{"agent_name": "causal_micro_lab_agent_loop", "data_source": "causal_micro_lab_final_eval_v3", "env_spec_json": "{\"agent\": {}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 0, \"task\": {\"state\": {\"available_experiment_ids\": [0, 1, 2, 3, 4, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 17, 18, 19, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 4, \"family_bucket\": \"cross_family\", \"maximum_separation\": 0.2222222222222222, \"mean_separation\": 0.12037037037037036, \"minimum_separation\": 0.05555555555555555, \"normalized_mean_separation\": 0.12037037037037036, \"oracle_budget_representation_error\": 0.0, \"oracle_singleton_representation_error\": 0.06944444444444445, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.06944444444444445, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 4}, \"private\": {\"hidden_mode_id\": \"3b16825d37fb9be7d8c6f1823b5c218abdb60aa76b402c49edfcd7d58a4ef7d8\", \"valid_mode_ids\": [\"23b6afc6c3c355d9bba0eee27f1da082011022cca673b404678078ec95424423\", \"3b16825d37fb9be7d8c6f1823b5c218abdb60aa76b402c49edfcd7d58a4ef7d8\", \"3d509d85b6d3a2c967cfacd249eb593bb16b220cb4e9aeb713b1a4616b7e7206\", \"e204585bdb070e4b16ac3e9880ae87ed0e01902a340c5964ebb79af940f2a98c\"]}, \"state_id\": \"2127799b113e502003afc07d\", \"visible_experiments\": [{\"experiment_id\": 5, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 16, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 1}, \"intervention\": \"DO_Z1_0\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 20, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 0}}, {\"experiment_id\": 35, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 1}}]}}}", "extra_info": {"index": 0, "max_global_steps": 0, "min_global_steps": 0}, "index": 0, "prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=1\n intervention: none\n observed: Z1=1, Z2=1, Y=1\n\nExperiment 2:\n inputs: X1=0, X2=1, X3=1\n intervention: set Z1=0\n observed: Z1=0, Z2=0, Y=0\n\nExperiment 3:\n inputs: X1=1, X2=0, X3=0\n intervention: none\n observed: Z1=1, Z2=0, Y=0\n\nExperiment 4:\n inputs: X1=1, X2=1, X3=1\n intervention: none\n observed: Z1=1, Z2=1, Y=1\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "raw_prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=1\n intervention: none\n observed: Z1=1, Z2=1, Y=1\n\nExperiment 2:\n inputs: X1=0, X2=1, X3=1\n intervention: set Z1=0\n observed: Z1=0, Z2=0, Y=0\n\nExperiment 3:\n inputs: X1=1, X2=0, X3=0\n intervention: none\n observed: Z1=1, Z2=0, Y=0\n\nExperiment 4:\n inputs: X1=1, X2=1, X3=1\n intervention: none\n observed: Z1=1, Z2=1, Y=1\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "reward_model": {"style": "rule"}, "state_json": "{\"available_experiment_ids\": [0, 1, 2, 3, 4, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 17, 18, 19, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 4, \"family_bucket\": \"cross_family\", \"maximum_separation\": 0.2222222222222222, \"mean_separation\": 0.12037037037037036, \"minimum_separation\": 0.05555555555555555, \"normalized_mean_separation\": 0.12037037037037036, \"oracle_budget_representation_error\": 0.0, \"oracle_singleton_representation_error\": 0.06944444444444445, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.06944444444444445, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 4}, \"private\": {\"hidden_mode_id\": \"3b16825d37fb9be7d8c6f1823b5c218abdb60aa76b402c49edfcd7d58a4ef7d8\", \"valid_mode_ids\": [\"23b6afc6c3c355d9bba0eee27f1da082011022cca673b404678078ec95424423\", \"3b16825d37fb9be7d8c6f1823b5c218abdb60aa76b402c49edfcd7d58a4ef7d8\", \"3d509d85b6d3a2c967cfacd249eb593bb16b220cb4e9aeb713b1a4616b7e7206\", \"e204585bdb070e4b16ac3e9880ae87ed0e01902a340c5964ebb79af940f2a98c\"]}, \"state_id\": \"2127799b113e502003afc07d\", \"visible_experiments\": [{\"experiment_id\": 5, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 16, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 1}, \"intervention\": \"DO_Z1_0\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 20, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 0}}, {\"experiment_id\": 35, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 1}}]}"} {"agent_name": "causal_micro_lab_agent_loop", "data_source": "causal_micro_lab_final_eval_v3", "env_spec_json": "{\"agent\": {}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 1, \"task\": {\"state\": {\"available_experiment_ids\": [0, 1, 2, 3, 4, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 19, 20, 21, 22, 23, 24, 26, 27, 28, 29, 31, 32, 33, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 4, \"family_bucket\": \"cross_family\", \"maximum_separation\": 0.2222222222222222, \"mean_separation\": 0.12962962962962965, \"minimum_separation\": 0.1111111111111111, \"normalized_mean_separation\": 0.12962962962962965, \"oracle_budget_representation_error\": 0.0, \"oracle_singleton_representation_error\": 0.08333333333333333, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.08333333333333333, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 4}, \"private\": {\"hidden_mode_id\": \"0484be52783b0448fe89f3076822101587ceee0189b2f582d8f6bf51bc92fe7c\", \"valid_mode_ids\": [\"0484be52783b0448fe89f3076822101587ceee0189b2f582d8f6bf51bc92fe7c\", \"21f2be4a5f667cef82718567552bde4e7285b36b7894e2cad37aa8de9e914381\", \"415a89b6319c01f6d3b05be88b199970b47c6e6a2a6b134738301387d828fdb7\", \"46549da9a07a99319effc90447ef50cf2f1c73eee2e9fadf4e81229a4bdc32c0\"]}, \"state_id\": \"88638545f4115af1c446f961\", \"visible_experiments\": [{\"experiment_id\": 5, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 18, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 1}, \"intervention\": \"DO_Z2_0\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 0}}, {\"experiment_id\": 25, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 30, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}]}}}", "extra_info": {"index": 1, "max_global_steps": 0, "min_global_steps": 0}, "index": 1, "prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=1\n intervention: none\n observed: Z1=0, Z2=1, Y=0\n\nExperiment 2:\n inputs: X1=0, X2=1, X3=1\n intervention: set Z2=0\n observed: Z1=1, Z2=0, Y=1\n\nExperiment 3:\n inputs: X1=1, X2=0, X3=1\n intervention: none\n observed: Z1=0, Z2=0, Y=0\n\nExperiment 4:\n inputs: X1=1, X2=1, X3=0\n intervention: none\n observed: Z1=0, Z2=1, Y=0\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "raw_prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=1\n intervention: none\n observed: Z1=0, Z2=1, Y=0\n\nExperiment 2:\n inputs: X1=0, X2=1, X3=1\n intervention: set Z2=0\n observed: Z1=1, Z2=0, Y=1\n\nExperiment 3:\n inputs: X1=1, X2=0, X3=1\n intervention: none\n observed: Z1=0, Z2=0, Y=0\n\nExperiment 4:\n inputs: X1=1, X2=1, X3=0\n intervention: none\n observed: Z1=0, Z2=1, Y=0\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "reward_model": {"style": "rule"}, "state_json": "{\"available_experiment_ids\": [0, 1, 2, 3, 4, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 19, 20, 21, 22, 23, 24, 26, 27, 28, 29, 31, 32, 33, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 4, \"family_bucket\": \"cross_family\", \"maximum_separation\": 0.2222222222222222, \"mean_separation\": 0.12962962962962965, \"minimum_separation\": 0.1111111111111111, \"normalized_mean_separation\": 0.12962962962962965, \"oracle_budget_representation_error\": 0.0, \"oracle_singleton_representation_error\": 0.08333333333333333, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.08333333333333333, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 4}, \"private\": {\"hidden_mode_id\": \"0484be52783b0448fe89f3076822101587ceee0189b2f582d8f6bf51bc92fe7c\", \"valid_mode_ids\": [\"0484be52783b0448fe89f3076822101587ceee0189b2f582d8f6bf51bc92fe7c\", \"21f2be4a5f667cef82718567552bde4e7285b36b7894e2cad37aa8de9e914381\", \"415a89b6319c01f6d3b05be88b199970b47c6e6a2a6b134738301387d828fdb7\", \"46549da9a07a99319effc90447ef50cf2f1c73eee2e9fadf4e81229a4bdc32c0\"]}, \"state_id\": \"88638545f4115af1c446f961\", \"visible_experiments\": [{\"experiment_id\": 5, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 18, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 1}, \"intervention\": \"DO_Z2_0\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 0}}, {\"experiment_id\": 25, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 30, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}]}"} {"agent_name": "causal_micro_lab_agent_loop", "data_source": "causal_micro_lab_final_eval_v3", "env_spec_json": "{\"agent\": {}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 2, \"task\": {\"state\": {\"available_experiment_ids\": [0, 1, 2, 3, 5, 6, 7, 8, 9, 11, 12, 13, 14, 15, 16, 17, 18, 19, 21, 22, 23, 24, 25, 26, 27, 28, 29, 31, 32, 33, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 4, \"family_bucket\": \"cross_family\", \"maximum_separation\": 0.2222222222222222, \"mean_separation\": 0.12962962962962962, \"minimum_separation\": 0.1111111111111111, \"normalized_mean_separation\": 0.12962962962962962, \"oracle_budget_representation_error\": 