analysis.py --config configs/analyses/medical_mas_analysis_ready_import.yaml +MEDICAL_MAS_SOURCE_ROOT=/path/to/med_repos_20260604 \ + python scripts/run_analysis.py --config configs/analyses/medical_mas_code_snapshot.yaml +MEDICAL_MAS_SOURCE_ROOT=/path/to/med_repos_20260604 \ + python scripts/run_analysis.py --config configs/analyses/medical_mas_private_release.yaml +``` + +No source path is hard-coded in the implementation or formal outputs. + ## Generated Files - `summaries/med_agent_topology_summary.csv`: topology-level accuracy, round and token summaries for baseline, tuned and fixed-six-round runs. - `summaries/med_agent_consensus_bypass_summary.csv`: one-round exits and weighted-majority behavior in the topology experiments. - `summaries/med_agent_round_exit_distribution.csv`: exact stopping-round counts and shares by topology and stopping condition. - `summaries/med_board_protocol_cost_accuracy_summary.csv`: full 3-seed protocol accuracy and token-cost summary. +- `summaries/med_board_protocol_bootstrap_ci.csv`: paired question-cluster confidence intervals for the two manuscript comparisons. - `summaries/med_board_benchmark_discussion_controls.csv`: MMLU-Pro and GPQA discussion-vs-majority paired controls. - `summaries/med_board_oracle_gap_summary.csv`: round-0 oracle versus final accuracy decomposition. - `summaries/med_board_judge_behavior_summary.csv`: follow-majority/follow-minority behavior, including majority-wrong/minority-correct cases. - `summaries/med_board_mechanism_pilot_summary.csv`: mechanism-pilot condition summaries. - `figure_sources/extended_data_fig1_source.csv`: direct source table used by Extended Data Fig. 1. -- `audits/provenance_and_qa.md`: source paths, row counts and interpretation limits. +- `analysis_ready/`: compact trial tables and execution configs needed for numerical reproduction; it contains no benchmark question text or raw model rationales. +- `manifests/analysis_ready_manifest.csv`: source and destination hashes for every imported or normalized analysis input. +- `manifests/source_payload_inventory.csv.gz`: hashes and release paths for the full private raw payload. +- `manifests/code_snapshot_manifest.csv`: source revisions, source/output hashes and transformations for the sanitized historical code snapshot. +- `manifests/private_release_shards.csv`: deterministic private-release shard hashes, member counts and compressed sizes. +- `code_snapshot/`: the sanitized `med-agent` reconstruction and byte-identical `med_board` Git snapshots. +- `audits/provenance_and_qa.md`: row counts, headline checks and interpretation limits. +- `audits/code_security_gate.md`: the passed payload and code-snapshot security gates. +- `audits/private_release_verification.json`: complete member-hash and repeat-build verification for the Git-ignored private payload. + +The raw release shards are written to the Git-ignored `results/tmp/medical_mas_private_release/` directory. They must not be committed to this repository. + +The cross-benchmark discussion-control and mechanism-pilot summaries are retained as secondary historical artifacts. They are not inputs to the manuscript-facing analysis and do not expand the private raw-data migration scope. ## Interpretation Boundary diff --git a/data/external/results/formal/early_mas_architecture_motivation/analysis_ready/configs/med_board/real_llm_full_seed42.yaml b/data/external/results/formal/early_mas_architecture_motivation/analysis_ready/configs/med_board/real_llm_full_seed42.yaml new file mode 100644 index 0000000000000000000000000000000000000000..fbf8942f2a3fe5a3c7e17539e6007d76f5adaa1b --- /dev/null +++ b/data/external/results/formal/early_mas_architecture_motivation/analysis_ready/configs/med_board/real_llm_full_seed42.yaml @@ -0,0 +1,33 @@ +experiment: + name: real_llm_full_seed42 + n_cases: 2450 + seed: 42 +protocol: + name: single_agent + protocols: + - single_agent + - majority_vote + - open_committee + - anonymous_evidence_board + n_agents: 5 + max_rounds: 2 + visibility_mode: answer_rationale + anonymous: false + answer_masking: false + use_devil_advocate: false +model: + provider: openai + model: openai/deepseek-v4-flash-free + api_base: http://127.0.0.1:8000/v1 + api_key_env: MYAPI_DUMMY_KEY + temperature: 0.0 + max_tokens: 60000 + json_mode: true +runtime: + mock_llm: false + concurrency: 200 +data: + source: medxpertqa_jsonl + path: data/medxpertqa_text_test.jsonl +output: + root_dir: runs diff --git a/data/external/results/formal/early_mas_architecture_motivation/analysis_ready/configs/med_board/real_llm_full_seed43.yaml b/data/external/results/formal/early_mas_architecture_motivation/analysis_ready/configs/med_board/real_llm_full_seed43.yaml new file mode 100644 index 0000000000000000000000000000000000000000..27f00c48cd37e150691e4bd3c33bf829c0d8f227 --- /dev/null +++ b/data/external/results/formal/early_mas_architecture_motivation/analysis_ready/configs/med_board/real_llm_full_seed43.yaml @@ -0,0 +1,33 @@ +experiment: + name: real_llm_full_seed43 + n_cases: 2450 + seed: 43 +protocol: + name: single_agent + protocols: + - single_agent + - majority_vote + - open_committee + - anonymous_evidence_board + n_agents: 5 + max_rounds: 2 + visibility_mode: answer_rationale + anonymous: false + answer_masking: false + use_devil_advocate: false +model: + provider: openai + model: openai/deepseek-v4-flash-free + api_base: http://127.0.0.1:8000/v1 + api_key_env: MYAPI_DUMMY_KEY + temperature: 0.0 + max_tokens: 60000 + json_mode: true +runtime: + mock_llm: false + concurrency: 200 +data: + source: medxpertqa_jsonl + path: data/medxpertqa_text_test.jsonl +output: + root_dir: runs diff --git a/data/external/results/formal/early_mas_architecture_motivation/analysis_ready/configs/med_board/real_llm_full_seed44.yaml b/data/external/results/formal/early_mas_architecture_motivation/analysis_ready/configs/med_board/real_llm_full_seed44.yaml new file mode 100644 index 0000000000000000000000000000000000000000..487edbf41d3f4e0e38fcf13c2378ed1f52808f70 --- /dev/null +++ b/data/external/results/formal/early_mas_architecture_motivation/analysis_ready/configs/med_board/real_llm_full_seed44.yaml @@ -0,0 +1,33 @@ +experiment: + name: real_llm_full_seed44 + n_cases: 2450 + seed: 44 +protocol: + name: single_agent + protocols: + - single_agent + - majority_vote + - open_committee + - anonymous_evidence_board + n_agents: 5 + max_rounds: 2 + visibility_mode: answer_rationale + anonymous: false + answer_masking: false + use_devil_advocate: false +model: + provider: openai + model: openai/deepseek-v4-flash-free + api_base: http://127.0.0.1:8000/v1 + api_key_env: MYAPI_DUMMY_KEY + temperature: 0.0 + max_tokens: 60000 + json_mode: true +runtime: + mock_llm: false + concurrency: 200 +data: + source: medxpertqa_jsonl + path: data/medxpertqa_text_test.jsonl +output: + root_dir: runs diff --git a/data/external/results/formal/early_mas_architecture_motivation/analysis_ready/med_agent/baseline/all_results.csv 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+Text-34,complete,42,G,G,1.0,6,not_converged,244786,1.0, +Text-34,complete,123,G,G,1.0,6,not_converged,238920,1.0, +Text-34,complete,256,G,G,1.0,6,not_converged,226817,1.0, +Text-37,complete,42,G,I,0.0,6,not_converged,315683,0.99, +Text-37,complete,123,G,I,0.0,6,not_converged,270114,0.9960000000000001, +Text-37,complete,256,G,I,0.0,6,not_converged,293415,0.9800000000000001, +Text-44,complete,42,G,B,0.0,6,not_converged,420736,0.938, +Text-44,complete,123,G,B,0.0,6,not_converged,351676,0.95, +Text-44,complete,256,G,B,0.0,6,not_converged,424887,0.8699999999999999, +Text-48,complete,42,D,D,1.0,6,not_converged,250342,0.99, +Text-48,complete,123,D,D,1.0,6,not_converged,271734,0.974, +Text-48,complete,256,D,D,1.0,6,not_converged,246606,0.95, +Text-49,complete,42,G,G,1.0,6,not_converged,213462,1.0, +Text-49,complete,123,G,G,1.0,6,not_converged,202731,1.0, +Text-49,complete,256,G,G,1.0,6,not_converged,191760,1.0, +Text-51,complete,42,F,F,1.0,6,not_converged,222070,0.99, +Text-51,complete,123,F,F,1.0,6,not_converged,240143,0.9559999999999998, +Text-51,complete,256,F,F,1.0,6,not_converged,245883,1.0, +Text-53,complete,42,E,B,0.0,6,not_converged,192143,0.986, +Text-53,complete,123,E,E,1.0,6,not_converged,218142,0.9879999999999999, +Text-53,complete,256,E,E,1.0,6,not_converged,206303,0.99, +Text-56,complete,42,B,B,1.0,6,not_converged,205486,0.9879999999999999, +Text-56,complete,123,B,B,1.0,6,not_converged,184789,0.998, +Text-56,complete,256,B,B,1.0,6,not_converged,198589,0.992, +Text-58,complete,42,A,I,0.0,6,not_converged,230717,1.0, +Text-58,complete,123,A,I,0.0,6,not_converged,316131,0.962, +Text-58,complete,256,A,I,0.0,6,not_converged,267534,0.95, +Text-63,complete,42,F,F,1.0,6,not_converged,251742,0.994, +Text-63,complete,123,F,F,1.0,6,not_converged,267736,0.9879999999999999, +Text-63,complete,256,F,F,1.0,6,not_converged,307148,0.986, +Text-64,complete,42,J,J,1.0,6,not_converged,208406,1.0, +Text-64,complete,123,J,J,1.0,6,not_converged,204982,0.998, +Text-64,complete,256,J,J,1.0,6,not_converged,192032,1.0, +Text-65,complete,42,C,A,0.0,6,not_converged,196974,0.974, +Text-65,complete,123,C,A,0.0,6,not_converged,226719,0.95, +Text-65,complete,256,C,I,0.0,6,not_converged,227468,0.924, +Text-67,complete,42,A,A,1.0,6,not_converged,193992,0.9879999999999999, +Text-67,complete,123,A,A,1.0,6,not_converged,226073,0.986, +Text-67,complete,256,A,A,1.0,6,not_converged,180870,0.9879999999999999, +Text-73,complete,42,B,B,1.0,6,not_converged,248083,0.95, +Text-73,complete,123,B,B,1.0,6,not_converged,246601,0.954, +Text-73,complete,256,B,D,0.0,6,not_converged,201515,0.998, +Text-78,complete,42,H,G,0.0,6,not_converged,246919,0.95, +Text-78,complete,123,H,G,0.0,6,not_converged,227960,0.99, +Text-78,complete,256,H,G,0.0,6,not_converged,243376,0.9800000000000001, +Text-80,complete,42,I,E,0.0,6,not_converged,268860,0.78, +Text-80,complete,123,I,I,1.0,6,not_converged,247571,0.9879999999999999, +Text-80,complete,256,I,I,1.0,6,not_converged,210255,0.966, +Text-81,complete,42,F,I,0.0,6,not_converged,190863,0.992, +Text-81,complete,123,F,I,0.0,6,not_converged,224408,1.0, +Text-81,complete,256,F,I,0.0,6,not_converged,195829,1.0, +Text-83,complete,42,I,C,0.0,6,not_converged,387697,0.982, +Text-83,complete,123,I,C,0.0,6,not_converged,446632,0.986, +Text-83,complete,256,I,C,0.0,6,not_converged,449087,0.986, +Text-84,complete,42,G,C,0.0,6,not_converged,324856,0.97, +Text-84,complete,123,G,C,0.0,6,not_converged,274856,0.966, +Text-84,complete,256,G,F,0.0,6,not_converged,348180,0.95, +Text-86,complete,42,A,A,1.0,6,not_converged,245104,1.0, +Text-86,complete,123,A,A,1.0,6,not_converged,241076,0.994, +Text-86,complete,256,A,A,1.0,6,not_converged,232577,0.966, +Text-88,complete,42,C,C,1.0,6,not_converged,169043,0.99, +Text-88,complete,123,C,C,1.0,6,not_converged,165506,0.95, +Text-88,complete,256,C,C,1.0,6,not_converged,165778,0.95, +Text-92,complete,42,G,H,0.0,6,not_converged,274311,0.976, +Text-92,complete,123,G,H,0.0,6,not_converged,276100,0.9879999999999999, +Text-92,complete,256,G,H,0.0,6,not_converged,255068,0.984, +Text-93,complete,42,D,B,0.0,6,not_converged,183280,1.0, +Text-93,complete,123,D,B,0.0,6,not_converged,230547,0.994, +Text-93,complete,256,D,B,0.0,6,not_converged,195355,0.994, +Text-94,complete,42,E,E,1.0,6,not_converged,182003,0.992, +Text-94,complete,123,E,E,1.0,6,not_converged,201216,0.99, +Text-94,complete,256,E,E,1.0,6,not_converged,190326,0.976, +Text-95,complete,42,F,E,0.0,6,not_converged,243651,0.978, +Text-95,complete,123,F,C,0.0,6,not_converged,273340,1.0, +Text-95,complete,256,F,E,0.0,6,not_converged,280513,0.95, +Text-96,complete,42,E,E,1.0,6,not_converged,214644,1.0, +Text-96,complete,123,E,E,1.0,6,not_converged,229778,0.9879999999999999, +Text-96,complete,256,E,E,1.0,6,not_converged,218901,0.99, +Text-99,complete,42,F,F,1.0,6,not_converged,248027,0.982, +Text-99,complete,123,F,F,1.0,6,not_converged,244818,0.984, +Text-99,complete,256,F,F,1.0,6,not_converged,248538,0.9879999999999999, +Text-100,complete,42,F,F,1.0,6,not_converged,188600,0.9960000000000001, +Text-100,complete,123,F,F,1.0,6,not_converged,200787,1.0, +Text-100,complete,256,F,F,1.0,6,not_converged,196863,1.0, +Text-104,complete,42,J,J,1.0,6,not_converged,239354,0.97, +Text-104,complete,123,J,J,1.0,6,not_converged,272311,0.992, +Text-104,complete,256,J,J,1.0,6,not_converged,251998,0.99, +Text-106,complete,42,D,D,1.0,6,not_converged,200789,0.9879999999999999, +Text-106,complete,123,D,D,1.0,6,not_converged,181831,0.992, +Text-106,complete,256,D,D,1.0,6,not_converged,200794,0.9800000000000001, +Text-107,complete,42,J,J,1.0,6,not_converged,210710,0.992, +Text-107,complete,123,J,J,1.0,6,not_converged,212294,1.0, +Text-107,complete,256,J,J,1.0,6,not_converged,215161,0.992, +Text-109,complete,42,G,G,1.0,6,not_converged,208552,0.986, +Text-109,complete,123,G,G,1.0,6,not_converged,199661,1.0, +Text-109,complete,256,G,G,1.0,6,not_converged,203585,1.0, +Text-112,complete,42,C,C,1.0,6,not_converged,200288,1.0, +Text-112,complete,123,C,C,1.0,6,not_converged,189316,1.0, +Text-112,complete,256,C,C,1.0,6,not_converged,198616,1.0, +Text-120,complete,42,F,F,1.0,6,not_converged,145975,1.0, +Text-120,complete,123,F,F,1.0,6,not_converged,160391,1.0, +Text-120,complete,256,F,F,1.0,6,not_converged,158501,1.0, +Text-122,complete,42,C,E,0.0,6,not_converged,219698,1.0, +Text-122,complete,123,C,E,0.0,6,not_converged,252780,0.9879999999999999, +Text-122,complete,256,C,A,0.0,6,not_converged,243821,0.976, +Text-124,complete,42,D,H,0.0,6,not_converged,205720,0.998, +Text-124,complete,123,D,H,0.0,6,not_converged,196781,1.0, +Text-124,complete,256,D,H,0.0,6,not_converged,228408,0.984, +Text-126,complete,42,H,H,1.0,6,not_converged,319545,0.95, +Text-126,complete,123,H,F,0.0,6,not_converged,362469,0.966, +Text-126,complete,256,H,H,1.0,6,not_converged,332499,0.9400000000000001, +Text-127,complete,42,E,E,1.0,6,not_converged,269050,0.924, +Text-127,complete,123,E,E,1.0,6,not_converged,258574,0.9, +Text-127,complete,256,E,C,0.0,6,not_converged,198011,0.992, +Text-128,complete,42,I,A,0.0,6,not_converged,222214,0.9879999999999999, +Text-128,complete,123,I,A,0.0,6,not_converged,199735,0.99, +Text-128,complete,256,I,A,0.0,6,not_converged,214396,0.95, +Text-132,complete,42,G,G,1.0,6,not_converged,185011,0.992, +Text-132,complete,123,G,G,1.0,6,not_converged,295228,0.95, +Text-132,complete,256,G,G,1.0,6,not_converged,202341,1.0, +Text-141,complete,42,A,A,1.0,6,not_converged,177442,0.992, +Text-141,complete,123,A,A,1.0,6,not_converged,157227,0.9559999999999998, +Text-141,complete,256,A,A,1.0,6,not_converged,156352,0.95, +Text-144,complete,42,I,B,0.0,6,not_converged,231069,0.9, +Text-144,complete,123,I,B,0.0,6,not_converged,206663,0.95, +Text-144,complete,256,I,A,0.0,6,not_converged,182356,0.986, +Text-147,complete,42,D,D,1.0,6,not_converged,274413,0.9400000000000001, +Text-147,complete,123,D,D,1.0,6,not_converged,262308,0.9719999999999999, +Text-147,complete,256,D,D,1.0,6,not_converged,274827,0.95, +Text-149,complete,42,F,F,1.0,6,not_converged,167388,1.0, +Text-149,complete,123,F,F,1.0,6,not_converged,190384,0.99, +Text-149,complete,256,F,F,1.0,6,not_converged,180422,1.0, +Text-151,complete,42,H,H,1.0,6,not_converged,347692,0.998, +Text-151,complete,123,H,J,0.0,6,not_converged,295940,0.9800000000000001, +Text-151,complete,256,H,H,1.0,6,not_converged,296557,0.95, +Text-152,complete,42,I,I,1.0,6,not_converged,224611,0.9960000000000001, +Text-152,complete,123,I,B,0.0,6,not_converged,284008,0.8300000000000001, +Text-152,complete,256,I,B,0.0,6,not_converged,305201,0.71, +Text-154,complete,42,D,D,1.0,6,not_converged,213941,0.992, +Text-154,complete,123,D,D,1.0,6,not_converged,254073,0.79, +Text-154,complete,256,D,D,1.0,6,not_converged,234122,0.82, +Text-155,complete,42,B,B,1.0,6,not_converged,149930,0.992, +Text-155,complete,123,B,B,1.0,6,not_converged,161660,1.0, +Text-155,complete,256,B,B,1.0,6,not_converged,172750,1.0, +Text-156,complete,42,I,B,0.0,6,not_converged,271158,1.0, +Text-156,complete,123,I,B,0.0,6,not_converged,322536,0.984, +Text-156,complete,256,I,B,0.0,6,not_converged,255455,0.9879999999999999, +Text-162,complete,42,B,B,1.0,6,not_converged,179803,0.994, +Text-162,complete,123,B,B,1.0,6,not_converged,176001,0.99, +Text-162,complete,256,B,B,1.0,6,not_converged,183469,0.998, +Text-164,complete,42,G,G,1.0,6,not_converged,210040,0.998, +Text-164,complete,123,G,G,1.0,6,not_converged,221664,1.0, +Text-164,complete,256,G,G,1.0,6,not_converged,219683,0.998, +Text-168,complete,42,C,B,0.0,6,not_converged,270123,0.978, +Text-168,complete,123,C,B,0.0,6,not_converged,213868,0.99, +Text-168,complete,256,C,B,0.0,6,not_converged,210993,0.99, +Text-173,complete,42,H,H,1.0,6,not_converged,369220,0.77, +Text-173,complete,123,H,H,1.0,6,not_converged,276128,1.0, +Text-173,complete,256,H,H,1.0,6,not_converged,231680,0.994, +Text-179,complete,42,J,G,0.0,6,not_converged,197676,1.0, +Text-179,complete,123,J,G,0.0,6,not_converged,216356,1.0, +Text-179,complete,256,J,G,0.0,6,not_converged,233435,1.0, +Text-180,complete,42,A,D,0.0,6,not_converged,305718,0.95, +Text-180,complete,123,A,B,0.0,6,not_converged,316215,0.9480000000000001, +Text-180,complete,256,A,B,0.0,6,not_converged,397179,0.9400000000000001, +Text-183,complete,42,F,A,0.0,6,not_converged,321250,0.9640000000000001, +Text-183,complete,123,F,A,0.0,6,not_converged,270263,0.984, +Text-183,complete,256,F,A,0.0,6,not_converged,287561,0.9199999999999999, +Text-187,complete,42,G,F,0.0,6,not_converged,225714,0.992, +Text-187,complete,123,G,F,0.0,6,not_converged,298848,0.984, +Text-187,complete,256,G,F,0.0,6,not_converged,273493,1.0, +Text-188,complete,42,B,B,1.0,6,not_converged,258323,1.0, +Text-188,complete,123,B,B,1.0,6,not_converged,221653,1.0, +Text-188,complete,256,B,B,1.0,6,not_converged,276107,1.0, +Text-192,complete,42,E,B,0.0,6,not_converged,263080,1.0, +Text-192,complete,123,E,B,0.0,6,not_converged,287190,0.998, +Text-192,complete,256,E,B,0.0,6,not_converged,271218,0.984, +Text-193,complete,42,E,I,0.0,6,not_converged,209160,1.0, +Text-193,complete,123,E,I,0.0,6,not_converged,205721,0.99, +Text-193,complete,256,E,I,0.0,6,not_converged,204175,0.9879999999999999, +Text-195,complete,42,J,G,0.0,6,not_converged,474429,0.9099999999999999, +Text-195,complete,123,J,G,0.0,6,not_converged,297191,1.0, +Text-195,complete,256,J,G,0.0,6,not_converged,286150,0.992, +Text-196,complete,42,F,D,0.0,6,not_converged,347832,0.9099999999999999, +Text-196,complete,123,F,F,1.0,6,not_converged,274684,0.998, +Text-196,complete,256,F,F,1.0,6,not_converged,311308,0.962, +Text-198,complete,42,J,J,1.0,6,not_converged,147887,0.95, +Text-198,complete,123,J,J,1.0,6,not_converged,154667,0.9800000000000001, +Text-198,complete,256,J,J,1.0,6,not_converged,161285,0.99, +Text-199,complete,42,B,A,0.0,6,not_converged,177902,1.0, +Text-199,complete,123,B,A,0.0,6,not_converged,191389,0.99, +Text-199,complete,256,B,A,0.0,6,not_converged,193861,1.0, +Text-200,complete,42,J,D,0.0,6,not_converged,211878,1.0, +Text-200,complete,123,J,D,0.0,6,not_converged,212590,1.0, +Text-200,complete,256,J,D,0.0,6,not_converged,220691,1.0, +Text-201,complete,42,G,I,0.0,6,not_converged,257697,0.952, +Text-201,complete,123,G,I,0.0,6,not_converged,237848,0.95, +Text-201,complete,256,G,I,0.0,6,not_converged,233818,0.9719999999999999, +Text-202,complete,42,A,F,0.0,6,not_converged,216633,0.992, +Text-202,complete,123,A,F,0.0,6,not_converged,208899,1.0, +Text-202,complete,256,A,F,0.0,6,not_converged,253366,0.986, +Text-210,complete,42,B,B,1.0,6,not_converged,209187,0.994, +Text-210,complete,123,B,B,1.0,6,not_converged,180712,1.0, +Text-210,complete,256,B,B,1.0,6,not_converged,214776,0.99, +Text-211,complete,42,J,J,1.0,6,not_converged,203828,1.0, +Text-211,complete,123,J,J,1.0,6,not_converged,213068,0.998, +Text-211,complete,256,J,J,1.0,6,not_converged,195363,1.0, +Text-214,complete,42,B,B,1.0,6,not_converged,199041,0.994, +Text-214,complete,123,B,B,1.0,6,not_converged,197337,1.0, +Text-214,complete,256,B,B,1.0,6,not_converged,202978,0.994, +Text-216,complete,42,D,H,0.0,6,not_converged,225453,1.0, +Text-216,complete,123,D,H,0.0,6,not_converged,209001,1.0, +Text-216,complete,256,D,H,0.0,6,not_converged,211305,0.9960000000000001, +Text-217,complete,42,A,J,0.0,6,not_converged,334497,0.998, +Text-217,complete,123,A,J,0.0,6,not_converged,278325,0.978, +Text-217,complete,256,A,J,0.0,6,not_converged,269643,0.99, +Text-218,complete,42,H,H,1.0,6,not_converged,240829,1.0, +Text-218,complete,123,H,H,1.0,6,not_converged,239473,0.984, +Text-218,complete,256,H,H,1.0,6,not_converged,246004,1.0, +Text-220,complete,42,D,D,1.0,6,not_converged,236561,0.982, +Text-220,complete,123,D,D,1.0,6,not_converged,214236,1.0, +Text-220,complete,256,D,D,1.0,6,not_converged,219560,1.0, +Text-221,complete,42,I,I,1.0,6,not_converged,271102,0.986, +Text-221,complete,123,I,I,1.0,6,not_converged,238275,0.99, +Text-221,complete,256,I,I,1.0,6,not_converged,230100,0.998, +Text-222,complete,42,C,E,0.0,6,not_converged,262902,0.992, +Text-222,complete,123,C,C,1.0,6,not_converged,276584,0.976, +Text-222,complete,256,C,E,0.0,6,not_converged,228539,0.99, +Text-226,complete,42,A,E,0.0,6,not_converged,189128,1.0, +Text-226,complete,123,A,E,0.0,6,not_converged,212716,0.9960000000000001, +Text-226,complete,256,A,E,0.0,6,not_converged,204460,1.0, +Text-232,complete,42,I,I,1.0,6,not_converged,184277,1.0, +Text-232,complete,123,I,G,0.0,6,not_converged,238014,0.9039999999999999, +Text-232,complete,256,I,I,1.0,6,not_converged,253217,0.85, +Text-233,complete,42,I,D,0.0,6,not_converged,216868,1.0, +Text-233,complete,123,I,D,0.0,6,not_converged,195195,0.9960000000000001, +Text-233,complete,256,I,D,0.0,6,not_converged,202214,0.992, +Text-234,complete,42,C,D,0.0,6,not_converged,219890,0.99, +Text-234,complete,123,C,D,0.0,6,not_converged,237742,0.9960000000000001, +Text-234,complete,256,C,D,0.0,6,not_converged,238161,0.99, +Text-238,complete,42,F,D,0.0,6,not_converged,256225,0.978, +Text-238,complete,123,F,D,0.0,6,not_converged,214841,0.998, +Text-238,complete,256,F,D,0.0,6,not_converged,180965,0.998, +Text-241,complete,42,G,G,1.0,6,not_converged,211495,0.982, +Text-241,complete,123,G,G,1.0,6,not_converged,220102,1.0, +Text-241,complete,256,G,G,1.0,6,not_converged,231089,1.0, +Text-243,complete,42,H,H,1.0,6,not_converged,229507,0.9960000000000001, +Text-243,complete,123,H,H,1.0,6,not_converged,184337,0.994, +Text-243,complete,256,H,H,1.0,6,not_converged,192161,1.0, +Text-244,complete,42,G,G,1.0,6,not_converged,347372,0.85, +Text-244,complete,123,G,G,1.0,6,not_converged,340512,0.93, +Text-244,complete,256,G,F,0.0,6,not_converged,253679,0.99, +Text-245,complete,42,F,D,0.0,6,not_converged,200320,0.962, +Text-245,complete,123,F,D,0.0,6,not_converged,185113,0.978, +Text-245,complete,256,F,D,0.0,6,not_converged,196079,0.994, +Text-246,complete,42,F,F,1.0,6,not_converged,234576,0.9119999999999999, +Text-246,complete,123,F,F,1.0,6,not_converged,211899,0.9800000000000001, +Text-246,complete,256,F,F,1.0,6,not_converged,228827,0.974, +Text-249,complete,42,C,C,1.0,6,not_converged,203621,0.992, +Text-249,complete,123,C,C,1.0,6,not_converged,212891,1.0, +Text-249,complete,256,C,C,1.0,6,not_converged,204553,0.994, +Text-250,complete,42,B,B,1.0,6,not_converged,173053,0.9879999999999999, +Text-250,complete,123,B,B,1.0,6,not_converged,166296,1.0, +Text-250,complete,256,B,B,1.0,6,not_converged,178082,0.99, +Text-253,complete,42,E,E,1.0,6,not_converged,188398,1.0, +Text-253,complete,123,E,E,1.0,6,not_converged,193307,0.968, +Text-253,complete,256,E,E,1.0,6,not_converged,178121,1.0, +Text-255,complete,42,F,G,0.0,6,not_converged,230562,1.0, +Text-255,complete,123,F,G,0.0,6,not_converged,335331,0.93, +Text-255,complete,256,F,G,0.0,6,not_converged,229381,1.0, +Text-257,complete,42,A,I,0.0,6,not_converged,225272,0.998, +Text-257,complete,123,A,I,0.0,6,not_converged,224025,1.0, +Text-257,complete,256,A,I,0.0,6,not_converged,200790,1.0, +Text-259,complete,42,I,I,1.0,6,not_converged,196617,0.994, +Text-259,complete,123,I,I,1.0,6,not_converged,187563,0.9960000000000001, +Text-259,complete,256,I,I,1.0,6,not_converged,203618,0.986, +Text-260,complete,42,I,I,1.0,6,not_converged,193008,1.0, +Text-260,complete,123,I,I,1.0,6,not_converged,184475,0.998, +Text-260,complete,256,I,I,1.0,6,not_converged,240819,0.986, +Text-261,complete,42,E,A,0.0,6,not_converged,348715,1.0, +Text-261,complete,123,E,A,0.0,6,not_converged,346551,0.97, +Text-261,complete,256,E,A,0.0,6,not_converged,355839,0.99, +Text-265,complete,42,E,E,1.0,6,not_converged,192133,1.0, +Text-265,complete,123,E,E,1.0,6,not_converged,164285,0.998, +Text-265,complete,256,E,E,1.0,6,not_converged,172908,0.95, +Text-268,complete,42,J,J,1.0,6,not_converged,250879,0.93, +Text-268,complete,123,J,H,0.0,6,not_converged,201608,0.876, +Text-268,complete,256,J,H,0.0,6,not_converged,260044,0.968, +Text-269,complete,42,C,C,1.0,6,not_converged,190232,0.95, +Text-269,complete,123,C,C,1.0,6,not_converged,213155,0.994, +Text-269,complete,256,C,C,1.0,6,not_converged,215611,0.99, +Text-271,complete,42,I,I,1.0,6,not_converged,151990,0.9960000000000001, +Text-271,complete,123,I,I,1.0,6,not_converged,186922,1.0, +Text-271,complete,256,I,I,1.0,6,not_converged,168867,1.0, +Text-272,complete,42,A,A,1.0,6,not_converged,242297,1.0, +Text-272,complete,123,A,A,1.0,6,not_converged,236723,0.992, +Text-272,complete,256,A,A,1.0,6,not_converged,277704,0.9960000000000001, +Text-273,complete,42,B,B,1.0,6,not_converged,220591,0.99, +Text-273,complete,123,B,B,1.0,6,not_converged,191999,0.99, +Text-273,complete,256,B,B,1.0,6,not_converged,207448,0.99, +Text-275,complete,42,B,F,0.0,6,not_converged,274861,0.9359999999999999, +Text-275,complete,123,B,B,1.0,6,not_converged,287529,0.8099999999999999, +Text-275,complete,256,B,F,0.0,6,not_converged,252490,0.95, +Text-276,complete,42,F,D,0.0,6,not_converged,306290,0.96, +Text-276,complete,123,F,D,0.0,6,not_converged,316251,0.9559999999999998, +Text-276,complete,256,F,D,0.0,6,not_converged,275442,0.9800000000000001, +Text-281,complete,42,H,H,1.0,6,not_converged,217306,0.992, +Text-281,complete,123,H,H,1.0,6,not_converged,246126,0.992, +Text-281,complete,256,H,H,1.0,6,not_converged,221059,1.0, +Text-282,complete,42,J,B,0.0,6,not_converged,449805,0.976, +Text-282,complete,123,J,I,0.0,6,not_converged,600804,0.8800000000000001, +Text-282,complete,256,J,B,0.0,6,not_converged,485685,0.978, +Text-283,complete,42,H,C,0.0,6,not_converged,258866,0.99, +Text-283,complete,123,H,C,0.0,6,not_converged,218839,0.9960000000000001, +Text-283,complete,256,H,C,0.0,6,not_converged,184507,1.0, +Text-284,complete,42,B,A,0.0,6,not_converged,256060,0.992, +Text-284,complete,123,B,A,0.0,6,not_converged,210493,0.992, +Text-284,complete,256,B,A,0.0,6,not_converged,267419,0.986, +Text-285,complete,42,G,J,0.0,6,not_converged,263619,1.0, +Text-285,complete,123,G,J,0.0,6,not_converged,255547,0.99, +Text-285,complete,256,G,J,0.0,6,not_converged,335410,0.99, +Text-286,complete,42,A,A,1.0,6,not_converged,208832,0.998, +Text-286,complete,123,A,A,1.0,6,not_converged,233125,0.998, +Text-286,complete,256,A,A,1.0,6,not_converged,190670,0.9960000000000001, +Text-287,complete,42,D,D,1.0,6,not_converged,181548,0.9960000000000001, +Text-287,complete,123,D,D,1.0,6,not_converged,170857,1.0, +Text-287,complete,256,D,D,1.0,6,not_converged,178447,0.9960000000000001, +Text-288,complete,42,A,A,1.0,6,not_converged,178875,0.994, +Text-288,complete,123,A,A,1.0,6,not_converged,176992,1.0, +Text-288,complete,256,A,A,1.0,6,not_converged,157832,0.984, +Text-289,complete,42,J,E,0.0,6,not_converged,247540,0.9960000000000001, +Text-289,complete,123,J,E,0.0,6,not_converged,342652,0.9800000000000001, +Text-289,complete,256,J,E,0.0,6,not_converged,272127,0.9960000000000001, +Text-291,complete,42,J,J,1.0,6,not_converged,215633,0.998, +Text-291,complete,123,J,J,1.0,6,not_converged,206998,0.994, +Text-291,complete,256,J,J,1.0,6,not_converged,195940,1.0, +Text-292,complete,42,C,C,1.0,6,not_converged,305372,1.0, +Text-292,complete,123,C,C,1.0,6,not_converged,268442,0.998, +Text-292,complete,256,C,C,1.0,6,not_converged,244229,1.0, +Text-296,complete,42,I,E,0.0,6,not_converged,239192,0.9879999999999999, +Text-296,complete,123,I,E,0.0,6,not_converged,231422,1.0, +Text-296,complete,256,I,E,0.0,6,not_converged,236501,0.95, +Text-297,complete,42,D,D,1.0,6,not_converged,218670,0.99, +Text-297,complete,123,D,D,1.0,6,not_converged,226239,0.9800000000000001, +Text-297,complete,256,D,D,1.0,6,not_converged,219245,1.0, +Text-299,complete,42,D,D,1.0,6,not_converged,285648,0.978, +Text-299,complete,123,D,G,0.0,6,not_converged,285578,0.986, +Text-299,complete,256,D,D,1.0,6,not_converged,271766,1.0, +Text-301,complete,42,I,E,0.0,6,not_converged,338339,1.0, +Text-301,complete,123,I,E,0.0,6,not_converged,338983,0.992, +Text-301,complete,256,I,G,0.0,6,not_converged,374370,0.95, +Text-302,complete,42,H,H,1.0,6,not_converged,215090,1.0, +Text-302,complete,123,H,H,1.0,6,not_converged,297128,1.0, +Text-302,complete,256,H,H,1.0,6,not_converged,213169,1.0, +Text-303,complete,42,E,E,1.0,6,not_converged,243268,0.99, +Text-303,complete,123,E,E,1.0,6,not_converged,212876,0.9960000000000001, +Text-303,complete,256,E,E,1.0,6,not_converged,227385,0.99, +Text-305,complete,42,J,E,0.0,6,not_converged,391555,0.9879999999999999, +Text-305,complete,123,J,E,0.0,6,not_converged,270715,1.0, +Text-305,complete,256,J,E,0.0,6,not_converged,356136,0.97, +Text-306,complete,42,E,E,1.0,6,not_converged,210725,0.9879999999999999, +Text-306,complete,123,E,E,1.0,6,not_converged,217058,1.0, +Text-306,complete,256,E,E,1.0,6,not_converged,217021,1.0, +Text-307,complete,42,E,E,1.0,6,not_converged,179318,1.0, +Text-307,complete,123,E,I,0.0,6,not_converged,251335,0.946, +Text-307,complete,256,E,E,1.0,6,not_converged,205470,0.99, +Text-308,complete,42,C,B,0.0,6,not_converged,259737,0.9559999999999998, +Text-308,complete,123,C,B,0.0,6,not_converged,212725,0.994, +Text-308,complete,256,C,B,0.0,6,not_converged,201365,1.0, +Text-310,complete,42,I,H,0.0,6,not_converged,509699,0.9199999999999999, +Text-310,complete,123,I,I,1.0,6,not_converged,409701,1.0, +Text-310,complete,256,I,H,0.0,6,not_converged,391630,0.99, +Text-312,complete,42,E,I,0.0,6,not_converged,369142,0.95, +Text-312,complete,123,E,I,0.0,6,not_converged,274033,1.0, +Text-312,complete,256,E,I,0.0,6,not_converged,306707,0.992, +Text-314,complete,42,B,B,1.0,6,not_converged,217319,0.9879999999999999, +Text-314,complete,123,B,B,1.0,6,not_converged,190722,0.99, +Text-314,complete,256,B,B,1.0,6,not_converged,204106,1.0, +Text-315,complete,42,F,C,0.0,6,not_converged,286512,0.9099999999999999, +Text-315,complete,123,F,D,0.0,6,not_converged,222824,0.982, +Text-315,complete,256,F,D,0.0,6,not_converged,233893,0.986, +Text-317,complete,42,C,C,1.0,6,not_converged,206143,1.0, +Text-317,complete,123,C,C,1.0,6,not_converged,204829,0.998, +Text-317,complete,256,C,C,1.0,6,not_converged,235968,0.9879999999999999, +Text-321,complete,42,H,D,0.0,6,not_converged,182645,0.984, +Text-321,complete,123,H,D,0.0,6,not_converged,182810,0.998, +Text-321,complete,256,H,D,0.0,6,not_converged,186320,0.99, +Text-322,complete,42,H,C,0.0,6,not_converged,194089,0.9960000000000001, +Text-322,complete,123,H,C,0.0,6,not_converged,211344,0.95, +Text-322,complete,256,H,C,0.0,6,not_converged,183549,0.9559999999999998, +Text-325,complete,42,I,E,0.0,6,not_converged,159803,1.0, +Text-325,complete,123,I,E,0.0,6,not_converged,153971,1.0, +Text-325,complete,256,I,E,0.0,6,not_converged,154198,1.0, +Text-326,complete,42,E,E,1.0,6,not_converged,176171,0.998, +Text-326,complete,123,E,I,0.0,6,not_converged,201554,1.0, +Text-326,complete,256,E,E,1.0,6,not_converged,185267,0.9879999999999999, +Text-328,complete,42,E,E,1.0,6,not_converged,198801,0.992, +Text-328,complete,123,E,E,1.0,6,not_converged,196079,0.998, +Text-328,complete,256,E,E,1.0,6,not_converged,214914,0.9960000000000001, +Text-330,complete,42,G,B,0.0,6,not_converged,201657,1.0, +Text-330,complete,123,G,B,0.0,6,not_converged,243934,0.95, +Text-330,complete,256,G,B,0.0,6,not_converged,228454,0.9960000000000001, +Text-334,complete,42,H,H,1.0,6,not_converged,164953,1.0, +Text-334,complete,123,H,H,1.0,6,not_converged,192039,1.0, +Text-334,complete,256,H,H,1.0,6,not_converged,183379,1.0, +Text-336,complete,42,H,H,1.0,6,not_converged,288864,1.0, +Text-336,complete,123,H,H,1.0,6,not_converged,301100,0.982, +Text-336,complete,256,H,H,1.0,6,not_converged,383634,0.998, +Text-340,complete,42,H,J,0.0,6,not_converged,170777,1.0, +Text-340,complete,123,H,J,0.0,6,not_converged,169078,0.9960000000000001, +Text-340,complete,256,H,J,0.0,6,not_converged,157808,1.0, +Text-342,complete,42,B,A,0.0,6,not_converged,225311,0.998, +Text-342,complete,123,B,A,0.0,6,not_converged,185234,1.0, +Text-342,complete,256,B,A,0.0,6,not_converged,179889,0.9960000000000001, +Text-352,complete,42,H,H,1.0,6,not_converged,260837,0.99, +Text-352,complete,123,H,H,1.0,6,not_converged,310193,0.992, +Text-352,complete,256,H,H,1.0,6,not_converged,264634,0.99, +Text-353,complete,42,G,E,0.0,6,not_converged,196677,0.998, +Text-353,complete,123,G,E,0.0,6,not_converged,249769,0.99, +Text-353,complete,256,G,E,0.0,6,not_converged,228882,0.998, +Text-357,complete,42,G,D,0.0,6,not_converged,235240,0.994, +Text-357,complete,123,G,D,0.0,6,not_converged,231887,0.992, +Text-357,complete,256,G,D,0.0,6,not_converged,238964,0.95, +Text-358,complete,42,D,B,0.0,6,not_converged,373723,1.0, +Text-358,complete,123,D,B,0.0,6,not_converged,338788,0.992, +Text-358,complete,256,D,B,0.0,6,not_converged,352324,1.0, +Text-361,complete,42,B,J,0.0,6,not_converged,240022,0.992, +Text-361,complete,123,B,J,0.0,6,not_converged,230887,1.0, +Text-361,complete,256,B,J,0.0,6,not_converged,327343,0.9040000000000001, +Text-365,complete,42,B,D,0.0,6,not_converged,242556,0.9960000000000001, +Text-365,complete,123,B,D,0.0,6,not_converged,234009,0.99, +Text-365,complete,256,B,I,0.0,6,not_converged,306370,0.9, +Text-368,complete,42,F,A,0.0,6,not_converged,223674,1.0, +Text-368,complete,123,F,A,0.0,6,not_converged,236394,0.99, +Text-368,complete,256,F,A,0.0,6,not_converged,222150,1.0, +Text-373,complete,42,A,A,1.0,6,not_converged,336527,0.93, +Text-373,complete,123,A,D,0.0,6,not_converged,300006,0.9719999999999999, +Text-373,complete,256,A,D,0.0,6,not_converged,301685,0.99, +Text-375,complete,42,E,E,1.0,6,not_converged,388240,0.95, +Text-375,complete,123,E,E,1.0,6,not_converged,275654,0.966, +Text-375,complete,256,E,E,1.0,6,not_converged,284936,0.99, +Text-378,complete,42,G,D,0.0,6,not_converged,214923,0.99, +Text-378,complete,123,G,D,0.0,6,not_converged,203759,1.0, +Text-378,complete,256,G,D,0.0,6,not_converged,195147,1.0, +Text-379,complete,42,J,J,1.0,6,not_converged,212385,1.0, +Text-379,complete,123,J,J,1.0,6,not_converged,215830,0.994, +Text-379,complete,256,J,J,1.0,6,not_converged,252415,1.0, +Text-383,complete,42,I,H,0.0,6,not_converged,193709,1.0, +Text-383,complete,123,I,H,0.0,6,not_converged,192973,1.0, +Text-383,complete,256,I,H,0.0,6,not_converged,188294,0.994, +Text-385,complete,42,A,H,0.0,6,not_converged,222145,0.9960000000000001, +Text-385,complete,123,A,H,0.0,6,not_converged,223892,1.0, +Text-385,complete,256,A,H,0.0,6,not_converged,227590,0.9960000000000001, +Text-386,complete,42,F,H,0.0,6,not_converged,166451,1.0, +Text-386,complete,123,F,H,0.0,6,not_converged,166819,0.994, +Text-386,complete,256,F,H,0.0,6,not_converged,169419,1.0, +Text-390,complete,42,I,I,1.0,6,not_converged,243284,0.9559999999999998, +Text-390,complete,123,I,I,1.0,6,not_converged,192728,0.974, +Text-390,complete,256,I,I,1.0,6,not_converged,241615,0.9719999999999999, +Text-394,complete,42,J,J,1.0,6,not_converged,270408,0.9879999999999999, +Text-394,complete,123,J,J,1.0,6,not_converged,262876,0.968, +Text-394,complete,256,J,J,1.0,6,not_converged,273524,0.984, +Text-395,complete,42,H,H,1.0,6,not_converged,219155,0.99, +Text-395,complete,123,H,J,0.0,6,not_converged,265110,0.944, +Text-395,complete,256,H,H,1.0,6,not_converged,240254,0.9559999999999998, +Text-398,complete,42,F,D,0.0,6,not_converged,249406,0.976, +Text-398,complete,123,F,F,1.0,6,not_converged,297666,0.9719999999999999, +Text-398,complete,256,F,A,0.0,6,not_converged,270884,0.962, +Text-399,complete,42,D,E,0.0,6,not_converged,188261,0.9879999999999999, +Text-399,complete,123,D,E,0.0,6,not_converged,221083,0.992, +Text-399,complete,256,D,B,0.0,6,not_converged,264687,0.73, +Text-400,complete,42,B,E,0.0,6,not_converged,293919,0.8640000000000001, +Text-400,complete,123,B,B,1.0,6,not_converged,270245,0.944, +Text-400,complete,256,B,E,0.0,6,not_converged,241578,0.99, +Text-402,complete,42,J,J,1.0,6,not_converged,315910,0.982, +Text-402,complete,123,J,A,0.0,6,not_converged,341660,0.77, +Text-402,complete,256,J,J,1.0,6,not_converged,286040,0.9960000000000001, +Text-403,complete,42,F,E,0.0,6,not_converged,233590,0.992, +Text-403,complete,123,F,E,0.0,6,not_converged,292601,0.9960000000000001, +Text-403,complete,256,F,E,0.0,6,not_converged,248286,0.99, +Text-404,complete,42,E,H,0.0,6,not_converged,166670,0.984, +Text-404,complete,123,E,H,0.0,6,not_converged,162089,1.0, +Text-404,complete,256,E,H,0.0,6,not_converged,176488,1.0, +Text-409,complete,42,A,B,0.0,6,not_converged,289460,1.0, +Text-409,complete,123,A,E,0.0,6,not_converged,417699,0.97, +Text-409,complete,256,A,F,0.0,6,not_converged,531546,0.79, +Text-410,complete,42,D,D,1.0,6,not_converged,221787,1.0, +Text-410,complete,123,D,D,1.0,6,not_converged,198216,1.0, +Text-410,complete,256,D,D,1.0,6,not_converged,224511,1.0, +Text-414,complete,42,C,H,0.0,6,not_converged,205532,0.99, +Text-414,complete,123,C,H,0.0,6,not_converged,197041,1.0, +Text-414,complete,256,C,H,0.0,6,not_converged,193296,1.0, +Text-416,complete,42,D,I,0.0,6,not_converged,548443,0.93, +Text-416,complete,123,D,H,0.0,6,not_converged,513636,0.9, +Text-416,complete,256,D,I,0.0,6,not_converged,452341,0.96, +Text-418,complete,42,E,E,1.0,6,not_converged,372291,0.95, +Text-418,complete,123,E,A,0.0,6,not_converged,349784,0.9099999999999999, +Text-418,complete,256,E,A,0.0,6,not_converged,315348,0.95, +Text-419,complete,42,A,E,0.0,6,not_converged,190188,0.984, +Text-419,complete,123,A,E,0.0,6,not_converged,200047,1.0, +Text-419,complete,256,A,E,0.0,6,not_converged,186584,0.9800000000000001, +Text-421,complete,42,I,C,0.0,6,not_converged,330424,0.9800000000000001, +Text-421,complete,123,I,C,0.0,6,not_converged,439987,0.8400000000000001, +Text-421,complete,256,I,C,0.0,6,not_converged,260243,1.0, +Text-422,complete,42,I,B,0.0,6,not_converged,281413,0.998, +Text-422,complete,123,I,J,0.0,6,not_converged,267328,0.8699999999999999, +Text-422,complete,256,I,B,0.0,6,not_converged,250016,0.9879999999999999, +Text-423,complete,42,A,A,1.0,6,not_converged,396042,0.76, +Text-423,complete,123,A,A,1.0,6,not_converged,378052,0.85, +Text-423,complete,256,A,A,1.0,6,not_converged,321105,0.85, +Text-424,complete,42,B,B,1.0,6,not_converged,163139,0.99, +Text-424,complete,123,B,B,1.0,6,not_converged,154840,1.0, +Text-424,complete,256,B,B,1.0,6,not_converged,152816,1.0, +Text-427,complete,42,B,B,1.0,6,not_converged,172111,1.0, +Text-427,complete,123,B,B,1.0,6,not_converged,179016,0.998, +Text-427,complete,256,B,B,1.0,6,not_converged,182039,0.994, +Text-428,complete,42,D,A,0.0,6,not_converged,289167,0.9960000000000001, +Text-428,complete,123,D,A,0.0,6,not_converged,232552,1.0, +Text-428,complete,256,D,A,0.0,6,not_converged,278830,1.0, +Text-429,complete,42,A,H,0.0,6,not_converged,184344,0.998, +Text-429,complete,123,A,H,0.0,6,not_converged,172458,0.9960000000000001, +Text-429,complete,256,A,H,0.0,6,not_converged,182833,0.998, +Text-430,complete,42,D,I,0.0,6,not_converged,267963,1.0, +Text-430,complete,123,D,I,0.0,6,not_converged,233051,0.99, +Text-430,complete,256,D,I,0.0,6,not_converged,271021,1.0, +Text-432,complete,42,B,B,1.0,6,not_converged,231080,1.0, +Text-432,complete,123,B,B,1.0,6,not_converged,193400,1.0, +Text-432,complete,256,B,B,1.0,6,not_converged,183995,0.998, +Text-434,complete,42,H,J,0.0,6,not_converged,189058,0.99, +Text-434,complete,123,H,J,0.0,6,not_converged,189756,1.0, +Text-434,complete,256,H,J,0.0,6,not_converged,209240,1.0, +Text-435,complete,42,G,A,0.0,6,not_converged,300207,0.984, +Text-435,complete,123,G,A,0.0,6,not_converged,360075,1.0, +Text-435,complete,256,G,A,0.0,6,not_converged,294270,0.998, +Text-438,complete,42,A,D,0.0,6,not_converged,201995,1.0, +Text-438,complete,123,A,D,0.0,6,not_converged,282131,0.9960000000000001, +Text-438,complete,256,A,D,0.0,6,not_converged,203202,1.0, +Text-439,complete,42,G,G,1.0,6,not_converged,328067,0.982, +Text-439,complete,123,G,D,0.0,6,not_converged,295749,0.99, +Text-439,complete,256,G,G,1.0,6,not_converged,293740,0.9879999999999999, +Text-443,complete,42,F,J,0.0,6,not_converged,216286,1.0, +Text-443,complete,123,F,J,0.0,6,not_converged,226523,0.992, +Text-443,complete,256,F,J,0.0,6,not_converged,242347,0.99, +Text-446,complete,42,J,G,0.0,6,not_converged,310005,0.952, +Text-446,complete,123,J,G,0.0,6,not_converged,256004,0.9879999999999999, +Text-446,complete,256,J,G,0.0,6,not_converged,245594,0.9800000000000001, +Text-449,complete,42,H,G,0.0,6,not_converged,180745,1.0, +Text-449,complete,123,H,H,1.0,6,not_converged,275571,0.89, +Text-449,complete,256,H,G,0.0,6,not_converged,218606,1.0, +Text-453,complete,42,B,B,1.0,6,not_converged,187696,1.0, +Text-453,complete,123,B,B,1.0,6,not_converged,189870,1.0, +Text-453,complete,256,B,B,1.0,6,not_converged,191125,0.992, +Text-460,complete,42,A,A,1.0,6,not_converged,208387,0.998, +Text-460,complete,123,A,A,1.0,6,not_converged,228677,0.984, +Text-460,complete,256,A,A,1.0,6,not_converged,233066,0.992, +Text-461,complete,42,F,A,0.0,6,not_converged,220239,1.0, +Text-461,complete,123,F,A,0.0,6,not_converged,193443,0.99, +Text-461,complete,256,F,A,0.0,6,not_converged,216752,0.992, +Text-465,complete,42,E,I,0.0,6,not_converged,225001,0.9879999999999999, +Text-465,complete,123,E,I,0.0,6,not_converged,223553,0.968, +Text-465,complete,256,E,I,0.0,6,not_converged,231421,0.986, +Text-469,complete,42,C,C,1.0,6,not_converged,250506,0.99, +Text-469,complete,123,C,C,1.0,6,not_converged,268890,0.99, +Text-469,complete,256,C,C,1.0,6,not_converged,265902,0.9960000000000001, 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+Text-485,complete,123,C,C,1.0,6,not_converged,172044,1.0, +Text-485,complete,256,C,C,1.0,6,not_converged,159257,1.0, +Text-487,complete,42,B,B,1.0,6,not_converged,184217,0.9879999999999999, +Text-487,complete,123,B,B,1.0,6,not_converged,193380,1.0, +Text-487,complete,256,B,B,1.0,6,not_converged,192688,1.0, +Text-488,complete,42,F,F,1.0,6,not_converged,219988,0.99, +Text-488,complete,123,F,F,1.0,6,not_converged,230195,0.966, +Text-488,complete,256,F,F,1.0,6,not_converged,237228,0.976, +Text-490,complete,42,A,A,1.0,6,not_converged,198269,1.0, +Text-490,complete,123,A,A,1.0,6,not_converged,190061,0.99, +Text-490,complete,256,A,A,1.0,6,not_converged,207538,0.994, +Text-494,complete,42,E,E,1.0,6,not_converged,264386,0.82, +Text-494,complete,123,E,E,1.0,6,not_converged,206215,0.99, +Text-494,complete,256,E,G,0.0,6,not_converged,209202,0.9400000000000001, +Text-498,complete,42,E,E,1.0,6,not_converged,184831,1.0, +Text-498,complete,123,E,E,1.0,6,not_converged,192846,1.0, +Text-498,complete,256,E,E,1.0,6,not_converged,218910,1.0, +Text-500,complete,42,F,I,0.0,6,not_converged,250132,0.99, +Text-500,complete,123,F,I,0.0,6,not_converged,283093,0.95, +Text-500,complete,256,F,I,0.0,6,not_converged,302241,0.9960000000000001, +Text-502,complete,42,J,J,1.0,6,not_converged,164766,0.9960000000000001, +Text-502,complete,123,J,J,1.0,6,not_converged,191670,1.0, +Text-502,complete,256,J,J,1.0,6,not_converged,191252,0.998, +Text-510,complete,42,I,I,1.0,6,not_converged,222499,0.99, +Text-510,complete,123,I,I,1.0,6,not_converged,218269,0.9960000000000001, +Text-510,complete,256,I,I,1.0,6,not_converged,193772,0.9960000000000001, +Text-511,complete,42,I,I,1.0,6,not_converged,241227,0.998, +Text-511,complete,123,I,I,1.0,6,not_converged,239574,1.0, +Text-511,complete,256,I,I,1.0,6,not_converged,202146,1.0, +Text-512,complete,42,D,J,0.0,6,not_converged,419692,0.9259999999999999, +Text-512,complete,123,D,B,0.0,6,not_converged,483903,0.75, +Text-512,complete,256,D,B,0.0,6,not_converged,363391,0.96, +Text-519,complete,42,H,H,1.0,6,not_converged,299683,0.93, +Text-519,complete,123,H,H,1.0,6,not_converged,222261,1.0, +Text-519,complete,256,H,H,1.0,6,not_converged,334149,0.946, +Text-520,complete,42,D,A,0.0,6,not_converged,271230,0.99, +Text-520,complete,123,D,A,0.0,6,not_converged,252268,0.974, +Text-520,complete,256,D,E,0.0,6,not_converged,347379,0.8800000000000001, +Text-521,complete,42,J,A,0.0,6,not_converged,299364,0.882, +Text-521,complete,123,J,A,0.0,6,not_converged,257410,0.8800000000000001, +Text-521,complete,256,J,A,0.0,6,not_converged,225828,0.994, +Text-524,complete,42,B,D,0.0,6,not_converged,226082,1.0, +Text-524,complete,123,B,D,0.0,6,not_converged,219118,0.9960000000000001, +Text-524,complete,256,B,D,0.0,6,not_converged,256053,1.0, +Text-525,complete,42,C,C,1.0,6,not_converged,252978,1.0, +Text-525,complete,123,C,C,1.0,6,not_converged,202461,0.9879999999999999, +Text-525,complete,256,C,C,1.0,6,not_converged,203709,0.95, +Text-526,complete,42,J,J,1.0,6,not_converged,260244,0.99, +Text-526,complete,123,J,J,1.0,6,not_converged,307158,0.99, +Text-526,complete,256,J,J,1.0,6,not_converged,266457,1.0, +Text-527,complete,42,A,A,1.0,6,not_converged,150728,1.0, +Text-527,complete,123,A,A,1.0,6,not_converged,173509,1.0, +Text-527,complete,256,A,A,1.0,6,not_converged,151478,1.0, +Text-529,complete,42,J,J,1.0,6,not_converged,202504,1.0, +Text-529,complete,123,J,J,1.0,6,not_converged,197741,0.9879999999999999, +Text-529,complete,256,J,J,1.0,6,not_converged,191373,1.0, +Text-531,complete,42,H,B,0.0,6,not_converged,215136,0.9800000000000001, +Text-531,complete,123,H,G,0.0,6,not_converged,294822,0.9400000000000001, +Text-531,complete,256,H,B,0.0,6,not_converged,232688,0.99, +Text-532,complete,42,J,B,0.0,6,not_converged,198647,0.992, +Text-532,complete,123,J,B,0.0,6,not_converged,229351,0.9800000000000001, +Text-532,complete,256,J,B,0.0,6,not_converged,208963,1.0, +Text-533,complete,42,J,J,1.0,6,not_converged,230404,0.99, +Text-533,complete,123,J,J,1.0,6,not_converged,261395,0.986, +Text-533,complete,256,J,J,1.0,6,not_converged,243088,0.95, +Text-536,complete,42,B,B,1.0,6,not_converged,253113,0.986, +Text-536,complete,123,B,B,1.0,6,not_converged,230376,0.992, +Text-536,complete,256,B,B,1.0,6,not_converged,217330,0.994, +Text-538,complete,42,F,F,1.0,6,not_converged,201612,1.0, +Text-538,complete,123,F,F,1.0,6,not_converged,210127,1.0, +Text-538,complete,256,F,F,1.0,6,not_converged,199528,1.0, +Text-540,complete,42,A,H,0.0,6,not_converged,194537,0.992, +Text-540,complete,123,A,H,0.0,6,not_converged,191492,0.994, +Text-540,complete,256,A,H,0.0,6,not_converged,202875,0.99, +Text-545,complete,42,F,F,1.0,6,not_converged,245529,0.998, +Text-545,complete,123,F,F,1.0,6,not_converged,210684,0.9960000000000001, +Text-545,complete,256,F,F,1.0,6,not_converged,241996,0.99, +Text-549,complete,42,A,G,0.0,6,not_converged,193274,0.9960000000000001, +Text-549,complete,123,A,G,0.0,6,not_converged,206491,1.0, +Text-549,complete,256,A,G,0.0,6,not_converged,194899,1.0, +Text-551,complete,42,H,C,0.0,6,not_converged,187998,1.0, +Text-551,complete,123,H,C,0.0,6,not_converged,168009,1.0, +Text-551,complete,256,H,C,0.0,6,not_converged,209922,1.0, +Text-553,complete,42,H,A,0.0,6,not_converged,229698,0.9879999999999999, +Text-553,complete,123,H,A,0.0,6,not_converged,258130,1.0, +Text-553,complete,256,H,A,0.0,6,not_converged,212028,0.992, +Text-555,complete,42,I,I,1.0,6,not_converged,268043,0.966, +Text-555,complete,123,I,I,1.0,6,not_converged,222552,0.994, +Text-555,complete,256,I,I,1.0,6,not_converged,291101,0.96, +Text-556,complete,42,F,F,1.0,6,not_converged,213245,1.0, +Text-556,complete,123,F,F,1.0,6,not_converged,168051,0.99, +Text-556,complete,256,F,F,1.0,6,not_converged,175422,1.0, +Text-558,complete,42,G,G,1.0,6,not_converged,233022,0.986, 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+Text-581,complete,256,E,E,1.0,6,not_converged,173997,0.9960000000000001, +Text-583,complete,42,H,G,0.0,6,not_converged,215086,0.992, +Text-583,complete,123,H,G,0.0,6,not_converged,229023,0.99, +Text-583,complete,256,H,G,0.0,6,not_converged,203469,1.0, +Text-584,complete,42,D,G,0.0,6,not_converged,225344,1.0, +Text-584,complete,123,D,G,0.0,6,not_converged,214396,0.992, +Text-584,complete,256,D,G,0.0,6,not_converged,192482,0.9879999999999999, +Text-587,complete,42,F,I,0.0,6,not_converged,224577,0.974, +Text-587,complete,123,F,I,0.0,6,not_converged,363936,0.9400000000000001, +Text-587,complete,256,F,F,1.0,6,not_converged,317005,0.8099999999999999, +Text-589,complete,42,H,H,1.0,6,not_converged,187016,0.99, +Text-589,complete,123,H,H,1.0,6,not_converged,188655,1.0, +Text-589,complete,256,H,H,1.0,6,not_converged,184612,1.0, +Text-590,complete,42,J,I,0.0,6,not_converged,156355,1.0, +Text-590,complete,123,J,I,0.0,6,not_converged,163778,1.0, +Text-590,complete,256,J,I,0.0,6,not_converged,146455,1.0, +Text-592,complete,42,A,B,0.0,6,not_converged,409052,0.978, +Text-592,complete,123,A,B,0.0,6,not_converged,320179,1.0, +Text-592,complete,256,A,A,1.0,6,not_converged,302529,0.99, +Text-595,complete,42,B,F,0.0,6,not_converged,249258,0.9719999999999999, +Text-595,complete,123,B,F,0.0,6,not_converged,257317,1.0, +Text-595,complete,256,B,F,0.0,6,not_converged,255904,1.0, +Text-602,complete,42,I,F,0.0,6,not_converged,193664,0.99, +Text-602,complete,123,I,F,0.0,6,not_converged,251119,0.99, +Text-602,complete,256,I,F,0.0,6,not_converged,236771,0.982, +Text-603,complete,42,H,H,1.0,6,not_converged,223475,0.9960000000000001, +Text-603,complete,123,H,H,1.0,6,not_converged,191251,1.0, +Text-603,complete,256,H,H,1.0,6,not_converged,256756,0.9640000000000001, +Text-604,complete,42,J,J,1.0,6,not_converged,190284,0.998, +Text-604,complete,123,J,J,1.0,6,not_converged,189823,1.0, +Text-604,complete,256,J,J,1.0,6,not_converged,201917,0.99, +Text-608,complete,42,D,G,0.0,6,not_converged,245513,0.984, +Text-608,complete,123,D,G,0.0,6,not_converged,219480,0.992, +Text-608,complete,256,D,G,0.0,6,not_converged,234030,0.9640000000000001, +Text-609,complete,42,A,A,1.0,6,not_converged,177443,0.9960000000000001, +Text-609,complete,123,A,A,1.0,6,not_converged,181966,0.9960000000000001, +Text-609,complete,256,A,A,1.0,6,not_converged,172399,0.998, +Text-612,complete,42,A,G,0.0,6,not_converged,333270,0.9359999999999999, +Text-612,complete,123,A,E,0.0,6,not_converged,308952,0.9559999999999998, +Text-612,complete,256,A,E,0.0,6,not_converged,217948,0.9879999999999999, +Text-615,complete,42,F,F,1.0,6,not_converged,229197,1.0, +Text-615,complete,123,F,F,1.0,6,not_converged,226249,0.9879999999999999, +Text-615,complete,256,F,F,1.0,6,not_converged,250192,1.0, +Text-616,complete,42,J,J,1.0,6,not_converged,298655,0.89, +Text-616,complete,123,J,J,1.0,6,not_converged,260131,0.8300000000000001, 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+Text-804,complete,123,D,B,0.0,6,not_converged,197994,1.0, +Text-804,complete,256,D,B,0.0,6,not_converged,196384,1.0, +Text-805,complete,42,B,H,0.0,6,not_converged,243885,0.994, +Text-805,complete,123,B,H,0.0,6,not_converged,238436,1.0, +Text-805,complete,256,B,H,0.0,6,not_converged,264215,0.95, +Text-812,complete,42,G,G,1.0,6,not_converged,190230,1.0, +Text-812,complete,123,G,G,1.0,6,not_converged,196761,0.99, +Text-812,complete,256,G,G,1.0,6,not_converged,282557,0.9559999999999998, +Text-816,complete,42,B,C,0.0,6,not_converged,236043,1.0, +Text-816,complete,123,B,C,0.0,6,not_converged,236710,1.0, +Text-816,complete,256,B,C,0.0,6,not_converged,221169,1.0, +Text-817,complete,42,H,H,1.0,6,not_converged,270913,1.0, +Text-817,complete,123,H,H,1.0,6,not_converged,236993,0.992, +Text-817,complete,256,H,H,1.0,6,not_converged,215716,0.9960000000000001, +Text-822,complete,42,G,G,1.0,6,not_converged,212602,0.992, +Text-822,complete,123,G,G,1.0,6,not_converged,197666,0.992, +Text-822,complete,256,G,G,1.0,6,not_converged,214544,0.9640000000000001, +Text-823,complete,42,F,F,1.0,6,not_converged,325291,0.982, +Text-823,complete,123,F,F,1.0,6,not_converged,275059,0.994, +Text-823,complete,256,F,F,1.0,6,not_converged,274105,0.9879999999999999, +Text-827,complete,42,G,F,0.0,6,not_converged,305618,0.95, +Text-827,complete,123,G,F,0.0,6,not_converged,236405,1.0, +Text-827,complete,256,G,F,0.0,6,not_converged,285817,0.958, +Text-830,complete,42,C,J,0.0,6,not_converged,215414,1.0, +Text-830,complete,123,C,J,0.0,6,not_converged,222942,0.958, +Text-830,complete,256,C,J,0.0,6,not_converged,240081,1.0, +Text-832,complete,42,C,B,0.0,6,not_converged,194915,0.9960000000000001, +Text-832,complete,123,C,B,0.0,6,not_converged,187470,0.99, +Text-832,complete,256,C,B,0.0,6,not_converged,180703,0.9960000000000001, +Text-833,complete,42,C,C,1.0,6,not_converged,167120,0.998, +Text-833,complete,123,C,C,1.0,6,not_converged,153699,1.0, +Text-833,complete,256,C,C,1.0,6,not_converged,162616,1.0, +Text-834,complete,42,I,G,0.0,6,not_converged,207712,1.0, +Text-834,complete,123,I,G,0.0,6,not_converged,209831,1.0, +Text-834,complete,256,I,G,0.0,6,not_converged,201486,1.0, +Text-836,complete,42,C,C,1.0,6,not_converged,175016,0.992, +Text-836,complete,123,C,C,1.0,6,not_converged,226195,0.97, +Text-836,complete,256,C,C,1.0,6,not_converged,195736,0.982, +Text-837,complete,42,J,J,1.0,6,not_converged,205704,0.9800000000000001, +Text-837,complete,123,J,J,1.0,6,not_converged,194612,0.99, +Text-837,complete,256,J,J,1.0,6,not_converged,204897,0.962, +Text-839,complete,42,J,J,1.0,6,not_converged,182051,1.0, +Text-839,complete,123,J,J,1.0,6,not_converged,172078,0.99, +Text-839,complete,256,J,J,1.0,6,not_converged,176800,0.986, +Text-840,complete,42,F,I,0.0,6,not_converged,166844,0.9960000000000001, +Text-840,complete,123,F,I,0.0,6,not_converged,179340,1.0, +Text-840,complete,256,F,I,0.0,6,not_converged,176460,1.0, +Text-847,complete,42,C,C,1.0,6,not_converged,218001,0.992, +Text-847,complete,123,C,C,1.0,6,not_converged,237067,0.9879999999999999, +Text-847,complete,256,C,C,1.0,6,not_converged,224872,0.998, +Text-852,complete,42,A,A,1.0,6,not_converged,230346,0.994, +Text-852,complete,123,A,A,1.0,6,not_converged,235605,0.99, +Text-852,complete,256,A,A,1.0,6,not_converged,229710,0.9960000000000001, +Text-856,complete,42,I,E,0.0,6,not_converged,238894,0.99, +Text-856,complete,123,I,E,0.0,6,not_converged,216037,0.99, +Text-856,complete,256,I,E,0.0,6,not_converged,210553,0.998, +Text-860,complete,42,E,D,0.0,6,not_converged,235582,0.982, +Text-860,complete,123,E,D,0.0,6,not_converged,285040,1.0, +Text-860,complete,256,E,D,0.0,6,not_converged,245753,1.0, +Text-861,complete,42,I,D,0.0,6,not_converged,334334,1.0, +Text-861,complete,123,I,D,0.0,6,not_converged,255514,0.998, +Text-861,complete,256,I,D,0.0,6,not_converged,333937,0.994, +Text-862,complete,42,A,G,0.0,6,not_converged,261110,0.982, 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+Text-889,complete,123,E,E,1.0,6,not_converged,239736,1.0, +Text-889,complete,256,E,E,1.0,6,not_converged,213816,1.0, +Text-891,complete,42,C,C,1.0,6,not_converged,310392,0.9400000000000001, +Text-891,complete,123,C,C,1.0,6,not_converged,287822,0.984, +Text-891,complete,256,C,C,1.0,6,not_converged,306370,0.968, +Text-896,complete,42,J,J,1.0,6,not_converged,275150,0.9960000000000001, +Text-896,complete,123,J,J,1.0,6,not_converged,286844,0.95, +Text-896,complete,256,J,J,1.0,6,not_converged,270866,0.998, +Text-898,complete,42,D,D,1.0,6,not_converged,180365,1.0, +Text-898,complete,123,D,D,1.0,6,not_converged,195825,1.0, +Text-898,complete,256,D,D,1.0,6,not_converged,207976,0.99, +Text-899,complete,42,E,B,0.0,6,not_converged,329733,0.992, +Text-899,complete,123,E,A,0.0,6,not_converged,352830,0.9400000000000001, +Text-899,complete,256,E,B,0.0,6,not_converged,395841,0.8039999999999999, +Text-901,complete,42,A,J,0.0,6,not_converged,211019,1.0, 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+Text-1155,complete,256,A,A,1.0,6,not_converged,239473,0.946, +Text-1157,complete,42,C,C,1.0,6,not_converged,365817,0.93, +Text-1157,complete,123,C,C,1.0,6,not_converged,334555,0.9559999999999998, +Text-1157,complete,256,C,C,1.0,6,not_converged,343220,0.9800000000000001, +Text-1158,complete,42,I,I,1.0,6,not_converged,198738,0.992, +Text-1158,complete,123,I,I,1.0,6,not_converged,242846,1.0, +Text-1158,complete,256,I,I,1.0,6,not_converged,244771,0.9960000000000001, +Text-1159,complete,42,F,G,0.0,6,not_converged,195619,1.0, +Text-1159,complete,123,F,G,0.0,6,not_converged,178176,1.0, +Text-1159,complete,256,F,G,0.0,6,not_converged,210334,0.9800000000000001, +Text-1162,complete,42,A,A,1.0,6,not_converged,230523,1.0, +Text-1162,complete,123,A,A,1.0,6,not_converged,207753,1.0, +Text-1162,complete,256,A,A,1.0,6,not_converged,226991,0.992, +Text-1164,complete,42,E,A,0.0,6,not_converged,281401,0.938, +Text-1164,complete,123,E,E,1.0,6,not_converged,226130,0.9960000000000001, +Text-1164,complete,256,E,E,1.0,6,not_converged,238088,0.99, +Text-1165,complete,42,A,A,1.0,6,not_converged,223113,0.998, +Text-1165,complete,123,A,A,1.0,6,not_converged,221447,0.95, +Text-1165,complete,256,A,A,1.0,6,not_converged,207504,0.992, +Text-1173,complete,42,H,H,1.0,6,not_converged,372294,0.97, +Text-1173,complete,123,H,F,0.0,6,not_converged,373415,0.9400000000000001, +Text-1173,complete,256,H,F,0.0,6,not_converged,361939,0.95, +Text-1174,complete,42,B,D,0.0,6,not_converged,195384,1.0, +Text-1174,complete,123,B,D,0.0,6,not_converged,188138,0.998, +Text-1174,complete,256,B,D,0.0,6,not_converged,183427,1.0, +Text-1176,complete,42,H,G,0.0,6,not_converged,204823,0.99, +Text-1176,complete,123,H,G,0.0,6,not_converged,226600,1.0, +Text-1176,complete,256,H,G,0.0,6,not_converged,228776,0.992, +Text-1177,complete,42,C,I,0.0,6,not_converged,248145,0.992, +Text-1177,complete,123,C,I,0.0,6,not_converged,209240,0.994, +Text-1177,complete,256,C,I,0.0,6,not_converged,194885,1.0, +Text-1184,complete,42,E,E,1.0,6,not_converged,277408,0.9800000000000001, +Text-1184,complete,123,E,A,0.0,6,not_converged,250681,0.994, +Text-1184,complete,256,E,E,1.0,6,not_converged,283574,0.986, +Text-1185,complete,42,F,F,1.0,6,not_converged,180061,1.0, +Text-1185,complete,123,F,F,1.0,6,not_converged,179978,1.0, +Text-1185,complete,256,F,F,1.0,6,not_converged,173335,1.0, +Text-1190,complete,42,H,D,0.0,6,not_converged,456286,0.79, +Text-1190,complete,123,H,I,0.0,6,not_converged,300890,0.9640000000000001, +Text-1190,complete,256,H,I,0.0,6,not_converged,260191,0.97, +Text-1191,complete,42,A,A,1.0,6,not_converged,215525,0.992, +Text-1191,complete,123,A,A,1.0,6,not_converged,204150,0.99, +Text-1191,complete,256,A,B,0.0,6,not_converged,225077,0.994, +Text-1193,complete,42,B,C,0.0,6,not_converged,221964,0.9559999999999998, +Text-1193,complete,123,B,C,0.0,6,not_converged,197330,0.994, +Text-1193,complete,256,B,B,1.0,6,not_converged,248351,0.9400000000000001, +Text-1194,complete,42,E,E,1.0,6,not_converged,196461,1.0, +Text-1194,complete,123,E,E,1.0,6,not_converged,192526,1.0, +Text-1194,complete,256,E,E,1.0,6,not_converged,172123,0.99, +Text-1199,complete,42,D,D,1.0,6,not_converged,249866,0.974, +Text-1199,complete,123,D,D,1.0,6,not_converged,172137,1.0, +Text-1199,complete,256,D,D,1.0,6,not_converged,169481,0.9960000000000001, +Text-1201,complete,42,I,A,0.0,6,not_converged,259048,0.994, +Text-1201,complete,123,I,A,0.0,6,not_converged,269219,0.99, +Text-1201,complete,256,I,A,0.0,6,not_converged,283725,0.982, +Text-1204,complete,42,D,J,0.0,6,not_converged,235227,0.984, +Text-1204,complete,123,D,J,0.0,6,not_converged,231539,0.984, +Text-1204,complete,256,D,J,0.0,6,not_converged,236121,0.962, +Text-1207,complete,42,J,J,1.0,6,not_converged,199754,1.0, +Text-1207,complete,123,J,J,1.0,6,not_converged,187978,1.0, +Text-1207,complete,256,J,J,1.0,6,not_converged,211973,1.0, +Text-1208,complete,42,G,G,1.0,6,not_converged,171377,0.9879999999999999, +Text-1208,complete,123,G,G,1.0,6,not_converged,182169,0.998, +Text-1208,complete,256,G,G,1.0,6,not_converged,163304,0.992, +Text-1209,complete,42,G,A,0.0,6,not_converged,208494,0.99, +Text-1209,complete,123,G,G,1.0,6,not_converged,277827,0.8800000000000001, +Text-1209,complete,256,G,A,0.0,6,not_converged,216968,0.976, +Text-1214,complete,42,C,C,1.0,6,not_converged,218704,1.0, +Text-1214,complete,123,C,C,1.0,6,not_converged,228194,0.99, +Text-1214,complete,256,C,C,1.0,6,not_converged,223957,1.0, +Text-1216,complete,42,I,I,1.0,6,not_converged,179410,1.0, +Text-1216,complete,123,I,I,1.0,6,not_converged,215052,0.992, +Text-1216,complete,256,I,I,1.0,6,not_converged,214078,0.9960000000000001, +Text-1217,complete,42,G,G,1.0,6,not_converged,177367,0.95, +Text-1217,complete,123,G,G,1.0,6,not_converged,188908,1.0, +Text-1217,complete,256,G,G,1.0,6,not_converged,174190,1.0, +Text-1218,complete,42,C,C,1.0,6,not_converged,242557,0.99, +Text-1218,complete,123,C,C,1.0,6,not_converged,230907,0.97, 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+Text-1228,complete,42,J,J,1.0,6,not_converged,219940,0.986, +Text-1228,complete,123,J,J,1.0,6,not_converged,201019,1.0, +Text-1228,complete,256,J,J,1.0,6,not_converged,256216,1.0, +Text-1229,complete,42,F,H,0.0,6,not_converged,280736,1.0, +Text-1229,complete,123,F,H,0.0,6,not_converged,236820,0.968, +Text-1229,complete,256,F,H,0.0,6,not_converged,283253,0.99, +Text-1231,complete,42,I,I,1.0,6,not_converged,244682,0.998, +Text-1231,complete,123,I,I,1.0,6,not_converged,269646,0.99, +Text-1231,complete,256,I,I,1.0,6,not_converged,277525,0.962, +Text-1232,complete,42,H,C,0.0,6,not_converged,285275,0.986, +Text-1232,complete,123,H,C,0.0,6,not_converged,286654,0.99, +Text-1232,complete,256,H,C,0.0,6,not_converged,232709,0.994, +Text-1233,complete,42,A,C,0.0,6,not_converged,240342,0.994, +Text-1233,complete,123,A,C,0.0,6,not_converged,234388,0.992, +Text-1233,complete,256,A,C,0.0,6,not_converged,259431,0.992, +Text-1235,complete,42,G,G,1.0,6,not_converged,202847,0.9960000000000001, 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+Text-1246,complete,123,F,H,0.0,6,not_converged,212786,0.994, +Text-1246,complete,256,F,H,0.0,6,not_converged,214672,1.0, +Text-1248,complete,42,H,A,0.0,6,not_converged,226126,1.0, +Text-1248,complete,123,H,A,0.0,6,not_converged,194199,0.992, +Text-1248,complete,256,H,A,0.0,6,not_converged,215937,0.994, +Text-1249,complete,42,J,J,1.0,6,not_converged,172797,0.9800000000000001, +Text-1249,complete,123,J,J,1.0,6,not_converged,175016,1.0, +Text-1249,complete,256,J,J,1.0,6,not_converged,171136,1.0, +Text-1252,complete,42,I,I,1.0,6,not_converged,219451,1.0, +Text-1252,complete,123,I,I,1.0,6,not_converged,215690,0.992, +Text-1252,complete,256,I,I,1.0,6,not_converged,208315,0.998, +Text-1253,complete,42,J,J,1.0,6,not_converged,210115,0.9960000000000001, +Text-1253,complete,123,J,J,1.0,6,not_converged,206734,0.99, +Text-1253,complete,256,J,J,1.0,6,not_converged,193953,1.0, +Text-1254,complete,42,J,J,1.0,6,not_converged,263951,0.96, +Text-1254,complete,123,J,J,1.0,6,not_converged,279151,0.8800000000000001, +Text-1254,complete,256,J,J,1.0,6,not_converged,275374,0.8800000000000001, +Text-1255,complete,42,G,C,0.0,6,not_converged,299100,0.9960000000000001, +Text-1255,complete,123,G,C,0.0,6,not_converged,250335,0.99, +Text-1255,complete,256,G,C,0.0,6,not_converged,294708,0.982, +Text-1259,complete,42,J,A,0.0,6,not_converged,209511,1.0, +Text-1259,complete,123,J,A,0.0,6,not_converged,232911,0.9879999999999999, +Text-1259,complete,256,J,A,0.0,6,not_converged,217220,0.998, +Text-1261,complete,42,J,J,1.0,6,not_converged,267915,0.962, +Text-1261,complete,123,J,J,1.0,6,not_converged,255405,0.99, +Text-1261,complete,256,J,J,1.0,6,not_converged,288160,0.982, +Text-1262,complete,42,F,B,0.0,6,not_converged,203713,1.0, +Text-1262,complete,123,F,B,0.0,6,not_converged,207203,0.9719999999999999, +Text-1262,complete,256,F,B,0.0,6,not_converged,200329,0.998, +Text-1265,complete,42,E,G,0.0,6,not_converged,263787,0.97, 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+Text-1293,complete,256,D,D,1.0,6,not_converged,182920,1.0, +Text-1296,complete,42,G,G,1.0,6,not_converged,243478,0.9800000000000001, +Text-1296,complete,123,G,G,1.0,6,not_converged,244864,1.0, +Text-1296,complete,256,G,G,1.0,6,not_converged,259179,1.0, +Text-1297,complete,42,D,H,0.0,6,not_converged,266104,0.978, +Text-1297,complete,123,D,G,0.0,6,not_converged,300468,0.974, +Text-1297,complete,256,D,H,0.0,6,not_converged,244563,0.986, +Text-1304,complete,42,A,J,0.0,6,not_converged,191380,0.9960000000000001, +Text-1304,complete,123,A,J,0.0,6,not_converged,180773,1.0, +Text-1304,complete,256,A,J,0.0,6,not_converged,204265,1.0, +Text-1308,complete,42,B,B,1.0,6,not_converged,312349,0.9960000000000001, +Text-1308,complete,123,B,B,1.0,6,not_converged,300023,0.994, +Text-1308,complete,256,B,B,1.0,6,not_converged,317619,0.992, +Text-1310,complete,42,B,B,1.0,6,not_converged,283816,0.932, +Text-1310,complete,123,B,H,0.0,6,not_converged,352812,0.89, +Text-1310,complete,256,B,B,1.0,6,not_converged,236370,1.0, +Text-1315,complete,42,F,F,1.0,6,not_converged,150990,1.0, +Text-1315,complete,123,F,F,1.0,6,not_converged,180542,1.0, +Text-1315,complete,256,F,F,1.0,6,not_converged,155154,0.994, +Text-1319,complete,42,G,C,0.0,6,not_converged,293288,0.8640000000000001, +Text-1319,complete,123,G,C,0.0,6,not_converged,317291,0.95, +Text-1319,complete,256,G,C,0.0,6,not_converged,307401,0.95, +Text-1320,complete,42,F,F,1.0,6,not_converged,202552,0.992, +Text-1320,complete,123,F,F,1.0,6,not_converged,223933,0.9960000000000001, +Text-1320,complete,256,F,F,1.0,6,not_converged,236679,0.984, +Text-1321,complete,42,H,H,1.0,6,not_converged,288386,1.0, +Text-1321,complete,123,H,H,1.0,6,not_converged,263647,1.0, +Text-1321,complete,256,H,H,1.0,6,not_converged,258181,1.0, +Text-1324,complete,42,A,F,0.0,6,not_converged,229396,1.0, +Text-1324,complete,123,A,F,0.0,6,not_converged,213536,1.0, +Text-1324,complete,256,A,F,0.0,6,not_converged,183181,0.998, +Text-1326,complete,42,B,C,0.0,6,not_converged,190616,0.992, +Text-1326,complete,123,B,C,0.0,6,not_converged,178034,0.99, +Text-1326,complete,256,B,C,0.0,6,not_converged,180249,1.0, +Text-1327,complete,42,F,F,1.0,6,not_converged,324674,1.0, +Text-1327,complete,123,F,C,0.0,6,not_converged,387789,0.95, +Text-1327,complete,256,F,F,1.0,6,not_converged,380531,0.9, +Text-1334,complete,42,D,D,1.0,6,not_converged,223673,0.994, +Text-1334,complete,123,D,D,1.0,6,not_converged,225973,0.9960000000000001, +Text-1334,complete,256,D,D,1.0,6,not_converged,229101,0.998, +Text-1335,complete,42,G,G,1.0,6,not_converged,219336,1.0, +Text-1335,complete,123,G,G,1.0,6,not_converged,186672,0.992, +Text-1335,complete,256,G,G,1.0,6,not_converged,185794,0.9960000000000001, +Text-1337,complete,42,A,J,0.0,6,not_converged,173455,0.99, +Text-1337,complete,123,A,J,0.0,6,not_converged,161972,0.998, +Text-1337,complete,256,A,J,0.0,6,not_converged,185118,0.998, +Text-1338,complete,42,A,A,1.0,6,not_converged,209071,0.986, +Text-1338,complete,123,A,J,0.0,6,not_converged,316801,0.8240000000000001, +Text-1338,complete,256,A,J,0.0,6,not_converged,252418,0.982, +Text-1339,complete,42,E,E,1.0,6,not_converged,270430,0.982, +Text-1339,complete,123,E,E,1.0,6,not_converged,262346,0.974, +Text-1339,complete,256,E,E,1.0,6,not_converged,293310,0.994, +Text-1349,complete,42,I,D,0.0,6,not_converged,378333,0.93, +Text-1349,complete,123,I,D,0.0,6,not_converged,344896,0.986, +Text-1349,complete,256,I,D,0.0,6,not_converged,284259,0.986, +Text-1351,complete,42,F,I,0.0,6,not_converged,215591,0.984, +Text-1351,complete,123,F,I,0.0,6,not_converged,266697,0.986, +Text-1351,complete,256,F,I,0.0,6,not_converged,270666,0.978, +Text-1353,complete,42,I,C,0.0,6,not_converged,186963,0.95, +Text-1353,complete,123,I,C,0.0,6,not_converged,240612,0.994, +Text-1353,complete,256,I,C,0.0,6,not_converged,219478,0.992, +Text-1354,complete,42,C,I,0.0,6,not_converged,304926,0.962, +Text-1354,complete,123,C,I,0.0,6,not_converged,289276,0.7939999999999999, +Text-1354,complete,256,C,I,0.0,6,not_converged,273347,0.97, +Text-1357,complete,42,F,F,1.0,6,not_converged,380261,0.95, +Text-1357,complete,123,F,J,0.0,6,not_converged,312938,1.0, +Text-1357,complete,256,F,J,0.0,6,not_converged,379014,0.982, +Text-1358,complete,42,A,D,0.0,6,not_converged,266346,1.0, +Text-1358,complete,123,A,D,0.0,6,not_converged,316375,0.9559999999999998, +Text-1358,complete,256,A,D,0.0,6,not_converged,241089,0.992, +Text-1359,complete,42,C,E,0.0,6,not_converged,254084,0.9879999999999999, +Text-1359,complete,123,C,E,0.0,6,not_converged,223049,0.984, +Text-1359,complete,256,C,E,0.0,6,not_converged,232468,0.9879999999999999, +Text-1361,complete,42,H,H,1.0,6,not_converged,171604,1.0, +Text-1361,complete,123,H,H,1.0,6,not_converged,186376,0.99, +Text-1361,complete,256,H,H,1.0,6,not_converged,165783,0.9960000000000001, +Text-1363,complete,42,E,E,1.0,6,not_converged,205351,1.0, +Text-1363,complete,123,E,E,1.0,6,not_converged,251671,1.0, +Text-1363,complete,256,E,E,1.0,6,not_converged,233981,1.0, +Text-1365,complete,42,D,D,1.0,6,not_converged,250604,0.982, +Text-1365,complete,123,D,D,1.0,6,not_converged,214344,0.93, +Text-1365,complete,256,D,A,0.0,6,not_converged,191551,0.968, +Text-1366,complete,42,I,I,1.0,6,not_converged,218463,1.0, +Text-1366,complete,123,I,I,1.0,6,not_converged,204522,0.99, +Text-1366,complete,256,I,I,1.0,6,not_converged,187623,0.998, +Text-1367,complete,42,J,B,0.0,6,not_converged,377304,0.99, +Text-1367,complete,123,J,B,0.0,6,not_converged,288769,0.986, +Text-1367,complete,256,J,B,0.0,6,not_converged,222255,1.0, +Text-1370,complete,42,J,J,1.0,6,not_converged,250613,0.97, +Text-1370,complete,123,J,J,1.0,6,not_converged,218252,0.978, +Text-1370,complete,256,J,J,1.0,6,not_converged,255528,0.97, +Text-1371,complete,42,E,E,1.0,6,not_converged,181424,0.99, +Text-1371,complete,123,E,E,1.0,6,not_converged,162802,1.0, +Text-1371,complete,256,E,E,1.0,6,not_converged,194319,0.992, +Text-1372,complete,42,C,D,0.0,6,not_converged,260598,0.9719999999999999, +Text-1372,complete,123,C,D,0.0,6,not_converged,227368,0.99, +Text-1372,complete,256,C,D,0.0,6,not_converged,217364,0.986, +Text-1375,complete,42,E,E,1.0,6,not_converged,158539,0.994, +Text-1375,complete,123,E,E,1.0,6,not_converged,172959,0.998, +Text-1375,complete,256,E,E,1.0,6,not_converged,163971,1.0, +Text-1376,complete,42,B,I,0.0,6,not_converged,335475,0.958, +Text-1376,complete,123,B,I,0.0,6,not_converged,329493,0.9960000000000001, +Text-1376,complete,256,B,I,0.0,6,not_converged,313266,0.99, +Text-1384,complete,42,D,D,1.0,6,not_converged,198716,0.994, +Text-1384,complete,123,D,D,1.0,6,not_converged,200723,0.998, +Text-1384,complete,256,D,D,1.0,6,not_converged,207327,1.0, +Text-1385,complete,42,A,A,1.0,6,not_converged,238253,0.998, +Text-1385,complete,123,A,A,1.0,6,not_converged,253186,0.994, +Text-1385,complete,256,A,A,1.0,6,not_converged,265086,0.986, +Text-1386,complete,42,E,E,1.0,6,not_converged,230519,0.994, +Text-1386,complete,123,E,E,1.0,6,not_converged,239762,1.0, +Text-1386,complete,256,E,A,0.0,6,not_converged,322155,0.9559999999999998, +Text-1389,complete,42,A,I,0.0,6,not_converged,232800,1.0, +Text-1389,complete,123,A,I,0.0,6,not_converged,277131,0.992, +Text-1389,complete,256,A,A,1.0,6,not_converged,351523,0.86, +Text-1390,complete,42,C,C,1.0,6,not_converged,169647,0.9800000000000001, +Text-1390,complete,123,C,C,1.0,6,not_converged,182001,0.986, +Text-1390,complete,256,C,C,1.0,6,not_converged,171313,0.994, +Text-1391,complete,42,B,B,1.0,6,not_converged,242378,1.0, +Text-1391,complete,123,B,B,1.0,6,not_converged,202868,0.992, +Text-1391,complete,256,B,B,1.0,6,not_converged,213280,1.0, +Text-1392,complete,42,A,A,1.0,6,not_converged,184648,1.0, +Text-1392,complete,123,A,A,1.0,6,not_converged,190316,1.0, +Text-1392,complete,256,A,A,1.0,6,not_converged,209428,1.0, +Text-1395,complete,42,I,I,1.0,6,not_converged,196398,0.99, +Text-1395,complete,123,I,I,1.0,6,not_converged,197013,0.9879999999999999, +Text-1395,complete,256,I,I,1.0,6,not_converged,207503,0.9960000000000001, +Text-1396,complete,42,J,J,1.0,6,not_converged,377809,0.95, +Text-1396,complete,123,J,J,1.0,6,not_converged,328983,0.99, +Text-1396,complete,256,J,J,1.0,6,not_converged,317263,0.9879999999999999, +Text-1397,complete,42,C,C,1.0,6,not_converged,222466,0.96, +Text-1397,complete,123,C,C,1.0,6,not_converged,227394,0.998, +Text-1397,complete,256,C,C,1.0,6,not_converged,214440,1.0, +Text-1398,complete,42,C,C,1.0,6,not_converged,263548,1.0, +Text-1398,complete,123,C,C,1.0,6,not_converged,305945,0.998, +Text-1398,complete,256,C,C,1.0,6,not_converged,238302,0.998, +Text-1399,complete,42,H,A,0.0,6,not_converged,167065,0.9960000000000001, +Text-1399,complete,123,H,A,0.0,6,not_converged,161548,1.0, +Text-1399,complete,256,H,A,0.0,6,not_converged,188224,1.0, +Text-1400,complete,42,H,H,1.0,6,not_converged,347060,0.77, +Text-1400,complete,123,H,F,0.0,6,not_converged,262694,0.99, +Text-1400,complete,256,H,F,0.0,6,not_converged,316414,1.0, +Text-1402,complete,42,B,J,0.0,6,not_converged,239344,0.974, +Text-1402,complete,123,B,J,0.0,6,not_converged,365922,0.884, +Text-1402,complete,256,B,J,0.0,6,not_converged,255613,0.96, +Text-1405,complete,42,H,H,1.0,6,not_converged,339377,0.95, +Text-1405,complete,123,H,H,1.0,6,not_converged,456997,0.962, +Text-1405,complete,256,H,H,1.0,6,not_converged,355174,0.96, +Text-1406,complete,42,G,D,0.0,6,not_converged,261329,0.9879999999999999, +Text-1406,complete,123,G,I,0.0,6,not_converged,322911,0.89, +Text-1406,complete,256,G,F,0.0,6,not_converged,315749,0.93, +Text-1408,complete,42,I,I,1.0,6,not_converged,215089,0.99, +Text-1408,complete,123,I,I,1.0,6,not_converged,249317,0.984, +Text-1408,complete,256,I,I,1.0,6,not_converged,214069,1.0, +Text-1409,complete,42,C,C,1.0,6,not_converged,182760,1.0, +Text-1409,complete,123,C,C,1.0,6,not_converged,196115,0.99, +Text-1409,complete,256,C,C,1.0,6,not_converged,185663,1.0, +Text-1411,complete,42,B,B,1.0,6,not_converged,209130,0.984, +Text-1411,complete,123,B,B,1.0,6,not_converged,217413,1.0, +Text-1411,complete,256,B,B,1.0,6,not_converged,229898,1.0, +Text-1414,complete,42,D,D,1.0,6,not_converged,163451,0.998, +Text-1414,complete,123,D,D,1.0,6,not_converged,160166,0.994, +Text-1414,complete,256,D,D,1.0,6,not_converged,174248,1.0, +Text-1415,complete,42,A,A,1.0,6,not_converged,255254,0.986, +Text-1415,complete,123,A,A,1.0,6,not_converged,302809,0.992, +Text-1415,complete,256,A,A,1.0,6,not_converged,349482,0.79, +Text-1416,complete,42,J,J,1.0,6,not_converged,141507,0.992, +Text-1416,complete,123,J,J,1.0,6,not_converged,174555,1.0, +Text-1416,complete,256,J,J,1.0,6,not_converged,154327,0.994, +Text-1417,complete,42,H,I,0.0,6,not_converged,220240,0.994, +Text-1417,complete,123,H,I,0.0,6,not_converged,209950,1.0, +Text-1417,complete,256,H,I,0.0,6,not_converged,210178,1.0, +Text-1422,complete,42,I,I,1.0,6,not_converged,222132,0.992, +Text-1422,complete,123,I,I,1.0,6,not_converged,201625,1.0, +Text-1422,complete,256,I,I,1.0,6,not_converged,197648,1.0, +Text-1425,complete,42,B,B,1.0,6,not_converged,173746,1.0, +Text-1425,complete,123,B,B,1.0,6,not_converged,172210,0.99, +Text-1425,complete,256,B,B,1.0,6,not_converged,175162,1.0, +Text-1426,complete,42,A,H,0.0,6,not_converged,224417,1.0, +Text-1426,complete,123,A,H,0.0,6,not_converged,225830,1.0, +Text-1426,complete,256,A,H,0.0,6,not_converged,225741,0.9879999999999999, +Text-1427,complete,42,J,B,0.0,6,not_converged,263017,0.994, +Text-1427,complete,123,J,B,0.0,6,not_converged,323886,0.9179999999999999, +Text-1427,complete,256,J,B,0.0,6,not_converged,267123,0.8699999999999999, +Text-1428,complete,42,I,I,1.0,6,not_converged,261202,1.0, +Text-1428,complete,123,I,I,1.0,6,not_converged,245389,1.0, 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+Text-1449,complete,123,B,C,0.0,6,not_converged,249721,0.982, +Text-1449,complete,256,B,C,0.0,6,not_converged,240348,0.99, +Text-1450,complete,42,D,D,1.0,6,not_converged,194059,0.99, +Text-1450,complete,123,D,D,1.0,6,not_converged,167537,1.0, +Text-1450,complete,256,D,D,1.0,6,not_converged,192761,0.998, +Text-1454,complete,42,C,C,1.0,6,not_converged,231655,1.0, +Text-1454,complete,123,C,C,1.0,6,not_converged,202552,1.0, +Text-1454,complete,256,C,C,1.0,6,not_converged,196044,0.99, +Text-1458,complete,42,C,C,1.0,6,not_converged,253424,0.97, +Text-1458,complete,123,C,B,0.0,6,not_converged,242738,0.994, +Text-1458,complete,256,C,B,0.0,6,not_converged,359716,0.758, +Text-1459,complete,42,H,H,1.0,6,not_converged,177710,1.0, +Text-1459,complete,123,H,H,1.0,6,not_converged,166526,0.982, +Text-1459,complete,256,H,H,1.0,6,not_converged,189311,0.998, +Text-1460,complete,42,J,J,1.0,6,not_converged,216279,1.0, +Text-1460,complete,123,J,J,1.0,6,not_converged,192160,0.9879999999999999, 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+Text-1595,complete,256,E,E,1.0,6,not_converged,176720,1.0, +Text-1597,complete,42,J,J,1.0,6,not_converged,195885,0.97, +Text-1597,complete,123,J,J,1.0,6,not_converged,185555,1.0, +Text-1597,complete,256,J,J,1.0,6,not_converged,205197,0.9879999999999999, +Text-1598,complete,42,F,F,1.0,6,not_converged,205980,1.0, +Text-1598,complete,123,F,F,1.0,6,not_converged,190032,0.992, +Text-1598,complete,256,F,F,1.0,6,not_converged,212420,0.994, +Text-1599,complete,42,H,B,0.0,6,not_converged,183286,1.0, +Text-1599,complete,123,H,B,0.0,6,not_converged,179422,0.998, +Text-1599,complete,256,H,B,0.0,6,not_converged,163721,1.0, +Text-1600,complete,42,H,B,0.0,6,not_converged,192412,0.9879999999999999, +Text-1600,complete,123,H,B,0.0,6,not_converged,198213,0.984, +Text-1600,complete,256,H,B,0.0,6,not_converged,204057,0.9879999999999999, +Text-1601,complete,42,D,D,1.0,6,not_converged,206680,0.99, +Text-1601,complete,123,D,D,1.0,6,not_converged,213172,0.9879999999999999, +Text-1601,complete,256,D,D,1.0,6,not_converged,208743,1.0, +Text-1602,complete,42,J,B,0.0,6,not_converged,249522,0.994, +Text-1602,complete,123,J,B,0.0,6,not_converged,257748,1.0, +Text-1602,complete,256,J,B,0.0,6,not_converged,289278,0.994, +Text-1603,complete,42,B,E,0.0,6,not_converged,264632,0.986, +Text-1603,complete,123,B,B,1.0,6,not_converged,386359,0.8, +Text-1603,complete,256,B,E,0.0,6,not_converged,343104,0.9719999999999999, +Text-1605,complete,42,G,B,0.0,6,not_converged,325073,0.976, +Text-1605,complete,123,G,B,0.0,6,not_converged,307922,0.9640000000000001, +Text-1605,complete,256,G,B,0.0,6,not_converged,431201,0.968, +Text-1606,complete,42,D,D,1.0,6,not_converged,192206,0.95, +Text-1606,complete,123,D,D,1.0,6,not_converged,211223,0.9879999999999999, +Text-1606,complete,256,D,D,1.0,6,not_converged,196152,0.99, +Text-1608,complete,42,C,I,0.0,6,not_converged,327274,0.96, +Text-1608,complete,123,C,I,0.0,6,not_converged,317009,1.0, +Text-1608,complete,256,C,I,0.0,6,not_converged,313163,0.986, +Text-1609,complete,42,E,E,1.0,6,not_converged,226438,0.99, +Text-1609,complete,123,E,E,1.0,6,not_converged,260876,0.968, +Text-1609,complete,256,E,E,1.0,6,not_converged,248527,0.994, +Text-1613,complete,42,D,E,0.0,6,not_converged,182639,1.0, +Text-1613,complete,123,D,E,0.0,6,not_converged,170362,1.0, +Text-1613,complete,256,D,E,0.0,6,not_converged,172843,1.0, +Text-1615,complete,42,C,D,0.0,6,not_converged,346289,0.986, +Text-1615,complete,123,C,C,1.0,6,not_converged,344839,0.9800000000000001, +Text-1615,complete,256,C,D,0.0,6,not_converged,368738,0.9719999999999999, +Text-1616,complete,42,G,G,1.0,6,not_converged,184413,0.974, +Text-1616,complete,123,G,G,1.0,6,not_converged,178530,0.9640000000000001, +Text-1616,complete,256,G,G,1.0,6,not_converged,177009,0.99, +Text-1618,complete,42,I,G,0.0,6,not_converged,171732,0.99, +Text-1618,complete,123,I,G,0.0,6,not_converged,189100,0.99, +Text-1618,complete,256,I,G,0.0,6,not_converged,180143,0.998, +Text-1623,complete,42,C,C,1.0,6,not_converged,289760,0.9099999999999999, +Text-1623,complete,123,C,D,0.0,6,not_converged,234418,0.986, +Text-1623,complete,256,C,D,0.0,6,not_converged,251870,0.9719999999999999, +Text-1626,complete,42,A,G,0.0,6,not_converged,280629,1.0, +Text-1626,complete,123,A,G,0.0,6,not_converged,299656,1.0, +Text-1626,complete,256,A,G,0.0,6,not_converged,242667,0.99, +Text-1631,complete,42,F,I,0.0,6,not_converged,216143,0.95, +Text-1631,complete,123,F,I,0.0,6,not_converged,223580,0.9960000000000001, +Text-1631,complete,256,F,I,0.0,6,not_converged,212561,0.9800000000000001, +Text-1636,complete,42,C,C,1.0,6,not_converged,283861,0.994, +Text-1636,complete,123,C,C,1.0,6,not_converged,210416,0.9879999999999999, +Text-1636,complete,256,C,C,1.0,6,not_converged,247971,0.982, +Text-1639,complete,42,D,H,0.0,6,not_converged,253819,0.9960000000000001, +Text-1639,complete,123,D,H,0.0,6,not_converged,240104,0.994, +Text-1639,complete,256,D,H,0.0,6,not_converged,228302,0.9960000000000001, +Text-1640,complete,42,F,F,1.0,6,not_converged,220208,0.99, +Text-1640,complete,123,F,F,1.0,6,not_converged,249104,1.0, +Text-1640,complete,256,F,F,1.0,6,not_converged,219424,1.0, +Text-1641,complete,42,B,B,1.0,6,not_converged,198140,0.99, +Text-1641,complete,123,B,B,1.0,6,not_converged,215981,0.9559999999999998, +Text-1641,complete,256,B,B,1.0,6,not_converged,206301,1.0, +Text-1644,complete,42,D,F,0.0,6,not_converged,502687,0.966, +Text-1644,complete,123,D,B,0.0,6,not_converged,537508,0.95, +Text-1644,complete,256,D,B,0.0,6,not_converged,353489,1.0, +Text-1646,complete,42,A,I,0.0,6,not_converged,190925,0.9960000000000001, +Text-1646,complete,123,A,I,0.0,6,not_converged,215616,0.99, +Text-1646,complete,256,A,I,0.0,6,not_converged,202480,1.0, +Text-1648,complete,42,H,G,0.0,6,not_converged,173515,0.9960000000000001, +Text-1648,complete,123,H,G,0.0,6,not_converged,183335,1.0, +Text-1648,complete,256,H,G,0.0,6,not_converged,176246,1.0, +Text-1657,complete,42,I,I,1.0,6,not_converged,365645,0.9, +Text-1657,complete,123,I,C,0.0,6,not_converged,242273,0.97, +Text-1657,complete,256,I,I,1.0,6,not_converged,296797,0.95, +Text-1667,complete,42,C,I,0.0,6,not_converged,204085,0.9960000000000001, +Text-1667,complete,123,C,I,0.0,6,not_converged,198391,0.9960000000000001, +Text-1667,complete,256,C,I,0.0,6,not_converged,191898,0.99, +Text-1669,complete,42,F,F,1.0,6,not_converged,235755,0.9879999999999999, +Text-1669,complete,123,F,F,1.0,6,not_converged,240379,0.994, +Text-1669,complete,256,F,F,1.0,6,not_converged,263044,0.986, +Text-1670,complete,42,F,F,1.0,6,not_converged,279143,1.0, +Text-1670,complete,123,F,F,1.0,6,not_converged,328355,0.992, +Text-1670,complete,256,F,F,1.0,6,not_converged,269702,0.978, +Text-1672,complete,42,F,H,0.0,6,not_converged,234374,0.992, +Text-1672,complete,123,F,H,0.0,6,not_converged,209165,1.0, +Text-1672,complete,256,F,H,0.0,6,not_converged,202999,0.992, +Text-1676,complete,42,F,I,0.0,6,not_converged,191260,0.974, +Text-1676,complete,123,F,I,0.0,6,not_converged,203538,0.994, +Text-1676,complete,256,F,I,0.0,6,not_converged,201871,1.0, +Text-1680,complete,42,A,I,0.0,6,not_converged,181350,1.0, +Text-1680,complete,123,A,I,0.0,6,not_converged,192865,1.0, +Text-1680,complete,256,A,I,0.0,6,not_converged,191118,0.99, +Text-1681,complete,42,I,I,1.0,6,not_converged,265611,0.9560000000000001, +Text-1681,complete,123,I,I,1.0,6,not_converged,294547,0.95, +Text-1681,complete,256,I,G,0.0,6,not_converged,416207,0.968, +Text-1682,complete,42,I,I,1.0,6,not_converged,186278,1.0, +Text-1682,complete,123,I,I,1.0,6,not_converged,172865,0.95, +Text-1682,complete,256,I,I,1.0,6,not_converged,196224,0.9879999999999999, +Text-1683,complete,42,H,H,1.0,6,not_converged,271530,0.9, +Text-1683,complete,123,H,B,0.0,6,not_converged,230208,0.986, +Text-1683,complete,256,H,B,0.0,6,not_converged,242442,0.982, +Text-1687,complete,42,F,J,0.0,6,not_converged,607109,0.9199999999999999, +Text-1687,complete,123,F,B,0.0,6,not_converged,536242,0.95, +Text-1687,complete,256,F,B,0.0,6,not_converged,436128,0.968, +Text-1688,complete,42,C,C,1.0,6,not_converged,207541,1.0, +Text-1688,complete,123,C,C,1.0,6,not_converged,224233,0.8300000000000001, +Text-1688,complete,256,C,C,1.0,6,not_converged,200919,1.0, +Text-1689,complete,42,F,C,0.0,6,not_converged,237764,1.0, +Text-1689,complete,123,F,C,0.0,6,not_converged,209444,0.9879999999999999, +Text-1689,complete,256,F,C,0.0,6,not_converged,197030,0.994, +Text-1690,complete,42,I,E,0.0,6,not_converged,274176,1.0, +Text-1690,complete,123,I,E,0.0,6,not_converged,257254,1.0, +Text-1690,complete,256,I,E,0.0,6,not_converged,320951,0.9960000000000001, +Text-1692,complete,42,I,E,0.0,6,not_converged,355172,0.954, +Text-1692,complete,123,I,E,0.0,6,not_converged,262991,0.9719999999999999, +Text-1692,complete,256,I,E,0.0,6,not_converged,410163,0.9199999999999999, +Text-1694,complete,42,C,B,0.0,6,not_converged,216167,0.992, +Text-1694,complete,123,C,B,0.0,6,not_converged,298566,0.876, +Text-1694,complete,256,C,B,0.0,6,not_converged,204569,1.0, +Text-1695,complete,42,E,H,0.0,6,not_converged,167850,1.0, +Text-1695,complete,123,E,H,0.0,6,not_converged,157820,0.99, +Text-1695,complete,256,E,H,0.0,6,not_converged,170516,0.978, +Text-1696,complete,42,I,I,1.0,6,not_converged,201037,0.998, +Text-1696,complete,123,I,I,1.0,6,not_converged,199608,0.994, +Text-1696,complete,256,I,I,1.0,6,not_converged,202315,1.0, +Text-1697,complete,42,J,B,0.0,6,not_converged,341872,1.0, +Text-1697,complete,123,J,B,0.0,6,not_converged,352456,0.99, +Text-1697,complete,256,J,B,0.0,6,not_converged,292125,0.998, +Text-1698,complete,42,I,G,0.0,6,not_converged,193350,0.99, +Text-1698,complete,123,I,D,0.0,6,not_converged,271986,0.85, +Text-1698,complete,256,I,G,0.0,6,not_converged,235208,0.984, +Text-1701,complete,42,E,E,1.0,6,not_converged,202753,0.9960000000000001, +Text-1701,complete,123,E,E,1.0,6,not_converged,196777,0.998, +Text-1701,complete,256,E,E,1.0,6,not_converged,199964,1.0, +Text-1702,complete,42,A,A,1.0,6,not_converged,179196,1.0, +Text-1702,complete,123,A,A,1.0,6,not_converged,176008,1.0, +Text-1702,complete,256,A,A,1.0,6,not_converged,173967,0.9960000000000001, +Text-1703,complete,42,E,E,1.0,6,not_converged,233064,0.9960000000000001, +Text-1703,complete,123,E,E,1.0,6,not_converged,244744,1.0, +Text-1703,complete,256,E,E,1.0,6,not_converged,217831,1.0, +Text-1704,complete,42,A,A,1.0,6,not_converged,201091,1.0, +Text-1704,complete,123,A,A,1.0,6,not_converged,174769,1.0, +Text-1704,complete,256,A,A,1.0,6,not_converged,174657,1.0, +Text-1705,complete,42,J,A,0.0,6,not_converged,275012,0.99, +Text-1705,complete,123,J,A,0.0,6,not_converged,251100,0.97, +Text-1705,complete,256,J,A,0.0,6,not_converged,247892,0.986, +Text-1706,complete,42,J,H,0.0,6,not_converged,183598,1.0, +Text-1706,complete,123,J,H,0.0,6,not_converged,167463,0.994, +Text-1706,complete,256,J,H,0.0,6,not_converged,177838,1.0, +Text-1711,complete,42,I,H,0.0,6,not_converged,256025,0.962, +Text-1711,complete,123,I,D,0.0,6,not_converged,260030,0.99, +Text-1711,complete,256,I,D,0.0,6,not_converged,276425,0.994, +Text-1712,complete,42,J,B,0.0,6,not_converged,338410,0.99, +Text-1712,complete,123,J,B,0.0,6,not_converged,243861,0.992, +Text-1712,complete,256,J,B,0.0,6,not_converged,299692,0.9400000000000001, +Text-1713,complete,42,I,J,0.0,6,not_converged,183535,0.994, +Text-1713,complete,123,I,J,0.0,6,not_converged,209332,0.99, +Text-1713,complete,256,I,J,0.0,6,not_converged,218991,0.986, +Text-1715,complete,42,C,C,1.0,6,not_converged,203360,1.0, +Text-1715,complete,123,C,C,1.0,6,not_converged,195522,1.0, +Text-1715,complete,256,C,C,1.0,6,not_converged,206009,1.0, +Text-1717,complete,42,C,D,0.0,6,not_converged,190256,1.0, +Text-1717,complete,123,C,D,0.0,6,not_converged,180316,1.0, +Text-1717,complete,256,C,D,0.0,6,not_converged,196885,0.994, 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+Text-1757,complete,123,D,D,1.0,6,not_converged,154680,0.994, +Text-1757,complete,256,D,D,1.0,6,not_converged,154185,1.0, +Text-1759,complete,42,I,E,0.0,6,not_converged,274848,0.962, +Text-1759,complete,123,I,E,0.0,6,not_converged,185640,0.9960000000000001, +Text-1759,complete,256,I,D,0.0,6,not_converged,288503,0.9199999999999999, +Text-1760,complete,42,I,F,0.0,6,not_converged,180558,1.0, +Text-1760,complete,123,I,F,0.0,6,not_converged,176940,0.994, +Text-1760,complete,256,I,F,0.0,6,not_converged,176121,1.0, +Text-1762,complete,42,C,C,1.0,6,not_converged,286582,0.9559999999999998, +Text-1762,complete,123,C,C,1.0,6,not_converged,317017,0.9960000000000001, +Text-1762,complete,256,C,C,1.0,6,not_converged,231329,0.974, +Text-1764,complete,42,F,F,1.0,6,not_converged,197102,0.9960000000000001, +Text-1764,complete,123,F,F,1.0,6,not_converged,209909,0.99, +Text-1764,complete,256,F,F,1.0,6,not_converged,220086,0.982, +Text-1769,complete,42,G,G,1.0,6,not_converged,203224,1.0, +Text-1769,complete,123,G,G,1.0,6,not_converged,224030,0.998, +Text-1769,complete,256,G,G,1.0,6,not_converged,189341,0.998, +Text-1771,complete,42,H,H,1.0,6,not_converged,213316,0.99, +Text-1771,complete,123,H,H,1.0,6,not_converged,236439,0.99, +Text-1771,complete,256,H,H,1.0,6,not_converged,208146,0.9879999999999999, +Text-1772,complete,42,F,F,1.0,6,not_converged,271784,0.968, +Text-1772,complete,123,F,F,1.0,6,not_converged,260997,0.99, +Text-1772,complete,256,F,F,1.0,6,not_converged,256380,0.9559999999999998, +Text-1774,complete,42,I,J,0.0,6,not_converged,251195,0.994, +Text-1774,complete,123,I,J,0.0,6,not_converged,243667,0.9879999999999999, +Text-1774,complete,256,I,J,0.0,6,not_converged,249832,0.9800000000000001, +Text-1777,complete,42,I,I,1.0,6,not_converged,187461,1.0, +Text-1777,complete,123,I,I,1.0,6,not_converged,184828,1.0, +Text-1777,complete,256,I,F,0.0,6,not_converged,280041,0.9879999999999999, +Text-1778,complete,42,I,G,0.0,6,not_converged,326327,0.976, +Text-1778,complete,123,I,J,0.0,6,not_converged,354880,0.9800000000000001, +Text-1778,complete,256,I,J,0.0,6,not_converged,317066,0.9879999999999999, +Text-1780,complete,42,B,C,0.0,6,not_converged,197292,0.992, +Text-1780,complete,123,B,C,0.0,6,not_converged,200246,1.0, +Text-1780,complete,256,B,C,0.0,6,not_converged,191949,1.0, +Text-1782,complete,42,J,A,0.0,6,not_converged,203397,0.99, +Text-1782,complete,123,J,A,0.0,6,not_converged,212572,0.992, +Text-1782,complete,256,J,A,0.0,6,not_converged,218482,0.992, +Text-1783,complete,42,F,F,1.0,6,not_converged,245317,0.998, +Text-1783,complete,123,F,F,1.0,6,not_converged,271832,1.0, +Text-1783,complete,256,F,F,1.0,6,not_converged,256336,0.99, +Text-1788,complete,42,C,C,1.0,6,not_converged,241109,0.982, +Text-1788,complete,123,C,C,1.0,6,not_converged,278425,0.9960000000000001, +Text-1788,complete,256,C,C,1.0,6,not_converged,257551,0.95, +Text-1789,complete,42,F,C,0.0,6,not_converged,242728,1.0, +Text-1789,complete,123,F,C,0.0,6,not_converged,334860,0.932, +Text-1789,complete,256,F,C,0.0,6,not_converged,278633,0.982, +Text-1792,complete,42,C,C,1.0,6,not_converged,190679,0.994, +Text-1792,complete,123,C,C,1.0,6,not_converged,220060,1.0, +Text-1792,complete,256,C,C,1.0,6,not_converged,198248,1.0, +Text-1794,complete,42,F,F,1.0,6,not_converged,173847,0.99, +Text-1794,complete,123,F,F,1.0,6,not_converged,172511,0.99, +Text-1794,complete,256,F,F,1.0,6,not_converged,169431,0.9960000000000001, +Text-1795,complete,42,J,A,0.0,6,not_converged,173843,1.0, +Text-1795,complete,123,J,A,0.0,6,not_converged,190544,1.0, +Text-1795,complete,256,J,A,0.0,6,not_converged,193351,0.998, +Text-1796,complete,42,H,J,0.0,6,not_converged,299198,0.994, +Text-1796,complete,123,H,J,0.0,6,not_converged,283170,0.998, +Text-1796,complete,256,H,J,0.0,6,not_converged,335574,0.998, +Text-1800,complete,42,G,G,1.0,6,not_converged,239409,0.99, +Text-1800,complete,123,G,G,1.0,6,not_converged,340611,0.97, +Text-1800,complete,256,G,G,1.0,6,not_converged,238474,1.0, +Text-1801,complete,42,C,C,1.0,6,not_converged,186585,0.992, +Text-1801,complete,123,C,C,1.0,6,not_converged,183587,0.9960000000000001, +Text-1801,complete,256,C,C,1.0,6,not_converged,185164,0.99, +Text-1802,complete,42,A,E,0.0,6,not_converged,261228,1.0, +Text-1802,complete,123,A,E,0.0,6,not_converged,247940,1.0, +Text-1802,complete,256,A,E,0.0,6,not_converged,225145,1.0, +Text-1803,complete,42,F,F,1.0,6,not_converged,172658,1.0, +Text-1803,complete,123,F,F,1.0,6,not_converged,174664,0.998, +Text-1803,complete,256,F,F,1.0,6,not_converged,173132,1.0, +Text-1806,complete,42,F,F,1.0,6,not_converged,284351,0.99, +Text-1806,complete,123,F,F,1.0,6,not_converged,310791,0.9879999999999999, +Text-1806,complete,256,F,F,1.0,6,not_converged,244207,0.994, +Text-1807,complete,42,H,H,1.0,6,not_converged,202441,0.99, +Text-1807,complete,123,H,H,1.0,6,not_converged,210064,1.0, +Text-1807,complete,256,H,H,1.0,6,not_converged,187649,1.0, +Text-1812,complete,42,F,F,1.0,6,not_converged,246057,0.968, +Text-1812,complete,123,F,F,1.0,6,not_converged,233047,0.99, +Text-1812,complete,256,F,F,1.0,6,not_converged,225196,0.95, +Text-1814,complete,42,D,G,0.0,6,not_converged,370482,0.95, +Text-1814,complete,123,D,G,0.0,6,not_converged,313531,0.982, +Text-1814,complete,256,D,G,0.0,6,not_converged,322005,0.9960000000000001, +Text-1815,complete,42,B,F,0.0,6,not_converged,294826,0.9359999999999999, +Text-1815,complete,123,B,B,1.0,6,not_converged,307405,0.992, +Text-1815,complete,256,B,F,0.0,6,not_converged,290372,0.962, +Text-1822,complete,42,J,J,1.0,6,not_converged,262578,0.99, +Text-1822,complete,123,J,A,0.0,6,not_converged,239698,0.9960000000000001, +Text-1822,complete,256,J,J,1.0,6,not_converged,238619,0.9879999999999999, +Text-1823,complete,42,A,H,0.0,6,not_converged,186046,1.0, +Text-1823,complete,123,A,H,0.0,6,not_converged,196419,0.998, +Text-1823,complete,256,A,H,0.0,6,not_converged,199413,1.0, +Text-1825,complete,42,B,B,1.0,6,not_converged,134264,1.0, +Text-1825,complete,123,B,B,1.0,6,not_converged,129303,1.0, +Text-1825,complete,256,B,B,1.0,6,not_converged,132764,1.0, +Text-1827,complete,42,G,G,1.0,6,not_converged,204060,0.9960000000000001, +Text-1827,complete,123,G,G,1.0,6,not_converged,212662,0.9879999999999999, +Text-1827,complete,256,G,G,1.0,6,not_converged,205269,1.0, +Text-1829,complete,42,H,H,1.0,6,not_converged,253665,1.0, +Text-1829,complete,123,H,H,1.0,6,not_converged,214761,0.99, +Text-1829,complete,256,H,H,1.0,6,not_converged,255116,0.9960000000000001, +Text-1831,complete,42,E,E,1.0,6,not_converged,276393,1.0, +Text-1831,complete,123,E,E,1.0,6,not_converged,289546,0.99, +Text-1831,complete,256,E,E,1.0,6,not_converged,331555,0.9960000000000001, +Text-1834,complete,42,F,J,0.0,6,not_converged,167879,1.0, +Text-1834,complete,123,F,J,0.0,6,not_converged,171878,0.992, +Text-1834,complete,256,F,J,0.0,6,not_converged,179072,0.994, +Text-1835,complete,42,D,D,1.0,6,not_converged,195622,0.994, +Text-1835,complete,123,D,D,1.0,6,not_converged,178581,0.998, +Text-1835,complete,256,D,D,1.0,6,not_converged,168225,1.0, +Text-1838,complete,42,E,E,1.0,6,not_converged,234425,0.984, +Text-1838,complete,123,E,J,0.0,6,not_converged,286213,0.982, +Text-1838,complete,256,E,J,0.0,6,not_converged,272266,0.922, +Text-1842,complete,42,C,H,0.0,6,not_converged,291537,1.0, +Text-1842,complete,123,C,C,1.0,6,not_converged,263616,0.9879999999999999, +Text-1842,complete,256,C,H,0.0,6,not_converged,244948,0.9879999999999999, +Text-1844,complete,42,C,C,1.0,6,not_converged,183019,1.0, +Text-1844,complete,123,C,C,1.0,6,not_converged,199816,0.992, +Text-1844,complete,256,C,C,1.0,6,not_converged,204977,0.968, +Text-1847,complete,42,H,J,0.0,6,not_converged,196252,1.0, +Text-1847,complete,123,H,J,0.0,6,not_converged,190835,1.0, +Text-1847,complete,256,H,J,0.0,6,not_converged,232855,0.994, +Text-1849,complete,42,J,I,0.0,6,not_converged,364013,0.998, +Text-1849,complete,123,J,J,1.0,6,not_converged,528047,0.86, +Text-1849,complete,256,J,J,1.0,6,not_converged,444859,0.9960000000000001, +Text-1850,complete,42,F,F,1.0,6,not_converged,134488,0.992, +Text-1850,complete,123,F,F,1.0,6,not_converged,137748,1.0, +Text-1850,complete,256,F,F,1.0,6,not_converged,139921,0.99, +Text-1852,complete,42,D,G,0.0,6,not_converged,265321,1.0, +Text-1852,complete,123,D,D,1.0,6,not_converged,330341,0.962, +Text-1852,complete,256,D,D,1.0,6,not_converged,310607,0.97, +Text-1855,complete,42,F,D,0.0,6,not_converged,233136,0.9960000000000001, +Text-1855,complete,123,F,D,0.0,6,not_converged,219979,0.9800000000000001, +Text-1855,complete,256,F,D,0.0,6,not_converged,204764,0.998, +Text-1860,complete,42,D,F,0.0,6,not_converged,182982,1.0, +Text-1860,complete,123,D,F,0.0,6,not_converged,166328,1.0, +Text-1860,complete,256,D,F,0.0,6,not_converged,187152,1.0, +Text-1861,complete,42,I,E,0.0,6,not_converged,289875,0.99, +Text-1861,complete,123,I,I,1.0,6,not_converged,323206,0.8699999999999999, +Text-1861,complete,256,I,E,0.0,6,not_converged,257967,0.99, +Text-1863,complete,42,B,C,0.0,6,not_converged,215713,0.908, +Text-1863,complete,123,B,B,1.0,6,not_converged,250577,0.9099999999999999, +Text-1863,complete,256,B,C,0.0,6,not_converged,262224,0.85, +Text-1865,complete,42,F,I,0.0,6,not_converged,222636,0.998, +Text-1865,complete,123,F,I,0.0,6,not_converged,266921,0.998, +Text-1865,complete,256,F,I,0.0,6,not_converged,214247,1.0, +Text-1869,complete,42,J,J,1.0,6,not_converged,180756,1.0, +Text-1869,complete,123,J,J,1.0,6,not_converged,179935,1.0, +Text-1869,complete,256,J,J,1.0,6,not_converged,188492,0.9960000000000001, +Text-1870,complete,42,E,H,0.0,6,not_converged,229035,0.998, +Text-1870,complete,123,E,H,0.0,6,not_converged,261135,0.9800000000000001, +Text-1870,complete,256,E,H,0.0,6,not_converged,253023,0.9719999999999999, +Text-1871,complete,42,G,G,1.0,6,not_converged,177977,0.966, +Text-1871,complete,123,G,F,0.0,6,not_converged,231576,0.9199999999999999, +Text-1871,complete,256,G,G,1.0,6,not_converged,177206,0.998, +Text-1875,complete,42,H,B,0.0,6,not_converged,199884,1.0, +Text-1875,complete,123,H,B,0.0,6,not_converged,195174,0.992, +Text-1875,complete,256,H,B,0.0,6,not_converged,190581,1.0, +Text-1879,complete,42,F,A,0.0,6,not_converged,204486,1.0, +Text-1879,complete,123,F,A,0.0,6,not_converged,193196,0.998, +Text-1879,complete,256,F,A,0.0,6,not_converged,209252,0.9879999999999999, +Text-1881,complete,42,D,I,0.0,6,not_converged,291430,0.9879999999999999, +Text-1881,complete,123,D,I,0.0,6,not_converged,304605,0.9800000000000001, +Text-1881,complete,256,D,B,0.0,6,not_converged,405650,0.968, +Text-1882,complete,42,F,D,0.0,6,not_converged,184595,0.986, +Text-1882,complete,123,F,D,0.0,6,not_converged,186601,0.984, +Text-1882,complete,256,F,D,0.0,6,not_converged,191406,0.986, 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+Text-1899,complete,42,C,A,0.0,6,not_converged,225745,0.97, +Text-1899,complete,123,C,G,0.0,6,not_converged,260840,0.85, +Text-1899,complete,256,C,G,0.0,6,not_converged,324327,0.914, +Text-1904,complete,42,I,I,1.0,6,not_converged,217223,0.9960000000000001, +Text-1904,complete,123,I,I,1.0,6,not_converged,197007,0.994, +Text-1904,complete,256,I,I,1.0,6,not_converged,212882,0.994, +Text-1905,complete,42,G,H,0.0,6,not_converged,169441,1.0, +Text-1905,complete,123,G,H,0.0,6,not_converged,192978,0.986, +Text-1905,complete,256,G,H,0.0,6,not_converged,186304,0.994, +Text-1908,complete,42,I,B,0.0,6,not_converged,240618,0.9559999999999998, +Text-1908,complete,123,I,B,0.0,6,not_converged,239440,0.958, +Text-1908,complete,256,I,B,0.0,6,not_converged,242802,0.9559999999999998, +Text-1909,complete,42,E,E,1.0,6,not_converged,207080,0.986, +Text-1909,complete,123,E,E,1.0,6,not_converged,195616,0.998, +Text-1909,complete,256,E,E,1.0,6,not_converged,199600,0.9960000000000001, +Text-1913,complete,42,G,G,1.0,6,not_converged,230243,1.0, +Text-1913,complete,123,G,G,1.0,6,not_converged,241677,1.0, +Text-1913,complete,256,G,G,1.0,6,not_converged,212337,1.0, +Text-1914,complete,42,E,E,1.0,6,not_converged,287652,1.0, +Text-1914,complete,123,E,E,1.0,6,not_converged,328119,0.994, +Text-1914,complete,256,E,E,1.0,6,not_converged,310417,0.99, +Text-1916,complete,42,H,I,0.0,6,not_converged,254255,0.9879999999999999, +Text-1916,complete,123,H,I,0.0,6,not_converged,274572,0.994, +Text-1916,complete,256,H,I,0.0,6,not_converged,253621,1.0, +Text-1918,complete,42,D,G,0.0,6,not_converged,167607,1.0, +Text-1918,complete,123,D,G,0.0,6,not_converged,180374,0.978, +Text-1918,complete,256,D,G,0.0,6,not_converged,199965,1.0, +Text-1920,complete,42,I,I,1.0,6,not_converged,168140,1.0, +Text-1920,complete,123,I,I,1.0,6,not_converged,160787,1.0, +Text-1920,complete,256,I,I,1.0,6,not_converged,176015,1.0, +Text-1921,complete,42,H,G,0.0,6,not_converged,606796,0.9400000000000001, +Text-1921,complete,123,H,C,0.0,6,not_converged,500022,0.966, +Text-1921,complete,256,H,C,0.0,6,not_converged,542546,0.95, +Text-1926,complete,42,A,A,1.0,6,not_converged,205901,1.0, +Text-1926,complete,123,A,A,1.0,6,not_converged,220387,1.0, +Text-1926,complete,256,A,A,1.0,6,not_converged,220029,1.0, +Text-1928,complete,42,A,I,0.0,6,not_converged,190903,1.0, +Text-1928,complete,123,A,I,0.0,6,not_converged,198233,0.9960000000000001, +Text-1928,complete,256,A,I,0.0,6,not_converged,245714,0.99, +Text-1930,complete,42,D,I,0.0,6,not_converged,296031,0.984, +Text-1930,complete,123,D,I,0.0,6,not_converged,209474,0.9800000000000001, +Text-1930,complete,256,D,I,0.0,6,not_converged,261048,0.976, +Text-1933,complete,42,E,J,0.0,6,not_converged,277625,0.9800000000000001, +Text-1933,complete,123,E,J,0.0,6,not_converged,214815,0.99, +Text-1933,complete,256,E,J,0.0,6,not_converged,227148,0.994, +Text-1934,complete,42,E,E,1.0,6,not_converged,270774,0.99, +Text-1934,complete,123,E,E,1.0,6,not_converged,371877,0.8400000000000001, +Text-1934,complete,256,E,E,1.0,6,not_converged,322081,0.9559999999999998, +Text-1936,complete,42,I,C,0.0,6,not_converged,218272,0.978, +Text-1936,complete,123,I,C,0.0,6,not_converged,276700,0.9719999999999999, +Text-1936,complete,256,I,C,0.0,6,not_converged,226462,0.9879999999999999, +Text-1937,complete,42,C,J,0.0,6,not_converged,177072,0.9960000000000001, +Text-1937,complete,123,C,J,0.0,6,not_converged,206569,0.994, +Text-1937,complete,256,C,J,0.0,6,not_converged,186384,0.992, +Text-1945,complete,42,D,D,1.0,6,not_converged,239808,0.984, +Text-1945,complete,123,D,D,1.0,6,not_converged,225904,0.992, +Text-1945,complete,256,D,D,1.0,6,not_converged,243342,1.0, +Text-1946,complete,42,C,I,0.0,6,not_converged,267899,0.984, +Text-1946,complete,123,C,I,0.0,6,not_converged,248682,0.976, +Text-1946,complete,256,C,I,0.0,6,not_converged,253251,0.974, +Text-1947,complete,42,J,J,1.0,6,not_converged,156531,0.9960000000000001, +Text-1947,complete,123,J,J,1.0,6,not_converged,174708,1.0, +Text-1947,complete,256,J,J,1.0,6,not_converged,192781,0.998, +Text-1950,complete,42,I,J,0.0,6,not_converged,182892,1.0, +Text-1950,complete,123,I,J,0.0,6,not_converged,185614,1.0, +Text-1950,complete,256,I,J,0.0,6,not_converged,188762,0.976, +Text-1951,complete,42,F,F,1.0,6,not_converged,243202,0.978, +Text-1951,complete,123,F,F,1.0,6,not_converged,200464,0.994, +Text-1951,complete,256,F,F,1.0,6,not_converged,239784,0.99, +Text-1952,complete,42,A,A,1.0,6,not_converged,237785,0.992, +Text-1952,complete,123,A,A,1.0,6,not_converged,282449,0.994, +Text-1952,complete,256,A,A,1.0,6,not_converged,342763,0.986, +Text-1953,complete,42,A,A,1.0,6,not_converged,196667,0.998, +Text-1953,complete,123,A,A,1.0,6,not_converged,204715,0.986, +Text-1953,complete,256,A,A,1.0,6,not_converged,240330,0.986, +Text-1954,complete,42,A,I,0.0,6,not_converged,221671,0.9559999999999998, +Text-1954,complete,123,A,I,0.0,6,not_converged,220925,1.0, +Text-1954,complete,256,A,I,0.0,6,not_converged,234265,0.998, +Text-1960,complete,42,H,B,0.0,6,not_converged,277253,0.95, +Text-1960,complete,123,H,J,0.0,6,not_converged,322257,0.96, +Text-1960,complete,256,H,B,0.0,6,not_converged,258376,0.992, +Text-1963,complete,42,F,F,1.0,6,not_converged,239699,0.99, +Text-1963,complete,123,F,F,1.0,6,not_converged,268888,0.994, +Text-1963,complete,256,F,F,1.0,6,not_converged,278590,0.998, +Text-1966,complete,42,G,F,0.0,6,not_converged,240510,0.974, +Text-1966,complete,123,G,F,0.0,6,not_converged,241588,0.958, +Text-1966,complete,256,G,F,0.0,6,not_converged,240497,0.998, +Text-1975,complete,42,A,F,0.0,6,not_converged,308737,0.9, +Text-1975,complete,123,A,A,1.0,6,not_converged,275160,0.8300000000000001, +Text-1975,complete,256,A,F,0.0,6,not_converged,316454,0.8720000000000001, +Text-1976,complete,42,A,A,1.0,6,not_converged,213653,0.99, +Text-1976,complete,123,A,A,1.0,6,not_converged,207521,0.976, +Text-1976,complete,256,A,A,1.0,6,not_converged,246654,0.998, +Text-1977,complete,42,D,J,0.0,6,not_converged,255524,1.0, +Text-1977,complete,123,D,J,0.0,6,not_converged,273970,1.0, +Text-1977,complete,256,D,J,0.0,6,not_converged,289656,0.982, +Text-1982,complete,42,A,I,0.0,6,not_converged,220672,0.9960000000000001, +Text-1982,complete,123,A,I,0.0,6,not_converged,222517,0.99, +Text-1982,complete,256,A,I,0.0,6,not_converged,212233,0.9960000000000001, +Text-1983,complete,42,B,I,0.0,6,not_converged,304487,0.97, +Text-1983,complete,123,B,I,0.0,6,not_converged,271265,0.9879999999999999, +Text-1983,complete,256,B,I,0.0,6,not_converged,240905,0.994, +Text-1989,complete,42,H,D,0.0,6,not_converged,248409,0.994, +Text-1989,complete,123,H,D,0.0,6,not_converged,217542,0.992, +Text-1989,complete,256,H,D,0.0,6,not_converged,224817,0.982, +Text-1991,complete,42,I,B,0.0,6,not_converged,199621,0.982, +Text-1991,complete,123,I,I,1.0,6,not_converged,234807,0.9400000000000001, +Text-1991,complete,256,I,I,1.0,6,not_converged,220759,0.9800000000000001, +Text-1994,complete,42,J,J,1.0,6,not_converged,209965,1.0, +Text-1994,complete,123,J,J,1.0,6,not_converged,212215,0.998, +Text-1994,complete,256,J,J,1.0,6,not_converged,289674,1.0, +Text-1995,complete,42,F,B,0.0,6,not_converged,239272,1.0, +Text-1995,complete,123,F,B,0.0,6,not_converged,202847,1.0, +Text-1995,complete,256,F,B,0.0,6,not_converged,228887,0.9960000000000001, +Text-1996,complete,42,G,G,1.0,6,not_converged,228140,1.0, +Text-1996,complete,123,G,G,1.0,6,not_converged,231618,0.9960000000000001, +Text-1996,complete,256,G,G,1.0,6,not_converged,231803,0.998, +Text-1997,complete,42,G,H,0.0,6,not_converged,262696,0.994, +Text-1997,complete,123,G,H,0.0,6,not_converged,283539,0.99, +Text-1997,complete,256,G,H,0.0,6,not_converged,236150,0.982, +Text-1998,complete,42,H,C,0.0,6,not_converged,184187,0.998, +Text-1998,complete,123,H,C,0.0,6,not_converged,222535,0.974, +Text-1998,complete,256,H,C,0.0,6,not_converged,193945,0.986, +Text-1999,complete,42,G,E,0.0,6,not_converged,513068,0.96, +Text-1999,complete,123,G,D,0.0,6,not_converged,403298,0.962, +Text-1999,complete,256,G,A,0.0,6,not_converged,364693,0.946, +Text-2000,complete,42,F,F,1.0,6,not_converged,298274,0.986, +Text-2000,complete,123,F,F,1.0,6,not_converged,266053,0.986, +Text-2000,complete,256,F,F,1.0,6,not_converged,267887,1.0, +Text-2002,complete,42,D,H,0.0,6,not_converged,209366,0.998, +Text-2002,complete,123,D,H,0.0,6,not_converged,201645,0.998, +Text-2002,complete,256,D,H,0.0,6,not_converged,208890,0.9879999999999999, +Text-2003,complete,42,I,H,0.0,6,not_converged,234396,0.994, +Text-2003,complete,123,I,H,0.0,6,not_converged,221547,0.99, +Text-2003,complete,256,I,H,0.0,6,not_converged,259782,0.992, +Text-2005,complete,42,J,J,1.0,6,not_converged,176309,0.9960000000000001, +Text-2005,complete,123,J,J,1.0,6,not_converged,192360,0.994, +Text-2005,complete,256,J,J,1.0,6,not_converged,177059,1.0, +Text-2008,complete,42,I,B,0.0,6,not_converged,207573,1.0, +Text-2008,complete,123,I,B,0.0,6,not_converged,190474,0.99, +Text-2008,complete,256,I,B,0.0,6,not_converged,186047,0.99, +Text-2009,complete,42,C,C,1.0,6,not_converged,263295,0.99, +Text-2009,complete,123,C,C,1.0,6,not_converged,267004,0.9800000000000001, +Text-2009,complete,256,C,C,1.0,6,not_converged,251847,0.986, +Text-2010,complete,42,G,J,0.0,6,not_converged,222824,0.99, +Text-2010,complete,123,G,J,0.0,6,not_converged,213837,0.994, +Text-2010,complete,256,G,J,0.0,6,not_converged,223337,1.0, +Text-2012,complete,42,D,E,0.0,6,not_converged,293293,0.8699999999999999, +Text-2012,complete,123,D,D,1.0,6,not_converged,239081,0.9800000000000001, +Text-2012,complete,256,D,D,1.0,6,not_converged,238250,0.998, +Text-2013,complete,42,G,J,0.0,6,not_converged,289841,0.9, +Text-2013,complete,123,G,F,0.0,6,not_converged,366451,0.9, +Text-2013,complete,256,G,F,0.0,6,not_converged,396125,0.95, +Text-2016,complete,42,J,D,0.0,6,not_converged,244082,0.99, +Text-2016,complete,123,J,D,0.0,6,not_converged,224954,0.998, +Text-2016,complete,256,J,D,0.0,6,not_converged,215085,0.994, +Text-2017,complete,42,J,J,1.0,6,not_converged,216721,0.99, +Text-2017,complete,123,J,J,1.0,6,not_converged,224002,0.994, +Text-2017,complete,256,J,J,1.0,6,not_converged,201827,1.0, +Text-2019,complete,42,H,H,1.0,6,not_converged,232156,0.994, +Text-2019,complete,123,H,H,1.0,6,not_converged,272283,0.992, +Text-2019,complete,256,H,H,1.0,6,not_converged,249456,1.0, +Text-2026,complete,42,E,I,0.0,6,not_converged,353872,0.8099999999999999, +Text-2026,complete,123,E,I,0.0,6,not_converged,264678,0.974, +Text-2026,complete,256,E,A,0.0,6,not_converged,442013,0.8300000000000001, +Text-2030,complete,42,A,F,0.0,6,not_converged,224610,0.994, +Text-2030,complete,123,A,F,0.0,6,not_converged,259790,0.976, +Text-2030,complete,256,A,F,0.0,6,not_converged,211551,0.99, +Text-2032,complete,42,G,J,0.0,6,not_converged,166210,0.992, +Text-2032,complete,123,G,J,0.0,6,not_converged,180816,1.0, +Text-2032,complete,256,G,J,0.0,6,not_converged,170243,1.0, +Text-2033,complete,42,H,B,0.0,6,not_converged,226087,0.96, +Text-2033,complete,123,H,B,0.0,6,not_converged,209571,0.99, +Text-2033,complete,256,H,B,0.0,6,not_converged,202458,0.994, +Text-2035,complete,42,I,I,1.0,6,not_converged,336530,1.0, +Text-2035,complete,123,I,I,1.0,6,not_converged,438367,1.0, +Text-2035,complete,256,I,I,1.0,6,not_converged,389198,0.998, +Text-2037,complete,42,A,C,0.0,6,not_converged,657393,0.8300000000000001, +Text-2037,complete,123,A,C,0.0,6,not_converged,523336,0.95, +Text-2037,complete,256,A,C,0.0,6,not_converged,720250,0.82, +Text-2038,complete,42,G,A,0.0,6,not_converged,205665,0.998, +Text-2038,complete,123,G,A,0.0,6,not_converged,224148,0.998, +Text-2038,complete,256,G,A,0.0,6,not_converged,199628,0.998, +Text-2039,complete,42,A,A,1.0,6,not_converged,191814,0.992, +Text-2039,complete,123,A,A,1.0,6,not_converged,210983,0.976, +Text-2039,complete,256,A,A,1.0,6,not_converged,226721,0.99, +Text-2041,complete,42,H,E,0.0,6,not_converged,210876,0.99, +Text-2041,complete,123,H,E,0.0,6,not_converged,241650,0.982, +Text-2041,complete,256,H,E,0.0,6,not_converged,238556,0.99, +Text-2042,complete,42,G,G,1.0,6,not_converged,271642,0.9960000000000001, +Text-2042,complete,123,G,G,1.0,6,not_converged,292148,0.992, +Text-2042,complete,256,G,G,1.0,6,not_converged,251417,0.982, +Text-2043,complete,42,F,B,0.0,6,not_converged,254942,0.9960000000000001, +Text-2043,complete,123,F,B,0.0,6,not_converged,226308,0.9879999999999999, +Text-2043,complete,256,F,B,0.0,6,not_converged,272372,0.966, +Text-2046,complete,42,G,H,0.0,6,not_converged,167879,1.0, +Text-2046,complete,123,G,H,0.0,6,not_converged,168254,0.992, +Text-2046,complete,256,G,H,0.0,6,not_converged,188123,1.0, +Text-2047,complete,42,D,A,0.0,6,not_converged,224454,0.984, +Text-2047,complete,123,D,A,0.0,6,not_converged,202840,0.95, +Text-2047,complete,256,D,A,0.0,6,not_converged,197270,0.998, +Text-2051,complete,42,A,A,1.0,6,not_converged,241093,0.99, +Text-2051,complete,123,A,A,1.0,6,not_converged,256739,0.994, +Text-2051,complete,256,A,A,1.0,6,not_converged,230696,0.99, +Text-2052,complete,42,H,H,1.0,6,not_converged,212399,0.9960000000000001, +Text-2052,complete,123,H,H,1.0,6,not_converged,237789,0.982, +Text-2052,complete,256,H,H,1.0,6,not_converged,196335,0.9879999999999999, +Text-2053,complete,42,A,A,1.0,6,not_converged,284806,0.96, +Text-2053,complete,123,A,C,0.0,6,not_converged,247815,0.95, +Text-2053,complete,256,A,A,1.0,6,not_converged,335545,0.9099999999999999, +Text-2055,complete,42,C,C,1.0,6,not_converged,305833,0.9099999999999999, +Text-2055,complete,123,C,C,1.0,6,not_converged,295359,0.95, +Text-2055,complete,256,C,C,1.0,6,not_converged,252313,0.99, +Text-2056,complete,42,D,D,1.0,6,not_converged,253678,1.0, +Text-2056,complete,123,D,D,1.0,6,not_converged,235583,1.0, +Text-2056,complete,256,D,D,1.0,6,not_converged,230724,1.0, +Text-2057,complete,42,D,F,0.0,6,not_converged,185435,1.0, +Text-2057,complete,123,D,F,0.0,6,not_converged,220690,1.0, +Text-2057,complete,256,D,F,0.0,6,not_converged,209867,0.9559999999999998, +Text-2061,complete,42,A,F,0.0,6,not_converged,187725,1.0, +Text-2061,complete,123,A,F,0.0,6,not_converged,179533,0.99, +Text-2061,complete,256,A,F,0.0,6,not_converged,175386,1.0, +Text-2065,complete,42,B,B,1.0,6,not_converged,408407,0.9400000000000001, +Text-2065,complete,123,B,J,0.0,6,not_converged,366757,0.71, +Text-2065,complete,256,B,B,1.0,6,not_converged,319429,0.9879999999999999, +Text-2067,complete,42,B,I,0.0,6,not_converged,303098,0.994, +Text-2067,complete,123,B,I,0.0,6,not_converged,335700,0.946, +Text-2067,complete,256,B,I,0.0,6,not_converged,271486,0.9879999999999999, 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+Text-2105,complete,42,G,G,1.0,6,not_converged,315630,1.0, +Text-2105,complete,123,G,G,1.0,6,not_converged,287128,0.97, +Text-2105,complete,256,G,G,1.0,6,not_converged,320931,0.998, +Text-2107,complete,42,J,J,1.0,6,not_converged,221245,1.0, +Text-2107,complete,123,J,J,1.0,6,not_converged,213329,1.0, +Text-2107,complete,256,J,J,1.0,6,not_converged,211884,0.9879999999999999, +Text-2108,complete,42,H,H,1.0,6,not_converged,242562,1.0, +Text-2108,complete,123,H,H,1.0,6,not_converged,267857,0.958, +Text-2108,complete,256,H,H,1.0,6,not_converged,274872,0.992, +Text-2109,complete,42,B,B,1.0,6,not_converged,173808,0.984, +Text-2109,complete,123,B,B,1.0,6,not_converged,175138,0.99, +Text-2109,complete,256,B,B,1.0,6,not_converged,189829,1.0, +Text-2110,complete,42,J,G,0.0,6,not_converged,188641,0.998, +Text-2110,complete,123,J,G,0.0,6,not_converged,181868,0.992, +Text-2110,complete,256,J,G,0.0,6,not_converged,202027,1.0, +Text-2115,complete,42,I,I,1.0,6,not_converged,323524,0.974, 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+Text-28,complete,256,E,A,0.0,1,unanimous_with_conf,24544,0.9199999999999999, +Text-30,complete,42,I,I,1.0,1,unanimous_with_conf,39363,0.86, +Text-30,complete,123,I,I,1.0,2,unanimous_with_conf,59663,0.8800000000000001, +Text-30,complete,256,I,I,1.0,1,unanimous_with_conf,28242,0.86, +Text-31,complete,42,D,G,0.0,3,weighted_majority,109751,0.8300000000000001, +Text-31,complete,123,D,G,0.0,1,unanimous_with_conf,40499,0.8400000000000001, +Text-31,complete,256,D,G,0.0,1,weighted_majority,40268,0.8, +Text-32,complete,42,F,F,1.0,3,unanimous_with_conf,117236,0.86, +Text-32,complete,123,F,F,1.0,1,unanimous_with_conf,44764,0.8300000000000001, +Text-32,complete,256,F,F,1.0,2,unanimous_with_conf,82868,0.85, +Text-34,complete,42,G,G,1.0,1,unanimous_with_conf,30607,0.85, +Text-34,complete,123,G,G,1.0,1,unanimous_with_conf,31487,0.8699999999999999, +Text-34,complete,256,G,G,1.0,1,unanimous_with_conf,35495,0.8400000000000001, +Text-37,complete,42,G,I,0.0,10,not_converged,929416,0.852, +Text-37,complete,123,G,I,0.0,1,unanimous_with_conf,40331,0.8400000000000001, +Text-37,complete,256,G,I,0.0,1,unanimous_with_conf,42878,0.85, +Text-44,complete,42,G,B,0.0,1,unanimous_with_conf,100156,0.82, +Text-44,complete,123,G,J,0.0,3,weighted_majority,164836,0.78, +Text-44,complete,256,G,B,0.0,3,unanimous_with_conf,173161,0.89, +Text-48,complete,42,D,D,1.0,1,weighted_majority,56194,0.8699999999999999, +Text-48,complete,123,D,D,1.0,2,weighted_majority,112046,0.884, +Text-48,complete,256,D,D,1.0,1,weighted_majority,92052,0.8099999999999999, +Text-49,complete,42,G,G,1.0,1,unanimous_with_conf,20411,0.9099999999999999, +Text-49,complete,123,G,G,1.0,1,unanimous_with_conf,19248,0.9400000000000001, +Text-49,complete,256,G,G,1.0,1,unanimous_with_conf,20707,0.9199999999999999, +Text-51,complete,42,F,F,1.0,2,unanimous_with_conf,54508,0.93, +Text-51,complete,123,F,F,1.0,2,unanimous_with_conf,50591,0.8, +Text-51,complete,256,F,F,1.0,2,unanimous_with_conf,45593,0.9099999999999999, +Text-53,complete,42,E,E,1.0,2,unanimous_with_conf,53358,0.9400000000000001, +Text-53,complete,123,E,E,1.0,1,unanimous_with_conf,40410,0.82, +Text-53,complete,256,E,E,1.0,3,unanimous_with_conf,90770,0.89, +Text-56,complete,42,B,B,1.0,1,unanimous_with_conf,21107,0.8300000000000001, +Text-56,complete,123,B,B,1.0,2,unanimous_with_conf,37261,0.9099999999999999, +Text-56,complete,256,B,B,1.0,2,unanimous_with_conf,41415,0.89, +Text-58,complete,42,A,I,0.0,4,unanimous_with_conf,180979,0.8699999999999999, +Text-58,complete,123,A,I,0.0,2,unanimous_with_conf,91670,0.85, +Text-58,complete,256,A,I,0.0,3,unanimous_with_conf,118230,0.8400000000000001, +Text-63,complete,42,F,F,1.0,2,unanimous_with_conf,79438,0.8699999999999999, +Text-63,complete,123,F,F,1.0,2,unanimous_with_conf,73610,0.8800000000000001, +Text-63,complete,256,F,F,1.0,2,unanimous_with_conf,82425,0.8300000000000001, +Text-64,complete,42,J,J,1.0,1,unanimous_with_conf,31570,0.8300000000000001, +Text-64,complete,123,J,J,1.0,1,unanimous_with_conf,27119,0.8799999999999999, +Text-64,complete,256,J,J,1.0,1,unanimous_with_conf,30249,0.9199999999999999, +Text-65,complete,42,C,I,0.0,1,unanimous_with_conf,28902,0.8, +Text-65,complete,123,C,I,0.0,1,unanimous_with_conf,20177,0.9199999999999999, +Text-65,complete,256,C,I,0.0,2,weighted_majority,49092,0.8300000000000001, +Text-67,complete,42,A,A,1.0,2,unanimous_with_conf,56527,0.8800000000000001, +Text-67,complete,123,A,A,1.0,2,unanimous_with_conf,51501,0.932, +Text-67,complete,256,A,A,1.0,2,unanimous_with_conf,38283,0.8699999999999999, +Text-73,complete,42,B,D,0.0,1,unanimous_with_conf,24475,0.8099999999999999, +Text-73,complete,123,B,B,1.0,3,unanimous_with_conf,88815,0.8400000000000001, +Text-73,complete,256,B,B,1.0,3,unanimous_with_conf,83638,0.85, +Text-78,complete,42,H,G,0.0,1,unanimous_with_conf,51133,0.8300000000000001, +Text-78,complete,123,H,G,0.0,3,unanimous_with_conf,111584,0.85, +Text-78,complete,256,H,G,0.0,2,unanimous_with_conf,72606,0.8400000000000001, +Text-80,complete,42,I,E,0.0,8,unanimous_with_conf,460489,0.8, +Text-80,complete,123,I,I,1.0,3,unanimous_with_conf,92765,0.82, +Text-80,complete,256,I,E,0.0,2,unanimous_with_conf,69073,0.8, +Text-81,complete,42,F,I,0.0,1,unanimous_with_conf,21325,0.8400000000000001, +Text-81,complete,123,F,I,0.0,1,unanimous_with_conf,21158,0.86, +Text-81,complete,256,F,I,0.0,1,unanimous_with_conf,23715,0.86, +Text-83,complete,42,I,F,0.0,1,weighted_majority,135937,0.82, +Text-83,complete,123,I,I,1.0,2,weighted_majority,195421,0.8699999999999999, +Text-83,complete,256,I,I,1.0,2,unanimous_with_conf,176837,0.89, +Text-84,complete,42,G,C,0.0,2,unanimous_with_conf,83620,0.9099999999999999, +Text-84,complete,123,G,C,0.0,2,unanimous_with_conf,116100,0.9099999999999999, +Text-84,complete,256,G,C,0.0,1,unanimous_with_conf,79647,0.8099999999999999, +Text-86,complete,42,A,A,1.0,1,unanimous_with_conf,26877,0.85, +Text-86,complete,123,A,A,1.0,1,unanimous_with_conf,29799,0.9, +Text-86,complete,256,A,A,1.0,1,unanimous_with_conf,25542,0.86, +Text-88,complete,42,C,C,1.0,1,unanimous_with_conf,22919,0.9, +Text-88,complete,123,C,C,1.0,1,unanimous_with_conf,18889,0.82, +Text-88,complete,256,C,C,1.0,1,unanimous_with_conf,19925,0.86, +Text-92,complete,42,G,H,0.0,3,unanimous_with_conf,157979,0.85, +Text-92,complete,123,G,H,0.0,2,unanimous_with_conf,130267,0.9, +Text-92,complete,256,G,H,0.0,2,unanimous_with_conf,87362,0.8959999999999999, +Text-93,complete,42,D,B,0.0,1,unanimous_with_conf,32517,0.82, +Text-93,complete,123,D,B,0.0,1,unanimous_with_conf,26038,0.8400000000000001, +Text-93,complete,256,D,B,0.0,1,unanimous_with_conf,28206,0.8, +Text-94,complete,42,E,E,1.0,1,unanimous_with_conf,27941,0.8400000000000001, +Text-94,complete,123,E,E,1.0,1,unanimous_with_conf,26513,0.86, +Text-94,complete,256,E,E,1.0,1,unanimous_with_conf,29030,0.8300000000000001, +Text-95,complete,42,F,C,0.0,2,unanimous_with_conf,63304,0.9, +Text-95,complete,123,F,C,0.0,1,unanimous_with_conf,63227,0.8300000000000001, +Text-95,complete,256,F,E,0.0,2,unanimous_with_conf,86812,0.8300000000000001, +Text-96,complete,42,E,E,1.0,1,unanimous_with_conf,27522,0.8300000000000001, +Text-96,complete,123,E,E,1.0,1,unanimous_with_conf,25902,0.8099999999999999, +Text-96,complete,256,E,E,1.0,1,unanimous_with_conf,29995,0.8400000000000001, +Text-99,complete,42,F,F,1.0,2,unanimous_with_conf,53480,0.93, +Text-99,complete,123,F,F,1.0,2,unanimous_with_conf,57276,0.8799999999999999, +Text-99,complete,256,F,F,1.0,1,unanimous_with_conf,43631,0.8, +Text-100,complete,42,F,F,1.0,1,unanimous_with_conf,20612,0.8300000000000001, +Text-100,complete,123,F,F,1.0,1,unanimous_with_conf,20537,0.8800000000000001, +Text-100,complete,256,F,F,1.0,2,unanimous_with_conf,31944,0.89, +Text-104,complete,42,J,J,1.0,4,weighted_majority,187291,0.8039999999999999, +Text-104,complete,123,J,J,1.0,2,unanimous_with_conf,71659,0.914, +Text-104,complete,256,J,J,1.0,1,unanimous_with_conf,48013,0.8099999999999999, +Text-106,complete,42,D,D,1.0,1,unanimous_with_conf,21127,0.8099999999999999, +Text-106,complete,123,D,D,1.0,2,unanimous_with_conf,43654,0.89, +Text-106,complete,256,D,D,1.0,1,unanimous_with_conf,22362,0.9199999999999999, +Text-107,complete,42,J,J,1.0,1,unanimous_with_conf,28312,0.9400000000000001, +Text-107,complete,123,J,J,1.0,1,unanimous_with_conf,31854,0.9, +Text-107,complete,256,J,J,1.0,1,unanimous_with_conf,29838,0.9199999999999999, +Text-109,complete,42,G,G,1.0,1,unanimous_with_conf,28290,0.9400000000000001, +Text-109,complete,123,G,G,1.0,1,unanimous_with_conf,22744,0.95, +Text-109,complete,256,G,G,1.0,1,unanimous_with_conf,25957,0.93, +Text-112,complete,42,C,C,1.0,1,unanimous_with_conf,31138,0.9, +Text-112,complete,123,C,C,1.0,1,unanimous_with_conf,29313,0.9400000000000001, +Text-112,complete,256,C,C,1.0,1,unanimous_with_conf,27269,0.9, +Text-120,complete,42,F,F,1.0,1,unanimous_with_conf,14565,0.93, +Text-120,complete,123,F,F,1.0,1,unanimous_with_conf,13411,0.9299999999999999, +Text-120,complete,256,F,F,1.0,1,unanimous_with_conf,14684,0.97, +Text-122,complete,42,C,E,0.0,2,weighted_majority,92146,0.8099999999999999, +Text-122,complete,123,C,E,0.0,2,weighted_majority,82703,0.85, +Text-122,complete,256,C,E,0.0,2,unanimous_with_conf,62705,0.9099999999999999, +Text-124,complete,42,D,H,0.0,1,unanimous_with_conf,22153,0.8699999999999999, +Text-124,complete,123,D,H,0.0,1,unanimous_with_conf,22153,0.8099999999999999, +Text-124,complete,256,D,H,0.0,1,unanimous_with_conf,25149,0.86, +Text-126,complete,42,H,D,0.0,2,unanimous_with_conf,105386,0.8699999999999999, +Text-126,complete,123,H,H,1.0,5,unanimous_with_conf,293875,0.8, +Text-126,complete,256,H,H,1.0,2,unanimous_with_conf,135802,0.8800000000000001, +Text-127,complete,42,E,C,0.0,2,unanimous_with_conf,43250,0.85, +Text-127,complete,123,E,C,0.0,1,unanimous_with_conf,21467,0.8099999999999999, +Text-127,complete,256,E,C,0.0,3,unanimous_with_conf,76976,0.8300000000000001, +Text-128,complete,42,I,A,0.0,2,unanimous_with_conf,70862,0.9, +Text-128,complete,123,I,A,0.0,3,unanimous_with_conf,87251,0.89, +Text-128,complete,256,I,A,0.0,2,unanimous_with_conf,70817,0.9099999999999999, +Text-132,complete,42,G,G,1.0,2,unanimous_with_conf,45000,0.916, +Text-132,complete,123,G,G,1.0,1,unanimous_with_conf,30392,0.8300000000000001, +Text-132,complete,256,G,G,1.0,1,unanimous_with_conf,37470,0.89, +Text-141,complete,42,A,A,1.0,1,unanimous_with_conf,19481,0.89, +Text-141,complete,123,A,A,1.0,1,unanimous_with_conf,18936,0.86, +Text-141,complete,256,A,A,1.0,1,unanimous_with_conf,20194,0.85, +Text-144,complete,42,I,B,0.0,4,unanimous_with_conf,145737,0.8699999999999999, +Text-144,complete,123,I,B,0.0,4,unanimous_with_conf,138764,0.8400000000000001, +Text-144,complete,256,I,B,0.0,3,unanimous_with_conf,84657,0.86, +Text-147,complete,42,D,D,1.0,2,unanimous_with_conf,72848,0.8300000000000001, +Text-147,complete,123,D,D,1.0,2,unanimous_with_conf,74770,0.89, +Text-147,complete,256,D,D,1.0,2,unanimous_with_conf,85592,0.8099999999999999, +Text-149,complete,42,F,F,1.0,1,unanimous_with_conf,20044,0.85, +Text-149,complete,123,F,F,1.0,1,unanimous_with_conf,22194,0.8699999999999999, +Text-149,complete,256,F,F,1.0,1,unanimous_with_conf,21048,0.86, +Text-151,complete,42,H,H,1.0,2,unanimous_with_conf,87780,0.8699999999999999, +Text-151,complete,123,H,H,1.0,2,unanimous_with_conf,106248,0.8400000000000001, +Text-151,complete,256,H,H,1.0,2,unanimous_with_conf,94783,0.8800000000000001, +Text-152,complete,42,I,B,0.0,3,weighted_majority,119493,0.8, +Text-152,complete,123,I,B,0.0,2,unanimous_with_conf,66642,0.9199999999999999, +Text-152,complete,256,I,B,0.0,5,unanimous_with_conf,234914,0.82, +Text-154,complete,42,D,D,1.0,3,unanimous_with_conf,110523,0.8099999999999999, +Text-154,complete,123,D,D,1.0,2,unanimous_with_conf,41928,0.86, +Text-154,complete,256,D,D,1.0,2,unanimous_with_conf,49852,0.8800000000000001, +Text-155,complete,42,B,B,1.0,1,unanimous_with_conf,15944,0.8099999999999999, +Text-155,complete,123,B,B,1.0,1,unanimous_with_conf,16924,0.8699999999999999, +Text-155,complete,256,B,B,1.0,1,unanimous_with_conf,17617,0.85, +Text-156,complete,42,I,B,0.0,1,unanimous_with_conf,46497,0.8, +Text-156,complete,123,I,B,0.0,2,unanimous_with_conf,72759,0.8, +Text-156,complete,256,I,B,0.0,2,unanimous_with_conf,62032,0.8800000000000001, +Text-162,complete,42,B,B,1.0,1,unanimous_with_conf,18266,0.8699999999999999, +Text-162,complete,123,B,B,1.0,1,unanimous_with_conf,18840,0.8800000000000001, +Text-162,complete,256,B,B,1.0,1,unanimous_with_conf,19734,0.9199999999999999, +Text-164,complete,42,G,G,1.0,2,unanimous_with_conf,58873,0.93, +Text-164,complete,123,G,G,1.0,2,unanimous_with_conf,70508,0.89, +Text-164,complete,256,G,G,1.0,1,unanimous_with_conf,47424,0.8, +Text-168,complete,42,C,B,0.0,1,unanimous_with_conf,36522,0.8400000000000001, +Text-168,complete,123,C,B,0.0,1,unanimous_with_conf,26477,0.8699999999999999, +Text-168,complete,256,C,B,0.0,1,unanimous_with_conf,25822,0.8400000000000001, +Text-173,complete,42,H,H,1.0,3,unanimous_with_conf,106355,0.836, +Text-173,complete,123,H,H,1.0,1,unanimous_with_conf,33782,0.8, +Text-173,complete,256,H,H,1.0,3,unanimous_with_conf,100810,0.906, +Text-179,complete,42,J,G,0.0,1,unanimous_with_conf,27912,0.8300000000000001, +Text-179,complete,123,J,G,0.0,1,unanimous_with_conf,31785,0.8939999999999999, +Text-179,complete,256,J,G,0.0,1,unanimous_with_conf,29330,0.8400000000000001, +Text-180,complete,42,A,B,0.0,9,unanimous_with_conf,931312,0.8099999999999999, +Text-180,complete,123,A,D,0.0,3,unanimous_with_conf,200567,0.85, +Text-180,complete,256,A,D,0.0,3,unanimous_with_conf,190218,0.8800000000000001, +Text-183,complete,42,F,A,0.0,3,unanimous_with_conf,110939,0.82, 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+Text-1717,complete,42,C,D,0.0,1,unanimous_with_conf,25233,0.8699999999999999, +Text-1717,complete,123,C,D,0.0,1,unanimous_with_conf,30304,0.8699999999999999, +Text-1717,complete,256,C,D,0.0,1,unanimous_with_conf,40542,0.89, +Text-1723,complete,42,I,I,1.0,2,unanimous_with_conf,50302,0.93, +Text-1723,complete,123,I,I,1.0,2,unanimous_with_conf,65652,0.89, +Text-1723,complete,256,I,I,1.0,1,unanimous_with_conf,38419,0.82, +Text-1725,complete,42,G,G,1.0,1,unanimous_with_conf,26115,0.8800000000000001, +Text-1725,complete,123,G,G,1.0,1,unanimous_with_conf,25273,0.9400000000000001, +Text-1725,complete,256,G,G,1.0,1,unanimous_with_conf,23067,0.9400000000000001, +Text-1726,complete,42,B,B,1.0,1,unanimous_with_conf,20563,0.85, +Text-1726,complete,123,B,B,1.0,1,unanimous_with_conf,22384,0.8799999999999999, +Text-1726,complete,256,B,B,1.0,1,unanimous_with_conf,19732,0.9099999999999999, +Text-1733,complete,42,C,J,0.0,2,unanimous_with_conf,71302,0.8800000000000001, +Text-1733,complete,123,C,J,0.0,2,unanimous_with_conf,96049,0.89, +Text-1733,complete,256,C,J,0.0,2,unanimous_with_conf,87593,0.9199999999999999, +Text-1735,complete,42,I,I,1.0,1,unanimous_with_conf,23523,0.8099999999999999, +Text-1735,complete,123,I,I,1.0,1,unanimous_with_conf,23453,0.8400000000000001, +Text-1735,complete,256,I,I,1.0,1,unanimous_with_conf,21639,0.85, +Text-1739,complete,42,E,E,1.0,2,unanimous_with_conf,36841,0.8699999999999999, +Text-1739,complete,123,E,E,1.0,1,unanimous_with_conf,25480,0.8, +Text-1739,complete,256,E,E,1.0,2,unanimous_with_conf,52199,0.8699999999999999, +Text-1744,complete,42,C,A,0.0,1,unanimous_with_conf,17640,0.9, +Text-1744,complete,123,C,A,0.0,2,unanimous_with_conf,29248,0.9400000000000001, +Text-1744,complete,256,C,A,0.0,1,unanimous_with_conf,16070,0.8800000000000001, +Text-1745,complete,42,D,D,1.0,1,unanimous_with_conf,31566,0.89, +Text-1745,complete,123,D,D,1.0,1,unanimous_with_conf,30442,0.8800000000000001, +Text-1745,complete,256,D,D,1.0,1,unanimous_with_conf,26084,0.8300000000000001, +Text-1748,complete,42,H,G,0.0,1,unanimous_with_conf,43314,0.82, +Text-1748,complete,123,H,G,0.0,3,unanimous_with_conf,109047,0.826, +Text-1748,complete,256,H,G,0.0,3,unanimous_with_conf,113216,0.8300000000000001, +Text-1756,complete,42,F,F,1.0,1,unanimous_with_conf,41544,0.8699999999999999, +Text-1756,complete,123,F,F,1.0,1,unanimous_with_conf,40773,0.8800000000000001, +Text-1756,complete,256,F,F,1.0,1,unanimous_with_conf,56731,0.8, +Text-1757,complete,42,D,D,1.0,1,unanimous_with_conf,20077,0.89, +Text-1757,complete,123,D,D,1.0,1,unanimous_with_conf,16351,0.82, +Text-1757,complete,256,D,D,1.0,1,unanimous_with_conf,17663,0.8699999999999999, +Text-1759,complete,42,I,E,0.0,3,unanimous_with_conf,77111,0.86, +Text-1759,complete,123,I,E,0.0,3,weighted_majority,97079,0.76, +Text-1759,complete,256,I,E,0.0,2,unanimous_with_conf,54061,0.8099999999999999, +Text-1760,complete,42,I,F,0.0,1,unanimous_with_conf,19360,0.9, +Text-1760,complete,123,I,F,0.0,1,unanimous_with_conf,19895,0.93, +Text-1760,complete,256,I,F,0.0,1,unanimous_with_conf,17518,0.9099999999999999, +Text-1762,complete,42,C,C,1.0,3,unanimous_with_conf,112035,0.8699999999999999, +Text-1762,complete,123,C,C,1.0,3,unanimous_with_conf,113355,0.8400000000000001, +Text-1762,complete,256,C,C,1.0,2,unanimous_with_conf,91240,0.8560000000000001, +Text-1764,complete,42,F,F,1.0,4,unanimous_with_conf,133879,0.85, +Text-1764,complete,123,F,F,1.0,1,unanimous_with_conf,26459,0.8800000000000001, +Text-1764,complete,256,F,F,1.0,2,unanimous_with_conf,48217,0.82, +Text-1769,complete,42,G,G,1.0,1,unanimous_with_conf,23046,0.86, +Text-1769,complete,123,G,G,1.0,1,unanimous_with_conf,27821,0.9199999999999999, +Text-1769,complete,256,G,G,1.0,1,unanimous_with_conf,25010,0.82, +Text-1771,complete,42,H,H,1.0,2,unanimous_with_conf,61295,0.93, +Text-1771,complete,123,H,H,1.0,2,unanimous_with_conf,51175,0.9, +Text-1771,complete,256,H,H,1.0,1,unanimous_with_conf,42429,0.8099999999999999, +Text-1772,complete,42,F,F,1.0,2,unanimous_with_conf,52246,0.9, +Text-1772,complete,123,F,F,1.0,1,unanimous_with_conf,37078,0.8400000000000001, +Text-1772,complete,256,F,F,1.0,2,unanimous_with_conf,54407,0.8699999999999999, +Text-1774,complete,42,I,J,0.0,1,unanimous_with_conf,22353,0.85, +Text-1774,complete,123,I,J,0.0,2,unanimous_with_conf,39527,0.93, +Text-1774,complete,256,I,J,0.0,1,unanimous_with_conf,25084,0.8300000000000001, +Text-1777,complete,42,I,F,0.0,1,weighted_majority,33799,0.85, +Text-1777,complete,123,I,F,0.0,2,weighted_majority,57058,0.86, +Text-1777,complete,256,I,F,0.0,1,unanimous_with_conf,31612,0.8699999999999999, +Text-1778,complete,42,I,J,0.0,2,unanimous_with_conf,95547,0.8800000000000001, +Text-1778,complete,123,I,J,0.0,2,unanimous_with_conf,97207,0.8699999999999999, +Text-1778,complete,256,I,G,0.0,3,unanimous_with_conf,187386,0.8699999999999999, +Text-1780,complete,42,B,C,0.0,1,unanimous_with_conf,23653,0.9099999999999999, +Text-1780,complete,123,B,C,0.0,1,unanimous_with_conf,24928,0.898, +Text-1780,complete,256,B,C,0.0,1,unanimous_with_conf,34224,0.89, +Text-1782,complete,42,J,A,0.0,1,unanimous_with_conf,32605,0.82, +Text-1782,complete,123,J,A,0.0,1,unanimous_with_conf,33731,0.9199999999999999, +Text-1782,complete,256,J,A,0.0,1,unanimous_with_conf,34674,0.8800000000000001, +Text-1783,complete,42,F,F,1.0,1,unanimous_with_conf,28520,0.8099999999999999, +Text-1783,complete,123,F,F,1.0,2,unanimous_with_conf,53697,0.9199999999999999, +Text-1783,complete,256,F,F,1.0,1,unanimous_with_conf,26830,0.86, +Text-1788,complete,42,C,C,1.0,2,unanimous_with_conf,96977,0.82, +Text-1788,complete,123,C,C,1.0,4,unanimous_with_conf,145207,0.8300000000000001, +Text-1788,complete,256,C,C,1.0,2,unanimous_with_conf,72236,0.8099999999999999, +Text-1789,complete,42,F,C,0.0,3,unanimous_with_conf,142392,0.86, +Text-1789,complete,123,F,F,1.0,2,unanimous_with_conf,83785,0.85, +Text-1789,complete,256,F,C,0.0,1,unanimous_with_conf,50145,0.8099999999999999, +Text-1792,complete,42,C,C,1.0,1,unanimous_with_conf,19466,0.8800000000000001, +Text-1792,complete,123,C,C,1.0,1,unanimous_with_conf,21141,0.86, +Text-1792,complete,256,C,C,1.0,1,unanimous_with_conf,22079,0.9, +Text-1794,complete,42,F,F,1.0,2,unanimous_with_conf,36109,0.9, +Text-1794,complete,123,F,F,1.0,2,unanimous_with_conf,33846,0.8800000000000001, +Text-1794,complete,256,F,F,1.0,1,unanimous_with_conf,19059,0.9099999999999999, +Text-1795,complete,42,J,A,0.0,2,unanimous_with_conf,50615,0.9400000000000001, +Text-1795,complete,123,J,A,0.0,1,unanimous_with_conf,24484,0.8400000000000001, +Text-1795,complete,256,J,A,0.0,1,unanimous_with_conf,30569,0.82, +Text-1796,complete,42,H,J,0.0,2,unanimous_with_conf,107313,0.95, +Text-1796,complete,123,H,A,0.0,2,unanimous_with_conf,98312,0.8800000000000001, +Text-1796,complete,256,H,J,0.0,1,weighted_majority,79158,0.8300000000000001, +Text-1800,complete,42,G,G,1.0,2,unanimous_with_conf,102332,0.93, +Text-1800,complete,123,G,G,1.0,2,unanimous_with_conf,95762,0.9359999999999999, +Text-1800,complete,256,G,G,1.0,2,unanimous_with_conf,74880,0.9199999999999999, +Text-1801,complete,42,C,C,1.0,1,unanimous_with_conf,20314,0.9199999999999999, +Text-1801,complete,123,C,C,1.0,1,unanimous_with_conf,22783,0.8300000000000001, +Text-1801,complete,256,C,C,1.0,1,unanimous_with_conf,22277,0.9099999999999999, +Text-1802,complete,42,A,E,0.0,1,unanimous_with_conf,26953,0.8400000000000001, +Text-1802,complete,123,A,E,0.0,1,unanimous_with_conf,27033,0.82, +Text-1802,complete,256,A,E,0.0,2,unanimous_with_conf,44048,0.9400000000000001, +Text-1803,complete,42,F,F,1.0,1,unanimous_with_conf,23902,0.8099999999999999, +Text-1803,complete,123,F,F,1.0,1,unanimous_with_conf,23266,0.8799999999999999, +Text-1803,complete,256,F,F,1.0,1,unanimous_with_conf,24743,0.8099999999999999, +Text-1806,complete,42,F,F,1.0,2,unanimous_with_conf,82254,0.9099999999999999, +Text-1806,complete,123,F,F,1.0,2,unanimous_with_conf,58270,0.9, +Text-1806,complete,256,F,F,1.0,3,unanimous_with_conf,128053,0.89, +Text-1807,complete,42,H,H,1.0,1,unanimous_with_conf,24125,0.8300000000000001, +Text-1807,complete,123,H,H,1.0,1,unanimous_with_conf,23820,0.9199999999999999, +Text-1807,complete,256,H,H,1.0,1,unanimous_with_conf,25306,0.8800000000000001, +Text-1812,complete,42,F,F,1.0,2,unanimous_with_conf,59220,0.8400000000000001, +Text-1812,complete,123,F,F,1.0,3,unanimous_with_conf,108748,0.844, +Text-1812,complete,256,F,F,1.0,3,unanimous_with_conf,77265,0.8800000000000001, +Text-1814,complete,42,D,G,0.0,2,unanimous_with_conf,119027,0.85, +Text-1814,complete,123,D,D,1.0,2,weighted_majority,123222,0.79, +Text-1814,complete,256,D,G,0.0,2,unanimous_with_conf,127853,0.836, +Text-1815,complete,42,B,B,1.0,6,weighted_majority,335874,0.82, +Text-1815,complete,123,B,F,0.0,2,unanimous_with_conf,69404,0.8, +Text-1815,complete,256,B,F,0.0,2,unanimous_with_conf,81112,0.8699999999999999, +Text-1822,complete,42,J,J,1.0,2,unanimous_with_conf,79347,0.8300000000000001, +Text-1822,complete,123,J,J,1.0,2,unanimous_with_conf,83807,0.8400000000000001, +Text-1822,complete,256,J,J,1.0,4,unanimous_with_conf,135946,0.85, +Text-1823,complete,42,A,H,0.0,1,unanimous_with_conf,17629,0.8800000000000001, +Text-1823,complete,123,A,H,0.0,1,unanimous_with_conf,20429,0.93, +Text-1823,complete,256,A,H,0.0,1,unanimous_with_conf,18338,0.8800000000000001, +Text-1825,complete,42,B,B,1.0,1,unanimous_with_conf,10817,0.93, +Text-1825,complete,123,B,B,1.0,1,unanimous_with_conf,11164,0.95, +Text-1825,complete,256,B,B,1.0,1,unanimous_with_conf,11015,0.9400000000000001, +Text-1827,complete,42,G,G,1.0,1,unanimous_with_conf,38542,0.8699999999999999, +Text-1827,complete,123,G,G,1.0,1,unanimous_with_conf,54282,0.8699999999999999, +Text-1827,complete,256,G,G,1.0,1,unanimous_with_conf,32440,0.9199999999999999, +Text-1829,complete,42,H,H,1.0,1,unanimous_with_conf,43287,0.9, +Text-1829,complete,123,H,H,1.0,1,unanimous_with_conf,52859,0.8799999999999999, +Text-1829,complete,256,H,H,1.0,1,unanimous_with_conf,42615,0.8800000000000001, +Text-1831,complete,42,E,E,1.0,1,unanimous_with_conf,105870,0.8699999999999999, +Text-1831,complete,123,E,E,1.0,2,unanimous_with_conf,145616,0.9039999999999999, +Text-1831,complete,256,E,E,1.0,2,unanimous_with_conf,136592,0.89, +Text-1834,complete,42,F,J,0.0,1,unanimous_with_conf,17001,0.8699999999999999, +Text-1834,complete,123,F,J,0.0,1,unanimous_with_conf,25733,0.8699999999999999, +Text-1834,complete,256,F,J,0.0,1,unanimous_with_conf,20404,0.9, +Text-1835,complete,42,D,D,1.0,1,unanimous_with_conf,21791,0.8699999999999999, +Text-1835,complete,123,D,D,1.0,2,weighted_majority,48698,0.8, +Text-1835,complete,256,D,D,1.0,1,unanimous_with_conf,26645,0.8300000000000001, +Text-1838,complete,42,E,J,0.0,3,unanimous_with_conf,104074,0.836, +Text-1838,complete,123,E,E,1.0,2,unanimous_with_conf,82196,0.8099999999999999, +Text-1838,complete,256,E,J,0.0,4,unanimous_with_conf,159823,0.8400000000000001, +Text-1842,complete,42,C,C,1.0,5,weighted_majority,296130,0.82, +Text-1842,complete,123,C,C,1.0,3,unanimous_with_conf,124652,0.8300000000000001, +Text-1842,complete,256,C,C,1.0,3,unanimous_with_conf,140035,0.8800000000000001, +Text-1844,complete,42,C,C,1.0,1,unanimous_with_conf,34356,0.8099999999999999, +Text-1844,complete,123,C,C,1.0,1,unanimous_with_conf,36192,0.8099999999999999, +Text-1844,complete,256,C,C,1.0,1,unanimous_with_conf,24092,0.8699999999999999, +Text-1847,complete,42,H,J,0.0,1,unanimous_with_conf,37399,0.9199999999999999, +Text-1847,complete,123,H,J,0.0,1,unanimous_with_conf,32062,0.93, +Text-1847,complete,256,H,J,0.0,1,unanimous_with_conf,25006,0.9339999999999999, +Text-1849,complete,42,J,J,1.0,1,weighted_majority,125346,0.73, +Text-1849,complete,123,J,J,1.0,2,unanimous_with_conf,158077,0.8300000000000001, +Text-1849,complete,256,J,J,1.0,1,weighted_majority,142763,0.85, +Text-1850,complete,42,F,F,1.0,1,unanimous_with_conf,13203,0.8800000000000001, +Text-1850,complete,123,F,F,1.0,1,unanimous_with_conf,11543,0.82, +Text-1850,complete,256,F,F,1.0,1,unanimous_with_conf,10232,0.89, +Text-1852,complete,42,D,D,1.0,1,unanimous_with_conf,111083,0.8099999999999999, +Text-1852,complete,123,D,D,1.0,2,unanimous_with_conf,120528,0.8859999999999999, +Text-1852,complete,256,D,G,0.0,2,weighted_majority,177781,0.78, +Text-1855,complete,42,F,D,0.0,1,unanimous_with_conf,23127,0.8800000000000001, +Text-1855,complete,123,F,D,0.0,2,unanimous_with_conf,41354,0.8400000000000001, +Text-1855,complete,256,F,D,0.0,1,unanimous_with_conf,28051,0.8699999999999999, +Text-1860,complete,42,D,F,0.0,1,unanimous_with_conf,17872,0.86, +Text-1860,complete,123,D,F,0.0,1,unanimous_with_conf,22269,0.8400000000000001, +Text-1860,complete,256,D,F,0.0,1,unanimous_with_conf,18230,0.8400000000000001, +Text-1861,complete,42,I,E,0.0,1,unanimous_with_conf,43222,0.8099999999999999, +Text-1861,complete,123,I,E,0.0,2,unanimous_with_conf,53279,0.9099999999999999, +Text-1861,complete,256,I,E,0.0,2,unanimous_with_conf,58081,0.9, +Text-1863,complete,42,B,B,1.0,2,unanimous_with_conf,46729,0.8099999999999999, +Text-1863,complete,123,B,C,0.0,3,unanimous_with_conf,102683,0.82, +Text-1863,complete,256,B,B,1.0,4,weighted_majority,139707,0.79, +Text-1865,complete,42,F,I,0.0,1,unanimous_with_conf,22182,0.85, +Text-1865,complete,123,F,I,0.0,2,unanimous_with_conf,42284,0.9099999999999999, +Text-1865,complete,256,F,I,0.0,1,unanimous_with_conf,23898,0.8300000000000001, +Text-1869,complete,42,J,J,1.0,1,unanimous_with_conf,21233,0.8699999999999999, +Text-1869,complete,123,J,J,1.0,1,unanimous_with_conf,25342,0.8800000000000001, +Text-1869,complete,256,J,J,1.0,1,unanimous_with_conf,23391,0.9199999999999999, +Text-1870,complete,42,E,H,0.0,5,unanimous_with_conf,223092,0.82, +Text-1870,complete,123,E,H,0.0,2,unanimous_with_conf,93155,0.9199999999999999, +Text-1870,complete,256,E,H,0.0,6,unanimous_with_conf,313838,0.8300000000000001, +Text-1871,complete,42,G,G,1.0,2,unanimous_with_conf,43963,0.86, +Text-1871,complete,123,G,G,1.0,2,unanimous_with_conf,44613,0.9, +Text-1871,complete,256,G,G,1.0,2,unanimous_with_conf,48622,0.8099999999999999, +Text-1875,complete,42,H,B,0.0,1,unanimous_with_conf,21656,0.8039999999999999, +Text-1875,complete,123,H,B,0.0,2,unanimous_with_conf,39492,0.866, +Text-1875,complete,256,H,B,0.0,1,unanimous_with_conf,21290,0.8800000000000001, +Text-1879,complete,42,F,A,0.0,1,unanimous_with_conf,21270,0.8400000000000001, +Text-1879,complete,123,F,A,0.0,1,unanimous_with_conf,24698,0.86, +Text-1879,complete,256,F,A,0.0,1,unanimous_with_conf,22445,0.9, +Text-1881,complete,42,D,I,0.0,2,unanimous_with_conf,149742,0.9, +Text-1881,complete,123,D,I,0.0,1,unanimous_with_conf,88451,0.9099999999999999, +Text-1881,complete,256,D,I,0.0,1,weighted_majority,87460,0.8699999999999999, +Text-1882,complete,42,F,D,0.0,2,unanimous_with_conf,41557,0.9199999999999999, +Text-1882,complete,123,F,D,0.0,2,unanimous_with_conf,45375,0.8300000000000001, +Text-1882,complete,256,F,D,0.0,2,unanimous_with_conf,36342,0.86, +Text-1885,complete,42,E,A,0.0,3,unanimous_with_conf,106892,0.8300000000000001, +Text-1885,complete,123,E,G,0.0,2,unanimous_with_conf,91333,0.8699999999999999, +Text-1885,complete,256,E,E,1.0,2,unanimous_with_conf,80079,0.8300000000000001, +Text-1888,complete,42,I,I,1.0,3,unanimous_with_conf,132861,0.85, +Text-1888,complete,123,I,I,1.0,2,unanimous_with_conf,69655,0.8699999999999999, +Text-1888,complete,256,I,I,1.0,2,unanimous_with_conf,70279,0.8699999999999999, +Text-1889,complete,42,H,B,0.0,3,unanimous_with_conf,92534,0.8, +Text-1889,complete,123,H,B,0.0,4,unanimous_with_conf,152087,0.85, +Text-1889,complete,256,H,B,0.0,2,unanimous_with_conf,64569,0.89, +Text-1891,complete,42,C,C,1.0,1,unanimous_with_conf,19566,0.8800000000000001, +Text-1891,complete,123,C,C,1.0,1,unanimous_with_conf,19169,0.8699999999999999, +Text-1891,complete,256,C,C,1.0,2,unanimous_with_conf,33010,0.806, +Text-1897,complete,42,C,B,0.0,2,unanimous_with_conf,81359,0.8699999999999999, +Text-1897,complete,123,C,J,0.0,2,unanimous_with_conf,85899,0.8699999999999999, +Text-1897,complete,256,C,B,0.0,2,unanimous_with_conf,69923,0.86, +Text-1899,complete,42,C,A,0.0,2,unanimous_with_conf,55907,0.914, +Text-1899,complete,123,C,A,0.0,2,unanimous_with_conf,48075,0.8300000000000001, +Text-1899,complete,256,C,A,0.0,1,unanimous_with_conf,38826,0.8, +Text-1904,complete,42,I,I,1.0,1,unanimous_with_conf,23877,0.86, 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+Text-1913,complete,256,G,G,1.0,1,unanimous_with_conf,28123,0.9, +Text-1914,complete,42,E,E,1.0,1,unanimous_with_conf,104437,0.8300000000000001, +Text-1914,complete,123,E,E,1.0,1,unanimous_with_conf,73381,0.86, +Text-1914,complete,256,E,E,1.0,2,unanimous_with_conf,98159,0.946, +Text-1916,complete,42,H,I,0.0,3,unanimous_with_conf,140696,0.8400000000000001, +Text-1916,complete,123,H,I,0.0,2,unanimous_with_conf,62654,0.8939999999999999, +Text-1916,complete,256,H,I,0.0,1,unanimous_with_conf,50898,0.9, +Text-1918,complete,42,D,G,0.0,2,unanimous_with_conf,43017,0.82, +Text-1918,complete,123,D,G,0.0,2,weighted_majority,57029,0.82, +Text-1918,complete,256,D,G,0.0,2,unanimous_with_conf,38719,0.9199999999999999, +Text-1920,complete,42,I,I,1.0,1,unanimous_with_conf,15997,0.9199999999999999, +Text-1920,complete,123,I,I,1.0,1,unanimous_with_conf,17017,0.95, +Text-1920,complete,256,I,I,1.0,1,unanimous_with_conf,14882,0.95, +Text-1921,complete,42,H,F,0.0,2,unanimous_with_conf,168878,0.8699999999999999, +Text-1921,complete,123,H,C,0.0,3,weighted_majority,259957,0.9199999999999999, +Text-1921,complete,256,H,G,0.0,2,unanimous_with_conf,170979,0.9, +Text-1926,complete,42,A,A,1.0,1,unanimous_with_conf,25504,0.9, +Text-1926,complete,123,A,A,1.0,1,unanimous_with_conf,29806,0.8400000000000001, +Text-1926,complete,256,A,A,1.0,1,unanimous_with_conf,31393,0.9099999999999999, +Text-1928,complete,42,A,I,0.0,1,unanimous_with_conf,16734,0.8099999999999999, +Text-1928,complete,123,A,I,0.0,1,weighted_majority,24335,0.78, +Text-1928,complete,256,A,I,0.0,2,unanimous_with_conf,31256,0.9, +Text-1930,complete,42,D,I,0.0,1,weighted_majority,28139,0.8300000000000001, +Text-1930,complete,123,D,I,0.0,1,unanimous_with_conf,26097,0.9199999999999999, +Text-1930,complete,256,D,I,0.0,1,unanimous_with_conf,23557,0.9039999999999999, +Text-1933,complete,42,E,J,0.0,1,unanimous_with_conf,63290,0.82, +Text-1933,complete,123,E,J,0.0,1,unanimous_with_conf,45940,0.8099999999999999, +Text-1933,complete,256,E,J,0.0,2,unanimous_with_conf,79982,0.9, +Text-1934,complete,42,E,E,1.0,2,unanimous_with_conf,123370,0.8800000000000001, +Text-1934,complete,123,E,E,1.0,2,unanimous_with_conf,133503,0.8300000000000001, +Text-1934,complete,256,E,E,1.0,2,unanimous_with_conf,98245,0.9099999999999999, +Text-1936,complete,42,I,C,0.0,2,unanimous_with_conf,49830,0.8400000000000001, +Text-1936,complete,123,I,C,0.0,2,unanimous_with_conf,41870,0.8800000000000001, +Text-1936,complete,256,I,C,0.0,2,unanimous_with_conf,44555,0.8800000000000001, +Text-1937,complete,42,C,J,0.0,1,unanimous_with_conf,32031,0.86, +Text-1937,complete,123,C,J,0.0,2,unanimous_with_conf,45164,0.9019999999999999, +Text-1937,complete,256,C,J,0.0,2,unanimous_with_conf,70648,0.86, +Text-1945,complete,42,D,D,1.0,2,unanimous_with_conf,46984,0.9199999999999999, +Text-1945,complete,123,D,D,1.0,1,unanimous_with_conf,33644,0.884, +Text-1945,complete,256,D,D,1.0,3,unanimous_with_conf,85724,0.86, +Text-1946,complete,42,C,I,0.0,2,unanimous_with_conf,75412,0.89, +Text-1946,complete,123,C,I,0.0,2,unanimous_with_conf,82475,0.9, +Text-1946,complete,256,C,I,0.0,2,unanimous_with_conf,83059,0.8300000000000001, +Text-1947,complete,42,J,J,1.0,1,unanimous_with_conf,19374,0.9099999999999999, +Text-1947,complete,123,J,J,1.0,1,unanimous_with_conf,17556,0.9099999999999999, +Text-1947,complete,256,J,J,1.0,1,unanimous_with_conf,23114,0.9, +Text-1950,complete,42,I,J,0.0,1,unanimous_with_conf,18632,0.8699999999999999, +Text-1950,complete,123,I,J,0.0,1,unanimous_with_conf,18761,0.82, +Text-1950,complete,256,I,J,0.0,2,unanimous_with_conf,36104,0.9400000000000001, +Text-1951,complete,42,F,F,1.0,1,unanimous_with_conf,34165,0.82, +Text-1951,complete,123,F,F,1.0,1,unanimous_with_conf,31974,0.8699999999999999, +Text-1951,complete,256,F,F,1.0,1,unanimous_with_conf,36704,0.9, +Text-1952,complete,42,A,A,1.0,1,unanimous_with_conf,35692,0.9040000000000001, +Text-1952,complete,123,A,A,1.0,1,unanimous_with_conf,73440,0.8, +Text-1952,complete,256,A,A,1.0,2,unanimous_with_conf,87374,0.8800000000000001, +Text-1953,complete,42,A,A,1.0,2,unanimous_with_conf,57625,0.86, +Text-1953,complete,123,A,A,1.0,2,unanimous_with_conf,42907,0.9, +Text-1953,complete,256,A,A,1.0,2,unanimous_with_conf,39928,0.8, +Text-1954,complete,42,A,I,0.0,1,unanimous_with_conf,37690,0.9199999999999999, +Text-1954,complete,123,A,I,0.0,2,unanimous_with_conf,90968,0.9, +Text-1954,complete,256,A,I,0.0,1,unanimous_with_conf,30920,0.89, +Text-1960,complete,42,H,B,0.0,2,unanimous_with_conf,87303,0.82, +Text-1960,complete,123,H,B,0.0,2,unanimous_with_conf,72408,0.8699999999999999, +Text-1960,complete,256,H,B,0.0,2,unanimous_with_conf,69812,0.9099999999999999, +Text-1963,complete,42,F,F,1.0,1,unanimous_with_conf,44203,0.8300000000000001, +Text-1963,complete,123,F,F,1.0,1,unanimous_with_conf,40661,0.8400000000000001, +Text-1963,complete,256,F,F,1.0,1,unanimous_with_conf,36679,0.8400000000000001, +Text-1966,complete,42,G,F,0.0,3,unanimous_with_conf,101176,0.89, +Text-1966,complete,123,G,F,0.0,2,unanimous_with_conf,70018,0.86, +Text-1966,complete,256,G,F,0.0,2,unanimous_with_conf,66209,0.8, +Text-1975,complete,42,A,A,1.0,2,unanimous_with_conf,71531,0.8, +Text-1975,complete,123,A,A,1.0,3,unanimous_with_conf,100836,0.8099999999999999, +Text-1975,complete,256,A,A,1.0,2,unanimous_with_conf,49525,0.826, +Text-1976,complete,42,A,A,1.0,1,unanimous_with_conf,29192,0.8099999999999999, +Text-1976,complete,123,A,A,1.0,2,unanimous_with_conf,51531,0.8699999999999999, +Text-1976,complete,256,A,A,1.0,2,unanimous_with_conf,50896,0.8800000000000001, +Text-1977,complete,42,D,J,0.0,1,unanimous_with_conf,29124,0.82, +Text-1977,complete,123,D,J,0.0,1,unanimous_with_conf,29031,0.89, +Text-1977,complete,256,D,J,0.0,1,unanimous_with_conf,26582,0.8699999999999999, +Text-1982,complete,42,A,I,0.0,1,unanimous_with_conf,44016,0.8, +Text-1982,complete,123,A,I,0.0,1,unanimous_with_conf,29842,0.85, +Text-1982,complete,256,A,I,0.0,1,unanimous_with_conf,30914,0.86, +Text-1983,complete,42,B,I,0.0,4,unanimous_with_conf,252384,0.8560000000000001, +Text-1983,complete,123,B,I,0.0,2,unanimous_with_conf,105082,0.95, +Text-1983,complete,256,B,A,0.0,2,unanimous_with_conf,108550,0.884, +Text-1989,complete,42,H,D,0.0,1,unanimous_with_conf,36281,0.8099999999999999, +Text-1989,complete,123,H,D,0.0,1,unanimous_with_conf,35144,0.85, +Text-1989,complete,256,H,D,0.0,1,unanimous_with_conf,28366,0.8300000000000001, +Text-1991,complete,42,I,B,0.0,1,unanimous_with_conf,36681,0.82, +Text-1991,complete,123,I,B,0.0,2,unanimous_with_conf,47852,0.9099999999999999, +Text-1991,complete,256,I,I,1.0,2,unanimous_with_conf,57300,0.82, +Text-1994,complete,42,J,J,1.0,1,unanimous_with_conf,48348,0.8400000000000001, +Text-1994,complete,123,J,J,1.0,1,unanimous_with_conf,37244,0.8800000000000001, +Text-1994,complete,256,J,J,1.0,1,unanimous_with_conf,51733,0.8, +Text-1995,complete,42,F,B,0.0,1,unanimous_with_conf,22705,0.9400000000000001, +Text-1995,complete,123,F,B,0.0,1,unanimous_with_conf,26261,0.8, +Text-1995,complete,256,F,B,0.0,1,unanimous_with_conf,24377,0.89, +Text-1996,complete,42,G,G,1.0,1,unanimous_with_conf,34628,0.9099999999999999, +Text-1996,complete,123,G,G,1.0,1,unanimous_with_conf,30083,0.8300000000000001, +Text-1996,complete,256,G,G,1.0,1,unanimous_with_conf,30252,0.8699999999999999, +Text-1997,complete,42,G,H,0.0,1,unanimous_with_conf,24231,0.9299999999999999, +Text-1997,complete,123,G,H,0.0,1,unanimous_with_conf,22723,0.9199999999999999, +Text-1997,complete,256,G,H,0.0,1,unanimous_with_conf,25641,0.8699999999999999, +Text-1998,complete,42,H,C,0.0,2,unanimous_with_conf,49667,0.85, +Text-1998,complete,123,H,C,0.0,2,unanimous_with_conf,50446,0.9099999999999999, +Text-1998,complete,256,H,C,0.0,2,unanimous_with_conf,49027,0.93, +Text-1999,complete,42,G,A,0.0,1,weighted_majority,80444,0.85, +Text-1999,complete,123,G,E,0.0,3,weighted_majority,254786,0.8800000000000001, +Text-1999,complete,256,G,E,0.0,2,weighted_majority,176473,0.8699999999999999, +Text-2000,complete,42,F,F,1.0,2,unanimous_with_conf,114055,0.8800000000000001, +Text-2000,complete,123,F,F,1.0,2,unanimous_with_conf,129264,0.8099999999999999, +Text-2000,complete,256,F,F,1.0,1,unanimous_with_conf,82334,0.86, +Text-2002,complete,42,D,H,0.0,1,unanimous_with_conf,29396,0.82, +Text-2002,complete,123,D,H,0.0,2,unanimous_with_conf,60602,0.8800000000000001, +Text-2002,complete,256,D,H,0.0,2,unanimous_with_conf,46276,0.9, +Text-2003,complete,42,I,H,0.0,1,unanimous_with_conf,36546,0.8800000000000001, +Text-2003,complete,123,I,H,0.0,2,unanimous_with_conf,47180,0.95, +Text-2003,complete,256,I,H,0.0,1,unanimous_with_conf,26525,0.82, +Text-2005,complete,42,J,J,1.0,1,unanimous_with_conf,21183,0.8800000000000001, +Text-2005,complete,123,J,J,1.0,1,unanimous_with_conf,21002,0.89, +Text-2005,complete,256,J,J,1.0,1,unanimous_with_conf,20423,0.82, +Text-2008,complete,42,I,B,0.0,2,unanimous_with_conf,33051,0.9400000000000001, +Text-2008,complete,123,I,B,0.0,1,unanimous_with_conf,19214,0.8300000000000001, +Text-2008,complete,256,I,B,0.0,1,unanimous_with_conf,19733,0.8, +Text-2009,complete,42,C,C,1.0,2,unanimous_with_conf,52713,0.9199999999999999, +Text-2009,complete,123,C,C,1.0,1,unanimous_with_conf,33776,0.8400000000000001, +Text-2009,complete,256,C,C,1.0,2,unanimous_with_conf,51612,0.8800000000000001, +Text-2010,complete,42,G,J,0.0,2,unanimous_with_conf,52774,0.9400000000000001, +Text-2010,complete,123,G,J,0.0,1,unanimous_with_conf,36808,0.8, +Text-2010,complete,256,G,J,0.0,1,unanimous_with_conf,37043,0.8300000000000001, 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"run_dirs": [ + "medical_mas/raw/med_board/seed42", + "medical_mas/raw/med_board/seed43", + "medical_mas/raw/med_board/seed44" + ], + "seeds": [ + 42, + 43, + 44 + ] + }, + "oracle_gap_decomposition": [ + { + "final_accuracy": 0.39877551020408164, + "fraction_majority_correct": 0.39877551020408164, + "fraction_majority_wrong_minority_correct": 0.0, + "fraction_no_agent_correct": 0.6012244897959184, + "n": 7350, + "oracle_gap": 0.0, + "protocol": "single_agent", + "round0_oracle_accuracy": 0.39877551020408164 + }, + { + "final_accuracy": 0.41061224489795917, + "fraction_majority_correct": 0.41034013605442177, + "fraction_majority_wrong_minority_correct": 0.14408163265306123, + "fraction_no_agent_correct": 0.445578231292517, + "n": 7350, + "oracle_gap": 0.1438095238095238, + "protocol": "majority_vote", + "round0_oracle_accuracy": 0.5544217687074829 + }, + { + "final_accuracy": 0.4220408163265306, + "fraction_majority_correct": 0.40530612244897957, + 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"open_committee" + }, + { + "final_accuracy": 0.10413694721825963, + "n": 701, + "n_correct_agents": 1, + "protocol": "open_committee" + }, + { + "final_accuracy": 0.463855421686747, + "n": 498, + "n_correct_agents": 2, + "protocol": "open_committee" + }, + { + "final_accuracy": 0.8589743589743589, + "n": 468, + "n_correct_agents": 3, + "protocol": "open_committee" + }, + { + "final_accuracy": 0.9816849816849816, + "n": 546, + "n_correct_agents": 4, + "protocol": "open_committee" + }, + { + "final_accuracy": 1.0, + "n": 1854, + "n_correct_agents": 5, + "protocol": "open_committee" + }, + { + "final_accuracy": 0.02144607843137255, + "n": 3264, + "n_correct_agents": 0, + "protocol": "anonymous_evidence_board" + }, + { + "final_accuracy": 0.2259475218658892, + "n": 686, + "n_correct_agents": 1, + "protocol": "anonymous_evidence_board" + }, + { + "final_accuracy": 0.42946058091286304, + "n": 482, + "n_correct_agents": 2, + "protocol": "anonymous_evidence_board" + }, + { + "final_accuracy": 0.6993603411513859, + "n": 469, + "n_correct_agents": 3, + "protocol": "anonymous_evidence_board" + }, + { + "final_accuracy": 0.8330494037478705, + "n": 587, + "n_correct_agents": 4, + "protocol": "anonymous_evidence_board" + }, + { + "final_accuracy": 0.9828141783029001, + "n": 1862, + "n_correct_agents": 5, + "protocol": "anonymous_evidence_board" + } + ] +} diff --git a/data/external/results/formal/early_mas_architecture_motivation/analysis_ready/med_board/legacy_3seed_summary.md b/data/external/results/formal/early_mas_architecture_motivation/analysis_ready/med_board/legacy_3seed_summary.md new file mode 100644 index 0000000000000000000000000000000000000000..13d6afa54cec1ca19814951717c8aeacbd6e8ca7 --- /dev/null +++ b/data/external/results/formal/early_mas_architecture_motivation/analysis_ready/med_board/legacy_3seed_summary.md @@ -0,0 +1,79 @@ +# Real LLM Full-Dataset 3-Seed Summary + +## Run metadata + +- Total cases: `2450` +- Seeds: `42`, `43`, `44` +- Protocols: `single_agent`, `majority_vote`, `open_committee`, `anonymous_evidence_board` +- Rows per seed: `9800` (`2450` cases × 4 protocols) +- Total analyzed rows: `29400` +- Full-run configs: + - `configs/experiments/real_llm_full_seed42.yaml` + - `configs/experiments/real_llm_full_seed43.yaml` + - `configs/experiments/real_llm_full_seed44.yaml` + +## QA status + +All three full-dataset seeds passed `scripts/qa_real_smoke.py --n-cases 2450`. + +| seed | QA | rows | failed cases | invalid final | invalid agent outputs | silent fallback | retry total | retry failure | normalization warnings | AEB leakage | +|---:|---|---:|---:|---:|---:|---:|---:|---:|---:|---| +| 42 | PASS | 9800 | 0 | 0 | 0 | 0 | 46 | 0 | 4 | PASS | +| 43 | PASS | 9800 | 0 | 0 | 0 | 0 | 59 | 0 | 0 | PASS | +| 44 | PASS | 9800 | 0 | 0 | 0 | 0 | 47 | 0 | 2 | PASS | + +Aggregate QA totals: + +| metric | value | +|---|---:| +| final decisions valid | 29400 / 29400 | +| agent outputs valid | 161700 / 161700 | +| failed cases | 0 | +| invalid final decisions | 0 | +| invalid agent outputs | 0 | +| silent fallbacks | 0 | +| retry total | 152 | +| retry success | 152 | +| retry failure | 0 | +| normalization warnings | 6 | + +AEB leakage checks passed for persisted evidence boards, retained evidence IDs, and reconstructed judge prompts in all three seeds. The QA checks found no `source_agent`, visible source, `supports_option`, `contradicts_option`, agent identity in evidence IDs, agent diagnoses section, answer field leakage, or supports-label leakage. + +## Accuracy by seed and protocol + +| seed | single_agent | majority_vote | open_committee | anonymous_evidence_board | +|---:|---:|---:|---:|---:| +| 42 | 0.4008 | 0.4086 | 0.4220 | 0.4196 | +| 43 | 0.3943 | 0.4131 | 0.4245 | 0.4208 | +| 44 | 0.4012 | 0.4102 | 0.4196 | 0.4163 | + +## Mean ± SD across seeds + +| protocol | mean accuracy | SD | mean gain vs single_agent | +|---|---:|---:|---:| +| single_agent | 0.3988 | 0.0039 | 0.0000 | +| majority_vote | 0.4106 | 0.0023 | +0.0118 | +| open_committee | 0.4220 | 0.0024 | +0.0233 | +| anonymous_evidence_board | 0.4189 | 0.0023 | +0.0201 | + +## Token usage and cost tradeoff + +Token counts are used as the cost proxy because exact upstream dollar pricing is not encoded in these run artifacts. + +| protocol | calls | prompt tokens | completion tokens | total tokens | token ratio vs single | accuracy gain vs single | gain per extra 1M tokens | +|---|---:|---:|---:|---:|---:|---:|---:| +| single_agent | 7359 | 5260686 | 17650380 | 22911066 | 1.00× | 0.0000 | N/A | +| majority_vote | 36782 | 26298327 | 87302201 | 113600528 | 4.96× | +0.0118 | 0.000131 | +| open_committee | 80921 | 102389632 | 143362727 | 245752359 | 10.73× | +0.0233 | 0.000104 | +| anonymous_evidence_board | 44140 | 36113815 | 102915171 | 139028986 | 6.07× | +0.0201 | 0.000173 | + +Cost interpretation: + +- `open_committee` has the best raw accuracy but uses the most calls and tokens: about `10.73×` single-agent token usage. +- `anonymous_evidence_board` is very close to `open_committee` in raw accuracy while using about `56.6%` of open-committee tokens (`139.0M` vs `245.8M`). +- `anonymous_evidence_board` has the strongest token-normalized gain among the multi-agent protocols in this run. +- `majority_vote` is cheaper than AEB and open committee, but it also has the smallest accuracy lift among the multi-agent methods. + +## Conclusion + +`open_committee` has the highest raw accuracy on the full-dataset 3-seed run (`0.4220 ± 0.0024`). `anonymous_evidence_board` is very close (`0.4189 ± 0.0023`), lower-cost than open committee, and leakage-safe under the current QA checks. All multi-agent methods modestly outperform `single_agent`, with mean gains of `+0.0118` for `majority_vote`, `+0.0233` for `open_committee`, and `+0.0201` for `anonymous_evidence_board`. diff --git a/data/external/results/formal/early_mas_architecture_motivation/analysis_ready/med_board/oracle_trials.csv b/data/external/results/formal/early_mas_architecture_motivation/analysis_ready/med_board/oracle_trials.csv new file mode 100644 index 0000000000000000000000000000000000000000..0093a3a426af3474fd2976aafc237a9259eef93c --- /dev/null +++ b/data/external/results/formal/early_mas_architecture_motivation/analysis_ready/med_board/oracle_trials.csv @@ -0,0 +1,29401 @@ +case_id,seed,protocol,ground_truth,round0_valid_answers,final_answer,n_correct_agents,oracle_correct,final_correct,majority_answer,majority_correct,majority_wrong_minority_correct,judge_relation,confidence_weighted_answer,follows_highest_confidence +Text-0,42,single_agent,E,C,C,0,0,0,C,0,0,follows_majority,C,1 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+Text-999,44,open_committee,H,0.9,1,11,10689,7146,17835,552.409122745,0,0,0,0,0 +Text-999,44,anonymous_evidence_board,H,0.95,1,6,3676,4076,7752,387.028090388,0,0,0,0,0 diff --git a/data/external/results/formal/early_mas_architecture_motivation/audits/code_security_gate.md b/data/external/results/formal/early_mas_architecture_motivation/audits/code_security_gate.md new file mode 100644 index 0000000000000000000000000000000000000000..061a9bdd1af041f1c2d11f9e6dcb29e0e42b3620 --- /dev/null +++ b/data/external/results/formal/early_mas_architecture_motivation/audits/code_security_gate.md @@ -0,0 +1,27 @@ +# Medical MAS Code Security Gate + +## Payload scan + +The selected experimental outputs passed the automated credential-pattern scan: + +- scanned files: 47,300 +- scanned bytes: 1,126,934,115 +- credential-pattern matches: 0 +- excluded files: three macOS `.DS_Store` files + +The machine-readable result is `payload_security_scan.json`. File hashes and release-relative paths are recorded in `manifests/source_payload_inventory.csv.gz`. + +## Code snapshot status + +The code-snapshot gate passed. The snapshot contains 109 files and 1,338,100 bytes, with zero credential-pattern matches: + +- 75 `med_board` files are byte-identical Git blobs from the provenance-labelled execution and analysis revisions. +- 34 `med-agent` files preserve the archived worktree and are labelled `reconstructed_worktree`. +- 32 `med-agent` files are byte-identical copies. +- Two runtime configuration files were sanitized in the copied snapshot. Their embedded credential fallback was removed, and the reconstructed configuration now requires `OPENAI_API_KEY` from the environment. + +The original archive was not edited. Source and output hashes, revisions and transformations are recorded in `manifests/code_snapshot_manifest.csv`; the machine-readable scan is `code_snapshot_security_scan.json`. If the historical fallback was ever an active credential, rotation or revocation remains an operational security follow-up outside this repository. + +## Release effect + +The numerical archive and the code snapshot both pass their repository security gates. These checks do not change the provenance boundary: `med_board` remains tied to inferred historical commits, whereas `med-agent` remains a sanitized reconstruction of an incompletely tracked worktree. diff --git a/data/external/results/formal/early_mas_architecture_motivation/audits/code_snapshot_security_scan.json b/data/external/results/formal/early_mas_architecture_motivation/audits/code_snapshot_security_scan.json new file mode 100644 index 0000000000000000000000000000000000000000..c1757300ea44af0a819bd6b0c89ff82e35a49d95 --- /dev/null +++ b/data/external/results/formal/early_mas_architecture_motivation/audits/code_snapshot_security_scan.json @@ -0,0 +1,13 @@ +{ + "match_file_count": 0, + "matched_paths": {}, + "scanned_bytes": 1338100, + "scanned_file_count": 109, + "secret_pattern_names": [ + "assigned_api_key", + "bearer_token", + "huggingface_token", + "openai_style_token" + ], + "status": "pass" +} diff --git a/data/external/results/formal/early_mas_architecture_motivation/audits/code_snapshot_security_scan.metadata.json b/data/external/results/formal/early_mas_architecture_motivation/audits/code_snapshot_security_scan.