0.0, \"oracle_singleton_representation_error\": 0.08333333333333333, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.08333333333333333, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 4}, \"private\": {\"hidden_mode_id\": \"9e7adf4347bfdd97f9262dfbf76094e0e3fd33dac3dfa46f40d5622b3df7d32a\", \"valid_mode_ids\": [\"3be3285d83d1467dff3ad08da7db4f91f16ff9cb070ee64ca04ea85f7bb44444\", \"77443a83203f71b06d1602df56a1ea73a5ea69e14b01d9b6679ecaafb74cdf35\", \"9e7adf4347bfdd97f9262dfbf76094e0e3fd33dac3dfa46f40d5622b3df7d32a\", \"f3816eaf040fa736bcd61c7c247a0d31b32c97705470e0609f9b284cd220a424\"]}, \"state_id\": \"f180f1f4d644cea9a3b5908d\", \"visible_experiments\": [{\"experiment_id\": 4, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z2_1\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 10, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 0}}, {\"experiment_id\": 20, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 0}}, {\"experiment_id\": 30, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 1}}]}}}", "extra_info": {"index": 2, "max_global_steps": 0, "min_global_steps": 0}, "index": 2, "prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=0\n intervention: set Z2=1\n observed: Z1=0, Z2=1, Y=0\n\nExperiment 2:\n inputs: X1=0, X2=1, X3=0\n intervention: none\n observed: Z1=1, Z2=0, Y=0\n\nExperiment 3:\n inputs: X1=1, X2=0, X3=0\n intervention: none\n observed: Z1=1, Z2=0, Y=0\n\nExperiment 4:\n inputs: X1=1, X2=1, X3=0\n intervention: none\n observed: Z1=1, Z2=1, Y=1\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "raw_prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=0\n intervention: set Z2=1\n observed: Z1=0, Z2=1, Y=0\n\nExperiment 2:\n inputs: X1=0, X2=1, X3=0\n intervention: none\n observed: Z1=1, Z2=0, Y=0\n\nExperiment 3:\n inputs: X1=1, X2=0, X3=0\n intervention: none\n observed: Z1=1, Z2=0, Y=0\n\nExperiment 4:\n inputs: X1=1, X2=1, X3=0\n intervention: none\n observed: Z1=1, Z2=1, Y=1\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "reward_model": {"style": "rule"}, "state_json": "{\"available_experiment_ids\": [0, 1, 2, 3, 5, 6, 7, 8, 9, 11, 12, 13, 14, 15, 16, 17, 18, 19, 21, 22, 23, 24, 25, 26, 27, 28, 29, 31, 32, 33, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 4, \"family_bucket\": \"cross_family\", \"maximum_separation\": 0.2222222222222222, \"mean_separation\": 0.12962962962962962, \"minimum_separation\": 0.1111111111111111, \"normalized_mean_separation\": 0.12962962962962962, \"oracle_budget_representation_error\": 0.0, \"oracle_singleton_representation_error\": 0.08333333333333333, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.08333333333333333, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 4}, \"private\": {\"hidden_mode_id\": \"9e7adf4347bfdd97f9262dfbf76094e0e3fd33dac3dfa46f40d5622b3df7d32a\", \"valid_mode_ids\": [\"3be3285d83d1467dff3ad08da7db4f91f16ff9cb070ee64ca04ea85f7bb44444\", \"77443a83203f71b06d1602df56a1ea73a5ea69e14b01d9b6679ecaafb74cdf35\", \"9e7adf4347bfdd97f9262dfbf76094e0e3fd33dac3dfa46f40d5622b3df7d32a\", \"f3816eaf040fa736bcd61c7c247a0d31b32c97705470e0609f9b284cd220a424\"]}, \"state_id\": \"f180f1f4d644cea9a3b5908d\", \"visible_experiments\": [{\"experiment_id\": 4, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z2_1\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 10, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 0}}, {\"experiment_id\": 20, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 0}}, {\"experiment_id\": 30, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 1}}]}"} {"agent_name": "causal_micro_lab_agent_loop", "data_source": "causal_micro_lab_final_eval_v3", "env_spec_json": "{\"agent\": {}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 3, \"task\": {\"state\": {\"available_experiment_ids\": [0, 1, 2, 3, 4, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 37, 38, 39], \"metadata\": {\"evidence_size\": 4, \"family_bucket\": \"within_family\", \"maximum_separation\": 0.19444444444444445, \"mean_separation\": 0.13425925925925927, \"minimum_separation\": 0.1111111111111111, \"normalized_mean_separation\": 0.13425925925925927, \"oracle_budget_representation_error\": 0.0, \"oracle_singleton_representation_error\": 0.09027777777777778, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.09027777777777778, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 4}, \"private\": {\"hidden_mode_id\": \"d2e2e1c9d75c09282d39fa97c70b44a6143e1193a8bbaa64b015556d2c60623b\", \"valid_mode_ids\": [\"31c671e7397cf25690aa36288912981a43fa347c726cddcc0abed4578cce123b\", \"4ef8a8980c97a7c77b60bd751d7275a6475820c7ebaeabdd8271ccb2c6278042\", \"8b7448ef8afd9837726d9130e7045551432480860ceeb3e81a85e3b53b82027f\", \"d2e2e1c9d75c09282d39fa97c70b44a6143e1193a8bbaa64b015556d2c60623b\"]}, \"state_id\": \"3cf795e93ba6c7a95bb0e2ba\", \"visible_experiments\": [{\"experiment_id\": 5, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 20, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 35, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 36, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 1}, \"intervention\": \"DO_Z1_0\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}]}}}", "extra_info": {"index": 3, "max_global_steps": 0, "min_global_steps": 0}, "index": 3, "prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=1\n intervention: none\n observed: Z1=1, Z2=1, Y=0\n\nExperiment 2:\n inputs: X1=1, X2=0, X3=0\n intervention: none\n observed: Z1=1, Z2=1, Y=0\n\nExperiment 3:\n inputs: X1=1, X2=1, X3=1\n intervention: none\n observed: Z1=1, Z2=1, Y=1\n\nExperiment 4:\n inputs: X1=1, X2=1, X3=1\n intervention: set Z1=0\n observed: Z1=0, Z2=1, Y=0\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "raw_prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=1\n intervention: none\n observed: Z1=1, Z2=1, Y=0\n\nExperiment 2:\n inputs: X1=1, X2=0, X3=0\n intervention: none\n observed: Z1=1, Z2=1, Y=0\n\nExperiment 3:\n inputs: X1=1, X2=1, X3=1\n intervention: none\n observed: Z1=1, Z2=1, Y=1\n\nExperiment 4:\n inputs: X1=1, X2=1, X3=1\n intervention: set Z1=0\n observed: Z1=0, Z2=1, Y=0\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "reward_model": {"style": "rule"}, "state_json": "{\"available_experiment_ids\": [0, 1, 2, 3, 4, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 37, 38, 39], \"metadata\": {\"evidence_size\": 4, \"family_bucket\": \"within_family\", \"maximum_separation\": 0.19444444444444445, \"mean_separation\": 0.13425925925925927, \"minimum_separation\": 0.1111111111111111, \"normalized_mean_separation\": 0.13425925925925927, \"oracle_budget_representation_error\": 0.0, \"oracle_singleton_representation_error\": 0.09027777777777778, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.09027777777777778, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 4}, \"private\": {\"hidden_mode_id\": \"d2e2e1c9d75c09282d39fa97c70b44a6143e1193a8bbaa64b015556d2c60623b\", \"valid_mode_ids\": [\"31c671e7397cf25690aa36288912981a43fa347c726cddcc0abed4578cce123b\", \"4ef8a8980c97a7c77b60bd751d7275a6475820c7ebaeabdd8271ccb2c6278042\", \"8b7448ef8afd9837726d9130e7045551432480860ceeb3e81a85e3b53b82027f\", \"d2e2e1c9d75c09282d39fa97c70b44a6143e1193a8bbaa64b015556d2c60623b\"]}, \"state_id\": \"3cf795e93ba6c7a95bb0e2ba\", \"visible_experiments\": [{\"experiment_id\": 5, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 20, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 35, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 36, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 1}, \"intervention\": \"DO_Z1_0\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}]}"} {"agent_name": "causal_micro_lab_agent_loop", "data_source": "causal_micro_lab_final_eval_v3", "env_spec_json": "{\"agent\": {}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 4, \"task\": {\"state\": {\"available_experiment_ids\": [0, 1, 2, 3, 4, 6, 7, 8, 9, 11, 12, 13, 14, 15, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 4, \"family_bucket\": \"mixed\", \"maximum_separation\": 0.2777777777777778, \"mean_separation\": 0.1574074074074074, \"minimum_separation\": 0.1111111111111111, \"normalized_mean_separation\": 0.1574074074074074, \"oracle_budget_representation_error\": 0.0, \"oracle_singleton_representation_error\": 0.09722222222222222, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.09722222222222222, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 4}, \"private\": {\"hidden_mode_id\": \"98142c1cca03d15b91131d3d6eace4e40bb37be1bd44d1bc745753a16584d6e2\", \"valid_mode_ids\": [\"0d8298500e632b40ad8c9e81b333c9d50949faec14d69b5a819b857da88c125f\", \"28aecf8aea38a8bbccf979d42fa76d27707d07838d20adc8920ad798124b6af4\", \"98142c1cca03d15b91131d3d6eace4e40bb37be1bd44d1bc745753a16584d6e2\", \"b8cdb80b2ba247893cf485fe94f1bd7c9ef4cb1e1b1dc8ef865bb39d1e2f2163\"]}, \"state_id\": \"237d62292cf2ca71847d4582\", \"visible_experiments\": [{\"experiment_id\": 5, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 10, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 0}}, {\"experiment_id\": 16, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 