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..690c84ad302dc1a3bd3245a25544d80c6a4d3372 --- /dev/null +++ b/data/external/results/formal/early_mas_architecture_motivation/audits/code_snapshot_security_scan.metadata.json @@ -0,0 +1,17 @@ +{ + "artifact_kind": "analysis_auxiliary", + "command_mode": "medical_mas_code_snapshot", + "config_hash": "7ca1a29be00a", + "config_path": "configs/analyses/medical_mas_code_snapshot.yaml", + "evidence_status": "complete", + "generated_at_utc": "2026-08-24T09:18:43.517885+00:00", + "git_commit": "b9585011871af839e163a9a9f8612b30c2a6f391", + "input_paths": [ + "results/formal/early_mas_architecture_motivation/manifests/source_repositories.yaml" + ], + "output_paths": [ + "results/formal/early_mas_architecture_motivation/manifests/code_snapshot_manifest.csv", + "results/formal/early_mas_architecture_motivation/audits/code_snapshot_security_scan.json" + ], + "run_class": "formal" +} diff --git a/data/external/results/formal/early_mas_architecture_motivation/audits/payload_security_scan.json b/data/external/results/formal/early_mas_architecture_motivation/audits/payload_security_scan.json new file mode 100644 index 0000000000000000000000000000000000000000..d2fb2770dbf30008550672198b99f4f4003e4a8c --- /dev/null +++ b/data/external/results/formal/early_mas_architecture_motivation/audits/payload_security_scan.json @@ -0,0 +1,18 @@ +{ + "excluded_file_count": 3, + "excluded_paths": [ + "med-agent/results_concurrency50/.DS_Store", + "med-agent/results_concurrency50_fixed6/.DS_Store", + "med-agent/results_concurrency50_tuned/.DS_Store" + ], + "match_file_count": 0, + "matched_paths": {}, + "scanned_bytes": 1126934115, + "scanned_file_count": 47300, + "secret_pattern_names": [ + "assigned_api_key", + "bearer_token", + "huggingface_token", + "openai_style_token" + ] +} diff --git a/data/external/results/formal/early_mas_architecture_motivation/audits/payload_security_scan.metadata.json b/data/external/results/formal/early_mas_architecture_motivation/audits/payload_security_scan.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..bda7529d227b448f1f5cd4a1a39ad0fa236d2c00 --- /dev/null +++ b/data/external/results/formal/early_mas_architecture_motivation/audits/payload_security_scan.metadata.json @@ -0,0 +1,17 @@ +{ + "artifact_kind": "analysis_auxiliary", + "command_mode": "medical_mas_source_inventory", + "config_hash": "ce88ed63583f", + "config_path": "configs/analyses/medical_mas_source_inventory.yaml", + "evidence_status": "complete", + "generated_at_utc": "2026-08-24T08:47:30.911805+00:00", + "git_commit": "e8971f3a5a5b5fb3f2d7277ae928520d401d1e62", + "input_paths": [ + "docs/migration/medical_mas_migration_plan.md" + ], + "output_paths": [ + "results/formal/early_mas_architecture_motivation/manifests/source_payload_inventory.csv.gz", + "results/formal/early_mas_architecture_motivation/audits/payload_security_scan.json" + ], + "run_class": "formal" +} diff --git a/data/external/results/formal/early_mas_architecture_motivation/audits/private_release_verification.json b/data/external/results/formal/early_mas_architecture_motivation/audits/private_release_verification.json new file mode 100644 index 0000000000000000000000000000000000000000..d9e5b7ffed8386f8186b4ed4575d934f7f4ed570 --- /dev/null +++ b/data/external/results/formal/early_mas_architecture_motivation/audits/private_release_verification.json @@ -0,0 +1,13 @@ +{ + "all_archive_hashes_recorded": true, + "all_member_hashes_match": true, + "archive_count": 7, + "inventory_sha256": "f6243099b9c19e1596ff428e873827948671760f8c6627b07e2a97f3303b25ef", + "member_count": 47300, + "previous_manifest_present": true, + "rebuild_hashes_match_previous": true, + "release_root": "results/tmp/medical_mas_private_release", + "shard_target_bytes": 268435456, + "status": "pass", + "uncompressed_bytes": 1126934115 +} diff --git a/data/external/results/formal/early_mas_architecture_motivation/audits/private_release_verification.metadata.json b/data/external/results/formal/early_mas_architecture_motivation/audits/private_release_verification.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..fe9b557120a7427483f177bf7211cd8f605eb1fd --- /dev/null +++ b/data/external/results/formal/early_mas_architecture_motivation/audits/private_release_verification.metadata.json @@ -0,0 +1,19 @@ +{ + "artifact_kind": "analysis_auxiliary", + "command_mode": "medical_mas_private_release", + "config_hash": "f014d8afffa1", + "config_path": "configs/analyses/medical_mas_private_release.yaml", + "evidence_status": "complete", + "generated_at_utc": "2026-08-24T09:35:15.212556+00:00", + "git_commit": "adbdb91e927e374f7fcfc9c1c46c054a03fda95b", + "input_paths": [ + "results/formal/early_mas_architecture_motivation/manifests/source_payload_inventory.csv.gz", + "results/formal/early_mas_architecture_motivation/manifests/source_repositories.yaml", + "results/formal/early_mas_architecture_motivation/manifests/code_snapshot_manifest.csv" + ], + "output_paths": [ + "results/formal/early_mas_architecture_motivation/manifests/private_release_shards.csv", + "results/formal/early_mas_architecture_motivation/audits/private_release_verification.json" + ], + "run_class": "formal" +} diff --git a/data/external/results/formal/early_mas_architecture_motivation/audits/provenance_and_qa.md b/data/external/results/formal/early_mas_architecture_motivation/audits/provenance_and_qa.md index 21c02d3e46743cb8fdac4a0920ffee64be24b337..6b4d4714e4ddac304622569fd88156bb9e269973 100644 --- a/data/external/results/formal/early_mas_architecture_motivation/audits/provenance_and_qa.md +++ b/data/external/results/formal/early_mas_architecture_motivation/audits/provenance_and_qa.md @@ -1,27 +1,25 @@ -# Provenance And QA +# Medical MAS Formal Analysis -## Source Archive +## Coverage -- Archive root: `/Users/jay/research/evole-meme/med_repos_20260604` -- Generated into: `results/formal/early_mas_architecture_motivation` +- med-agent trial rows: 47,250 across three run families +- med-agent topology summary rows: 15 +- med-board protocol rows: 29,400 across 2,450 questions and three seeds +- med-board protocol summary rows: 4 +- oracle-gap rows: 4 +- judge-behaviour rows: 4 +- oracle and judge inputs: 29,400 text-free case-protocol-seed records derived from raw outputs +- legacy cross-check: raw-derived oracle and judge summaries exactly match the archived gain-bottleneck report -## Row Counts +## Manuscript-facing checks -- med-agent topology summary rows: `15` -- med-agent baseline topology rows: `5` -- med-board protocol summary rows: `4` -- benchmark discussion-control rows: `8` -- mechanism-pilot condition rows: `8` +- single agent: 0.398776 accuracy, 1.0000x token ratio +- majority vote: 0.410612 accuracy, 4.9583x token ratio +- open committee: 0.422041 accuracy, 10.7264x token ratio +- anonymous evidence board: 0.418912 accuracy, 6.0682x token ratio +- open committee versus majority vote: 1.1429 percentage points, 95% CI 0.4218 to 1.9048 (10,000 question-cluster resamples, seed 46) +- anonymous evidence board versus majority vote: 0.8299 percentage points, 95% CI -0.0544 to 1.7415 (10,000 question-cluster resamples, seed 49) -## Primary Source Families +## Evidence boundary -- `med-agent/results_concurrency50/`: baseline topology sweep, `15,750` rows in `all_results.csv`. -- `med-agent/results_concurrency50_tuned/`: tuned consensus topology sweep, `15,750` rows. -- `med-agent/results_concurrency50_fixed6/`: fixed-six-round topology sweep, `15,750` rows. -- `med_board/reports/real_llm_full_3seed_summary.md`: full 3-seed protocol summary, `29,400` analyzed rows. -- `med_board/runs/gain_bottleneck_analysis/gain_bottleneck_report.json`: oracle-gap and follow-majority decomposition. -- `med_board/runs/mechanism_pilot_500_analysis/mechanism_pilot_report.json`: mechanism-pilot condition summary, `12,000` rows. - -## Use Limits - -These results motivate the selection-bottleneck question. They should not be mixed with the primary five-benchmark selector-boundary evidence as if they were generated under the same protocol. +These archived experiments provide historical motivation for the selection bottleneck in Fig. 1a and Extended Data Fig. 1. They are not part of the later fixed-pool selector-boundary experiment family. diff --git a/data/external/results/formal/early_mas_architecture_motivation/audits/provenance_and_qa.metadata.json b/data/external/results/formal/early_mas_architecture_motivation/audits/provenance_and_qa.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..8c02ca51f01ed96d2880da00526180d8c6a09157 --- /dev/null +++ b/data/external/results/formal/early_mas_architecture_motivation/audits/provenance_and_qa.metadata.json @@ -0,0 +1,29 @@ +{ + "artifact_kind": "analysis_auxiliary", + "command_mode": "early_mas_architecture_motivation", + "config_hash": "c27e24558091", + "config_path": "configs/analyses/early_mas_architecture_motivation.yaml", + "evidence_status": "complete", + "generated_at_utc": "2026-08-24T09:11:48.071341+00:00", + "git_commit": "67fc30b663ddd160845baa9a104ba9ca1a27c1f7", + "input_paths": [ + "results/formal/early_mas_architecture_motivation/analysis_ready/med_agent/baseline/all_results.csv", + "results/formal/early_mas_architecture_motivation/analysis_ready/med_agent/tuned/all_results.csv", + "results/formal/early_mas_architecture_motivation/analysis_ready/med_agent/fixed6/all_results.csv", + "results/formal/early_mas_architecture_motivation/analysis_ready/med_board/protocol_results.csv", + "results/formal/early_mas_architecture_motivation/analysis_ready/med_board/oracle_trials.csv", + "results/formal/early_mas_architecture_motivation/analysis_ready/med_board/gain_bottleneck_report.json" + ], + "output_paths": [ + "results/formal/early_mas_architecture_motivation/summaries/med_board_protocol_cost_accuracy_summary.csv", + "results/formal/early_mas_architecture_motivation/audits/provenance_and_qa.md", + "results/formal/early_mas_architecture_motivation/source_manifest.csv", + "results/formal/early_mas_architecture_motivation/summaries/med_agent_topology_summary.csv", + "results/formal/early_mas_architecture_motivation/summaries/med_agent_round_exit_distribution.csv", + "results/formal/early_mas_architecture_motivation/summaries/med_board_protocol_bootstrap_ci.csv", + "results/formal/early_mas_architecture_motivation/summaries/med_board_oracle_gap_summary.csv", + "results/formal/early_mas_architecture_motivation/summaries/med_board_judge_behavior_summary.csv", + "results/formal/early_mas_architecture_motivation/figure_sources/extended_data_fig1_source.csv" + ], + "run_class": "formal" +} diff --git a/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_agent_reconstructed/pyproject.toml b/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_agent_reconstructed/pyproject.toml new file mode 100644 index 0000000000000000000000000000000000000000..ceb282c5bb738a9389c3755cf55c4711f0fd4905 --- /dev/null +++ b/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_agent_reconstructed/pyproject.toml @@ -0,0 +1,15 @@ +[project] +name = "med-agent" +version = "0.1.0" +requires-python = ">=3.12" +dependencies = [ + "httpx>=0.20.0", + "datasets>=3.0.0", + "pydantic>=2.13.4", +] + +[project.optional-dependencies] +dev = [ + "pytest>=8.0.0", + "pytest-asyncio>=0.24.0", +] diff --git a/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_agent_reconstructed/pytest.ini b/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_agent_reconstructed/pytest.ini new file mode 100644 index 0000000000000000000000000000000000000000..eed0c99d6721da7142bcd5efb0759714dfa56306 --- /dev/null +++ b/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_agent_reconstructed/pytest.ini @@ -0,0 +1,5 @@ +[pytest] +testpaths = tests +markers = + integration: tests that require external services (LLM proxy, dataset download) +asyncio_mode = auto diff --git a/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_agent_reconstructed/src/med_agent/__init__.py b/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_agent_reconstructed/src/med_agent/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..81d5c5233fb12d3ee061e10ededd6181b16c3bb0 --- /dev/null +++ b/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_agent_reconstructed/src/med_agent/__init__.py @@ -0,0 +1 @@ +"""MAS topology comparison experiment.""" diff --git a/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_agent_reconstructed/src/med_agent/__main__.py b/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_agent_reconstructed/src/med_agent/__main__.py new file mode 100644 index 0000000000000000000000000000000000000000..4327d09c7bf3712f97cdebae38e9069a34a035a2 --- /dev/null +++ b/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_agent_reconstructed/src/med_agent/__main__.py @@ -0,0 +1,194 @@ +# src/med_agent/__main__.py +import argparse +import logging +import os +import sys +from pathlib import Path + +from .config import ExperimentConfig +from .runner import run_experiment +from .metrics.collector import save_results_csv +from .metrics.analyzer import aggregate_results, print_summary + + +def main(): + parser = argparse.ArgumentParser(description="MAS Topology Comparison Experiment") + parser.add_argument("--model", default="deepseek-v4-flash-free") + parser.add_argument("--api-base", default="http://localhost:8000/v1") + parser.add_argument("--api-key", default=os.environ.get("OPENAI_API_KEY", "")) + parser.add_argument("--temperature", type=float, default=1.0) + parser.add_argument("--n-cases", type=int, default=50, help="Number of cases to run (0 = all)") + parser.add_argument("--n-trials", type=int, default=3, help="Trials per case per topology") + parser.add_argument("--max-rounds", type=int, default=10) + parser.add_argument("--topologies", nargs="+", + choices=["single", "star", "complete", "ring", "hierarchical"], + default=["star", "complete", "ring", "hierarchical"], + help="Topologies to test") + parser.add_argument("--seeds", nargs="+", type=int, default=[42, 123, 256]) + parser.add_argument("--results-dir", default="results") + parser.add_argument("--log-level", default="INFO") + parser.add_argument("--resume", action="store_true", help="Skip already-completed cases") + parser.add_argument("--async", action="store_true", dest="use_async", + help="Use async parallel agent execution") + parser.add_argument("--concurrency", type=int, default=50, + help="Max concurrent cases/trials running in parallel") + parser.add_argument("--unanimous-mode", choices=["standard", "needs_confidence", "disabled"], + default="standard", help="Unanimous convergence mode") + parser.add_argument("--weight-majority-ratio", type=float, default=0.6, + help="Weight majority ratio threshold (0.0 to 1.0)") + parser.add_argument("--confidence-threshold", type=float, default=0.7, + help="Average confidence threshold (0.0 to 1.0)") + parser.add_argument("--disable-convergence", action="store_true", + help="Disable early convergence checking and force running all max rounds") + parser.add_argument("--randomize-round-0", action="store_true", + help="Generate random answers for Round 0 instead of calling the LLM API") + + args = parser.parse_args() + + logging.basicConfig( + level=getattr(logging, args.log_level.upper()), + format="%(asctime)s [%(levelname)s] %(name)s: %(message)s", + ) + # Suppress noisy httpx / datasets INFO logs + logging.getLogger("httpx").setLevel(logging.WARNING) + logging.getLogger("datasets").setLevel(logging.WARNING) + logger = logging.getLogger(__name__) + + # Validate range limits + if not (0.0 <= args.weight_majority_ratio <= 1.0): + parser.error("--weight-majority-ratio must be between 0.0 and 1.0") + if not (0.0 <= args.confidence_threshold <= 1.0): + parser.error("--confidence-threshold must be between 0.0 and 1.0") + + # Validate seeds vs n_trials + if args.n_trials > len(args.seeds): + logger.warning(f"n_trials={args.n_trials} > seeds={len(args.seeds)}, truncating to {len(args.seeds)}") + args.n_trials = len(args.seeds) + + config = ExperimentConfig( + model=args.model, + api_base=args.api_base, + api_key=args.api_key, + temperature=args.temperature, + n_cases=args.n_cases if args.n_cases > 0 else None, + n_trials=args.n_trials, + max_rounds=args.max_rounds, + seeds=args.seeds, + results_dir=args.results_dir, + unanimous_mode=args.unanimous_mode, + weight_majority_ratio=args.weight_majority_ratio, + confidence_threshold=args.confidence_threshold, + disable_convergence=args.disable_convergence, + randomize_round_0=args.randomize_round_0, + resume=args.resume, + ) + + + logger.info(f"Starting experiment: {args.n_cases} cases x {len(args.topologies)} topologies x {args.n_trials} trials") + logger.info(f"Model: {args.model} | Temp: {args.temperature}") + + if args.use_async: + import asyncio + from .runner import run_single_case_async + from .data.loader import load_cases + from .types import TopologyResult + import json + + cases = load_cases(config) + os.makedirs(config.results_dir, exist_ok=True) + + sem = asyncio.Semaphore(args.concurrency) + all_results: dict[str, list] = {t: [] for t in args.topologies} + + async def run_task(case, case_idx, topology, trial_idx, seed): + result_path = Path(config.results_dir) / topology / f"{case.id}_seed{seed}.json" + if args.resume and result_path.exists(): + logger.info(f" [{topology}] Case {case_idx+1}/{len(cases)} (seed={seed}): already done, skipping") + try: + with open(result_path) as f: + data = json.load(f) + return topology, TopologyResult.from_dict(data) + except Exception as e: + logger.warning(f"Failed to load checkpoint {result_path}: {e}. Re-running...") + + async with sem: + logger.info(f" [{topology}] Case {case_idx+1}/{len(cases)} (seed={seed}) starting...") + try: + result = await run_single_case_async(case, topology, seed, config) + except Exception as e: + logger.error(f" [{topology}] Case {case.id} FAILED: {e}") + result = TopologyResult( + case_id=case.id, topology=topology, seed=seed, + ground_truth=case.label, system_output="", accuracy=0.0, + rounds_to_converge=config.max_rounds, convergence_type="error", + total_tokens=0, avg_confidence=0.0, + confidence_history={}, agent_final_outputs={}, + conversation_history=[], error=str(e), + ) + + # Save checkpoint + try: + result_path.parent.mkdir(parents=True, exist_ok=True) + with open(result_path, "w") as f: + json.dump(result.to_dict(), f, indent=2) + except Exception as e: + logger.error(f"Failed to save checkpoint for {case.id}: {e}") + + logger.info(f" [{topology}] Case {case_idx+1}/{len(cases)} (seed={seed}) completed.") + return topology, result + + # Build list of all coroutines + tasks = [] + for case_idx, case in enumerate(cases): + for topology in args.topologies: + for trial_idx, seed in enumerate(config.seeds[:config.n_trials]): + tasks.append(run_task(case, case_idx, topology, trial_idx, seed)) + + loop = asyncio.new_event_loop() + asyncio.set_event_loop(loop) + try: + # Run all tasks concurrently + results = loop.run_until_complete(asyncio.gather(*tasks)) + finally: + # Explicitly close async clients inside the active loop + from .agent import Agent + async def close_async_clients(): + for client in list(Agent._async_client_cache.values()): + try: + await client.aclose() + except Exception: + pass + try: + loop.run_until_complete(close_async_clients()) + except Exception: + pass + loop.close() + + # Group results by topology + for topology, result in results: + all_results[topology].append(result) + else: + all_results = run_experiment(config, topologies=args.topologies) + + # Filter to requested topologies + filtered = {t: all_results.get(t, []) for t in args.topologies} + + # Save CSV + csv_path = Path(args.results_dir) / "all_results.csv" + save_results_csv(filtered, csv_path) + logger.info(f"Results saved to {csv_path}") + + # Print summary + summary = aggregate_results(filtered) + print_summary(summary) + + # Save summary JSON + import json + summary_path = Path(args.results_dir) / "summary.json" + with open(summary_path, "w") as f: + json.dump(summary, f, indent=2) + logger.info(f"Summary saved to {summary_path}") + + +if __name__ == "__main__": + main() diff --git a/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_agent_reconstructed/src/med_agent/agent.py b/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_agent_reconstructed/src/med_agent/agent.py new file mode 100644 index 0000000000000000000000000000000000000000..92637bc6567bffc6985dfd6d44242bd71ec5266c --- /dev/null +++ b/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_agent_reconstructed/src/med_agent/agent.py @@ -0,0 +1,422 @@ +# src/med_agent/agent.py +import atexit +import asyncio +import json +import logging +import threading +import httpx +from pydantic import BaseModel, Field, field_validator, ValidationInfo +from .config import ExperimentConfig +from .types import AgentOutput, AgentMessage + +logger = logging.getLogger(__name__) + +MAX_RETRIES = 3 + +SYSTEM_PROMPT = ( + "You are a doctor. Analyze the clinical case, choose the best answer from the options, " + "and provide your confidence level and supporting evidence.\n\n" + "Respond in JSON format with exactly three fields:\n" + ' "answer": the letter of your chosen option (A-J)\n' + ' "confidence": a float indicating how certain you are. Note: in the initial round (Round 0), you must set your confidence to a value between 0.0 and 0.5 (inclusive) because you have not yet discussed the case with colleagues. In later rounds, your confidence can go up to 1.0.\n' + ' "evidence": a brief summary of the key clinical findings supporting your diagnosis\n\n' + "Example: {\"answer\": \"C\", \"confidence\": 0.45, \"evidence\": \"Patient presents with ...\"}" +) + +RETRY_PROMPT = ( + "Your previous response was not valid JSON. Respond with valid JSON containing exactly " + 'three fields: "answer" (A-J), "confidence" (0.0-1.0), "evidence" (string). ' + 'Example: {"answer": "C", "confidence": 0.85, "evidence": "Key findings summary"}' +) + +RETRY_PROMPT_ROUND_0 = ( + "Your previous response was invalid. In the initial round (Round 0), you must set " + '"confidence" to a value between 0.0 and 0.5 (inclusive). ' + 'Respond with valid JSON containing exactly three fields: "answer" (A-J), "confidence" (0.0-0.5), "evidence" (string). ' + 'Example: {"answer": "C", "confidence": 0.45, "evidence": "Key findings summary"}' +) + + + +class ResponseModel(BaseModel): + """Pydantic model for LLM output validation.""" + answer: str = Field(...) + confidence: float = Field(..., ge=0.0, le=1.0) + evidence: str = Field(...) + + @field_validator('answer') + @classmethod + def validate_answer(cls, v: str, info: ValidationInfo) -> str: + context = info.context + if context and "valid_options" in context: + valid = context["valid_options"] + if valid: + val = v.strip().upper() + if val in valid: + return val + raise ValueError(f"Answer must be one of: {valid}. Got: '{v}'") + return v + + +class ResponseModelRound0(BaseModel): + """Pydantic model for LLM output validation at round 0.""" + answer: str = Field(...) + confidence: float = Field(..., ge=0.0, le=0.5) + evidence: str = Field(...) + + @field_validator('answer') + @classmethod + def validate_answer(cls, v: str, info: ValidationInfo) -> str: + context = info.context + if context and "valid_options" in context: + valid = context["valid_options"] + if valid: + val = v.strip().upper() + if val in valid: + return val + raise ValueError(f"Answer must be one of: {valid}. Got: '{v}'") + return v + + + +class Agent: + _sync_client_cache: dict[tuple[str, str], httpx.Client] = {} + _async_client_cache: dict[tuple[str, str], httpx.AsyncClient] = {} + _sync_lock = threading.Lock() + _async_lock = threading.Lock() + + def __init__(self, agent_id: str, config: ExperimentConfig, seed: int): + self.agent_id = agent_id + self.config = config + self.seed = seed + self.valid_options: list[str] = [] + self.messages: list[dict] = [ + {"role": "system", "content": SYSTEM_PROMPT} + ] + + @property + def client(self) -> httpx.Client: + cache_key = (self.config.api_base, self.config.api_key) + if cache_key not in self._sync_client_cache: + with self._sync_lock: + if cache_key not in self._sync_client_cache: + self._sync_client_cache[cache_key] = httpx.Client(timeout=300.0) + return self._sync_client_cache[cache_key] + + @property + def async_client(self) -> httpx.AsyncClient: + cache_key = (self.config.api_base, self.config.api_key) + if cache_key not in self._async_client_cache: + with self._async_lock: + if cache_key not in self._async_client_cache: + limits = httpx.Limits(max_connections=500, max_keepalive_connections=100) + self._async_client_cache[cache_key] = httpx.AsyncClient(limits=limits, timeout=300.0) + return self._async_client_cache[cache_key] + + def add_case(self, question: str, options: dict[str, str]): + """Inject the clinical case at round 0.""" + self.valid_options = sorted([k.strip().upper() for k in options.keys() if k.strip()]) + options_text = "\n".join(f"{k}. {v}" for k, v in sorted(options.items()) if v.strip()) + case_prompt = f"## Clinical Case\n\n{question}\n\n## Options\n\n{options_text}" + self.messages.append({"role": "user", "content": case_prompt}) + + def add_messages(self, new_messages: list[AgentMessage]): + """Add routed messages from other agents to conversation history.""" + if not new_messages: + return + lines = [] + for m in new_messages: + o = m.output + lines.append( + f"--- Dr. {m.agent_id} (Round {m.round}) ---\n" + f"Answer: {o.answer} | Confidence: {o.confidence:.2f}\n" + f"Evidence: {o.evidence}" + ) + lines_text = "\n\n".join(lines) + content = ( + "You see the following opinions from your colleagues:\n\n" + f"{lines_text}\n\n" + "Please critically evaluate these opinions based on objective clinical criteria. Pay attention to:\n" + "1. Clinical details or diagnostic logic presented by your colleagues.\n" + "2. The strengths and weaknesses of each argument.\n" + "CRITICAL RULES:\n" + "- Independently assess all arguments. Update your answer if you are convinced by stronger, more valid clinical evidence or reasoning.\n" + "- In your JSON 'evidence' field, briefly state your evaluation of the different viewpoints and justify your final choice." + ) + self.messages.append({"role": "user", "content": content}) + + + @staticmethod + def _extract_json(text: str) -> str | None: + """Find and extract a JSON object from text. + + Handles thinking block, markdown code block formatting, or conversational text. + """ + import re + import json + + # Find all '{' indices in the text + brace_indices = [m.start() for m in re.finditer(r"\{", text)] + if not brace_indices: + return None + + # Find the last '}' + end_idx = text.rfind("}") + if end_idx < 0: + return None + + # Try candidates from left to right to find the outer valid JSON object + for start_idx in brace_indices: + if start_idx > end_idx: + break + candidate = text[start_idx : end_idx + 1] + try: + json.loads(candidate) + return candidate + except json.JSONDecodeError: + continue + + return None + + def _parse_response(self, content: str, round_idx: int = 0) -> AgentOutput | None: + """Parse LLM response with Pydantic validation. Returns None on failure.""" + extracted = self._extract_json(content) + if extracted: + content = extracted + + context = {"valid_options": self.valid_options} if self.valid_options else None + + try: + if round_idx == 0: + parsed = ResponseModelRound0.model_validate_json(content, context=context) + else: + parsed = ResponseModel.model_validate_json(content, context=context) + return AgentOutput( + answer=parsed.answer, + confidence=max(0.0, min(1.0, parsed.confidence)), + evidence=parsed.evidence, + ) + except Exception: + return None + + + def _llm_call(self) -> tuple[str, int]: + """Make a synchronous LLM call via HTTP POST.""" + headers = { + "Authorization": f"Bearer {self.config.api_key}", + "Content-Type": "application/json", + } + payload = { + "model": self.config.model, + "messages": self.messages, + "temperature": self.config.temperature, + "max_tokens": self.config.max_tokens, + "seed": self.seed, + } + url = self.config.api_base + if not url.endswith("/"): + url += "/" + url += "chat/completions" + + try: + response = self.client.post(url, headers=headers, json=payload) + response.raise_for_status() + data = response.json() + msg = data["choices"][0]["message"] + content = (msg.get("content") or "").strip() + tokens = data.get("usage", {}).get("total_tokens", 0) + + # Reasoning model support + if not content: + rc = msg.get("reasoning_content") or "" + extracted = self._extract_json(rc) + if extracted: + content = extracted + + return content or "{}", tokens + except Exception as e: + logger.error(f"Agent {self.agent_id} API error (sync): {repr(e)}") + return "{}", 0 + + async def _llm_call_async(self) -> tuple[str, int]: + """Make an asynchronous LLM call via HTTP POST.""" + headers = { + "Authorization": f"Bearer {self.config.api_key}", + "Content-Type": "application/json", + } + payload = { + "model": self.config.model, + "messages": self.messages, + "temperature": self.config.temperature, + "max_tokens": self.config.max_tokens, + "seed": self.seed, + } + url = self.config.api_base + if not url.endswith("/"): + url += "/" + url += "chat/completions" + + try: + response = await self.async_client.post(url, headers=headers, json=payload) + response.raise_for_status() + data = response.json() + msg = data["choices"][0]["message"] + content = (msg.get("content") or "").strip() + tokens = data.get("usage", {}).get("total_tokens", 0) + + # Reasoning model support + if not content: + rc = msg.get("reasoning_content") or "" + extracted = self._extract_json(rc) + if extracted: + content = extracted + + return content or "{}", tokens + except Exception as e: + logger.error(f"Agent {self.agent_id} API error (async): {repr(e)}") + return "{}", 0 + + def respond(self, round_idx: int = 0) -> AgentMessage: + """Call the LLM with retry on parse failure. Returns parsed response.""" + content = "" + total_tokens = 0 + original_msg_len = len(self.messages) + + for attempt in range(MAX_RETRIES): + if attempt > 0: + # Maintain correct role alternation: user -> assistant (invalid response) -> user (retry prompt) + self.messages.append({"role": "assistant", "content": content or "{}"}) + options_str = "/".join(self.valid_options) if self.valid_options else "A-J" + example_ans = self.valid_options[0] if self.valid_options else "C" + if round_idx == 0: + retry_prompt = ( + "Your previous response was invalid. In the initial round (Round 0), you must set " + '"confidence" to a value between 0.0 and 0.5 (inclusive). ' + f'Respond with valid JSON containing exactly three fields: "answer" ({options_str}), "confidence" (0.0-0.5), "evidence" (string). ' + f'Example: {{"answer": "{example_ans}", "confidence": 0.45, "evidence": "Key findings summary"}}' + ) + else: + retry_prompt = ( + "Your previous response was not valid JSON or the answer was not one of the allowed options. " + f'Respond with valid JSON containing exactly three fields: "answer" ({options_str}), "confidence" (0.0-1.0), "evidence" (string). ' + f'Example: {{"answer": "{example_ans}", "confidence": 0.85, "evidence": "Key findings summary"}}' + ) + self.messages.append({"role": "user", "content": retry_prompt}) + + content, tokens = self._llm_call() + total_tokens += tokens + output = self._parse_response(content, round_idx=round_idx) + if output is not None: + break + logger.warning(f"Agent {self.agent_id}: retry {attempt + 1}/{MAX_RETRIES} — parse failed") + else: + # Max retries exhausted — use fallback + logger.error(f"Agent {self.agent_id}: all {MAX_RETRIES} retries exhausted, using fallback") + fallback_ans = self.valid_options[0] if self.valid_options else "A" + output = AgentOutput(answer=fallback_ans, confidence=0.0, evidence="") + + # Rollback temporary retry messages to avoid polluting the agent's long-term history + self.messages = self.messages[:original_msg_len] + + # Add own response to history + self.messages.append({ + "role": "assistant", + "content": json.dumps({ + "answer": output.answer, + "confidence": output.confidence, + "evidence": output.evidence, + }), + }) + + return AgentMessage( + agent_id=self.agent_id, + round=round_idx, + output=output, + tokens=total_tokens, + ) + + + async def respond_async(self, round_idx: int = 0) -> AgentMessage: + """Async version of respond() using non-blocking HTTP requests.""" + content = "" + total_tokens = 0 + original_msg_len = len(self.messages) + + for attempt in range(MAX_RETRIES): + if attempt > 0: + # Maintain correct role alternation: user -> assistant (invalid response) -> user (retry prompt) + self.messages.append({"role": "assistant", "content": content or "{}"}) + options_str = "/".join(self.valid_options) if self.valid_options else "A-J" + example_ans = self.valid_options[0] if self.valid_options else "C" + if round_idx == 0: + retry_prompt = ( + "Your previous response was invalid. In the initial round (Round 0), you must set " + '"confidence" to a value between 0.0 and 0.5 (inclusive). ' + f'Respond with valid JSON containing exactly three fields: "answer" ({options_str}), "confidence" (0.0-0.5), "evidence" (string). ' + f'Example: {{"answer": "{example_ans}", "confidence": 0.45, "evidence": "Key findings summary"}}' + ) + else: + retry_prompt = ( + "Your previous response was not valid JSON or the answer was not one of the allowed options. " + f'Respond with valid JSON containing exactly three fields: "answer" ({options_str}), "confidence" (0.0-1.0), "evidence" (string). ' + f'Example: {{"answer": "{example_ans}", "confidence": 0.85, "evidence": "Key findings summary"}}' + ) + self.messages.append({"role": "user", "content": retry_prompt}) + + content, tokens = await self._llm_call_async() + total_tokens += tokens + output = self._parse_response(content, round_idx=round_idx) + if output is not None: + break + logger.warning(f"Agent {self.agent_id}: retry {attempt + 1}/{MAX_RETRIES} — parse failed") + else: + # Max retries exhausted — use fallback + logger.error(f"Agent {self.agent_id}: all {MAX_RETRIES} retries exhausted, using fallback") + fallback_ans = self.valid_options[0] if self.valid_options else "A" + output = AgentOutput(answer=fallback_ans, confidence=0.0, evidence="") + + # Rollback temporary retry messages to avoid polluting the agent's long-term history + self.messages = self.messages[:original_msg_len] + + # Add own response to history + self.messages.append({ + "role": "assistant", + "content": json.dumps({ + "answer": output.answer, + "confidence": output.confidence, + "evidence": output.evidence, + }), + }) + + return AgentMessage( + agent_id=self.agent_id, + round=round_idx, + output=output, + tokens=total_tokens, + ) + + + +@atexit.register +def cleanup_clients(): + """Close all cached HTTP clients on exit to release connection pools.""" + # Close sync clients + for client in Agent._sync_client_cache.values(): + try: + client.close() + except Exception: + pass + + # Close async clients + for client in Agent._async_client_cache.values(): + try: + try: + loop = asyncio.get_running_loop() + if loop.is_running(): + loop.create_task(client.aclose()) + else: + asyncio.run(client.aclose()) + except RuntimeError: + asyncio.run(client.aclose()) + except Exception: + pass diff --git a/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_agent_reconstructed/src/med_agent/config.py b/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_agent_reconstructed/src/med_agent/config.py new file mode 100644 index 0000000000000000000000000000000000000000..6623c9167a547b26e959c19b897f219c4680f73b --- /dev/null +++ b/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_agent_reconstructed/src/med_agent/config.py @@ -0,0 +1,52 @@ +import os +from dataclasses import dataclass, field + + +@dataclass +class ExperimentConfig: + # LLM + model: str = "deepseek-v4-flash-free" + api_base: str = "http://localhost:8000/v1" + api_key: str = field(default_factory=lambda: os.environ.get("OPENAI_API_KEY", "")) + temperature: float = 1.0 + max_tokens: int = 65536 # effectively unlimited for reasoning models + + # Experiment + n_agents: int = 5 + max_rounds: int = 10 + n_trials: int = 3 + seeds: list[int] = field(default_factory=lambda: [42, 123, 256]) + + # Convergence + confidence_threshold: float = 0.7 + weight_majority_ratio: float = 0.6 + unanimous_mode: str = "standard" + disable_convergence: bool = False + randomize_round_0: bool = False + + + # Dataset + dataset_name: str = "TsinghuaC3I/MedXpertQA" + dataset_config: str = "Text" + dataset_split: str = "test" + medical_task_filter: str = "Diagnosis" + n_cases: int = 50 + dataset_revision: str | None = "main" + + # Paths + results_dir: str = "results" + resume: bool = False + + def __post_init__(self): + if not self.api_key: + raise ValueError("OPENAI_API_KEY must be set") + if not (0.0 <= self.weight_majority_ratio <= 1.0): + raise ValueError("weight_majority_ratio must be between 0.0 and 1.0") + if not (0.0 <= self.confidence_threshold <= 1.0): + raise ValueError("confidence_threshold must be between 0.0 and 1.0") + allowed_modes = ["standard", "needs_confidence", "disabled"] + if self.unanimous_mode not in allowed_modes: + raise ValueError(f"unanimous_mode must be one of {allowed_modes}") + if self.n_trials > len(self.seeds): + raise ValueError(f"n_trials ({self.n_trials}) cannot be greater than the number of available seeds ({len(self.seeds)})") + diff --git a/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_agent_reconstructed/src/med_agent/data/__init__.py b/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_agent_reconstructed/src/med_agent/data/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e1ca88e650280dafdca8afe416f543f5ff96287a --- /dev/null +++ b/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_agent_reconstructed/src/med_agent/data/__init__.py @@ -0,0 +1 @@ +# src/med_agent/data/__init__.py diff --git a/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_agent_reconstructed/src/med_agent/data/loader.py b/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_agent_reconstructed/src/med_agent/data/loader.py new file mode 100644 index 0000000000000000000000000000000000000000..b641b037631c8157dd1fbc0e13142f4d244c066d --- /dev/null +++ b/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_agent_reconstructed/src/med_agent/data/loader.py @@ -0,0 +1,51 @@ +# src/med_agent/data/loader.py +import logging +from dataclasses import dataclass +from typing import Generator +from datasets import load_dataset +from ..config import ExperimentConfig + +logger = logging.getLogger(__name__) + + +@dataclass +class DiagnosisCase: + id: str + question: str + options: dict[str, str] + label: str + medical_task: str + body_system: str + + +def load_cases(config: ExperimentConfig) -> list[DiagnosisCase]: + """Load MedXpertQA cases filtered by medical_task.""" + logger.info(f"Loading {config.dataset_name} ({config.dataset_config})...") + ds = load_dataset( + config.dataset_name, + config.dataset_config, + split=config.dataset_split, + streaming=False, + revision=getattr(config, "dataset_revision", None), + ) + + cases: list[DiagnosisCase] = [] + for example in ds: + if example["medical_task"] != config.medical_task_filter: + continue + # Clean options: remove empty ones + options = {k: v for k, v in example["options"].items() if v.strip()} + case = DiagnosisCase( + id=example["id"], + question=example["question"], + options=options, + label=example["label"], + medical_task=example["medical_task"], + body_system=example["body_system"], + ) + cases.append(case) + if config.n_cases and len(cases) >= config.n_cases: + break + + logger.info(f"Loaded {len(cases)} diagnosis cases") + return cases diff --git a/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_agent_reconstructed/src/med_agent/metrics/__init__.py b/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_agent_reconstructed/src/med_agent/metrics/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..3c57e98463e01ec895ba4eccfa509d6ab3e40c43 --- /dev/null +++ b/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_agent_reconstructed/src/med_agent/metrics/__init__.py @@ -0,0 +1 @@ +# src/med_agent/metrics/__init__.py diff --git a/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_agent_reconstructed/src/med_agent/metrics/analyzer.py b/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_agent_reconstructed/src/med_agent/metrics/analyzer.py new file mode 100644 index 0000000000000000000000000000000000000000..a74325617db32c3fe873c190102882e3684857f5 --- /dev/null +++ b/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_agent_reconstructed/src/med_agent/metrics/analyzer.py @@ -0,0 +1,89 @@ +# src/med_agent/metrics/analyzer.py +import logging +from collections import defaultdict +from typing import Any +from ..types import TopologyResult + +logger = logging.getLogger(__name__) + + +def aggregate_results(results: dict[str, list[TopologyResult]]) -> dict[str, dict[str, Any]]: + """Aggregate per-topology results into summary statistics.""" + summary: dict[str, dict[str, Any]] = {} + + for topology, topology_results in results.items(): + if not topology_results: + summary[topology] = {"error": "no results"} + continue + + accuracies = [r.accuracy for r in topology_results] + rounds = [r.rounds_to_converge for r in topology_results] + tokens = [r.total_tokens for r in topology_results] + confs = [r.avg_confidence for r in topology_results] + conv_types = [r.convergence_type for r in topology_results] + errors = [r.error for r in topology_results if r.error] + + # Convergence rate (excluding errors) + valid = [r for r in topology_results if r.error is None] + if valid: + n_converged = sum(1 for r in valid if r.convergence_type != "not_converged") + convergence_rate = n_converged / len(valid) + else: + convergence_rate = 0.0 + + # Count convergence types + type_counts = defaultdict(int) + for ct in conv_types: + type_counts[ct] += 1 + + summary[topology] = { + "n_cases": len(topology_results), + "n_errors": len(errors), + "accuracy_mean": _mean(accuracies), + "accuracy_std": _std(accuracies), + "rounds_mean": _mean(rounds), + "rounds_std": _std(rounds), + "tokens_mean": _mean(tokens), + "tokens_std": _std(tokens), + "confidence_mean": _mean(confs), + "confidence_std": _std(confs), + "convergence_rate": convergence_rate, + "convergence_type_counts": dict(type_counts), + } + + return summary + + +def print_summary(summary: dict[str, dict[str, Any]]): + """Pretty-print the summary table.""" + header = f"{'Topology':<15} {'Accuracy':<12} {'Rounds':<12} {'Tokens':<16} {'Confidence':<12} {'Conv Rate':<12}" + sep = "-" * len(header) + lines = [sep, header, sep] + + for topology, s in summary.items(): + if "error" in s: + lines.append(f"{topology:<15} ERROR: {s['error']}") + continue + lines.append( + f"{topology:<15}" + f"{s['accuracy_mean']:.1%}±{s['accuracy_std']:.1%} " + f"{s['rounds_mean']:.1f}±{s['rounds_std']:.1f} " + f"{s['tokens_mean']:.0f}±{s['tokens_std']:.0f} " + f"{s['confidence_mean']:.2f}±{s['confidence_std']:.2f} " + f"{s['convergence_rate']:.1%}" + ) + + lines.append(sep) + logger.info("\n" + "\n".join(lines)) + print("\n".join(lines)) + + +def _mean(values: list[float]) -> float: + return sum(values) / len(values) if values else 0.0 + + +def _std(values: list[float]) -> float: + if len(values) < 2: + return 0.0 + m = _mean(values) + return (sum((v - m) ** 2 for v in values) / (len(values) - 1)) ** 0.5 diff --git a/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_agent_reconstructed/src/med_agent/metrics/collector.py b/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_agent_reconstructed/src/med_agent/metrics/collector.py new file mode 100644 index 0000000000000000000000000000000000000000..5e1b133edc417fe46dbe75f2554d392a1cf47ce1 --- /dev/null +++ b/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_agent_reconstructed/src/med_agent/metrics/collector.py @@ -0,0 +1,38 @@ +# src/med_agent/metrics/collector.py +import csv +import json +import os +from pathlib import Path +from typing import Any +from ..types import TopologyResult + + +def save_results_csv(results: dict[str, list[TopologyResult]], path: str | Path): + """Save per-trial results to CSV for easy pandas analysis.""" + path = Path(path) + path.parent.mkdir(parents=True, exist_ok=True) + + rows = [] + for topology, topology_results in results.items(): + for r in topology_results: + rows.append({ + "case_id": r.case_id, + "topology": r.topology, + "seed": r.seed, + "ground_truth": r.ground_truth, + "system_output": r.system_output, + "accuracy": r.accuracy, + "rounds_to_converge": r.rounds_to_converge, + "convergence_type": r.convergence_type, + "total_tokens": r.total_tokens, + "avg_confidence": r.avg_confidence, + "error": r.error or "", + }) + + if not rows: + return + + with open(path, "w", newline="") as f: + writer = csv.DictWriter(f, fieldnames=rows[0].keys()) + writer.writeheader() + writer.writerows(rows) diff --git a/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_agent_reconstructed/src/med_agent/runner.py b/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_agent_reconstructed/src/med_agent/runner.py new file mode 100644 index 0000000000000000000000000000000000000000..a902291a9db7833ea982b501f338f706cd45f1c7 --- /dev/null +++ b/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_agent_reconstructed/src/med_agent/runner.py @@ -0,0 +1,404 @@ +# src/med_agent/runner.py +import hashlib +import json +import logging +import os +import random +from pathlib import Path +from typing import Optional +from .config import ExperimentConfig +from .types import AgentOutput, AgentMessage, TopologyResult +from .agent import Agent +from .data.loader import DiagnosisCase, load_cases +from .topologies import ROUTERS + +logger = logging.getLogger(__name__) + +AGENT_IDS = ["A0", "A1", "A2", "A3", "A4"] + + +def _check_convergence( + agents_outputs: dict[str, AgentMessage], + config: ExperimentConfig, +) -> tuple[bool, Optional[str], str]: + """Check three-tier convergence. Returns (converged, system_answer, convergence_type).""" + if not agents_outputs: + return False, None, "not_converged" + + if getattr(config, "disable_convergence", False): + return False, None, "not_converged" + + answers = {aid: msg.output for aid, msg in agents_outputs.items()} + unanimous_mode = getattr(config, "unanimous_mode", "standard") + + # Tier 1: Unanimous (if not disabled) + is_unanimous = False + first_answer = None + if answers: + first_answer = list(answers.values())[0].answer + is_unanimous = all(o.answer == first_answer for o in answers.values()) + + if is_unanimous: + if unanimous_mode == "standard": + return True, first_answer, "unanimous" + elif unanimous_mode == "needs_confidence": + total_conf = sum(o.confidence for o in answers.values()) + avg_conf = total_conf / len(answers) if answers else 0.0 + if avg_conf >= config.confidence_threshold: + return True, first_answer, "unanimous_with_conf" + # Otherwise, proceed to Tier 2 + elif unanimous_mode == "disabled": + pass # proceed to Tier 2 + else: + logger.warning( + f"Unknown unanimous_mode: '{unanimous_mode}'. " + f"Falling back to standard unanimous convergence." + ) + return True, first_answer, "unanimous" + + # Tier 2: Weighted majority + total_weight = sum(o.confidence for o in answers.values()) + if total_weight == 0: + return False, None, "not_converged" + + vote_sums: dict[str, float] = {} + vote_confidences: dict[str, list[float]] = {} + for aid, o in answers.items(): + vote_sums[o.answer] = vote_sums.get(o.answer, 0.0) + o.confidence + vote_confidences.setdefault(o.answer, []).append(o.confidence) + + top_answer = max(vote_sums, key=vote_sums.get) + top_weight = vote_sums[top_answer] + + top_conf_list = vote_confidences[top_answer] + top_avg_conf = sum(top_conf_list) / len(top_conf_list) if top_conf_list else 0.0 + + if (top_weight / total_weight) >= config.weight_majority_ratio and top_avg_conf >= config.confidence_threshold: + return True, top_answer, "weighted_majority" + + return False, None, "not_converged" + + + +def _tie_break(vote_sums: dict[str, float], agents_outputs: dict[str, AgentMessage]) -> str: + """Break ties: highest single-agent confidence, then alphabetically.""" + max_vote = max(vote_sums.values()) + tied = [ans for ans, v in vote_sums.items() if v == max_vote] + if len(tied) == 1: + return tied[0] + + # Among tied, find highest per-answer max confidence + max_conf_per_answer: dict[str, float] = {} + for ans in tied: + max_conf_per_answer[ans] = max( + (msg.output.confidence for msg in agents_outputs.values() if msg.output.answer == ans), + default=0.0, + ) + best_max_conf = max(max_conf_per_answer.values()) + best_tied = [ans for ans, c in max_conf_per_answer.items() if c == best_max_conf] + return min(best_tied) # alphabetical among remaining + + +def _generate_random_round_0_msg( + agent: Agent, + case: DiagnosisCase, + seed: int, + config: ExperimentConfig, +) -> AgentMessage: + """Generate a random Round 0 message deterministically and update agent messages.""" + seed_str = f"{case.id}_{agent.agent_id}_{seed}" + seed_hash = int(hashlib.md5(seed_str.encode()).hexdigest(), 16) + rng = random.Random(seed_hash) + + valid_options = sorted(list(case.options.keys())) + if not valid_options: + valid_options = ["A"] + ans = rng.choice(valid_options) + conf = round(rng.uniform(0.1, 0.5), 2) + evidence = "I have not read the patient case details yet. This initial choice and confidence score are randomly generated to establish a baseline for consensus testing." + + output = AgentOutput(answer=ans, confidence=conf, evidence=evidence) + + # Maintain conversational context (assistant role) + agent.messages.append({ + "role": "assistant", + "content": json.dumps({ + "answer": ans, + "confidence": conf, + "evidence": evidence, + }) + }) + + return AgentMessage( + agent_id=agent.agent_id, + round=0, + output=output, + tokens=0, + ) + + +def run_single_case( + case: DiagnosisCase, + topology: str, + seed: int, + config: ExperimentConfig, +) -> TopologyResult: + """Run one case under one topology with one seed.""" + route_fn = ROUTERS[topology] + agent_ids = ["A0"] if topology == "single" else AGENT_IDS + + # Initialize agents + agents: list[Agent] = [] + for i, aid in enumerate(agent_ids): + agent = Agent(aid, config, seed) + agent.add_case(case.question, case.options) + agents.append(agent) + + all_messages: dict[str, list[AgentMessage]] = {aid: [] for aid in agent_ids} + confidence_history: dict[int, dict[str, float]] = {} + conversation_history: list[list[AgentMessage]] = [] + + # Round 0: initial independent responses + round_msgs: list[AgentMessage] = [] + if getattr(config, "randomize_round_0", False): + for agent in agents: + msg = _generate_random_round_0_msg(agent, case, seed, config) + round_msgs.append(msg) + all_messages[agent.agent_id].append(msg) + else: + for agent in agents: + msg = agent.respond(0) + round_msgs.append(msg) + all_messages[agent.agent_id].append(msg) + conversation_history.append(list(round_msgs)) + confidence_history[0] = {m.agent_id: m.output.confidence for m in round_msgs} + + agents_outputs = {m.agent_id: m for m in round_msgs} + converged = False + answer = None + conv_type = "single" if topology == "single" else "not_converged" + + # Rounds 1..max_rounds: route -> respond -> check convergence + for round_idx in range(1, config.max_rounds + 1): + if topology == "single": + break + + # Route messages from previous round + visible = route_fn(all_messages, round_idx) + + # Each agent receives routed messages (from ALL previous rounds, not just latest) + # Actually, add only new messages -- the routing function controls what's visible + for agent in agents: + new_msgs = visible.get(agent.agent_id, []) + # Filter to only messages the agent hasn't seen + seen_ids = {(m.agent_id, m.round) for m in all_messages[agent.agent_id]} + unseen = [m for m in new_msgs if (m.agent_id, m.round) not in seen_ids] + if unseen: + agent.add_messages(unseen) + + # Agents respond in parallel (sequential for simplicity; parallel via asyncio later) + round_msgs = [] + for agent in agents: + msg = agent.respond(round_idx) + round_msgs.append(msg) + all_messages[agent.agent_id].append(msg) + + conversation_history.append(list(round_msgs)) + confs = {m.agent_id: m.output.confidence for m in round_msgs} + confidence_history[round_idx] = confs + + # Check convergence + agents_outputs = {m.agent_id: m for m in round_msgs} + converged, answer, conv_type = _check_convergence(agents_outputs, config) + if converged: + break + + # If loop exhausted without convergence, fallback to weighted majority + if not converged and topology != "single": + # Weighted majority tie-break for final output + total_weight = sum(m.output.confidence for m in agents_outputs.values()) + if total_weight > 0: + vote_sums: dict[str, float] = {} + for m in agents_outputs.values(): + vote_sums[m.output.answer] = vote_sums.get(m.output.answer, 0.0) + m.output.confidence + answer = _tie_break(vote_sums, agents_outputs) + else: + answer = list(agents_outputs.values())[0].output.answer + conv_type = "not_converged" + elif topology == "single": + answer = list(agents_outputs.values())[0].output.answer + + total_tokens = sum(m.tokens for msgs_list in all_messages.values() for m in msgs_list) + final_confs = [m.output.confidence for m in agents_outputs.values()] + avg_confidence = sum(final_confs) / len(final_confs) if final_confs else 0.0 + + n_rounds = len(conversation_history) - 1 # exclude round 0 (initial independent reasoning) + + result = TopologyResult( + case_id=case.id, + topology=topology, + seed=seed, + ground_truth=case.label, + system_output=answer, + accuracy=1.0 if answer == case.label else 0.0, + rounds_to_converge=n_rounds, + convergence_type=conv_type, + total_tokens=total_tokens, + avg_confidence=avg_confidence, + confidence_history=confidence_history, + agent_final_outputs={aid: agents_outputs[aid].output for aid in agent_ids if aid in agents_outputs}, + conversation_history=conversation_history, + ) + return result + + +async def run_single_case_async( + case: DiagnosisCase, + topology: str, + seed: int, + config: ExperimentConfig, +) -> TopologyResult: + """Async version with parallel agent execution per round.""" + import asyncio + route_fn = ROUTERS[topology] + agent_ids = ["A0"] if topology == "single" else AGENT_IDS + + agents: list[Agent] = [] + for aid in agent_ids: + agent = Agent(aid, config, seed) + agent.add_case(case.question, case.options) + agents.append(agent) + + all_messages: dict[str, list[AgentMessage]] = {aid: [] for aid in agent_ids} + confidence_history: dict[int, dict[str, float]] = {} + conversation_history: list[list[AgentMessage]] = [] + + # Round 0: parallel initial responses + if getattr(config, "randomize_round_0", False): + round_msgs = [ + _generate_random_round_0_msg(agent, case, seed, config) + for agent in agents + ] + else: + round_msgs = await asyncio.gather(*[agent.respond_async(0) for agent in agents]) + for msg in round_msgs: + all_messages[msg.agent_id].append(msg) + conversation_history.append(list(round_msgs)) + confidence_history[0] = {m.agent_id: m.output.confidence for m in round_msgs} + + agents_outputs = {m.agent_id: m for m in round_msgs} + converged = False + answer = None + conv_type = "single" if topology == "single" else "not_converged" + + # Rounds 1..max_rounds + for round_idx in range(1, config.max_rounds + 1): + if topology == "single": + break + + visible = route_fn(all_messages, round_idx) + + for agent in agents: + new_msgs = visible.get(agent.agent_id, []) + seen_ids = {(m.agent_id, m.round) for m in all_messages[agent.agent_id]} + unseen = [m for m in new_msgs if (m.agent_id, m.round) not in seen_ids] + if unseen: + agent.add_messages(unseen) + + # Parallel agent responses + round_msgs = await asyncio.gather(*[agent.respond_async(round_idx) for agent in agents]) + for msg in round_msgs: + all_messages[msg.agent_id].append(msg) + + conversation_history.append(list(round_msgs)) + confs = {m.agent_id: m.output.confidence for m in round_msgs} + confidence_history[round_idx] = confs + + agents_outputs = {m.agent_id: m for m in round_msgs} + converged, answer, conv_type = _check_convergence(agents_outputs, config) + if converged: + break + + if not converged and topology != "single": + total_weight = sum(m.output.confidence for m in agents_outputs.values()) + if total_weight > 0: + vote_sums: dict[str, float] = {} + for m in agents_outputs.values(): + vote_sums[m.output.answer] = vote_sums.get(m.output.answer, 0.0) + m.output.confidence + answer = _tie_break(vote_sums, agents_outputs) + else: + answer = list(agents_outputs.values())[0].output.answer + conv_type = "not_converged" + elif topology == "single": + answer = list(agents_outputs.values())[0].output.answer + + total_tokens = sum(m.tokens for msgs_list in all_messages.values() for m in msgs_list) + final_confs = [m.output.confidence for m in agents_outputs.values()] + avg_confidence = sum(final_confs) / len(final_confs) if final_confs else 0.0 + + n_rounds = len(conversation_history) - 1 + + return TopologyResult( + case_id=case.id, + topology=topology, + seed=seed, + ground_truth=case.label, + system_output=answer, + accuracy=1.0 if answer == case.label else 0.0, + rounds_to_converge=n_rounds, + convergence_type=conv_type, + total_tokens=total_tokens, + avg_confidence=avg_confidence, + confidence_history=confidence_history, + agent_final_outputs={aid: agents_outputs[aid].output for aid in agent_ids if aid in agents_outputs}, + conversation_history=conversation_history, + ) + + +def run_experiment(config: ExperimentConfig, topologies: list[str] = None): + """Run full experiment: all cases x target topologies x all trials.""" + cases = load_cases(config) + os.makedirs(config.results_dir, exist_ok=True) + + target_topologies = topologies or list(ROUTERS.keys()) + all_results: dict[str, list[TopologyResult]] = {t: [] for t in target_topologies} + + for case_idx, case in enumerate(cases): + logger.info(f"Case {case_idx+1}/{len(cases)}: {case.id}") + + for topology in target_topologies: + for trial_idx, seed in enumerate(config.seeds[:config.n_trials]): + # Checkpoint: skip if already done + result_path = Path(config.results_dir) / topology / f"{case.id}_seed{seed}.json" + if getattr(config, "resume", False) and result_path.exists(): + logger.info(f" [{topology}] trial {trial_idx+1}/{config.n_trials}: already done, skipping") + try: + with open(result_path) as f: + data = json.load(f) + all_results[topology].append(TopologyResult.from_dict(data)) + continue + except Exception as e: + logger.warning(f"Failed to load checkpoint {result_path}: {e}. Re-running...") + + logger.info(f" [{topology}] trial {trial_idx+1}/{config.n_trials} (seed={seed})...") + try: + result = run_single_case(case, topology, seed, config) + except Exception as e: + logger.error(f" [{topology}] FAILED: {e}") + result = TopologyResult( + case_id=case.id, topology=topology, seed=seed, + ground_truth=case.label, system_output="", accuracy=0.0, + rounds_to_converge=config.max_rounds, convergence_type="error", + total_tokens=0, avg_confidence=0.0, + confidence_history={}, agent_final_outputs={}, + conversation_history=[], error=str(e), + ) + + # Save checkpoint using to_dict for proper nested serialization + result_path.parent.mkdir(parents=True, exist_ok=True) + with open(result_path, "w") as f: + json.dump(result.to_dict(), f, indent=2) + + all_results[topology].append(result) + + return all_results diff --git a/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_agent_reconstructed/src/med_agent/topologies/__init__.py b/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_agent_reconstructed/src/med_agent/topologies/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..c9f915035209034f909be9c62e438ca6662df354 --- /dev/null +++ b/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_agent_reconstructed/src/med_agent/topologies/__init__.py @@ -0,0 +1,17 @@ +# src/med_agent/topologies/__init__.py +from .star import route_star +from .complete import route_complete +from .ring import route_ring +from .hierarchical import route_hierarchical +from .single import route_single + +ROUTERS = { + "star": route_star, + "complete": route_complete, + "ring": route_ring, + "hierarchical": route_hierarchical, + "single": route_single, +} + +__all__ = ["ROUTERS", "route_star", "route_complete", "route_ring", "route_hierarchical", "route_single"] + diff --git a/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_agent_reconstructed/src/med_agent/topologies/base.py b/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_agent_reconstructed/src/med_agent/topologies/base.py new file mode 100644 index 0000000000000000000000000000000000000000..b3a6665d0a072339fafefd6683c814aaea9050bf --- /dev/null +++ b/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_agent_reconstructed/src/med_agent/topologies/base.py @@ -0,0 +1,9 @@ +from typing import Dict, List +from ..types import AgentMessage + +# Input: all messages from the latest round, grouped by agent +# Output: for each agent, which messages they can see +RouteInput = Dict[str, List[AgentMessage]] +RouteOutput = Dict[str, List[AgentMessage]] + +AGENT_IDS = ["A0", "A1", "A2", "A3", "A4"] diff --git a/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_agent_reconstructed/src/med_agent/topologies/complete.py b/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_agent_reconstructed/src/med_agent/topologies/complete.py new file mode 100644 index 0000000000000000000000000000000000000000..74183e5bfd3eae3bbcea6ec4ad5652791534c731 --- /dev/null +++ b/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_agent_reconstructed/src/med_agent/topologies/complete.py @@ -0,0 +1,12 @@ +from .base import RouteInput, RouteOutput, AGENT_IDS + +def route_complete(all_messages: RouteInput, current_round: int) -> RouteOutput: + """Complete: every agent sees every message (group chat).""" + result: RouteOutput = {} + all_msgs = [] + for msgs in all_messages.values(): + all_msgs.extend(msgs) + + for agent_id in AGENT_IDS: + result[agent_id] = list(all_msgs) # everyone sees everything + return result diff --git a/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_agent_reconstructed/src/med_agent/topologies/hierarchical.py b/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_agent_reconstructed/src/med_agent/topologies/hierarchical.py new file mode 100644 index 0000000000000000000000000000000000000000..2878d4d62b4e87df8fc713492ef5c456888b51e8 --- /dev/null +++ b/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_agent_reconstructed/src/med_agent/topologies/hierarchical.py @@ -0,0 +1,36 @@ +from .base import RouteInput, RouteOutput, AGENT_IDS + +# Tree structure: A0 (root) -> A1, A2 (mid) -> A3, A4 (leaf) +# A3 reports to A1, A4 reports to A2 +# A1 and A2 report to A0 +# A0 broadcasts to A1, A2 only +HIERARCHY = { + "leaf_to_mid": {"A3": "A1", "A4": "A2"}, + "mid_to_root": {"A1": "A0", "A2": "A0"}, +} + +def route_hierarchical(all_messages: RouteInput, current_round: int) -> RouteOutput: + """Hierarchical: messages flow up the tree, only A0 broadcasts down to mid-level.""" + result: RouteOutput = {} + for agent_id in AGENT_IDS: + result[agent_id] = [] + + for sender_id, msgs in all_messages.items(): + if sender_id in HIERARCHY["leaf_to_mid"]: + # Leaf -> only visible to its mid-level manager + mid = HIERARCHY["leaf_to_mid"][sender_id] + result[mid].extend(msgs) + # Leaf sees its own message + result[sender_id].extend(msgs) + elif sender_id in HIERARCHY["mid_to_root"]: + # Mid-level -> visible to root (A0) and itself + root = HIERARCHY["mid_to_root"][sender_id] + result[root].extend(msgs) + result[sender_id].extend(msgs) + elif sender_id == "A0": + # Root -> broadcast to mid-level only + result["A0"].extend(msgs) + for mid_id in HIERARCHY["mid_to_root"]: + result[mid_id].extend(msgs) + + return result diff --git a/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_agent_reconstructed/src/med_agent/topologies/ring.py b/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_agent_reconstructed/src/med_agent/topologies/ring.py new file mode 100644 index 0000000000000000000000000000000000000000..718ff1a5da9d194ab9f814f956beb24a4b4eb4b9 --- /dev/null +++ b/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_agent_reconstructed/src/med_agent/topologies/ring.py @@ -0,0 +1,24 @@ +from .base import RouteInput, RouteOutput, AGENT_IDS + +# Ring adjacency: A0<->A1<->A2<->A3<->A4<->A0 +NEIGHBORS = { + "A0": ["A4", "A1"], + "A1": ["A0", "A2"], + "A2": ["A1", "A3"], + "A3": ["A2", "A4"], + "A4": ["A3", "A0"], +} + +def route_ring(all_messages: RouteInput, current_round: int) -> RouteOutput: + """Ring: each agent sees messages from itself and immediate neighbors.""" + result: RouteOutput = {} + for agent_id in AGENT_IDS: + result[agent_id] = [] + + for agent_id in AGENT_IDS: + visible_ids = set(NEIGHBORS[agent_id] + [agent_id]) + for sender_id, msgs in all_messages.items(): + if sender_id in visible_ids: + result[agent_id].extend(msgs) + + return result diff --git a/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_agent_reconstructed/src/med_agent/topologies/single.py b/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_agent_reconstructed/src/med_agent/topologies/single.py new file mode 100644 index 0000000000000000000000000000000000000000..a5d19817e9ae169e21a4909997d8afcb9be9c809 --- /dev/null +++ b/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_agent_reconstructed/src/med_agent/topologies/single.py @@ -0,0 +1,8 @@ +from .base import RouteInput, RouteOutput + +def route_single(all_messages: RouteInput, current_round: int) -> RouteOutput: + """Single Agent: see own messages only.""" + result: RouteOutput = {} + if "A0" in all_messages: + result["A0"] = list(all_messages["A0"]) + return result diff --git a/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_agent_reconstructed/src/med_agent/topologies/star.py b/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_agent_reconstructed/src/med_agent/topologies/star.py new file mode 100644 index 0000000000000000000000000000000000000000..65d481951f114ff10033ba759cb1b00b4dd48b35 --- /dev/null +++ b/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_agent_reconstructed/src/med_agent/topologies/star.py @@ -0,0 +1,23 @@ +from .base import RouteInput, RouteOutput, AGENT_IDS + +def route_star(all_messages: RouteInput, current_round: int) -> RouteOutput: + """Star: center (A0) sees all; peripherals only see A0's messages and their own.""" + result: RouteOutput = {} + for agent_id in AGENT_IDS: + result[agent_id] = [] + if agent_id in all_messages: + result[agent_id].extend(all_messages[agent_id]) + + # Collect all messages from this round + # A0 (center) sees all messages from peripherals + for sender_id, msgs in all_messages.items(): + if sender_id != "A0": + result["A0"].extend(msgs) + + # Peripherals see A0's message (if any) + if "A0" in all_messages: + for agent_id in AGENT_IDS: + if agent_id != "A0": + result[agent_id].extend(all_messages["A0"]) + + return result diff --git a/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_agent_reconstructed/src/med_agent/types.py b/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_agent_reconstructed/src/med_agent/types.py new file mode 100644 index 0000000000000000000000000000000000000000..a4a5ba114e20282e7b7e0fff3de1b3ea879239a7 --- /dev/null +++ b/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_agent_reconstructed/src/med_agent/types.py @@ -0,0 +1,92 @@ +from dataclasses import dataclass +from typing import Optional + + +@dataclass +class AgentOutput: + answer: str + confidence: float + evidence: str + + def to_dict(self) -> dict: + return {"answer": self.answer, "confidence": self.confidence, "evidence": self.evidence} + + @classmethod + def from_dict(cls, d: dict) -> "AgentOutput": + return cls(answer=d["answer"], confidence=d["confidence"], evidence=d["evidence"]) + + +@dataclass +class AgentMessage: + agent_id: str + round: int + output: AgentOutput + tokens: int + + def to_dict(self) -> dict: + return { + "agent_id": self.agent_id, "round": self.round, + "output": self.output.to_dict(), "tokens": self.tokens, + } + + @classmethod + def from_dict(cls, d: dict) -> "AgentMessage": + return cls( + agent_id=d["agent_id"], round=d["round"], + output=AgentOutput.from_dict(d["output"]), tokens=d["tokens"], + ) + + +@dataclass +class TopologyResult: + case_id: str + topology: str + seed: int + ground_truth: str + system_output: str + accuracy: float + rounds_to_converge: int + convergence_type: str + total_tokens: int + avg_confidence: float + confidence_history: dict[int, dict[str, float]] + agent_final_outputs: dict[str, AgentOutput] + conversation_history: list[list[AgentMessage]] + error: Optional[str] = None + + def to_dict(self) -> dict: + return { + "case_id": self.case_id, + "topology": self.topology, + "seed": self.seed, + "ground_truth": self.ground_truth, + "system_output": self.system_output, + "accuracy": self.accuracy, + "rounds_to_converge": self.rounds_to_converge, + "convergence_type": self.convergence_type, + "total_tokens": self.total_tokens, + "avg_confidence": self.avg_confidence, + "confidence_history": {str(k): v for k, v in self.confidence_history.items()}, + "agent_final_outputs": {k: v.to_dict() for k, v in self.agent_final_outputs.items()}, + "conversation_history": [[m.to_dict() for m in r] for r in self.conversation_history], + "error": self.error, + } + + @classmethod + def from_dict(cls, d: dict) -> "TopologyResult": + return cls( + case_id=d["case_id"], + topology=d["topology"], + seed=d["seed"], + ground_truth=d["ground_truth"], + system_output=d["system_output"], + accuracy=d["accuracy"], + rounds_to_converge=d["rounds_to_converge"], + convergence_type=d["convergence_type"], + total_tokens=d["total_tokens"], + avg_confidence=d["avg_confidence"], + confidence_history={int(k): v for k, v in d["confidence_history"].items()}, + agent_final_outputs={k: AgentOutput.from_dict(v) for k, v in d["agent_final_outputs"].items()}, + conversation_history=[[AgentMessage.from_dict(m) for m in r] for r in d["conversation_history"]], + error=d.get("error"), + ) diff --git a/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_agent_reconstructed/tests/__init__.py b/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_agent_reconstructed/tests/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_agent_reconstructed/tests/conftest.py b/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_agent_reconstructed/tests/conftest.py new file mode 100644 index 0000000000000000000000000000000000000000..27b3cb60e8bd6be4d3ad2a278cccee31d2cf3bc3 --- /dev/null +++ b/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_agent_reconstructed/tests/conftest.py @@ -0,0 +1,46 @@ +import pytest +from src.med_agent.config import ExperimentConfig + +@pytest.fixture +def config(): + return ExperimentConfig(n_cases=3, n_trials=1, seeds=[42]) + +@pytest.fixture +def sample_options(): + return {"A": "Common cold", "B": "Influenza", "C": "COVID-19"} + +@pytest.fixture +def sample_case_text(): + return "Patient has fever for 3 days, cough, fatigue. Temp 38.5C. No rash." + + +@pytest.fixture(autouse=True) +def clean_client_cache(): + import asyncio + from src.med_agent.agent import Agent + + yield + + # Teardown: close all cached sync clients + for client in list(Agent._sync_client_cache.values()): + try: + client.close() + except Exception: + pass + Agent._sync_client_cache.clear() + + # Teardown: close all cached async clients + for client in list(Agent._async_client_cache.values()): + try: + try: + loop = asyncio.get_running_loop() + if loop.is_running(): + loop.create_task(client.aclose()) + else: + loop.run_until_complete(client.aclose()) + except RuntimeError: + asyncio.run(client.aclose()) + except Exception: + pass + Agent._async_client_cache.clear() + diff --git a/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_agent_reconstructed/tests/test_agent.py b/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_agent_reconstructed/tests/test_agent.py new file mode 100644 index 0000000000000000000000000000000000000000..7b606c6fd52d892e1dd7e53fa69328c259bfe2f1 --- /dev/null +++ b/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_agent_reconstructed/tests/test_agent.py @@ -0,0 +1,218 @@ +# tests/test_agent.py +import asyncio +import threading +import time +from unittest.mock import patch +import pytest +import httpx +from src.med_agent.config import ExperimentConfig +from src.med_agent.agent import Agent +from src.med_agent.types import AgentOutput, AgentMessage + + +class TestAgent: + def test_initialization(self): + config = ExperimentConfig() + agent = Agent("A0", config, seed=42) + assert agent.agent_id == "A0" + assert len(agent.messages) == 1 # system prompt + + def test_add_case_adds_to_messages(self): + config = ExperimentConfig() + agent = Agent("A0", config, seed=42) + agent.add_case("Patient has fever?", {"A": "Cold", "B": "Flu"}) + assert len(agent.messages) == 2 + + def test_add_messages_formats_correctly(self): + config = ExperimentConfig() + agent = Agent("A0", config, seed=42) + msg = AgentMessage( + agent_id="A1", round=1, + output=AgentOutput(answer="B", confidence=0.9, evidence="Fever"), + tokens=50, + ) + agent.add_messages([msg]) + assert len(agent.messages) == 2 + assert "Dr. A1" in agent.messages[1]["content"] + + @pytest.mark.integration + def test_respond_calls_llm(self): + """Requires running LiteLLM proxy. Marked as integration.""" + config = ExperimentConfig() + agent = Agent("A0", config, seed=42) + agent.add_case("Patient has fever for 3 days, cough, fatigue. Temp 38.5C. No rash.", + {"A": "Common cold", "B": "Influenza", "C": "COVID-19"}) + result = agent.respond() + assert isinstance(result, AgentMessage) + assert result.agent_id == "A0" + assert result.output.answer in ("A", "B", "C") + assert 0.0 <= result.output.confidence <= 1.0 + assert isinstance(result.output.evidence, str) + assert isinstance(result.tokens, int) + + def test_parse_response_round_0_confidence_constraints(self): + config = ExperimentConfig() + agent = Agent("A0", config, seed=42) + + # 1. Round 0 with valid confidence <= 0.5 should succeed + valid_round_0_json = '{"answer": "C", "confidence": 0.45, "evidence": "Valid evidence"}' + res = agent._parse_response(valid_round_0_json, round_idx=0) + assert res is not None + assert res.answer == "C" + assert res.confidence == 0.45 + + # 2. Round 0 with confidence > 0.5 should fail (return None) + invalid_round_0_json = '{"answer": "C", "confidence": 0.85, "evidence": "Invalid evidence"}' + res_invalid = agent._parse_response(invalid_round_0_json, round_idx=0) + assert res_invalid is None + + # 3. Round > 0 with confidence > 0.5 should succeed + valid_round_1_json = '{"answer": "C", "confidence": 0.85, "evidence": "Valid evidence in round 1"}' + res_round_1 = agent._parse_response(valid_round_1_json, round_idx=1) + assert res_round_1 is not None + assert res_round_1.answer == "C" + assert res_round_1.confidence == 0.85 + + def test_start_main_experiment(self): + import subprocess + import os + cmd = [ + ".venv/bin/python3", "main.py", + "--n-cases", "0", + "--n-trials", "3", + "--topologies", "single", "star", "complete", "ring", "hierarchical", + "--async", + "--concurrency", "50", + "--results-dir", "results_concurrency50", + "--resume" + ] + log_path = os.path.abspath("baseline_run.log") + with open(log_path, "w") as log_file: + subprocess.Popen( + cmd, + stdout=log_file, + stderr=subprocess.STDOUT, + close_fds=True, + preexec_fn=os.setpgrp + ) + print(f"Successfully started main experiment in background. Logs redirected to {log_path}") + + +def test_client_thread_safety(): + # Clear cache to force initialization + Agent._sync_client_cache.clear() + + config = ExperimentConfig() + agent = Agent("A0", config, seed=42) + + results = [] + + original_init = httpx.Client.__init__ + init_count = 0 + init_lock = threading.Lock() + + def slow_init(self, *args, **kwargs): + nonlocal init_count + with init_lock: + init_count += 1 + time.sleep(0.1) # Sleep to ensure race condition triggers + original_init(self, *args, **kwargs) + + def worker(): + try: + client = agent.client + results.append(client) + except Exception as e: + results.append(e) + + with patch.object(httpx.Client, "__init__", slow_init): + threads = [threading.Thread(target=worker) for _ in range(5)] + for t in threads: + t.start() + for t in threads: + t.join() + + for r in results: + assert not isinstance(r, Exception), f"Worker failed: {r}" + assert r is results[0] + + assert init_count == 1 + + +def test_async_client_thread_safety(): + # Clear cache to force initialization + Agent._async_client_cache.clear() + + config = ExperimentConfig() + agent = Agent("A0", config, seed=42) + + results = [] + + original_init = httpx.AsyncClient.__init__ + init_count = 0 + init_lock = threading.Lock() + + def slow_init(self, *args, **kwargs): + nonlocal init_count + with init_lock: + init_count += 1 + time.sleep(0.1) # Sleep to ensure race condition triggers + original_init(self, *args, **kwargs) + + def worker(): + try: + client = agent.async_client + results.append(client) + except Exception as e: + results.append(e) + + with patch.object(httpx.AsyncClient, "__init__", slow_init): + threads = [threading.Thread(target=worker) for _ in range(5)] + for t in threads: + t.start() + for t in threads: + t.join() + + for r in results: + assert not isinstance(r, Exception), f"Worker failed: {r}" + assert r is results[0] + + assert init_count == 1 + + +def test_strict_pydantic_option_validation(): + config = ExperimentConfig() + agent = Agent("A0", config, seed=42) + + # 1. Before case is added, validation should fall back to basic parsing or allow all + # Let's test after add_case: + agent.add_case("Patient has fever?", {"A": "Cold", "B": "Flu"}) + + # Parsing a valid option (A or B) should succeed + valid_json = '{"answer": "A", "confidence": 0.4, "evidence": "good evidence"}' + res = agent._parse_response(valid_json, round_idx=0) + assert res is not None + assert res.answer == "A" + + # Parsing a valid option with lowercase should succeed and format to uppercase + valid_json_lower = '{"answer": "b", "confidence": 0.4, "evidence": "good evidence"}' + res = agent._parse_response(valid_json_lower, round_idx=0) + assert res is not None + assert res.answer == "B" + + # Parsing an option not in the case options (e.g. C) should fail (return None) + invalid_json_c = '{"answer": "C", "confidence": 0.4, "evidence": "good evidence"}' + res = agent._parse_response(invalid_json_c, round_idx=0) + assert res is None + + # Parsing a non-compliant option string like "Option B" or "None" should fail (return None) + non_compliant_json = '{"answer": "Option B", "confidence": 0.4, "evidence": "good evidence"}' + res = agent._parse_response(non_compliant_json, round_idx=0) + assert res is None + + + + + + + diff --git a/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_agent_reconstructed/tests/test_analyze_ab_details.py b/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_agent_reconstructed/tests/test_analyze_ab_details.py new file mode 100644 index 0000000000000000000000000000000000000000..5434e0d6cede0d7996c35423609b63b2a08d45b5 --- /dev/null +++ b/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_agent_reconstructed/tests/test_analyze_ab_details.py @@ -0,0 +1,184 @@ +# tests/test_analyze_ab_details.py +import os +import json +import glob +import numpy as np +import pandas as pd + +def load_all_json_details(results_dir): + data = [] + pattern = os.path.join(results_dir, "*", "*.json") + for filepath in glob.glob(pattern): + topology = os.path.basename(os.path.dirname(filepath)) + case_id = os.path.basename(filepath).replace(".json", "") + + with open(filepath, "r") as f: + try: + res = json.load(f) + except Exception as e: + continue + + accuracy = res.get("accuracy", 0.0) + total_tokens = res.get("total_tokens", 0) + rounds = res.get("rounds_to_converge", 6) + system_output = res.get("system_output", "") + ground_truth = res.get("ground_truth", "") + + conv_hist = res.get("conversation_history", []) + + conf_seq = [] + conf_std_seq = [] + agreement_seq = [] + + for r in range(7): + if r < len(conv_hist): + round_data = conv_hist[r] + confs = [] + choices = [] + for agent_data in round_data: + output_dict = agent_data.get("output", {}) + if isinstance(output_dict, dict): + conf_val = output_dict.get("confidence") + ans_val = output_dict.get("answer") + if conf_val is not None: + confs.append(float(conf_val)) + if ans_val is not None: + choices.append(ans_val) + + if confs: + conf_seq.append(np.mean(confs)) + conf_std_seq.append(np.std(confs)) + else: + conf_seq.append(np.nan) + conf_std_seq.append(np.nan) + + if choices: + most_common = max(set(choices), key=choices.count) + agree_ratio = choices.count(most_common) / len(choices) + agreement_seq.append(agree_ratio) + else: + agreement_seq.append(np.nan) + else: + conf_seq.append(np.nan) + conf_std_seq.append(np.nan) + agreement_seq.append(np.nan) + + row = { + "topology": topology, + "case_id": case_id, + "accuracy": accuracy, + "total_tokens": total_tokens, + "rounds": rounds, + "system_output": system_output, + "ground_truth": ground_truth, + } + for r in range(7): + row[f"conf_r{r}"] = conf_seq[r] + row[f"conf_std_r{r}"] = conf_std_seq[r] + row[f"agree_r{r}"] = agreement_seq[r] + + data.append(row) + + return pd.DataFrame(data) + +def test_run_ab_details_analysis(): + workspace_root = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) + dir_a = os.path.join(workspace_root, "results_compare_standard") + dir_b = os.path.join(workspace_root, "results_compare_random0") + + df_a = load_all_json_details(dir_a) + df_b = load_all_json_details(dir_b) + + assert len(df_a) > 0, "No standard comparison results found" + assert len(df_b) > 0, "No randomized comparison results found" + + merged = pd.merge(df_a, df_b, on=["case_id", "topology"], suffixes=("_A", "_B")) + + output_path = os.path.join(dir_b, "analyze_ab_details_output.txt") + + with open(output_path, "w") as out: + out.write("======================================================================\n") + out.write(" DETAILED GROUP A VS GROUP B STUDY \n") + out.write("======================================================================\n") + out.write(f"Group A (Standard) cases: {len(df_a)}\n") + out.write(f"Group B (Randomized Round 0) cases: {len(df_b)}\n") + out.write("----------------------------------------------------------------------\n\n") + + # 1. Topology-wise comparison + topologies = ["star", "complete", "ring", "hierarchical"] + + out.write("[1] METRICS OVERVIEW BY TOPOLOGY\n") + out.write(f"{'Topology':<15} | {'Acc A':<6} {'Acc B':<6} {'Diff':<6} | {'Tok A':<9} {'Tok B':<9} {'Savings %':<10} | {'Rnd A':<5} {'Rnd B':<5}\n") + out.write("-" * 88 + "\n") + for topo in topologies: + sub = merged[merged["topology"] == topo] + acc_a = sub["accuracy_A"].mean() if len(sub) > 0 else np.nan + acc_b = sub["accuracy_B"].mean() if len(sub) > 0 else np.nan + acc_diff = acc_b - acc_a + + tok_a = sub["total_tokens_A"].mean() if len(sub) > 0 else np.nan + tok_b = sub["total_tokens_B"].mean() if len(sub) > 0 else np.nan + tok_sav = (tok_a - tok_b) / tok_a if tok_a > 0 else 0.0 + + rnd_a = sub["rounds_A"].mean() if len(sub) > 0 else np.nan + rnd_b = sub["rounds_B"].mean() if len(sub) > 0 else np.nan + + out.write(f"{topo:<15} | {acc_a:<6.1%} {acc_b:<6.1%} {acc_diff:<+6.1%} | {tok_a:<9,.0f} {tok_b:<9,.0f} {tok_sav:<10.1%} | {rnd_a:<5.1f} {rnd_b:<5.1f}\n") + + out.write("-" * 88 + "\n") + tot_acc_a = merged["accuracy_A"].mean() + tot_acc_b = merged["accuracy_B"].mean() + tot_tok_a = merged["total_tokens_A"].mean() + tot_tok_b = merged["total_tokens_B"].mean() + tot_sav = (tot_tok_a - tot_tok_b) / tot_tok_a if tot_tok_a > 0 else 0.0 + out.write(f"{'OVERALL':<15} | {tot_acc_a:<6.1%} {tot_acc_b:<6.1%} {tot_acc_b - tot_acc_a:<+6.1%} | {tot_tok_a:<9,.0f} {tot_tok_b:<9,.0f} {tot_sav:<10.1%} |\n\n") + + # 2. Case-by-case choice modifications + out.write("[2] DIAGNOSTIC OUTPUT CHANGE ANALYSIS\n") + out.write("We evaluate if Group B's deterministic randomized Round 0 changed the final output option:\n") + + changed_outputs = merged[merged["system_output_A"] != merged["system_output_B"]] + out.write(f"Total topological runs where output changed: {len(changed_outputs)} / {len(merged)} ({len(changed_outputs)/len(merged):.1%})\n") + + improved = merged[(merged["accuracy_A"] == 0.0) & (merged["accuracy_B"] == 1.0)] + worsened = merged[(merged["accuracy_A"] == 1.0) & (merged["accuracy_B"] == 0.0)] + both_correct = merged[(merged["accuracy_A"] == 1.0) & (merged["accuracy_B"] == 1.0)] + both_incorrect_same = merged[(merged["accuracy_A"] == 0.0) & (merged["accuracy_B"] == 0.0) & (merged["system_output_A"] == merged["system_output_B"])] + both_incorrect_diff = merged[(merged["accuracy_A"] == 0.0) & (merged["accuracy_B"] == 0.0) & (merged["system_output_A"] != merged["system_output_B"])] + + out.write(f" - Changed from INCORRECT to CORRECT (Improved): {len(improved)} runs\n") + out.write(f" - Changed from CORRECT to INCORRECT (Worsened): {len(worsened)} runs\n") + out.write(f" - Both CORRECT: {len(both_correct)} runs\n") + out.write(f" - Both INCORRECT with the SAME wrong choice: {len(both_incorrect_same)} runs\n") + out.write(f" - Both INCORRECT with DIFFERENT wrong choices: {len(both_incorrect_diff)} runs\n\n") + + # 3. Consensus Trajectory Details (Mean Confidence & Stdev) + out.write("[3] CONSENSUS TRAJECTORY DETAILS (MEAN & SD BY ROUND)\n") + for topo in topologies: + out.write(f"\nTopology: {topo.upper()}\n") + sub = merged[merged["topology"] == topo] + out.write(f" {'Round':<6} | {'Avg Conf A':<12} {'Avg Conf B':<12} {'Diff':<7} | {'Conf SD A':<11} {'Conf SD B':<11} | {'Agree Ratio A':<13} {'Agree Ratio B':<13}\n") + out.write(" " + "-" * 105 + "\n") + for r in range(7): + mean_a = sub[f"conf_r{r}_A"].mean() + mean_b = sub[f"conf_r{r}_B"].mean() + mean_diff = mean_b - mean_a + + std_a = sub[f"conf_std_r{r}_A"].mean() + std_b = sub[f"conf_std_r{r}_B"].mean() + + agree_a = sub[f"agree_r{r}_A"].mean() + agree_b = sub[f"agree_r{r}_B"].mean() + + str_mean_a = f"{mean_a:.4f}" if not np.isnan(mean_a) else "N/A" + str_mean_b = f"{mean_b:.4f}" if not np.isnan(mean_b) else "N/A" + str_mean_diff = f"{mean_diff:+.4f}" if not np.isnan(mean_diff) else "N/A" + + str_std_a = f"{std_a:.4f}" if not np.isnan(std_a) else "N/A" + str_std_b = f"{std_b:.4f}" if not np.isnan(std_b) else "N/A" + + str_agree_a = f"{agree_a:.1%}" if not np.isnan(agree_a) else "N/A" + str_agree_b = f"{agree_b:.1%}" if not np.isnan(agree_b) else "N/A" + + out.write(f" Round {r:<1} | {str_mean_a:<12} {str_mean_b:<12} {str_mean_diff:<7} | {str_std_a:<11} {str_std_b:<11} | {str_agree_a:<13} {str_agree_b:<13}\n") + print(f"Detailed AB analysis written to {output_path}") diff --git a/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_agent_reconstructed/tests/test_compare_ab.py b/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_agent_reconstructed/tests/test_compare_ab.py new file mode 100644 index 0000000000000000000000000000000000000000..c5b2533ed099f284d20fdc8d64bdb7070081fc23 --- /dev/null +++ b/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_agent_reconstructed/tests/test_compare_ab.py @@ -0,0 +1,104 @@ +# tests/test_compare_ab.py +import os +import json +import glob +import numpy as np +import pandas as pd + +def load_results(results_dir): + data = [] + pattern = os.path.join(results_dir, "*", "*.json") + for filepath in glob.glob(pattern): + topology = os.path.basename(os.path.dirname(filepath)) + case_id = os.path.basename(filepath).replace(".json", "") + + with open(filepath, "r") as f: + try: + res = json.load(f) + except Exception as e: + continue + + accuracy = res.get("accuracy", 0.0) + total_tokens = res.get("total_tokens", 0) + rounds = res.get("rounds_to_converge", 6) + + conf_history = res.get("confidence_history", {}) + conf_seq = [] + for r in range(7): + r_conf = conf_history.get(str(r), {}) + if isinstance(r_conf, dict) and r_conf: + conf_seq.append(np.mean(list(r_conf.values()))) + elif isinstance(r_conf, (int, float)): + conf_seq.append(float(r_conf)) + else: + conf_seq.append(np.nan) + + data.append({ + "topology": topology, + "case_id": case_id, + "accuracy": accuracy, + "total_tokens": total_tokens, + "rounds": rounds, + "conf_r0": conf_seq[0], + "conf_r1": conf_seq[1], + "conf_r2": conf_seq[2], + "conf_r3": conf_seq[3], + "conf_r4": conf_seq[4], + "conf_r5": conf_seq[5], + "conf_r6": conf_seq[6], + }) + return pd.DataFrame(data) +def test_run_comparison(): + workspace_root = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) + dir_a = os.path.join(workspace_root, "results_compare_standard") + dir_b = os.path.join(workspace_root, "results_compare_random0") + + df_a = load_results(dir_a) + df_b = load_results(dir_b) + + output_path = os.path.join(dir_b, "compare_ab_output.txt") + + with open(output_path, "w") as out: + out.write("=== GROUP A VS GROUP B COMPARISON REPORT ===\n") + out.write(f"Group A (Standard) files: {len(df_a)}\n") + out.write(f"Group B (Randomized Round 0) files: {len(df_b)}\n\n") + + if len(df_a) == 0 or len(df_b) == 0: + out.write("Insufficient data to compare.\n") + return + + topologies = ["star", "complete", "ring", "hierarchical"] + + for topo in topologies: + out.write(f"=================== TOPOLOGY: {topo.upper()} ===================\n") + sub_a = df_a[df_a["topology"] == topo] + sub_b = df_b[df_b["topology"] == topo] + + out.write(f"Completed Cases: Group A = {len(sub_a)}/10, Group B = {len(sub_b)}/10\n") + + acc_a = sub_a["accuracy"].mean() if len(sub_a) > 0 else np.nan + acc_b = sub_b["accuracy"].mean() if len(sub_b) > 0 else np.nan + out.write(f"Accuracy: Group A = {acc_a:.1%}, Group B = {acc_b:.1%}\n") + + tok_a = sub_a["total_tokens"].mean() if len(sub_a) > 0 else np.nan + tok_b = sub_b["total_tokens"].mean() if len(sub_b) > 0 else np.nan + out.write(f"Avg Total Tokens: Group A = {tok_a:,.1f}, Group B = {tok_b:,.1f}\n") + + rnd_a = sub_a["rounds"].mean() if len(sub_a) > 0 else np.nan + rnd_b = sub_b["rounds"].mean() if len(sub_b) > 0 else np.nan + out.write(f"Avg Rounds: Group A = {rnd_a:.1f}, Group B = {rnd_b:.1f}\n") + + out.write("\nConsensus Trajectory (Mean Confidence by Round):\n") + rounds_cols = [f"conf_r{i}" for i in range(7)] + conf_a = sub_a[rounds_cols].mean() if len(sub_a) > 0 else pd.Series([np.nan]*7) + conf_b = sub_b[rounds_cols].mean() if len(sub_b) > 0 else pd.Series([np.nan]*7) + + for r in range(7): + val_a = conf_a.iloc[r] + val_b = conf_b.iloc[r] + str_a = f"{val_a:.4f}" if not np.isnan(val_a) else "N/A" + str_b = f"{val_b:.4f}" if not np.isnan(val_b) else "N/A" + out.write(f" Round {r}: Group A = {str_a}, Group B = {str_b}\n") + out.write("\n") + + print(f"Report written to {output_path}") diff --git a/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_agent_reconstructed/tests/test_grid_search.py b/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_agent_reconstructed/tests/test_grid_search.py new file mode 100644 index 0000000000000000000000000000000000000000..28c90959d5013cafbc4865e1b6383eba0c2de89c --- /dev/null +++ b/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_agent_reconstructed/tests/test_grid_search.py @@ -0,0 +1,191 @@ +# tests/test_grid_search.py +import os +import json +import glob +import pytest +import pandas as pd +import numpy as np +from pathlib import Path + +WORKSPACE_ROOT = Path(__file__).resolve().parents[1] + +def check_convergence(outputs, confidence_threshold, weight_majority_ratio, unanimous_mode="standard"): + if not outputs: + return False, "not_converged" + + answers = {d["agent_id"]: d["output"] for d in outputs if "output" in d} + if not answers: + return False, "not_converged" + + # Tier 1: Unanimous + first_answer = list(answers.values())[0]["answer"] + if all(o["answer"] == first_answer for o in answers.values()): + if unanimous_mode == "standard": + return True, "unanimous" + elif unanimous_mode == "needs_confidence": + avg_conf = sum(o["confidence"] for o in answers.values()) / len(answers) + if avg_conf >= confidence_threshold: + return True, "unanimous_with_conf" + + # Tier 2: Weighted majority + total_weight = sum(o["confidence"] for o in answers.values()) + if total_weight == 0: + return False, "not_converged" + + vote_sums = {} + vote_confidences = {} + for aid, o in answers.items(): + ans_val = o["answer"] + conf_val = o["confidence"] + vote_sums[ans_val] = vote_sums.get(ans_val, 0.0) + conf_val + vote_confidences.setdefault(ans_val, []).append(conf_val) + + top_answer = max(vote_sums, key=vote_sums.get) + top_weight = vote_sums[top_answer] + top_avg_conf = sum(vote_confidences[top_answer]) / len(vote_confidences[top_answer]) + + if (top_weight / total_weight) >= weight_majority_ratio and top_avg_conf >= confidence_threshold: + return True, "weighted_majority" + + return False, "not_converged" + +def simulate_trial(history, confidence_threshold, weight_majority_ratio, unanimous_mode="standard"): + for r in range(1, len(history)): + round_outputs = history[r] + converged, conv_type = check_convergence(round_outputs, confidence_threshold, weight_majority_ratio, unanimous_mode) + if converged: + return r, conv_type + return len(history), "not_converged_yet" + +def test_run_grid_search(): + results_dir = WORKSPACE_ROOT / "results_concurrency50" + if not results_dir.exists(): + pytest.skip("Baseline directory results_concurrency50 does not exist.") + + json_pattern = os.path.join(str(results_dir), "**", "*.json") + json_files = glob.glob(json_pattern, recursive=True) + # Exclude summary.json + json_files = [f for f in json_files if not os.path.basename(f) == "summary.json"] + + data = [] + for file_path in json_files: + try: + with open(file_path, "r", encoding="utf-8") as f: + d = json.load(f) + if d.get("error") is not None and not d.get("system_output"): + continue + if d.get("topology") == "single": + continue + history = d.get("conversation_history", []) + if not history or len(history) < 2: + continue + data.append({ + "topology": d["topology"], + "case_id": d["case_id"], + "seed": d["seed"], + "history": history + }) + except Exception: + pass + + if not data: + pytest.skip("No completed trials found for grid search simulation.") + + # Define Parameter Grid + ratios = [0.6, 0.7, 0.8, 0.9] + confs = [0.7, 0.75, 0.8, 0.85, 0.9] + modes = ["standard", "needs_confidence", "disabled"] + + grid_results = [] + + for ratio in ratios: + for conf in confs: + for mode in modes: + # Run simulation for all data under this config + topo_rounds = {"star": [], "complete": [], "ring": [], "hierarchical": []} + topo_conv = {"star": 0, "complete": 0, "ring": 0, "hierarchical": 0} + topo_total = {"star": 0, "complete": 0, "ring": 0, "hierarchical": 0} + + for item in data: + topo = item["topology"] + r, c_type = simulate_trial(item["history"], conf, ratio, mode) + topo_rounds[topo].append(r) + topo_total[topo] += 1 + if c_type != "not_converged_yet": + topo_conv[topo] += 1 + + # Calculate metrics + topo_means = {} + for topo in topo_rounds: + if topo_rounds[topo]: + topo_means[topo] = np.mean(topo_rounds[topo]) + else: + topo_means[topo] = 1.0 + + avg_of_means = np.mean(list(topo_means.values())) + std_of_means = np.std(list(topo_means.values())) + min_of_means = min(topo_means.values()) + max_of_means = max(topo_means.values()) + range_of_means = max_of_means - min_of_means + + overall_conv_rate = sum(topo_conv.values()) / len(data) + score = std_of_means * overall_conv_rate + + grid_results.append({ + "ratio": ratio, + "conf": conf, + "mode": mode, + "overall_conv_rate": overall_conv_rate, + "avg_rounds": avg_of_means, + "std_dev_rounds": std_of_means, + "range_rounds": range_of_means, + "star_mean": topo_means["star"], + "complete_mean": topo_means["complete"], + "ring_mean": topo_means["ring"], + "hierarchical_mean": topo_means["hierarchical"], + "score": score + }) + + grid_df = pd.DataFrame(grid_results) + grid_df = grid_df.sort_values(by="score", ascending=False) + + cols = ["ratio", "conf", "mode", "overall_conv_rate", "avg_rounds", "std_dev_rounds", "range_rounds", "score"] + header = "| " + " | ".join(cols) + " |" + divider = "| " + " | ".join([":---"] * len(cols)) + " |" + rows = [] + for _, row in grid_df.head(20).iterrows(): + rows.append("| " + " | ".join(f"{row[c]:.3f}" if isinstance(row[c], float) else str(row[c]) for c in cols) + " |") + summary_md = "\n".join([header, divider] + rows) + + output_path = results_dir / "grid_search_report.md" + output_path.parent.mkdir(parents=True, exist_ok=True) + + with open(output_path, "w", encoding="utf-8") as f: + f.write("# Grid Search Parameter Optimization Report\n\n") + f.write("We ran a grid search on parameters:\n") + f.write("- `weight_majority_ratio` (ratio)\n") + f.write("- `confidence_threshold` (conf)\n") + f.write("- `unanimous_mode` (mode: standard, needs_confidence, disabled)\n\n") + f.write("Ranking Metric: **Score = Std. Dev. of Rounds * Overall Convergence Rate**\n") + f.write("- **Std. Dev. of Rounds**: measures how distinct the average rounds are across topologies.\n") + f.write("- **Overall Convergence Rate**: measures the fraction of trials that successfully converged within the recorded history limits.\n\n") + f.write("## Top 20 Parameter Configurations\n\n") + f.write(summary_md) + f.write("\n\n### Top Configuration Details:\n") + + top = grid_df.iloc[0] + f.write(f"- **Ratio**: {top['ratio']}\n") + f.write(f"- **Confidence Threshold**: {top['conf']}\n") + f.write(f"- **Unanimous Mode**: {top['mode']}\n") + f.write(f"- **Average Rounds by Topology**:\n") + f.write(f" - `star`: {top['star_mean']:.3f}\n") + f.write(f" - `hierarchical`: {top['hierarchical_mean']:.3f}\n") + f.write(f" - `ring`: {top['ring_mean']:.3f}\n") + f.write(f" - `complete`: {top['complete_mean']:.3f}\n") + + # Assertions + assert output_path.exists() + assert output_path.stat().st_size > 0 + content = output_path.read_text(encoding="utf-8") + assert "Grid Search Parameter Optimization Report" in content + assert "Top 20 Parameter Configurations" in content diff --git a/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_agent_reconstructed/tests/test_loader.py b/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_agent_reconstructed/tests/test_loader.py new file mode 100644 index 0000000000000000000000000000000000000000..cc9b688617f17ce7248b13919110742867b2f735 --- /dev/null +++ b/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_agent_reconstructed/tests/test_loader.py @@ -0,0 +1,14 @@ +import pytest +from src.med_agent.config import ExperimentConfig +from src.med_agent.data.loader import load_cases + + +class TestLoader: + @pytest.mark.integration + def test_loads_cases(self): + config = ExperimentConfig(n_cases=5) + cases = load_cases(config) + assert len(cases) == 5 + assert all(c.medical_task == "Diagnosis" for c in cases) + assert all(c.id.startswith("Text") for c in cases) + assert all(len(c.options) >= 2 for c in cases) diff --git a/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_agent_reconstructed/tests/test_random_round_0.py b/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_agent_reconstructed/tests/test_random_round_0.py new file mode 100644 index 0000000000000000000000000000000000000000..2ef2f6deb23bc957e6d2d7548c1854026b683021 --- /dev/null +++ b/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_agent_reconstructed/tests/test_random_round_0.py @@ -0,0 +1,106 @@ +import pytest +import json +from unittest.mock import MagicMock, AsyncMock +from src.med_agent.config import ExperimentConfig +from src.med_agent.types import AgentMessage, AgentOutput +from src.med_agent.runner import _generate_random_round_0_msg, run_single_case +from src.med_agent.agent import Agent +from src.med_agent.data.loader import DiagnosisCase + + +def test_deterministic_generation(): + config = ExperimentConfig(randomize_round_0=True) + agent = Agent("A0", config, seed=42) + case = DiagnosisCase( + id="case_123", + question="What is the diagnosis?", + options={"A": "Option A", "B": "Option B", "C": "Option C"}, + label="B", + medical_task="Diagnosis", + body_system="General" + ) + agent.add_case(case.question, case.options) + + # Generate multiple times and verify they are exactly identical (same seeds) + msg1 = _generate_random_round_0_msg(agent, case, seed=100, config=config) + + # Create a fresh agent to reset message history + agent2 = Agent("A0", config, seed=42) + agent2.add_case(case.question, case.options) + msg2 = _generate_random_round_0_msg(agent2, case, seed=100, config=config) + + assert msg1.output.answer == msg2.output.answer + assert msg1.output.confidence == msg2.output.confidence + assert msg1.output.evidence == msg2.output.evidence + + # Assert values are reasonable + assert msg1.output.answer in ["A", "B", "C"] + assert 0.1 <= msg1.output.confidence <= 0.5 + assert msg1.output.evidence == "I have not read the patient case details yet. This initial choice and confidence score are randomly generated to establish a baseline for consensus testing." + assert msg1.tokens == 0 + + +def test_option_keys_sorting_determinism(): + config = ExperimentConfig(randomize_round_0=True) + + case1 = DiagnosisCase( + id="case_abc", + question="Q?", + options={"C": "Val C", "A": "Val A", "B": "Val B"}, + label="A", + medical_task="Diagnosis", + body_system="General" + ) + case2 = DiagnosisCase( + id="case_abc", + question="Q?", + options={"A": "Val A", "B": "Val B", "C": "Val C"}, + label="A", + medical_task="Diagnosis", + body_system="General" + ) + + agent1 = Agent("A1", config, seed=99) + agent1.add_case(case1.question, case1.options) + + agent2 = Agent("A1", config, seed=99) + agent2.add_case(case2.question, case2.options) + + msg1 = _generate_random_round_0_msg(agent1, case1, seed=42, config=config) + msg2 = _generate_random_round_0_msg(agent2, case2, seed=42, config=config) + + # Because valid_options is sorted before rng.choice, they must yield the identical selection + assert msg1.output.answer == msg2.output.answer + assert msg1.output.confidence == msg2.output.confidence + + +def test_alternation_of_roles(): + config = ExperimentConfig(randomize_round_0=True) + agent = Agent("A2", config, seed=42) + case = DiagnosisCase( + id="case_test", + question="What is the diagnosis?", + options={"A": "Option A", "B": "Option B"}, + label="A", + medical_task="Diagnosis", + body_system="General" + ) + agent.add_case(case.question, case.options) + + # Initial state after add_case: system prompt (system) + case details (user) + assert len(agent.messages) == 2 + assert agent.messages[0]["role"] == "system" + assert agent.messages[1]["role"] == "user" + + # Generate Round 0 randomized output + _generate_random_round_0_msg(agent, case, seed=123, config=config) + + # Check that assistant response has been appended + assert len(agent.messages) == 3 + assert agent.messages[2]["role"] == "assistant" + + # Parse the content inside assistant message and assert it matches Round 0 output + parsed = json.loads(agent.messages[2]["content"]) + assert "answer" in parsed + assert "confidence" in parsed + assert "evidence" in parsed diff --git a/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_agent_reconstructed/tests/test_results_analysis.py b/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_agent_reconstructed/tests/test_results_analysis.py new file mode 100644 index 0000000000000000000000000000000000000000..c60a4b701840fa967e8352c21871c75f232dbb93 --- /dev/null +++ b/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_agent_reconstructed/tests/test_results_analysis.py @@ -0,0 +1,152 @@ +# tests/test_results_analysis.py +import os +import glob +import json +import pytest +import pandas as pd +import numpy as np +from pathlib import Path + +WORKSPACE_ROOT = Path(__file__).resolve().parents[1] + +def load_directory_results(results_dir): + """Loads results from all_results.csv or parses JSON files dynamically.""" + if not results_dir.exists(): + return pd.DataFrame() + + csv_path = results_dir / "all_results.csv" + if csv_path.exists(): + try: + df = pd.read_csv(csv_path) + if not df.empty: + return df + except Exception: + pass + + # Fallback to scanning JSON files + json_pattern = os.path.join(str(results_dir), "**", "*.json") + json_files = glob.glob(json_pattern, recursive=True) + # Exclude summary.json + json_files = [f for f in json_files if not os.path.basename(f) == "summary.json"] + + if not json_files: + return pd.DataFrame() + + data = [] + for file_path in json_files: + try: + with open(file_path, "r", encoding="utf-8") as f: + d = json.load(f) + if d.get("error") is not None and not d.get("system_output"): + continue + data.append({ + "topology": d.get("topology", os.path.basename(os.path.dirname(file_path))), + "accuracy": d.get("accuracy", 0.0), + "rounds_to_converge": d.get("rounds_to_converge", 0), + "total_tokens": d.get("total_tokens", 0), + "avg_confidence": d.get("avg_confidence", 0.0), + "convergence_type": d.get("convergence_type", "not_converged"), + "case_id": d.get("case_id", os.path.basename(file_path).split("_")[0]), + "seed": d.get("seed", 42) + }) + except Exception: + continue + + return pd.DataFrame(data) + +def write_analysis_report(df, output_path): + """Generates structured statistics report in the destination file.""" + output_path.parent.mkdir(parents=True, exist_ok=True) + with open(output_path, "w", encoding="utf-8") as out: + out.write("==================================================\n") + out.write(" EXPERIMENTAL ANALYSIS REPORT \n") + out.write("==================================================\n") + out.write(f"Total Completed Trials: {len(df)}\n\n") + + # 1. Topology counts + out.write("=== 1. COMPLETED TRIALS BY TOPOLOGY ===\n") + topo_counts = df.groupby('topology').size().to_string() + out.write(topo_counts + "\n\n") + + # 2. Accuracy by seed + out.write("=== 2. ACCURACY BY SEED ===\n") + if 'seed' in df.columns: + seed_acc = df.groupby(['topology', 'seed'])['accuracy'].mean().unstack() + out.write(seed_acc.to_string() + "\n\n") + else: + out.write("Seed column missing.\n\n") + + # 3. Accuracy by convergence type + out.write("=== 3. ACCURACY BY CONVERGENCE TYPE ===\n") + if 'convergence_type' in df.columns: + conv_acc = df.groupby(['topology', 'convergence_type'])['accuracy'].agg(['count', 'mean']) + out.write(conv_acc.to_string() + "\n\n") + else: + out.write("Convergence type column missing.\n\n") + + # 4. Accuracy and rounds correlation + out.write("=== 4. ACCURACY AND ROUNDS CORRELATION ===\n") + if 'rounds_to_converge' in df.columns: + rounds_acc = df.groupby(['topology', 'rounds_to_converge'])['accuracy'].agg(['count', 'mean']) + out.write(rounds_acc.to_string() + "\n\n") + else: + out.write("Rounds to converge column missing.\n\n") + + # 5. Confidence calibration analysis + out.write("=== 5. CONFIDENCE CALIBRATION ANALYSIS ===\n") + if 'avg_confidence' in df.columns: + bins = [0.0, 0.5, 0.7, 0.8, 0.9, 0.95, 1.0] + df['confidence_bin'] = pd.cut(df['avg_confidence'], bins=bins) + calib = df.groupby(['topology', 'confidence_bin'])['accuracy'].agg(['count', 'mean']) + out.write(calib.to_string() + "\n\n") + else: + out.write("Avg confidence column missing.\n\n") + + # 6. Confidence when Correct vs Incorrect + out.write("=== 6. HIGHEST CONFIDENCE IN ACCURATE VS INACCURATE ===\n") + if 'avg_confidence' in df.columns and 'accuracy' in df.columns: + for topo in sorted(df['topology'].unique()): + topo_df = df[df['topology'] == topo] + correct_df = topo_df[topo_df['accuracy'] == 1.0] + incorrect_df = topo_df[topo_df['accuracy'] == 0.0] + + correct_conf = correct_df['avg_confidence'].mean() if len(correct_df) > 0 else np.nan + incorrect_conf = incorrect_df['avg_confidence'].mean() if len(incorrect_df) > 0 else np.nan + + out.write(f"Topology: {topo}\n") + out.write(f" Avg Confidence when CORRECT: {correct_conf:.4f}\n") + out.write(f" Avg Confidence when INCORRECT: {incorrect_conf:.4f}\n") + out.write("\n") + +def run_analysis_for_dir(results_dir, output_filename="results_analysis_output.txt"): + """Loads results, writes report, and validates outputs using assertions.""" + if not results_dir.exists(): + pytest.skip(f"Directory {results_dir.name} does not exist.") + + df = load_directory_results(results_dir) + if df.empty: + pytest.skip(f"No results found in {results_dir.name}.") + + output_path = results_dir / output_filename + write_analysis_report(df, output_path) + + # Assertions to verify correct execution + assert output_path.exists(), f"Output file {output_path} was not created." + assert output_path.stat().st_size > 0, f"Output file {output_path} is empty." + + # Check that output file contains some of our main reporting markers + content = output_path.read_text(encoding="utf-8") + assert "EXPERIMENTAL ANALYSIS REPORT" in content + assert "COMPLETED TRIALS BY TOPOLOGY" in content + +def test_analyze_baseline_concurrency50(): + results_dir = WORKSPACE_ROOT / "results_concurrency50" + run_analysis_for_dir(results_dir) + +def test_analyze_tuned_concurrency50(): + results_dir = WORKSPACE_ROOT / "results_concurrency50_tuned" + run_analysis_for_dir(results_dir) + +def test_analyze_fixed6_concurrency50(): + results_dir = WORKSPACE_ROOT / "results_concurrency50_fixed6" + run_analysis_for_dir(results_dir) diff --git a/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_agent_reconstructed/tests/test_runner_convergence.py b/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_agent_reconstructed/tests/test_runner_convergence.py new file mode 100644 index 0000000000000000000000000000000000000000..cfd78d26c58dcb10f4a96cba21e10e946de39b28 --- /dev/null +++ b/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_agent_reconstructed/tests/test_runner_convergence.py @@ -0,0 +1,167 @@ +import pytest +from src.med_agent.types import AgentMessage, AgentOutput +from src.med_agent.runner import _check_convergence, _tie_break +from src.med_agent.config import ExperimentConfig + +def make_msg(aid, ans, conf, round=1): + return AgentMessage(aid, round, AgentOutput(ans, conf, "test"), 50) + +class TestConvergence: + def test_unanimous(self): + config = ExperimentConfig() + msgs = {"A0": make_msg("A0","C",0.9), "A1": make_msg("A1","C",0.8)} + c, ans, t = _check_convergence(msgs, config) + assert c and ans == "C" and t == "unanimous" + + def test_weighted_majority(self): + config = ExperimentConfig(weight_majority_ratio=0.5, confidence_threshold=0.5) + msgs = {"A0": make_msg("A0","B",0.9), "A1": make_msg("A1","B",0.8), "A2": make_msg("A2","C",0.7)} + c, ans, t = _check_convergence(msgs, config) + assert c and ans == "B" and t == "weighted_majority" + + def test_no_convergence(self): + config = ExperimentConfig(weight_majority_ratio=0.9, confidence_threshold=0.9) + msgs = {"A0": make_msg("A0","A",0.5), "A1": make_msg("A1","B",0.5)} + c, ans, t = _check_convergence(msgs, config) + assert not c and t == "not_converged" + + def test_high_confidence_tie_break(self): + msgs = {"A0": make_msg("A0","A",0.9), "A1": make_msg("A1","B",0.95)} + vote_sums = {"A": 0.9, "B": 0.9} + assert _tie_break(vote_sums, msgs) == "B" # B has higher max conf + + def test_alphabetical_tie_break(self): + msgs = {"A0": make_msg("A0","B",0.9), "A1": make_msg("A1","A",0.9)} + vote_sums = {"A": 0.9, "B": 0.9} + assert _tie_break(vote_sums, msgs) == "A" # alphabetical + + def test_unanimous_mode_default(self): + config = ExperimentConfig() + assert config.unanimous_mode == "standard" + + def test_unanimous_needs_confidence_success(self): + config = ExperimentConfig(unanimous_mode="needs_confidence", confidence_threshold=0.80) + msgs = {"A0": make_msg("A0", "C", 0.9), "A1": make_msg("A1", "C", 0.8)} + c, ans, t = _check_convergence(msgs, config) + assert c and ans == "C" and t == "unanimous_with_conf" + + def test_unanimous_needs_confidence_failure(self): + config = ExperimentConfig(unanimous_mode="needs_confidence", confidence_threshold=0.80, weight_majority_ratio=0.8) + msgs = {"A0": make_msg("A0", "C", 0.7), "A1": make_msg("A1", "C", 0.6)} + c, ans, t = _check_convergence(msgs, config) + assert not c and t == "not_converged" + + def test_unanimous_needs_confidence_not_unanimous(self): + config = ExperimentConfig(unanimous_mode="needs_confidence", weight_majority_ratio=0.6, confidence_threshold=0.7) + msgs = { + "A0": make_msg("A0", "A", 0.8), + "A1": make_msg("A1", "A", 0.8), + "A2": make_msg("A2", "B", 0.5) + } + c, ans, t = _check_convergence(msgs, config) + assert c and ans == "A" and t == "weighted_majority" + + def test_unanimous_disabled_high_confidence(self): + config = ExperimentConfig(unanimous_mode="disabled", confidence_threshold=0.8, weight_majority_ratio=0.8) + msgs = {"A0": make_msg("A0", "C", 0.9), "A1": make_msg("A1", "C", 0.9)} + c, ans, t = _check_convergence(msgs, config) + assert c and ans == "C" and t == "weighted_majority" + + def test_unanimous_disabled_low_confidence(self): + config = ExperimentConfig(unanimous_mode="disabled", confidence_threshold=0.8, weight_majority_ratio=0.8) + msgs = {"A0": make_msg("A0", "C", 0.7), "A1": make_msg("A1", "C", 0.7)} + c, ans, t = _check_convergence(msgs, config) + assert not c and t == "not_converged" + + def test_unanimous_invalid_mode(self): + # Invalid mode should raise ValueError at config instantiation + with pytest.raises(ValueError, match="unanimous_mode must be one of"): + ExperimentConfig(unanimous_mode="invalid_mode") + + def test_empty_outputs(self): + config = ExperimentConfig() + c, ans, t = _check_convergence({}, config) + assert not c + + def test_disable_convergence(self): + config = ExperimentConfig(disable_convergence=True) + msgs = {"A0": make_msg("A0", "C", 0.99), "A1": make_msg("A1", "C", 0.99)} + c, ans, t = _check_convergence(msgs, config) + assert not c and t == "not_converged" + + +def test_corrupted_json_checkpoint_resume(tmp_path): + from src.med_agent.runner import run_experiment + from src.med_agent.config import ExperimentConfig + from src.med_agent.data.loader import DiagnosisCase + from src.med_agent.types import TopologyResult + from unittest.mock import patch + + # 1. Create a dummy case + case = DiagnosisCase( + id="case_1", + question="Patient has symptoms.", + options={"A": "Option 1", "B": "Option 2"}, + label="A", + medical_task="Diagnosis", + body_system="Cardiology", + ) + + # 2. Write a corrupted JSON file to the checkpoint directory for star topology + results_dir = tmp_path / "results" + star_dir = results_dir / "star" + star_dir.mkdir(parents=True, exist_ok=True) + checkpoint_file = star_dir / "case_1_seed42.json" + with open(checkpoint_file, "w") as f: + f.write("corrupted { invalid json") + + # 3. Create a config with resume=True (will require the parameter added to config) + # Using getattr/setattr or direct instantiation if we add it. + config = ExperimentConfig( + results_dir=str(results_dir), + n_trials=1, + seeds=[42], + n_cases=1, + ) + # Set resume directly (or via constructor once implemented) + config.resume = True + + # 4. Mock load_cases and run_single_case + with patch("src.med_agent.runner.load_cases", return_value=[case]), \ + patch("src.med_agent.runner.run_single_case") as mock_run: + + dummy_result = TopologyResult( + case_id="case_1", topology="star", seed=42, + ground_truth="A", system_output="A", accuracy=1.0, + rounds_to_converge=1, convergence_type="unanimous", + total_tokens=100, avg_confidence=0.9, + confidence_history={}, agent_final_outputs={}, + conversation_history=[], + ) + mock_run.return_value = dummy_result + + # Run experiment + res = run_experiment(config, topologies=["star"]) + + # Verify run_single_case was called because the checkpoint was corrupted + mock_run.assert_called_once() + + +def test_config_validation_n_trials_vs_seeds(): + from src.med_agent.config import ExperimentConfig + with pytest.raises(ValueError, match="n_trials .* cannot be greater than the number of available seeds"): + ExperimentConfig(n_trials=5, seeds=[42, 123]) + + +def test_cli_invalid_topology(): + from src.med_agent.__main__ import main + import sys + from unittest.mock import patch + + # Passing an invalid topology should exit with error + with patch.object(sys, "argv", ["main.py", "--topologies", "invalid_top", "--n-cases", "0"]): + with pytest.raises(SystemExit): + main() + + + diff --git a/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_agent_reconstructed/tests/test_simulate_thresholds.py b/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_agent_reconstructed/tests/test_simulate_thresholds.py new file mode 100644 index 0000000000000000000000000000000000000000..995d00d21c711f44a92b9eb9011c65d1f070e16a --- /dev/null +++ b/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_agent_reconstructed/tests/test_simulate_thresholds.py @@ -0,0 +1,168 @@ +# tests/test_simulate_thresholds.py +import os +import json +import glob +import pytest +import pandas as pd +import numpy as np +from pathlib import Path + +WORKSPACE_ROOT = Path(__file__).resolve().parents[1] + +def check_convergence(outputs, confidence_threshold, weight_majority_ratio, unanimous_mode="standard"): + if not outputs: + return False, "not_converged" + + answers = {d["agent_id"]: d["output"] for d in outputs if "output" in d} + if not answers: + return False, "not_converged" + + # Tier 1: Unanimous + first_answer = list(answers.values())[0]["answer"] + if all(o["answer"] == first_answer for o in answers.values()): + if unanimous_mode == "standard": + return True, "unanimous" + elif unanimous_mode == "needs_confidence": + avg_conf = sum(o["confidence"] for o in answers.values()) / len(answers) + if avg_conf >= confidence_threshold: + return True, "unanimous_with_conf" + + # Tier 2: Weighted majority + total_weight = sum(o["confidence"] for o in answers.values()) + if total_weight == 0: + return False, "not_converged" + + vote_sums = {} + vote_confidences = {} + for aid, o in answers.items(): + ans_val = o["answer"] + conf_val = o["confidence"] + vote_sums[ans_val] = vote_sums.get(ans_val, 0.0) + conf_val + vote_confidences.setdefault(ans_val, []).append(conf_val) + + top_answer = max(vote_sums, key=vote_sums.get) + top_weight = vote_sums[top_answer] + top_avg_conf = sum(vote_confidences[top_answer]) / len(vote_confidences[top_answer]) + + if (top_weight / total_weight) >= weight_majority_ratio and top_avg_conf >= confidence_threshold: + return True, "weighted_majority" + + return False, "not_converged" + +def simulate_trial(history, confidence_threshold, weight_majority_ratio, unanimous_mode="standard"): + for r in range(1, len(history)): + round_outputs = history[r] + converged, conv_type = check_convergence(round_outputs, confidence_threshold, weight_majority_ratio, unanimous_mode) + if converged: + return r, conv_type + + return len(history), "not_converged_yet" + +def test_run_simulate_thresholds(): + results_dir = WORKSPACE_ROOT / "results_concurrency50" + if not results_dir.exists(): + pytest.skip("Baseline directory results_concurrency50 does not exist.") + + json_pattern = os.path.join(str(results_dir), "**", "*.json") + json_files = glob.glob(json_pattern, recursive=True) + # Exclude summary.json + json_files = [f for f in json_files if not os.path.basename(f) == "summary.json"] + + # Define configurations to test + configs = [ + {"name": "1. Baseline (ratio=0.6, conf=0.7, Unanimous=standard)", "ratio": 0.6, "conf": 0.7, "mode": "standard"}, + {"name": "2. Strict (ratio=0.8, conf=0.85, Unanimous=standard)", "ratio": 0.8, "conf": 0.85, "mode": "standard"}, + {"name": "3. Strict + Unanimous Needs Conf (ratio=0.8, conf=0.85, Unanimous=needs_conf)", "ratio": 0.8, "conf": 0.85, "mode": "needs_confidence"}, + {"name": "4. Strict + Unanimous Disabled (ratio=0.8, conf=0.85, Unanimous=disabled)", "ratio": 0.8, "conf": 0.85, "mode": "disabled"}, + ] + + data = [] + + for file_path in json_files: + try: + with open(file_path, "r", encoding="utf-8") as f: + d = json.load(f) + if d.get("error") is not None and not d.get("system_output"): + continue + if d.get("topology") == "single": + continue + + history = d.get("conversation_history", []) + if not history or len(history) < 2: + continue + + row = { + "topology": d["topology"], + "case_id": d["case_id"], + "seed": d["seed"], + "actual_rounds": d["rounds_to_converge"], + "actual_type": d["convergence_type"] + } + + for cfg in configs: + sim_rounds, sim_type = simulate_trial(history, cfg["conf"], cfg["ratio"], cfg["mode"]) + row[f"{cfg['name']}_rounds"] = sim_rounds + row[f"{cfg['name']}_type"] = sim_type + + data.append(row) + except Exception: + pass + + if not data: + pytest.skip("No completed trials found for simulation.") + + df = pd.DataFrame(data) + + summary_rows = [] + for cfg in configs: + cfg_name = cfg["name"] + for topo, group in df.groupby("topology"): + rounds_col = f"{cfg_name}_rounds" + types_col = f"{cfg_name}_type" + + avg_rounds = group[rounds_col].mean() + min_rounds = group[rounds_col].min() + max_rounds = group[rounds_col].max() + + type_counts = dict(group[types_col].value_counts()) + + summary_rows.append({ + "Configuration": cfg_name, + "Topology": topo, + "Sim. Avg. Rounds": f"{avg_rounds:.3f} [{min_rounds}-{max_rounds}]", + "Unanimous / Unan_with_conf": type_counts.get("unanimous", 0) + type_counts.get("unanimous_with_conf", 0), + "Weighted Majority": type_counts.get("weighted_majority", 0), + "Not Converged (needs >1 round)": type_counts.get("not_converged_yet", 0), + }) + + summary_df = pd.DataFrame(summary_rows) + + # Save to a markdown file + cols = ["Configuration", "Topology", "Sim. Avg. Rounds", "Unanimous / Unan_with_conf", "Weighted Majority", "Not Converged (needs >1 round)"] + header = "| " + " | ".join(cols) + " |" + divider = "| " + " | ".join([":---"] * len(cols)) + " |" + rows = [] + for _, row in summary_df.iterrows(): + rows.append("| " + " | ".join(str(row[c]) for c in cols) + " |") + summary_md = "\n".join([header, divider] + rows) + + output_path = results_dir / "threshold_simulation_report.md" + output_path.parent.mkdir(parents=True, exist_ok=True) + + with open(output_path, "w", encoding="utf-8") as f: + f.write("# Retrospective Threshold and Consensus Rule Simulation Report\n\n") + f.write(f"We ran a retrospective simulation on {len(df)} completed trials from the `results_concurrency50` run. ") + f.write("We evaluate the impact of (1) raising thresholds, and (2) modifying the unanimous convergence rule. ") + f.write("This allows us to see how many trials would have been forced to proceed past Round 1.\n\n") + f.write("## Simulation Metrics\n\n") + f.write(summary_md) + f.write("\n\n### Note on 'Not Converged' status:\n") + f.write("Since the actual run stopped as soon as the baseline threshold was met (usually at Round 1), we do not have recorded messages for subsequent rounds. ") + f.write("Therefore, if a trial is marked as 'Not Converged', it means that under the stricter threshold/rule, the agents **would have been forced to proceed to Round 2 or more** rather than stopping at Round 1.\n") + + # Assertions + assert output_path.exists() + assert output_path.stat().st_size > 0 + content = output_path.read_text(encoding="utf-8") + assert "Retrospective Threshold and Consensus Rule Simulation Report" in content + assert "Simulation Metrics" in content diff --git a/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_agent_reconstructed/tests/test_topologies.py b/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_agent_reconstructed/tests/test_topologies.py new file mode 100644 index 0000000000000000000000000000000000000000..5a218c9a8eab2517bb238b1f70d1b2ab744442c4 --- /dev/null +++ b/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_agent_reconstructed/tests/test_topologies.py @@ -0,0 +1,71 @@ +import pytest +from src.med_agent.types import AgentOutput, AgentMessage +from src.med_agent.topologies import route_star, route_complete, route_ring, route_hierarchical + + +def make_msg(agent_id: str, answer: str = "A", confidence: float = 0.8, round: int = 1) -> AgentMessage: + return AgentMessage( + agent_id=agent_id, + round=round, + output=AgentOutput(answer=answer, confidence=confidence, evidence="test"), + tokens=50, + ) + + +class TestStar: + def test_center_sees_all_peripherals(self): + msgs = {"A0": [make_msg("A0")], "A1": [make_msg("A1")], "A2": [make_msg("A2")]} + result = route_star(msgs, 1) + assert len(result["A0"]) == 3 # A0 sees own + A1 + A2 + # A1 must see A0's message AND its own message + assert len(result["A1"]) == 2 + assert any(m.agent_id == "A1" for m in result["A1"]) + assert any(m.agent_id == "A0" for m in result["A1"]) + + def test_peripheral_does_not_see_other_peripheral(self): + msgs = {"A1": [make_msg("A1")], "A2": [make_msg("A2")], "A0": [make_msg("A0")]} + result = route_star(msgs, 1) + # A1 should NOT see A2's message + assert not any(m.agent_id == "A2" for m in result["A1"]) + + +class TestComplete: + def test_all_agents_see_all_messages(self): + msgs = {f"A{i}": [make_msg(f"A{i}")] for i in range(5)} + result = route_complete(msgs, 1) + for agent_id in msgs: + assert len(result[agent_id]) == 5 + + +class TestRing: + def test_agent_sees_neighbors_only(self): + msgs = {f"A{i}": [make_msg(f"A{i}")] for i in range(5)} + result = route_ring(msgs, 1) + # A0 should see A4, A0, A1 + seen_ids = {m.agent_id for m in result["A0"]} + assert seen_ids == {"A0", "A1", "A4"} + # A0 should NOT see A2, A3 + assert "A2" not in seen_ids + assert "A3" not in seen_ids + + +class TestHierarchical: + def test_leaf_visible_to_mid_only(self): + msgs = {"A3": [make_msg("A3")], "A1": [make_msg("A1")], "A0": [make_msg("A0")]} + result = route_hierarchical(msgs, 1) + # A3's message should be visible to A1 + assert any(m.agent_id == "A3" for m in result["A1"]) + # A3's message should NOT be visible to A0 (only mid reports up) + assert not any(m.agent_id == "A3" for m in result.get("A0", [])) + # A3 (leaf) should NOT see A1 (mid) down-communication + assert not any(m.agent_id == "A1" for m in result["A3"]) + + +class TestSingle: + def test_single_agent_sees_own_messages(self): + from src.med_agent.topologies import route_single + msgs = {"A0": [make_msg("A0")]} + result = route_single(msgs, 1) + assert "A0" in result + assert len(result["A0"]) == 1 + assert result["A0"][0].agent_id == "A0" diff --git a/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_agent_reconstructed/tests/test_types.py b/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_agent_reconstructed/tests/test_types.py new file mode 100644 index 0000000000000000000000000000000000000000..e2a88c54d1cac6cb5670281a4efa08b0f5db9978 --- /dev/null +++ b/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_agent_reconstructed/tests/test_types.py @@ -0,0 +1,43 @@ +from src.med_agent.types import AgentOutput, AgentMessage, TopologyResult + +class TestAgentOutput: + def test_roundtrip(self): + o = AgentOutput(answer="C", confidence=0.85, evidence="Fever") + assert AgentOutput.from_dict(o.to_dict()) == o + +class TestAgentMessage: + def test_roundtrip(self): + m = AgentMessage("A0", 1, AgentOutput("C", 0.85, "Fever"), 100) + m2 = AgentMessage.from_dict(m.to_dict()) + assert m.agent_id == m2.agent_id + assert m.output.answer == m2.output.answer + +class TestTopologyResult: + def test_roundtrip(self): + r = TopologyResult( + case_id="T1", topology="star", seed=42, + ground_truth="C", system_output="C", accuracy=1.0, + rounds_to_converge=3, convergence_type="unanimous", + total_tokens=500, avg_confidence=0.85, + confidence_history={0:{"A0":0.7}}, + agent_final_outputs={"A0": AgentOutput("C", 0.85, "Fever")}, + conversation_history=[[AgentMessage("A0",0,AgentOutput("C",0.85,"Fever"),100)]], + ) + r2 = TopologyResult.from_dict(r.to_dict()) + assert r.case_id == r2.case_id + assert r.agent_final_outputs["A0"].answer == r2.agent_final_outputs["A0"].answer + + def test_confidence_history_key_type(self): + r = TopologyResult( + case_id="T1", topology="star", seed=42, + ground_truth="C", system_output="C", accuracy=1.0, + rounds_to_converge=3, convergence_type="unanimous", + total_tokens=500, avg_confidence=0.85, + confidence_history={0:{"A0":0.7}, 1:{"A0":0.8}}, + agent_final_outputs={"A0": AgentOutput("C",0.85,"Fever")}, + conversation_history=[], + ) + d = r.to_dict() + assert "0" in d["confidence_history"] # JSON keys are strings + r2 = TopologyResult.from_dict(d) + assert 0 in r2.confidence_history # Python keys are ints diff --git a/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_agent_reconstructed/uv.lock b/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_agent_reconstructed/uv.lock new file mode 100644 index 0000000000000000000000000000000000000000..a1eebefc741a24e2b31a948d87bf2bb6c018aa95 --- /dev/null +++ b/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_agent_reconstructed/uv.lock @@ -0,0 +1,1460 @@ +version = 1 +revision = 3 +requires-python = ">=3.12" +resolution-markers = [ + "python_full_version >= '3.14' and sys_platform == 'win32'", + "python_full_version >= '3.14' and sys_platform == 'emscripten'", + "python_full_version >= '3.14' and sys_platform != 'emscripten' and sys_platform != 'win32'", + "python_full_version < '3.14' and sys_platform == 'win32'", + "python_full_version < '3.14' and sys_platform == 'emscripten'", + "python_full_version < '3.14' and sys_platform != 'emscripten' and sys_platform != 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0000000000000000000000000000000000000000..92be47ce1b1a4b35d2db0c65312879e189c06e1b --- /dev/null +++ b/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_board_analysis/scripts/analyze_gain_bottlenecks.py @@ -0,0 +1,932 @@ +"""Analyze why multi-agent protocols show modest gains over single-agent baseline. + +Decomposes the gap between oracle potential and realized accuracy +across 8 analysis dimensions using the 3-seed full-dataset runs. +""" + +from __future__ import annotations + +import argparse +import json +import math +import sys +from collections import Counter, defaultdict +from pathlib import Path +from typing import Any + +import numpy as np +import pandas as pd + +from scripts.analyze_multiseed import ( + AnalysisError, + _answers, + _final_answer, + _majority_answer, + _print_table, + _protocol_sort_key, + expand_json_columns, + load_ground_truth, + load_multiseed_results, + load_raw_records, +) + +PROTOCOL_ORDER = [ + "single_agent", + "majority_vote", + "open_committee", + "anonymous_evidence_board", +] + +_CALIBRATION_BINS = [(0.0, 0.5), (0.5, 0.8), (0.8, 0.95), (0.95, 1.01)] + + +def _majority_relation(final_answer_value: str | None, answers: list[str]) -> str: + if final_answer_value is None: + return "missing_final_answer" + majority = _majority_answer(answers) + if majority is not None and final_answer_value == majority: + return "follows_majority" + if final_answer_value in set(answers): + return "follows_minority" + return "novel_answer" + + +def _num_correct_agents(round0_outputs: list[dict[str, Any]], label: str) -> int: + return sum(1 for answer in _answers(round0_outputs) if answer == label) + + +def _round0_oracle_correct(round0_outputs: list[dict[str, Any]], label: str) -> bool: + return label in set(_answers(round0_outputs)) + + +def _confidence_answers(round0_outputs: list[dict[str, Any]]) -> list[tuple[float, str]]: + result: list[tuple[float, str]] = [] + for output in round0_outputs: + answer = output.get("answer") + if answer and output.get("answer_valid", True) is not False: + confidence = output.get("confidence") + if confidence is not None: + result.append((float(confidence), str(answer))) + return result + + +def _records_index(records: list[dict[str, Any]], labels: dict[str, str]) -> dict[tuple[str, str, int], dict[str, Any]]: + indexed: dict[tuple[str, str, int], dict[str, Any]] = {} + for record in records: + case_id = str(record.get("case_id", "")) + protocol = str(record.get("protocol_name", "")) + seed = int(record.get("seed", 0)) + label = labels.get(case_id) + if not case_id or not protocol or label is None: + continue + indexed[(case_id, protocol, seed)] = {**record, "_label": label} + return indexed + + +def _records_for_protocol(indexed: dict[tuple[str, str, int], dict[str, Any]], protocol: str) -> list[dict[str, Any]]: + return [record for (_, p, _), record in indexed.items() if p == protocol] + + +def _is_majority_wrong_minority_correct(record: dict[str, Any]) -> bool: + answers = _answers(record.get("round0_outputs", [])) + if len(set(answers)) <= 1: + return False + majority = _majority_answer(answers) + if majority is None: + return False + label = record.get("_label", "") + return majority != label and label in set(answers) + + +def _confidence_weighted_answer(round0_outputs: list[dict[str, Any]]) -> str | None: + pairs = _confidence_answers(round0_outputs) + if not pairs: + return None + sums: dict[str, float] = defaultdict(float) + for conf, answer in pairs: + sums[answer] += conf + return max(sums, key=sums.get) + + +# ── Section 1: Round0 correct-agent-count decomposition ────────────── + + +def compute_correct_agent_count_decomposition( + indexed: dict[tuple[str, str, int], dict[str, Any]], +) -> list[dict[str, Any]]: + rows: list[dict[str, Any]] = [] + for protocol in PROTOCOL_ORDER: + buckets: dict[int, list[bool]] = defaultdict(list) + for record in _records_for_protocol(indexed, protocol): + n_correct = _num_correct_agents(record.get("round0_outputs", []), record["_label"]) + buckets[n_correct].append(_final_answer(record) == record["_label"]) + for count in range(6): + values = buckets.get(count, []) + rows.append({ + "protocol": protocol, + "n_correct_agents": count, + "n": len(values), + "final_accuracy": sum(values) / len(values) if values else 0.0, + }) + return rows + + +# ── Section 2: Oracle gap decomposition ──────────────────────────── + + +def compute_oracle_gap_decomposition( + indexed: dict[tuple[str, str, int], dict[str, Any]], +) -> list[dict[str, Any]]: + rows: list[dict[str, Any]] = [] + for protocol in PROTOCOL_ORDER: + records = _records_for_protocol(indexed, protocol) + n = len(records) + if n == 0: + continue + oracle_correct = 0 + final_correct = 0 + no_agent_correct = 0 + majority_correct = 0 + mwmc = 0 + for record in records: + label = record["_label"] + outputs = record.get("round0_outputs", []) + answers = _answers(outputs) + if _round0_oracle_correct(outputs, label): + oracle_correct += 1 + if _final_answer(record) == label: + final_correct += 1 + if _num_correct_agents(outputs, label) == 0: + no_agent_correct += 1 + majority = _majority_answer(answers) + if majority is not None: + if majority == label: + majority_correct += 1 + elif label in set(answers): + mwmc += 1 + rows.append({ + "protocol": protocol, + "n": n, + "round0_oracle_accuracy": oracle_correct / n, + "final_accuracy": final_correct / n, + "oracle_gap": (oracle_correct - final_correct) / n, + "fraction_no_agent_correct": no_agent_correct / n, + "fraction_majority_correct": majority_correct / n, + "fraction_majority_wrong_minority_correct": mwmc / n, + }) + return rows + + +# ── Section 3: Error correlation analysis ────────────────────────── + + +def compute_error_correlation(frame: pd.DataFrame) -> dict[str, Any]: + pivot = frame.pivot_table( + index=["case_id", "seed"], columns="variant", values="accuracy", aggfunc="first" + ) + protocols = [p for p in PROTOCOL_ORDER if p in pivot.columns] + pairs = [ + (protocols[i], protocols[j]) + for i in range(len(protocols)) + for j in range(i + 1, len(protocols)) + ] + pair_results: list[dict[str, Any]] = [] + for left, right in pairs: + pair = pivot[[left, right]].dropna() + if len(pair) == 0: + continue + both_correct = ((pair[left] > 0) & (pair[right] > 0)).sum() + both_wrong = ((pair[left] <= 0) & (pair[right] <= 0)).sum() + left_ok_right_wrong = ((pair[left] > 0) & (pair[right] <= 0)).sum() + left_wrong_right_ok = ((pair[left] <= 0) & (pair[right] > 0)).sum() + n = len(pair) + disagreed = left_ok_right_wrong + left_wrong_right_ok + pair_results.append({ + "pair": f"{left} vs {right}", + "n": int(n), + "both_correct": int(both_correct), + "both_wrong": int(both_wrong), + f"{left}_correct_{right}_wrong": int(left_ok_right_wrong), + f"{left}_wrong_{right}_correct": int(left_wrong_right_ok), + "agreement": float((both_correct + both_wrong) / n), + "error_correlation": float(both_wrong / (both_wrong + disagreed)) if (both_wrong + disagreed) > 0 else 0.0, + "error_correlation_n": int(both_wrong + disagreed), + }) + valid = pivot[[p for p in protocols if p in pivot.columns]].dropna() + n_correct_protocols = (valid > 0).sum(axis=1) + pk = len(protocols) + histogram = [{"n_correct": k, "count": int(v)} for k, v in sorted(Counter(n_correct_protocols).items())] + return { + "pairwise": pair_results, + "all_correct_rate": float((n_correct_protocols == pk).mean()), + "all_wrong_rate": float((n_correct_protocols == 0).mean()), + "n_protocols": pk, + "n_correct_protocols_histogram": histogram, + } + + +# ── Section 4: Judge behavior analysis ──────────────────────────── + + +def compute_judge_behavior( + indexed: dict[tuple[str, str, int], dict[str, Any]], +) -> dict[str, Any]: + results: dict[str, Any] = {} + for protocol in ("open_committee", "anonymous_evidence_board"): + records = _records_for_protocol(indexed, protocol) + full: dict[str, list[bool]] = { + "follows_majority": [], + "follows_minority": [], + "novel_answer": [], + "follows_highest_confidence": [], + } + mwmc_subset: dict[str, list[bool]] = {k: [] for k in full} + for record in records: + label = record["_label"] + outputs = record.get("round0_outputs", []) + answers = _answers(outputs) + final = _final_answer(record) + relation = _majority_relation(final, answers) + is_mwmc = _is_majority_wrong_minority_correct(record) + for key in ("follows_majority", "follows_minority", "novel_answer"): + in_bucket = relation == key + full[key].append(in_bucket) + if is_mwmc: + mwmc_subset[key].append(in_bucket) + cwa = _confidence_weighted_answer(outputs) + follows_conf = bool(final is not None and cwa is not None and final == cwa) + full["follows_highest_confidence"].append(follows_conf) + if is_mwmc: + mwmc_subset["follows_highest_confidence"].append(follows_conf) + results[protocol] = { + "full_dataset": { + k: float(sum(v) / len(v)) if v else 0.0 for k, v in full.items() + }, + "majority_wrong_minority_correct_subset": { + k: float(sum(v) / len(v)) if v else 0.0 for k, v in mwmc_subset.items() + }, + } + return results + + +# ── Section 5: Discussion transition analysis (open_committee) ──── + + +def compute_discussion_transitions( + indexed: dict[tuple[str, str, int], dict[str, Any]], +) -> dict[str, Any]: + records = _records_for_protocol(indexed, "open_committee") + agent_changes: dict[str, int] = defaultdict(int) + correct_increase = 0 + correct_decrease = 0 + increase_final_correct = 0 + decrease_final_correct = 0 + decrease_final_wrong = 0 + for record in records: + label = record["_label"] + round0 = record.get("round0_outputs", []) + revised = record.get("revised_outputs", []) + r0_by_id: dict[str, dict] = {o.get("agent_id", ""): o for o in round0 if o.get("agent_id")} + r1_by_id: dict[str, dict] = {o.get("agent_id", ""): o for o in revised if o.get("agent_id")} + r0_correct = 0 + r1_correct = 0 + for agent_id, before in r0_by_id.items(): + after = r1_by_id.get(agent_id) + if after is None: + continue + bc = before.get("answer") == label + ac = after.get("answer") == label + if bc and ac: + agent_changes["correct_to_correct"] += 1 + elif bc and not ac: + agent_changes["correct_to_wrong"] += 1 + elif not bc and ac: + agent_changes["wrong_to_correct"] += 1 + else: + agent_changes["wrong_to_wrong"] += 1 + if bc: + r0_correct += 1 + if ac: + r1_correct += 1 + if r1_correct > r0_correct: + correct_increase += 1 + if _final_answer(record) == label: + increase_final_correct += 1 + elif r1_correct < r0_correct: + correct_decrease += 1 + if _final_answer(record) == label: + decrease_final_correct += 1 + else: + decrease_final_wrong += 1 + n = len(records) + return { + "n_cases": n, + "agent_transitions": dict(agent_changes), + "n_correct_agents_increase": correct_increase, + "n_correct_agents_decrease": correct_decrease, + "case_rescue_count": increase_final_correct, + "case_damage_count": decrease_final_wrong, + "final_accuracy_when_correct_agents_increase": float(increase_final_correct / correct_increase) if correct_increase else 0.0, + "final_accuracy_when_correct_agents_decrease": float(decrease_final_correct / correct_decrease) if correct_decrease else 0.0, + "final_error_rate_when_correct_agents_decrease": float(decrease_final_wrong / correct_decrease) if correct_decrease else 0.0, + } + + +# ── Section 6: Confidence calibration ────────────────────────────── + + +def compute_confidence_calibration( + indexed: dict[tuple[str, str, int], dict[str, Any]], +) -> dict[str, Any]: + by_protocol: dict[str, Any] = {} + for protocol in PROTOCOL_ORDER: + records = _records_for_protocol(indexed, protocol) + conf_pairs: list[tuple[float, str, str]] = [] # (conf, answer, label) + for record in records: + label = record["_label"] + for conf, answer in _confidence_answers(record.get("round0_outputs", [])): + conf_pairs.append((conf, answer, label)) + correct_confs = [c for c, a, l in conf_pairs if a == l] + wrong_confs = [c for c, a, l in conf_pairs if a != l] + bins = [] + for low, high in _CALIBRATION_BINS: + in_bin = [(c, a == l) for c, a, l in conf_pairs if low <= c < high] + bins.append({ + "bin": f"{low}-{high}", + "n": len(in_bin), + "accuracy": float(sum(1 for _, correct in in_bin if correct) / len(in_bin)) if in_bin else 0.0, + "mean_confidence": float(sum(c for c, _ in in_bin) / len(in_bin)) if in_bin else 0.0, + }) + by_protocol[protocol] = { + "n_confidence_pairs": len(conf_pairs), + "mean_confidence_correct": float(sum(correct_confs) / len(correct_confs)) if correct_confs else 0.0, + "mean_confidence_wrong": float(sum(wrong_confs) / len(wrong_confs)) if wrong_confs else 0.0, + "binned_calibration": bins, + } + + # Majority comparison + majority_comp: list[dict[str, Any]] = [] + for protocol in PROTOCOL_ORDER: + records = _records_for_protocol(indexed, protocol) + simple_ok = 0 + weighted_ok = 0 + n = 0 + for record in records: + label = record["_label"] + outputs = record.get("round0_outputs", []) + answers = _answers(outputs) + simple = _majority_answer(answers) + if simple is not None: + simple_ok += int(simple == label) + cwa = _confidence_weighted_answer(outputs) + if cwa is not None: + weighted_ok += int(cwa == label) + n += 1 + majority_comp.append({ + "protocol": protocol, + "n": n, + "simple_majority_accuracy": float(simple_ok / n) if n else 0.0, + "confidence_weighted_majority_accuracy": float(weighted_ok / n) if n else 0.0, + }) + + # MWMC confidence comparison + mwmc_conf: list[dict[str, Any]] = [] + for protocol in PROTOCOL_ORDER: + records = _records_for_protocol(indexed, protocol) + majority_confs: list[float] = [] + minority_confs: list[float] = [] + for record in records: + if not _is_majority_wrong_minority_correct(record): + continue + label = record["_label"] + majority = _majority_answer(_answers(record.get("round0_outputs", []))) + for conf, answer in _confidence_answers(record.get("round0_outputs", [])): + if answer == majority: + majority_confs.append(conf) + elif answer == label: + minority_confs.append(conf) + mwmc_conf.append({ + "protocol": protocol, + "n_cases": len(records), + "mean_majority_confidence": float(sum(majority_confs) / len(majority_confs)) if majority_confs else 0.0, + "n_majority_agents": len(majority_confs), + "mean_minority_confidence": float(sum(minority_confs) / len(minority_confs)) if minority_confs else 0.0, + "n_minority_agents": len(minority_confs), + }) + + return { + "by_protocol": by_protocol, + "majority_comparison": majority_comp, + "mwmc_confidence_comparison": mwmc_conf, + } + + +# ── Section 7: AEB compression diagnostics ───────────────────────── + + +def compute_aeb_compression( + indexed: dict[tuple[str, str, int], dict[str, Any]], +) -> dict[str, Any]: + aeb_records = _records_for_protocol(indexed, "anonymous_evidence_board") + oc_records = _records_for_protocol(indexed, "open_committee") + oc_by_key: dict[tuple[str, int], dict] = { + (str(r.get("case_id")), int(r.get("seed", 0))): r for r in oc_records + } + + paired: list[dict[str, Any]] = [] + for record in aeb_records: + case_id = str(record.get("case_id", "")) + seed = int(record.get("seed", 0)) + oc = oc_by_key.get((case_id, seed)) + if oc is None: + continue + label = record["_label"] + aeb_correct = _final_answer(record) == label + oc_correct = _final_answer(oc) == label + retention = _metrics_value(record, "evidence_retention_rate") + board = record.get("evidence_board") or {} + n_units = len(board.get("units", [])) if isinstance(board, dict) else 0 + retained_ids = _retained_ids(record) + paired.append({ + "case_id": case_id, + "seed": seed, + "aeb_correct": aeb_correct, + "oc_correct": oc_correct, + "aeb_win": aeb_correct and not oc_correct, + "aeb_loss": not aeb_correct and oc_correct, + "evidence_retention_rate": retention, + "n_evidence_units": n_units, + "n_retained_ids": len(retained_ids), + }) + + n = len(paired) + wins = [p for p in paired if p["aeb_win"]] + losses = [p for p in paired if p["aeb_loss"]] + ties = [p for p in paired if not p["aeb_win"] and not p["aeb_loss"]] + + # Win rate by retention decile + if paired: + retentions = sorted({p["evidence_retention_rate"] for p in paired}) + n_deciles = min(10, len(retentions)) + if n_deciles > 1: + boundaries = [retentions[int(len(retentions) * frac / n_deciles)] for frac in range(1, n_deciles)] + else: + boundaries = [] + decile_results: list[dict[str, Any]] = [] + low = 0.0 + for idx, high in enumerate(boundaries + [1.01]): + in_decile = [p for p in paired if low <= p["evidence_retention_rate"] < high] + d_wins = sum(1 for p in in_decile if p["aeb_win"]) + d_losses = sum(1 for p in in_decile if p["aeb_loss"]) + decile_results.append({ + "decile": idx, + "range": f"{low:.2f}-{high:.2f}", + "n": len(in_decile), + "aeb_win_rate": float(d_wins / (d_wins + d_losses)) if (d_wins + d_losses) > 0 else 0.0, + }) + low = high + else: + decile_results = [] + + # Novel answer subset + novel_correct: list[bool] = [] + for p in paired: + aeb_record = _find_record_by_key(indexed, p["case_id"], "anonymous_evidence_board", p["seed"]) + if aeb_record is None: + continue + answers = _answers(aeb_record.get("round0_outputs", [])) + final = _final_answer(aeb_record) + if final is not None and final not in set(answers): + novel_correct.append(aeb_record["_label"] == final) + + # MWMC + sole correct rescue + rescue_correct: list[bool] = [] + aeb_only = _records_for_protocol(indexed, "anonymous_evidence_board") + for record in aeb_only: + if not _is_majority_wrong_minority_correct(record): + continue + answers = _answers(record.get("round0_outputs", [])) + label = record["_label"] + # Check if the correct-minority answer is the only occurrence of label in answers + if answers.count(label) == 1: + rescue_correct.append(_final_answer(record) == label) + + return { + "n_paired": n, + "n_aeb_wins": len(wins), + "n_aeb_losses": len(losses), + "n_ties": len(ties), + "aeb_win_rate": float(len(wins) / (len(wins) + len(losses))) if (wins or losses) else 0.0, + "mean_retention_wins": float(sum(p["evidence_retention_rate"] for p in wins) / len(wins)) if wins else 0.0, + "mean_retention_losses": float(sum(p["evidence_retention_rate"] for p in losses) / len(losses)) if losses else 0.0, + "mean_units_wins": float(sum(p["n_evidence_units"] for p in wins) / len(wins)) if wins else 0.0, + "mean_units_losses": float(sum(p["n_evidence_units"] for p in losses) / len(losses)) if losses else 0.0, + "mean_retained_ids_wins": float(sum(p["n_retained_ids"] for p in wins) / len(wins)) if wins else 0.0, + "mean_retained_ids_losses": float(sum(p["n_retained_ids"] for p in losses) / len(losses)) if losses else 0.0, + "win_rate_by_retention_decile": decile_results, + "novel_answer_n": len(novel_correct), + "novel_answer_accuracy": float(sum(1 for c in novel_correct if c) / len(novel_correct)) if novel_correct else 0.0, + "mwmc_sole_correct_rescue_n": len(rescue_correct), + "mwmc_sole_correct_rescue_rate": float(sum(1 for c in rescue_correct if c) / len(rescue_correct)) if rescue_correct else 0.0, + } + + +def _metrics_value(record: dict[str, Any], key: str) -> float: + metrics = record.get("metrics") or {} + if isinstance(metrics, str): + try: + metrics = json.loads(metrics) + except (json.JSONDecodeError, TypeError): + return 0.0 + return float(metrics.get(key, 0.0) or 0.0) + + +def _retained_ids(record: dict[str, Any]) -> list[str]: + fd = record.get("final_decision") or {} + ids = fd.get("retained_evidence_ids") or [] + if isinstance(ids, list): + return [str(item) for item in ids] + return [] + + +def _find_record_by_key( + indexed: dict[tuple[str, str, int], dict[str, Any]], + case_id: str, protocol: str, seed: int, +) -> dict[str, Any] | None: + return indexed.get((case_id, protocol, seed)) + + +# ── Section 8: Difficulty strata ────────────────────────────────── + + +def compute_difficulty_strata( + frame: pd.DataFrame, + indexed: dict[tuple[str, str, int], dict[str, Any]], +) -> list[dict[str, Any]]: + pivot = frame.pivot_table( + index=["case_id", "seed"], columns="variant", values="accuracy", aggfunc="first" + ) + protocols = [p for p in PROTOCOL_ORDER if p in pivot.columns] + valid = pivot[[p for p in protocols]].dropna() + global_strata: dict[str, set[tuple[str, int]]] = defaultdict(set) + for (case_id, seed), row in valid.iterrows(): + single_ok = row.get("single_agent", 0) > 0 + mv_ok = row.get("majority_vote", 0) > 0 + oc_ok = row.get("open_committee", 0) > 0 + aeb_ok = row.get("anonymous_evidence_board", 0) > 0 + multi_ok = [mv_ok, oc_ok, aeb_ok] + n_multi_ok = sum(multi_ok) + if single_ok and all(multi_ok): + global_strata["global_all_correct"].add((str(case_id), int(seed))) + if not single_ok and all(not item for item in multi_ok): + global_strata["global_all_wrong"].add((str(case_id), int(seed))) + if single_ok and n_multi_ok == 0: + global_strata["global_single_correct_multi_wrong"].add((str(case_id), int(seed))) + if not single_ok and n_multi_ok >= 2: + global_strata["global_single_wrong_multi_correct"].add((str(case_id), int(seed))) + + protocol_strata: dict[str, dict[str, set[tuple[str, int]]]] = { + protocol: defaultdict(set) for protocol in PROTOCOL_ORDER + } + for protocol in PROTOCOL_ORDER: + for record in _records_for_protocol(indexed, protocol): + case_id = str(record.get("case_id", "")) + seed = int(record.get("seed", 0)) + if (case_id, seed) not in valid.index: + continue + label = record["_label"] + outputs = record.get("round0_outputs", []) + n_correct = _num_correct_agents(outputs, label) + if n_correct == 0: + protocol_strata[protocol]["protocol_no_round0_agent_correct"].add((case_id, seed)) + if n_correct >= 3: + protocol_strata[protocol]["protocol_majority_correct"].add((case_id, seed)) + if _is_majority_wrong_minority_correct(record): + protocol_strata[protocol]["protocol_majority_wrong_minority_correct"].add((case_id, seed)) + + rows: list[dict[str, Any]] = [] + for protocol in PROTOCOL_ORDER: + row: dict[str, Any] = {"protocol": protocol} + for name, case_seeds in sorted(global_strata.items()): + correct = 0 + n = 0 + for case_id, seed in case_seeds: + record = indexed.get((case_id, protocol, seed)) + if record is None: + continue + n += 1 + correct += int(_final_answer(record) == record["_label"]) + row[name] = float(correct / n) if n else 0.0 + row[f"{name}_n"] = n + for name, case_seeds in sorted(protocol_strata[protocol].items()): + correct = 0 + n = 0 + for case_id, seed in case_seeds: + record = indexed.get((case_id, protocol, seed)) + if record is None: + continue + n += 1 + correct += int(_final_answer(record) == record["_label"]) + row[name] = float(correct / n) if n else 0.0 + row[f"{name}_n"] = n + rows.append(row) + return rows + + +# ── Report building and output ───────────────────────────────────── + + +def build_report( + run_dirs: list[Path], + data_path: Path, + output_dir: Path, +) -> dict[str, Any]: + frame = load_multiseed_results(run_dirs) + if "accuracy" not in frame.columns: + frame = expand_json_columns(frame) + raw_records = load_raw_records(run_dirs) + labels = load_ground_truth(data_path) + indexed = _records_index(raw_records, labels) + return { + "metadata": { + "run_dirs": [str(p) for p in run_dirs], + "data_path": str(data_path), + "n_rows": int(len(frame)), + "n_raw_records": len(raw_records), + "n_indexed": len(indexed), + "n_case_ids": int(frame["case_id"].nunique()) if "case_id" in frame.columns else 0, + "seeds": sorted(int(s) for s in frame["seed"].unique()) if "seed" in frame.columns else [], + }, + "round0_correct_agent_count": compute_correct_agent_count_decomposition(indexed), + "oracle_gap_decomposition": compute_oracle_gap_decomposition(indexed), + "error_correlation": compute_error_correlation(frame), + "judge_behavior": compute_judge_behavior(indexed), + "discussion_transitions": compute_discussion_transitions(indexed), + "confidence_calibration": compute_confidence_calibration(indexed), + "aeb_compression": compute_aeb_compression(indexed), + "difficulty_strata": compute_difficulty_strata(frame, indexed), + } + + +def print_summary(report: dict[str, Any]) -> None: + meta = report["metadata"] + print(f"=== Gain Bottleneck Analysis ===") + print(f"Seeds: {meta['seeds']} Rows: {meta['n_rows']} Indexed: {meta['n_indexed']}") + print(f"Case IDs: {meta['n_case_ids']} Run dirs: {len(meta['run_dirs'])}") + + _print_table( + "Section 1: Round0 correct-agent-count decomposition", + ["protocol", "n_correct", "n", "final_accuracy"], + [ + [r["protocol"], str(r["n_correct_agents"]), str(r["n"]), f"{r['final_accuracy']:.4f}"] + for r in report["round0_correct_agent_count"] + ], + ) + + _print_table( + "Section 2: Oracle gap decomposition", + ["protocol", "n", "oracle", "final", "gap", "no_agent", "majority_correct", "mwmc"], + [ + [ + r["protocol"], str(r["n"]), + f"{r['round0_oracle_accuracy']:.4f}", f"{r['final_accuracy']:.4f}", + f"{r['oracle_gap']:.4f}", f"{r['fraction_no_agent_correct']:.4f}", + f"{r['fraction_majority_correct']:.4f}", f"{r['fraction_majority_wrong_minority_correct']:.4f}", + ] + for r in report["oracle_gap_decomposition"] + ], + ) + + _print_table( + "Section 3: Error correlation (pairwise)", + ["pair", "n", "both_correct", "both_wrong", "agreement", "error_corr", "ec_n"], + [ + [ + r["pair"], str(r["n"]), str(r["both_correct"]), str(r["both_wrong"]), + f"{r['agreement']:.4f}", f"{r['error_correlation']:.4f}", str(r["error_correlation_n"]), + ] + for r in report["error_correlation"]["pairwise"] + ], + ) + ec = report["error_correlation"] + print(f" all_correct_rate={ec['all_correct_rate']:.4f} all_wrong_rate={ec['all_wrong_rate']:.4f}") + print(f" n_correct_protocols_histogram: {ec['n_correct_protocols_histogram']}") + + _print_table( + "Section 4: Judge behavior (full dataset)", + ["protocol", "follows_majority", "follows_minority", "novel", "follows_conf"], + [ + [ + protocol, + f"{data['full_dataset']['follows_majority']:.4f}", + f"{data['full_dataset']['follows_minority']:.4f}", + f"{data['full_dataset']['novel_answer']:.4f}", + f"{data['full_dataset']['follows_highest_confidence']:.4f}", + ] + for protocol, data in report["judge_behavior"].items() + ], + ) + _print_table( + "Section 4: Judge behavior (MWMC subset)", + ["protocol", "follows_majority", "follows_minority", "novel", "follows_conf"], + [ + [ + protocol, + f"{data['majority_wrong_minority_correct_subset']['follows_majority']:.4f}", + f"{data['majority_wrong_minority_correct_subset']['follows_minority']:.4f}", + f"{data['majority_wrong_minority_correct_subset']['novel_answer']:.4f}", + f"{data['majority_wrong_minority_correct_subset']['follows_highest_confidence']:.4f}", + ] + for protocol, data in report["judge_behavior"].items() + ], + ) + + dt = report["discussion_transitions"] + print(f"\n=== Section 5: Discussion transitions (open_committee) ===") + print(f" cases={dt['n_cases']} agent_transitions={dt['agent_transitions']}") + print(f" correct_agents_increase={dt['n_correct_agents_increase']} → accuracy={dt['final_accuracy_when_correct_agents_increase']:.4f}") + print( + f" correct_agents_decrease={dt['n_correct_agents_decrease']} → " + f"accuracy={dt['final_accuracy_when_correct_agents_decrease']:.4f} " + f"error_rate={dt['final_error_rate_when_correct_agents_decrease']:.4f}" + ) + print(f" case_rescue={dt['case_rescue_count']} case_damage={dt['case_damage_count']}") + + cc = report["confidence_calibration"] + _print_table( + "Section 6: Confidence calibration", + ["protocol", "n_pairs", "mean_conf_correct", "mean_conf_wrong"], + [ + [ + p, str(d["n_confidence_pairs"]), + f"{d['mean_confidence_correct']:.4f}", f"{d['mean_confidence_wrong']:.4f}", + ] + for p, d in cc["by_protocol"].items() + ], + ) + _print_table( + "Section 6: Majority comparison", + ["protocol", "n", "simple_majority", "confidence_weighted"], + [ + [r["protocol"], str(r["n"]), f"{r['simple_majority_accuracy']:.4f}", f"{r['confidence_weighted_majority_accuracy']:.4f}"] + for r in cc["majority_comparison"] + ], + ) + + aeb = report["aeb_compression"] + _print_table( + "Section 7: AEB compression", + ["metric", "value"], + [ + ["n_paired", str(aeb["n_paired"])], + ["aeb_win_rate", f"{aeb['aeb_win_rate']:.4f}"], + ["mean_retention_wins", f"{aeb['mean_retention_wins']:.4f}"], + ["mean_retention_losses", f"{aeb['mean_retention_losses']:.4f}"], + ["mean_units_wins", f"{aeb['mean_units_wins']:.2f}"], + ["mean_units_losses", f"{aeb['mean_units_losses']:.2f}"], + ["novel_answer_n", str(aeb["novel_answer_n"])], + ["novel_answer_accuracy", f"{aeb['novel_answer_accuracy']:.4f}"], + ["mwmc_sole_rescue_n", str(aeb["mwmc_sole_correct_rescue_n"])], + ["mwmc_sole_rescue_rate", f"{aeb['mwmc_sole_correct_rescue_rate']:.4f}"], + ], + ) + + difficulty_keys = sorted({ + key + for row in report["difficulty_strata"] + for key in row + if key != "protocol" + }) + _print_table( + "Section 8: Difficulty strata", + ["protocol", *difficulty_keys], + [ + [row["protocol"]] + [ + f"{row.get(k, 0.0):.4f}" if isinstance(row.get(k, 0.0), float) else str(row.get(k, 0)) + for k in difficulty_keys + ] + for row in report["difficulty_strata"] + ], + ) + + +def write_json_report(report: dict[str, Any], path: Path) -> None: + path.parent.mkdir(parents=True, exist_ok=True) + path.write_text(json.dumps(report, indent=2, sort_keys=True), encoding="utf-8") + + +def _table(headers: list[str], rows: list[list[Any]]) -> str: + lines = ["| " + " | ".join(headers) + " |", "| " + " | ".join("---" for _ in headers) + " |"] + for row in rows: + lines.append("| " + " | ".join(str(item) for item in row) + " |") + return "\n".join(lines) + + +def write_markdown_report(report: dict[str, Any], path: Path) -> None: + path.parent.mkdir(parents=True, exist_ok=True) + meta = report["metadata"] + sections: list[str] = [ + "# Gain Bottleneck Analysis", + f"Seeds: {meta['seeds']} | Rows: {meta['n_rows']} | Case IDs: {meta['n_case_ids']}", + "## Section 1: Round0 correct-agent-count decomposition", + _table( + ["protocol", "n_correct", "n", "final_accuracy"], + [[r["protocol"], str(r["n_correct_agents"]), str(r["n"]), f"{r['final_accuracy']:.4f}"] + for r in report["round0_correct_agent_count"]], + ), + "## Section 2: Oracle gap decomposition", + _table( + ["protocol", "n", "oracle", "final", "gap", "no_agent", "majority_correct", "mwmc"], + [[r["protocol"], str(r["n"]), f"{r['round0_oracle_accuracy']:.4f}", f"{r['final_accuracy']:.4f}", + f"{r['oracle_gap']:.4f}", f"{r['fraction_no_agent_correct']:.4f}", + f"{r['fraction_majority_correct']:.4f}", f"{r['fraction_majority_wrong_minority_correct']:.4f}"] + for r in report["oracle_gap_decomposition"]], + ), + "## Section 3: Error correlation", + _table( + ["pair", "n", "both_correct", "both_wrong", "agreement", "error_corr"], + [[r["pair"], str(r["n"]), str(r["both_correct"]), str(r["both_wrong"]), + f"{r['agreement']:.4f}", f"{r['error_correlation']:.4f}"] + for r in report["error_correlation"]["pairwise"]], + ), + f"- all_correct_rate = {report['error_correlation']['all_correct_rate']:.4f}", + f"- all_wrong_rate = {report['error_correlation']['all_wrong_rate']:.4f}", + f"- n_correct_protocols histogram: {report['error_correlation']['n_correct_protocols_histogram']}", + "## Section 4: Judge behavior", + "### Full dataset", + _table( + ["protocol", "follows_majority", "follows_minority", "novel", "follows_conf"], + [[protocol, f"{data['full_dataset']['follows_majority']:.4f}", + f"{data['full_dataset']['follows_minority']:.4f}", + f"{data['full_dataset']['novel_answer']:.4f}", + f"{data['full_dataset']['follows_highest_confidence']:.4f}"] + for protocol, data in report["judge_behavior"].items()], + ), + "### MWMC subset", + _table( + ["protocol", "follows_majority", "follows_minority", "novel", "follows_conf"], + [[protocol, f"{data['majority_wrong_minority_correct_subset']['follows_majority']:.4f}", + f"{data['majority_wrong_minority_correct_subset']['follows_minority']:.4f}", + f"{data['majority_wrong_minority_correct_subset']['novel_answer']:.4f}", + f"{data['majority_wrong_minority_correct_subset']['follows_highest_confidence']:.4f}"] + for protocol, data in report["judge_behavior"].items()], + ), + "## Section 5: Discussion transitions (open_committee)", + f"- Agent transitions: {report['discussion_transitions']['agent_transitions']}", + f"- correct_agents_increase → accuracy: {report['discussion_transitions']['final_accuracy_when_correct_agents_increase']:.4f}", + f"- correct_agents_decrease → accuracy: {report['discussion_transitions']['final_accuracy_when_correct_agents_decrease']:.4f}", + f"- correct_agents_decrease → error rate: {report['discussion_transitions']['final_error_rate_when_correct_agents_decrease']:.4f}", + f"- case_rescue: {report['discussion_transitions']['case_rescue_count']}", + f"- case_damage: {report['discussion_transitions']['case_damage_count']}", + "## Section 6: Confidence calibration", + _table( + ["protocol", "n_pairs", "mean_conf_correct", "mean_conf_wrong"], + [[p, str(d["n_confidence_pairs"]), f"{d['mean_confidence_correct']:.4f}", f"{d['mean_confidence_wrong']:.4f}"] + for p, d in report["confidence_calibration"]["by_protocol"].items()], + ), + _table( + ["protocol", "simple_majority", "conf_weighted"], + [[r["protocol"], f"{r['simple_majority_accuracy']:.4f}", f"{r['confidence_weighted_majority_accuracy']:.4f}"] + for r in report["confidence_calibration"]["majority_comparison"]], + ), + "## Section 7: AEB compression", + f"- AEB win rate vs open_committee: {report['aeb_compression']['aeb_win_rate']:.4f}", + f"- Novel answer accuracy: {report['aeb_compression']['novel_answer_accuracy']:.4f}", + f"- MWMC sole-correct rescue rate: {report['aeb_compression']['mwmc_sole_correct_rescue_rate']:.4f}", + "## Section 8: Difficulty strata", + _table( + ["protocol"] + sorted( + k for k in report["difficulty_strata"][0] if k != "protocol" + ), + [[row["protocol"]] + [ + f"{row[k]:.4f}" if isinstance(row[k], float) else str(row[k]) + for k in sorted(k for k in row if k != "protocol")] + for row in report["difficulty_strata"]], + ), + ] + path.write_text("\n\n".join(sections) + "\n", encoding="utf-8") + + +def main(argv: list[str] | None = None) -> int: + parser = argparse.ArgumentParser(description="Analyze multi-agent gain bottlenecks.") + parser.add_argument("--run-dirs", nargs="+", type=Path, required=True) + parser.add_argument("--data-path", type=Path, default=Path("data/medxpertqa_text_test.jsonl")) + parser.add_argument("--output-dir", type=Path, default=Path("runs/gain_bottleneck_analysis")) + args = parser.parse_args(argv) + try: + report = build_report(args.run_dirs, args.data_path, args.output_dir) + except AnalysisError as exc: + print(f"ERROR: {exc}", file=sys.stderr) + return 1 + print_summary(report) + json_path = args.output_dir / "gain_bottleneck_report.json" + markdown_path = Path("reports") / "gain_bottleneck_analysis.md" + write_json_report(report, json_path) + write_markdown_report(report, markdown_path) + print(f"\nJSON report: {json_path}") + print(f"Markdown report: {markdown_path}") + return 0 + + +if __name__ == "__main__": + sys.exit(main()) diff --git a/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_board_analysis/scripts/analyze_multiseed.py b/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_board_analysis/scripts/analyze_multiseed.py new file mode 100644 index 0000000000000000000000000000000000000000..b03a1f6d34611460c94cb2987a6b70ab3d7c993d --- /dev/null +++ b/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_board_analysis/scripts/analyze_multiseed.py @@ -0,0 +1,506 @@ +"""Analyze multiple MedBoard real LLM run directories.""" + +from __future__ import annotations + +import argparse +import json +import math +import sys +from collections import Counter +from pathlib import Path +from typing import Any + +import numpy as np +import pandas as pd +from scipy.stats import binomtest + +from medboard.adapters.medxpertqa import load_medxpertqa_jsonl +from medboard.storage.jsonl_logger import read_jsonl + +PROTOCOL_ORDER = [ + "single_agent", + "majority_vote", + "open_committee", + "anonymous_evidence_board", +] +COMPARISONS = [ + ("majority_vote", "single_agent"), + ("open_committee", "single_agent"), + ("anonymous_evidence_board", "single_agent"), + ("anonymous_evidence_board", "open_committee"), + ("open_committee", "majority_vote"), +] + + +class AnalysisError(RuntimeError): + pass + + +def _json_dict(value: Any) -> dict[str, Any]: + if isinstance(value, dict): + return value + if isinstance(value, str) and value: + return json.loads(value) + return {} + + +def expand_json_columns(frame: pd.DataFrame) -> pd.DataFrame: + expanded = frame.copy() + metrics = expanded["metrics"].map(_json_dict) if "metrics" in expanded else pd.Series([{}] * len(expanded)) + token_usage = expanded["token_usage"].map(_json_dict) if "token_usage" in expanded else pd.Series([{}] * len(expanded)) + for key in sorted({key for item in metrics for key in item}): + expanded[key] = metrics.map(lambda item, key=key: item.get(key)) + for key in sorted({key for item in token_usage for key in item}): + expanded[key] = token_usage.map(lambda item, key=key: item.get(key)) + if "variant" not in expanded and "protocol_name" in expanded: + expanded["variant"] = expanded["protocol_name"] + if "protocol_name" not in expanded and "variant" in expanded: + expanded["protocol_name"] = expanded["variant"] + if "accuracy" not in expanded: + expanded["accuracy"] = 0.0 + expanded["accuracy"] = pd.to_numeric(expanded["accuracy"], errors="coerce").fillna(0.0) + for key in [ + "n_calls", + "prompt_tokens", + "completion_tokens", + "total_tokens", + "latency_total", + "retry_count_total", + "retry_success_count", + "retry_failure_count", + "invalid_agent_outputs_count", + "invalid_final_decision_count", + ]: + if key not in expanded: + expanded[key] = 0 + expanded[key] = pd.to_numeric(expanded[key], errors="coerce").fillna(0.0) + return expanded + + +def load_multiseed_results(run_dirs: list[Path]) -> pd.DataFrame: + frames = [] + for run_dir in run_dirs: + aggregated = run_dir / "aggregated_results.parquet" + if not aggregated.exists(): + raise AnalysisError(f"Missing aggregated results: {aggregated}") + frame = pd.read_parquet(aggregated) + if frame.empty: + raise AnalysisError(f"Aggregated results are empty: {aggregated}") + frame = expand_json_columns(frame) + frame["run_dir"] = str(run_dir) + frames.append(frame) + combined = pd.concat(frames, ignore_index=True) + if combined.empty: + raise AnalysisError("No rows loaded from run dirs") + return combined + + +def load_ground_truth(data_path: Path) -> dict[str, str]: + if not data_path.exists(): + raise AnalysisError(f"Missing data path: {data_path}") + return {case.case_id: case.correct_label for case in load_medxpertqa_jsonl(data_path)} + + +def load_raw_records(run_dirs: list[Path]) -> list[dict[str, Any]]: + records: list[dict[str, Any]] = [] + for run_dir in run_dirs: + if not run_dir.exists(): + raise AnalysisError(f"Missing run dir: {run_dir}") + for protocol_dir in sorted(path for path in run_dir.iterdir() if path.is_dir()): + raw_path = protocol_dir / "raw" / "cases.jsonl" + if not raw_path.exists(): + continue + for record in read_jsonl(raw_path): + record.setdefault("protocol_name", protocol_dir.name) + records.append(record) + return records + + +def seed_wise_accuracy(frame: pd.DataFrame) -> list[dict[str, Any]]: + rows = [] + grouped = frame.groupby(["seed", "variant"], sort=True) + for (seed, protocol), group in grouped: + rows.append( + { + "seed": int(seed), + "protocol": str(protocol), + "n": int(len(group)), + "accuracy": float(group["accuracy"].mean()), + } + ) + return sorted(rows, key=lambda row: (row["seed"], _protocol_sort_key(row["protocol"]))) + + +def accuracy_mean_sd(frame: pd.DataFrame) -> list[dict[str, Any]]: + seed_rows = pd.DataFrame(seed_wise_accuracy(frame)) + rows = [] + for protocol, group in seed_rows.groupby("protocol", sort=False): + values = group["accuracy"].astype(float) + rows.append( + { + "protocol": protocol, + "mean_accuracy": float(values.mean()), + "sd_accuracy": float(values.std(ddof=1)) if len(values) > 1 else 0.0, + "n_seeds": int(len(values)), + } + ) + return sorted(rows, key=lambda row: _protocol_sort_key(row["protocol"])) + + +def _paired_pivot(frame: pd.DataFrame, left: str, right: str) -> pd.DataFrame: + subset = frame[frame["variant"].isin({left, right})] + pivot = subset.pivot_table(index=["case_id", "seed"], columns="variant", values="accuracy", aggfunc="first") + return pivot.dropna(subset=[left, right]) + + +def _mcnemar_exact_p(wins: int, losses: int) -> float: + discordant = wins + losses + if discordant == 0: + return 1.0 + return float(binomtest(min(wins, losses), discordant, 0.5, alternative="two-sided").pvalue) + + +def _sign_test_p(wins: int, losses: int) -> float: + discordant = wins + losses + if discordant == 0: + return 1.0 + return float(binomtest(min(wins, losses), discordant, 0.5, alternative="two-sided").pvalue) + + +def pooled_paired_comparison(frame: pd.DataFrame, left: str, right: str) -> dict[str, Any]: + pivot = _paired_pivot(frame, left, right) + left_correct = pivot[left] > 0 + right_correct = pivot[right] > 0 + wins = int((left_correct & ~right_correct).sum()) + losses = int((~left_correct & right_correct).sum()) + ties = int((left_correct == right_correct).sum()) + n = int(len(pivot)) + left_accuracy = float(left_correct.mean()) if n else 0.0 + right_accuracy = float(right_correct.mean()) if n else 0.0 + return { + "comparison": f"{left} vs {right}", + "left": left, + "right": right, + "n_paired": n, + "left_accuracy": left_accuracy, + "right_accuracy": right_accuracy, + "accuracy_diff": left_accuracy - right_accuracy, + "wins": wins, + "losses": losses, + "ties": ties, + "mcnemar_p": _mcnemar_exact_p(wins, losses), + "sign_test_p": _sign_test_p(wins, losses), + } + + +def pooled_paired_comparisons(frame: pd.DataFrame) -> list[dict[str, Any]]: + return [pooled_paired_comparison(frame, left, right) for left, right in COMPARISONS] + + +def cluster_bootstrap_difference(frame: pd.DataFrame, left: str, right: str, n_bootstrap: int, seed: int) -> dict[str, Any]: + pivot = _paired_pivot(frame, left, right) + if pivot.empty: + return { + "comparison": f"{left} vs {right}", + "observed_diff": 0.0, + "ci_low": 0.0, + "ci_high": 0.0, + "n_clusters": 0, + "n_paired": 0, + } + pivot = pivot.reset_index() + pivot["diff"] = pivot[left].astype(float) - pivot[right].astype(float) + case_diffs = pivot.groupby("case_id")["diff"].apply(lambda values: values.to_numpy(dtype=float)) + case_ids = np.asarray(case_diffs.index.to_list(), dtype=object) + arrays = list(case_diffs) + observed = float(pivot["diff"].mean()) + if n_bootstrap <= 0 or len(case_ids) <= 1: + return { + "comparison": f"{left} vs {right}", + "observed_diff": observed, + "ci_low": observed, + "ci_high": observed, + "n_clusters": int(len(case_ids)), + "n_paired": int(len(pivot)), + } + rng = np.random.default_rng(seed) + values = np.empty(n_bootstrap, dtype=float) + for index in range(n_bootstrap): + selected = rng.integers(0, len(arrays), size=len(arrays)) + sample = np.concatenate([arrays[item] for item in selected]) + values[index] = sample.mean() + return { + "comparison": f"{left} vs {right}", + "observed_diff": observed, + "ci_low": float(np.percentile(values, 2.5)), + "ci_high": float(np.percentile(values, 97.5)), + "n_clusters": int(len(case_ids)), + "n_paired": int(len(pivot)), + } + + +def cluster_bootstrap_differences(frame: pd.DataFrame, n_bootstrap: int, seed: int) -> list[dict[str, Any]]: + rows = [] + for index, (left, right) in enumerate(COMPARISONS): + rows.append(cluster_bootstrap_difference(frame, left, right, n_bootstrap=n_bootstrap, seed=seed + index)) + return rows + + +def cost_normalized_gain(frame: pd.DataFrame, baseline: str = "single_agent") -> list[dict[str, Any]]: + grouped = frame.groupby("variant", sort=False) + if baseline not in grouped.groups: + raise AnalysisError(f"Missing baseline protocol: {baseline}") + base = grouped.get_group(baseline) + base_accuracy = float(base["accuracy"].mean()) + base_correct = float(base["accuracy"].sum()) + base_tokens = float(base["total_tokens"].sum()) + base_calls = float(base["n_calls"].sum()) + rows = [] + for protocol, group in grouped: + accuracy = float(group["accuracy"].mean()) + correct = float(group["accuracy"].sum()) + tokens = float(group["total_tokens"].sum()) + calls = float(group["n_calls"].sum()) + extra_tokens = tokens - base_tokens + extra_correct = correct - base_correct + rows.append( + { + "protocol": protocol, + "accuracy": accuracy, + "accuracy_gain_vs_single": accuracy - base_accuracy, + "correct": correct, + "extra_correct_vs_single": extra_correct, + "n_calls": int(calls), + "total_tokens": int(tokens), + "token_ratio_vs_single": tokens / base_tokens if base_tokens else math.nan, + "call_ratio_vs_single": calls / base_calls if base_calls else math.nan, + "extra_tokens_vs_single": int(extra_tokens), + "extra_tokens_per_extra_correct": extra_tokens / extra_correct if extra_tokens > 0 and extra_correct > 0 else math.nan, + "accuracy_gain_per_extra_million_tokens": (accuracy - base_accuracy) / (extra_tokens / 1_000_000) if extra_tokens > 0 else math.nan, + } + ) + return sorted(rows, key=lambda row: _protocol_sort_key(row["protocol"])) + + +def _answers(outputs: list[dict[str, Any]]) -> list[str]: + return [str(output.get("answer")) for output in outputs if output.get("answer") and output.get("answer_valid", True) is not False] + + +def _majority_answer(answers: list[str]) -> str | None: + if not answers: + return None + counts = Counter(answers) + max_count = max(counts.values()) + winners = {answer for answer, count in counts.items() if count == max_count} + for answer in answers: + if answer in winners: + return answer + return sorted(winners)[0] + + +def _final_answer(record: dict[str, Any]) -> str | None: + answer = record.get("final_decision", {}).get("answer") + return str(answer) if answer is not None else None + + +def minority_rescue_subset(records: list[dict[str, Any]], correct_label: dict[str, str]) -> list[dict[str, Any]]: + buckets: dict[str, dict[str, list[bool]]] = {} + for record in records: + protocol = str(record.get("protocol_name") or "") + case_id = str(record.get("case_id") or "") + label = correct_label.get(case_id) + if not protocol or label is None: + continue + if protocol not in buckets: + buckets[protocol] = { + "all_agents_agree": [], + "round0_disagreement": [], + "majority_wrong_minority_correct": [], + } + answers = _answers(record.get("round0_outputs", [])) + final_correct = _final_answer(record) == label + if len(set(answers)) <= 1: + buckets[protocol]["all_agents_agree"].append(final_correct) + continue + buckets[protocol]["round0_disagreement"].append(final_correct) + majority = _majority_answer(answers) + minority = [answer for answer in answers if answer != majority] + if majority != label and label in minority: + buckets[protocol]["majority_wrong_minority_correct"].append(final_correct) + rows = [] + for protocol, protocol_buckets in buckets.items(): + row: dict[str, Any] = {"protocol": protocol} + for name, values in protocol_buckets.items(): + row[f"{name}_n"] = len(values) + row[f"{name}_accuracy"] = float(sum(values) / len(values)) if values else 0.0 + rows.append(row) + return sorted(rows, key=lambda row: _protocol_sort_key(row["protocol"])) + + +def retry_invalid_summary(frame: pd.DataFrame) -> list[dict[str, Any]]: + rows = [] + for protocol, group in frame.groupby("variant", sort=False): + rows.append( + { + "protocol": protocol, + "invalid_agent_outputs_count": int(group["invalid_agent_outputs_count"].sum()), + "invalid_final_decision_count": int(group["invalid_final_decision_count"].sum()), + "retry_count_total": int(group["retry_count_total"].sum()), + "retry_success_count": int(group["retry_success_count"].sum()), + "retry_failure_count": int(group["retry_failure_count"].sum()), + } + ) + return sorted(rows, key=lambda row: _protocol_sort_key(row["protocol"])) + + +def _protocol_sort_key(protocol: str) -> tuple[int, str]: + try: + return (PROTOCOL_ORDER.index(protocol), protocol) + except ValueError: + return (len(PROTOCOL_ORDER), protocol) + + +def _print_table(title: str, headers: list[str], rows: list[list[str]]) -> None: + print(f"\n=== {title} ===") + widths = [len(header) for header in headers] + for row in rows: + for index, value in enumerate(row): + widths[index] = max(widths[index], len(value)) + print(" ".join(header.ljust(widths[index]) for index, header in enumerate(headers))) + print(" ".join("-" * width for width in widths)) + for row in rows: + print(" ".join(value.ljust(widths[index]) for index, value in enumerate(row))) + + +def print_report(report: dict[str, Any]) -> None: + _print_table( + "Seed-wise accuracy", + ["seed", "protocol", "n", "accuracy"], + [[str(row["seed"]), row["protocol"], str(row["n"]), f"{row['accuracy']:.4f}"] for row in report["seed_wise_accuracy"]], + ) + _print_table( + "Accuracy mean ± SD", + ["protocol", "mean", "sd", "n_seeds"], + [[row["protocol"], f"{row['mean_accuracy']:.4f}", f"{row['sd_accuracy']:.4f}", str(row["n_seeds"])] for row in report["accuracy_mean_sd"]], + ) + _print_table( + "Pooled paired comparison with McNemar/sign test", + ["comparison", "n", "diff", "wins", "losses", "ties", "mcnemar_p", "sign_p"], + [ + [ + row["comparison"], + str(row["n_paired"]), + f"{row['accuracy_diff']:.4f}", + str(row["wins"]), + str(row["losses"]), + str(row["ties"]), + f"{row['mcnemar_p']:.6f}", + f"{row['sign_test_p']:.6f}", + ] + for row in report["pooled_paired_comparison"] + ], + ) + _print_table( + "Cluster bootstrap CI by case_id", + ["comparison", "diff", "ci_low", "ci_high", "clusters"], + [ + [row["comparison"], f"{row['observed_diff']:.4f}", f"{row['ci_low']:.4f}", f"{row['ci_high']:.4f}", str(row["n_clusters"])] + for row in report["cluster_bootstrap_ci"] + ], + ) + _print_table( + "Cost-normalized gain", + ["protocol", "accuracy", "gain", "tokens", "ratio", "extra_correct", "tokens/extra_correct"], + [ + [ + row["protocol"], + f"{row['accuracy']:.4f}", + f"{row['accuracy_gain_vs_single']:.4f}", + str(row["total_tokens"]), + f"{row['token_ratio_vs_single']:.2f}", + f"{row['extra_correct_vs_single']:.1f}", + "n/a" if math.isnan(row["extra_tokens_per_extra_correct"]) else f"{row['extra_tokens_per_extra_correct']:.1f}", + ] + for row in report["cost_normalized_gain"] + ], + ) + _print_table( + "Minority rescue subset analysis", + ["protocol", "disagree_n", "disagree_acc", "minority_n", "minority_acc", "agree_n", "agree_acc"], + [ + [ + row["protocol"], + str(row["round0_disagreement_n"]), + f"{row['round0_disagreement_accuracy']:.4f}", + str(row["majority_wrong_minority_correct_n"]), + f"{row['majority_wrong_minority_correct_accuracy']:.4f}", + str(row["all_agents_agree_n"]), + f"{row['all_agents_agree_accuracy']:.4f}", + ] + for row in report["minority_rescue_subset"] + ], + ) + _print_table( + "Retry and invalid summary", + ["protocol", "invalid_agent", "invalid_final", "retries", "retry_ok", "retry_fail"], + [ + [ + row["protocol"], + str(row["invalid_agent_outputs_count"]), + str(row["invalid_final_decision_count"]), + str(row["retry_count_total"]), + str(row["retry_success_count"]), + str(row["retry_failure_count"]), + ] + for row in report["retry_invalid_summary"] + ], + ) + + +def build_report(run_dirs: list[Path], data_path: Path, n_bootstrap: int, seed: int) -> dict[str, Any]: + frame = load_multiseed_results(run_dirs) + raw_records = load_raw_records(run_dirs) + correct_label = load_ground_truth(data_path) + return { + "metadata": { + "run_dirs": [str(path) for path in run_dirs], + "data_path": str(data_path), + "n_rows": int(len(frame)), + "n_case_ids": int(frame["case_id"].nunique()), + "seeds": sorted(int(seed_value) for seed_value in frame["seed"].unique()), + "n_bootstrap": n_bootstrap, + "bootstrap_seed": seed, + }, + "seed_wise_accuracy": seed_wise_accuracy(frame), + "accuracy_mean_sd": accuracy_mean_sd(frame), + "pooled_paired_comparison": pooled_paired_comparisons(frame), + "cluster_bootstrap_ci": cluster_bootstrap_differences(frame, n_bootstrap=n_bootstrap, seed=seed), + "cost_normalized_gain": cost_normalized_gain(frame), + "minority_rescue_subset": minority_rescue_subset(raw_records, correct_label), + "retry_invalid_summary": retry_invalid_summary(frame), + } + + +def main(argv: list[str] | None = None) -> int: + parser = argparse.ArgumentParser(description="Analyze multiple MedBoard real LLM seeds.") + parser.add_argument("--run-dirs", nargs="+", type=Path, required=True, help="Run directories containing aggregated_results.parquet.") + parser.add_argument("--data-path", type=Path, default=Path("data/medxpertqa_text_test.jsonl"), help="Dataset JSONL for ground-truth labels.") + parser.add_argument("--n-bootstrap", type=int, default=10000, help="Cluster bootstrap resamples for paired protocol differences.") + parser.add_argument("--seed", type=int, default=42, help="Random seed for bootstrap.") + parser.add_argument("--json-out", type=Path, help="Optional path for a JSON report.") + args = parser.parse_args(argv) + try: + report = build_report(args.run_dirs, args.data_path, args.n_bootstrap, args.seed) + except AnalysisError as exc: + print(f"ERROR: {exc}", file=sys.stderr) + return 1 + print_report(report) + if args.json_out: + args.json_out.parent.mkdir(parents=True, exist_ok=True) + args.json_out.write_text(json.dumps(report, indent=2, sort_keys=True), encoding="utf-8") + print(f"\nJSON report: {args.json_out}") + return 0 + + +if __name__ == "__main__": + sys.exit(main()) diff --git a/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_board_analysis/tests/test_analyze_gain_bottlenecks.py b/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_board_analysis/tests/test_analyze_gain_bottlenecks.py new file mode 100644 index 0000000000000000000000000000000000000000..fca2a2930a35813a6a542295f94dafa81aa5a445 --- /dev/null +++ b/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_board_analysis/tests/test_analyze_gain_bottlenecks.py @@ -0,0 +1,309 @@ +"""Tests for analyze_gain_bottlenecks.py.""" + +from __future__ import annotations + +import json +from pathlib import Path + +import pandas as pd + +from scripts import analyze_gain_bottlenecks as agb +from scripts.analyze_gain_bottlenecks import _records_index + + +def _agent(answer: str, index: int, *, confidence: float = 0.95, answer_valid: bool = True) -> dict: + return { + "agent_id": f"agent_{index}", + "round_id": 0, + "answer": answer, + "confidence": confidence, + "answer_valid": answer_valid, + } + + +def _revised_agent(answer: str, index: int, *, answer_valid: bool = True) -> dict: + return { + "agent_id": f"agent_{index}", + "round_id": 1, + "answer": answer, + "confidence": 0.95, + "answer_valid": answer_valid, + } + + +def _raw_case( + case_id: str, + protocol: str, + seed: int, + round0_answers: list[str], + final_answer: str, + *, + round0_confs: list[float] | None = None, + revised_answers: list[str] | None = None, + metrics: dict | None = None, +) -> dict: + agents = [_agent(answer, i, confidence=round0_confs[i] if round0_confs else 0.95) for i, answer in enumerate(round0_answers)] + revised = [] + if revised_answers: + revised = [_revised_agent(answer, i) for i, answer in enumerate(revised_answers)] + return { + "case_id": case_id, + "protocol_name": protocol, + "seed": seed, + "round0_outputs": agents, + "revised_outputs": revised, + "final_decision": {"answer": final_answer, "answer_valid": True}, + "metrics": metrics or {}, + "token_usage": {}, + } + + +def _agg_row( + case_id: str, variant: str, seed: int, accuracy: float, + *, total_tokens: int = 100 +) -> dict: + return { + "variant": variant, + "case_id": case_id, + "protocol_name": variant, + "seed": seed, + "answer": "A" if accuracy else "B", + "confidence": 0.8, + "metrics": json.dumps({ + "accuracy": accuracy, + "invalid_agent_outputs_count": 0, + "invalid_final_decision_count": 0, + "evidence_retention_rate": 0.5, + }), + "token_usage": json.dumps({ + "n_calls": 1, + "prompt_tokens": 50, + "completion_tokens": 50, + "total_tokens": total_tokens, + "latency_total": 1.0, + }), + "raw_log_path": None, + } + + +def _index_from(records: list[dict], labels: dict[str, str]) -> dict: + return _records_index(records, labels) + + +# ── Tests ──────────────────────────────────────────────────────────── + + +class TestHelpers: + def test_majority_relation(self): + assert agb._majority_relation("A", ["A", "A", "B"]) == "follows_majority" + assert agb._majority_relation("B", ["A", "A", "B"]) == "follows_minority" + assert agb._majority_relation("C", ["A", "A", "B"]) == "novel_answer" + assert agb._majority_relation(None, ["A", "A", "B"]) == "missing_final_answer" + + def test_num_correct_agents(self): + outputs = [_agent("A", 0), _agent("B", 1), _agent("A", 2)] + assert agb._num_correct_agents(outputs, "A") == 2 + assert agb._num_correct_agents(outputs, "C") == 0 + + def test_round0_oracle_correct(self): + outputs = [_agent("A", 0), _agent("B", 1)] + assert agb._round0_oracle_correct(outputs, "A") is True + assert agb._round0_oracle_correct(outputs, "C") is False + + def test_confidence_answers(self): + outputs = [ + _agent("A", 0, confidence=0.9), + _agent("B", 1, confidence=0.8), + ] + assert agb._confidence_answers(outputs) == [(0.9, "A"), (0.8, "B")] + + def test_confidence_answers_skips_invalid(self): + outputs = [ + _agent("A", 0, answer_valid=False), + ] + assert agb._confidence_answers(outputs) == [] + + def test_is_majority_wrong_minority_correct(self): + record = _raw_case("c1", "test", 1, ["B", "B", "A", "A", "C"], "A") + record["_label"] = "A" + assert agb._is_majority_wrong_minority_correct(record) is True + + record2 = _raw_case("c2", "test", 1, ["A", "A", "A", "B", "B"], "A") + record2["_label"] = "A" + assert agb._is_majority_wrong_minority_correct(record2) is False + + def test_confidence_weighted_answer(self): + outputs = [ + _agent("A", 0, confidence=0.5), + _agent("B", 1, confidence=0.9), + _agent("B", 2, confidence=0.5), + ] + assert agb._confidence_weighted_answer(outputs) == "B" + + def test_confidence_weighted_answer_empty(self): + assert agb._confidence_weighted_answer([]) is None + + +class TestSection1: + def test_correct_agent_count_decomposition(self): + """Verify bucket counts for 3/5-correct and 1/5-correct cases.""" + records = [ + _raw_case("c1", "open_committee", 1, ["A", "A", "A", "B", "B"], "A"), # 3 correct, final correct + _raw_case("c2", "open_committee", 1, ["A", "B", "B", "C", "C"], "B"), # 1 correct, final wrong + ] + labels = {"c1": "A", "c2": "A"} + indexed = _index_from(records, labels) + + rows = agb.compute_correct_agent_count_decomposition(indexed) + oc_rows = [r for r in rows if r["protocol"] == "open_committee"] + + bucket_3 = next(r for r in oc_rows if r["n_correct_agents"] == 3) + assert bucket_3["n"] == 1 + assert bucket_3["final_accuracy"] == 1.0 + + bucket_1 = next(r for r in oc_rows if r["n_correct_agents"] == 1) + assert bucket_1["n"] == 1 + assert bucket_1["final_accuracy"] == 0.0 + + for r in oc_rows: + if r["n_correct_agents"] not in (1, 3): + assert r["n"] == 0 + + +class TestSection2: + def test_oracle_gap_decomposition(self): + """Case where oracle=1.0 but final=0.0 → gap should be 1.0.""" + records = [ + _raw_case("c1", "single_agent", 1, ["A", "B", "C", "D", "E"], "F"), # oracle=1, final wrong + _raw_case("c2", "single_agent", 1, ["A", "B", "C", "D", "E"], "A"), # oracle=1, final correct + ] + labels = {"c1": "A", "c2": "A"} + indexed = _index_from(records, labels) + + rows = agb.compute_oracle_gap_decomposition(indexed) + sa = next(r for r in rows if r["protocol"] == "single_agent") + + assert sa["round0_oracle_accuracy"] == 1.0 + assert sa["final_accuracy"] == 0.5 + assert sa["oracle_gap"] == 0.5 + + +class TestSection3: + def test_error_correlation(self): + """Simple pairwise test: 2 cases, 2 protocols.""" + frame = pd.DataFrame([ + _agg_row("c1", "single_agent", 1, 1.0), + _agg_row("c1", "majority_vote", 1, 0.0), + _agg_row("c2", "single_agent", 1, 0.0), + _agg_row("c2", "majority_vote", 1, 0.0), + ]) + frame = agb.expand_json_columns(frame) + + result = agb.compute_error_correlation(frame) + assert result["pairwise"][0]["both_correct"] == 0 + assert result["pairwise"][0]["both_wrong"] == 1 + + +class TestSection4: + def test_judge_follows_majority_vs_minority(self): + """Verify both follows_majority and follows_minority classification.""" + records = [ + # follows_majority: 3 agents say A, final says A + _raw_case("c1", "open_committee", 1, ["A", "A", "A", "B", "B"], "A"), + # follows_minority: 3 agents say B, 2 say A, final says A + _raw_case("c2", "open_committee", 1, ["B", "B", "B", "A", "A"], "A"), + ] + labels = {"c1": "A", "c2": "A"} + indexed = _index_from(records, labels) + + result = agb.compute_judge_behavior(indexed) + oc = result["open_committee"]["full_dataset"] + assert oc["follows_majority"] == 0.5 + assert oc["follows_minority"] == 0.5 + + +class TestSection5: + def test_discussion_transition_counts(self): + """Verify agent transition counts: correct_to_correct, correct_to_wrong, etc.""" + records = [ + _raw_case( + "c1", "open_committee", 1, + ["A", "A", "B"], # round0: two correct agents + "B", + revised_answers=["B", "B", "B"], # revised: zero correct agents + ), + ] + labels = {"c1": "A"} + indexed = _index_from(records, labels) + + result = agb.compute_discussion_transitions(indexed) + trans = result["agent_transitions"] + assert trans.get("correct_to_correct", 0) >= 0 + assert trans.get("correct_to_wrong", 0) >= 0 + assert trans.get("wrong_to_correct", 0) >= 0 + assert trans.get("wrong_to_wrong", 0) >= 0 + assert result["final_accuracy_when_correct_agents_decrease"] == 0.0 + assert result["final_error_rate_when_correct_agents_decrease"] == 1.0 + total = sum(trans.values()) + assert total == 3 # 3 agents with both round0 and revised + + +class TestSection6: + def test_confidence_weighted_differs_from_simple(self): + """Weighted majority should pick B when simple picks A, given confidence distribution.""" + records = [ + _raw_case( + "c1", "majority_vote", 1, + ["A", "A", "A", "B", "B"], # simple majority = A + "A", + round0_confs=[0.5, 0.5, 0.5, 0.95, 0.95], # weighted sums: A=1.5, B=1.9 + ), + ] + labels = {"c1": "A"} + indexed = _index_from(records, labels) + + result = agb.compute_confidence_calibration(indexed) + mv = next(r for r in result["majority_comparison"] if r["protocol"] == "majority_vote") + assert mv["simple_majority_accuracy"] == 1.0 # simple majority picks A which is correct + assert mv["confidence_weighted_majority_accuracy"] == 0.0 # weighted picks B which is wrong + + +class TestSection8: + def test_difficulty_strata_distinguishes_global_and_protocol_specific_keys(self): + records = [ + _raw_case("c1", "single_agent", 1, ["A"], "A"), + _raw_case("c1", "majority_vote", 1, ["B", "B", "B", "A", "A"], "B"), + _raw_case("c1", "open_committee", 1, ["B", "B", "B", "A", "A"], "B"), + _raw_case("c1", "anonymous_evidence_board", 1, ["A", "A", "A", "B", "B"], "A"), + ] + labels = {"c1": "A"} + indexed = _index_from(records, labels) + frame = pd.DataFrame([ + _agg_row("c1", "single_agent", 1, 1.0), + _agg_row("c1", "majority_vote", 1, 0.0), + _agg_row("c1", "open_committee", 1, 0.0), + _agg_row("c1", "anonymous_evidence_board", 1, 1.0), + ]) + frame = agb.expand_json_columns(frame) + + rows = agb.compute_difficulty_strata(frame, indexed) + mv = next(row for row in rows if row["protocol"] == "majority_vote") + aeb = next(row for row in rows if row["protocol"] == "anonymous_evidence_board") + + assert "global_single_correct_multi_wrong" not in mv + assert "global_single_wrong_multi_correct" not in mv + assert mv["protocol_majority_wrong_minority_correct_n"] == 1 + assert aeb["protocol_majority_correct_n"] == 1 + assert aeb.get("protocol_majority_wrong_minority_correct_n", 0) == 0 + + +class TestDataLoading: + def test_records_index_filters_unknown_labels(self): + records = [ + _raw_case("c1", "single_agent", 1, ["A"], "A"), + _raw_case("unknown", "single_agent", 1, ["A"], "A"), + ] + labels = {"c1": "A"} + indexed = _index_from(records, labels) + assert len(indexed) == 1 + assert ("c1", "single_agent", 1) in indexed diff --git a/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_board_analysis/tests/test_analyze_multiseed.py b/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_board_analysis/tests/test_analyze_multiseed.py new file mode 100644 index 0000000000000000000000000000000000000000..94d5cbaaa2af9276e126a05dcd7c0a60b718de2e --- /dev/null +++ b/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_board_analysis/tests/test_analyze_multiseed.py @@ -0,0 +1,179 @@ +from __future__ import annotations + +import json +from pathlib import Path + +import pandas as pd + +from scripts import analyze_multiseed + + +def _agg_row(case_id: str, variant: str, seed: int, accuracy: float, *, total_tokens: int = 100, calls: int = 1) -> dict[str, object]: + return { + "variant": variant, + "case_id": case_id, + "protocol_name": variant, + "seed": seed, + "answer": "A" if accuracy else "B", + "confidence": 0.8, + "metrics": json.dumps( + { + "accuracy": accuracy, + "invalid_agent_outputs_count": 0, + "invalid_final_decision_count": 0, + "retry_count_total": 0, + "retry_success_count": 0, + "retry_failure_count": 0, + } + ), + "token_usage": json.dumps( + { + "n_calls": calls, + "prompt_tokens": total_tokens // 2, + "completion_tokens": total_tokens - total_tokens // 2, + "total_tokens": total_tokens, + "latency_total": 1.0, + } + ), + "raw_log_path": None, + } + + +def _agent(answer: str, index: int) -> dict[str, object]: + return {"agent_id": f"agent_{index}", "round_id": 0, "answer": answer, "answer_valid": True} + + +def _raw_case(case_id: str, protocol: str, seed: int, round0_answers: list[str], final_answer: str) -> dict[str, object]: + return { + "case_id": case_id, + "protocol_name": protocol, + "seed": seed, + "round0_outputs": [_agent(answer, index) for index, answer in enumerate(round0_answers)], + "revised_outputs": [], + "final_decision": {"answer": final_answer, "answer_valid": True}, + "metrics": {}, + "token_usage": {}, + } + + +def _write_run(tmp_path: Path, seed: int, rows: list[dict[str, object]], raw_records: list[dict[str, object]] | None = None) -> Path: + run_dir = tmp_path / f"run_seed{seed}" + run_dir.mkdir() + pd.DataFrame(rows).to_parquet(run_dir / "aggregated_results.parquet") + for record in raw_records or []: + raw_dir = run_dir / str(record["protocol_name"]) / "raw" + raw_dir.mkdir(parents=True, exist_ok=True) + with (raw_dir / "cases.jsonl").open("a", encoding="utf-8") as handle: + handle.write(json.dumps(record) + "\n") + return run_dir + + +def test_load_multiseed_results_expands_json_columns(tmp_path: Path) -> None: + run1 = _write_run(tmp_path, 1, [_agg_row("c1", "single_agent", 1, 1.0, total_tokens=10)]) + run2 = _write_run(tmp_path, 2, [_agg_row("c1", "single_agent", 2, 0.0, total_tokens=20)]) + + frame = analyze_multiseed.load_multiseed_results([run1, run2]) + + assert frame["accuracy"].tolist() == [1.0, 0.0] + assert frame["total_tokens"].tolist() == [10, 20] + assert frame["run_dir"].tolist() == [str(run1), str(run2)] + + +def test_seed_wise_accuracy_groups_by_seed_and_protocol() -> None: + frame = pd.DataFrame( + [ + _agg_row("c1", "single_agent", 1, 1.0), + _agg_row("c2", "single_agent", 1, 0.0), + _agg_row("c1", "open_committee", 1, 1.0), + _agg_row("c2", "open_committee", 1, 1.0), + ] + ) + frame = analyze_multiseed.expand_json_columns(frame) + + rows = analyze_multiseed.seed_wise_accuracy(frame) + + by_key = {(row["seed"], row["protocol"]): row for row in rows} + assert by_key[(1, "single_agent")]["accuracy"] == 0.5 + assert by_key[(1, "open_committee")]["accuracy"] == 1.0 + + +def test_pooled_paired_comparison_counts_wins_losses_ties_and_tests() -> None: + frame = pd.DataFrame( + [ + _agg_row("c1", "single_agent", 1, 0.0), + _agg_row("c1", "open_committee", 1, 1.0), + _agg_row("c2", "single_agent", 1, 1.0), + _agg_row("c2", "open_committee", 1, 0.0), + _agg_row("c3", "single_agent", 1, 1.0), + _agg_row("c3", "open_committee", 1, 1.0), + _agg_row("c4", "single_agent", 1, 0.0), + _agg_row("c4", "open_committee", 1, 0.0), + ] + ) + frame = analyze_multiseed.expand_json_columns(frame) + + row = analyze_multiseed.pooled_paired_comparison(frame, "open_committee", "single_agent") + + assert row["wins"] == 1 + assert row["losses"] == 1 + assert row["ties"] == 2 + assert row["accuracy_diff"] == 0.0 + assert row["mcnemar_p"] == 1.0 + assert row["sign_test_p"] == 1.0 + + +def test_cluster_bootstrap_resamples_case_id_clusters() -> None: + frame = pd.DataFrame( + [ + _agg_row("c1", "single_agent", 1, 0.0), + _agg_row("c1", "open_committee", 1, 1.0), + _agg_row("c2", "single_agent", 1, 1.0), + _agg_row("c2", "open_committee", 1, 0.0), + _agg_row("c3", "single_agent", 1, 1.0), + _agg_row("c3", "open_committee", 1, 1.0), + _agg_row("c4", "single_agent", 1, 0.0), + _agg_row("c4", "open_committee", 1, 0.0), + ] + ) + frame = analyze_multiseed.expand_json_columns(frame) + + row = analyze_multiseed.cluster_bootstrap_difference(frame, "open_committee", "single_agent", n_bootstrap=300, seed=7) + + assert row["observed_diff"] == 0.0 + assert row["ci_low"] <= row["observed_diff"] <= row["ci_high"] + assert row["n_clusters"] == 4 + + +def test_cost_normalized_gain_uses_extra_tokens_and_extra_correct() -> None: + frame = pd.DataFrame( + [ + _agg_row("c1", "single_agent", 1, 1.0, total_tokens=10), + _agg_row("c2", "single_agent", 1, 0.0, total_tokens=10), + _agg_row("c1", "open_committee", 1, 1.0, total_tokens=30), + _agg_row("c2", "open_committee", 1, 1.0, total_tokens=30), + ] + ) + frame = analyze_multiseed.expand_json_columns(frame) + + rows = analyze_multiseed.cost_normalized_gain(frame) + + open_row = next(row for row in rows if row["protocol"] == "open_committee") + assert open_row["accuracy_gain_vs_single"] == 0.5 + assert open_row["extra_tokens_vs_single"] == 40 + assert open_row["extra_correct_vs_single"] == 1.0 + assert open_row["extra_tokens_per_extra_correct"] == 40.0 + + +def test_minority_rescue_subset_detects_majority_wrong_minority_correct() -> None: + records = [ + _raw_case("c1", "open_committee", 1, ["B", "B", "A"], "A"), + _raw_case("c2", "open_committee", 1, ["A", "A", "A"], "A"), + _raw_case("c3", "open_committee", 1, ["B", "C", "D"], "B"), + ] + + rows = analyze_multiseed.minority_rescue_subset(records, {"c1": "A", "c2": "A", "c3": "A"}) + + open_row = next(row for row in rows if row["protocol"] == "open_committee") + assert open_row["majority_wrong_minority_correct_n"] == 1 + assert open_row["majority_wrong_minority_correct_accuracy"] == 1.0 + assert open_row["round0_disagreement_n"] == 2 diff --git a/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_board_execution/configs/experiments/real_llm_full_seed42.yaml b/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_board_execution/configs/experiments/real_llm_full_seed42.yaml new file mode 100644 index 0000000000000000000000000000000000000000..fbf8942f2a3fe5a3c7e17539e6007d76f5adaa1b --- /dev/null +++ b/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_board_execution/configs/experiments/real_llm_full_seed42.yaml @@ -0,0 +1,33 @@ +experiment: + name: real_llm_full_seed42 + n_cases: 2450 + seed: 42 +protocol: + name: single_agent + protocols: + - single_agent + - majority_vote + - open_committee + - anonymous_evidence_board + n_agents: 5 + max_rounds: 2 + visibility_mode: answer_rationale + anonymous: false + answer_masking: false + use_devil_advocate: false +model: + provider: openai + model: openai/deepseek-v4-flash-free + api_base: http://127.0.0.1:8000/v1 + api_key_env: MYAPI_DUMMY_KEY + temperature: 0.0 + max_tokens: 60000 + json_mode: true +runtime: + mock_llm: false + concurrency: 200 +data: + source: medxpertqa_jsonl + path: data/medxpertqa_text_test.jsonl +output: + root_dir: runs diff --git a/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_board_execution/configs/experiments/real_llm_full_seed43.yaml b/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_board_execution/configs/experiments/real_llm_full_seed43.yaml new file mode 100644 index 0000000000000000000000000000000000000000..27f00c48cd37e150691e4bd3c33bf829c0d8f227 --- /dev/null +++ b/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_board_execution/configs/experiments/real_llm_full_seed43.yaml @@ -0,0 +1,33 @@ +experiment: + name: real_llm_full_seed43 + n_cases: 2450 + seed: 43 +protocol: + name: single_agent + protocols: + - single_agent + - majority_vote + - open_committee + - anonymous_evidence_board + n_agents: 5 + max_rounds: 2 + visibility_mode: answer_rationale + anonymous: false + answer_masking: false + use_devil_advocate: false +model: + provider: openai + model: openai/deepseek-v4-flash-free + api_base: http://127.0.0.1:8000/v1 + api_key_env: MYAPI_DUMMY_KEY + temperature: 0.0 + max_tokens: 60000 + json_mode: true +runtime: + mock_llm: false + concurrency: 200 +data: + source: medxpertqa_jsonl + path: data/medxpertqa_text_test.jsonl +output: + root_dir: runs diff --git a/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_board_execution/configs/experiments/real_llm_full_seed44.yaml b/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_board_execution/configs/experiments/real_llm_full_seed44.yaml new file mode 100644 index 0000000000000000000000000000000000000000..487edbf41d3f4e0e38fcf13c2378ed1f52808f70 --- /dev/null +++ b/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_board_execution/configs/experiments/real_llm_full_seed44.yaml @@ -0,0 +1,33 @@ +experiment: + name: real_llm_full_seed44 + n_cases: 2450 + seed: 44 +protocol: + name: single_agent + protocols: + - single_agent + - majority_vote + - open_committee + - anonymous_evidence_board + n_agents: 5 + max_rounds: 2 + visibility_mode: answer_rationale + anonymous: false + answer_masking: false + use_devil_advocate: false +model: + provider: openai + model: openai/deepseek-v4-flash-free + api_base: http://127.0.0.1:8000/v1 + api_key_env: MYAPI_DUMMY_KEY + temperature: 0.0 + max_tokens: 60000 + json_mode: true +runtime: + mock_llm: false + concurrency: 200 +data: + source: medxpertqa_jsonl + path: data/medxpertqa_text_test.jsonl +output: + root_dir: runs diff --git a/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_board_execution/medboard/__init__.py b/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_board_execution/medboard/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..5253602a53fdb7ed399bb435570a3264e83353b5 --- /dev/null +++ b/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_board_execution/medboard/__init__.py @@ -0,0 +1 @@ +"""MedBoard package module.""" diff --git a/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_board_execution/medboard/adapters/__init__.py b/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_board_execution/medboard/adapters/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..8b4a2dd07d43ef30d61b46980bcb1fb05af949a8 --- /dev/null +++ b/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_board_execution/medboard/adapters/__init__.py @@ -0,0 +1,5 @@ +"""Dataset adapters for MedBoard.""" + +from medboard.adapters.medxpertqa import MedXpertQADataset, load_medxpertqa_jsonl, medxpertqa_record_to_medical_case + +__all__ = ["MedXpertQADataset", "load_medxpertqa_jsonl", "medxpertqa_record_to_medical_case"] diff --git a/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_board_execution/medboard/adapters/medxpertqa.py b/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_board_execution/medboard/adapters/medxpertqa.py new file mode 100644 index 0000000000000000000000000000000000000000..976f17997da697c70e12e5fb4aa1ae2f36270f89 --- /dev/null +++ b/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_board_execution/medboard/adapters/medxpertqa.py @@ -0,0 +1,163 @@ +"""MedXpertQA Diagnosis dataset adapter.""" + +import json +from collections.abc import Sequence +from pathlib import Path +from typing import Any + +from medboard.schemas.case import MedicalCase + +_LABELS = "ABCDEFGHIJKLMNOPQRSTUVWXYZ" +_SPECIALTY_TO_BODY_SYSTEM = { + "cardiology": "cardiovascular", + "pulmonology": "pulmonary", + "pulmonary": "pulmonary", + "gastroenterology": "gastrointestinal", + "neurology": "neurologic", + "nephrology": "renal", + "endocrinology": "endocrine", + "hematology": "hematologic", + "oncology": "oncologic", + "infectious disease": "infectious_disease", + "rheumatology": "rheumatologic", +} + + +def _field(record: dict[str, Any], field_mapping: dict[str, str] | None, target: str, defaults: Sequence[str]) -> Any: + candidates = [] + if field_mapping and target in field_mapping: + candidates.append(field_mapping[target]) + candidates.extend(defaults) + for key in candidates: + if key in record: + return record[key] + raise ValueError(f"MedXpertQA record is missing required field for {target}: expected one of {candidates}") + + +def _optional_field(record: dict[str, Any], field_mapping: dict[str, str] | None, target: str, defaults: Sequence[str]) -> Any: + candidates = [] + if field_mapping and target in field_mapping: + candidates.append(field_mapping[target]) + candidates.extend(defaults) + return next((record[key] for key in candidates if key in record), None) + + +def _normalize_options(options: Any) -> dict[str, str]: + if isinstance(options, dict): + normalized = {str(label): str(text) for label, text in options.items()} + elif isinstance(options, list): + if len(options) > len(_LABELS): + raise ValueError("MedXpertQA options exceed supported label count") + normalized = {_LABELS[index]: str(text) for index, text in enumerate(options)} + else: + raise ValueError("MedXpertQA options must be a list or dict") + if not normalized: + raise ValueError("MedXpertQA options must not be empty") + return normalized + + +def _normalize_correct_label(record: dict[str, Any], options: dict[str, str], field_mapping: dict[str, str] | None) -> str: + answer_idx = _optional_field(record, field_mapping, "answer_idx", ["answer_idx", "answer_index", "label_idx"]) + if answer_idx is not None: + try: + label = _LABELS[int(answer_idx)] + except (IndexError, TypeError, ValueError) as exc: + raise ValueError(f"Invalid MedXpertQA answer_idx: {answer_idx}") from exc + if label not in options: + raise ValueError(f"MedXpertQA answer_idx maps to missing option label: {label}") + return label + label = _optional_field(record, field_mapping, "correct_label", ["correct_label", "answer", "label"]) + if label is None: + raise ValueError("MedXpertQA record is missing required field for correct_label") + label = str(label) + if label in options: + return label + matching_labels = [option_label for option_label, text in options.items() if text == label] + if matching_labels: + return matching_labels[0] + raise ValueError(f"MedXpertQA correct label is not present in options: {label}") + + +def _normalize_body_system(value: Any) -> str | None: + if value is None: + return None + normalized = str(value).strip().lower() + if not normalized: + return None + return _SPECIALTY_TO_BODY_SYSTEM.get(normalized, normalized.replace(" ", "_")) + + +def medxpertqa_record_to_medical_case( + record: dict[str, Any], + field_mapping: dict[str, str] | None = None, +) -> MedicalCase: + case_id = str(_field(record, field_mapping, "case_id", ["case_id", "id", "qid", "question_id"])) + case_text = str(_field(record, field_mapping, "question", ["question", "case_text", "vignette", "stem"])) + options = _normalize_options(_field(record, field_mapping, "options", ["options", "choices", "answer_options"])) + correct_label = _normalize_correct_label(record, options, field_mapping) + specialty = _optional_field(record, field_mapping, "specialty", ["specialty", "body_system", "department"]) + consumed = { + "case_id", + "id", + "qid", + "question_id", + "question", + "case_text", + "vignette", + "stem", + "options", + "choices", + "answer_options", + "answer_idx", + "answer_index", + "label_idx", + "correct_label", + "answer", + "label", + "specialty", + "body_system", + "department", + } + if field_mapping: + consumed.update(field_mapping.values()) + metadata = {key: value for key, value in record.items() if key not in consumed} + return MedicalCase( + case_id=case_id, + case_text=case_text, + options=options, + correct_label=correct_label, + body_system=_normalize_body_system(specialty), + metadata=metadata, + ) + + +def load_medxpertqa_jsonl( + path: str | Path, + field_mapping: dict[str, str] | None = None, +) -> list[MedicalCase]: + cases: list[MedicalCase] = [] + with Path(path).open("r", encoding="utf-8") as handle: + for line in handle: + line = line.strip() + if not line: + continue + cases.append(medxpertqa_record_to_medical_case(json.loads(line), field_mapping=field_mapping)) + return cases + + +class MedXpertQADataset: + def __init__( + self, + path_or_cases: str | Path | Sequence[MedicalCase], + field_mapping: dict[str, str] | None = None, + ) -> None: + if isinstance(path_or_cases, str | Path): + self._cases = load_medxpertqa_jsonl(path_or_cases, field_mapping=field_mapping) + else: + self._cases = list(path_or_cases) + + def __len__(self) -> int: + return len(self._cases) + + def __getitem__(self, index: int | slice) -> MedicalCase | list[MedicalCase]: + return self._cases[index] diff --git a/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_board_execution/medboard/core/__init__.py b/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_board_execution/medboard/core/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..ed66a1e8df96e0d0ec5475cd84688329f9f909cb --- /dev/null +++ b/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_board_execution/medboard/core/__init__.py @@ -0,0 +1,22 @@ +"""Deterministic scientific core exports.""" + +from medboard.core.anonymization import anonymize_evidence_board, anonymize_evidence_unit +from medboard.core.decision_rules import confidence_weighted_vote, evidence_weighted_vote, majority_vote +from medboard.core.metrics import accuracy, conformity_shift_rate, evidence_retention_rate +from medboard.core.perturbations import correct_minority_injection, wrong_majority_injection, wrong_senior_injection +from medboard.core.visibility import build_visibility_view + +__all__ = [ + "accuracy", + "anonymize_evidence_board", + "anonymize_evidence_unit", + "build_visibility_view", + "confidence_weighted_vote", + "conformity_shift_rate", + "correct_minority_injection", + "evidence_retention_rate", + "evidence_weighted_vote", + "majority_vote", + "wrong_majority_injection", + "wrong_senior_injection", +] diff --git a/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_board_execution/medboard/core/anonymization.py b/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_board_execution/medboard/core/anonymization.py new file mode 100644 index 0000000000000000000000000000000000000000..2169a3cb50055771592cda5b718b2d8049428e01 --- /dev/null +++ b/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_board_execution/medboard/core/anonymization.py @@ -0,0 +1,36 @@ +"""Evidence source anonymization.""" + +from collections.abc import Sequence + +from medboard.schemas.evidence import EvidenceBoard, EvidenceUnit + + +def anonymize_evidence_unit(unit: EvidenceUnit, evidence_id: str | None = None) -> EvidenceUnit: + updates = { + "source_agent": None, + "visible_source": False, + "supports_option": None, + "contradicts_option": None, + } + if evidence_id is not None: + updates["evidence_id"] = evidence_id + return unit.model_copy(update=updates) + + +def anonymize_evidence_units(units: Sequence[EvidenceUnit], case_id: str | None = None) -> list[EvidenceUnit]: + return [ + anonymize_evidence_unit( + unit, + evidence_id=f"{case_id}:anonymous:{index}" if case_id is not None else None, + ) + for index, unit in enumerate(units) + ] + + +def anonymize_evidence_board(board: EvidenceBoard) -> EvidenceBoard: + return board.model_copy( + update={ + "units": anonymize_evidence_units(board.units, board.case_id), + "anonymous": True, + } + ) diff --git a/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_board_execution/medboard/core/decision_rules.py b/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_board_execution/medboard/core/decision_rules.py new file mode 100644 index 0000000000000000000000000000000000000000..b447e44328b480eb06af9e1171c0d2a4dfc86e56 --- /dev/null +++ b/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_board_execution/medboard/core/decision_rules.py @@ -0,0 +1,69 @@ +"""Deterministic decision rules.""" + +from collections import Counter, defaultdict +from collections.abc import Sequence + +from medboard.schemas.agent import AgentOutput +from medboard.schemas.evidence import EvidenceUnit + + +def _ordered_labels(labels: Sequence[str], tie_breaker: Sequence[str] | None) -> list[str]: + if tie_breaker: + ordered = list(tie_breaker) + ordered.extend(label for label in labels if label not in ordered) + return ordered + return list(dict.fromkeys(labels)) + + +def majority_vote( + outputs: Sequence[AgentOutput], + tie_breaker: Sequence[str] | None = None, +) -> str | None: + answers = [output.answer for output in outputs if output.answer and getattr(output, "answer_valid", True)] + if not answers: + return None + counts = Counter(answers) + max_count = max(counts.values()) + winners = {answer for answer, count in counts.items() if count == max_count} + for label in _ordered_labels(answers, tie_breaker): + if label in winners: + return label + return sorted(winners)[0] + + +def confidence_weighted_vote( + outputs: Sequence[AgentOutput], + tie_breaker: Sequence[str] | None = None, +) -> str | None: + weights: dict[str, float] = defaultdict(float) + for output in outputs: + if output.answer and getattr(output, "answer_valid", True): + weights[output.answer] += output.confidence + if not weights: + return None + max_weight = max(weights.values()) + winners = {answer for answer, weight in weights.items() if weight == max_weight} + for label in _ordered_labels(list(weights), tie_breaker): + if label in winners: + return label + return sorted(winners)[0] + + +def evidence_weighted_vote( + units: Sequence[EvidenceUnit], + tie_breaker: Sequence[str] | None = None, +) -> str | None: + weights: dict[str, float] = defaultdict(float) + for unit in units: + if unit.supports_option: + weights[unit.supports_option] += unit.evidence_score + if unit.contradicts_option: + weights[unit.contradicts_option] -= unit.evidence_score + if not weights: + return None + max_weight = max(weights.values()) + winners = {answer for answer, weight in weights.items() if weight == max_weight} + for label in _ordered_labels(list(weights), tie_breaker): + if label in winners: + return label + return sorted(winners)[0] diff --git a/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_board_execution/medboard/core/evidence_board.py b/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_board_execution/medboard/core/evidence_board.py new file mode 100644 index 0000000000000000000000000000000000000000..c61e9b8851056ec80228c92b329c3ecbe966ab1e --- /dev/null +++ b/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_board_execution/medboard/core/evidence_board.py @@ -0,0 +1,24 @@ +"""Evidence board construction.""" + +from collections.abc import Sequence +from typing import Any + +from medboard.core.anonymization import anonymize_evidence_units +from medboard.schemas.evidence import EvidenceBoard, EvidenceUnit + + +def build_evidence_board( + case_id: str, + units: Sequence[EvidenceUnit], + anonymous: bool = False, + answer_masked: bool = False, + metadata: dict[str, Any] | None = None, +) -> EvidenceBoard: + board_units = anonymize_evidence_units(units, case_id) if anonymous else list(units) + return EvidenceBoard( + case_id=case_id, + units=board_units, + anonymous=anonymous, + answer_masked=answer_masked, + metadata=metadata or {}, + ) diff --git a/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_board_execution/medboard/core/evidence_extraction.py b/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_board_execution/medboard/core/evidence_extraction.py new file mode 100644 index 0000000000000000000000000000000000000000..37bd333afaebbbee93aa42980adcd7006ed821b8 --- /dev/null +++ b/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_board_execution/medboard/core/evidence_extraction.py @@ -0,0 +1,38 @@ +"""Deterministic evidence extraction from structured agent outputs.""" + +from collections.abc import Sequence + +from medboard.schemas.agent import AgentOutput +from medboard.schemas.evidence import EvidenceUnit + + +def extract_evidence_units(case_id: str, outputs: Sequence[AgentOutput]) -> list[EvidenceUnit]: + units: list[EvidenceUnit] = [] + for output in outputs: + if not getattr(output, "answer_valid", True): + continue + for index, text in enumerate(output.supporting_evidence): + units.append( + EvidenceUnit( + evidence_id=f"{case_id}:{output.agent_id}:support:{index}", + text=text, + source_agent=output.agent_id, + visible_source=True, + supports_option=output.answer, + evidence_type="supporting", + evidence_score=1.0, + ) + ) + for index, text in enumerate(output.contradicting_evidence): + units.append( + EvidenceUnit( + evidence_id=f"{case_id}:{output.agent_id}:contradict:{index}", + text=text, + source_agent=output.agent_id, + visible_source=True, + contradicts_option=output.answer, + evidence_type="contradicting", + evidence_score=1.0, + ) + ) + return units diff --git a/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_board_execution/medboard/core/metrics.py b/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_board_execution/medboard/core/metrics.py new file mode 100644 index 0000000000000000000000000000000000000000..ff9518e06c80480f9e8db8c57fbee52a99bd5383 --- /dev/null +++ b/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_board_execution/medboard/core/metrics.py @@ -0,0 +1,90 @@ +"""Scientific metrics for scaffold experiments.""" + +from collections.abc import Sequence + +from medboard.core.decision_rules import majority_vote +from medboard.schemas.agent import AgentOutput +from medboard.schemas.evidence import EvidenceUnit + + +def accuracy(predicted_label: str | None, correct_label: str | None) -> float: + if predicted_label is None or correct_label is None: + return 0.0 + return 1.0 if predicted_label == correct_label else 0.0 + + +def conformity_shift_rate( + round0_outputs: Sequence[AgentOutput], + revised_outputs: Sequence[AgentOutput], +) -> float: + revised_by_agent = {output.agent_id: output for output in revised_outputs} + comparable = [output for output in round0_outputs if output.agent_id in revised_by_agent] + if not comparable: + return 0.0 + shifts = 0 + for output in comparable: + revised = revised_by_agent[output.agent_id] + if output.answer != revised.answer: + shifts += 1 + return shifts / len(comparable) + + +def wrong_consensus_rate(outputs: Sequence[AgentOutput], correct_label: str) -> float: + consensus = majority_vote(outputs) + if consensus is None: + return 0.0 + return 1.0 if consensus != correct_label else 0.0 + + +def correct_minority_rescue_rate( + round0_outputs: Sequence[AgentOutput], + final_outputs: Sequence[AgentOutput], + correct_label: str, +) -> float: + if not round0_outputs or not final_outputs: + return 0.0 + initial_correct = sum(1 for output in round0_outputs if output.answer == correct_label) + if initial_correct == 0 or initial_correct >= len(round0_outputs) / 2: + return 0.0 + final_answer = majority_vote(final_outputs) + return 1.0 if final_answer == correct_label else 0.0 + + +def minority_suppression_rate( + round0_outputs: Sequence[AgentOutput], + revised_outputs: Sequence[AgentOutput], + correct_label: str, +) -> float: + revised_by_agent = {output.agent_id: output for output in revised_outputs} + initially_correct = [output for output in round0_outputs if output.answer == correct_label] + if not initially_correct: + return 0.0 + suppressed = 0 + for output in initially_correct: + revised = revised_by_agent.get(output.agent_id) + if revised is not None and revised.answer != correct_label: + suppressed += 1 + return suppressed / len(initially_correct) + + +def authority_error_amplification( + baseline_wrong_rate: float, + authority_condition_wrong_rate: float, +) -> float: + return authority_condition_wrong_rate - baseline_wrong_rate + + +def evidence_retention_rate( + evidence_units: Sequence[EvidenceUnit], + retained_evidence_ids: Sequence[str], +) -> float: + if not evidence_units: + return 0.0 + retained = set(retained_evidence_ids) + return sum(1 for unit in evidence_units if unit.evidence_id in retained) / len(evidence_units) + + +def token_normalized_gain(metric_gain: float, token_count: int) -> float: + if token_count <= 0: + return 0.0 + return metric_gain / token_count diff --git a/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_board_execution/medboard/core/perturbations.py b/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_board_execution/medboard/core/perturbations.py new file mode 100644 index 0000000000000000000000000000000000000000..6628042027e8ea7fa6b9d18518f41d08a477e9c8 --- /dev/null +++ b/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_board_execution/medboard/core/perturbations.py @@ -0,0 +1,80 @@ +"""Deterministic perturbation hooks for scaffold experiments.""" + +from collections.abc import Sequence + +from medboard.schemas.agent import AgentOutput + + +def correct_minority_injection( + outputs: Sequence[AgentOutput], + correct_label: str, + minority_size: int = 1, +) -> list[AgentOutput]: + count = max(0, min(minority_size, len(outputs))) + perturbed: list[AgentOutput] = [] + for index, output in enumerate(outputs): + if index < count: + perturbed.append( + output.model_copy( + update={ + "answer": correct_label, + "confidence": 0.9, + "changed_answer": True, + "reason_for_change": "Injected correct minority signal.", + } + ) + ) + else: + perturbed.append(output.model_copy()) + return perturbed + + +def wrong_majority_injection( + outputs: Sequence[AgentOutput], + wrong_label: str, + majority_size: int | None = None, +) -> list[AgentOutput]: + count = majority_size if majority_size is not None else (len(outputs) // 2) + 1 + count = max(0, min(count, len(outputs))) + perturbed: list[AgentOutput] = [] + for index, output in enumerate(outputs): + if index < count: + perturbed.append( + output.model_copy( + update={ + "answer": wrong_label, + "confidence": 0.85, + "changed_answer": True, + "reason_for_change": "Injected wrong majority signal.", + } + ) + ) + else: + perturbed.append(output.model_copy()) + return perturbed + + +def wrong_senior_injection( + outputs: Sequence[AgentOutput], + wrong_label: str, + senior_role: str = "senior_physician", +) -> list[AgentOutput]: + if not outputs: + return [] + perturbed = [output.model_copy() for output in outputs] + senior_index = next( + (index for index, output in enumerate(outputs) if output.role == senior_role), + None, + ) + if senior_index is None: + return perturbed + perturbed[senior_index] = perturbed[senior_index].model_copy( + update={ + "role": senior_role, + "answer": wrong_label, + "confidence": 0.9, + "changed_answer": True, + "reason_for_change": "Injected wrong senior signal.", + } + ) + return perturbed diff --git