1}, \"intervention\": \"DO_Z1_0\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 35, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 1}}]}}}", "extra_info": {"index": 4, "max_global_steps": 0, "min_global_steps": 0}, "index": 4, "prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=1\n intervention: none\n observed: Z1=1, Z2=1, Y=1\n\nExperiment 2:\n inputs: X1=0, X2=1, X3=0\n intervention: none\n observed: Z1=1, Z2=0, Y=1\n\nExperiment 3:\n inputs: X1=0, X2=1, X3=1\n intervention: set Z1=0\n observed: Z1=0, Z2=0, Y=1\n\nExperiment 4:\n inputs: X1=1, X2=1, X3=1\n intervention: none\n observed: Z1=1, Z2=1, Y=1\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "raw_prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=1\n intervention: none\n observed: Z1=1, Z2=1, Y=1\n\nExperiment 2:\n inputs: X1=0, X2=1, X3=0\n intervention: none\n observed: Z1=1, Z2=0, Y=1\n\nExperiment 3:\n inputs: X1=0, X2=1, X3=1\n intervention: set Z1=0\n observed: Z1=0, Z2=0, Y=1\n\nExperiment 4:\n inputs: X1=1, X2=1, X3=1\n intervention: none\n observed: Z1=1, Z2=1, Y=1\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "reward_model": {"style": "rule"}, "state_json": "{\"available_experiment_ids\": [0, 1, 2, 3, 4, 6, 7, 8, 9, 11, 12, 13, 14, 15, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 4, \"family_bucket\": \"mixed\", \"maximum_separation\": 0.2777777777777778, \"mean_separation\": 0.1574074074074074, \"minimum_separation\": 0.1111111111111111, \"normalized_mean_separation\": 0.1574074074074074, \"oracle_budget_representation_error\": 0.0, \"oracle_singleton_representation_error\": 0.09722222222222222, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.09722222222222222, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 4}, \"private\": {\"hidden_mode_id\": \"98142c1cca03d15b91131d3d6eace4e40bb37be1bd44d1bc745753a16584d6e2\", \"valid_mode_ids\": [\"0d8298500e632b40ad8c9e81b333c9d50949faec14d69b5a819b857da88c125f\", \"28aecf8aea38a8bbccf979d42fa76d27707d07838d20adc8920ad798124b6af4\", \"98142c1cca03d15b91131d3d6eace4e40bb37be1bd44d1bc745753a16584d6e2\", \"b8cdb80b2ba247893cf485fe94f1bd7c9ef4cb1e1b1dc8ef865bb39d1e2f2163\"]}, \"state_id\": \"237d62292cf2ca71847d4582\", \"visible_experiments\": [{\"experiment_id\": 5, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 10, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 0}}, {\"experiment_id\": 16, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 1}, \"intervention\": \"DO_Z1_0\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 35, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 1}}]}"} {"agent_name": "causal_micro_lab_agent_loop", "data_source": "causal_micro_lab_final_eval_v3", "env_spec_json": "{\"agent\": {}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 5, \"task\": {\"state\": {\"available_experiment_ids\": [1, 2, 3, 4, 5, 6, 7, 8, 9, 11, 12, 13, 14, 15, 16, 17, 18, 19, 21, 22, 23, 24, 25, 26, 27, 28, 29, 31, 32, 33, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 4, \"family_bucket\": \"within_family\", \"maximum_separation\": 0.16666666666666666, \"mean_separation\": 0.1527777777777778, \"minimum_separation\": 0.1388888888888889, \"normalized_mean_separation\": 0.1527777777777778, \"oracle_budget_representation_error\": 0.0, \"oracle_singleton_representation_error\": 0.10416666666666667, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.10416666666666667, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 4}, \"private\": {\"hidden_mode_id\": \"2234aa0c90fbc0d1852bca23311fa5ce600cce117af9305a5560d3a71fc3ef5a\", \"valid_mode_ids\": [\"2234aa0c90fbc0d1852bca23311fa5ce600cce117af9305a5560d3a71fc3ef5a\", \"22f7be6b030b024f1185fe03ca68bb9c80f5b99810b1a684f2b18065c4d9eacc\", \"ac753247a828dc5e1c5dc48f06d288e62a62e9449267b4b79a9f69d63ad4c3a7\", \"c27f95e47536e884b592a91479396da269e668d8724befde880ff673e2ac0d08\"]}, \"state_id\": \"19c0271a4495a01f2f8107a2\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 10, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 0}}, {\"experiment_id\": 20, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 0}}, {\"experiment_id\": 30, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 0}}]}}}", "extra_info": {"index": 5, "max_global_steps": 0, "min_global_steps": 0}, "index": 5, "prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=0\n intervention: none\n observed: Z1=0, Z2=0, Y=0\n\nExperiment 2:\n inputs: X1=0, X2=1, X3=0\n intervention: none\n observed: Z1=1, Z2=0, Y=1\n\nExperiment 3:\n inputs: X1=1, X2=0, X3=0\n intervention: none\n observed: Z1=1, Z2=0, Y=1\n\nExperiment 4:\n inputs: X1=1, X2=1, X3=0\n intervention: none\n observed: Z1=1, Z2=0, Y=0\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "raw_prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=0\n intervention: none\n observed: Z1=0, Z2=0, Y=0\n\nExperiment 2:\n inputs: X1=0, X2=1, X3=0\n intervention: none\n observed: Z1=1, Z2=0, Y=1\n\nExperiment 3:\n inputs: X1=1, X2=0, X3=0\n intervention: none\n observed: Z1=1, Z2=0, Y=1\n\nExperiment 4:\n inputs: X1=1, X2=1, X3=0\n intervention: none\n observed: Z1=1, Z2=0, Y=0\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "reward_model": {"style": "rule"}, "state_json": "{\"available_experiment_ids\": [1, 2, 3, 4, 5, 6, 7, 8, 9, 11, 12, 13, 14, 15, 16, 17, 18, 19, 21, 22, 23, 24, 25, 26, 27, 28, 29, 31, 32, 33, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 4, \"family_bucket\": \"within_family\", \"maximum_separation\": 0.16666666666666666, \"mean_separation\": 0.1527777777777778, \"minimum_separation\": 0.1388888888888889, \"normalized_mean_separation\": 0.1527777777777778, \"oracle_budget_representation_error\": 0.0, \"oracle_singleton_representation_error\": 0.10416666666666667, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.10416666666666667, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 4}, \"private\": {\"hidden_mode_id\": \"2234aa0c90fbc0d1852bca23311fa5ce600cce117af9305a5560d3a71fc3ef5a\", \"valid_mode_ids\": [\"2234aa0c90fbc0d1852bca23311fa5ce600cce117af9305a5560d3a71fc3ef5a\", \"22f7be6b030b024f1185fe03ca68bb9c80f5b99810b1a684f2b18065c4d9eacc\", \"ac753247a828dc5e1c5dc48f06d288e62a62e9449267b4b79a9f69d63ad4c3a7\", \"c27f95e47536e884b592a91479396da269e668d8724befde880ff673e2ac0d08\"]}, \"state_id\": \"19c0271a4495a01f2f8107a2\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 10, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 0}}, {\"experiment_id\": 20, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 0}}, {\"experiment_id\": 30, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 0}}]}"} {"agent_name": "causal_micro_lab_agent_loop", "data_source": "causal_micro_lab_final_eval_v3", "env_spec_json": "{\"agent\": {}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 6, \"task\": {\"state\": {\"available_experiment_ids\": [0, 1, 2, 3, 4, 6, 7, 8, 9, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 4, \"family_bucket\": \"mixed\", \"maximum_separation\": 0.19444444444444445, \"mean_separation\": 0.14814814814814814, \"minimum_separation\": 0.1111111111111111, \"normalized_mean_separation\": 0.14814814814814814, \"oracle_budget_representation_error\": 0.0, \"oracle_singleton_representation_error\": 0.11111111111111112, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.11111111111111112, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 4}, \"private\": {\"hidden_mode_id\": \"0bcbd49d515e6d3f151312cd036219b5cacd38c0c401aa35ebd5202218491c21\", \"valid_mode_ids\": [\"0bcbd49d515e6d3f151312cd036219b5cacd38c0c401aa35ebd5202218491c21\", \"9b612f8ec496de61395a99755de7c279abcd8ea71577f53a36cac336eabbb178\", \"e3319abc6b32a86cc408098493ffb2d8cf2bee7fa63a5d042a182b1a1c3a995c\", \"ee79c90ef0a7ca4a229d7ff92d524e95d77f7c044b7380463f11fe030874b7d3\"]}, \"state_id\": \"85e492e5533ce29cec5412c1\", \"visible_experiments\": [{\"experiment_id\": 5, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 0}}, {\"experiment_id\": 10, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 0}}, {\"experiment_id\": 22, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z1_1\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 35, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 1}}]}}}", "extra_info": {"index": 6, "max_global_steps": 0, "min_global_steps": 0}, "index": 6, "prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=1\n intervention: none\n observed: Z1=1, Z2=0, Y=0\n\nExperiment 2:\n inputs: X1=0, X2=1, X3=0\n intervention: none\n observed: Z1=1, Z2=0, Y=1\n\nExperiment 3:\n inputs: X1=1, X2=0, X3=0\n intervention: set Z1=1\n observed: Z1=1, Z2=1, Y=0\n\nExperiment 4:\n inputs: X1=1, X2=1, X3=1\n intervention: none\n observed: Z1=1, Z2=1, Y=1\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "raw_prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=1\n intervention: none\n observed: Z1=1, Z2=0, Y=0\n\nExperiment 2:\n inputs: X1=0, X2=1, X3=0\n intervention: none\n observed: Z1=1, Z2=0, Y=1\n\nExperiment 3:\n inputs: X1=1, X2=0, X3=0\n intervention: set Z1=1\n observed: Z1=1, Z2=1, Y=0\n\nExperiment 4:\n inputs: X1=1, X2=1, X3=1\n intervention: none\n observed: Z1=1, Z2=1, Y=1\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "reward_model": {"style": "rule"}, "state_json": "{\"available_experiment_ids\": [0, 1, 2, 3, 4, 6, 7, 8, 9, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 4, \"family_bucket\": \"mixed\", \"maximum_separation\": 0.19444444444444445, \"mean_separation\": 0.14814814814814814, \"minimum_separation\": 0.1111111111111111, \"normalized_mean_separation\": 0.14814814814814814, \"oracle_budget_representation_error\": 0.0, \"oracle_singleton_representation_error\": 0.11111111111111112, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.11111111111111112, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 4}, \"private\": {\"hidden_mode_id\": \"0bcbd49d515e6d3f151312cd036219b5cacd38c0c401aa35ebd5202218491c21\", \"valid_mode_ids\": [\"0bcbd49d515e6d3f151312cd036219b5cacd38c0c401aa35ebd5202218491c21\", \"9b612f8ec496de61395a99755de7c279abcd8ea71577f53a36cac336eabbb178\", \"e3319abc6b32a86cc408098493ffb2d8cf2bee7fa63a5d042a182b1a1c3a995c\", \"ee79c90ef0a7ca4a229d7ff92d524e95d77f7c044b7380463f11fe030874b7d3\"]}, \"state_id\": \"85e492e5533ce29cec5412c1\", \"visible_experiments\": [{\"experiment_id\": 5, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 0}}, {\"experiment_id\": 10, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 0}}, {\"experiment_id\": 22, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z1_1\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 35, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 1}}]}"} {"agent_name": "causal_micro_lab_agent_loop", "data_source": "causal_micro_lab_final_eval_v3", "env_spec_json": "{\"agent\": {}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 7, \"task\": {\"state\": {\"available_experiment_ids\": [1, 2, 3, 4, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 4, \"family_bucket\": \"within_family\", \"maximum_separation\": 0.3333333333333333, \"mean_separation\": 0.19444444444444445, \"minimum_separation\": 0.16666666666666666, \"normalized_mean_separation\": 0.19444444444444445, \"oracle_budget_representation_error\": 0.0, \"oracle_singleton_representation_error\": 0.125, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.125, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 4}, \"private\": {\"hidden_mode_id\": \"9b36e687f40997d441b6aa722428411169243f334af70ba1c343a13f4e4f6fac\", \"valid_mode_ids\": [\"6da21d69779155a43199e1e64659a56fddb226cdf5ec401af831772213bdeb83\", \"9b36e687f40997d441b6aa722428411169243f334af70ba1c343a13f4e4f6fac\", \"beb5bb966717e884296e29f26a98eb3c399176f05931a19090e58c46a7c5ddef\", \"ca86984c91e86202a671be04bd67b29b34e98a94ce47ded50a2ff7fce5944cba\"]}, \"state_id\": \"2e75321ff6624e8ba49745c8\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 5, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 20, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 35, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 0}}]}}}", "extra_info": {"index": 7, "max_global_steps": 0, "min_global_steps": 0}, "index": 7, "prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=0\n intervention: none\n observed: Z1=0, Z2=0, Y=0\n\nExperiment 2:\n inputs: X1=0, X2=0, X3=1\n intervention: none\n observed: Z1=0, Z2=0, Y=1\n\nExperiment 3:\n inputs: X1=1, X2=0, X3=0\n intervention: none\n observed: Z1=0, Z2=0, Y=1\n\nExperiment 4:\n inputs: X1=1, X2=1, X3=1\n intervention: none\n observed: Z1=1, Z2=0, Y=1\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "raw_prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=0\n intervention: none\n observed: Z1=0, Z2=0, Y=0\n\nExperiment 2:\n inputs: X1=0, X2=0, X3=1\n intervention: none\n observed: Z1=0, Z2=0, Y=1\n\nExperiment 3:\n inputs: X1=1, X2=0, X3=0\n intervention: none\n observed: Z1=0, Z2=0, Y=1\n\nExperiment 4:\n inputs: X1=1, X2=1, X3=1\n intervention: none\n observed: Z1=1, Z2=0, Y=1\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "reward_model": {"style": "rule"}, "state_json": "{\"available_experiment_ids\": [1, 2, 3, 4, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 4, \"family_bucket\": \"within_family\", \"maximum_separation\": 0.3333333333333333, \"mean_separation\": 0.19444444444444445, \"minimum_separation\": 0.16666666666666666, \"normalized_mean_separation\": 0.19444444444444445, \"oracle_budget_representation_error\": 0.0, \"oracle_singleton_representation_error\": 0.125, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.125, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 4}, \"private\": {\"hidden_mode_id\": \"9b36e687f40997d441b6aa722428411169243f334af70ba1c343a13f4e4f6fac\", \"valid_mode_ids\": [\"6da21d69779155a43199e1e64659a56fddb226cdf5ec401af831772213bdeb83\", \"9b36e687f40997d441b6aa722428411169243f334af70ba1c343a13f4e4f6fac\", \"beb5bb966717e884296e29f26a98eb3c399176f05931a19090e58c46a7c5ddef\", \"ca86984c91e86202a671be04bd67b29b34e98a94ce47ded50a2ff7fce5944cba\"]}, \"state_id\": \"2e75321ff6624e8ba49745c8\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 5, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 20, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 35, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 0}}]}"} {"agent_name": "causal_micro_lab_agent_loop", "data_source": "causal_micro_lab_final_eval_v3", "env_spec_json": "{\"agent\": {}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 8, \"task\": {\"state\": {\"available_experiment_ids\": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 11, 12, 13, 14, 15, 16, 17, 18, 19, 21, 22, 23, 24, 26, 27, 28, 29, 31, 32, 33, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 4, \"family_bucket\": \"cross_family\", \"maximum_separation\": 0.3888888888888889, \"mean_separation\": 0.23148148148148148, \"minimum_separation\": 0.1111111111111111, \"normalized_mean_separation\": 0.23148148148148148, \"oracle_budget_representation_error\": 0.0, \"oracle_singleton_representation_error\": 0.125, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.125, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 4}, \"private\": {\"hidden_mode_id\": \"44279784c88504bbcf374008f6bdbe331d594362029910e01f8a86592dddc76d\", \"valid_mode_ids\": [\"0e016e0cdf647b8137ff1c683d71852a0bc25dac6224896f6ad060b5b85ebdfc\", \"383bd8af812770f5a1f97ba1f1874be69a5dfeda9fa6a9db35aadc571b09d437\", \"44279784c88504bbcf374008f6bdbe331d594362029910e01f8a86592dddc76d\", \"b4944a75160c5e31e84e442bc8a29970216b795fc2af261341197ea5104c215e\"]}, \"state_id\": \"85a592c0082b5cd4832b9540\", \"visible_experiments\": [{\"experiment_id\": 10, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 0}}, {\"experiment_id\": 20, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 0}}, {\"experiment_id\": 25, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 0}}, {\"experiment_id\": 30, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 1}}]}}}", "extra_info": {"index": 8, "max_global_steps": 0, "min_global_steps": 0}, "index": 8, "prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=1, X3=0\n intervention: none\n observed: Z1=1, Z2=0, Y=0\n\nExperiment 2:\n inputs: X1=1, X2=0, X3=0\n intervention: none\n observed: Z1=1, Z2=0, Y=1\n\nExperiment 3:\n inputs: X1=1, X2=0, X3=1\n intervention: none\n observed: Z1=1, Z2=0, Y=1\n\nExperiment 4:\n inputs: X1=1, X2=1, X3=0\n intervention: none\n observed: Z1=1, Z2=1, Y=1\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "raw_prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=1, X3=0\n intervention: none\n observed: Z1=1, Z2=0, Y=0\n\nExperiment 2:\n inputs: X1=1, X2=0, X3=0\n intervention: none\n observed: Z1=1, Z2=0, Y=1\n\nExperiment 3:\n inputs: X1=1, X2=0, X3=1\n intervention: none\n observed: Z1=1, Z2=0, Y=1\n\nExperiment 4:\n inputs: X1=1, X2=1, X3=0\n intervention: none\n observed: Z1=1, Z2=1, Y=1\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "reward_model": {"style": "rule"}, "state_json": "{\"available_experiment_ids\": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 11, 12, 13, 14, 15, 16, 17, 18, 19, 21, 22, 23, 24, 26, 27, 28, 29, 31, 32, 33, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 4, \"family_bucket\": \"cross_family\", \"maximum_separation\": 0.3888888888888889, \"mean_separation\": 0.23148148148148148, \"minimum_separation\": 0.1111111111111111, \"normalized_mean_separation\": 0.23148148148148148, \"oracle_budget_representation_error\": 0.0, \"oracle_singleton_representation_error\": 0.125, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.125, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 4}, \"private\": {\"hidden_mode_id\": \"44279784c88504bbcf374008f6bdbe331d594362029910e01f8a86592dddc76d\", \"valid_mode_ids\": [\"0e016e0cdf647b8137ff1c683d71852a0bc25dac6224896f6ad060b5b85ebdfc\", \"383bd8af812770f5a1f97ba1f1874be69a5dfeda9fa6a9db35aadc571b09d437\", \"44279784c88504bbcf374008f6bdbe331d594362029910e01f8a86592dddc76d\", \"b4944a75160c5e31e84e442bc8a29970216b795fc2af261341197ea5104c215e\"]}, \"state_id\": \"85a592c0082b5cd4832b9540\", \"visible_experiments\": [{\"experiment_id\": 10, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 