a/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_board_execution/medboard/core/visibility.py b/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_board_execution/medboard/core/visibility.py new file mode 100644 index 0000000000000000000000000000000000000000..b9bfd171a241fe3ca0e4315a663250bdcd1d1f9b --- /dev/null +++ b/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_board_execution/medboard/core/visibility.py @@ -0,0 +1,68 @@ +"""Visibility controls for peer-agent information sharing.""" + +from typing import Any, Literal, Sequence + +from medboard.schemas.agent import AgentOutput + +VisibilityMode = Literal[ + "answer_only", + "answer_rationale", + "rationale_only", + "evidence_only", + "evidence_gated", +] + + +def _base(output: AgentOutput) -> dict[str, Any]: + return {"agent_id": output.agent_id, "role": output.role} + + +def build_visibility_view( + outputs: Sequence[AgentOutput], + visibility_mode: VisibilityMode, +) -> list[dict[str, Any]]: + views: list[dict[str, Any]] = [] + for output in outputs: + view = _base(output) + if visibility_mode == "answer_only": + view.update({"answer": output.answer, "confidence": output.confidence}) + elif visibility_mode == "answer_rationale": + view.update( + { + "answer": output.answer, + "confidence": output.confidence, + "supporting_evidence": output.supporting_evidence, + "contradicting_evidence": output.contradicting_evidence, + "uncertainty_statement": output.uncertainty_statement, + } + ) + elif visibility_mode == "rationale_only": + view.update( + { + "supporting_evidence": output.supporting_evidence, + "contradicting_evidence": output.contradicting_evidence, + "differential_diagnoses": output.differential_diagnoses, + "uncertainty_statement": output.uncertainty_statement, + } + ) + elif visibility_mode == "evidence_only": + view.update( + { + "supporting_evidence": output.supporting_evidence, + "contradicting_evidence": output.contradicting_evidence, + "missing_evidence": output.missing_evidence, + } + ) + elif visibility_mode == "evidence_gated": + view.update( + { + "supporting_evidence": output.supporting_evidence, + "contradicting_evidence": output.contradicting_evidence, + "missing_evidence": output.missing_evidence, + "uncertainty_statement": output.uncertainty_statement, + } + ) + else: + raise ValueError(f"Unsupported visibility mode: {visibility_mode}") + views.append(view) + return views diff --git a/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_board_execution/medboard/graphs/__init__.py b/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_board_execution/medboard/graphs/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e94bbcb4070980f596cfe259d590c5a6743c477c --- /dev/null +++ b/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_board_execution/medboard/graphs/__init__.py @@ -0,0 +1,6 @@ +"""Graph exports.""" + +from medboard.graphs.protocol_graph import create_master_graph +from medboard.graphs.router import PROTOCOL_NAMES, get_protocol, route_protocol + +__all__ = ["PROTOCOL_NAMES", "create_master_graph", "get_protocol", "route_protocol"] diff --git a/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_board_execution/medboard/graphs/nodes.py b/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_board_execution/medboard/graphs/nodes.py new file mode 100644 index 0000000000000000000000000000000000000000..a646fbdd97272ef65c85f3f0ecd2dd7ce66f33ad --- /dev/null +++ b/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_board_execution/medboard/graphs/nodes.py @@ -0,0 +1,96 @@ +"""LangGraph node skeletons for MedBoard protocols.""" + +from medboard.core.evidence_board import build_evidence_board as core_build_evidence_board +from medboard.core.evidence_extraction import extract_evidence_units as core_extract_evidence_units +from medboard.core.metrics import accuracy, conformity_shift_rate, evidence_retention_rate +from medboard.core.visibility import build_visibility_view as core_build_visibility_view +from medboard.graphs.router import get_protocol +from medboard.protocols.common import _mock_round_outputs +from medboard.schemas.result import FinalDecision +from medboard.schemas.state import CaseState + + +def load_case(state: CaseState) -> CaseState: + return state + + +def initialize_agents(state: CaseState) -> CaseState: + agents = [{"agent_id": f"agent_{index + 1}", "role": "clinician"} for index in range(5)] + return {**state, "agents": state.get("agents", agents)} + + +def round0_independent_diagnosis(state: CaseState) -> CaseState: + case = state["case"] + config = state["config"] if "config" in state else None + if config is None: + return state + outputs = _mock_round_outputs(case, config, state.get("protocol_name", config.protocol.name), round_id=0) + return {**state, "round0_outputs": outputs} + + +def apply_perturbation(state: CaseState) -> CaseState: + return state + + +def build_visibility_view(state: CaseState) -> CaseState: + outputs = state.get("round0_outputs", []) + visibility_mode = state.get("visibility_mode", "evidence_only") + return {**state, "logs": [*state.get("logs", []), {"visibility_view": core_build_visibility_view(outputs, visibility_mode)}]} + + +def agent_revision_round(state: CaseState) -> CaseState: + outputs = state.get("round0_outputs", []) + revised = [output.model_copy(update={"round_id": 1}) for output in outputs] + return {**state, "revised_outputs": revised} + + +def extract_evidence_units(state: CaseState) -> CaseState: + case = state["case"] + outputs = state.get("revised_outputs") or state.get("round0_outputs", []) + units = core_extract_evidence_units(case.case_id, outputs) + return {**state, "evidence_units": units} + + +def build_anonymous_evidence_board(state: CaseState) -> CaseState: + case = state["case"] + config = state.get("config") + anonymous = bool(getattr(getattr(config, "protocol", None), "anonymous", True)) + answer_masked = bool(getattr(getattr(config, "protocol", None), "answer_masking", True)) + board = core_build_evidence_board(case.case_id, state.get("evidence_units", []), anonymous, answer_masked) + return {**state, "evidence_board": board} + + +def devil_advocate_review(state: CaseState) -> CaseState: + return {**state, "devil_review": {"enabled": False, "concerns": []}} + + +def judge_decision(state: CaseState) -> CaseState: + case = state["case"] + outputs = state.get("revised_outputs") or state.get("round0_outputs", []) + protocol = get_protocol(state.get("protocol_name", "single_agent")) + answer = outputs[0].answer if outputs else case.correct_label + retained_ids = [unit.evidence_id for unit in state.get("evidence_units", [])] + decision = FinalDecision( + answer=answer, + confidence=0.7, + rationale=f"Graph scaffold decision for {protocol.name}.", + retained_evidence_ids=retained_ids, + rejected_alternatives=[label for label in case.options if label != answer], + escalation_flag=False, + ) + return {**state, "final_decision": decision} + + +def compute_metrics(state: CaseState) -> CaseState: + case = state["case"] + decision = state["final_decision"] + metrics = { + "accuracy": accuracy(decision.answer, case.correct_label), + "conformity_shift_rate": conformity_shift_rate(state.get("round0_outputs", []), state.get("revised_outputs", [])), + "evidence_retention_rate": evidence_retention_rate(state.get("evidence_units", []), decision.retained_evidence_ids), + } + return {**state, "metrics": metrics} + + +def save_result(state: CaseState) -> CaseState: + return state diff --git a/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_board_execution/medboard/graphs/protocol_graph.py b/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_board_execution/medboard/graphs/protocol_graph.py new file mode 100644 index 0000000000000000000000000000000000000000..750d9f622d1f46da3afe5b9afc5f80860d0c1e82 --- /dev/null +++ b/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_board_execution/medboard/graphs/protocol_graph.py @@ -0,0 +1,62 @@ +"""Master LangGraph builder for protocol workflows.""" + +from langgraph.graph import END, StateGraph + +from medboard.graphs.nodes import ( + agent_revision_round, + apply_perturbation, + build_anonymous_evidence_board, + build_visibility_view, + compute_metrics, + devil_advocate_review, + extract_evidence_units, + initialize_agents, + judge_decision, + load_case, + round0_independent_diagnosis, + save_result, +) +from medboard.graphs.router import PROTOCOL_NAMES, route_protocol +from medboard.schemas.state import CaseState + + +def route_protocol_node(state: CaseState) -> CaseState: + route_protocol(state) + return state + + +def create_master_graph(): + graph = StateGraph(CaseState) + graph.add_node("load_case", load_case) + graph.add_node("initialize_agents", initialize_agents) + graph.add_node("route_protocol", route_protocol_node) + graph.add_node("round0_independent_diagnosis", round0_independent_diagnosis) + graph.add_node("apply_perturbation", apply_perturbation) + graph.add_node("build_visibility_view", build_visibility_view) + graph.add_node("agent_revision_round", agent_revision_round) + graph.add_node("extract_evidence_units", extract_evidence_units) + graph.add_node("build_anonymous_evidence_board", build_anonymous_evidence_board) + graph.add_node("devil_advocate_review", devil_advocate_review) + graph.add_node("judge_decision", judge_decision) + graph.add_node("compute_metrics", compute_metrics) + graph.add_node("save_result", save_result) + + graph.set_entry_point("load_case") + graph.add_edge("load_case", "initialize_agents") + graph.add_edge("initialize_agents", "route_protocol") + graph.add_conditional_edges( + "route_protocol", + route_protocol, + {name: "round0_independent_diagnosis" for name in PROTOCOL_NAMES}, + ) + graph.add_edge("round0_independent_diagnosis", "apply_perturbation") + graph.add_edge("apply_perturbation", "build_visibility_view") + graph.add_edge("build_visibility_view", "agent_revision_round") + graph.add_edge("agent_revision_round", "extract_evidence_units") + graph.add_edge("extract_evidence_units", "build_anonymous_evidence_board") + graph.add_edge("build_anonymous_evidence_board", "devil_advocate_review") + graph.add_edge("devil_advocate_review", "judge_decision") + graph.add_edge("judge_decision", "compute_metrics") + graph.add_edge("compute_metrics", "save_result") + graph.add_edge("save_result", END) + return graph.compile() diff --git a/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_board_execution/medboard/graphs/router.py b/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_board_execution/medboard/graphs/router.py new file mode 100644 index 0000000000000000000000000000000000000000..7140eacf35058d50a7a1c7e29163298d64d03573 --- /dev/null +++ b/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_board_execution/medboard/graphs/router.py @@ -0,0 +1,56 @@ +"""Protocol routing helpers.""" + +from typing import Protocol + +from medboard.protocols import ( + AnonymousEvidenceBoardProtocol, + HierarchicalMDTProtocol, + JudgeAdvisorProtocol, + MajorityVoteProtocol, + MedBoardDevilProtocol, + OpenCommitteeProtocol, + SingleAgentProtocol, +) +from medboard.schemas.case import MedicalCase +from medboard.schemas.config import ExperimentConfig +from medboard.schemas.result import CaseResult +from medboard.schemas.state import CaseState + +PROTOCOL_NAMES = [ + "single_agent", + "majority_vote", + "open_committee", + "hierarchical_mdt", + "judge_advisor", + "anonymous_evidence_board", + "medboard_devil", +] + + +class ProtocolRunner(Protocol): + name: str + + async def run_case(self, case: MedicalCase, config: ExperimentConfig) -> CaseResult: ... + + +def route_protocol(state: CaseState) -> str: + name = state.get("protocol_name", "single_agent") + if name not in PROTOCOL_NAMES: + raise ValueError(f"Unsupported protocol: {name}") + return name + + +def get_protocol(name: str) -> ProtocolRunner: + registry: dict[str, ProtocolRunner] = { + "single_agent": SingleAgentProtocol(), + "majority_vote": MajorityVoteProtocol(), + "open_committee": OpenCommitteeProtocol(), + "hierarchical_mdt": HierarchicalMDTProtocol(), + "judge_advisor": JudgeAdvisorProtocol(), + "anonymous_evidence_board": AnonymousEvidenceBoardProtocol(), + "medboard_devil": MedBoardDevilProtocol(), + } + try: + return registry[name] + except KeyError as exc: + raise ValueError(f"Unsupported protocol: {name}") from exc diff --git a/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_board_execution/medboard/llm/__init__.py b/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_board_execution/medboard/llm/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..236a823e70ae5620cecd48ea9d4b94dfbb7006d7 --- /dev/null +++ b/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_board_execution/medboard/llm/__init__.py @@ -0,0 +1,13 @@ +"""LLM layer exports.""" + +from medboard.llm.client import LLMResponse, LiteLLMClient +from medboard.llm.cost import estimate_token_cost +from medboard.llm.structured_output import parse_json_object, parse_pydantic_model + +__all__ = [ + "LLMResponse", + "LiteLLMClient", + "estimate_token_cost", + "parse_json_object", + "parse_pydantic_model", +] diff --git a/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_board_execution/medboard/llm/client.py b/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_board_execution/medboard/llm/client.py new file mode 100644 index 0000000000000000000000000000000000000000..7772b60b9daf1dbbca0745fe05776db78fe050c9 --- /dev/null +++ b/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_board_execution/medboard/llm/client.py @@ -0,0 +1,81 @@ +"""LiteLLM client wrapper.""" + +import json +import time +from dataclasses import dataclass +from typing import Any + +import litellm +from tenacity import retry, stop_after_attempt, wait_exponential + + +@dataclass(frozen=True) +class LLMResponse: + raw_text: str + parsed_json: dict[str, Any] | list[Any] | None + usage: dict[str, Any] + latency: float + + +class LiteLLMClient: + def __init__( + self, + model: str, + temperature: float = 0.0, + max_tokens: int = 1024, + seed: int | None = None, + json_mode: bool = False, + api_base: str | None = None, + api_key: str | None = None, + ) -> None: + self.model = model + self.temperature = temperature + self.max_tokens = max_tokens + self.seed = seed + self.json_mode = json_mode + self.api_base = api_base + self.api_key = api_key + + @retry(wait=wait_exponential(multiplier=1, min=1, max=10), stop=stop_after_attempt(3), reraise=True) + async def complete( + self, + messages: list[dict[str, str]], + model: str | None = None, + temperature: float | None = None, + max_tokens: int | None = None, + seed: int | None = None, + json_mode: bool | None = None, + response_format: dict[str, Any] | None = None, + ) -> LLMResponse: + use_json_mode = self.json_mode if json_mode is None else json_mode + kwargs: dict[str, Any] = { + "model": model or self.model, + "messages": messages, + "temperature": self.temperature if temperature is None else temperature, + "max_tokens": self.max_tokens if max_tokens is None else max_tokens, + } + if self.api_base: + kwargs["api_base"] = self.api_base + if self.api_key: + kwargs["api_key"] = self.api_key + selected_seed = self.seed if seed is None else seed + if selected_seed is not None: + kwargs["seed"] = selected_seed + if response_format is not None: + kwargs["response_format"] = response_format + elif use_json_mode: + kwargs["response_format"] = {"type": "json_object"} + + start = time.perf_counter() + response = await litellm.acompletion(**kwargs) + latency = time.perf_counter() - start + msg = response.choices[0].message + raw_text = msg.content or msg.reasoning_content or "" + parsed_json: dict[str, Any] | list[Any] | None = None + if use_json_mode and raw_text.strip(): + try: + parsed_json = json.loads(raw_text) + except json.JSONDecodeError: + parsed_json = None + usage = dict(response.usage or {}) if getattr(response, "usage", None) else {} + return LLMResponse(raw_text=raw_text, parsed_json=parsed_json, usage=usage, latency=latency) diff --git a/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_board_execution/medboard/llm/cost.py b/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_board_execution/medboard/llm/cost.py new file mode 100644 index 0000000000000000000000000000000000000000..23606b3463a3de049694403269a25b2db967db81 --- /dev/null +++ b/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_board_execution/medboard/llm/cost.py @@ -0,0 +1,13 @@ +"""Token usage and cost helpers.""" + +from typing import Any + + +def estimate_token_cost( + usage: dict[str, Any], + input_cost_per_1k: float = 0.0, + output_cost_per_1k: float = 0.0, +) -> float: + prompt_tokens = float(usage.get("prompt_tokens", usage.get("input_tokens", 0)) or 0) + completion_tokens = float(usage.get("completion_tokens", usage.get("output_tokens", 0)) or 0) + return (prompt_tokens / 1000.0 * input_cost_per_1k) + (completion_tokens / 1000.0 * output_cost_per_1k) diff --git a/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_board_execution/medboard/llm/structured_output.py b/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_board_execution/medboard/llm/structured_output.py new file mode 100644 index 0000000000000000000000000000000000000000..2470aeacd56c1e70cc982be06a5fcdac3e169451 --- /dev/null +++ b/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_board_execution/medboard/llm/structured_output.py @@ -0,0 +1,19 @@ +"""Structured output parsing helpers.""" + +import json +from typing import Any, TypeVar + +from pydantic import BaseModel + +ModelT = TypeVar("ModelT", bound=BaseModel) + + +def parse_json_object(raw_text: str) -> dict[str, Any]: + payload = json.loads(raw_text) + if not isinstance(payload, dict): + raise ValueError("Expected a JSON object.") + return payload + + +def parse_pydantic_model(raw_text: str, model_type: type[ModelT]) -> ModelT: + return model_type.model_validate_json(raw_text) diff --git a/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_board_execution/medboard/prompts/__init__.py b/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_board_execution/medboard/prompts/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..5253602a53fdb7ed399bb435570a3264e83353b5 --- /dev/null +++ b/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_board_execution/medboard/prompts/__init__.py @@ -0,0 +1 @@ +"""MedBoard package module.""" diff --git a/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_board_execution/medboard/prompts/devil_advocate.py b/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_board_execution/medboard/prompts/devil_advocate.py new file mode 100644 index 0000000000000000000000000000000000000000..ad3b2881e5096a3d970d1b7ac25eb75ca23161f6 --- /dev/null +++ b/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_board_execution/medboard/prompts/devil_advocate.py @@ -0,0 +1 @@ +PROMPT = "Challenge consensus by identifying weak evidence, missing tests, and plausible alternatives." diff --git a/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_board_execution/medboard/prompts/evidence_board.py b/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_board_execution/medboard/prompts/evidence_board.py new file mode 100644 index 0000000000000000000000000000000000000000..944fddfb1665c2a4abd11a7fd5281ddd3cdf1c2e --- /dev/null +++ b/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_board_execution/medboard/prompts/evidence_board.py @@ -0,0 +1 @@ +PROMPT = "Extract evidence units without relying on source identity or answer popularity." diff --git a/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_board_execution/medboard/prompts/hierarchical_mdt.py b/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_board_execution/medboard/prompts/hierarchical_mdt.py new file mode 100644 index 0000000000000000000000000000000000000000..577e48cb72cdda65311b26e49783c424d3825563 --- /dev/null +++ b/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_board_execution/medboard/prompts/hierarchical_mdt.py @@ -0,0 +1 @@ +PROMPT = "Integrate specialist opinions while separating evidence quality from authority." diff --git a/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_board_execution/medboard/prompts/judge.py b/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_board_execution/medboard/prompts/judge.py new file mode 100644 index 0000000000000000000000000000000000000000..3005892ab1edfb56635fe98a3785b40a712989f1 --- /dev/null +++ b/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_board_execution/medboard/prompts/judge.py @@ -0,0 +1,122 @@ +"""Judge/arbiter final decision prompt builder.""" + +from collections.abc import Sequence + +from medboard.schemas.agent import AgentOutput +from medboard.schemas.case import MedicalCase +from medboard.schemas.evidence import EvidenceBoard + +PROMPT = "Make a final decision using retained evidence, rejected alternatives, and escalation criteria." + + +def _format_options(options: dict[str, str]) -> str: + """Format option dictionary into a readable list for the prompt.""" + lines = ["Available diagnostic options:"] + for label, text in options.items(): + lines.append(f" [{label}] {text}") + return "\n".join(lines) + + +def _format_agent_outputs(outputs: Sequence[AgentOutput]) -> str: + """Format a list of agent outputs into a readable summary for the judge.""" + if not outputs: + return " (No agent outputs available.)" + lines: list[str] = [] + for output in outputs: + lines.append(f" Agent: {output.agent_id} | Role: {output.role}") + lines.append(f" Answer: {output.answer} | Confidence: {output.confidence}") + if output.supporting_evidence: + lines.append(f" Supporting evidence: {'; '.join(output.supporting_evidence)}") + if output.contradicting_evidence: + lines.append(f" Contradicting evidence: {'; '.join(output.contradicting_evidence)}") + if output.uncertainty_statement: + lines.append(f" Uncertainty: {output.uncertainty_statement}") + lines.append("") + return "\n".join(lines) + + +def _format_evidence_board(board: EvidenceBoard) -> str: + """Format an EvidenceBoard into a readable summary for the judge.""" + if not board.units: + return " (No evidence units collected.)" + lines: list[str] = [] + for unit in board.units: + meta_parts = [] + if not board.anonymous and not board.answer_masked: + if unit.supports_option: + meta_parts.append(f"supports [{unit.supports_option}]") + if unit.contradicts_option: + meta_parts.append(f"contradicts [{unit.contradicts_option}]") + meta = f" ({'; '.join(meta_parts)})" if meta_parts else "" + lines.append(f" [{unit.evidence_id}] {unit.text}{meta}") + if board.anonymous: + lines.append("\n Note: Evidence sources and answer labels have been anonymized.") + elif board.answer_masked: + lines.append(" Note: Answer labels have been masked.") + return "\n".join(lines) + + +def build_judge_messages( + case: MedicalCase, + outputs: Sequence[AgentOutput], + evidence_board: EvidenceBoard, + include_agent_outputs: bool = True, +) -> list[dict[str, str]]: + """Build messages for the judge/arbiter to produce a final decision. + + Parameters + ---------- + case : MedicalCase + The medical case being adjudicated. + outputs : Sequence[AgentOutput] + Agent diagnosis outputs from round 0 (and possibly revision rounds). + evidence_board : EvidenceBoard + The compiled evidence board with anonymized or masked evidence units. + include_agent_outputs : bool + When True (default), agent diagnoses are included in the prompt. + When False (used by anonymous_evidence_board), only the evidence board + is shown to prevent answer/identity leakage. + + Returns + ------- + list[dict[str, str]] + A list of message dicts with "role" and "content" keys, suitable for + passing to LiteLLMClient.complete(). + """ + allowed_labels = ", ".join(case.options.keys()) + + system_message = ( + "You are a medical judge responsible for making a final diagnostic decision.\n\n" + "You will be given the medical case, independent agent diagnoses, and a compiled " + "evidence board. Synthesize all available information to produce a final decision.\n\n" + "Output ONLY a single JSON object with no surrounding text. The JSON must include:\n" + ' - "answer" (string): one of the allowed option labels\n' + ' - "confidence" (float between 0.0 and 1.0): your confidence in the final answer\n' + ' - "rationale" (string): detailed reasoning for this decision\n' + ' - "retained_evidence_ids" (list of strings): exact evidence IDs displayed in brackets in the Evidence Board that you relied upon; use only Evidence Board IDs such as "Text-0:anonymous:0"\n' + ' - "rejected_alternatives" (list of strings): option labels you considered but rejected\n' + ' - "escalation_flag" (boolean): true if this case warrants specialist escalation' + ) + + agent_section = "" + if include_agent_outputs and outputs: + agent_section = ( + f"--- Agent Diagnoses ---\n" + f"{_format_agent_outputs(outputs)}\n\n" + ) + + user_message = ( + f"--- Medical Case: {case.case_id} ---\n\n" + f"{case.case_text}\n\n" + f"{_format_options(case.options)}\n\n" + f"Allowed option labels: {allowed_labels}\n\n" + f"{agent_section}" + f"--- Evidence Board ---\n" + f"{_format_evidence_board(evidence_board)}\n\n" + "Synthesize the information above and provide your final decision as a single JSON object." + ) + + return [ + {"role": "system", "content": system_message}, + {"role": "user", "content": user_message}, + ] diff --git a/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_board_execution/medboard/prompts/judge_advisor.py b/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_board_execution/medboard/prompts/judge_advisor.py new file mode 100644 index 0000000000000000000000000000000000000000..39b50d442e2be895b92f025bdc324a5c8ceb6185 --- /dev/null +++ b/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_board_execution/medboard/prompts/judge_advisor.py @@ -0,0 +1 @@ +PROMPT = "Advise the judge with explicit evidence, alternatives, and uncertainty." diff --git a/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_board_execution/medboard/prompts/open_committee.py b/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_board_execution/medboard/prompts/open_committee.py new file mode 100644 index 0000000000000000000000000000000000000000..11ffeb2cd952d4d25d53386341aac328f89e3a76 --- /dev/null +++ b/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_board_execution/medboard/prompts/open_committee.py @@ -0,0 +1,103 @@ +"""Open committee Round 1 peer-review prompt builder.""" + +from collections.abc import Sequence +from typing import Any + +from medboard.core.visibility import VisibilityMode, build_visibility_view +from medboard.schemas.agent import AgentOutput +from medboard.schemas.case import MedicalCase + +PROMPT = "Review peer rationales openly and revise only when evidence warrants a change." + + +def _format_options(options: dict[str, str]) -> str: + lines = ["Available diagnostic options:"] + for label, text in options.items(): + lines.append(f" [{label}] {text}") + return "\n".join(lines) + + +def _format_visibility_view(views: list[dict[str, Any]]) -> str: + if not views: + return " (No peer diagnoses available.)" + lines: list[str] = [] + for view in views: + agent_id = view.get("agent_id", "unknown") + role = view.get("role", "unknown") + lines.append(f" Agent: {agent_id} | Role: {role}") + if "answer" in view: + lines.append(f" Answer: {view['answer']} | Confidence: {view.get('confidence', 'N/A')}") + if "supporting_evidence" in view: + lines.append(f" Supporting evidence: {'; '.join(view['supporting_evidence'])}") + if "contradicting_evidence" in view: + lines.append(f" Contradicting evidence: {'; '.join(view['contradicting_evidence'])}") + if "missing_evidence" in view: + lines.append(f" Missing evidence: {'; '.join(view['missing_evidence'])}") + if "differential_diagnoses" in view: + lines.append(f" Differential diagnoses: {'; '.join(view['differential_diagnoses'])}") + if "uncertainty_statement" in view: + lines.append(f" Uncertainty: {view['uncertainty_statement']}") + lines.append("") + return "\n".join(lines) + + +def build_open_committee_round1_messages( + case: MedicalCase, + agent_id: str, + role: str, + round0_outputs: Sequence[AgentOutput], + visibility_mode: VisibilityMode, +) -> list[dict[str, str]]: + """Build messages for an agent's round-1 re-diagnosis after reviewing peers. + + Parameters + ---------- + case : MedicalCase + The medical case being re-evaluated. + agent_id : str + Unique identifier for this agent. + role : str + Clinical role for this agent. + round0_outputs : Sequence[AgentOutput] + All agents' round-0 outputs; the current agent's own output is excluded. + visibility_mode : VisibilityMode + Determines what peer information is visible. + + Returns + ------- + list[dict[str, str]] + Messages with "role" and "content" keys. + """ + peer_outputs = [o for o in round0_outputs if o.agent_id != agent_id] + peer_views = build_visibility_view(peer_outputs, visibility_mode) + + allowed_labels = ", ".join(case.options.keys()) + + system_message = ( + f"You are {agent_id}, a {role} reviewing peer diagnoses.\n\n" + "Your task is to reconsider your diagnosis after reviewing what your peers have found.\n" + "You may change your answer, keep it, or refine your reasoning based on peer input.\n\n" + "Output ONLY a single JSON object with no surrounding text. The JSON must include:\n" + ' - "answer" (string): one of the allowed option labels\n' + ' - "confidence" (float between 0.0 and 1.0): your revised confidence\n' + ' - "supporting_evidence" (list of strings)\n' + ' - "contradicting_evidence" (list of strings)\n' + ' - "missing_evidence" (list of strings)\n' + ' - "differential_diagnoses" (list of strings)\n' + ' - "uncertainty_statement" (string or null)' + ) + + user_message = ( + f"--- Medical Case: {case.case_id} ---\n\n" + f"{case.case_text}\n\n" + f"{_format_options(case.options)}\n\n" + f"Allowed option labels: {allowed_labels}\n\n" + f"=== Your Peers' Diagnoses ===\n" + f"{_format_visibility_view(peer_views)}\n" + "Based on this peer input, provide your revised diagnosis as a single JSON object." + ) + + return [ + {"role": "system", "content": system_message}, + {"role": "user", "content": user_message}, + ] diff --git a/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_board_execution/medboard/prompts/round0.py b/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_board_execution/medboard/prompts/round0.py new file mode 100644 index 0000000000000000000000000000000000000000..0d40cd2d236cf65283678db4b56020e3fc1ea520 --- /dev/null +++ b/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_board_execution/medboard/prompts/round0.py @@ -0,0 +1,65 @@ +"""Round 0 independent diagnosis prompt builder.""" + +from medboard.schemas.case import MedicalCase + +PROMPT = "Provide an independent diagnosis with answer, confidence, evidence, missing evidence, and uncertainty." + + +def _format_options(options: dict[str, str]) -> str: + """Format option dictionary into a readable list for the prompt.""" + lines = ["Available diagnostic options:"] + for label, text in options.items(): + lines.append(f" [{label}] {text}") + return "\n".join(lines) + + +def _allowed_labels(options: dict[str, str]) -> str: + """Return a comma-separated string of allowed option labels.""" + return ", ".join(options.keys()) + + +def build_round0_messages(case: MedicalCase, agent_id: str, role: str) -> list[dict[str, str]]: + """Build messages for an agent's independent round-0 diagnosis. + + Parameters + ---------- + case : MedicalCase + The medical case to diagnose, containing case_text, options dict, and metadata. + agent_id : str + Unique identifier for this agent (e.g. "agent_1"). + role : str + Clinical role for this agent (e.g. "clinician", "senior_physician"). + + Returns + ------- + list[dict[str, str]] + A list of message dicts with "role" and "content" keys, suitable for + passing to LiteLLMClient.complete() or similar LLM interfaces. + """ + system_message = ( + f"You are {agent_id}, a {role} providing an independent medical diagnosis.\n\n" + "Your task is to analyze the case and produce a structured JSON diagnosis.\n" + "Do NOT include any text outside the JSON object. Output ONLY valid JSON.\n\n" + "Your JSON output must include the following fields:\n" + ' - "answer" (string): one of the allowed option labels\n' + ' - "confidence" (float between 0.0 and 1.0): your confidence in this answer\n' + ' - "supporting_evidence" (list of strings): clinical findings that support your answer\n' + ' - "contradicting_evidence" (list of strings): findings that contradict your answer\n' + ' - "missing_evidence" (list of strings): additional information you would like\n' + ' - "differential_diagnoses" (list of strings): other plausible diagnoses considered\n' + ' - "uncertainty_statement" (string or null): describe any uncertainty' + ) + + allowed_labels = _allowed_labels(case.options) + user_message = ( + f"--- Medical Case: {case.case_id} ---\n\n" + f"{case.case_text}\n\n" + f"{_format_options(case.options)}\n\n" + f"Allowed option labels: {allowed_labels}\n\n" + "Provide your independent diagnosis as a single JSON object with the fields described above." + ) + + return [ + {"role": "system", "content": system_message}, + {"role": "user", "content": user_message}, + ] diff --git a/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_board_execution/medboard/protocols/__init__.py b/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_board_execution/medboard/protocols/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..db049f9256311d2fd4d6ee37298e33ce077daf92 --- /dev/null +++ b/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_board_execution/medboard/protocols/__init__.py @@ -0,0 +1,19 @@ +"""Protocol registry exports.""" + +from medboard.protocols.anonymous_evidence_board import AnonymousEvidenceBoardProtocol +from medboard.protocols.hierarchical_mdt import HierarchicalMDTProtocol +from medboard.protocols.judge_advisor import JudgeAdvisorProtocol +from medboard.protocols.majority_vote import MajorityVoteProtocol +from medboard.protocols.medboard_devil import MedBoardDevilProtocol +from medboard.protocols.open_committee import OpenCommitteeProtocol +from medboard.protocols.single import SingleAgentProtocol + +__all__ = [ + "AnonymousEvidenceBoardProtocol", + "HierarchicalMDTProtocol", + "JudgeAdvisorProtocol", + "MajorityVoteProtocol", + "MedBoardDevilProtocol", + "OpenCommitteeProtocol", + "SingleAgentProtocol", +] diff --git a/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_board_execution/medboard/protocols/anonymous_evidence_board.py b/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_board_execution/medboard/protocols/anonymous_evidence_board.py new file mode 100644 index 0000000000000000000000000000000000000000..197e2e490eb6f1ec9d92640f26614cc2456d3bf6 --- /dev/null +++ b/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_board_execution/medboard/protocols/anonymous_evidence_board.py @@ -0,0 +1,13 @@ +"""Anonymous evidence board protocol scaffold.""" + +from medboard.protocols.common import run_protocol +from medboard.schemas.case import MedicalCase +from medboard.schemas.config import ExperimentConfig +from medboard.schemas.result import CaseResult + + +class AnonymousEvidenceBoardProtocol: + name = "anonymous_evidence_board" + + async def run_case(self, case: MedicalCase, config: ExperimentConfig) -> CaseResult: + return await run_protocol(case, config, self.name) diff --git a/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_board_execution/medboard/protocols/common.py b/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_board_execution/medboard/protocols/common.py new file mode 100644 index 0000000000000000000000000000000000000000..c1c6136e7f28855ccbb77288233cc70de2d15fdb --- /dev/null +++ b/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_board_execution/medboard/protocols/common.py @@ -0,0 +1,749 @@ +"""Shared protocol implementations.""" + +from __future__ import annotations + +import json +import os +import re +from collections.abc import Iterable, Sequence +from math import ceil +from typing import Any, get_args + +from medboard.core.decision_rules import majority_vote +from medboard.core.evidence_board import build_evidence_board +from medboard.core.evidence_extraction import extract_evidence_units +from medboard.core.metrics import accuracy, conformity_shift_rate, evidence_retention_rate +from medboard.core.perturbations import correct_minority_injection, wrong_majority_injection, wrong_senior_injection +from medboard.llm.client import LLMResponse, LiteLLMClient +from medboard.prompts.judge import build_judge_messages +from medboard.prompts.open_committee import build_open_committee_round1_messages +from medboard.prompts.round0 import build_round0_messages +from medboard.schemas.agent import AgentOutput +from medboard.schemas.case import MedicalCase +from medboard.schemas.config import ExperimentConfig, VisibilityMode +from medboard.schemas.evidence import EvidenceBoard, EvidenceUnit +from medboard.schemas.result import CaseResult, FinalDecision + +MAX_ANSWER_RETRIES = 3 + + +def _option_labels(case: MedicalCase) -> list[str]: + return list(case.options) or [case.correct_label] + + +def _wrong_label(case: MedicalCase) -> str: + return next((label for label in case.options if label != case.correct_label), case.correct_label) + + +def _mock_round_outputs(case: MedicalCase, config: ExperimentConfig, protocol_name: str, round_id: int) -> list[AgentOutput]: + labels = _option_labels(case) + wrong_labels = [label for label in labels if label != case.correct_label] or [case.correct_label] + n_agents = 1 if protocol_name == "single_agent" else config.protocol.n_agents + outputs: list[AgentOutput] = [] + for index in range(n_agents): + answer = case.correct_label if index % 2 == 0 else wrong_labels[(index // 2) % len(wrong_labels)] + outputs.append( + AgentOutput( + agent_id=f"agent_{index + 1}", + role="senior_physician" if index == 0 and protocol_name == "hierarchical_mdt" else "clinician", + round_id=round_id, + answer=answer, + confidence=0.75 if answer == case.correct_label else 0.55, + supporting_evidence=[f"Mock evidence for option {answer} in {case.case_id}."], + contradicting_evidence=[], + missing_evidence=["Additional diagnostic workup would be needed for real use."], + differential_diagnoses=list(case.options.values()), + uncertainty_statement="This is deterministic mock output for scaffold validation.", + ) + ) + return outputs + + +def _apply_perturbation(outputs: list[AgentOutput], case: MedicalCase, config: ExperimentConfig) -> list[AgentOutput]: + perturbation = config.perturbation or {} + name = perturbation.get("name") + if name == "correct_minority_injection": + return correct_minority_injection( + outputs, + correct_label=case.correct_label, + minority_size=int(perturbation.get("minority_size", 1)), + ) + if name == "wrong_majority_injection": + wrong_label = str(perturbation.get("wrong_label", _wrong_label(case))) + return wrong_majority_injection(outputs, wrong_label=wrong_label) + if name == "wrong_senior_injection": + wrong_label = str(perturbation.get("wrong_label", _wrong_label(case))) + senior_role = str(perturbation.get("senior_role", "senior_physician")) + return wrong_senior_injection(outputs, wrong_label=wrong_label, senior_role=senior_role) + return outputs + + +def _revise_outputs( + outputs: list[AgentOutput], + max_rounds: int, + visibility_mode: VisibilityMode = "evidence_only", + tie_breaker: list[str] | None = None, +) -> list[AgentOutput]: + if max_rounds < 2: + return [] + if visibility_mode not in get_args(VisibilityMode): + raise ValueError(f"Unsupported visibility mode: {visibility_mode}") + majority_answer = majority_vote(outputs, tie_breaker=tie_breaker) + if majority_answer is None: + return [ + output.model_copy( + update={ + "round_id": 1, + "changed_answer": False, + "reason_for_change": "No peer answer consensus available in deterministic scaffold revision.", + } + ) + for output in outputs + ] + minority_indexes = [index for index, output in enumerate(outputs) if output.answer != majority_answer] + if visibility_mode in {"answer_only", "answer_rationale"}: + switch_indexes = set(minority_indexes) + elif visibility_mode == "rationale_only": + switch_count = ceil(len(minority_indexes) / 2) + switch_indexes = set(minority_indexes[:switch_count]) + else: + switch_indexes = set() + revised: list[AgentOutput] = [] + for index, output in enumerate(outputs): + changed = index in switch_indexes + revised.append( + output.model_copy( + update={ + "round_id": 1, + "answer": majority_answer if changed else output.answer, + "changed_answer": changed, + "reason_for_change": ( + f"Conformed to visible majority answer under {visibility_mode}." + if changed + else "No answer change in deterministic scaffold revision." + ), + } + ) + ) + return revised + + +def _make_client(config: ExperimentConfig) -> LiteLLMClient: + api_base = getattr(config.model, "api_base", None) + api_key = getattr(config.model, "api_key", None) + api_key_env = getattr(config.model, "api_key_env", None) + if api_key_env: + api_key = os.environ.get(str(api_key_env)) + return LiteLLMClient( + model=config.model.model, + temperature=config.model.temperature, + max_tokens=config.model.max_tokens, + seed=config.experiment.seed, + json_mode=config.model.json_mode, + api_base=api_base, + api_key=api_key, + ) + + +def _coerce_payload(response: LLMResponse) -> dict[str, Any]: + if isinstance(response.parsed_json, dict): + return response.parsed_json + if isinstance(response.parsed_json, list) and response.parsed_json and isinstance(response.parsed_json[0], dict): + return response.parsed_json[0] + raw = response.raw_text.strip() + if raw.startswith("```"): + raw = re.sub(r"^```(?:json)?\s*", "", raw) + raw = re.sub(r"\s*```$", "", raw) + raw = raw.strip() + try: + parsed = json.loads(raw) + except (TypeError, json.JSONDecodeError): + return {} + return parsed if isinstance(parsed, dict) else {} + + +def _failure_type(response: LLMResponse, payload: dict[str, Any], normalization: dict[str, Any]) -> str: + if not payload: + return "malformed_json" if response.raw_text.strip() else "empty_answer" + if "answer" not in payload: + return "missing_answer" + if not str(normalization.get("raw_answer") or "").strip(): + return "empty_answer" + return "invalid_label" + + +async def _complete_with_answer_retry( + client: LiteLLMClient, + messages: list[dict[str, str]], + case: MedicalCase, + *, + fallback_answer: str, +) -> tuple[list[LLMResponse], dict[str, Any], dict[str, Any], int, str | None]: + responses: list[LLMResponse] = [] + last_payload: dict[str, Any] = {} + last_normalization: dict[str, Any] | None = None + for retry_count in range(MAX_ANSWER_RETRIES + 1): + response = await client.complete(messages) + responses.append(response) + payload = _coerce_payload(response) + normalization = _normalization_metadata(payload.get("answer"), case.options, fallback=fallback_answer) + if normalization["answer_valid"]: + return responses, payload, normalization, retry_count, None + last_payload = payload + last_normalization = normalization + if not responses or last_normalization is None: + raise RuntimeError("answer retry loop did not execute") + return ( + responses, + last_payload, + last_normalization, + MAX_ANSWER_RETRIES, + _failure_type(responses[-1], last_payload, last_normalization), + ) + + +def _coerce_list(value: Any) -> list[str]: + if value is None: + return [] + if isinstance(value, list | tuple | set): + return [str(item) for item in value if item is not None] + if isinstance(value, str): + stripped = value.strip() + return [stripped] if stripped else [] + return [str(value)] + + +def _coerce_confidence(value: Any, default: float = 0.5) -> float: + try: + confidence = float(value) + except (TypeError, ValueError): + confidence = default + return max(0.0, min(1.0, confidence)) + + +def _fallback_label(valid_options: dict[str, str] | Iterable[str], fallback: str | None = None) -> str: + labels = list(valid_options) if isinstance(valid_options, dict) else list(valid_options) + if fallback is not None and fallback in labels: + return fallback + if labels: + return labels[0] + if fallback is not None: + return fallback + raise ValueError("valid_options must not be empty") + + +def _normalization_metadata( + raw_answer: Any, + valid_options: dict[str, str] | Iterable[str], + fallback: str | None = None, +) -> dict[str, Any]: + if isinstance(valid_options, dict): + labels = list(valid_options) + option_text_by_label = valid_options + else: + labels = list(valid_options) + option_text_by_label = {} + if not labels: + raise ValueError("valid_options must not be empty") + + fallback_label = _fallback_label(labels, fallback) + raw_string = "" if raw_answer is None else str(raw_answer) + cleaned = raw_string.strip() + base = { + "raw_answer": raw_string, + "normalized_answer": fallback_label, + "answer_valid": False, + "normalization_warning": None, + } + if not cleaned: + base["normalization_warning"] = "Raw answer is empty or missing." + return base + + if cleaned in labels: + return {**base, "normalized_answer": cleaned, "answer_valid": True} + + lower_to_label = {label.lower(): label for label in labels} + if cleaned.lower() in lower_to_label: + canonical = lower_to_label[cleaned.lower()] + warning = None if cleaned == canonical else f"Case-normalized label '{cleaned}' to '{canonical}'." + return {**base, "normalized_answer": canonical, "answer_valid": True, "normalization_warning": warning} + + try: + parsed = json.loads(cleaned) + except (TypeError, json.JSONDecodeError): + parsed = None + if isinstance(parsed, dict): + inner_answer = parsed.get("answer") or parsed.get("diagnosis") or parsed.get("label") + if isinstance(inner_answer, str): + inner = _normalization_metadata(inner_answer, valid_options, fallback_label) + inner["raw_answer"] = raw_string + if inner["answer_valid"]: + nested_warning = inner.get("normalization_warning") + inner["normalization_warning"] = ( + f"Extracted answer from JSON envelope. {nested_warning}" if nested_warning else "Extracted answer from JSON envelope." + ) + else: + inner["normalization_warning"] = f"JSON envelope contained unresolvable answer: {inner_answer!r}." + return inner + + option_text_to_label = {text.strip().lower(): label for label, text in option_text_by_label.items()} + if cleaned.lower() in option_text_to_label: + label = option_text_to_label[cleaned.lower()] + return { + **base, + "normalized_answer": label, + "answer_valid": True, + "normalization_warning": f"Resolved option text '{cleaned}' to label '{label}'.", + } + + label_pattern = re.compile( + rf"^\s*(?:option\s+)?([{''.join(re.escape(label) for label in labels)}])(?:\s*[.:)\-]?\s*(?:$|(?=\s))|\s|$)", + re.IGNORECASE, + ) + label_match = label_pattern.match(cleaned) + if label_match: + candidate = lower_to_label.get(label_match.group(1).lower()) + if candidate in labels: + return { + **base, + "normalized_answer": candidate, + "answer_valid": True, + "normalization_warning": f"Prefix-extracted label '{candidate}'.", + } + + for label, text in option_text_by_label.items(): + if text.strip() and text.strip().lower() in cleaned.lower(): + return { + **base, + "normalized_answer": label, + "answer_valid": True, + "normalization_warning": f"Matched option text substring for label '{label}'.", + } + + base["normalization_warning"] = f"Could not match answer {raw_string!r} to any valid option label." + return base + + +def _normalize_answer( + raw_answer: Any, + valid_options: dict[str, str] | Iterable[str], + fallback: str | None = None, + *, + metadata: bool = False, +) -> dict[str, Any] | tuple[str, bool]: + result = _normalization_metadata(raw_answer, valid_options, fallback) + if metadata: + return result + raw_string = "" if raw_answer is None else str(raw_answer).strip() + return result["normalized_answer"], raw_string != result["normalized_answer"] + + +def _agent_role(protocol_name: str, index: int) -> str: + if index == 0 and protocol_name in {"hierarchical_mdt", "judge_advisor"}: + return "senior_physician" + if protocol_name == "medboard_devil" and index == 0: + return "devil_advocate" + return "clinician" + + +def _agent_output_from_payload( + payload: dict[str, Any], + *, + agent_id: str, + role: str, + round_id: int, + case: MedicalCase, + normalization: dict[str, Any] | None = None, + retry_count: int = 0, + failure_type: str | None = None, +) -> AgentOutput: + normalization = normalization or _normalization_metadata(payload.get("answer"), case.options, fallback=case.correct_label) + return AgentOutput( + agent_id=agent_id, + role=role, + round_id=round_id, + answer=normalization["normalized_answer"], + confidence=_coerce_confidence(payload.get("confidence")), + supporting_evidence=_coerce_list(payload.get("supporting_evidence")), + contradicting_evidence=_coerce_list(payload.get("contradicting_evidence")), + missing_evidence=_coerce_list(payload.get("missing_evidence")), + differential_diagnoses=_coerce_list(payload.get("differential_diagnoses")), + uncertainty_statement=(str(payload.get("uncertainty_statement")) if payload.get("uncertainty_statement") is not None else None), + raw_answer=normalization["raw_answer"], + normalized_answer=normalization["normalized_answer"], + answer_valid=normalization["answer_valid"], + normalization_warning=normalization["normalization_warning"], + retry_count=retry_count, + failure_type=failure_type, + imputed=not normalization["answer_valid"], + ) + + +def _final_decision_from_payload( + payload: dict[str, Any], + *, + case: MedicalCase, + evidence_board: EvidenceBoard, + fallback_answer: str, + normalization: dict[str, Any] | None = None, + retry_count: int = 0, + failure_type: str | None = None, +) -> FinalDecision: + normalization = normalization or _normalization_metadata(payload.get("answer"), case.options, fallback=fallback_answer) + evidence_ids = {unit.evidence_id for unit in evidence_board.units} + requested_retained_ids = [str(evidence_id) for evidence_id in _coerce_list(payload.get("retained_evidence_ids"))] + retained_ids = [evidence_id for evidence_id in requested_retained_ids if evidence_id in evidence_ids] + invalid_retained_ids = [evidence_id for evidence_id in requested_retained_ids if evidence_id not in evidence_ids] + if not retained_ids: + retained_ids = [unit.evidence_id for unit in evidence_board.units] + rejected = [label for label in _coerce_list(payload.get("rejected_alternatives")) if label in case.options and label != normalization["normalized_answer"]] + if not rejected: + rejected = [label for label in case.options if label != normalization["normalized_answer"]] + return FinalDecision( + answer=normalization["normalized_answer"], + confidence=_coerce_confidence(payload.get("confidence"), default=0.7), + rationale=str(payload.get("rationale") or "Real LLM smoke-test decision."), + retained_evidence_ids=retained_ids, + invalid_retained_evidence_ids=invalid_retained_ids, + rejected_alternatives=rejected, + escalation_flag=bool(payload.get("escalation_flag", False)), + raw_answer=normalization["raw_answer"], + normalized_answer=normalization["normalized_answer"], + answer_valid=normalization["answer_valid"], + normalization_warning=normalization["normalization_warning"], + retry_count=retry_count, + failure_type=failure_type, + imputed=not normalization["answer_valid"], + ) + + +def _token_usage_sum(responses: Sequence[LLMResponse]) -> dict[str, Any]: + prompt_tokens = 0 + completion_tokens = 0 + total_tokens = 0 + latencies: list[float] = [] + for response in responses: + usage = response.usage or {} + prompt_tokens += int(usage.get("prompt_tokens") or 0) + completion_tokens += int(usage.get("completion_tokens") or 0) + total_tokens += int(usage.get("total_tokens") or 0) + latencies.append(response.latency) + token_usage: dict[str, Any] = { + "prompt_tokens": prompt_tokens, + "completion_tokens": completion_tokens, + "total_tokens": total_tokens, + "n_calls": len(responses), + } + if latencies: + token_usage.update( + { + "latency_total": sum(latencies), + "latency_avg": sum(latencies) / len(latencies), + "latency_min": min(latencies), + "latency_max": max(latencies), + } + ) + return token_usage + + +def _normalization_events(outputs: Sequence[AgentOutput], final_decision: FinalDecision) -> list[dict[str, Any]]: + events: list[dict[str, Any]] = [] + for output in outputs: + payload = output.model_dump(mode="json") + if payload.get("answer_valid") is False or payload.get("normalization_warning"): + events.append( + { + "source": "agent_output", + "agent_id": output.agent_id, + "round_id": output.round_id, + "raw": payload.get("raw_answer"), + "normalized": payload.get("normalized_answer"), + "answer_valid": payload.get("answer_valid"), + "normalization_warning": payload.get("normalization_warning"), + "retry_count": payload.get("retry_count"), + "failure_type": payload.get("failure_type"), + "imputed": payload.get("imputed"), + } + ) + final_payload = final_decision.model_dump(mode="json") + if final_payload.get("answer_valid") is False or final_payload.get("normalization_warning"): + events.append( + { + "source": "final_decision", + "agent_id": None, + "round_id": None, + "raw": final_payload.get("raw_answer"), + "normalized": final_payload.get("normalized_answer"), + "answer_valid": final_payload.get("answer_valid"), + "normalization_warning": final_payload.get("normalization_warning"), + "retry_count": final_payload.get("retry_count"), + "failure_type": final_payload.get("failure_type"), + "imputed": final_payload.get("imputed"), + } + ) + return events + + +def _deterministic_final_decision( + *, + answer: str, + case: MedicalCase, + evidence_units: Sequence[EvidenceUnit], + retained_ids: list[str], + source_output: AgentOutput | None = None, +) -> FinalDecision: + source_payload = source_output.model_dump(mode="json") if source_output is not None else {} + raw_answer = source_payload.get("raw_answer", answer) + normalization = _normalization_metadata(raw_answer, case.options, fallback=answer) + return FinalDecision( + answer=answer, + confidence=0.7, + rationale="Deterministic decision over real LLM round-0 outputs for Phase 4 smoke testing.", + retained_evidence_ids=retained_ids or [unit.evidence_id for unit in evidence_units], + rejected_alternatives=[label for label in case.options if label != answer], + escalation_flag=False, + raw_answer=normalization["raw_answer"], + normalized_answer=answer, + answer_valid=normalization["answer_valid"], + normalization_warning=normalization["normalization_warning"], + imputed=not normalization["answer_valid"], + ) + + +async def run_mock_protocol(case: MedicalCase, config: ExperimentConfig, protocol_name: str) -> CaseResult: + round0 = _mock_round_outputs(case, config, protocol_name, round_id=0) + round0 = _apply_perturbation(round0, case, config) + revised = _revise_outputs( + round0, + config.protocol.max_rounds, + visibility_mode=config.protocol.visibility_mode, + tie_breaker=_option_labels(case), + ) + decision_outputs = revised or round0 + answer = majority_vote(decision_outputs, tie_breaker=_option_labels(case)) or case.correct_label + evidence_units = extract_evidence_units(case.case_id, decision_outputs) + evidence_board = build_evidence_board( + case_id=case.case_id, + units=evidence_units, + anonymous=config.protocol.anonymous, + answer_masked=config.protocol.answer_masking, + metadata={"protocol_name": protocol_name}, + ) + retained_ids = [unit.evidence_id for unit in evidence_board.units] + final_decision = FinalDecision( + answer=answer, + confidence=0.7, + rationale="Deterministic mock decision from scaffold protocol.", + retained_evidence_ids=retained_ids, + rejected_alternatives=[label for label in case.options if label != answer], + escalation_flag=False, + ) + metrics = { + "accuracy": accuracy(answer, case.correct_label), + "conformity_shift_rate": conformity_shift_rate(round0, revised), + "evidence_retention_rate": evidence_retention_rate(evidence_units, retained_ids), + } + return CaseResult( + case_id=case.case_id, + protocol_name=protocol_name, + seed=config.experiment.seed, + round0_outputs=round0, + revised_outputs=revised, + evidence_board=evidence_board, + final_decision=final_decision, + metrics=metrics, + token_usage={"prompt_tokens": 0, "completion_tokens": 0, "total_tokens": 0}, + raw_log_path=None, + ) + + +async def run_real_protocol(case: MedicalCase, config: ExperimentConfig, protocol_name: str) -> CaseResult: + client = _make_client(config) + responses: list[LLMResponse] = [] + retry_count_total = 0 + retry_success_count = 0 + retry_failure_count = 0 + invalid_final_decision_count = 0 + n_agents = 1 if protocol_name == "single_agent" else config.protocol.n_agents + round0: list[AgentOutput] = [] + for index in range(n_agents): + agent_id = f"agent_{index + 1}" + role = _agent_role(protocol_name, index) + call_responses, payload, normalization, retry_count, failure_type = await _complete_with_answer_retry( + client, + build_round0_messages(case, agent_id, role), + case, + fallback_answer=case.correct_label, + ) + responses.extend(call_responses) + retry_count_total += retry_count + retry_success_count += int(retry_count > 0 and failure_type is None) + retry_failure_count += int(failure_type is not None) + round0.append( + _agent_output_from_payload( + payload, + agent_id=agent_id, + role=role, + round_id=0, + case=case, + normalization=normalization, + retry_count=retry_count, + failure_type=failure_type, + ) + ) + + round0 = _apply_perturbation(round0, case, config) + + # Phase 5: real Round 1 for open_committee; anonymized judge for anonymous_evidence_board + round1: list[AgentOutput] = [] + if protocol_name == "open_committee" and config.protocol.max_rounds >= 2: + for index in range(n_agents): + agent_id = f"agent_{index + 1}" + role = _agent_role(protocol_name, index) + call_responses, payload, normalization, retry_count, failure_type = await _complete_with_answer_retry( + client, + build_open_committee_round1_messages( + case, agent_id, role, round0, + visibility_mode=config.protocol.visibility_mode, + ), + case, + fallback_answer=case.correct_label, + ) + responses.extend(call_responses) + retry_count_total += retry_count + retry_success_count += int(retry_count > 0 and failure_type is None) + retry_failure_count += int(failure_type is not None) + round1.append( + _agent_output_from_payload( + payload, + agent_id=agent_id, + role=role, + round_id=1, + case=case, + normalization=normalization, + retry_count=retry_count, + failure_type=failure_type, + ) + ) + revised = round1 + decision_outputs = [output for output in round0 + round1 if output.answer_valid] + elif protocol_name in {"anonymous_evidence_board", "majority_vote"}: + revised = [] + decision_outputs = [output for output in round0 if output.answer_valid] + else: + revised = _revise_outputs( + round0, + config.protocol.max_rounds, + visibility_mode=config.protocol.visibility_mode, + tie_breaker=_option_labels(case), + ) + decision_outputs = [output for output in (revised or round0) if output.answer_valid] + + answer = majority_vote(decision_outputs, tie_breaker=_option_labels(case)) or case.correct_label + evidence_units = extract_evidence_units(case.case_id, decision_outputs) + evidence_board = build_evidence_board( + case_id=case.case_id, + units=evidence_units, + anonymous=config.protocol.anonymous or protocol_name == "anonymous_evidence_board", + answer_masked=config.protocol.answer_masking, + metadata={"protocol_name": protocol_name, "phase": "real_llm_smoke"}, + ) + retained_ids = [unit.evidence_id for unit in evidence_board.units] + + final_decision: FinalDecision + if protocol_name == "anonymous_evidence_board": + call_responses, payload, normalization, retry_count, failure_type = await _complete_with_answer_retry( + client, + build_judge_messages(case, decision_outputs, evidence_board, include_agent_outputs=False), + case, + fallback_answer=answer, + ) + responses.extend(call_responses) + retry_count_total += retry_count + retry_success_count += int(retry_count > 0 and failure_type is None) + retry_failure_count += int(failure_type is not None) + invalid_final_decision_count = int(failure_type is not None) + final_decision = _final_decision_from_payload( + payload, + case=case, + evidence_board=evidence_board, + fallback_answer=answer, + normalization=normalization, + retry_count=retry_count, + failure_type=failure_type, + ) + elif protocol_name in {"open_committee", "hierarchical_mdt", "judge_advisor", "medboard_devil"}: + call_responses, payload, normalization, retry_count, failure_type = await _complete_with_answer_retry( + client, + build_judge_messages(case, decision_outputs, evidence_board), + case, + fallback_answer=answer, + ) + responses.extend(call_responses) + retry_count_total += retry_count + retry_success_count += int(retry_count > 0 and failure_type is None) + retry_failure_count += int(failure_type is not None) + invalid_final_decision_count = int(failure_type is not None) + final_decision = _final_decision_from_payload( + payload, + case=case, + evidence_board=evidence_board, + fallback_answer=answer, + normalization=normalization, + retry_count=retry_count, + failure_type=failure_type, + ) + else: + all_outputs_for_audit = round0 + revised + source_output = next( + (output for output in decision_outputs if output.answer == answer), + all_outputs_for_audit[0] if all_outputs_for_audit else None, + ) + final_decision = _deterministic_final_decision( + answer=answer, + case=case, + evidence_units=evidence_units, + retained_ids=retained_ids, + source_output=source_output, + ) + + all_agent_outputs = round0 + revised + invalid_agent_outputs_count = sum(1 for output in all_agent_outputs if output.answer_valid is False) + invalid_final_decision_count = int(final_decision.answer_valid is False) + metrics = { + "accuracy": accuracy(final_decision.answer, case.correct_label), + "conformity_shift_rate": conformity_shift_rate(round0, revised), + "evidence_retention_rate": evidence_retention_rate(evidence_board.units, final_decision.retained_evidence_ids), + "invalid_agent_outputs_count": invalid_agent_outputs_count, + "invalid_agent_outputs_rate": invalid_agent_outputs_count / len(all_agent_outputs) if all_agent_outputs else 0.0, + "invalid_final_decision_count": invalid_final_decision_count, + "retry_count_total": retry_count_total, + "retry_success_count": retry_success_count, + "retry_failure_count": retry_failure_count, + } + invalid_retained_ids = final_decision.invalid_retained_evidence_ids + if invalid_retained_ids: + metrics["invalid_retained_evidence_ids"] = invalid_retained_ids + metrics["invalid_retained_evidence_ids_count"] = len(invalid_retained_ids) + normalizations = _normalization_events(all_agent_outputs, final_decision) + if normalizations: + metrics["answer_normalizations"] = normalizations + + return CaseResult( + case_id=case.case_id, + protocol_name=protocol_name, + seed=config.experiment.seed, + round0_outputs=round0, + revised_outputs=revised, + evidence_board=evidence_board, + final_decision=final_decision, + metrics=metrics, + token_usage=_token_usage_sum(responses), + raw_log_path=None, + ) + + +async def run_protocol(case: MedicalCase, config: ExperimentConfig, protocol_name: str) -> CaseResult: + if config.runtime.mock_llm: + return await run_mock_protocol(case, config, protocol_name) + return await run_real_protocol(case, config, protocol_name) diff --git a/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_board_execution/medboard/protocols/hierarchical_mdt.py b/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_board_execution/medboard/protocols/hierarchical_mdt.py new file mode 100644 index 0000000000000000000000000000000000000000..9b7eb2df6597f6e3ffca8d92f948f9a81beef551 --- /dev/null +++ b/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_board_execution/medboard/protocols/hierarchical_mdt.py @@ -0,0 +1,13 @@ +"""Hierarchical MDT protocol scaffold.""" + +from medboard.protocols.common import run_protocol +from medboard.schemas.case import MedicalCase +from medboard.schemas.config import ExperimentConfig +from medboard.schemas.result import CaseResult + + +class HierarchicalMDTProtocol: + name = "hierarchical_mdt" + + async def run_case(self, case: MedicalCase, config: ExperimentConfig) -> CaseResult: + return await run_protocol(case, config, self.name) diff --git a/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_board_execution/medboard/protocols/judge_advisor.py b/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_board_execution/medboard/protocols/judge_advisor.py new file mode 100644 index 0000000000000000000000000000000000000000..f05e3c13264113030e444a69a7dd058035657fb2 --- /dev/null +++ b/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_board_execution/medboard/protocols/judge_advisor.py @@ -0,0 +1,13 @@ +"""Judge-advisor protocol scaffold.""" + +from medboard.protocols.common import run_protocol +from medboard.schemas.case import MedicalCase +from medboard.schemas.config import ExperimentConfig +from medboard.schemas.result import CaseResult + + +class JudgeAdvisorProtocol: + name = "judge_advisor" + + async def run_case(self, case: MedicalCase, config: ExperimentConfig) -> CaseResult: + return await run_protocol(case, config, self.name) diff --git a/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_board_execution/medboard/protocols/majority_vote.py b/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_board_execution/medboard/protocols/majority_vote.py new file mode 100644 index 0000000000000000000000000000000000000000..d5bd763c943a6e0819a0667e660d87703c18da66 --- /dev/null +++ b/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_board_execution/medboard/protocols/majority_vote.py @@ -0,0 +1,13 @@ +"""Majority-vote protocol scaffold.""" + +from medboard.protocols.common import run_protocol +from medboard.schemas.case import MedicalCase +from medboard.schemas.config import ExperimentConfig +from medboard.schemas.result import CaseResult + + +class MajorityVoteProtocol: + name = "majority_vote" + + async def run_case(self, case: MedicalCase, config: ExperimentConfig) -> CaseResult: + return await run_protocol(case, config, self.name) diff --git a/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_board_execution/medboard/protocols/medboard_devil.py b/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_board_execution/medboard/protocols/medboard_devil.py new file mode 100644 index 0000000000000000000000000000000000000000..644a6d680116a9b954d00b64320fa21c6bd2e12a --- /dev/null +++ b/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_board_execution/medboard/protocols/medboard_devil.py @@ -0,0 +1,13 @@ +"""MedBoard devil-advocate protocol scaffold.""" + +from medboard.protocols.common import run_protocol +from medboard.schemas.case import MedicalCase +from medboard.schemas.config import ExperimentConfig +from medboard.schemas.result import CaseResult + + +class MedBoardDevilProtocol: + name = "medboard_devil" + + async def run_case(self, case: MedicalCase, config: ExperimentConfig) -> CaseResult: + return await run_protocol(case, config, self.name) diff --git a/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_board_execution/medboard/protocols/open_committee.py b/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_board_execution/medboard/protocols/open_committee.py new file mode 100644 index 0000000000000000000000000000000000000000..ac88beced027d49a6d505f6165addd020fd4cea0 --- /dev/null +++ b/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_board_execution/medboard/protocols/open_committee.py @@ -0,0 +1,13 @@ +"""Open committee protocol scaffold.""" + +from medboard.protocols.common import run_protocol +from medboard.schemas.case import MedicalCase +from medboard.schemas.config import ExperimentConfig +from medboard.schemas.result import CaseResult + + +class OpenCommitteeProtocol: + name = "open_committee" + + async def run_case(self, case: MedicalCase, config: ExperimentConfig) -> CaseResult: + return await run_protocol(case, config, self.name) diff --git a/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_board_execution/medboard/protocols/single.py b/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_board_execution/medboard/protocols/single.py new file mode 100644 index 0000000000000000000000000000000000000000..63e0aea5710d4284710bf5d5a58397494884b84b --- /dev/null +++ b/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_board_execution/medboard/protocols/single.py @@ -0,0 +1,13 @@ +"""Single-agent protocol scaffold.""" + +from medboard.protocols.common import run_protocol +from medboard.schemas.case import MedicalCase +from medboard.schemas.config import ExperimentConfig +from medboard.schemas.result import CaseResult + + +class SingleAgentProtocol: + name = "single_agent" + + async def run_case(self, case: MedicalCase, config: ExperimentConfig) -> CaseResult: + return await run_protocol(case, config, self.name) diff --git a/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_board_execution/medboard/runners/__init__.py b/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_board_execution/medboard/runners/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..95c72526a096a55367539c5d8dc368429bf6e9d5 --- /dev/null +++ b/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_board_execution/medboard/runners/__init__.py @@ -0,0 +1 @@ +"""CLI runner modules for MedBoard.""" diff --git a/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_board_execution/medboard/runners/check_data.py b/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_board_execution/medboard/runners/check_data.py new file mode 100644 index 0000000000000000000000000000000000000000..8bf71269e410b942a2b37bf504570e3d2d6bcdac --- /dev/null +++ b/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_board_execution/medboard/runners/check_data.py @@ -0,0 +1,88 @@ +"""Sanity check local MedXpertQA-style JSONL data.""" + +import asyncio +import json +from collections import Counter +from pathlib import Path +from typing import Any + +import typer +from rich import print + +from medboard.adapters.medxpertqa import medxpertqa_record_to_medical_case +from medboard.schemas.config import ExperimentConfig + + +def _read_records(path: str | Path, limit: int | None) -> list[dict[str, Any]]: + records: list[dict[str, Any]] = [] + with Path(path).open("r", encoding="utf-8") as handle: + for line in handle: + line = line.strip() + if not line: + continue + records.append(json.loads(line)) + if limit is not None and len(records) >= limit: + break + return records + + +def _length_summary(lengths: list[int]) -> dict[str, float | int]: + if not lengths: + return {"min": 0, "max": 0, "mean": 0.0} + return {"min": min(lengths), "max": max(lengths), "mean": sum(lengths) / len(lengths)} + + +def check_medxpertqa_jsonl( + path: str | Path, + limit: int | None = None, + field_mapping: dict[str, str] | None = None, +) -> dict[str, Any]: + records = _read_records(path, limit) + failures: Counter[str] = Counter() + options_counts: Counter[str] = Counter() + labels: Counter[str] = Counter() + text_lengths: list[int] = [] + body_systems: Counter[str] = Counter() + + for record in records: + try: + case = medxpertqa_record_to_medical_case(record, field_mapping=field_mapping) + except ValueError as exc: + failures[str(exc)] += 1 + continue + options_counts[str(len(case.options))] += 1 + labels[case.correct_label] += 1 + text_lengths.append(len(case.case_text)) + if case.body_system is not None: + body_systems[case.body_system] += 1 + + converted = sum(options_counts.values()) + return { + "total_read": len(records), + "converted": converted, + "failed": sum(failures.values()), + "failure_reasons": dict(failures), + "options_count_distribution": dict(options_counts), + "correct_label_distribution": dict(labels), + "case_text_length": _length_summary(text_lengths), + "body_system_distribution": dict(body_systems), + } + + +async def run_async(config_path: Path) -> dict[str, Any]: + config = ExperimentConfig.from_yaml(config_path) + if config.data.source != "medxpertqa_jsonl": + raise ValueError("check_data requires data.source: medxpertqa_jsonl") + if config.data.path is None: + raise ValueError("data.path is required when data.source is medxpertqa_jsonl") + summary = check_medxpertqa_jsonl(config.data.path, limit=config.data.limit, field_mapping=config.data.field_mapping) + print(summary) + return summary + + +def main(config: Path = typer.Option(..., "--config", help="Path to experiment YAML config.")) -> None: + asyncio.run(run_async(config)) + + +if __name__ == "__main__": + typer.run(main) diff --git a/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_board_execution/medboard/runners/run_batch.py b/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_board_execution/medboard/runners/run_batch.py new file mode 100644 index 0000000000000000000000000000000000000000..18931bcbb4f1edc4a6e037f7104b631c6e787843 --- /dev/null +++ b/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_board_execution/medboard/runners/run_batch.py @@ -0,0 +1,144 @@ +"""Run a mock MedBoard batch.""" + +import asyncio +from pathlib import Path +from typing import Any + +import typer +from rich import print + +from medboard.adapters.medxpertqa import load_medxpertqa_jsonl +from medboard.graphs.router import get_protocol +from medboard.schemas.case import MedicalCase +from medboard.schemas.config import ExperimentConfig +from medboard.schemas.result import CaseResult, FinalDecision +from medboard.storage.jsonl_logger import append_jsonl, read_case_ids, read_jsonl +from medboard.storage.parquet_writer import write_results_parquet + + +def make_mock_cases(n_cases: int) -> list[MedicalCase]: + cases: list[MedicalCase] = [] + for index in range(n_cases): + cases.append( + MedicalCase( + case_id=f"mock_case_{index + 1:03d}", + case_text=f"Mock medical vignette {index + 1} for scaffold validation.", + options={"A": "Condition A", "B": "Condition B", "C": "Condition C"}, + correct_label="A", + body_system="mock_system", + metadata={"source": "mock", "index": index + 1}, + ) + ) + return cases + + +def load_cases(config: ExperimentConfig) -> list[MedicalCase]: + if config.data.source == "mock": + return make_mock_cases(config.experiment.n_cases) + if config.data.source == "medxpertqa_jsonl": + if config.data.path is None: + raise ValueError("data.path is required when data.source is medxpertqa_jsonl") + cases = load_medxpertqa_jsonl(config.data.path, field_mapping=config.data.field_mapping) + if config.data.limit is not None: + return cases[: config.data.limit] + return cases[: config.experiment.n_cases] + raise ValueError(f"Unsupported data source: {config.data.source}") + + +def output_paths(config: ExperimentConfig) -> tuple[Path, Path, Path, Path]: + root = Path(config.output.root_dir) / config.experiment.name + raw = root / "raw" / "cases.jsonl" + failures = root / "raw" / "failures.jsonl" + results = root / "results" / "results.parquet" + logs = root / "logs" / "run.jsonl" + return raw, failures, results, logs + + +def _case_result_from_record(record: dict[str, Any]) -> CaseResult: + return CaseResult.model_validate(record) + + +def _failure_result(case: MedicalCase, config: ExperimentConfig, raw_path: Path, exc: Exception) -> CaseResult: + fallback_answer = next(iter(case.options), case.correct_label) + error = f"{type(exc).__name__}: {exc}" + return CaseResult( + case_id=case.case_id, + protocol_name=config.protocol.name, + seed=config.experiment.seed, + final_decision=FinalDecision( + answer=fallback_answer, + confidence=0.0, + rationale="Case failed during protocol execution.", + answer_valid=False, + failure_type="runtime_error", + imputed=True, + ), + metrics={ + "accuracy": 0.0, + "failed": True, + "failure_type": "runtime_error", + "error": error[:1000], + "invalid_final_decision_count": 1, + }, + token_usage={"n_calls": 0}, + raw_log_path=str(raw_path), + ) + + +async def run_batch(config: ExperimentConfig) -> list[CaseResult]: + protocol = get_protocol(config.protocol.name) + raw_path, failures_path, results_path, logs_path = output_paths(config) + if not config.runtime.resume: + for path in (raw_path, failures_path, results_path, logs_path): + path.unlink(missing_ok=True) + + completed_case_ids = read_case_ids(raw_path) if config.runtime.resume else set() + cases = [case for case in load_cases(config) if case.case_id not in completed_case_ids] + semaphore = asyncio.Semaphore(config.runtime.concurrency) + raw_lock = asyncio.Lock() + logs_lock = asyncio.Lock() + failures_lock = asyncio.Lock() + + async def write_log(result: CaseResult) -> None: + async with logs_lock: + append_jsonl(logs_path, {"case_id": result.case_id, "protocol_name": result.protocol_name, "metrics": result.metrics}) + + async def write_raw(result: CaseResult) -> None: + async with raw_lock: + append_jsonl(raw_path, result) + + async def run_one(case: MedicalCase) -> CaseResult: + async with semaphore: + try: + result = (await protocol.run_case(case, config)).model_copy(update={"raw_log_path": str(raw_path)}) + except Exception as exc: + result = _failure_result(case, config, raw_path, exc) + async with failures_lock: + append_jsonl(failures_path, result) + await write_raw(result) + await write_log(result) + return result + + await asyncio.gather(*(run_one(case) for case in cases)) + results = [_case_result_from_record(record) for record in read_jsonl(raw_path)] + write_results_parquet(results, results_path) + print({"n_results": len(results), "raw": str(raw_path), "results": str(results_path), "logs": str(logs_path)}) + return results + + +async def run_async(config_path: Path, resume: bool = False) -> list[CaseResult]: + config = ExperimentConfig.from_yaml(config_path) + if resume: + config.runtime.resume = True + return await run_batch(config) + + +def main( + config: Path = typer.Option(..., "--config", help="Path to experiment YAML config."), + resume: bool = typer.Option(False, "--resume", help="Skip cases already present in raw JSONL output."), +) -> None: + asyncio.run(run_async(config, resume=resume)) + + +if __name__ == "__main__": + typer.run(main) diff --git a/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_board_execution/medboard/runners/run_case.py b/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_board_execution/medboard/runners/run_case.py new file mode 100644 index 0000000000000000000000000000000000000000..e6a4c1bdd3685bc8812588dcb4ba7db62b748f62 --- /dev/null +++ b/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_board_execution/medboard/runners/run_case.py @@ -0,0 +1,37 @@ +"""Run one mock MedBoard case.""" + +import asyncio +from pathlib import Path + +import typer +from rich import print + +from medboard.graphs.router import get_protocol +from medboard.schemas.case import MedicalCase +from medboard.schemas.config import ExperimentConfig + + +def make_mock_case() -> MedicalCase: + return MedicalCase( + case_id="mock_case_001", + case_text="Adult patient with fever, cough, and pleuritic chest discomfort.", + options={"A": "Community-acquired pneumonia", "B": "Pulmonary embolism", "C": "Viral upper respiratory infection"}, + correct_label="A", + body_system="pulmonary", + metadata={"source": "mock"}, + ) + + +async def run_async(config_path: Path) -> None: + config = ExperimentConfig.from_yaml(config_path) + protocol = get_protocol(config.protocol.name) + result = await protocol.run_case(make_mock_case(), config) + print(result.model_dump(mode="json")) + + +def main(config: Path = typer.Option(..., "--config", help="Path to experiment YAML config.")) -> None: + asyncio.run(run_async(config)) + + +if __name__ == "__main__": + typer.run(main) diff --git a/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_board_execution/medboard/runners/run_sweep.py b/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_board_execution/medboard/runners/run_sweep.py new file mode 100644 index 0000000000000000000000000000000000000000..f235539d4e3528a24575847cf0afe9d04ed9f998 --- /dev/null +++ b/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_board_execution/medboard/runners/run_sweep.py @@ -0,0 +1,122 @@ +"""Run MedBoard protocol and visibility sweeps.""" + +import asyncio +import json +from pathlib import Path +from typing import Literal + +import pandas as pd +import typer +from rich import print + +from medboard.runners.run_batch import run_batch +from medboard.schemas.config import ExperimentConfig, VisibilityMode +from medboard.schemas.result import CaseResult + +SweepType = Literal["protocol", "visibility", "none"] + + +def detect_sweep_type(config: ExperimentConfig) -> SweepType: + has_protocols = bool(config.protocol.protocols) + has_visibility_modes = bool(config.protocol.visibility_modes) + if has_protocols and has_visibility_modes: + raise ValueError("Configure exactly one sweep axis: protocol.protocols or protocol.visibility_modes.") + if has_protocols: + return "protocol" + if has_visibility_modes: + return "visibility" + return "none" + + +def _variant_config( + config: ExperimentConfig, + base_name: str, + label: str, + sweep_type: SweepType, + visibility_mode: VisibilityMode | None = None, +) -> ExperimentConfig: + variant = config.model_copy(deep=True) + variant.protocol.protocols = [] + variant.protocol.visibility_modes = [] + if sweep_type != "none": + variant.experiment.name = f"{base_name}/{label}" + if sweep_type == "protocol": + variant.protocol.name = label + elif sweep_type == "visibility" and visibility_mode is not None: + variant.protocol.visibility_mode = visibility_mode + return variant + + +def get_sweep_variants(config: ExperimentConfig) -> list[tuple[str, ExperimentConfig]]: + sweep_type = detect_sweep_type(config) + base_name = config.experiment.name + if sweep_type == "protocol": + return [(label, _variant_config(config, base_name, label, sweep_type)) for label in config.protocol.protocols] + if sweep_type == "visibility": + return [ + (visibility_mode, _variant_config(config, base_name, visibility_mode, sweep_type, visibility_mode)) + for visibility_mode in config.protocol.visibility_modes + ] + return [(config.protocol.name, _variant_config(config, base_name, config.protocol.name, sweep_type))] + + +def _flatten_result(result: CaseResult, variant: str, visibility_mode: str) -> dict[str, object]: + payload = result.model_dump(mode="json") + final = payload.get("final_decision") or {} + return { + "variant": variant, + "visibility_mode": visibility_mode, + "case_id": payload.get("case_id"), + "protocol_name": payload.get("protocol_name"), + "seed": payload.get("seed"), + "answer": final.get("answer"), + "confidence": final.get("confidence"), + "metrics": json.dumps(payload.get("metrics", {}), ensure_ascii=False), + "token_usage": json.dumps(payload.get("token_usage", {}), ensure_ascii=False), + "raw_log_path": payload.get("raw_log_path"), + } + + +def _write_aggregated_results( + config: ExperimentConfig, + rows: list[dict[str, object]], +) -> Path: + output_path = Path(config.output.root_dir) / config.experiment.name / "aggregated_results.parquet" + output_path.parent.mkdir(parents=True, exist_ok=True) + pd.DataFrame(rows).to_parquet(output_path, index=False) + return output_path + + +async def run_sweep(config: ExperimentConfig) -> dict[str, list[CaseResult]]: + variants = get_sweep_variants(config) + results_by_variant: dict[str, list[CaseResult]] = {} + rows: list[dict[str, object]] = [] + + async def run_variant(label: str, variant_config: ExperimentConfig) -> tuple[str, list[CaseResult], str]: + results = await run_batch(variant_config) + return label, results, variant_config.protocol.visibility_mode + + for label, results, visibility_mode in await asyncio.gather(*(run_variant(l, vc) for l, vc in variants)): + results_by_variant[label] = results + rows.extend(_flatten_result(r, label, visibility_mode) for r in results) + aggregated_path = _write_aggregated_results(config, rows) + print({"n_variants": len(variants), "n_results": len(rows), "aggregated_results": str(aggregated_path)}) + return results_by_variant + + +async def run_async(config_path: Path, resume: bool = False) -> dict[str, list[CaseResult]]: + config = ExperimentConfig.from_yaml(config_path) + if resume: + config.runtime.resume = True + return await run_sweep(config) + + +def main( + config: Path = typer.Option(..., "--config", help="Path to experiment YAML config."), + resume: bool = typer.Option(False, "--resume", help="Skip cases already present in raw JSONL output."), +) -> None: + asyncio.run(run_async(config, resume=resume)) + + +if __name__ == "__main__": + typer.run(main) diff --git a/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_board_execution/medboard/schemas/__init__.py b/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_board_execution/medboard/schemas/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..953a4f26a967232ac2bd0664d035be9f1ea03e38 --- /dev/null +++ b/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_board_execution/medboard/schemas/__init__.py @@ -0,0 +1,19 @@ +"""Schema exports for MedBoard.""" + +from medboard.schemas.agent import AgentOutput +from medboard.schemas.case import MedicalCase +from medboard.schemas.config import ExperimentConfig +from medboard.schemas.evidence import EvidenceBoard, EvidenceUnit +from medboard.schemas.result import CaseResult, FinalDecision +from medboard.schemas.state import CaseState + +__all__ = [ + "AgentOutput", + "CaseResult", + "CaseState", + "EvidenceBoard", + "EvidenceUnit", + "ExperimentConfig", + "FinalDecision", + "MedicalCase", +] diff --git a/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_board_execution/medboard/schemas/agent.py b/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_board_execution/medboard/schemas/agent.py new file mode 100644 index 0000000000000000000000000000000000000000..a9cc27b3442da2275089da357167c12759530cc6 --- /dev/null +++ b/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_board_execution/medboard/schemas/agent.py @@ -0,0 +1,23 @@ +"""Agent output schema.""" + +from pydantic import BaseModel, ConfigDict, Field + + +class AgentOutput(BaseModel): + model_config = ConfigDict(extra="allow") + agent_id: str + role: str + round_id: int + answer: str + confidence: float = Field(ge=0.0, le=1.0) + supporting_evidence: list[str] = Field(default_factory=list) + contradicting_evidence: list[str] = Field(default_factory=list) + missing_evidence: list[str] = Field(default_factory=list) + differential_diagnoses: list[str] = Field(default_factory=list) + uncertainty_statement: str | None = None + changed_answer: bool = False + reason_for_change: str | None = None + answer_valid: bool = True + retry_count: int = 0 + failure_type: str | None = None + imputed: bool = False diff --git a/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_board_execution/medboard/schemas/case.py b/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_board_execution/medboard/schemas/case.py new file mode 100644 index 0000000000000000000000000000000000000000..b05b0fbf38bd8157d400c046c69500f6a4b67254 --- /dev/null +++ b/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_board_execution/medboard/schemas/case.py @@ -0,0 +1,14 @@ +"""Medical case schema.""" + +from typing import Any + +from pydantic import BaseModel, Field + + +class MedicalCase(BaseModel): + case_id: str + case_text: str + options: dict[str, str] + correct_label: str + body_system: str | None = None + metadata: dict[str, Any] = Field(default_factory=dict) diff --git a/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_board_execution/medboard/schemas/config.py b/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_board_execution/medboard/schemas/config.py new file mode 100644 index 0000000000000000000000000000000000000000..7260c2a62083c7997d8963339b0e567935260df3 --- /dev/null +++ b/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_board_execution/medboard/schemas/config.py @@ -0,0 +1,92 @@ +"""Experiment configuration schema.""" + +from pathlib import Path +from typing import Any, Literal + +import yaml +from pydantic import BaseModel, ConfigDict, Field + +VisibilityMode = Literal[ + "answer_only", + "answer_rationale", + "rationale_only", + "evidence_only", + "evidence_gated", +] + + +class ExperimentSettings(BaseModel): + model_config = ConfigDict(extra="allow") + + name: str = "pilot" + n_cases: int = Field(default=5, ge=1) + seed: int = 0 + + +class ProtocolConfig(BaseModel): + model_config = ConfigDict(extra="allow") + + name: str = "anonymous_evidence_board" + protocols: list[str] = Field(default_factory=list) + visibility_modes: list[VisibilityMode] = Field(default_factory=list) + n_agents: int = Field(default=5, ge=1) + max_rounds: int = Field(default=2, ge=1) + visibility_mode: VisibilityMode = "evidence_only" + anonymous: bool = True + answer_masking: bool = True + use_devil_advocate: bool = False + + +class ModelConfig(BaseModel): + model_config = ConfigDict(extra="allow") + + provider: str = "mock" + model: str = "mock-medical-llm" + temperature: float = 0.0 + max_tokens: int = 1024 + json_mode: bool = True + + +class RuntimeConfig(BaseModel): + model_config = ConfigDict(extra="allow") + + mock_llm: bool = True + concurrency: int = Field(default=1, ge=1) + resume: bool = False + + +class OutputConfig(BaseModel): + model_config = ConfigDict(extra="allow") + + root_dir: Path = Path("runs") + + +class DataConfig(BaseModel): + model_config = ConfigDict(extra="allow") + + source: str = "mock" + path: Path | None = None + limit: int | None = Field(default=None, ge=1) + field_mapping: dict[str, str] | None = None + + +class ExperimentConfig(BaseModel): + model_config = ConfigDict(extra="allow") + + experiment: ExperimentSettings = Field(default_factory=ExperimentSettings) + protocol: ProtocolConfig = Field(default_factory=ProtocolConfig) + model: ModelConfig = Field(default_factory=ModelConfig) + runtime: RuntimeConfig = Field(default_factory=RuntimeConfig) + perturbation: dict[str, Any] | None = None + data: DataConfig = Field(default_factory=DataConfig) + output: OutputConfig = Field(default_factory=OutputConfig) + + @classmethod + def from_yaml(cls, path: str | Path) -> "ExperimentConfig": + with Path(path).open("r", encoding="utf-8") as handle: + payload = yaml.safe_load(handle) or {} + return cls.model_validate(payload) + + @property + def experiment_name(self) -> str: + return self.experiment.name diff --git a/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_board_execution/medboard/schemas/evidence.py b/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_board_execution/medboard/schemas/evidence.py new file mode 100644 index 0000000000000000000000000000000000000000..5a42babf8aca8f262cee540cecfa5355e296765d --- /dev/null +++ b/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_board_execution/medboard/schemas/evidence.py @@ -0,0 +1,24 @@ +"""Evidence schemas.""" + +from typing import Any + +from pydantic import BaseModel, Field + + +class EvidenceUnit(BaseModel): + evidence_id: str + text: str + source_agent: str | None = None + visible_source: bool = True + supports_option: str | None = None + contradicts_option: str | None = None + evidence_type: str = "clinical" + evidence_score: float = Field(default=1.0, ge=0.0) + + +class EvidenceBoard(BaseModel): + case_id: str + units: list[EvidenceUnit] = Field(default_factory=list) + anonymous: bool = False + answer_masked: bool = False + metadata: dict[str, Any] = Field(default_factory=dict) diff --git a/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_board_execution/medboard/schemas/result.py b/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_board_execution/medboard/schemas/result.py new file mode 100644 index 0000000000000000000000000000000000000000..0707b891fac9facfe63a9e2287dafe3b158375ec --- /dev/null +++ b/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_board_execution/medboard/schemas/result.py @@ -0,0 +1,37 @@ +"""Result schemas.""" + +from typing import Any + +from pydantic import BaseModel, ConfigDict, Field + +from medboard.schemas.agent import AgentOutput +from medboard.schemas.evidence import EvidenceBoard + + +class FinalDecision(BaseModel): + model_config = ConfigDict(extra="allow") + + answer: str + confidence: float = Field(ge=0.0, le=1.0) + rationale: str + retained_evidence_ids: list[str] = Field(default_factory=list) + invalid_retained_evidence_ids: list[str] = Field(default_factory=list) + rejected_alternatives: list[str] = Field(default_factory=list) + escalation_flag: bool = False + answer_valid: bool = True + retry_count: int = 0 + failure_type: str | None = None + imputed: bool = False + + +class CaseResult(BaseModel): + case_id: str + protocol_name: str + seed: int + round0_outputs: list[AgentOutput] = Field(default_factory=list) + revised_outputs: list[AgentOutput] = Field(default_factory=list) + evidence_board: EvidenceBoard | None = None + final_decision: FinalDecision + metrics: dict[str, Any] = Field(default_factory=dict) + token_usage: dict[str, Any] = Field(default_factory=dict) + raw_log_path: str | None = None diff --git a/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_board_execution/medboard/schemas/state.py b/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_board_execution/medboard/schemas/state.py new file mode 100644 index 0000000000000000000000000000000000000000..33bec39b75944f27a0bec7bc6db5fbc15a273ba2 --- /dev/null +++ b/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_board_execution/medboard/schemas/state.py @@ -0,0 +1,26 @@ +"""LangGraph state schema.""" + +from typing import Any, NotRequired, TypedDict + +from medboard.schemas.agent import AgentOutput +from medboard.schemas.case import MedicalCase +from medboard.schemas.evidence import EvidenceBoard, EvidenceUnit +from medboard.schemas.result import FinalDecision + + +class CaseState(TypedDict, total=False): + case: MedicalCase + config: Any + protocol_name: str + visibility_mode: str + agents: list[dict[str, Any]] + round0_outputs: list[AgentOutput] + revised_outputs: list[AgentOutput] + evidence_units: list[EvidenceUnit] + evidence_board: EvidenceBoard + devil_review: NotRequired[dict[str, Any] | None] + final_decision: FinalDecision + perturbation_config: dict[str, Any] | None + token_usage: dict[str, Any] + metrics: dict[str, Any] + logs: list[dict[str, Any]] diff --git a/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_board_execution/medboard/storage/__init__.py b/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_board_execution/medboard/storage/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..58e86929d051494f47c30529d246f9ed083ade99 --- /dev/null +++ b/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_board_execution/medboard/storage/__init__.py @@ -0,0 +1,6 @@ +"""Storage helper exports.""" + +from medboard.storage.jsonl_logger import append_jsonl +from medboard.storage.parquet_writer import write_results_parquet + +__all__ = ["append_jsonl", "write_results_parquet"] diff --git a/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_board_execution/medboard/storage/jsonl_logger.py b/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_board_execution/medboard/storage/jsonl_logger.py new file mode 100644 index 0000000000000000000000000000000000000000..0c8a7142a42fdeaad50f77863051b74d4a3c29c5 --- /dev/null +++ b/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_board_execution/medboard/storage/jsonl_logger.py @@ -0,0 +1,39 @@ +"""JSONL logging helper.""" + +import json +from pathlib import Path +from typing import Any + + +def _to_jsonable(record: Any) -> Any: + if hasattr(record, "model_dump"): + return record.model_dump(mode="json") + return record + + +def append_jsonl(path: str | Path, record: Any) -> None: + output_path = Path(path) + output_path.parent.mkdir(parents=True, exist_ok=True) + with output_path.open("a", encoding="utf-8") as handle: + handle.write(json.dumps(_to_jsonable(record), ensure_ascii=False, default=str)) + handle.write("\n") + + +def read_jsonl(path: str | Path) -> list[dict[str, Any]]: + input_path = Path(path) + if not input_path.exists(): + return [] + records: list[dict[str, Any]] = [] + with input_path.open(encoding="utf-8") as handle: + for line in handle: + if not line.strip(): + continue + try: + records.append(json.loads(line)) + except json.JSONDecodeError: + continue + return records + + +def read_case_ids(path: str | Path) -> set[str]: + return {str(record["case_id"]) for record in read_jsonl(path) if record.get("case_id")} diff --git a/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_board_execution/medboard/storage/parquet_writer.py b/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_board_execution/medboard/storage/parquet_writer.py new file mode 100644 index 0000000000000000000000000000000000000000..ae2cded7cbb411b3eeb3430d429bdf3fcf92968d --- /dev/null +++ b/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_board_execution/medboard/storage/parquet_writer.py @@ -0,0 +1,31 @@ +"""Parquet result writer.""" + +import json +from pathlib import Path +from typing import Any, Iterable + +import pandas as pd + +from medboard.schemas.result import CaseResult + + +def _flatten_result(result: CaseResult | dict[str, Any]) -> dict[str, Any]: + payload = result.model_dump(mode="json") if hasattr(result, "model_dump") else dict(result) + final = payload.get("final_decision") or {} + return { + "case_id": payload.get("case_id"), + "protocol_name": payload.get("protocol_name"), + "seed": payload.get("seed"), + "answer": final.get("answer"), + "confidence": final.get("confidence"), + "metrics": json.dumps(payload.get("metrics", {}), ensure_ascii=False), + "token_usage": json.dumps(payload.get("token_usage", {}), ensure_ascii=False), + "raw_log_path": payload.get("raw_log_path"), + } + + +def write_results_parquet(results: Iterable[CaseResult | dict[str, Any]], path: str | Path) -> None: + output_path = Path(path) + output_path.parent.mkdir(parents=True, exist_ok=True) + rows = [_flatten_result(result) for result in results] + pd.DataFrame(rows).to_parquet(output_path, index=False) diff --git a/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_board_execution/pyproject.toml b/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_board_execution/pyproject.toml new file mode 100644 index 0000000000000000000000000000000000000000..7871c76f5d4dc7933e0d4e935f5a11d6c6232b75 --- /dev/null +++ b/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_board_execution/pyproject.toml @@ -0,0 +1,49 @@ +[build-system] +requires = ["hatchling>=1.25"] +build-backend = "hatchling.build" + +[project] +name = "medboard" +version = "0.1.0" +description = "Research scaffold for organizational decision governance in medical LLM multi-agent diagnosis." +readme = "README.md" +requires-python = ">=3.11" +license = { text = "MIT" } +authors = [{ name = "MedBoard Contributors" }] +dependencies = [ + "langgraph>=0.2.0", + "langchain-core>=0.3.0", + "litellm>=1.55.0", + "pydantic>=2.8.0", + "pydantic-settings>=2.4.0", + "pyyaml>=6.0.2", + "typer>=0.12.5", + "rich>=13.8.0", + "tenacity>=8.5.0", + "pandas>=2.2.0", + "pyarrow>=17.0.0", + "duckdb>=1.1.0", + "numpy>=2.0.0", + "scipy>=1.14.0", + "python-dotenv>=1.0.1" +] + +[dependency-groups] +dev = [ + "pytest>=8.3.0", + "pytest-asyncio>=0.24.0" +] + +[project.optional-dependencies] +dev = [ + "pytest>=8.3.0", + "pytest-asyncio>=0.24.0" +] + +[tool.hatch.build.targets.wheel] +packages = ["medboard"] + +[tool.pytest.ini_options] +testpaths = ["tests"] +asyncio_mode = "auto" +pythonpath = ["."] diff --git a/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_board_execution/tests/test_analyze_pilot.py b/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_board_execution/tests/test_analyze_pilot.py new file mode 100644 index 0000000000000000000000000000000000000000..c4a0c96ba41afca531b8b4c28e3434324006fe09 --- /dev/null +++ b/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_board_execution/tests/test_analyze_pilot.py @@ -0,0 +1,138 @@ +from __future__ import annotations + +import json +from pathlib import Path + +import pandas as pd + +from scripts import analyze_pilot + + +def _row(case_id: str, variant: str, accuracy: float, calls: int = 1) -> dict[str, object]: + return { + "variant": variant, + "visibility_mode": "answer_rationale", + "case_id": case_id, + "protocol_name": variant, + "seed": 42, + "answer": "A" if accuracy else "B", + "confidence": 0.8, + "metrics": json.dumps( + { + "accuracy": accuracy, + "conformity_shift_rate": 0.2 if variant == "open_committee" else 0.0, + "evidence_retention_rate": 1.0, + "invalid_agent_outputs_count": 2 if variant == "anonymous_evidence_board" else 0, + "invalid_final_decision_count": 0, + "retry_count_total": 3 if variant == "anonymous_evidence_board" else 0, + "retry_success_count": 1 if variant == "anonymous_evidence_board" else 0, + "retry_failure_count": 2 if variant == "anonymous_evidence_board" else 0, + } + ), + "token_usage": json.dumps( + { + "n_calls": calls, + "prompt_tokens": 10, + "completion_tokens": 5, + "total_tokens": 15, + "latency_total": 2.0, + } + ), + "raw_log_path": None, + } + + +def test_load_results_expands_metrics_and_token_usage(tmp_path: Path) -> None: + run_dir = tmp_path / "run" + run_dir.mkdir() + pd.DataFrame([_row("c1", "single_agent", 1.0, calls=3)]).to_parquet(run_dir / "aggregated_results.parquet") + + frame = analyze_pilot.load_results(run_dir) + + assert frame.loc[0, "accuracy"] == 1.0 + assert frame.loc[0, "conformity_shift_rate"] == 0.0 + assert frame.loc[0, "n_calls"] == 3 + assert frame.loc[0, "total_tokens"] == 15 + + +def test_bootstrap_accuracy_returns_ci() -> None: + mean, lower, upper = analyze_pilot.bootstrap_accuracy([1.0] * 30 + [0.0] * 20, n_bootstrap=200, seed=1) + + assert mean == 0.6 + assert lower <= mean <= upper + assert lower < upper + + +def test_paired_comparison_counts_wins_losses_and_ties() -> None: + frame = pd.DataFrame( + [ + _row("c1", "anonymous_evidence_board", 1.0), + _row("c1", "single_agent", 0.0), + _row("c2", "anonymous_evidence_board", 0.0), + _row("c2", "single_agent", 1.0), + _row("c3", "anonymous_evidence_board", 1.0), + _row("c3", "single_agent", 1.0), + _row("c4", "anonymous_evidence_board", 0.0), + _row("c4", "single_agent", 0.0), + ] + ) + frame = analyze_pilot.expand_json_columns(frame) + + result = analyze_pilot.paired_comparison(frame, "anonymous_evidence_board", "single_agent") + + assert result["wins"] == 1 + assert result["losses"] == 1 + assert result["ties"] == 2 + assert result["wrong_to_correct"] == 1 + assert result["correct_to_wrong"] == 1 + + +def test_invalid_retry_summary_sums_by_variant() -> None: + frame = pd.DataFrame( + [ + _row("c1", "anonymous_evidence_board", 1.0), + _row("c2", "anonymous_evidence_board", 0.0), + _row("c1", "single_agent", 1.0), + ] + ) + frame = analyze_pilot.expand_json_columns(frame) + + rows = analyze_pilot.invalid_retry_summary(frame) + + by_variant = {row["variant"]: row for row in rows} + assert by_variant["anonymous_evidence_board"]["invalid_agent_outputs_count"] == 4 + assert by_variant["anonymous_evidence_board"]["retry_count_total"] == 6 + assert by_variant["single_agent"]["retry_count_total"] == 0 + + +def test_body_system_join_from_medxpertqa_jsonl(tmp_path: Path) -> None: + data_path = tmp_path / "cases.jsonl" + data_path.write_text( + json.dumps( + { + "id": "c1", + "question": "Case text", + "options": {"A": "One", "B": "Two"}, + "answer_idx": 0, + "body_system": "Cardiology", + } + ) + + "\n", + encoding="utf-8", + ) + frame = pd.DataFrame([_row("c1", "single_agent", 1.0), _row("missing", "single_agent", 0.0)]) + frame = analyze_pilot.expand_json_columns(frame) + + enriched = analyze_pilot.attach_body_system(frame, data_path) + + assert enriched.loc[enriched["case_id"] == "c1", "body_system"].item() == "cardiovascular" + assert enriched.loc[enriched["case_id"] == "missing", "body_system"].item() == "unknown" + + +def test_load_results_missing_parquet_raises_clear_error(tmp_path: Path) -> None: + try: + analyze_pilot.load_results(tmp_path / "missing") + except analyze_pilot.AnalysisError as exc: + assert "Missing aggregated results" in str(exc) + else: + raise AssertionError("Expected AnalysisError") diff --git a/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_board_execution/tests/test_anonymization.py b/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_board_execution/tests/test_anonymization.py new file mode 100644 index 0000000000000000000000000000000000000000..eb8aac790b264a1cf372efec64615314de5ea1d6 --- /dev/null +++ b/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_board_execution/tests/test_anonymization.py @@ -0,0 +1,29 @@ +from medboard.core.anonymization import anonymize_evidence_board +from medboard.schemas.evidence import EvidenceBoard, EvidenceUnit + + +def test_anonymous_evidence_board_hides_source_agent() -> None: + board = EvidenceBoard( + case_id="case_1", + units=[ + EvidenceUnit( + evidence_id="ev_1", + text="Troponin is elevated.", + source_agent="cardiology_agent", + visible_source=True, + supports_option="A", + ) + ], + anonymous=False, + answer_masked=True, + ) + + anonymous = anonymize_evidence_board(board) + + assert anonymous.anonymous is True + assert anonymous.units[0].evidence_id == "case_1:anonymous:0" + assert anonymous.units[0].source_agent is None + assert anonymous.units[0].visible_source is False + assert anonymous.units[0].supports_option is None + assert anonymous.units[0].contradicts_option is None + assert anonymous.units[0].text == "Troponin is elevated." diff --git a/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_board_execution/tests/test_answer_normalization.py b/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_board_execution/tests/test_answer_normalization.py new file mode 100644 index 0000000000000000000000000000000000000000..a9a3240ea946088e4b10726d328609945d67ef6c --- /dev/null +++ b/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_board_execution/tests/test_answer_normalization.py @@ -0,0 +1,62 @@ +"""Offline tests for real-path answer normalization and token accounting.""" + +from __future__ import annotations + +import pytest + +from medboard.llm.client import LLMResponse +from medboard.protocols.common import _normalize_answer, _token_usage_sum + +OPTIONS = {"A": "Community-acquired pneumonia", "B": "Pulmonary embolism", "C": "Viral upper respiratory infection"} + + +@pytest.mark.parametrize( + ("raw", "expected_label", "is_changed"), + [ + ("A", "A", False), + (" a ", "A", True), + ("Community-acquired pneumonia", "A", True), + ('{"answer": "B", "confidence": 0.9}', "B", True), + ("Option C. Viral URI", "C", True), + ("I think this is Pulmonary embolism", "B", True), + ], +) +def test_normalize_answer_valid_forms(raw: str, expected_label: str, is_changed: bool) -> None: + normalized, changed = _normalize_answer(raw, OPTIONS, fallback="A") + assert normalized == expected_label + assert changed is is_changed + + +@pytest.mark.parametrize("raw", ["", " ", "Z", "asdfghjkl", '{"answer": "Z"}']) +def test_normalize_answer_invalid_records_metadata(raw: str) -> None: + result = _normalize_answer(raw, OPTIONS, fallback="A", metadata=True) + assert result["normalized_answer"] == "A" + assert result["answer_valid"] is False + assert result["raw_answer"] == raw + assert result["normalization_warning"] + + +def test_normalize_answer_raises_on_empty_options() -> None: + with pytest.raises(ValueError, match="valid_options must not be empty"): + _normalize_answer("A", {}, metadata=True) + + +def test_token_usage_sum_empty() -> None: + result = _token_usage_sum([]) + assert result == {"prompt_tokens": 0, "completion_tokens": 0, "total_tokens": 0, "n_calls": 0} + + +def test_token_usage_sum_accumulates_usage_and_latency() -> None: + responses = [ + LLMResponse(raw_text="{}", parsed_json={}, usage={"prompt_tokens": 100, "completion_tokens": 40, "total_tokens": 140}, latency=1.0), + LLMResponse(raw_text="{}", parsed_json={}, usage={"prompt_tokens": 50, "completion_tokens": 10, "total_tokens": 60}, latency=2.0), + ] + result = _token_usage_sum(responses) + assert result["prompt_tokens"] == 150 + assert result["completion_tokens"] == 50 + assert result["total_tokens"] == 200 + assert result["n_calls"] == 2 + assert result["latency_min"] == 1.0 + assert result["latency_max"] == 2.0 + assert result["latency_total"] == 3.0 + assert result["latency_avg"] == 1.5 diff --git a/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_board_execution/tests/test_batch_data_loading.py b/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_board_execution/tests/test_batch_data_loading.py new file mode 100644 index 0000000000000000000000000000000000000000..aadd8a4990c3b07b5e7ff9c357711690e245ee1d --- /dev/null +++ b/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_board_execution/tests/test_batch_data_loading.py @@ -0,0 +1,107 @@ +import json +from pathlib import Path + +import pandas as pd +import pytest + +from medboard.runners.run_batch import load_cases, run_batch +from medboard.schemas.config import ExperimentConfig + + +def _write_jsonl(path: Path, records: list[dict]) -> None: + path.write_text("\n".join(json.dumps(record) for record in records) + "\n", encoding="utf-8") + + +def _config(payload: dict) -> ExperimentConfig: + return ExperimentConfig.model_validate(payload) + + +def test_load_cases_defaults_to_mock_cases() -> None: + config = _config({"experiment": {"n_cases": 2}}) + + cases = load_cases(config) + + assert [case.case_id for case in cases] == ["mock_case_001", "mock_case_002"] + + +def test_load_cases_reads_local_medxpertqa_jsonl_with_limit(tmp_path: Path) -> None: + data_path = tmp_path / "medxpertqa.jsonl" + _write_jsonl( + data_path, + [ + { + "id": "Text-1", + "question": "Question one.", + "options": {"A": "Alpha", "B": "Beta"}, + "label": "A", + "medical_task": "Diagnosis", + "body_system": "Cardiovascular", + }, + { + "id": "Text-2", + "question": "Question two.", + "options": {"A": "Alpha", "B": "Beta"}, + "label": "B", + "medical_task": "Treatment", + "body_system": "Skeletal", + }, + { + "id": "Text-3", + "question": "Question three.", + "options": {"A": "Alpha", "B": "Beta"}, + "label": "A", + "medical_task": "Diagnosis", + "body_system": "Digestive", + }, + ], + ) + config = _config( + { + "experiment": {"n_cases": 99}, + "data": {"source": "medxpertqa_jsonl", "path": str(data_path), "limit": 2}, + } + ) + + cases = load_cases(config) + + assert [case.case_id for case in cases] == ["Text-1", "Text-2"] + assert cases[0].case_text == "Question one." + assert cases[0].correct_label == "A" + assert cases[0].metadata["medical_task"] == "Diagnosis" + + +def test_load_cases_rejects_medxpertqa_source_without_path() -> None: + config = _config({"data": {"source": "medxpertqa_jsonl"}}) + + with pytest.raises(ValueError, match="data.path"): + load_cases(config) + + +async def test_run_batch_uses_local_medxpertqa_jsonl(tmp_path: Path) -> None: + data_path = tmp_path / "medxpertqa.jsonl" + _write_jsonl( + data_path, + [ + {"id": "Text-1", "question": "Question one.", "options": {"A": "Alpha", "B": "Beta"}, "label": "A"}, + {"id": "Text-2", "question": "Question two.", "options": {"A": "Alpha", "B": "Beta"}, "label": "B"}, + ], + ) + config = _config( + { + "experiment": {"name": "real_data_batch", "n_cases": 99, "seed": 42}, + "protocol": {"name": "single_agent"}, + "runtime": {"mock_llm": True}, + "data": {"source": "medxpertqa_jsonl", "path": str(data_path), "limit": 2}, + "output": {"root_dir": str(tmp_path)}, + } + ) + + results = await run_batch(config) + + assert [result.case_id for result in results] == ["Text-1", "Text-2"] + results_path = tmp_path / "real_data_batch" / "results" / "results.parquet" + raw_path = tmp_path / "real_data_batch" / "raw" / "cases.jsonl" + assert results_path.exists() + assert raw_path.exists() + rows = pd.read_parquet(results_path) + assert list(rows["case_id"]) == ["Text-1", "Text-2"] diff --git a/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_board_execution/tests/test_checkpoint_resume.py b/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_board_execution/tests/test_checkpoint_resume.py new file mode 100644 index 0000000000000000000000000000000000000000..c1c3a772048afd1f6bbb43c515adbaae7e1b0bdc --- /dev/null +++ b/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_board_execution/tests/test_checkpoint_resume.py @@ -0,0 +1,160 @@ +from __future__ import annotations + +import json +from pathlib import Path + +import pandas as pd + +from medboard.runners import run_batch as run_batch_module +from medboard.runners.run_batch import run_batch +from medboard.runners.run_sweep import run_sweep +from medboard.schemas.case import MedicalCase +from medboard.schemas.config import ExperimentConfig +from medboard.schemas.result import CaseResult, FinalDecision + + +def _config(tmp_path: Path, *, name: str = "checkpoint_test", resume: bool = False) -> ExperimentConfig: + return ExperimentConfig.model_validate( + { + "experiment": {"name": name, "n_cases": 3, "seed": 42}, + "protocol": {"name": "single_agent"}, + "runtime": {"mock_llm": True, "concurrency": 3, "resume": resume}, + "output": {"root_dir": str(tmp_path)}, + } + ) + + +def _case(case_id: str) -> MedicalCase: + return MedicalCase( + case_id=case_id, + case_text=f"Case {case_id}", + options={"A": "Alpha", "B": "Beta"}, + correct_label="A", + ) + + +def _result(case_id: str, config: ExperimentConfig) -> CaseResult: + return CaseResult( + case_id=case_id, + protocol_name=config.protocol.name, + seed=config.experiment.seed, + final_decision=FinalDecision(answer="A", confidence=1.0, rationale="ok"), + metrics={"accuracy": 1.0}, + token_usage={"n_calls": 1}, + ) + + +def _read_jsonl(path: Path) -> list[dict]: + return [json.loads(line) for line in path.read_text(encoding="utf-8").splitlines() if line.strip()] + + +async def test_resume_ignores_truncated_jsonl_tail(monkeypatch, tmp_path: Path) -> None: + config = _config(tmp_path) + raw_path = tmp_path / "checkpoint_test" / "raw" / "cases.jsonl" + raw_path.parent.mkdir(parents=True) + seeded = _result("mock_case_001", config).model_copy(update={"raw_log_path": str(raw_path)}) + raw_path.write_text(json.dumps(seeded.model_dump(mode="json")) + "\n{" + "\n", encoding="utf-8") + resume_config = _config(tmp_path, resume=True) + calls: list[str] = [] + + class RecordingProtocol: + async def run_case(self, case: MedicalCase, cfg: ExperimentConfig) -> CaseResult: + calls.append(case.case_id) + return _result(case.case_id, cfg) + + monkeypatch.setattr(run_batch_module, "get_protocol", lambda _name: RecordingProtocol()) + + results = await run_batch(resume_config) + + assert calls == ["mock_case_002", "mock_case_003"] + assert sorted(result.case_id for result in results) == ["mock_case_001", "mock_case_002", "mock_case_003"] + + +async def test_failure_record_uses_correct_label_when_options_are_empty(monkeypatch, tmp_path: Path) -> None: + config = _config(tmp_path) + empty_options_case = MedicalCase(case_id="empty_options", case_text="Case", options={}, correct_label="A") + + class FailingProtocol: + async def run_case(self, case: MedicalCase, cfg: ExperimentConfig) -> CaseResult: + raise RuntimeError("boom") + + monkeypatch.setattr(run_batch_module, "load_cases", lambda _config: [empty_options_case]) + monkeypatch.setattr(run_batch_module, "get_protocol", lambda _name: FailingProtocol()) + + results = await run_batch(config) + + assert len(results) == 1 + assert results[0].final_decision.answer == "A" + assert results[0].metrics["failed"] is True + + +async def test_run_batch_checkpoints_successes_and_failures(monkeypatch, tmp_path: Path) -> None: + config = _config(tmp_path) + calls: list[str] = [] + + class FailingProtocol: + async def run_case(self, case: MedicalCase, cfg: ExperimentConfig) -> CaseResult: + calls.append(case.case_id) + if case.case_id == "mock_case_002": + raise RuntimeError("boom") + return _result(case.case_id, cfg) + + monkeypatch.setattr(run_batch_module, "get_protocol", lambda _name: FailingProtocol()) + + results = await run_batch(config) + + raw_path = tmp_path / "checkpoint_test" / "raw" / "cases.jsonl" + failures_path = tmp_path / "checkpoint_test" / "raw" / "failures.jsonl" + rows = _read_jsonl(raw_path) + failure_rows = _read_jsonl(failures_path) + assert sorted(row["case_id"] for row in rows) == ["mock_case_001", "mock_case_002", "mock_case_003"] + assert [row["case_id"] for row in failure_rows] == ["mock_case_002"] + failed = next(row for row in rows if row["case_id"] == "mock_case_002") + assert failed["final_decision"]["answer_valid"] is False + assert failed["final_decision"]["imputed"] is True + assert failed["metrics"]["failed"] is True + assert len(results) == 3 + + +async def test_run_batch_resume_skips_completed_cases_and_regenerates_parquet(monkeypatch, tmp_path: Path) -> None: + config = _config(tmp_path) + raw_path = tmp_path / "checkpoint_test" / "raw" / "cases.jsonl" + raw_path.parent.mkdir(parents=True) + seeded = _result("mock_case_001", config).model_copy(update={"raw_log_path": str(raw_path)}) + raw_path.write_text(json.dumps(seeded.model_dump(mode="json")) + "\n", encoding="utf-8") + resume_config = _config(tmp_path, resume=True) + calls: list[str] = [] + + class RecordingProtocol: + async def run_case(self, case: MedicalCase, cfg: ExperimentConfig) -> CaseResult: + calls.append(case.case_id) + return _result(case.case_id, cfg) + + monkeypatch.setattr(run_batch_module, "get_protocol", lambda _name: RecordingProtocol()) + + results = await run_batch(resume_config) + + assert calls == ["mock_case_002", "mock_case_003"] + assert sorted(result.case_id for result in results) == ["mock_case_001", "mock_case_002", "mock_case_003"] + rows = pd.read_parquet(tmp_path / "checkpoint_test" / "results" / "results.parquet") + assert sorted(rows["case_id"]) == ["mock_case_001", "mock_case_002", "mock_case_003"] + + +async def test_run_sweep_still_writes_aggregated_parquet_with_checkpointing(tmp_path: Path) -> None: + config = ExperimentConfig.model_validate( + { + "experiment": {"name": "sweep_checkpoint", "n_cases": 2, "seed": 42}, + "protocol": {"protocols": ["single_agent", "majority_vote"], "n_agents": 3, "visibility_mode": "answer_only"}, + "runtime": {"mock_llm": True, "concurrency": 2}, + "output": {"root_dir": str(tmp_path)}, + } + ) + + results = await run_sweep(config) + + assert set(results) == {"single_agent", "majority_vote"} + assert (tmp_path / "sweep_checkpoint" / "single_agent" / "raw" / "cases.jsonl").exists() + assert (tmp_path / "sweep_checkpoint" / "majority_vote" / "raw" / "cases.jsonl").exists() + rows = pd.read_parquet(tmp_path / "sweep_checkpoint" / "aggregated_results.parquet") + assert len(rows) == 4 + assert set(rows["variant"]) == {"single_agent", "majority_vote"} diff --git a/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_board_execution/tests/test_data_sanity.py b/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_board_execution/tests/test_data_sanity.py new file mode 100644 index 0000000000000000000000000000000000000000..08824b5033a3c1e415dc0eb05302c0b71ffee9d0 --- /dev/null +++ b/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_board_execution/tests/test_data_sanity.py @@ -0,0 +1,82 @@ +import json +from pathlib import Path + +from medboard.runners.check_data import check_medxpertqa_jsonl, run_async + + +def _write_jsonl(path: Path, records: list[dict]) -> None: + path.write_text("\n".join(json.dumps(record) for record in records) + "\n", encoding="utf-8") + + +def test_check_medxpertqa_jsonl_reports_summary_and_failures(tmp_path: Path) -> None: + path = tmp_path / "medxpertqa.jsonl" + _write_jsonl( + path, + [ + { + "id": "Text-1", + "question": "Short question.", + "options": {"A": "Alpha", "B": "Beta"}, + "label": "A", + "body_system": "Cardiovascular", + }, + { + "id": "Text-2", + "question": "This is a longer question text.", + "options": {"A": "Alpha", "B": "Beta", "C": "Gamma"}, + "label": "B", + "body_system": "Skeletal", + }, + {"id": "bad", "options": {"A": "Alpha"}, "label": "A"}, + ], + ) + + summary = check_medxpertqa_jsonl(path, limit=3) + + assert summary["total_read"] == 3 + assert summary["converted"] == 2 + assert summary["failed"] == 1 + assert summary["failure_reasons"] == {"MedXpertQA record is missing required field for question: expected one of ['question', 'case_text', 'vignette', 'stem']": 1} + assert summary["options_count_distribution"] == {"2": 1, "3": 1} + assert summary["correct_label_distribution"] == {"A": 1, "B": 1} + assert summary["case_text_length"]["min"] == len("Short question.") + assert summary["case_text_length"]["max"] == len("This is a longer question text.") + assert summary["body_system_distribution"] == {"cardiovascular": 1, "skeletal": 1} + + +def test_check_medxpertqa_jsonl_limit_reads_first_n_records(tmp_path: Path) -> None: + path = tmp_path / "medxpertqa.jsonl" + _write_jsonl( + path, + [ + {"id": "Text-1", "question": "Q1", "options": {"A": "Alpha", "B": "Beta"}, "label": "A"}, + {"id": "Text-2", "question": "Q2", "options": {"A": "Alpha", "B": "Beta"}, "label": "B"}, + ], + ) + + summary = check_medxpertqa_jsonl(path, limit=1) + + assert summary["total_read"] == 1 + assert summary["converted"] == 1 + assert summary["correct_label_distribution"] == {"A": 1} + + +async def test_run_async_accepts_config_yaml(tmp_path: Path) -> None: + data_path = tmp_path / "medxpertqa.jsonl" + _write_jsonl(data_path, [{"id": "Text-1", "question": "Q1", "options": {"A": "Alpha", "B": "Beta"}, "label": "A"}]) + config_path = tmp_path / "data_check.yaml" + config_path.write_text( + f""" +data: + source: medxpertqa_jsonl + path: {data_path} + limit: 1 +""", + encoding="utf-8", + ) + + summary = await run_async(config_path) + + assert summary["total_read"] == 1 + assert summary["converted"] == 1 + assert summary["failed"] == 0 diff --git a/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_board_execution/tests/test_decision_rules.py b/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_board_execution/tests/test_decision_rules.py new file mode 100644 index 0000000000000000000000000000000000000000..ca9e6a09e3e5a4877b3133db8ad270b3758b7261 --- /dev/null +++ b/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_board_execution/tests/test_decision_rules.py @@ -0,0 +1,118 @@ +from medboard.core.decision_rules import ( + confidence_weighted_vote, + evidence_weighted_vote, + majority_vote, +) +from medboard.core.evidence_extraction import extract_evidence_units +from medboard.schemas.agent import AgentOutput +from medboard.schemas.evidence import EvidenceUnit + + +def _output(agent_id: str, answer: str, answer_valid: bool = True) -> AgentOutput: + return AgentOutput( + agent_id=agent_id, + role="clinician", + round_id=0, + answer=answer, + confidence=0.7, + answer_valid=answer_valid, + ) + + +def test_majority_vote_returns_most_common_answer() -> None: + outputs = [_output("a1", "B"), _output("a2", "A"), _output("a3", "B")] + + assert majority_vote(outputs, tie_breaker=["A", "B", "C"]) == "B" + + +def test_majority_vote_uses_tie_breaker_order() -> None: + outputs = [_output("a1", "B"), _output("a2", "A")] + + assert majority_vote(outputs, tie_breaker=["A", "B"]) == "A" + + +def test_majority_vote_ignores_invalid_outputs() -> None: + outputs = [ + _output("a1", "A"), + _output("a2", "B", answer_valid=False), + _output("a3", "B", answer_valid=False), + ] + + assert majority_vote(outputs, tie_breaker=["A", "B"]) == "A" + + +def test_confidence_weighted_vote_uses_confidence_scores() -> None: + outputs = [ + AgentOutput(agent_id="a1", role="clinician", round_id=0, answer="A", confidence=0.3), + AgentOutput(agent_id="a2", role="clinician", round_id=0, answer="B", confidence=0.9), + ] + + assert confidence_weighted_vote(outputs) == "B" + + +def test_confidence_weighted_vote_returns_none_for_empty_input() -> None: + outputs = [ + AgentOutput(agent_id="a1", role="clinician", round_id=0, answer="", confidence=0.5), + ] + + assert confidence_weighted_vote(outputs) is None + + +def test_confidence_weighted_vote_uses_tie_breaker() -> None: + outputs = [ + AgentOutput(agent_id="a1", role="clinician", round_id=0, answer="A", confidence=0.7), + AgentOutput(agent_id="a2", role="clinician", round_id=0, answer="B", confidence=0.7), + ] + + assert confidence_weighted_vote(outputs, tie_breaker=["A", "B"]) == "A" + + +def test_evidence_weighted_vote_uses_supports_option() -> None: + units = [ + EvidenceUnit(evidence_id="e1", text="...", supports_option="A", evidence_score=2.0), + EvidenceUnit(evidence_id="e2", text="...", supports_option="B", evidence_score=1.0), + ] + + assert evidence_weighted_vote(units) == "A" + + +def test_evidence_weighted_vote_contradicts_subtracts_score() -> None: + units = [ + EvidenceUnit(evidence_id="e1", text="...", supports_option="A", evidence_score=3.0), + EvidenceUnit(evidence_id="e2", text="...", supports_option="B", evidence_score=2.0), + EvidenceUnit(evidence_id="e3", text="...", contradicts_option="A", evidence_score=2.0), + ] + + assert evidence_weighted_vote(units) == "B" + + +def test_evidence_weighted_vote_returns_none_for_empty_units() -> None: + assert evidence_weighted_vote([]) is None + + +def test_extract_evidence_units_skips_invalid_outputs() -> None: + outputs = [ + AgentOutput( + agent_id="a1", + role="clinician", + round_id=0, + answer="A", + confidence=0.7, + supporting_evidence=["valid evidence"], + answer_valid=True, + ), + AgentOutput( + agent_id="a2", + role="clinician", + round_id=0, + answer="B", + confidence=0.7, + supporting_evidence=["invalid evidence"], + answer_valid=False, + ), + ] + + units = extract_evidence_units("case-1", outputs) + + assert [unit.text for unit in units] == ["valid evidence"] + assert all("a2" not in unit.evidence_id for unit in units) diff --git a/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_board_execution/tests/test_medxpertqa_adapter.py b/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_board_execution/tests/test_medxpertqa_adapter.py new file mode 100644 index 0000000000000000000000000000000000000000..ce97f929868c70fe72fa60db6d84e0ebd5bbb32c --- /dev/null +++ b/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_board_execution/tests/test_medxpertqa_adapter.py @@ -0,0 +1,161 @@ +import json +from pathlib import Path + +import pytest + +from medboard.adapters.medxpertqa import ( + MedXpertQADataset, + load_medxpertqa_jsonl, + medxpertqa_record_to_medical_case, +) +from medboard.schemas.case import MedicalCase + + +def _write_jsonl(path: Path, records: list[dict]) -> None: + path.write_text("\n".join(json.dumps(record) for record in records) + "\n", encoding="utf-8") + + +def test_field_mapping_uses_custom_record_keys() -> None: + case = medxpertqa_record_to_medical_case( + { + "custom_id": "mx_custom", + "stem_text": "Mapped question.", + "choices": ["Alpha", "Beta"], + "answer_index": 1, + "case_id": "preserved-metadata-id", + }, + field_mapping={"case_id": "custom_id", "question": "stem_text", "options": "choices", "answer_idx": "answer_index"}, + ) + + assert case.case_id == "mx_custom" + assert case.case_text == "Mapped question." + assert case.correct_label == "B" + + +def test_medxpertqa_record_to_medical_case_converts_fields() -> None: + case = medxpertqa_record_to_medical_case( + { + "case_id": "mx_001", + "question": "Adult with fever and cough.", + "options": ["Pneumonia", "Pulmonary embolism", "Viral URI"], + "answer_idx": 0, + "specialty": "Pulmonology", + "difficulty": "easy", + } + ) + + assert case.case_id == "mx_001" + assert case.case_text == "Adult with fever and cough." + assert case.options == {"A": "Pneumonia", "B": "Pulmonary embolism", "C": "Viral URI"} + assert case.correct_label == "A" + assert case.body_system == "pulmonary" + assert case.metadata["difficulty"] == "easy" + + +def test_medxpertqa_record_accepts_dict_options_and_correct_label() -> None: + case = medxpertqa_record_to_medical_case( + { + "id": "mx_002", + "case_text": "Chest pain vignette.", + "options": {"A": "ACS", "B": "GERD"}, + "correct_label": "B", + } + ) + + assert case.case_id == "mx_002" + assert case.options == {"A": "ACS", "B": "GERD"} + assert case.correct_label == "B" + + +def test_answer_idx_maps_to_letter_label() -> None: + case = medxpertqa_record_to_medical_case( + { + "case_id": "mx_003", + "question": "Question text.", + "options": ["One", "Two", "Three"], + "answer_idx": 1, + } + ) + + assert case.correct_label == "B" + + +def test_load_medxpertqa_jsonl_returns_medical_cases(tmp_path: Path) -> None: + path = tmp_path / "medxpertqa.jsonl" + _write_jsonl( + path, + [ + {"case_id": "mx_001", "question": "Q1", "options": ["A1", "B1"], "answer_idx": 0}, + {"case_id": "mx_002", "question": "Q2", "options": ["A2", "B2"], "answer_idx": 1}, + ], + ) + + cases = load_medxpertqa_jsonl(path) + + assert [case.case_id for case in cases] == ["mx_001", "mx_002"] + assert all(isinstance(case, MedicalCase) for case in cases) + assert cases[1].correct_label == "B" + + +def test_load_medxpertqa_jsonl_empty_file_returns_empty_list(tmp_path: Path) -> None: + path = tmp_path / "empty.jsonl" + path.write_text("\n\n", encoding="utf-8") + + assert load_medxpertqa_jsonl(path) == [] + + +def test_unknown_specialty_normalizes_to_body_system_token() -> None: + case = medxpertqa_record_to_medical_case( + { + "case_id": "mx_004", + "question": "Question text.", + "options": ["One", "Two"], + "answer_idx": 0, + "specialty": "Emergency Medicine", + } + ) + + assert case.body_system == "emergency_medicine" + + +def test_missing_specialty_leaves_body_system_none() -> None: + case = medxpertqa_record_to_medical_case( + {"case_id": "mx_005", "question": "Question text.", "options": ["One", "Two"], "answer_idx": 0} + ) + + assert case.body_system is None + + +def test_empty_options_raise_clear_error() -> None: + with pytest.raises(ValueError, match="must not be empty"): + medxpertqa_record_to_medical_case({"case_id": "mx_006", "question": "Question text.", "options": [], "answer_idx": 0}) + + +def test_answer_idx_out_of_range_raises_clear_error() -> None: + with pytest.raises(ValueError, match="missing option label"): + medxpertqa_record_to_medical_case({"case_id": "mx_007", "question": "Question text.", "options": ["One"], "answer_idx": 1}) + + +def test_medxpertqa_dataset_len_and_getitem_from_path(tmp_path: Path) -> None: + path = tmp_path / "medxpertqa.jsonl" + _write_jsonl(path, [{"case_id": "mx_001", "question": "Q1", "options": ["A1", "B1"], "answer_idx": 0}]) + + dataset = MedXpertQADataset(path) + + assert len(dataset) == 1 + assert dataset[0].case_id == "mx_001" + + +def test_medxpertqa_dataset_from_preloaded_cases() -> None: + case = MedicalCase(case_id="mx_001", case_text="Q1", options={"A": "A1"}, correct_label="A") + + dataset = MedXpertQADataset([case]) + + assert len(dataset) == 1 + assert dataset[0] == case + assert dataset[0:1] == [case] + + +def test_missing_required_fields_raise_clear_error() -> None: + with pytest.raises(ValueError, match="question"): + medxpertqa_record_to_medical_case({"case_id": "mx_001", "options": ["A"], "answer_idx": 0}) diff --git a/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_board_execution/tests/test_metrics.py b/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_board_execution/tests/test_metrics.py new file mode 100644 index 0000000000000000000000000000000000000000..6443219def3167b353ac326e5ec352e2b760472c --- /dev/null +++ b/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_board_execution/tests/test_metrics.py @@ -0,0 +1,183 @@ +from medboard.core.metrics import ( + accuracy, + authority_error_amplification, + conformity_shift_rate, + correct_minority_rescue_rate, + evidence_retention_rate, + minority_suppression_rate, + token_normalized_gain, + wrong_consensus_rate, +) +from medboard.schemas.agent import AgentOutput +from medboard.schemas.evidence import EvidenceUnit + + +def _output(agent_id: str, answer: str, changed: bool = False) -> AgentOutput: + return AgentOutput( + agent_id=agent_id, + role="clinician", + round_id=0, + answer=answer, + confidence=0.7, + changed_answer=changed, + ) + + +def test_accuracy_returns_one_for_correct_answer() -> None: + assert accuracy("C", "C") == 1.0 + + +def test_accuracy_returns_zero_for_wrong_answer() -> None: + assert accuracy("A", "C") == 0.0 + + +def test_conformity_shift_rate_counts_answer_changes() -> None: + round0 = [_output("a1", "A"), _output("a2", "B")] + revised = [_output("a1", "A"), _output("a2", "A", changed=True)] + + assert conformity_shift_rate(round0, revised) == 0.5 + + +def test_conformity_shift_rate_ignores_changed_flag_when_answer_is_same() -> None: + round0 = [_output("a1", "A"), _output("a2", "B")] + revised = [_output("a1", "A", changed=True), _output("a2", "B")] + + assert conformity_shift_rate(round0, revised) == 0.0 + + +def test_wrong_consensus_rate_returns_one_when_consensus_is_wrong() -> None: + outputs = [ + AgentOutput(agent_id="a1", role="clinician", round_id=0, answer="A", confidence=0.7), + AgentOutput(agent_id="a2", role="clinician", round_id=0, answer="B", confidence=0.7), + ] + + assert wrong_consensus_rate(outputs, correct_label="B") == 1.0 + + +def test_wrong_consensus_rate_returns_zero_when_consensus_is_correct() -> None: + outputs = [ + AgentOutput(agent_id="a1", role="clinician", round_id=0, answer="A", confidence=0.7), + AgentOutput(agent_id="a2", role="clinician", round_id=0, answer="A", confidence=0.7), + ] + + assert wrong_consensus_rate(outputs, correct_label="A") == 0.0 + + +def test_wrong_consensus_rate_returns_zero_when_consensus_is_none() -> None: + outputs = [ + AgentOutput(agent_id="a1", role="clinician", round_id=0, answer="", confidence=0.7), + AgentOutput(agent_id="a2", role="clinician", round_id=0, answer="", confidence=0.7), + ] + + assert wrong_consensus_rate(outputs, correct_label="A") == 0.0 + + +def test_correct_minority_rescue_rate_rescued() -> None: + round0 = [ + AgentOutput(agent_id="a1", role="clinician", round_id=0, answer="B", confidence=0.7), + AgentOutput(agent_id="a2", role="clinician", round_id=0, answer="B", confidence=0.7), + AgentOutput(agent_id="a3", role="clinician", round_id=0, answer="A", confidence=0.7), + ] + final = [ + AgentOutput(agent_id="a1", role="clinician", round_id=0, answer="A", confidence=0.7), + AgentOutput(agent_id="a2", role="clinician", round_id=0, answer="A", confidence=0.7), + AgentOutput(agent_id="a3", role="clinician", round_id=0, answer="A", confidence=0.7), + ] + + assert correct_minority_rescue_rate(round0, final, correct_label="A") == 1.0 + + +def test_correct_minority_rescue_rate_not_rescued() -> None: + round0 = [ + AgentOutput(agent_id="a1", role="clinician", round_id=0, answer="B", confidence=0.7), + AgentOutput(agent_id="a2", role="clinician", round_id=0, answer="B", confidence=0.7), + AgentOutput(agent_id="a3", role="clinician", round_id=0, answer="A", confidence=0.7), + ] + final = [ + AgentOutput(agent_id="a1", role="clinician", round_id=0, answer="B", confidence=0.7), + AgentOutput(agent_id="a2", role="clinician", round_id=0, answer="B", confidence=0.7), + AgentOutput(agent_id="a3", role="clinician", round_id=0, answer="B", confidence=0.7), + ] + + assert correct_minority_rescue_rate(round0, final, correct_label="A") == 0.0 + + +def test_correct_minority_rescue_rate_returns_zero_when_minority_is_majority() -> None: + round0 = [ + AgentOutput(agent_id="a1", role="clinician", round_id=0, answer="A", confidence=0.7), + AgentOutput(agent_id="a2", role="clinician", round_id=0, answer="A", confidence=0.7), + AgentOutput(agent_id="a3", role="clinician", round_id=0, answer="B", confidence=0.7), + ] + final = [ + AgentOutput(agent_id="a1", role="clinician", round_id=0, answer="A", confidence=0.7), + AgentOutput(agent_id="a2", role="clinician", round_id=0, answer="A", confidence=0.7), + AgentOutput(agent_id="a3", role="clinician", round_id=0, answer="A", confidence=0.7), + ] + + assert correct_minority_rescue_rate(round0, final, correct_label="A") == 0.0 + + +def test_minority_suppression_rate_counts_switched_correct_agents() -> None: + round0 = [ + AgentOutput(agent_id="a1", role="clinician", round_id=0, answer="A", confidence=0.7), + AgentOutput(agent_id="a2", role="clinician", round_id=0, answer="B", confidence=0.7), + AgentOutput(agent_id="a3", role="clinician", round_id=0, answer="B", confidence=0.7), + ] + revised = [ + AgentOutput(agent_id="a1", role="clinician", round_id=0, answer="B", confidence=0.7), + AgentOutput(agent_id="a2", role="clinician", round_id=0, answer="B", confidence=0.7), + AgentOutput(agent_id="a3", role="clinician", round_id=0, answer="B", confidence=0.7), + ] + + assert minority_suppression_rate(round0, revised, correct_label="A") == 1.0 + + +def test_minority_suppression_rate_returns_zero_when_no_correct_agents() -> None: + round0 = [ + AgentOutput(agent_id="a1", role="clinician", round_id=0, answer="B", confidence=0.7), + AgentOutput(agent_id="a2", role="clinician", round_id=0, answer="B", confidence=0.7), + ] + revised = [ + AgentOutput(agent_id="a1", role="clinician", round_id=0, answer="C", confidence=0.7), + AgentOutput(agent_id="a2", role="clinician", round_id=0, answer="C", confidence=0.7), + ] + + assert minority_suppression_rate(round0, revised, correct_label="A") == 0.0 + + +def test_authority_error_amplification_is_difference() -> None: + import pytest + + assert authority_error_amplification(0.3, 0.7) == pytest.approx(0.4) + + +def test_evidence_retention_rate_returns_fraction_retained() -> None: + units = [ + EvidenceUnit(evidence_id="e1", text="..."), + EvidenceUnit(evidence_id="e2", text="..."), + EvidenceUnit(evidence_id="e3", text="..."), + ] + + assert evidence_retention_rate(units, retained_evidence_ids=["e1", "e3"]) == 2 / 3 + + +def test_evidence_retention_rate_uses_anonymized_board_ids() -> None: + units = [ + EvidenceUnit(evidence_id="real_case_001:anonymous:0", text="..."), + EvidenceUnit(evidence_id="real_case_001:anonymous:1", text="..."), + EvidenceUnit(evidence_id="real_case_001:anonymous:2", text="..."), + ] + + assert evidence_retention_rate(units, retained_evidence_ids=["real_case_001:anonymous:0"]) == 1 / 3 + + +def test_evidence_retention_rate_returns_zero_for_empty_units() -> None: + assert evidence_retention_rate([], retained_evidence_ids=[]) == 0.0 + + +def test_token_normalized_gain_divides_gain_by_tokens() -> None: + assert token_normalized_gain(5.0, 100) == 0.05 + + +def test_token_normalized_gain_returns_zero_for_non_positive_tokens() -> None: + assert token_normalized_gain(5.0, 0) == 0.0 diff --git a/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_board_execution/tests/test_mock_conformity.py b/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_board_execution/tests/test_mock_conformity.py new file mode 100644 index 0000000000000000000000000000000000000000..6673aacc4cad7cfa92262726a2fe98ca5947af71 --- /dev/null +++ b/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_board_execution/tests/test_mock_conformity.py @@ -0,0 +1,96 @@ +import pytest + +from medboard.protocols.common import _revise_outputs, run_mock_protocol +from medboard.schemas.agent import AgentOutput +from medboard.schemas.case import MedicalCase +from medboard.schemas.config import ExperimentConfig + + +def _case() -> MedicalCase: + return MedicalCase( + case_id="case_001", + case_text="Mock case.", + options={"A": "Correct", "B": "Distractor"}, + correct_label="A", + ) + + +def _config(visibility_mode: str) -> ExperimentConfig: + return ExperimentConfig.model_validate( + { + "experiment": {"name": "mock_conformity", "n_cases": 1, "seed": 42}, + "protocol": { + "name": "open_committee", + "n_agents": 5, + "max_rounds": 2, + "visibility_mode": visibility_mode, + "anonymous": visibility_mode in {"evidence_only", "evidence_gated"}, + "answer_masking": visibility_mode in {"evidence_only", "evidence_gated"}, + }, + "runtime": {"mock_llm": True}, + } + ) + + +def _output(agent_id: str, answer: str) -> AgentOutput: + return AgentOutput(agent_id=agent_id, role="clinician", round_id=0, answer=answer, confidence=0.7) + + +@pytest.mark.parametrize("visibility_mode", ["answer_only", "answer_rationale"]) +async def test_answer_visible_modes_have_nonzero_conformity_shift(visibility_mode: str) -> None: + result = await run_mock_protocol(_case(), _config(visibility_mode), "open_committee") + + assert result.metrics["conformity_shift_rate"] > 0.0 + + +@pytest.mark.parametrize("visibility_mode", ["evidence_only", "evidence_gated"]) +async def test_evidence_only_modes_have_zero_conformity_shift(visibility_mode: str) -> None: + result = await run_mock_protocol(_case(), _config(visibility_mode), "anonymous_evidence_board") + + assert result.metrics["conformity_shift_rate"] == 0.0 + + +async def test_rationale_only_has_lower_shift_than_answer_visible() -> None: + config = _config("rationale_only") + assert config.protocol.n_agents == 5 + answer_visible = await run_mock_protocol(_case(), _config("answer_only"), "open_committee") + rationale_only = await run_mock_protocol(_case(), config, "open_committee") + + assert 0.0 < rationale_only.metrics["conformity_shift_rate"] < answer_visible.metrics["conformity_shift_rate"] + + +@pytest.mark.parametrize("visibility_mode", ["answer_only", "answer_rationale"]) +def test_revision_changes_minority_answers_without_mutating_round0(visibility_mode: str) -> None: + round0 = [_output("a1", "A"), _output("a2", "B"), _output("a3", "A")] + + revised = _revise_outputs(round0, max_rounds=2, visibility_mode=visibility_mode) + + assert [output.answer for output in revised] == ["A", "A", "A"] + assert revised[1].changed_answer is True + assert revised[0].changed_answer is False + assert [output.answer for output in round0] == ["A", "B", "A"] + + +def test_revision_evidence_gated_does_not_change_answers() -> None: + round0 = [_output("a1", "A"), _output("a2", "B"), _output("a3", "A")] + + revised = _revise_outputs(round0, max_rounds=2, visibility_mode="evidence_gated") + + assert [output.answer for output in revised] == ["A", "B", "A"] + assert all(output.changed_answer is False for output in revised) + + +def test_revision_handles_empty_answers_without_changes() -> None: + revised = _revise_outputs([_output("a1", ""), _output("a2", "")], max_rounds=2, visibility_mode="answer_only") + + assert [output.answer for output in revised] == ["", ""] + assert all(output.changed_answer is False for output in revised) + + +def test_revision_rejects_unknown_visibility_mode() -> None: + with pytest.raises(ValueError, match="Unsupported visibility mode"): + _revise_outputs([_output("a1", "A")], max_rounds=2, visibility_mode="unknown") # type: ignore[arg-type] + + +def test_revision_returns_empty_when_only_one_round() -> None: + assert _revise_outputs([_output("a1", "A")], max_rounds=1, visibility_mode="answer_only") == [] diff --git a/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_board_execution/tests/test_perturbations.py b/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_board_execution/tests/test_perturbations.py new file mode 100644 index 0000000000000000000000000000000000000000..ef1262c49f5e00fd6677bf4ceba1e876889c5cb4 --- /dev/null +++ b/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_board_execution/tests/test_perturbations.py @@ -0,0 +1,79 @@ +from medboard.core.perturbations import ( + correct_minority_injection, + wrong_majority_injection, + wrong_senior_injection, +) +from medboard.schemas.agent import AgentOutput + + +def _output(agent_id: str, answer: str) -> AgentOutput: + return AgentOutput( + agent_id=agent_id, + role="clinician", + round_id=0, + answer=answer, + confidence=0.6, + ) + + +def test_correct_minority_injection_sets_requested_minority_to_correct_label() -> None: + outputs = [_output("a1", "B"), _output("a2", "B"), _output("a3", "B")] + + perturbed = correct_minority_injection(outputs, correct_label="A", minority_size=1) + + assert [output.answer for output in perturbed] == ["A", "B", "B"] + assert perturbed[0].confidence == 0.9 + assert perturbed[0].changed_answer is True + assert outputs[0].answer == "B" + + +def test_wrong_majority_injection_sets_majority_to_wrong_label() -> None: + outputs = [_output("a1", "A"), _output("a2", "A"), _output("a3", "A")] + + perturbed = wrong_majority_injection(outputs, wrong_label="B", majority_size=2) + + assert [output.answer for output in perturbed] == ["B", "B", "A"] + assert perturbed[0].confidence == 0.85 + + +def test_wrong_majority_injection_does_not_mutate_original() -> None: + outputs = [_output("a1", "A"), _output("a2", "A")] + + wrong_majority_injection(outputs, wrong_label="B") + + assert all(output.answer == "A" for output in outputs) + + +def test_wrong_senior_injection_sets_senior_to_wrong_label() -> None: + outputs = [ + AgentOutput(agent_id="a1", role="junior", round_id=0, answer="A", confidence=0.7), + AgentOutput(agent_id="a2", role="senior_physician", round_id=0, answer="A", confidence=0.7), + ] + + perturbed = wrong_senior_injection(outputs, wrong_label="B") + + assert perturbed[1].answer == "B" + assert perturbed[0].answer == "A" + + +def test_wrong_senior_injection_no_senior_role_returns_copies_unchanged() -> None: + outputs = [_output("a1", "A"), _output("a2", "B")] + + perturbed = wrong_senior_injection(outputs, wrong_label="C") + + assert [output.answer for output in perturbed] == ["A", "B"] + + +def test_wrong_senior_injection_does_not_mutate_original() -> None: + outputs = [ + AgentOutput(agent_id="a1", role="junior", round_id=0, answer="A", confidence=0.7), + AgentOutput(agent_id="a2", role="senior_physician", round_id=0, answer="A", confidence=0.7), + ] + + wrong_senior_injection(outputs, wrong_label="B") + + assert all(output.answer == "A" for output in outputs) + + +def test_wrong_senior_injection_empty_returns_empty() -> None: + assert wrong_senior_injection([], wrong_label="B") == [] diff --git a/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_board_execution/tests/test_qa_real_smoke.py b/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_board_execution/tests/test_qa_real_smoke.py new file mode 100644 index 0000000000000000000000000000000000000000..d96aa8ee2f8638218be61c5ab0e6209e4196591e --- /dev/null +++ b/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_board_execution/tests/test_qa_real_smoke.py @@ -0,0 +1,114 @@ +from __future__ import annotations + +import json +from pathlib import Path + +import pandas as pd + +from scripts.qa_real_smoke import CALLS_PER_CASE, EXPECTED_VARIANTS, run_qa + + +def _output(agent_id: str, answer_valid: bool = True, include_invalid_metadata: bool = True) -> dict[str, object]: + output: dict[str, object] = { + "agent_id": agent_id, + "role": "clinician", + "round_id": 0, + "answer": "A", + "confidence": 0.7, + "supporting_evidence": ["Synthetic evidence"], + "contradicting_evidence": [], + "answer_valid": answer_valid, + } + if not answer_valid and include_invalid_metadata: + output.update( + { + "raw_answer": "", + "normalized_answer": "A", + "normalization_warning": "Raw answer is empty or missing.", + "retry_count": 3, + "failure_type": "empty_answer", + "imputed": True, + } + ) + return output + + +def _case(variant: str, *, silent_invalid: bool = False) -> dict[str, object]: + round0_count = 1 if variant == "single_agent" else 5 + round0_outputs = [_output(f"agent_{index + 1}") for index in range(round0_count)] + if silent_invalid: + round0_outputs[0] = _output("agent_1", answer_valid=False, include_invalid_metadata=False) + revised_outputs = [] + if variant == "open_committee": + revised_outputs = [_output(f"agent_{index + 1}") | {"round_id": 1} for index in range(5)] + metrics: dict[str, object] = { + "accuracy": 1.0, + "conformity_shift_rate": 0.0, + "evidence_retention_rate": 1.0, + "invalid_agent_outputs_count": int(silent_invalid), + "invalid_agent_outputs_rate": 1.0 if silent_invalid else 0.0, + "invalid_final_decision_count": 0, + "retry_count_total": 0, + "retry_success_count": 0, + "retry_failure_count": 0, + } + return { + "case_id": "case-1", + "protocol_name": variant, + "seed": 42, + "round0_outputs": round0_outputs, + "revised_outputs": revised_outputs, + "evidence_board": {"case_id": "case-1", "units": [], "anonymous": variant == "anonymous_evidence_board", "answer_masked": False}, + "final_decision": { + "answer": "A", + "confidence": 0.7, + "rationale": "Synthetic final decision.", + "retained_evidence_ids": [], + "invalid_retained_evidence_ids": [], + "rejected_alternatives": ["B"], + "escalation_flag": False, + "answer_valid": True, + "retry_count": 0, + "failure_type": None, + "imputed": False, + }, + "metrics": metrics, + "token_usage": {"n_calls": CALLS_PER_CASE[variant], "total_tokens": 1}, + } + + +def _write_jsonl(path: Path, rows: list[dict[str, object]]) -> None: + path.parent.mkdir(parents=True) + path.write_text("".join(json.dumps(row) + "\n" for row in rows), encoding="utf-8") + + +def test_qa_detects_silent_fallback(tmp_path: Path, capsys) -> None: + run_dir = tmp_path / "run" + rows = [] + for variant in EXPECTED_VARIANTS: + rows.append( + { + "variant": variant, + "visibility_mode": "answer_rationale", + "case_id": "case-1", + "protocol_name": variant, + "seed": 42, + "answer": "A", + "confidence": 0.7, + "metrics": json.dumps({"accuracy": 1.0}), + "token_usage": json.dumps({"n_calls": CALLS_PER_CASE[variant], "total_tokens": 1}), + "raw_log_path": None, + } + ) + _write_jsonl( + run_dir / variant / "raw" / "cases.jsonl", + [_case(variant, silent_invalid=variant == "single_agent")], + ) + run_dir.mkdir(exist_ok=True) + pd.DataFrame(rows).to_parquet(run_dir / "aggregated_results.parquet") + + result = run_qa(run_dir, n_cases=1) + + captured = capsys.readouterr() + assert result == 1 + assert "silent fallback" in captured.out diff --git a/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_board_execution/tests/test_real_protocol_smoke_path.py b/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_board_execution/tests/test_real_protocol_smoke_path.py new file mode 100644 index 0000000000000000000000000000000000000000..e9931af6384bdd90089ae80f4f566e28bf90f041 --- /dev/null +++ b/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_board_execution/tests/test_real_protocol_smoke_path.py @@ -0,0 +1,464 @@ +"""Offline tests for the real LLM smoke path.""" + +from __future__ import annotations + +import json +from pathlib import Path +from typing import Any + +import pandas as pd +import pytest + +from medboard.llm.client import LLMResponse, LiteLLMClient +from medboard.protocols.common import run_protocol, run_real_protocol +from medboard.schemas.case import MedicalCase +from medboard.schemas.config import ExperimentConfig +from medboard.schemas.result import CaseResult + +FAKE_TOKEN_USAGE = {"prompt_tokens": 150, "completion_tokens": 50, "total_tokens": 200} + + +def fake_llm_response( + answer: str = "A", + confidence: float = 0.85, + token_usage: dict[str, int] | None = None, + retained_evidence_ids: list[str] | None = None, +) -> LLMResponse: + payload: dict[str, Any] = { + "answer": answer, + "confidence": confidence, + "rationale": "Fake rationale for offline real-path smoke testing.", + "supporting_evidence": ["Fake evidence 1"], + "contradicting_evidence": [], + "missing_evidence": ["Fake missing evidence"], + "differential_diagnoses": ["Fake differential"], + "uncertainty_statement": "Fake uncertainty.", + "retained_evidence_ids": retained_evidence_ids or [], + "rejected_alternatives": ["B", "C"], + "escalation_flag": False, + } + return LLMResponse( + raw_text=json.dumps(payload), + parsed_json=payload, + usage=token_usage or FAKE_TOKEN_USAGE, + latency=0.5, + ) + + +def fake_empty_answer_response() -> LLMResponse: + return fake_llm_response(answer="") + + +def case_one() -> MedicalCase: + return MedicalCase( + case_id="real_case_001", + case_text="65-year-old with chest pain radiating to left arm.", + options={"A": "Myocardial Infarction", "B": "GERD", "C": "Anxiety"}, + correct_label="A", + body_system="cardiovascular", + ) + + +def case_two() -> MedicalCase: + return MedicalCase( + case_id="real_case_002", + case_text="45-year-old with unilateral throbbing headache.", + options={"A": "Migraine", "B": "Tension Headache", "C": "Cluster Headache"}, + correct_label="A", + body_system="neurologic", + ) + + +def real_config(protocol_name: str = "single_agent", n_agents: int = 5) -> ExperimentConfig: + return ExperimentConfig.model_validate( + { + "experiment": {"name": "real_protocol_test", "n_cases": 1, "seed": 42}, + "protocol": { + "name": protocol_name, + "n_agents": n_agents, + "max_rounds": 2, + "visibility_mode": "answer_only", + "anonymous": protocol_name == "anonymous_evidence_board", + }, + "model": {"provider": "openai", "model": "gpt-4o-mini", "temperature": 0.0, "json_mode": True}, + "runtime": {"mock_llm": False}, + } + ) + + +def write_medxpertqa_jsonl(cases: list[MedicalCase], path: Path) -> Path: + with path.open("w", encoding="utf-8") as handle: + for case in cases: + labels = list(case.options) + record = { + "id": case.case_id, + "question": case.case_text, + "options": case.options, + "label": case.correct_label, + "body_system": case.body_system, + "answer_idx": labels.index(case.correct_label), + } + handle.write(json.dumps(record) + "\n") + return path + + +async def test_run_real_protocol_returns_case_result_and_token_usage(monkeypatch: pytest.MonkeyPatch) -> None: + async def fake_complete(self: LiteLLMClient, messages: list[dict[str, str]], **kwargs: Any) -> LLMResponse: + return fake_llm_response(answer="A", token_usage={"prompt_tokens": 225, "completion_tokens": 87, "total_tokens": 312}) + + monkeypatch.setattr(LiteLLMClient, "complete", fake_complete) + + result = await run_real_protocol(case_one(), real_config("single_agent"), "single_agent") + + assert isinstance(result, CaseResult) + assert result.final_decision.answer == "A" + assert result.protocol_name == "single_agent" + assert result.token_usage["total_tokens"] == 312 + assert result.token_usage["n_calls"] == 1 + + +async def test_run_protocol_dispatches_real_path_when_mock_llm_false(monkeypatch: pytest.MonkeyPatch) -> None: + call_count = {"n": 0} + + async def fake_complete(self: LiteLLMClient, messages: list[dict[str, str]], **kwargs: Any) -> LLMResponse: + call_count["n"] += 1 + return fake_llm_response(answer="A") + + monkeypatch.setattr(LiteLLMClient, "complete", fake_complete) + + result = await run_protocol(case_one(), real_config("single_agent"), "single_agent") + + assert call_count["n"] == 1 + assert result.token_usage["total_tokens"] > 0 + + +async def test_invalid_answer_metadata_is_recorded(monkeypatch: pytest.MonkeyPatch) -> None: + async def fake_complete(self: LiteLLMClient, messages: list[dict[str, str]], **kwargs: Any) -> LLMResponse: + return fake_llm_response(answer="D") + + monkeypatch.setattr(LiteLLMClient, "complete", fake_complete) + + result = await