0}}, {\"experiment_id\": 20, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 0}}, {\"experiment_id\": 25, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 0}}, {\"experiment_id\": 30, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 1}}]}"} {"agent_name": "causal_micro_lab_agent_loop", "data_source": "causal_micro_lab_final_eval_v3", "env_spec_json": "{\"agent\": {}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 9, \"task\": {\"state\": {\"available_experiment_ids\": [0, 1, 2, 3, 4, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 17, 18, 19, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 4, \"family_bucket\": \"mixed\", \"maximum_separation\": 0.25, \"mean_separation\": 0.17592592592592593, \"minimum_separation\": 0.1111111111111111, \"normalized_mean_separation\": 0.17592592592592593, \"oracle_budget_representation_error\": 0.0, \"oracle_singleton_representation_error\": 0.13194444444444445, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.13194444444444445, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 4}, \"private\": {\"hidden_mode_id\": \"130e1ec76809abc56fcde31e515483c7a04ea26c8eb089ecf9500f88f6d3658c\", \"valid_mode_ids\": [\"130e1ec76809abc56fcde31e515483c7a04ea26c8eb089ecf9500f88f6d3658c\", \"2781c98bdd1c8a4c42c2bdb0aedd64a9b772b79305de097bc47ed3e0f627356c\", \"7ac31d28a4bf624c8f73962c772d1e8e9f809a5f99f0edaa48d29c49c913e045\", \"9ac8e05fee0955ea0c1669836cbc970453d3a2a29dc886f7eab9fddd8e71c7d5\"]}, \"state_id\": \"ed0d477e19edad46dfda3c69\", \"visible_experiments\": [{\"experiment_id\": 5, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 16, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 1}, \"intervention\": \"DO_Z1_0\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 20, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 0}}, {\"experiment_id\": 35, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 1}}]}}}", "extra_info": {"index": 9, "max_global_steps": 0, "min_global_steps": 0}, "index": 9, "prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=1\n intervention: none\n observed: Z1=1, Z2=1, Y=0\n\nExperiment 2:\n inputs: X1=0, X2=1, X3=1\n intervention: set Z1=0\n observed: Z1=0, Z2=1, Y=0\n\nExperiment 3:\n inputs: X1=1, X2=0, X3=0\n intervention: none\n observed: Z1=1, Z2=0, Y=1\n\nExperiment 4:\n inputs: X1=1, X2=1, X3=1\n intervention: none\n observed: Z1=1, Z2=1, Y=1\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "raw_prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=1\n intervention: none\n observed: Z1=1, Z2=1, Y=0\n\nExperiment 2:\n inputs: X1=0, X2=1, X3=1\n intervention: set Z1=0\n observed: Z1=0, Z2=1, Y=0\n\nExperiment 3:\n inputs: X1=1, X2=0, X3=0\n intervention: none\n observed: Z1=1, Z2=0, Y=1\n\nExperiment 4:\n inputs: X1=1, X2=1, X3=1\n intervention: none\n observed: Z1=1, Z2=1, Y=1\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "reward_model": {"style": "rule"}, "state_json": "{\"available_experiment_ids\": [0, 1, 2, 3, 4, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 17, 18, 19, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 4, \"family_bucket\": \"mixed\", \"maximum_separation\": 0.25, \"mean_separation\": 0.17592592592592593, \"minimum_separation\": 0.1111111111111111, \"normalized_mean_separation\": 0.17592592592592593, \"oracle_budget_representation_error\": 0.0, \"oracle_singleton_representation_error\": 0.13194444444444445, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.13194444444444445, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 4}, \"private\": {\"hidden_mode_id\": \"130e1ec76809abc56fcde31e515483c7a04ea26c8eb089ecf9500f88f6d3658c\", \"valid_mode_ids\": [\"130e1ec76809abc56fcde31e515483c7a04ea26c8eb089ecf9500f88f6d3658c\", \"2781c98bdd1c8a4c42c2bdb0aedd64a9b772b79305de097bc47ed3e0f627356c\", \"7ac31d28a4bf624c8f73962c772d1e8e9f809a5f99f0edaa48d29c49c913e045\", \"9ac8e05fee0955ea0c1669836cbc970453d3a2a29dc886f7eab9fddd8e71c7d5\"]}, \"state_id\": \"ed0d477e19edad46dfda3c69\", \"visible_experiments\": [{\"experiment_id\": 5, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 16, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 1}, \"intervention\": \"DO_Z1_0\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 20, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 0}}, {\"experiment_id\": 35, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 1}}]}"} {"agent_name": "causal_micro_lab_agent_loop", "data_source": "causal_micro_lab_final_eval_v3", "env_spec_json": "{\"agent\": {}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 10, \"task\": {\"state\": {\"available_experiment_ids\": [1, 2, 3, 4, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 17, 18, 19, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 4, \"family_bucket\": \"within_family\", \"maximum_separation\": 0.2777777777777778, \"mean_separation\": 0.19444444444444445, \"minimum_separation\": 0.1111111111111111, \"normalized_mean_separation\": 0.19444444444444445, \"oracle_budget_representation_error\": 0.0, \"oracle_singleton_representation_error\": 0.1388888888888889, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.1388888888888889, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 4}, \"private\": {\"hidden_mode_id\": \"fec3174e97b64e36c20553960e046f548ebb46d1fdc6bd698a642d62521c1a95\", \"valid_mode_ids\": [\"14fa63dcf359260d281545bad38c4f5b65635d34eb04a47731935bf97f3c0852\", \"636878023f47b2b063ef297b6711c2d1201c5e1eff26f5572b1d5c1e9917a8b1\", \"e9cb311059c132074309110aa1ca78016d658b076275db70cb282c39cd0e291a\", \"fec3174e97b64e36c20553960e046f548ebb46d1fdc6bd698a642d62521c1a95\"]}, \"state_id\": \"09669964d6269234c5059c63\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 5, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 16, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 1}, \"intervention\": \"DO_Z1_0\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 20, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 1}}]}}}", "extra_info": {"index": 10, "max_global_steps": 0, "min_global_steps": 0}, "index": 10, "prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=0\n intervention: none\n observed: Z1=0, Z2=0, Y=1\n\nExperiment 2:\n inputs: X1=0, X2=0, X3=1\n intervention: none\n observed: Z1=1, Z2=1, Y=0\n\nExperiment 3:\n inputs: X1=0, X2=1, X3=1\n intervention: set Z1=0\n observed: Z1=0, Z2=0, Y=0\n\nExperiment 4:\n inputs: X1=1, X2=0, X3=0\n intervention: none\n observed: Z1=1, Z2=1, Y=1\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "raw_prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=0\n intervention: none\n observed: Z1=0, Z2=0, Y=1\n\nExperiment 2:\n inputs: X1=0, X2=0, X3=1\n intervention: none\n observed: Z1=1, Z2=1, Y=0\n\nExperiment 3:\n inputs: X1=0, X2=1, X3=1\n intervention: set Z1=0\n observed: Z1=0, Z2=0, Y=0\n\nExperiment 4:\n inputs: X1=1, X2=0, X3=0\n intervention: none\n observed: Z1=1, Z2=1, Y=1\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "reward_model": {"style": "rule"}, "state_json": "{\"available_experiment_ids\": [1, 2, 3, 4, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 17, 18, 19, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 4, \"family_bucket\": \"within_family\", \"maximum_separation\": 0.2777777777777778, \"mean_separation\": 0.19444444444444445, \"minimum_separation\": 0.1111111111111111, \"normalized_mean_separation\": 0.19444444444444445, \"oracle_budget_representation_error\": 0.0, \"oracle_singleton_representation_error\": 0.1388888888888889, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.1388888888888889, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 4}, \"private\": {\"hidden_mode_id\": \"fec3174e97b64e36c20553960e046f548ebb46d1fdc6bd698a642d62521c1a95\", \"valid_mode_ids\": [\"14fa63dcf359260d281545bad38c4f5b65635d34eb04a47731935bf97f3c0852\", \"636878023f47b2b063ef297b6711c2d1201c5e1eff26f5572b1d5c1e9917a8b1\", \"e9cb311059c132074309110aa1ca78016d658b076275db70cb282c39cd0e291a\", \"fec3174e97b64e36c20553960e046f548ebb46d1fdc6bd698a642d62521c1a95\"]}, \"state_id\": \"09669964d6269234c5059c63\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 5, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 16, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 1}, \"intervention\": \"DO_Z1_0\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 20, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 1}}]}"} {"agent_name": "causal_micro_lab_agent_loop", "data_source": "causal_micro_lab_final_eval_v3", "env_spec_json": "{\"agent\": {}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 11, \"task\": {\"state\": {\"available_experiment_ids\": [1, 2, 3, 4, 6, 7, 8, 9, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 3, \"family_bucket\": \"within_family\", \"maximum_separation\": 0.32432432432432434, \"mean_separation\": 0.24324324324324328, \"minimum_separation\": 0.16216216216216217, \"normalized_mean_separation\": 0.24324324324324328, \"oracle_budget_representation_error\": 0.0, \"oracle_singleton_representation_error\": 