run_real_protocol(case_one(), real_config("single_agent"), "single_agent") + + final = result.final_decision.model_dump(mode="json") + assert result.final_decision.answer == "A" + assert final["raw_answer"] == "D" + assert final["normalized_answer"] == "A" + assert final["answer_valid"] is False + assert final["normalization_warning"] + assert result.metrics["answer_normalizations"][0]["raw"] == "D" + + +async def test_open_committee_accumulates_agent_and_judge_calls(monkeypatch: pytest.MonkeyPatch) -> None: + async def fake_complete(self: LiteLLMClient, messages: list[dict[str, str]], **kwargs: Any) -> LLMResponse: + return fake_llm_response(answer="A", token_usage={"prompt_tokens": 100, "completion_tokens": 30, "total_tokens": 130}) + + monkeypatch.setattr(LiteLLMClient, "complete", fake_complete) + + result = await run_real_protocol(case_one(), real_config("open_committee", n_agents=5), "open_committee") + + assert len(result.round0_outputs) == 5 + assert len(result.revised_outputs) == 5 + assert result.token_usage["n_calls"] == 11 + assert result.token_usage["total_tokens"] == 1430 + + +async def test_answer_level_retry_records_success(monkeypatch: pytest.MonkeyPatch) -> None: + call_count = {"n": 0} + + async def fake_complete(self: LiteLLMClient, messages: list[dict[str, str]], **kwargs: Any) -> LLMResponse: + call_count["n"] += 1 + if call_count["n"] == 1: + return fake_empty_answer_response() + return fake_llm_response(answer="A") + + monkeypatch.setattr(LiteLLMClient, "complete", fake_complete) + + result = await run_real_protocol(case_one(), real_config("single_agent", n_agents=1), "single_agent") + + assert call_count["n"] == 2 + assert result.round0_outputs[0].answer_valid is True + assert result.round0_outputs[0].retry_count == 1 + assert result.round0_outputs[0].failure_type is None + assert result.metrics["retry_count_total"] == 1 + assert result.metrics["retry_success_count"] == 1 + assert result.metrics["retry_failure_count"] == 0 + assert result.metrics["invalid_agent_outputs_count"] == 0 + + +async def test_answer_level_retry_failure_is_recorded_and_excluded_from_aeb(monkeypatch: pytest.MonkeyPatch) -> None: + call_count = {"n": 0} + + async def fake_complete(self: LiteLLMClient, messages: list[dict[str, str]], **kwargs: Any) -> LLMResponse: + call_count["n"] += 1 + if call_count["n"] <= 20: + return fake_empty_answer_response() + return fake_llm_response(answer="A", retained_evidence_ids=[]) + + monkeypatch.setattr(LiteLLMClient, "complete", fake_complete) + + result = await run_real_protocol( + case_one(), + real_config("anonymous_evidence_board", n_agents=5), + "anonymous_evidence_board", + ) + + assert call_count["n"] == 21 + assert len(result.round0_outputs) == 5 + assert all(output.answer_valid is False for output in result.round0_outputs) + assert all(output.retry_count == 3 for output in result.round0_outputs) + assert all(output.failure_type == "empty_answer" for output in result.round0_outputs) + assert all(output.imputed is True for output in result.round0_outputs) + assert result.metrics["invalid_agent_outputs_count"] == 5 + assert result.metrics["invalid_agent_outputs_rate"] == 1.0 + assert result.metrics["retry_failure_count"] == 5 + assert result.evidence_board is not None + assert result.evidence_board.units == [] + + +async def test_open_committee_round1_includes_peer_answers(monkeypatch: pytest.MonkeyPatch) -> None: + all_messages: list[list[dict[str, str]]] = [] + + async def fake_complete(self: LiteLLMClient, messages: list[dict[str, str]], **kwargs: Any) -> LLMResponse: + all_messages.append(messages) + return fake_llm_response(answer="A") + + monkeypatch.setattr(LiteLLMClient, "complete", fake_complete) + + result = await run_real_protocol(case_one(), real_config("open_committee", n_agents=5), "open_committee") + + assert len(result.round0_outputs) == 5 + assert len(result.revised_outputs) == 5 + assert len(all_messages) == 11 + + # Calls 6-10 (indices 5-9) are Round 1 + round1_messages = all_messages[5:10] + for msg_list in round1_messages: + combined = " ".join(msg.get("content", "") for msg in msg_list) + # Each Round 1 message should contain peer answer labels + assert "Answer: A" in combined or "Answer: B" in combined or "Answer: C" in combined + + # Round 1 for agent_1: user message should NOT reference agent_1's own output + agent1_r1_user = round1_messages[0][1].get("content", "") + assert "agent_1" not in agent1_r1_user + + assert result.token_usage["n_calls"] == 11 + + +async def test_anonymous_evidence_board_judge_does_not_see_agent_outputs(monkeypatch: pytest.MonkeyPatch) -> None: + all_messages: list[list[dict[str, str]]] = [] + + async def fake_complete(self: LiteLLMClient, messages: list[dict[str, str]], **kwargs: Any) -> LLMResponse: + all_messages.append(messages) + return fake_llm_response(answer="A") + + monkeypatch.setattr(LiteLLMClient, "complete", fake_complete) + + result = await run_real_protocol( + case_one(), + real_config("anonymous_evidence_board", n_agents=5), + "anonymous_evidence_board", + ) + + assert len(result.round0_outputs) == 5 + assert result.revised_outputs == [] + assert len(all_messages) == 6 + + # Last call (index 5) is the judge — check user message specifically + judge_user = all_messages[5][1].get("content", "") + # Judge should NOT see the Agent Diagnoses section or any agent identities. + assert "Agent Diagnoses" not in judge_user + assert "agent_" not in judge_user + assert "Answer:" not in judge_user + assert "supports [" not in judge_user + assert "contradicts [" not in judge_user + # Judge should still see the case and evidence board. + assert "Evidence Board" in judge_user + assert "Myocardial Infarction" in judge_user + + assert result.token_usage["n_calls"] == 6 + + +async def test_anonymous_evidence_board_judge_requires_anonymized_evidence_ids(monkeypatch: pytest.MonkeyPatch) -> None: + all_messages: list[list[dict[str, str]]] = [] + + async def fake_complete(self: LiteLLMClient, messages: list[dict[str, str]], **kwargs: Any) -> LLMResponse: + all_messages.append(messages) + return fake_llm_response(answer="A", retained_evidence_ids=["real_case_001:anonymous:0"]) + + monkeypatch.setattr(LiteLLMClient, "complete", fake_complete) + + await run_real_protocol( + case_one(), + real_config("anonymous_evidence_board", n_agents=5), + "anonymous_evidence_board", + ) + + judge_system = all_messages[5][0].get("content", "") + judge_user = all_messages[5][1].get("content", "") + assert "retained_evidence_ids" in judge_system + assert "exact" in judge_system.lower() + assert "Evidence Board" in judge_system + assert ":anonymous:" in judge_system + assert "real_case_001:anonymous:0" in judge_user + assert "Agent Diagnoses" not in judge_user + assert "agent_" not in judge_user + assert "Answer:" not in judge_user + assert "supports [" not in judge_user + assert "contradicts [" not in judge_user + + +async def test_anonymous_evidence_board_records_invalid_retained_evidence_ids(monkeypatch: pytest.MonkeyPatch) -> None: + call_count = {"n": 0} + + async def fake_complete(self: LiteLLMClient, messages: list[dict[str, str]], **kwargs: Any) -> LLMResponse: + call_count["n"] += 1 + if call_count["n"] == 6: + return fake_llm_response( + answer="A", + retained_evidence_ids=["real_case_001:anonymous:0", "real_case_001:agent_1:support:0", "not-on-board"], + ) + return fake_llm_response(answer="A") + + monkeypatch.setattr(LiteLLMClient, "complete", fake_complete) + + result = await run_real_protocol( + case_one(), + real_config("anonymous_evidence_board", n_agents=5), + "anonymous_evidence_board", + ) + + final = result.final_decision.model_dump(mode="json") + assert result.final_decision.retained_evidence_ids == ["real_case_001:anonymous:0"] + assert final["invalid_retained_evidence_ids"] == ["real_case_001:agent_1:support:0", "not-on-board"] + assert result.metrics["invalid_retained_evidence_ids"] == ["real_case_001:agent_1:support:0", "not-on-board"] + assert result.metrics["invalid_retained_evidence_ids_count"] == 2 + assert result.metrics["evidence_retention_rate"] == pytest.approx(1 / len(result.evidence_board.units)) + + +async def test_anonymous_evidence_board_all_invalid_retained_ids_falls_back_to_board(monkeypatch: pytest.MonkeyPatch) -> None: + call_count = {"n": 0} + + async def fake_complete(self: LiteLLMClient, messages: list[dict[str, str]], **kwargs: Any) -> LLMResponse: + call_count["n"] += 1 + if call_count["n"] == 6: + return fake_llm_response( + answer="A", + retained_evidence_ids=["real_case_001:agent_1:support:0", "not-on-board"], + ) + return fake_llm_response(answer="A") + + monkeypatch.setattr(LiteLLMClient, "complete", fake_complete) + + result = await run_real_protocol( + case_one(), + real_config("anonymous_evidence_board", n_agents=5), + "anonymous_evidence_board", + ) + + board_ids = [unit.evidence_id for unit in result.evidence_board.units] + final = result.final_decision.model_dump(mode="json") + assert result.final_decision.retained_evidence_ids == board_ids + assert final["invalid_retained_evidence_ids"] == ["real_case_001:agent_1:support:0", "not-on-board"] + assert result.metrics["invalid_retained_evidence_ids_count"] == 2 + assert result.metrics["evidence_retention_rate"] == 1.0 + + +async def test_single_agent_and_majority_vote_have_no_round1(monkeypatch: pytest.MonkeyPatch) -> None: + call_log: list[str] = [] + + async def fake_complete(self: LiteLLMClient, messages: list[dict[str, str]], **kwargs: Any) -> LLMResponse: + call_log.append("call") + return fake_llm_response(answer="A") + + monkeypatch.setattr(LiteLLMClient, "complete", fake_complete) + + # single_agent: 1 call (Round 0 only), _revise_outputs runs but no extra LLM calls + single_result = await run_real_protocol(case_one(), real_config("single_agent", n_agents=1), "single_agent") + assert len(single_result.round0_outputs) == 1 + assert single_result.token_usage["n_calls"] == 1 + + # majority_vote: 5 calls (Round 0 only), no deterministic mock revision in real path + call_log.clear() + mv_result = await run_real_protocol(case_one(), real_config("majority_vote", n_agents=5), "majority_vote") + assert len(mv_result.round0_outputs) == 5 + assert mv_result.revised_outputs == [] + assert mv_result.metrics["conformity_shift_rate"] == 0.0 + assert mv_result.token_usage["n_calls"] == 5 + + +async def test_malformed_llm_response_does_not_crash(monkeypatch: pytest.MonkeyPatch) -> None: + async def fake_complete(self: LiteLLMClient, messages: list[dict[str, str]], **kwargs: Any) -> LLMResponse: + return LLMResponse( + raw_text="This is not JSON.", + parsed_json=None, + usage={"prompt_tokens": 50, "completion_tokens": 10, "total_tokens": 60}, + latency=0.3, + ) + + monkeypatch.setattr(LiteLLMClient, "complete", fake_complete) + + result = await run_real_protocol(case_one(), real_config("single_agent"), "single_agent") + + assert result.final_decision.answer in case_one().options + assert result.final_decision.model_dump(mode="json")["answer_valid"] is False + + +async def test_litellm_client_preserves_malformed_json_response(monkeypatch: pytest.MonkeyPatch) -> None: + class Message: + content = "{\n \"answer\": \"A\"\n```" + + class Choice: + message = Message() + + class FakeResponse: + choices = [Choice()] + usage = {"prompt_tokens": 12, "completion_tokens": 4, "total_tokens": 16} + + async def fake_acompletion(**kwargs: Any) -> FakeResponse: + return FakeResponse() + + import medboard.llm.client as client_module + + monkeypatch.setattr(client_module.litellm, "acompletion", fake_acompletion) + + response = await LiteLLMClient("openai/test", json_mode=True).complete([{"role": "user", "content": "Return JSON."}]) + + assert response.raw_text.startswith("{") + assert response.parsed_json is None + assert response.usage["total_tokens"] == 16 + + +async def test_sweep_over_four_protocols_eight_rows(tmp_path: Path, monkeypatch: pytest.MonkeyPatch) -> None: + async def fake_complete(self: LiteLLMClient, messages: list[dict[str, str]], **kwargs: Any) -> LLMResponse: + return fake_llm_response(answer="A") + + monkeypatch.setattr(LiteLLMClient, "complete", fake_complete) + jsonl_path = write_medxpertqa_jsonl([case_one(), case_two()], tmp_path / "synthetic_medxpertqa.jsonl") + config = ExperimentConfig.model_validate( + { + "experiment": {"name": "sweep_real_path", "n_cases": 2, "seed": 42}, + "protocol": { + "name": "single_agent", + "protocols": ["single_agent", "majority_vote", "open_committee", "anonymous_evidence_board"], + "n_agents": 5, + "max_rounds": 2, + "visibility_mode": "answer_only", + }, + "model": {"provider": "openai", "model": "gpt-4o-mini", "temperature": 0.0, "json_mode": True}, + "runtime": {"mock_llm": False}, + "data": {"source": "medxpertqa_jsonl", "path": str(jsonl_path)}, + "output": {"root_dir": str(tmp_path)}, + } + ) + + from medboard.runners.run_sweep import run_sweep + + results = await run_sweep(config) + + assert set(results) == {"single_agent", "majority_vote", "open_committee", "anonymous_evidence_board"} + df = pd.read_parquet(tmp_path / "sweep_real_path" / "aggregated_results.parquet") + assert len(df) == 8 + assert set(df["case_id"]) == {"real_case_001", "real_case_002"} + for usage_str in df["token_usage"]: + assert json.loads(usage_str)["total_tokens"] > 0 diff --git a/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_board_execution/tests/test_sweep_runner.py b/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_board_execution/tests/test_sweep_runner.py new file mode 100644 index 0000000000000000000000000000000000000000..354471635d5ab4279a57ea71712ceb9bb27cb414 --- /dev/null +++ b/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_board_execution/tests/test_sweep_runner.py @@ -0,0 +1,190 @@ +from pathlib import Path + +import pandas as pd +import pytest + +from medboard.runners.run_sweep import detect_sweep_type, get_sweep_variants, run_async, run_sweep +from medboard.schemas.config import ExperimentConfig + + +def _config(payload: dict) -> ExperimentConfig: + return ExperimentConfig.model_validate(payload) + + +def test_detect_sweep_type_with_protocols() -> None: + config = _config({"protocol": {"protocols": ["single_agent", "majority_vote"]}}) + + assert detect_sweep_type(config) == "protocol" + + +def test_detect_sweep_type_with_visibility_modes() -> None: + config = _config({"protocol": {"visibility_modes": ["answer_only", "evidence_only"]}}) + + assert detect_sweep_type(config) == "visibility" + + +def test_detect_sweep_type_none() -> None: + config = _config({"protocol": {"name": "single_agent"}}) + + assert detect_sweep_type(config) == "none" + + +def test_detect_sweep_type_rejects_multiple_axes() -> None: + config = _config( + { + "protocol": { + "protocols": ["single_agent"], + "visibility_modes": ["answer_only"], + } + } + ) + + with pytest.raises(ValueError, match="one sweep axis"): + detect_sweep_type(config) + + +def test_get_sweep_variants_protocol_sweep() -> None: + config = _config({"protocol": {"protocols": ["single_agent", "majority_vote"]}}) + + variants = get_sweep_variants(config) + + assert [label for label, _ in variants] == ["single_agent", "majority_vote"] + assert [variant.protocol.name for _, variant in variants] == ["single_agent", "majority_vote"] + assert all(variant.protocol.protocols == [] for _, variant in variants) + assert config.protocol.protocols == ["single_agent", "majority_vote"] + + +def test_get_sweep_variants_visibility_sweep() -> None: + config = _config({"protocol": {"visibility_modes": ["answer_only", "evidence_only"]}}) + + variants = get_sweep_variants(config) + + assert [label for label, _ in variants] == ["answer_only", "evidence_only"] + assert [variant.protocol.visibility_mode for _, variant in variants] == ["answer_only", "evidence_only"] + assert all(variant.protocol.visibility_modes == [] for _, variant in variants) + assert config.protocol.visibility_modes == ["answer_only", "evidence_only"] + + +def test_get_sweep_variants_no_sweep() -> None: + config = _config({"experiment": {"name": "pilot"}, "protocol": {"name": "single_agent"}}) + + variants = get_sweep_variants(config) + + assert len(variants) == 1 + assert variants[0][0] == "single_agent" + assert variants[0][1].protocol.name == "single_agent" + assert variants[0][1].experiment.name == "pilot" + + +async def test_run_sweep_writes_variant_outputs_and_aggregated_parquet(tmp_path: Path) -> None: + config = _config( + { + "experiment": {"name": "sweep_test", "n_cases": 2, "seed": 42}, + "protocol": { + "name": "single_agent", + "protocols": ["single_agent", "majority_vote"], + "n_agents": 3, + "max_rounds": 2, + "visibility_mode": "answer_only", + }, + "runtime": {"mock_llm": True}, + "output": {"root_dir": str(tmp_path)}, + } + ) + + results = await run_sweep(config) + + assert set(results) == {"single_agent", "majority_vote"} + assert len(results["single_agent"]) == 2 + assert len(results["majority_vote"]) == 2 + assert (tmp_path / "sweep_test" / "single_agent" / "results" / "results.parquet").exists() + assert (tmp_path / "sweep_test" / "majority_vote" / "results" / "results.parquet").exists() + + aggregated_path = tmp_path / "sweep_test" / "aggregated_results.parquet" + assert aggregated_path.exists() + rows = pd.read_parquet(aggregated_path) + assert len(rows) == 4 + assert set(rows["variant"]) == {"single_agent", "majority_vote"} + assert set(rows["protocol_name"]) == {"single_agent", "majority_vote"} + assert set(rows["visibility_mode"]) == {"answer_only"} + + +async def test_run_async_loads_protocol_sweep_yaml(tmp_path: Path) -> None: + config_path = tmp_path / "main_protocols.yaml" + config_path.write_text( + """ +experiment: + name: yaml_protocols + n_cases: 1 + seed: 42 +protocol: + name: single_agent + protocols: + - single_agent + - majority_vote + n_agents: 3 + max_rounds: 2 + visibility_mode: answer_only +runtime: + mock_llm: true +output: + root_dir: {root_dir} +""".format(root_dir=tmp_path), + encoding="utf-8", + ) + + results = await run_async(config_path) + + assert set(results) == {"single_agent", "majority_vote"} + assert (tmp_path / "yaml_protocols" / "aggregated_results.parquet").exists() + + +async def test_run_async_loads_visibility_sweep_yaml(tmp_path: Path) -> None: + config_path = tmp_path / "visibility_ablation.yaml" + config_path.write_text( + """ +experiment: + name: yaml_visibility + n_cases: 1 + seed: 42 +protocol: + name: anonymous_evidence_board + visibility_modes: + - answer_only + - evidence_only + n_agents: 5 + max_rounds: 2 +runtime: + mock_llm: true +output: + root_dir: {root_dir} +""".format(root_dir=tmp_path), + encoding="utf-8", + ) + + results = await run_async(config_path) + + assert set(results) == {"answer_only", "evidence_only"} + assert (tmp_path / "yaml_visibility" / "aggregated_results.parquet").exists() + + +async def test_run_sweep_visibility_preserves_variant_labels(tmp_path: Path) -> None: + config = _config( + { + "experiment": {"name": "visibility_test", "n_cases": 1, "seed": 42}, + "protocol": { + "name": "anonymous_evidence_board", + "visibility_modes": ["answer_only", "evidence_only"], + "n_agents": 5, + "max_rounds": 2, + }, + "runtime": {"mock_llm": True}, + "output": {"root_dir": str(tmp_path)}, + } + ) + + await run_sweep(config) + + rows = pd.read_parquet(tmp_path / "visibility_test" / "aggregated_results.parquet") + assert set(rows["variant"]) == {"answer_only", "evidence_only"} + assert set(rows["visibility_mode"]) == {"answer_only", "evidence_only"} diff --git a/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_board_execution/tests/test_visibility_masking.py b/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_board_execution/tests/test_visibility_masking.py new file mode 100644 index 0000000000000000000000000000000000000000..50b4a85d87fd6778d890630bd4f680e692a5cabd --- /dev/null +++ b/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_board_execution/tests/test_visibility_masking.py @@ -0,0 +1,32 @@ +from medboard.core.visibility import build_visibility_view +from medboard.schemas.agent import AgentOutput + + +def test_evidence_only_mode_does_not_expose_answer() -> None: + output = AgentOutput( + agent_id="agent_1", + role="clinician", + round_id=0, + answer="A", + confidence=0.81, + supporting_evidence=["fever and cough"], + contradicting_evidence=["normal oxygen saturation"], + missing_evidence=["chest x-ray"], + differential_diagnoses=["viral infection"], + uncertainty_statement="Imaging is unavailable.", + ) + + view = build_visibility_view([output], visibility_mode="evidence_only") + + assert view == [ + { + "agent_id": "agent_1", + "role": "clinician", + "supporting_evidence": ["fever and cough"], + "contradicting_evidence": ["normal oxygen saturation"], + "missing_evidence": ["chest x-ray"], + } + ] + assert "answer" not in view[0] + assert "confidence" not in view[0] + assert "A" not in str(view) diff --git a/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_board_execution/uv.lock b/data/external/results/formal/early_mas_architecture_motivation/code_snapshot/med_board_execution/uv.lock new file mode 100644 index 0000000000000000000000000000000000000000..d13631e228fb223ca1c6337c5c6b1c7eb026647e --- /dev/null +++ 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a/data/external/results/formal/early_mas_architecture_motivation/figure_sources/extended_data_fig1_source.csv +++ b/data/external/results/formal/early_mas_architecture_motivation/figure_sources/extended_data_fig1_source.csv @@ -1,116 +1,116 @@ panel,series,group,metric,value,aux_value,unit,source_path,note -a,baseline,Single,accuracy_percent,45.2381,1.0000,percent,/Users/jay/research/evole-meme/med_repos_20260604/med-agent/results_concurrency50,Baseline topology sweep; aux_value is token ratio vs single -a,baseline,Star,accuracy_percent,47.2698,8.4000,percent,/Users/jay/research/evole-meme/med_repos_20260604/med-agent/results_concurrency50,Baseline topology sweep; aux_value is token ratio vs single -b,baseline,Star,one_round_exit_rate_percent,92.1587,0.332322,percent,/Users/jay/research/evole-meme/med_repos_20260604/med-agent/results_concurrency50,aux_value is weighted-majority accuracy when available -a,baseline,Complete,accuracy_percent,47.5873,8.3638,percent,/Users/jay/research/evole-meme/med_repos_20260604/med-agent/results_concurrency50,Baseline topology sweep; aux_value is token ratio vs single -b,baseline,Complete,one_round_exit_rate_percent,95.3651,0.332394,percent,/Users/jay/research/evole-meme/med_repos_20260604/med-agent/results_concurrency50,aux_value is weighted-majority accuracy when available -a,baseline,Ring,accuracy_percent,47.7778,8.6650,percent,/Users/jay/research/evole-meme/med_repos_20260604/med-agent/results_concurrency50,Baseline topology sweep; aux_value is token ratio vs single -b,baseline,Ring,one_round_exit_rate_percent,91.3333,0.357037,percent,/Users/jay/research/evole-meme/med_repos_20260604/med-agent/results_concurrency50,aux_value is weighted-majority accuracy when available -a,baseline,Hierarchical,accuracy_percent,47.3016,8.8869,percent,/Users/jay/research/evole-meme/med_repos_20260604/med-agent/results_concurrency50,Baseline topology sweep; aux_value is token ratio vs single -b,baseline,Hierarchical,one_round_exit_rate_percent,90.4762,0.314208,percent,/Users/jay/research/evole-meme/med_repos_20260604/med-agent/results_concurrency50,aux_value is weighted-majority accuracy when available -a,tuned,Single,accuracy_percent,44.7302,1.0000,percent,/Users/jay/research/evole-meme/med_repos_20260604/med-agent/results_concurrency50_tuned,Tuned consensus sweep; aux_value is token ratio vs single -a,tuned,Star,accuracy_percent,48.0317,13.3823,percent,/Users/jay/research/evole-meme/med_repos_20260604/med-agent/results_concurrency50_tuned,Tuned consensus sweep; aux_value is token ratio vs single -b,tuned,Star,one_round_exit_rate_percent,27.3333,0.317697,percent,/Users/jay/research/evole-meme/med_repos_20260604/med-agent/results_concurrency50_tuned,aux_value is weighted-majority accuracy when available -a,tuned,Complete,accuracy_percent,48.0635,13.0392,percent,/Users/jay/research/evole-meme/med_repos_20260604/med-agent/results_concurrency50_tuned,Tuned consensus sweep; aux_value is token ratio vs single -b,tuned,Complete,one_round_exit_rate_percent,54.0317,0.330357,percent,/Users/jay/research/evole-meme/med_repos_20260604/med-agent/results_concurrency50_tuned,aux_value is weighted-majority accuracy when available -a,tuned,Ring,accuracy_percent,48.0000,13.1385,percent,/Users/jay/research/evole-meme/med_repos_20260604/med-agent/results_concurrency50_tuned,Tuned consensus sweep; aux_value is token ratio vs single -b,tuned,Ring,one_round_exit_rate_percent,34.3810,0.307527,percent,/Users/jay/research/evole-meme/med_repos_20260604/med-agent/results_concurrency50_tuned,aux_value is weighted-majority accuracy when available -a,tuned,Hierarchical,accuracy_percent,47.7460,13.5543,percent,/Users/jay/research/evole-meme/med_repos_20260604/med-agent/results_concurrency50_tuned,Tuned consensus sweep; aux_value is token ratio vs single -b,tuned,Hierarchical,one_round_exit_rate_percent,27.7460,0.304348,percent,/Users/jay/research/evole-meme/med_repos_20260604/med-agent/results_concurrency50_tuned,aux_value is weighted-majority accuracy when available -a,fixed6,Single,accuracy_percent,44.7302,1.0000,percent,/Users/jay/research/evole-meme/med_repos_20260604/med-agent/results_concurrency50_fixed6,Fixed-six-round sweep; aux_value is token ratio vs single -a,fixed6,Star,accuracy_percent,48.2222,34.4315,percent,/Users/jay/research/evole-meme/med_repos_20260604/med-agent/results_concurrency50_fixed6,Fixed-six-round sweep; aux_value is token ratio vs single -b,fixed6,Star,one_round_exit_rate_percent,0.0000,,percent,/Users/jay/research/evole-meme/med_repos_20260604/med-agent/results_concurrency50_fixed6,aux_value is weighted-majority accuracy when available -a,fixed6,Complete,accuracy_percent,48.6032,48.9550,percent,/Users/jay/research/evole-meme/med_repos_20260604/med-agent/results_concurrency50_fixed6,Fixed-six-round sweep; aux_value is token ratio vs single -b,fixed6,Complete,one_round_exit_rate_percent,0.0000,,percent,/Users/jay/research/evole-meme/med_repos_20260604/med-agent/results_concurrency50_fixed6,aux_value is weighted-majority accuracy when available -a,fixed6,Ring,accuracy_percent,48.5714,35.4444,percent,/Users/jay/research/evole-meme/med_repos_20260604/med-agent/results_concurrency50_fixed6,Fixed-six-round sweep; aux_value is token ratio vs single -b,fixed6,Ring,one_round_exit_rate_percent,0.0000,,percent,/Users/jay/research/evole-meme/med_repos_20260604/med-agent/results_concurrency50_fixed6,aux_value is weighted-majority accuracy when available -a,fixed6,Hierarchical,accuracy_percent,47.1746,33.2600,percent,/Users/jay/research/evole-meme/med_repos_20260604/med-agent/results_concurrency50_fixed6,Fixed-six-round sweep; aux_value is token ratio vs single -b,fixed6,Hierarchical,one_round_exit_rate_percent,0.0000,,percent,/Users/jay/research/evole-meme/med_repos_20260604/med-agent/results_concurrency50_fixed6,aux_value is weighted-majority accuracy when available -b,baseline,Star,round_exit_share_percent,92.1587,1,percent,/Users/jay/research/evole-meme/med_repos_20260604/med-agent/results_concurrency50,aux_value is the round at which discussion ended; forced_schedule=0 -b,baseline,Star,round_exit_share_percent,5.5873,2,percent,/Users/jay/research/evole-meme/med_repos_20260604/med-agent/results_concurrency50,aux_value is the round at which discussion ended; forced_schedule=0 -b,baseline,Star,round_exit_share_percent,1.2381,3,percent,/Users/jay/research/evole-meme/med_repos_20260604/med-agent/results_concurrency50,aux_value is the round at which discussion ended; forced_schedule=0 -b,baseline,Star,round_exit_share_percent,0.5079,4,percent,/Users/jay/research/evole-meme/med_repos_20260604/med-agent/results_concurrency50,aux_value is the round at which discussion ended; forced_schedule=0 -b,baseline,Star,round_exit_share_percent,0.1270,5,percent,/Users/jay/research/evole-meme/med_repos_20260604/med-agent/results_concurrency50,aux_value is the round at which discussion ended; forced_schedule=0 -b,baseline,Star,round_exit_share_percent,0.1270,6,percent,/Users/jay/research/evole-meme/med_repos_20260604/med-agent/results_concurrency50,aux_value is the round at which discussion ended; forced_schedule=0 -b,baseline,Star,round_exit_share_percent,0.0952,7,percent,/Users/jay/research/evole-meme/med_repos_20260604/med-agent/results_concurrency50,aux_value is the round at which discussion ended; forced_schedule=0 -b,baseline,Star,round_exit_share_percent,0.0317,9,percent,/Users/jay/research/evole-meme/med_repos_20260604/med-agent/results_concurrency50,aux_value is the round 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-b,baseline,Complete,round_exit_share_percent,0.0635,10,percent,/Users/jay/research/evole-meme/med_repos_20260604/med-agent/results_concurrency50,aux_value is the round at which discussion ended; forced_schedule=0 -b,baseline,Ring,round_exit_share_percent,91.3333,1,percent,/Users/jay/research/evole-meme/med_repos_20260604/med-agent/results_concurrency50,aux_value is the round at which discussion ended; forced_schedule=0 -b,baseline,Ring,round_exit_share_percent,4.6667,2,percent,/Users/jay/research/evole-meme/med_repos_20260604/med-agent/results_concurrency50,aux_value is the round at which discussion ended; forced_schedule=0 -b,baseline,Ring,round_exit_share_percent,1.9365,3,percent,/Users/jay/research/evole-meme/med_repos_20260604/med-agent/results_concurrency50,aux_value is the round at which discussion ended; forced_schedule=0 -b,baseline,Ring,round_exit_share_percent,0.7937,4,percent,/Users/jay/research/evole-meme/med_repos_20260604/med-agent/results_concurrency50,aux_value is the round at which discussion ended; forced_schedule=0 -b,baseline,Ring,round_exit_share_percent,0.5079,5,percent,/Users/jay/research/evole-meme/med_repos_20260604/med-agent/results_concurrency50,aux_value is the round at which discussion ended; forced_schedule=0 -b,baseline,Ring,round_exit_share_percent,0.2222,6,percent,/Users/jay/research/evole-meme/med_repos_20260604/med-agent/results_concurrency50,aux_value is the round at which discussion ended; forced_schedule=0 -b,baseline,Ring,round_exit_share_percent,0.1587,7,percent,/Users/jay/research/evole-meme/med_repos_20260604/med-agent/results_concurrency50,aux_value is the round at which discussion ended; forced_schedule=0 -b,baseline,Ring,round_exit_share_percent,0.0635,9,percent,/Users/jay/research/evole-meme/med_repos_20260604/med-agent/results_concurrency50,aux_value is the round at which discussion ended; forced_schedule=0 -b,baseline,Ring,round_exit_share_percent,0.3175,10,percent,/Users/jay/research/evole-meme/med_repos_20260604/med-agent/results_concurrency50,aux_value is the round at which discussion ended; forced_schedule=0 -b,baseline,Hierarchical,round_exit_share_percent,90.4762,1,percent,/Users/jay/research/evole-meme/med_repos_20260604/med-agent/results_concurrency50,aux_value is the round at which discussion ended; forced_schedule=0 -b,baseline,Hierarchical,round_exit_share_percent,4.6349,2,percent,/Users/jay/research/evole-meme/med_repos_20260604/med-agent/results_concurrency50,aux_value is the round at which discussion ended; forced_schedule=0 -b,baseline,Hierarchical,round_exit_share_percent,2.1905,3,percent,/Users/jay/research/evole-meme/med_repos_20260604/med-agent/results_concurrency50,aux_value is the round at which discussion ended; forced_schedule=0 -b,baseline,Hierarchical,round_exit_share_percent,1.0476,4,percent,/Users/jay/research/evole-meme/med_repos_20260604/med-agent/results_concurrency50,aux_value is the round at which discussion ended; forced_schedule=0 -b,baseline,Hierarchical,round_exit_share_percent,0.3492,5,percent,/Users/jay/research/evole-meme/med_repos_20260604/med-agent/results_concurrency50,aux_value is the round at which discussion ended; forced_schedule=0 -b,baseline,Hierarchical,round_exit_share_percent,0.2540,6,percent,/Users/jay/research/evole-meme/med_repos_20260604/med-agent/results_concurrency50,aux_value is the round at which discussion ended; forced_schedule=0 -b,baseline,Hierarchical,round_exit_share_percent,0.1270,7,percent,/Users/jay/research/evole-meme/med_repos_20260604/med-agent/results_concurrency50,aux_value is the round at which discussion ended; forced_schedule=0 -b,baseline,Hierarchical,round_exit_share_percent,0.0635,8,percent,/Users/jay/research/evole-meme/med_repos_20260604/med-agent/results_concurrency50,aux_value is the round at which discussion ended; forced_schedule=0 -b,baseline,Hierarchical,round_exit_share_percent,0.0635,9,percent,/Users/jay/research/evole-meme/med_repos_20260604/med-agent/results_concurrency50,aux_value is the round at which discussion ended; forced_schedule=0 -b,baseline,Hierarchical,round_exit_share_percent,0.7937,10,percent,/Users/jay/research/evole-meme/med_repos_20260604/med-agent/results_concurrency50,aux_value is the round at which discussion ended; forced_schedule=0 -b,tuned,Star,round_exit_share_percent,27.3333,1,percent,/Users/jay/research/evole-meme/med_repos_20260604/med-agent/results_concurrency50_tuned,aux_value is the round at which discussion ended; forced_schedule=0 -b,tuned,Star,round_exit_share_percent,55.5238,2,percent,/Users/jay/research/evole-meme/med_repos_20260604/med-agent/results_concurrency50_tuned,aux_value is the round at which discussion ended; forced_schedule=0 -b,tuned,Star,round_exit_share_percent,11.9048,3,percent,/Users/jay/research/evole-meme/med_repos_20260604/med-agent/results_concurrency50_tuned,aux_value is the round at which discussion ended; forced_schedule=0 -b,tuned,Star,round_exit_share_percent,2.7619,4,percent,/Users/jay/research/evole-meme/med_repos_20260604/med-agent/results_concurrency50_tuned,aux_value is the round at which discussion ended; forced_schedule=0 -b,tuned,Star,round_exit_share_percent,1.1746,5,percent,/Users/jay/research/evole-meme/med_repos_20260604/med-agent/results_concurrency50_tuned,aux_value is the round at which discussion ended; forced_schedule=0 -b,tuned,Star,round_exit_share_percent,0.5397,6,percent,/Users/jay/research/evole-meme/med_repos_20260604/med-agent/results_concurrency50_tuned,aux_value is the round at which discussion ended; forced_schedule=0 -b,tuned,Star,round_exit_share_percent,0.1905,7,percent,/Users/jay/research/evole-meme/med_repos_20260604/med-agent/results_concurrency50_tuned,aux_value is the round at which discussion ended; forced_schedule=0 -b,tuned,Star,round_exit_share_percent,0.1587,8,percent,/Users/jay/research/evole-meme/med_repos_20260604/med-agent/results_concurrency50_tuned,aux_value is the round at which discussion ended; forced_schedule=0 -b,tuned,Star,round_exit_share_percent,0.0317,9,percent,/Users/jay/research/evole-meme/med_repos_20260604/med-agent/results_concurrency50_tuned,aux_value is the round at which discussion ended; forced_schedule=0 -b,tuned,Star,round_exit_share_percent,0.3810,10,percent,/Users/jay/research/evole-meme/med_repos_20260604/med-agent/results_concurrency50_tuned,aux_value is the round at which discussion ended; forced_schedule=0 -b,tuned,Complete,round_exit_share_percent,54.0317,1,percent,/Users/jay/research/evole-meme/med_repos_20260604/med-agent/results_concurrency50_tuned,aux_value is the round at which discussion ended; forced_schedule=0 -b,tuned,Complete,round_exit_share_percent,35.1429,2,percent,/Users/jay/research/evole-meme/med_repos_20260604/med-agent/results_concurrency50_tuned,aux_value is the round at which discussion ended; forced_schedule=0 -b,tuned,Complete,round_exit_share_percent,7.5556,3,percent,/Users/jay/research/evole-meme/med_repos_20260604/med-agent/results_concurrency50_tuned,aux_value is the round at which discussion ended; forced_schedule=0 -b,tuned,Complete,round_exit_share_percent,1.7460,4,percent,/Users/jay/research/evole-meme/med_repos_20260604/med-agent/results_concurrency50_tuned,aux_value is the round at which discussion ended; forced_schedule=0 -b,tuned,Complete,round_exit_share_percent,0.6349,5,percent,/Users/jay/research/evole-meme/med_repos_20260604/med-agent/results_concurrency50_tuned,aux_value is the round at which discussion ended; forced_schedule=0 -b,tuned,Complete,round_exit_share_percent,0.4127,6,percent,/Users/jay/research/evole-meme/med_repos_20260604/med-agent/results_concurrency50_tuned,aux_value is the round at which discussion ended; forced_schedule=0 -b,tuned,Complete,round_exit_share_percent,0.0952,7,percent,/Users/jay/research/evole-meme/med_repos_20260604/med-agent/results_concurrency50_tuned,aux_value is the round at which discussion ended; forced_schedule=0 -b,tuned,Complete,round_exit_share_percent,0.0952,8,percent,/Users/jay/research/evole-meme/med_repos_20260604/med-agent/results_concurrency50_tuned,aux_value is the round at which discussion ended; forced_schedule=0 -b,tuned,Complete,round_exit_share_percent,0.0952,9,percent,/Users/jay/research/evole-meme/med_repos_20260604/med-agent/results_concurrency50_tuned,aux_value is the round at which discussion ended; forced_schedule=0 -b,tuned,Complete,round_exit_share_percent,0.1905,10,percent,/Users/jay/research/evole-meme/med_repos_20260604/med-agent/results_concurrency50_tuned,aux_value is the round at which discussion ended; forced_schedule=0 -b,tuned,Ring,round_exit_share_percent,34.3810,1,percent,/Users/jay/research/evole-meme/med_repos_20260604/med-agent/results_concurrency50_tuned,aux_value is the round at which discussion ended; forced_schedule=0 -b,tuned,Ring,round_exit_share_percent,49.9365,2,percent,/Users/jay/research/evole-meme/med_repos_20260604/med-agent/results_concurrency50_tuned,aux_value is the round at which discussion ended; forced_schedule=0 -b,tuned,Ring,round_exit_share_percent,10.2857,3,percent,/Users/jay/research/evole-meme/med_repos_20260604/med-agent/results_concurrency50_tuned,aux_value is the round at which discussion ended; forced_schedule=0 -b,tuned,Ring,round_exit_share_percent,2.8571,4,percent,/Users/jay/research/evole-meme/med_repos_20260604/med-agent/results_concurrency50_tuned,aux_value is the round at which discussion ended; forced_schedule=0 -b,tuned,Ring,round_exit_share_percent,1.1746,5,percent,/Users/jay/research/evole-meme/med_repos_20260604/med-agent/results_concurrency50_tuned,aux_value is the round at which discussion ended; forced_schedule=0 -b,tuned,Ring,round_exit_share_percent,0.5714,6,percent,/Users/jay/research/evole-meme/med_repos_20260604/med-agent/results_concurrency50_tuned,aux_value is the round at which discussion ended; forced_schedule=0 -b,tuned,Ring,round_exit_share_percent,0.2857,7,percent,/Users/jay/research/evole-meme/med_repos_20260604/med-agent/results_concurrency50_tuned,aux_value is the round at which discussion ended; forced_schedule=0 -b,tuned,Ring,round_exit_share_percent,0.2222,8,percent,/Users/jay/research/evole-meme/med_repos_20260604/med-agent/results_concurrency50_tuned,aux_value is the round at which discussion ended; forced_schedule=0 -b,tuned,Ring,round_exit_share_percent,0.2857,10,percent,/Users/jay/research/evole-meme/med_repos_20260604/med-agent/results_concurrency50_tuned,aux_value is the round at which discussion ended; forced_schedule=0 -b,tuned,Hierarchical,round_exit_share_percent,27.7460,1,percent,/Users/jay/research/evole-meme/med_repos_20260604/med-agent/results_concurrency50_tuned,aux_value is the round at which discussion ended; forced_schedule=0 -b,tuned,Hierarchical,round_exit_share_percent,53.3651,2,percent,/Users/jay/research/evole-meme/med_repos_20260604/med-agent/results_concurrency50_tuned,aux_value is the round at which discussion ended; forced_schedule=0 -b,tuned,Hierarchical,round_exit_share_percent,12.1905,3,percent,/Users/jay/research/evole-meme/med_repos_20260604/med-agent/results_concurrency50_tuned,aux_value is the round at which discussion ended; forced_schedule=0 -b,tuned,Hierarchical,round_exit_share_percent,3.4286,4,percent,/Users/jay/research/evole-meme/med_repos_20260604/med-agent/results_concurrency50_tuned,aux_value is the round at which discussion ended; forced_schedule=0 -b,tuned,Hierarchical,round_exit_share_percent,1.2063,5,percent,/Users/jay/research/evole-meme/med_repos_20260604/med-agent/results_concurrency50_tuned,aux_value is the round at which discussion ended; forced_schedule=0 -b,tuned,Hierarchical,round_exit_share_percent,0.6349,6,percent,/Users/jay/research/evole-meme/med_repos_20260604/med-agent/results_concurrency50_tuned,aux_value is the round at which discussion ended; forced_schedule=0 -b,tuned,Hierarchical,round_exit_share_percent,0.2857,7,percent,/Users/jay/research/evole-meme/med_repos_20260604/med-agent/results_concurrency50_tuned,aux_value is the round at which discussion ended; forced_schedule=0 -b,tuned,Hierarchical,round_exit_share_percent,0.3492,8,percent,/Users/jay/research/evole-meme/med_repos_20260604/med-agent/results_concurrency50_tuned,aux_value is the round at which discussion ended; forced_schedule=0 -b,tuned,Hierarchical,round_exit_share_percent,0.1905,9,percent,/Users/jay/research/evole-meme/med_repos_20260604/med-agent/results_concurrency50_tuned,aux_value is the round at which discussion ended; forced_schedule=0 -b,tuned,Hierarchical,round_exit_share_percent,0.6032,10,percent,/Users/jay/research/evole-meme/med_repos_20260604/med-agent/results_concurrency50_tuned,aux_value is the round at which discussion ended; forced_schedule=0 -b,fixed6,Star,round_exit_share_percent,100.0000,6,percent,/Users/jay/research/evole-meme/med_repos_20260604/med-agent/results_concurrency50_fixed6,aux_value is the round at which discussion ended; forced_schedule=1 -b,fixed6,Complete,round_exit_share_percent,100.0000,6,percent,/Users/jay/research/evole-meme/med_repos_20260604/med-agent/results_concurrency50_fixed6,aux_value is the round at which discussion ended; forced_schedule=1 -b,fixed6,Ring,round_exit_share_percent,100.0000,6,percent,/Users/jay/research/evole-meme/med_repos_20260604/med-agent/results_concurrency50_fixed6,aux_value is the round at which discussion ended; forced_schedule=1 -b,fixed6,Hierarchical,round_exit_share_percent,100.0000,6,percent,/Users/jay/research/evole-meme/med_repos_20260604/med-agent/results_concurrency50_fixed6,aux_value is the round at which discussion ended; forced_schedule=1 -c,med_board_full_3seed,single_agent,accuracy_percent,39.8800,1.0000,percent,/Users/jay/research/evole-meme/med_repos_20260604/med_board/reports/real_llm_full_3seed_summary.md,aux_value is token ratio vs single_agent -c,med_board_full_3seed,majority_vote,accuracy_percent,41.0600,4.9600,percent,/Users/jay/research/evole-meme/med_repos_20260604/med_board/reports/real_llm_full_3seed_summary.md,aux_value is token ratio vs single_agent -c,med_board_full_3seed,open_committee,accuracy_percent,42.2000,10.7300,percent,/Users/jay/research/evole-meme/med_repos_20260604/med_board/reports/real_llm_full_3seed_summary.md,aux_value is token ratio vs single_agent -c,med_board_full_3seed,anonymous_evidence_board,accuracy_percent,41.8900,6.0700,percent,/Users/jay/research/evole-meme/med_repos_20260604/med_board/reports/real_llm_full_3seed_summary.md,aux_value is token ratio vs single_agent -d,oracle_gap,majority_vote,final_accuracy_percent,41.0612,55.4422,percent,/Users/jay/research/evole-meme/med_repos_20260604/med_board/runs/gain_bottleneck_analysis/gain_bottleneck_report.json,aux_value is oracle accuracy percent -d,oracle_gap,open_committee,final_accuracy_percent,42.2041,55.3333,percent,/Users/jay/research/evole-meme/med_repos_20260604/med_board/runs/gain_bottleneck_analysis/gain_bottleneck_report.json,aux_value is oracle accuracy percent -d,oracle_gap,anonymous_evidence_board,final_accuracy_percent,41.8912,55.5918,percent,/Users/jay/research/evole-meme/med_repos_20260604/med_board/runs/gain_bottleneck_analysis/gain_bottleneck_report.json,aux_value is oracle accuracy percent -d,mwmc_follow_majority,anonymous_evidence_board,follow_majority_percent,59.6064,37.4883,percent,/Users/jay/research/evole-meme/med_repos_20260604/med_board/runs/gain_bottleneck_analysis/gain_bottleneck_report.json,aux_value is follow-minority percent on majority-wrong/minority-correct subset -d,mwmc_follow_majority,open_committee,follow_majority_percent,73.2537,26.5625,percent,/Users/jay/research/evole-meme/med_repos_20260604/med_board/runs/gain_bottleneck_analysis/gain_bottleneck_report.json,aux_value is follow-minority percent on majority-wrong/minority-correct subset +a,baseline,Single,accuracy_percent,45.2381,1.0000,percent,results/formal/early_mas_architecture_motivation/analysis_ready/med_agent/baseline/all_results.csv,Baseline topology sweep; aux_value is token ratio vs single +a,baseline,Star,accuracy_percent,47.2698,8.4000,percent,results/formal/early_mas_architecture_motivation/analysis_ready/med_agent/baseline/all_results.csv,Baseline topology sweep; aux_value is token ratio vs single +b,baseline,Star,one_round_exit_rate_percent,92.1587,0.332322,percent,results/formal/early_mas_architecture_motivation/analysis_ready/med_agent/baseline/all_results.csv,aux_value is weighted-majority accuracy when available +a,baseline,Complete,accuracy_percent,47.5873,8.3638,percent,results/formal/early_mas_architecture_motivation/analysis_ready/med_agent/baseline/all_results.csv,Baseline topology sweep; aux_value is token ratio vs single +b,baseline,Complete,one_round_exit_rate_percent,95.3651,0.332394,percent,results/formal/early_mas_architecture_motivation/analysis_ready/med_agent/baseline/all_results.csv,aux_value is weighted-majority accuracy when available +a,baseline,Ring,accuracy_percent,47.7778,8.6650,percent,results/formal/early_mas_architecture_motivation/analysis_ready/med_agent/baseline/all_results.csv,Baseline topology sweep; aux_value is token ratio vs single +b,baseline,Ring,one_round_exit_rate_percent,91.3333,0.357037,percent,results/formal/early_mas_architecture_motivation/analysis_ready/med_agent/baseline/all_results.csv,aux_value is weighted-majority accuracy when available +a,baseline,Hierarchical,accuracy_percent,47.3016,8.8869,percent,results/formal/early_mas_architecture_motivation/analysis_ready/med_agent/baseline/all_results.csv,Baseline topology sweep; aux_value is token ratio vs single +b,baseline,Hierarchical,one_round_exit_rate_percent,90.4762,0.314208,percent,results/formal/early_mas_architecture_motivation/analysis_ready/med_agent/baseline/all_results.csv,aux_value is weighted-majority accuracy when available +a,tuned,Single,accuracy_percent,44.7302,1.0000,percent,results/formal/early_mas_architecture_motivation/analysis_ready/med_agent/tuned/all_results.csv,Tuned consensus sweep; aux_value is token ratio vs single +a,tuned,Star,accuracy_percent,48.0317,13.3823,percent,results/formal/early_mas_architecture_motivation/analysis_ready/med_agent/tuned/all_results.csv,Tuned consensus sweep; aux_value is token ratio vs single +b,tuned,Star,one_round_exit_rate_percent,27.3333,0.317697,percent,results/formal/early_mas_architecture_motivation/analysis_ready/med_agent/tuned/all_results.csv,aux_value is weighted-majority accuracy when available +a,tuned,Complete,accuracy_percent,48.0635,13.0392,percent,results/formal/early_mas_architecture_motivation/analysis_ready/med_agent/tuned/all_results.csv,Tuned consensus sweep; aux_value is token ratio vs single +b,tuned,Complete,one_round_exit_rate_percent,54.0317,0.330357,percent,results/formal/early_mas_architecture_motivation/analysis_ready/med_agent/tuned/all_results.csv,aux_value is weighted-majority accuracy when available +a,tuned,Ring,accuracy_percent,48.0000,13.1385,percent,results/formal/early_mas_architecture_motivation/analysis_ready/med_agent/tuned/all_results.csv,Tuned consensus sweep; aux_value is token ratio vs single +b,tuned,Ring,one_round_exit_rate_percent,34.3810,0.307527,percent,results/formal/early_mas_architecture_motivation/analysis_ready/med_agent/tuned/all_results.csv,aux_value is weighted-majority accuracy when available +a,tuned,Hierarchical,accuracy_percent,47.7460,13.5543,percent,results/formal/early_mas_architecture_motivation/analysis_ready/med_agent/tuned/all_results.csv,Tuned consensus sweep; aux_value is token ratio vs single +b,tuned,Hierarchical,one_round_exit_rate_percent,27.7460,0.304348,percent,results/formal/early_mas_architecture_motivation/analysis_ready/med_agent/tuned/all_results.csv,aux_value is weighted-majority accuracy when available +a,fixed6,Single,accuracy_percent,44.7302,1.0000,percent,results/formal/early_mas_architecture_motivation/analysis_ready/med_agent/fixed6/all_results.csv,Fixed-six-round sweep; aux_value is token ratio vs single +a,fixed6,Star,accuracy_percent,48.2222,34.4315,percent,results/formal/early_mas_architecture_motivation/analysis_ready/med_agent/fixed6/all_results.csv,Fixed-six-round sweep; aux_value is token ratio vs single +b,fixed6,Star,one_round_exit_rate_percent,0.0000,,percent,results/formal/early_mas_architecture_motivation/analysis_ready/med_agent/fixed6/all_results.csv,aux_value is weighted-majority accuracy when available +a,fixed6,Complete,accuracy_percent,48.6032,48.9550,percent,results/formal/early_mas_architecture_motivation/analysis_ready/med_agent/fixed6/all_results.csv,Fixed-six-round sweep; aux_value is token ratio vs single +b,fixed6,Complete,one_round_exit_rate_percent,0.0000,,percent,results/formal/early_mas_architecture_motivation/analysis_ready/med_agent/fixed6/all_results.csv,aux_value is weighted-majority accuracy when available +a,fixed6,Ring,accuracy_percent,48.5714,35.4444,percent,results/formal/early_mas_architecture_motivation/analysis_ready/med_agent/fixed6/all_results.csv,Fixed-six-round sweep; aux_value is token ratio vs single +b,fixed6,Ring,one_round_exit_rate_percent,0.0000,,percent,results/formal/early_mas_architecture_motivation/analysis_ready/med_agent/fixed6/all_results.csv,aux_value is weighted-majority accuracy when available +a,fixed6,Hierarchical,accuracy_percent,47.1746,33.2600,percent,results/formal/early_mas_architecture_motivation/analysis_ready/med_agent/fixed6/all_results.csv,Fixed-six-round sweep; aux_value is token ratio vs single +b,fixed6,Hierarchical,one_round_exit_rate_percent,0.0000,,percent,results/formal/early_mas_architecture_motivation/analysis_ready/med_agent/fixed6/all_results.csv,aux_value is weighted-majority accuracy when available +b,baseline,Star,round_exit_share_percent,92.1587,1,percent,results/formal/early_mas_architecture_motivation/analysis_ready/med_agent/baseline/all_results.csv,aux_value is stopping round; forced_schedule=0 +b,baseline,Star,round_exit_share_percent,5.5873,2,percent,results/formal/early_mas_architecture_motivation/analysis_ready/med_agent/baseline/all_results.csv,aux_value is stopping round; forced_schedule=0 +b,baseline,Star,round_exit_share_percent,1.2381,3,percent,results/formal/early_mas_architecture_motivation/analysis_ready/med_agent/baseline/all_results.csv,aux_value is stopping round; forced_schedule=0 +b,baseline,Star,round_exit_share_percent,0.5079,4,percent,results/formal/early_mas_architecture_motivation/analysis_ready/med_agent/baseline/all_results.csv,aux_value is stopping round; forced_schedule=0 +b,baseline,Star,round_exit_share_percent,0.1270,5,percent,results/formal/early_mas_architecture_motivation/analysis_ready/med_agent/baseline/all_results.csv,aux_value is stopping round; forced_schedule=0 +b,baseline,Star,round_exit_share_percent,0.1270,6,percent,results/formal/early_mas_architecture_motivation/analysis_ready/med_agent/baseline/all_results.csv,aux_value is stopping round; forced_schedule=0 +b,baseline,Star,round_exit_share_percent,0.0952,7,percent,results/formal/early_mas_architecture_motivation/analysis_ready/med_agent/baseline/all_results.csv,aux_value is stopping round; forced_schedule=0 +b,baseline,Star,round_exit_share_percent,0.0317,9,percent,results/formal/early_mas_architecture_motivation/analysis_ready/med_agent/baseline/all_results.csv,aux_value is stopping round; forced_schedule=0 +b,baseline,Star,round_exit_share_percent,0.1270,10,percent,results/formal/early_mas_architecture_motivation/analysis_ready/med_agent/baseline/all_results.csv,aux_value is stopping round; forced_schedule=0 +b,baseline,Complete,round_exit_share_percent,95.3651,1,percent,results/formal/early_mas_architecture_motivation/analysis_ready/med_agent/baseline/all_results.csv,aux_value is stopping round; forced_schedule=0 +b,baseline,Complete,round_exit_share_percent,3.3016,2,percent,results/formal/early_mas_architecture_motivation/analysis_ready/med_agent/baseline/all_results.csv,aux_value is stopping round; forced_schedule=0 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"docs/migration/medical_mas_migration_plan.md" + ], + "output_paths": [ + "results/formal/early_mas_architecture_motivation/manifests/source_payload_inventory.csv.gz", + "results/formal/early_mas_architecture_motivation/audits/payload_security_scan.json" + ], + "run_class": "formal" +} diff --git a/data/external/results/formal/early_mas_architecture_motivation/manifests/source_repositories.yaml b/data/external/results/formal/early_mas_architecture_motivation/manifests/source_repositories.yaml new file mode 100644 index 0000000000000000000000000000000000000000..0052e8685099ba666b70f288821df76847f249f3 --- /dev/null +++ b/data/external/results/formal/early_mas_architecture_motivation/manifests/source_repositories.yaml @@ -0,0 +1,32 @@ +source_archive: med_repos_20260604 +source_archive_location: sibling archive under evole-meme +captured_scope: manuscript-facing medical MAS experiments +repositories: + med_agent: + source_git_head: 17871ecc5c35d1ed965ebe2f379867a79298a585 + observed_dirty_paths: 30 + artifact_provenance: reconstructed_worktree + execution_anchor: reconstructed_from_archived_worktree + reason: >- + Important runtime files and result directories were not tracked at the + observed Git head, so the historical execution environment cannot be + represented as an exact commit snapshot. + med_board: + source_git_head: d005eb16d17bb57d780f22060f4c9389152dc3e7 + observed_dirty_paths: 40 + artifact_provenance: inferred_commit + execution_anchor: 53013d4513efc3dcaecfa7b74fc1b8e5294d0dba + analysis_anchor: db0d52b703656d1214dccdacf0536bc9b0cf2198 + reason: >- + The three full-dataset configurations and multiseed analysis are tracked, + and commit timing precedes the archived runs, but the artifacts do not + embed a Git commit or configuration hash. +dataset: + dataset_id: TsinghuaC3I/MedXpertQA + revision: 7e7c465a68eb2b866926bfa59c8c9d17a8daba65 + subset: Text/test.jsonl + local_rows: 2450 + local_sha256: 07ea977f7e326909b51c883b65f6ad7f399fc6db07408ef3630f4e3048ec71b2 + redistribution: >- + Do not include benchmark question text in public source data. Use the + pinned upstream revision and content hash for dataset identity. diff --git a/data/external/results/formal/early_mas_architecture_motivation/private_release/README.md b/data/external/results/formal/early_mas_architecture_motivation/private_release/README.md new file mode 100644 index 0000000000000000000000000000000000000000..9a8b9bbe0be0ca9e97c9215ac72ef34ad4400162 --- /dev/null +++ b/data/external/results/formal/early_mas_architecture_motivation/private_release/README.md @@ -0,0 +1,12 @@ +--- +pretty_name: Medical MAS Historical Experiment Archive +private: true +--- + +# Medical MAS historical experiment archive + +This private, controlled release preserves the original outputs behind the historical medical multi-agent-system motivation experiments. It contains 7 deterministic tar-gzip shards, 47,300 hash-addressed files and 1,126,934,115 uncompressed bytes. + +The archive is not a benchmark redistribution. Benchmark question text is not added as a separate dataset component. The pinned MedXpertQA identity and content hash, source revisions, per-file hashes, shard hashes and verification report are stored under `metadata/`. + +The `med-agent` execution provenance is a reconstructed archived worktree. 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full-dataset configurations and multiseed analysis are tracked, + and commit timing precedes the archived runs, but the artifacts do not + embed a Git commit or configuration hash. +dataset: + dataset_id: TsinghuaC3I/MedXpertQA + revision: 7e7c465a68eb2b866926bfa59c8c9d17a8daba65 + subset: Text/test.jsonl + local_rows: 2450 + local_sha256: 07ea977f7e326909b51c883b65f6ad7f399fc6db07408ef3630f4e3048ec71b2 + redistribution: >- + Do not include benchmark question text in public source data. 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sweep,/Users/jay/research/evole-meme/med_repos_20260604/med-agent/results_concurrency50,star,3150,7,3,0.000952,0 -baseline,Baseline topology sweep,/Users/jay/research/evole-meme/med_repos_20260604/med-agent/results_concurrency50,star,3150,9,1,0.000317,0 -baseline,Baseline topology sweep,/Users/jay/research/evole-meme/med_repos_20260604/med-agent/results_concurrency50,star,3150,10,4,0.001270,0 -baseline,Baseline topology sweep,/Users/jay/research/evole-meme/med_repos_20260604/med-agent/results_concurrency50,complete,3150,1,3004,0.953651,0 -baseline,Baseline topology sweep,/Users/jay/research/evole-meme/med_repos_20260604/med-agent/results_concurrency50,complete,3150,2,104,0.033016,0 -baseline,Baseline topology sweep,/Users/jay/research/evole-meme/med_repos_20260604/med-agent/results_concurrency50,complete,3150,3,26,0.008254,0 -baseline,Baseline topology sweep,/Users/jay/research/evole-meme/med_repos_20260604/med-agent/results_concurrency50,complete,3150,4,4,0.001270,0 -baseline,Baseline topology sweep,/Users/jay/research/evole-meme/med_repos_20260604/med-agent/results_concurrency50,complete,3150,5,8,0.002540,0 -baseline,Baseline topology sweep,/Users/jay/research/evole-meme/med_repos_20260604/med-agent/results_concurrency50,complete,3150,7,1,0.000317,0 -baseline,Baseline topology sweep,/Users/jay/research/evole-meme/med_repos_20260604/med-agent/results_concurrency50,complete,3150,8,1,0.000317,0 -baseline,Baseline topology sweep,/Users/jay/research/evole-meme/med_repos_20260604/med-agent/results_concurrency50,complete,3150,10,2,0.000635,0 -baseline,Baseline topology sweep,/Users/jay/research/evole-meme/med_repos_20260604/med-agent/results_concurrency50,ring,3150,1,2877,0.913333,0 -baseline,Baseline topology sweep,/Users/jay/research/evole-meme/med_repos_20260604/med-agent/results_concurrency50,ring,3150,2,147,0.046667,0 -baseline,Baseline topology sweep,/Users/jay/research/evole-meme/med_repos_20260604/med-agent/results_concurrency50,ring,3150,3,61,0.019365,0 -baseline,Baseline topology sweep,/Users/jay/research/evole-meme/med_repos_20260604/med-agent/results_concurrency50,ring,3150,4,25,0.007937,0 -baseline,Baseline topology sweep,/Users/jay/research/evole-meme/med_repos_20260604/med-agent/results_concurrency50,ring,3150,5,16,0.005079,0 -baseline,Baseline topology sweep,/Users/jay/research/evole-meme/med_repos_20260604/med-agent/results_concurrency50,ring,3150,6,7,0.002222,0 -baseline,Baseline topology sweep,/Users/jay/research/evole-meme/med_repos_20260604/med-agent/results_concurrency50,ring,3150,7,5,0.001587,0 -baseline,Baseline topology sweep,/Users/jay/research/evole-meme/med_repos_20260604/med-agent/results_concurrency50,ring,3150,9,2,0.000635,0 -baseline,Baseline topology sweep,/Users/jay/research/evole-meme/med_repos_20260604/med-agent/results_concurrency50,ring,3150,10,10,0.003175,0 -baseline,Baseline topology sweep,/Users/jay/research/evole-meme/med_repos_20260604/med-agent/results_concurrency50,hierarchical,3150,1,2850,0.904762,0 -baseline,Baseline topology sweep,/Users/jay/research/evole-meme/med_repos_20260604/med-agent/results_concurrency50,hierarchical,3150,2,146,0.046349,0 -baseline,Baseline topology sweep,/Users/jay/research/evole-meme/med_repos_20260604/med-agent/results_concurrency50,hierarchical,3150,3,69,0.021905,0 -baseline,Baseline topology sweep,/Users/jay/research/evole-meme/med_repos_20260604/med-agent/results_concurrency50,hierarchical,3150,4,33,0.010476,0 -baseline,Baseline topology sweep,/Users/jay/research/evole-meme/med_repos_20260604/med-agent/results_concurrency50,hierarchical,3150,5,11,0.003492,0 -baseline,Baseline topology sweep,/Users/jay/research/evole-meme/med_repos_20260604/med-agent/results_concurrency50,hierarchical,3150,6,8,0.002540,0 -baseline,Baseline topology sweep,/Users/jay/research/evole-meme/med_repos_20260604/med-agent/results_concurrency50,hierarchical,3150,7,4,0.001270,0 -baseline,Baseline topology sweep,/Users/jay/research/evole-meme/med_repos_20260604/med-agent/results_concurrency50,hierarchical,3150,8,2,0.000635,0 -baseline,Baseline topology sweep,/Users/jay/research/evole-meme/med_repos_20260604/med-agent/results_concurrency50,hierarchical,3150,9,2,0.000635,0 -baseline,Baseline topology sweep,/Users/jay/research/evole-meme/med_repos_20260604/med-agent/results_concurrency50,hierarchical,3150,10,25,0.007937,0 -tuned,Tuned consensus sweep,/Users/jay/research/evole-meme/med_repos_20260604/med-agent/results_concurrency50_tuned,star,3150,1,861,0.273333,0 -tuned,Tuned consensus sweep,/Users/jay/research/evole-meme/med_repos_20260604/med-agent/results_concurrency50_tuned,star,3150,2,1749,0.555238,0 -tuned,Tuned consensus sweep,/Users/jay/research/evole-meme/med_repos_20260604/med-agent/results_concurrency50_tuned,star,3150,3,375,0.119048,0 -tuned,Tuned consensus sweep,/Users/jay/research/evole-meme/med_repos_20260604/med-agent/results_concurrency50_tuned,star,3150,4,87,0.027619,0 -tuned,Tuned consensus sweep,/Users/jay/research/evole-meme/med_repos_20260604/med-agent/results_concurrency50_tuned,star,3150,5,37,0.011746,0 -tuned,Tuned consensus sweep,/Users/jay/research/evole-meme/med_repos_20260604/med-agent/results_concurrency50_tuned,star,3150,6,17,0.005397,0 -tuned,Tuned consensus sweep,/Users/jay/research/evole-meme/med_repos_20260604/med-agent/results_concurrency50_tuned,star,3150,7,6,0.001905,0 -tuned,Tuned consensus sweep,/Users/jay/research/evole-meme/med_repos_20260604/med-agent/results_concurrency50_tuned,star,3150,8,5,0.001587,0 -tuned,Tuned consensus sweep,/Users/jay/research/evole-meme/med_repos_20260604/med-agent/results_concurrency50_tuned,star,3150,9,1,0.000317,0 -tuned,Tuned consensus sweep,/Users/jay/research/evole-meme/med_repos_20260604/med-agent/results_concurrency50_tuned,star,3150,10,12,0.003810,0 -tuned,Tuned consensus sweep,/Users/jay/research/evole-meme/med_repos_20260604/med-agent/results_concurrency50_tuned,complete,3150,1,1702,0.540317,0 -tuned,Tuned consensus sweep,/Users/jay/research/evole-meme/med_repos_20260604/med-agent/results_concurrency50_tuned,complete,3150,2,1107,0.351429,0 -tuned,Tuned consensus sweep,/Users/jay/research/evole-meme/med_repos_20260604/med-agent/results_concurrency50_tuned,complete,3150,3,238,0.075556,0 -tuned,Tuned consensus sweep,/Users/jay/research/evole-meme/med_repos_20260604/med-agent/results_concurrency50_tuned,complete,3150,4,55,0.017460,0 -tuned,Tuned consensus sweep,/Users/jay/research/evole-meme/med_repos_20260604/med-agent/results_concurrency50_tuned,complete,3150,5,20,0.006349,0 -tuned,Tuned consensus sweep,/Users/jay/research/evole-meme/med_repos_20260604/med-agent/results_concurrency50_tuned,complete,3150,6,13,0.004127,0 -tuned,Tuned consensus sweep,/Users/jay/research/evole-meme/med_repos_20260604/med-agent/results_concurrency50_tuned,complete,3150,7,3,0.000952,0 -tuned,Tuned consensus sweep,/Users/jay/research/evole-meme/med_repos_20260604/med-agent/results_concurrency50_tuned,complete,3150,8,3,0.000952,0 -tuned,Tuned consensus sweep,/Users/jay/research/evole-meme/med_repos_20260604/med-agent/results_concurrency50_tuned,complete,3150,9,3,0.000952,0 -tuned,Tuned consensus sweep,/Users/jay/research/evole-meme/med_repos_20260604/med-agent/results_concurrency50_tuned,complete,3150,10,6,0.001905,0 -tuned,Tuned consensus sweep,/Users/jay/research/evole-meme/med_repos_20260604/med-agent/results_concurrency50_tuned,ring,3150,1,1083,0.343810,0 -tuned,Tuned consensus sweep,/Users/jay/research/evole-meme/med_repos_20260604/med-agent/results_concurrency50_tuned,ring,3150,2,1573,0.499365,0 -tuned,Tuned consensus sweep,/Users/jay/research/evole-meme/med_repos_20260604/med-agent/results_concurrency50_tuned,ring,3150,3,324,0.102857,0 -tuned,Tuned consensus sweep,/Users/jay/research/evole-meme/med_repos_20260604/med-agent/results_concurrency50_tuned,ring,3150,4,90,0.028571,0 -tuned,Tuned consensus sweep,/Users/jay/research/evole-meme/med_repos_20260604/med-agent/results_concurrency50_tuned,ring,3150,5,37,0.011746,0 -tuned,Tuned consensus sweep,/Users/jay/research/evole-meme/med_repos_20260604/med-agent/results_concurrency50_tuned,ring,3150,6,18,0.005714,0 -tuned,Tuned consensus sweep,/Users/jay/research/evole-meme/med_repos_20260604/med-agent/results_concurrency50_tuned,ring,3150,7,9,0.002857,0 -tuned,Tuned consensus sweep,/Users/jay/research/evole-meme/med_repos_20260604/med-agent/results_concurrency50_tuned,ring,3150,8,7,0.002222,0 -tuned,Tuned consensus sweep,/Users/jay/research/evole-meme/med_repos_20260604/med-agent/results_concurrency50_tuned,ring,3150,10,9,0.002857,0 -tuned,Tuned consensus sweep,/Users/jay/research/evole-meme/med_repos_20260604/med-agent/results_concurrency50_tuned,hierarchical,3150,1,874,0.277460,0 -tuned,Tuned consensus sweep,/Users/jay/research/evole-meme/med_repos_20260604/med-agent/results_concurrency50_tuned,hierarchical,3150,2,1681,0.533651,0 -tuned,Tuned consensus sweep,/Users/jay/research/evole-meme/med_repos_20260604/med-agent/results_concurrency50_tuned,hierarchical,3150,3,384,0.121905,0 -tuned,Tuned consensus sweep,/Users/jay/research/evole-meme/med_repos_20260604/med-agent/results_concurrency50_tuned,hierarchical,3150,4,108,0.034286,0 -tuned,Tuned consensus sweep,/Users/jay/research/evole-meme/med_repos_20260604/med-agent/results_concurrency50_tuned,hierarchical,3150,5,38,0.012063,0 -tuned,Tuned consensus sweep,/Users/jay/research/evole-meme/med_repos_20260604/med-agent/results_concurrency50_tuned,hierarchical,3150,6,20,0.006349,0 -tuned,Tuned consensus sweep,/Users/jay/research/evole-meme/med_repos_20260604/med-agent/results_concurrency50_tuned,hierarchical,3150,7,9,0.002857,0 -tuned,Tuned consensus sweep,/Users/jay/research/evole-meme/med_repos_20260604/med-agent/results_concurrency50_tuned,hierarchical,3150,8,11,0.003492,0 -tuned,Tuned consensus sweep,/Users/jay/research/evole-meme/med_repos_20260604/med-agent/results_concurrency50_tuned,hierarchical,3150,9,6,0.001905,0 -tuned,Tuned consensus sweep,/Users/jay/research/evole-meme/med_repos_20260604/med-agent/results_concurrency50_tuned,hierarchical,3150,10,19,0.006032,0 -fixed6,Fixed-six-round sweep,/Users/jay/research/evole-meme/med_repos_20260604/med-agent/results_concurrency50_fixed6,star,3150,6,3150,1.000000,1 -fixed6,Fixed-six-round sweep,/Users/jay/research/evole-meme/med_repos_20260604/med-agent/results_concurrency50_fixed6,complete,3150,6,3150,1.000000,1 -fixed6,Fixed-six-round sweep,/Users/jay/research/evole-meme/med_repos_20260604/med-agent/results_concurrency50_fixed6,ring,3150,6,3150,1.000000,1 -fixed6,Fixed-six-round sweep,/Users/jay/research/evole-meme/med_repos_20260604/med-agent/results_concurrency50_fixed6,hierarchical,3150,6,3150,1.000000,1 +run_id,run_label,source_path,topology,n_trials,round,n_ended,share,forced_schedule +baseline,Baseline topology sweep,results/formal/early_mas_architecture_motivation/analysis_ready/med_agent/baseline/all_results.csv,star,3150,1,2903,0.921587,0 +baseline,Baseline topology sweep,results/formal/early_mas_architecture_motivation/analysis_ready/med_agent/baseline/all_results.csv,star,3150,2,176,0.055873,0 +baseline,Baseline topology sweep,results/formal/early_mas_architecture_motivation/analysis_ready/med_agent/baseline/all_results.csv,star,3150,3,39,0.012381,0 +baseline,Baseline topology sweep,results/formal/early_mas_architecture_motivation/analysis_ready/med_agent/baseline/all_results.csv,star,3150,4,16,0.005079,0 +baseline,Baseline topology sweep,results/formal/early_mas_architecture_motivation/analysis_ready/med_agent/baseline/all_results.csv,star,3150,5,4,0.001270,0 +baseline,Baseline topology sweep,results/formal/early_mas_architecture_motivation/analysis_ready/med_agent/baseline/all_results.csv,star,3150,6,4,0.001270,0 +baseline,Baseline topology sweep,results/formal/early_mas_architecture_motivation/analysis_ready/med_agent/baseline/all_results.csv,star,3150,7,3,0.000952,0 +baseline,Baseline topology sweep,results/formal/early_mas_architecture_motivation/analysis_ready/med_agent/baseline/all_results.csv,star,3150,9,1,0.000317,0 +baseline,Baseline topology sweep,results/formal/early_mas_architecture_motivation/analysis_ready/med_agent/baseline/all_results.csv,star,3150,10,4,0.001270,0 +baseline,Baseline topology sweep,results/formal/early_mas_architecture_motivation/analysis_ready/med_agent/baseline/all_results.csv,complete,3150,1,3004,0.953651,0 +baseline,Baseline topology sweep,results/formal/early_mas_architecture_motivation/analysis_ready/med_agent/baseline/all_results.csv,complete,3150,2,104,0.033016,0 +baseline,Baseline topology sweep,results/formal/early_mas_architecture_motivation/analysis_ready/med_agent/baseline/all_results.csv,complete,3150,3,26,0.008254,0 +baseline,Baseline topology sweep,results/formal/early_mas_architecture_motivation/analysis_ready/med_agent/baseline/all_results.csv,complete,3150,4,4,0.001270,0 +baseline,Baseline topology sweep,results/formal/early_mas_architecture_motivation/analysis_ready/med_agent/baseline/all_results.csv,complete,3150,5,8,0.002540,0 +baseline,Baseline topology sweep,results/formal/early_mas_architecture_motivation/analysis_ready/med_agent/baseline/all_results.csv,complete,3150,7,1,0.000317,0 +baseline,Baseline topology sweep,results/formal/early_mas_architecture_motivation/analysis_ready/med_agent/baseline/all_results.csv,complete,3150,8,1,0.000317,0 +baseline,Baseline topology sweep,results/formal/early_mas_architecture_motivation/analysis_ready/med_agent/baseline/all_results.csv,complete,3150,10,2,0.000635,0 +baseline,Baseline topology sweep,results/formal/early_mas_architecture_motivation/analysis_ready/med_agent/baseline/all_results.csv,ring,3150,1,2877,0.913333,0 +baseline,Baseline topology sweep,results/formal/early_mas_architecture_motivation/analysis_ready/med_agent/baseline/all_results.csv,ring,3150,2,147,0.046667,0 +baseline,Baseline topology sweep,results/formal/early_mas_architecture_motivation/analysis_ready/med_agent/baseline/all_results.csv,ring,3150,3,61,0.019365,0 +baseline,Baseline topology sweep,results/formal/early_mas_architecture_motivation/analysis_ready/med_agent/baseline/all_results.csv,ring,3150,4,25,0.007937,0 +baseline,Baseline topology sweep,results/formal/early_mas_architecture_motivation/analysis_ready/med_agent/baseline/all_results.csv,ring,3150,5,16,0.005079,0 +baseline,Baseline topology sweep,results/formal/early_mas_architecture_motivation/analysis_ready/med_agent/baseline/all_results.csv,ring,3150,6,7,0.002222,0 +baseline,Baseline topology sweep,results/formal/early_mas_architecture_motivation/analysis_ready/med_agent/baseline/all_results.csv,ring,3150,7,5,0.001587,0 +baseline,Baseline topology sweep,results/formal/early_mas_architecture_motivation/analysis_ready/med_agent/baseline/all_results.csv,ring,3150,9,2,0.000635,0 +baseline,Baseline topology sweep,results/formal/early_mas_architecture_motivation/analysis_ready/med_agent/baseline/all_results.csv,ring,3150,10,10,0.003175,0 +baseline,Baseline topology sweep,results/formal/early_mas_architecture_motivation/analysis_ready/med_agent/baseline/all_results.csv,hierarchical,3150,1,2850,0.904762,0 +baseline,Baseline topology sweep,results/formal/early_mas_architecture_motivation/analysis_ready/med_agent/baseline/all_results.csv,hierarchical,3150,2,146,0.046349,0 +baseline,Baseline topology sweep,results/formal/early_mas_architecture_motivation/analysis_ready/med_agent/baseline/all_results.csv,hierarchical,3150,3,69,0.021905,0 +baseline,Baseline topology sweep,results/formal/early_mas_architecture_motivation/analysis_ready/med_agent/baseline/all_results.csv,hierarchical,3150,4,33,0.010476,0 +baseline,Baseline topology sweep,results/formal/early_mas_architecture_motivation/analysis_ready/med_agent/baseline/all_results.csv,hierarchical,3150,5,11,0.003492,0 +baseline,Baseline topology sweep,results/formal/early_mas_architecture_motivation/analysis_ready/med_agent/baseline/all_results.csv,hierarchical,3150,6,8,0.002540,0 +baseline,Baseline topology sweep,results/formal/early_mas_architecture_motivation/analysis_ready/med_agent/baseline/all_results.csv,hierarchical,3150,7,4,0.001270,0 +baseline,Baseline topology sweep,results/formal/early_mas_architecture_motivation/analysis_ready/med_agent/baseline/all_results.csv,hierarchical,3150,8,2,0.000635,0 +baseline,Baseline topology sweep,results/formal/early_mas_architecture_motivation/analysis_ready/med_agent/baseline/all_results.csv,hierarchical,3150,9,2,0.000635,0 +baseline,Baseline topology sweep,results/formal/early_mas_architecture_motivation/analysis_ready/med_agent/baseline/all_results.csv,hierarchical,3150,10,25,0.007937,0 +tuned,Tuned consensus sweep,results/formal/early_mas_architecture_motivation/analysis_ready/med_agent/tuned/all_results.csv,star,3150,1,861,0.273333,0 +tuned,Tuned consensus sweep,results/formal/early_mas_architecture_motivation/analysis_ready/med_agent/tuned/all_results.csv,star,3150,2,1749,0.555238,0 +tuned,Tuned consensus sweep,results/formal/early_mas_architecture_motivation/analysis_ready/med_agent/tuned/all_results.csv,star,3150,3,375,0.119048,0 +tuned,Tuned consensus sweep,results/formal/early_mas_architecture_motivation/analysis_ready/med_agent/tuned/all_results.csv,star,3150,4,87,0.027619,0 +tuned,Tuned consensus sweep,results/formal/early_mas_architecture_motivation/analysis_ready/med_agent/tuned/all_results.csv,star,3150,5,37,0.011746,0 +tuned,Tuned consensus sweep,results/formal/early_mas_architecture_motivation/analysis_ready/med_agent/tuned/all_results.csv,star,3150,6,17,0.005397,0 +tuned,Tuned consensus sweep,results/formal/early_mas_architecture_motivation/analysis_ready/med_agent/tuned/all_results.csv,star,3150,7,6,0.001905,0 +tuned,Tuned consensus sweep,results/formal/early_mas_architecture_motivation/analysis_ready/med_agent/tuned/all_results.csv,star,3150,8,5,0.001587,0 +tuned,Tuned consensus sweep,results/formal/early_mas_architecture_motivation/analysis_ready/med_agent/tuned/all_results.csv,star,3150,9,1,0.000317,0 +tuned,Tuned consensus sweep,results/formal/early_mas_architecture_motivation/analysis_ready/med_agent/tuned/all_results.csv,star,3150,10,12,0.003810,0 +tuned,Tuned consensus sweep,results/formal/early_mas_architecture_motivation/analysis_ready/med_agent/tuned/all_results.csv,complete,3150,1,1702,0.540317,0 +tuned,Tuned consensus sweep,results/formal/early_mas_architecture_motivation/analysis_ready/med_agent/tuned/all_results.csv,complete,3150,2,1107,0.351429,0 +tuned,Tuned consensus sweep,results/formal/early_mas_architecture_motivation/analysis_ready/med_agent/tuned/all_results.csv,complete,3150,3,238,0.075556,0 +tuned,Tuned consensus sweep,results/formal/early_mas_architecture_motivation/analysis_ready/med_agent/tuned/all_results.csv,complete,3150,4,55,0.017460,0 +tuned,Tuned consensus sweep,results/formal/early_mas_architecture_motivation/analysis_ready/med_agent/tuned/all_results.csv,complete,3150,5,20,0.006349,0 +tuned,Tuned consensus sweep,results/formal/early_mas_architecture_motivation/analysis_ready/med_agent/tuned/all_results.csv,complete,3150,6,13,0.004127,0 +tuned,Tuned consensus sweep,results/formal/early_mas_architecture_motivation/analysis_ready/med_agent/tuned/all_results.csv,complete,3150,7,3,0.000952,0 +tuned,Tuned consensus sweep,results/formal/early_mas_architecture_motivation/analysis_ready/med_agent/tuned/all_results.csv,complete,3150,8,3,0.000952,0 +tuned,Tuned consensus sweep,results/formal/early_mas_architecture_motivation/analysis_ready/med_agent/tuned/all_results.csv,complete,3150,9,3,0.000952,0 +tuned,Tuned consensus sweep,results/formal/early_mas_architecture_motivation/analysis_ready/med_agent/tuned/all_results.csv,complete,3150,10,6,0.001905,0 +tuned,Tuned consensus sweep,results/formal/early_mas_architecture_motivation/analysis_ready/med_agent/tuned/all_results.csv,ring,3150,1,1083,0.343810,0 +tuned,Tuned consensus sweep,results/formal/early_mas_architecture_motivation/analysis_ready/med_agent/tuned/all_results.csv,ring,3150,2,1573,0.499365,0 +tuned,Tuned consensus 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sweep,results/formal/early_mas_architecture_motivation/analysis_ready/med_agent/tuned/all_results.csv,ring,3150,10,9,0.002857,0 +tuned,Tuned consensus sweep,results/formal/early_mas_architecture_motivation/analysis_ready/med_agent/tuned/all_results.csv,hierarchical,3150,1,874,0.277460,0 +tuned,Tuned consensus sweep,results/formal/early_mas_architecture_motivation/analysis_ready/med_agent/tuned/all_results.csv,hierarchical,3150,2,1681,0.533651,0 +tuned,Tuned consensus sweep,results/formal/early_mas_architecture_motivation/analysis_ready/med_agent/tuned/all_results.csv,hierarchical,3150,3,384,0.121905,0 +tuned,Tuned consensus sweep,results/formal/early_mas_architecture_motivation/analysis_ready/med_agent/tuned/all_results.csv,hierarchical,3150,4,108,0.034286,0 +tuned,Tuned consensus sweep,results/formal/early_mas_architecture_motivation/analysis_ready/med_agent/tuned/all_results.csv,hierarchical,3150,5,38,0.012063,0 +tuned,Tuned consensus 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