0.14864864864864866, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.14864864864864866, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 4}, \"private\": {\"hidden_mode_id\": \"91b59f4915c31fd19e32078149041519fe79e3c250417d3234c7d50aefc7072d\", \"valid_mode_ids\": [\"1648bbaed0e66f57afcccd07b883397b148e153c4ef318ed3be91e248b689d3c\", \"63e1ad18903f6342d7c658947f5cd1e95209264dea216e25aa7947ccbf6fb466\", \"91b59f4915c31fd19e32078149041519fe79e3c250417d3234c7d50aefc7072d\", \"9b075fee9a695514550eec4e06a11cbc737d2d38c159dd85a0ab1b848cd74fc8\"]}, \"state_id\": \"d12583f9b7f0496b556eeb71\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 0}}, {\"experiment_id\": 5, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 0}}, {\"experiment_id\": 10, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}]}}}", "extra_info": {"index": 11, "max_global_steps": 0, "min_global_steps": 0}, "index": 11, "prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=0\n intervention: none\n observed: Z1=1, Z2=0, Y=1\n\nExperiment 2:\n inputs: X1=0, X2=0, X3=1\n intervention: none\n observed: Z1=1, Z2=0, Y=0\n\nExperiment 3:\n inputs: X1=0, X2=1, X3=0\n intervention: none\n observed: Z1=0, Z2=1, Y=0\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "raw_prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=0\n intervention: none\n observed: Z1=1, Z2=0, Y=1\n\nExperiment 2:\n inputs: X1=0, X2=0, X3=1\n intervention: none\n observed: Z1=1, Z2=0, Y=0\n\nExperiment 3:\n inputs: X1=0, X2=1, X3=0\n intervention: none\n observed: Z1=0, Z2=1, Y=0\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "reward_model": {"style": "rule"}, "state_json": "{\"available_experiment_ids\": [1, 2, 3, 4, 6, 7, 8, 9, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 3, \"family_bucket\": \"within_family\", \"maximum_separation\": 0.32432432432432434, \"mean_separation\": 0.24324324324324328, \"minimum_separation\": 0.16216216216216217, \"normalized_mean_separation\": 0.24324324324324328, \"oracle_budget_representation_error\": 0.0, \"oracle_singleton_representation_error\": 0.14864864864864866, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.14864864864864866, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 4}, \"private\": {\"hidden_mode_id\": \"91b59f4915c31fd19e32078149041519fe79e3c250417d3234c7d50aefc7072d\", \"valid_mode_ids\": [\"1648bbaed0e66f57afcccd07b883397b148e153c4ef318ed3be91e248b689d3c\", \"63e1ad18903f6342d7c658947f5cd1e95209264dea216e25aa7947ccbf6fb466\", \"91b59f4915c31fd19e32078149041519fe79e3c250417d3234c7d50aefc7072d\", \"9b075fee9a695514550eec4e06a11cbc737d2d38c159dd85a0ab1b848cd74fc8\"]}, \"state_id\": \"d12583f9b7f0496b556eeb71\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 0}}, {\"experiment_id\": 5, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 0}}, {\"experiment_id\": 10, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}]}"} {"agent_name": "causal_micro_lab_agent_loop", "data_source": "causal_micro_lab_final_eval_v3", "env_spec_json": "{\"agent\": {}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 12, \"task\": {\"state\": {\"available_experiment_ids\": [0, 1, 2, 3, 4, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 4, \"family_bucket\": \"mixed\", \"maximum_separation\": 0.2777777777777778, \"mean_separation\": 0.20833333333333334, \"minimum_separation\": 0.16666666666666666, \"normalized_mean_separation\": 0.20833333333333334, \"oracle_budget_representation_error\": 0.0, \"oracle_singleton_representation_error\": 0.1527777777777778, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.1527777777777778, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 4}, \"private\": {\"hidden_mode_id\": \"2f9cc73ac48960e0a1238ff0e5c1717312172bcf1528eea343a3181e28b10d00\", \"valid_mode_ids\": [\"2f9cc73ac48960e0a1238ff0e5c1717312172bcf1528eea343a3181e28b10d00\", \"63304f76afe51df30568462b281f297b9f904811bada1b7ddb03e73379c0d509\", \"9c0f3fc07df0ac1e3242b73edd1eeaff3398e48b61755303030b752d8525570a\", \"baa8bc11e2d705ae50d05cd3f9085d0a4024d66dd53adb9304b112ff84e890ce\"]}, \"state_id\": \"3647d373453b93656f6d6b9b\", \"visible_experiments\": [{\"experiment_id\": 5, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 6, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 1}, \"intervention\": \"DO_Z1_0\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 20, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 35, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 1}}]}}}", "extra_info": {"index": 12, "max_global_steps": 0, "min_global_steps": 0}, "index": 12, "prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=1\n intervention: none\n observed: Z1=1, Z2=1, Y=1\n\nExperiment 2:\n inputs: X1=0, X2=0, X3=1\n intervention: set Z1=0\n observed: Z1=0, Z2=0, Y=1\n\nExperiment 3:\n inputs: X1=1, X2=0, X3=0\n intervention: none\n observed: Z1=0, Z2=1, Y=1\n\nExperiment 4:\n inputs: X1=1, X2=1, X3=1\n intervention: none\n observed: Z1=1, Z2=1, Y=1\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "raw_prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=1\n intervention: none\n observed: Z1=1, Z2=1, Y=1\n\nExperiment 2:\n inputs: X1=0, X2=0, X3=1\n intervention: set Z1=0\n observed: Z1=0, Z2=0, Y=1\n\nExperiment 3:\n inputs: X1=1, X2=0, X3=0\n intervention: none\n observed: Z1=0, Z2=1, Y=1\n\nExperiment 4:\n inputs: X1=1, X2=1, X3=1\n intervention: none\n observed: Z1=1, Z2=1, Y=1\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "reward_model": {"style": "rule"}, "state_json": "{\"available_experiment_ids\": [0, 1, 2, 3, 4, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 4, \"family_bucket\": \"mixed\", \"maximum_separation\": 0.2777777777777778, \"mean_separation\": 0.20833333333333334, \"minimum_separation\": 0.16666666666666666, \"normalized_mean_separation\": 0.20833333333333334, \"oracle_budget_representation_error\": 0.0, \"oracle_singleton_representation_error\": 0.1527777777777778, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.1527777777777778, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 4}, \"private\": {\"hidden_mode_id\": \"2f9cc73ac48960e0a1238ff0e5c1717312172bcf1528eea343a3181e28b10d00\", \"valid_mode_ids\": [\"2f9cc73ac48960e0a1238ff0e5c1717312172bcf1528eea343a3181e28b10d00\", \"63304f76afe51df30568462b281f297b9f904811bada1b7ddb03e73379c0d509\", \"9c0f3fc07df0ac1e3242b73edd1eeaff3398e48b61755303030b752d8525570a\", \"baa8bc11e2d705ae50d05cd3f9085d0a4024d66dd53adb9304b112ff84e890ce\"]}, \"state_id\": \"3647d373453b93656f6d6b9b\", \"visible_experiments\": [{\"experiment_id\": 5, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 6, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 1}, \"intervention\": \"DO_Z1_0\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 20, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 35, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 1}}]}"} {"agent_name": "causal_micro_lab_agent_loop", "data_source": "causal_micro_lab_final_eval_v3", "env_spec_json": "{\"agent\": {}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 13, \"task\": {\"state\": {\"available_experiment_ids\": [0, 1, 2, 3, 4, 6, 7, 8, 9, 10, 12, 13, 14, 15, 16, 17, 18, 19, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 3, \"family_bucket\": \"within_family\", \"maximum_separation\": 0.32432432432432434, \"mean_separation\": 0.27027027027027023, \"minimum_separation\": 0.16216216216216217, \"normalized_mean_separation\": 0.27027027027027023, \"oracle_budget_representation_error\": 0.0, \"oracle_singleton_representation_error\": 0.16216216216216217, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.16216216216216217, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 4}, \"private\": {\"hidden_mode_id\": \"9d604985ba8209e140a557afc44d848ac341a1041d1c1ed9873306a753d16d82\", \"valid_mode_ids\": [\"2c984193d1fa79129b7a64a27901d1964213ab5ab12ad1a8378baca8a02dda2b\", \"425c88d1f7cab941e118953bdfb350620098fe7fffd8efa917fb79058e728f8c\", \"807ecba11feef494b7e51dbc6a18bf281f26b24441930f607a55a4d8a97c4977\", \"9d604985ba8209e140a557afc44d848ac341a1041d1c1ed9873306a753d16d82\"]}, \"state_id\": \"4c9600f66710a8850c9f6d8f\", \"visible_experiments\": [{\"experiment_id\": 5, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 11, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 0}, \"intervention\": \"DO_Z1_0\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 20, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 0}}]}}}", "extra_info": {"index": 13, "max_global_steps": 0, "min_global_steps": 0}, "index": 13, "prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=1\n intervention: none\n observed: Z1=0, Z2=0, Y=1\n\nExperiment 2:\n inputs: X1=0, X2=1, X3=0\n intervention: set Z1=0\n observed: Z1=0, Z2=1, Y=0\n\nExperiment 3:\n inputs: X1=1, X2=0, X3=0\n intervention: none\n observed: Z1=1, Z2=0, Y=0\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "raw_prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=1\n intervention: none\n observed: Z1=0, Z2=0, Y=1\n\nExperiment 2:\n inputs: X1=0, X2=1, X3=0\n intervention: set Z1=0\n observed: Z1=0, Z2=1, Y=0\n\nExperiment 3:\n inputs: X1=1, X2=0, X3=0\n intervention: none\n observed: Z1=1, Z2=0, Y=0\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "reward_model": {"style": "rule"}, "state_json": "{\"available_experiment_ids\": [0, 1, 2, 3, 4, 6, 7, 8, 9, 10, 12, 13, 14, 15, 16, 17, 18, 19, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 3, \"family_bucket\": \"within_family\", \"maximum_separation\": 0.32432432432432434, \"mean_separation\": 0.27027027027027023, \"minimum_separation\": 0.16216216216216217, \"normalized_mean_separation\": 0.27027027027027023, \"oracle_budget_representation_error\": 0.0, \"oracle_singleton_representation_error\": 0.16216216216216217, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.16216216216216217, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 4}, \"private\": {\"hidden_mode_id\": \"9d604985ba8209e140a557afc44d848ac341a1041d1c1ed9873306a753d16d82\", \"valid_mode_ids\": [\"2c984193d1fa79129b7a64a27901d1964213ab5ab12ad1a8378baca8a02dda2b\", \"425c88d1f7cab941e118953bdfb350620098fe7fffd8efa917fb79058e728f8c\", \"807ecba11feef494b7e51dbc6a18bf281f26b24441930f607a55a4d8a97c4977\", \"9d604985ba8209e140a557afc44d848ac341a1041d1c1ed9873306a753d16d82\"]}, \"state_id\": \"4c9600f66710a8850c9f6d8f\", \"visible_experiments\": [{\"experiment_id\": 5, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 11, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 0}, \"intervention\": \"DO_Z1_0\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 20, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 0}}]}"} {"agent_name": "causal_micro_lab_agent_loop", "data_source": "causal_micro_lab_final_eval_v3", "env_spec_json": "{\"agent\": {}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 14, \"task\": {\"state\": {\"available_experiment_ids\": [1, 2, 3, 4, 6, 7, 8, 9, 10, 11, 12, 13, 14, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 4, \"family_bucket\": \"cross_family\", \"maximum_separation\": 0.2777777777777778, \"mean_separation\": 0.25, \"minimum_separation\": 0.1111111111111111, \"normalized_mean_separation\": 0.25, \"oracle_budget_representation_error\": 0.0, \"oracle_singleton_representation_error\": 0.16666666666666669, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.16666666666666669, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 4}, \"private\": {\"hidden_mode_id\": \"24dbd877a115846a3e1a08241015c0aa0b0e4a5b67ce3a143dfb73d61957ea00\", \"valid_mode_ids\": [\"24dbd877a115846a3e1a08241015c0aa0b0e4a5b67ce3a143dfb73d61957ea00\", \"2c9004d14a4b66bc81915dd7346b94579a81861388f2b0b910cc8040c90a1b4d\", \"6d98ca0e3a0f018896d5c64819c3e0e2f8d5786ee655334b32f1097e0738769b\", \"d20dc390bdf822324d5a4d0e51805df9c5921fb810eb2f43698f580f7a95169e\"]}, \"state_id\": \"d458b6a5a24f37a00721704a\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 0}}, {\"experiment_id\": 5, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 15, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 0}}, {\"experiment_id\": 28, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 1}, \"intervention\": \"DO_Z2_0\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 0}}]}}}", "extra_info": {"index": 14, "max_global_steps": 0, "min_global_steps": 0}, "index": 14, "prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=0\n intervention: none\n observed: Z1=1, Z2=0, Y=0\n\nExperiment 2:\n inputs: X1=0, X2=0, X3=1\n intervention: none\n observed: Z1=1, Z2=1, Y=1\n\nExperiment 3:\n inputs: X1=0, X2=1, X3=1\n intervention: none\n observed: Z1=1, Z2=0, Y=1\n\nExperiment 4:\n inputs: X1=1, X2=0, X3=1\n intervention: set Z2=0\n observed: Z1=0, Z2=0, Y=1\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "raw_prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=0\n intervention: none\n observed: Z1=1, Z2=0, Y=0\n\nExperiment 2:\n inputs: X1=0, X2=0, X3=1\n intervention: none\n observed: Z1=1, Z2=1, Y=1\n\nExperiment 3:\n inputs: X1=0, X2=1, X3=1\n intervention: none\n observed: Z1=1, Z2=0, Y=1\n\nExperiment 4:\n inputs: X1=1, X2=0, X3=1\n intervention: set Z2=0\n observed: Z1=0, Z2=0, Y=1\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "reward_model": {"style": "rule"}, "state_json": "{\"available_experiment_ids\": [1, 2, 3, 4, 6, 7, 8, 9, 10, 11, 12, 13, 14, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 4, \"family_bucket\": \"cross_family\", \"maximum_separation\": 0.2777777777777778, \"mean_separation\": 0.25, \"minimum_separation\": 0.1111111111111111, \"normalized_mean_separation\": 0.25, \"oracle_budget_representation_error\": 0.0, \"oracle_singleton_representation_error\": 0.16666666666666669, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.16666666666666669, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 4}, \"private\": {\"hidden_mode_id\": \"24dbd877a115846a3e1a08241015c0aa0b0e4a5b67ce3a143dfb73d61957ea00\", \"valid_mode_ids\": [\"24dbd877a115846a3e1a08241015c0aa0b0e4a5b67ce3a143dfb73d61957ea00\", \"2c9004d14a4b66bc81915dd7346b94579a81861388f2b0b910cc8040c90a1b4d\", \"6d98ca0e3a0f018896d5c64819c3e0e2f8d5786ee655334b32f1097e0738769b\", \"d20dc390bdf822324d5a4d0e51805df9c5921fb810eb2f43698f580f7a95169e\"]}, \"state_id\": \"d458b6a5a24f37a00721704a\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 0}}, {\"experiment_id\": 5, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 15, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 0}}, {\"experiment_id\": 28, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 1}, \"intervention\": \"DO_Z2_0\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 0}}]}"} {"agent_name": "causal_micro_lab_agent_loop", "data_source": "causal_micro_lab_final_eval_v3", "env_spec_json": "{\"agent\": {}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 15, \"task\": {\"state\": {\"available_experiment_ids\": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 16, 17, 18, 19, 20, 21, 22, 23, 24, 26, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 4, \"family_bucket\": \"cross_family\", \"maximum_separation\": 0.3055555555555556, \"mean_separation\": 0.24537037037037035, \"minimum_separation\": 0.2222222222222222, \"normalized_mean_separation\": 0.24537037037037035, \"oracle_budget_representation_error\": 0.0, \"oracle_singleton_representation_error\": 0.1736111111111111, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.1736111111111111, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 4}, \"private\": {\"hidden_mode_id\": \"d6ddd469951152cb054cb25a873a33fe1d6aec997f112efff39ff58821b15b69\", \"valid_mode_ids\": [\"5c82a8d10a6a206e3908a1ce9946034dc2adbaca9bbf38238711e2d1febeefa5\", \"d6ddd469951152cb054cb25a873a33fe1d6aec997f112efff39ff58821b15b69\", \"d97113fb7ec4bfb34cfc07b693fbb63a3d65494c7dbe733c126b660209ed4547\", \"f1afdf9e6a214dbce3591b7584f33aef0cab1011767384e3354e500e6aa4c4ff\"]}, \"state_id\": \"018cea508233fe7daf017044\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 15, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 25, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 27, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 1}, \"intervention\": \"DO_Z1_1\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 0}}]}}}", "extra_info": {"index": 15, "max_global_steps": 0, "min_global_steps": 0}, "index": 15, "prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=0\n intervention: none\n observed: Z1=0, Z2=0, Y=0\n\nExperiment 2:\n inputs: X1=0, X2=1, X3=1\n intervention: none\n observed: Z1=0, Z2=0, Y=0\n\nExperiment 3:\n inputs: X1=1, X2=0, X3=1\n intervention: none\n observed: Z1=0, Z2=1, Y=1\n\nExperiment 4:\n inputs: X1=1, X2=0, X3=1\n intervention: set Z1=1\n observed: Z1=1, Z2=0, Y=0\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "raw_prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=0\n intervention: none\n observed: Z1=0, Z2=0, Y=0\n\nExperiment 2:\n inputs: X1=0, X2=1, X3=1\n intervention: none\n observed: Z1=0, Z2=0, Y=0\n\nExperiment 3:\n inputs: X1=1, X2=0, X3=1\n intervention: none\n observed: Z1=0, Z2=1, Y=1\n\nExperiment 4:\n inputs: X1=1, X2=0, X3=1\n intervention: set Z1=1\n observed: Z1=1, Z2=0, Y=0\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "reward_model": {"style": "rule"}, "state_json": "{\"available_experiment_ids\": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 16, 17, 18, 19, 20, 21, 22, 23, 24, 26, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 4, \"family_bucket\": \"cross_family\", \"maximum_separation\": 0.3055555555555556, \"mean_separation\": 0.24537037037037035, \"minimum_separation\": 0.2222222222222222, \"normalized_mean_separation\": 0.24537037037037035, \"oracle_budget_representation_error\": 0.0, \"oracle_singleton_representation_error\": 0.1736111111111111, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.1736111111111111, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 4}, \"private\": {\"hidden_mode_id\": \"d6ddd469951152cb054cb25a873a33fe1d6aec997f112efff39ff58821b15b69\", \"valid_mode_ids\": [\"5c82a8d10a6a206e3908a1ce9946034dc2adbaca9bbf38238711e2d1febeefa5\", \"d6ddd469951152cb054cb25a873a33fe1d6aec997f112efff39ff58821b15b69\", \"d97113fb7ec4bfb34cfc07b693fbb63a3d65494c7dbe733c126b660209ed4547\", \"f1afdf9e6a214dbce3591b7584f33aef0cab1011767384e3354e500e6aa4c4ff\"]}, \"state_id\": \"018cea508233fe7daf017044\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 15, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 25, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 27, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 1}, \"intervention\": \"DO_Z1_1\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 0}}]}"} {"agent_name": "causal_micro_lab_agent_loop", "data_source": "causal_micro_lab_final_eval_v3", "env_spec_json": "{\"agent\": {}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 16, \"task\": {\"state\": {\"available_experiment_ids\": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 16, 17, 18, 19, 21, 22, 23, 24, 25, 26, 27, 28, 29, 31, 32, 33, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 4, \"family_bucket\": \"cross_family\", \"maximum_separation\": 0.5, \"mean_separation\": 0.3055555555555555, \"minimum_separation\": 0.1111111111111111, \"normalized_mean_separation\": 0.3055555555555555, \"oracle_budget_representation_error\": 0.0, \"oracle_singleton_representation_error\": 0.18055555555555558, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.18055555555555558, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 4}, \"private\": {\"hidden_mode_id\": \"0e675d5095aa9e5cfaec38d6bc148ba61605027440412a7609527c61468168a3\", \"valid_mode_ids\": [\"0e675d5095aa9e5cfaec38d6bc148ba61605027440412a7609527c61468168a3\", \"93abb6c6ba7e2b5667c5fbab7101b395625d17151410d9cfd94826c152fdb6c1\", \"b546442163bdc28513b75f1f8fba641296224687c6912afba9371fcef70f4736\", \"ed4346e05dc1a23d65abf6b8096bbbb59887ae67b105947960e02f9497eec913\"]}, \"state_id\": \"35c45e03b0abc3ba1daefbab\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 15, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 0}}, {\"experiment_id\": 20, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 0}}, {\"experiment_id\": 30, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 0}}]}}}", "extra_info": {"index": 16, "max_global_steps": 0, "min_global_steps": 0}, "index": 16, "prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=0\n intervention: none\n observed: Z1=0, Z2=0, Y=0\n\nExperiment 2:\n inputs: X1=0, X2=1, X3=1\n intervention: none\n observed: Z1=1, Z2=0, Y=0\n\nExperiment 3:\n inputs: X1=1, X2=0, X3=0\n intervention: none\n observed: Z1=1, Z2=0, Y=1\n\nExperiment 4:\n inputs: X1=1, X2=1, X3=0\n intervention: none\n observed: Z1=0, Z2=0, Y=1\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "raw_prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=0\n intervention: none\n observed: Z1=0, Z2=0, Y=0\n\nExperiment 2:\n inputs: X1=0, X2=1, X3=1\n intervention: none\n observed: Z1=1, Z2=0, Y=0\n\nExperiment 3:\n inputs: X1=1, X2=0, X3=0\n intervention: none\n observed: Z1=1, Z2=0, Y=1\n\nExperiment 4:\n inputs: X1=1, X2=1, X3=0\n intervention: none\n observed: Z1=0, Z2=0, Y=1\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "reward_model": {"style": "rule"}, "state_json": "{\"available_experiment_ids\": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 16, 17, 18, 19, 21, 22, 23, 24, 25, 26, 27, 28, 29, 31, 32, 33, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 4, \"family_bucket\": \"cross_family\", \"maximum_separation\": 0.5, \"mean_separation\": 0.3055555555555555, \"minimum_separation\": 0.1111111111111111, \"normalized_mean_separation\": 0.3055555555555555, \"oracle_budget_representation_error\": 0.0, \"oracle_singleton_representation_error\": 0.18055555555555558, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.18055555555555558, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 4}, \"private\": {\"hidden_mode_id\": \"0e675d5095aa9e5cfaec38d6bc148ba61605027440412a7609527c61468168a3\", \"valid_mode_ids\": [\"0e675d5095aa9e5cfaec38d6bc148ba61605027440412a7609527c61468168a3\", \"93abb6c6ba7e2b5667c5fbab7101b395625d17151410d9cfd94826c152fdb6c1\", \"b546442163bdc28513b75f1f8fba641296224687c6912afba9371fcef70f4736\", \"ed4346e05dc1a23d65abf6b8096bbbb59887ae67b105947960e02f9497eec913\"]}, \"state_id\": \"35c45e03b0abc3ba1daefbab\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 15, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 0}}, {\"experiment_id\": 20, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 0}}, {\"experiment_id\": 30, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 0}}]}"} {"agent_name": "causal_micro_lab_agent_loop", "data_source": "causal_micro_lab_final_eval_v3", "env_spec_json": "{\"agent\": {}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 17, \"task\": {\"state\": {\"available_experiment_ids\": [1, 2, 3, 4, 6, 7, 8, 9, 10, 11, 12, 13, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 3, \"family_bucket\": \"within_family\", \"maximum_separation\": 0.32432432432432434, \"mean_separation\": 0.2882882882882883, \"minimum_separation\": 0.10810810810810811, \"normalized_mean_separation\": 0.2882882882882883, \"oracle_budget_representation_error\": 0.0, \"oracle_singleton_representation_error\": 0.1891891891891892, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.1891891891891892, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 4}, \"private\": {\"hidden_mode_id\": \"8be7ed376b7d2be63a0ac8d46f6b0908dd77798c0efd42ccf3d62795383d0281\", \"valid_mode_ids\": [\"4023b438014bc74dbc230f5ca6dbf7fd67bc16b1ccff1b03cca29fa7fe0d064f\", \"6ef1f96fde0647709065833b49cfd760eb7150d376b2a1ef105ec1778088225e\", \"8be7ed376b7d2be63a0ac8d46f6b0908dd77798c0efd42ccf3d62795383d0281\", \"a385a6b4845822f8be64338cab6d65e85bd01cdbc867b92e66141977576b6452\"]}, \"state_id\": \"49b88192e805e971f113b06f\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 5, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 14, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 0}, \"intervention\": \"DO_Z2_1\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}]}}}", "extra_info": {"index": 17, "max_global_steps": 0, "min_global_steps": 0}, "index": 17, "prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=0\n intervention: none\n observed: Z1=1, Z2=1, Y=1\n\nExperiment 2:\n inputs: X1=0, X2=0, X3=1\n intervention: none\n observed: Z1=0, Z2=1, Y=0\n\nExperiment 3:\n inputs: X1=0, X2=1, X3=0\n intervention: set Z2=1\n observed: Z1=1, Z2=1, Y=0\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "raw_prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=0\n intervention: none\n observed: Z1=1, Z2=1, Y=1\n\nExperiment 2:\n inputs: X1=0, X2=0, X3=1\n intervention: none\n observed: Z1=0, Z2=1, Y=0\n\nExperiment 3:\n inputs: X1=0, X2=1, X3=0\n intervention: set Z2=1\n observed: Z1=1, Z2=1, Y=0\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "reward_model": {"style": "rule"}, "state_json": "{\"available_experiment_ids\": [1, 2, 3, 4, 6, 7, 8, 9, 10, 11, 12, 13, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 3, \"family_bucket\": \"within_family\", \"maximum_separation\": 0.32432432432432434, \"mean_separation\": 0.2882882882882883, \"minimum_separation\": 0.10810810810810811, \"normalized_mean_separation\": 0.2882882882882883, \"oracle_budget_representation_error\": 0.0, \"oracle_singleton_representation_error\": 0.1891891891891892, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.1891891891891892, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 4}, \"private\": {\"hidden_mode_id\": \"8be7ed376b7d2be63a0ac8d46f6b0908dd77798c0efd42ccf3d62795383d0281\", \"valid_mode_ids\": [\"4023b438014bc74dbc230f5ca6dbf7fd67bc16b1ccff1b03cca29fa7fe0d064f\", \"6ef1f96fde0647709065833b49cfd760eb7150d376b2a1ef105ec1778088225e\", \"8be7ed376b7d2be63a0ac8d46f6b0908dd77798c0efd42ccf3d62795383d0281\", \"a385a6b4845822f8be64338cab6d65e85bd01cdbc867b92e66141977576b6452\"]}, \"state_id\": \"49b88192e805e971f113b06f\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 5, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 14, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 0}, \"intervention\": \"DO_Z2_1\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}]}"} {"agent_name": "causal_micro_lab_agent_loop", "data_source": "causal_micro_lab_final_eval_v3", "env_spec_json": "{\"agent\": {}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 18, \"task\": {\"state\": {\"available_experiment_ids\": [1, 2, 3, 4, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 21, 22, 23, 24, 25, 26, 27, 28, 29, 31, 32, 33, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 4, \"family_bucket\": \"cross_family\", \"maximum_separation\": 0.3888888888888889, \"mean_separation\": 0.27777777777777773, \"minimum_separation\": 0.1111111111111111, \"normalized_mean_separation\": 0.27777777777777773, \"oracle_budget_representation_error\": 0.0, \"oracle_singleton_representation_error\": 0.19444444444444448, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.19444444444444448, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 4}, \"private\": {\"hidden_mode_id\": \"afd72ea73a3c083f46f28b8c3798650e8aa447990ce543ee733a8841917c8c76\", \"valid_mode_ids\": [\"32a4e0f61ce33542164881d30b4158b5e75af1b57767d90d355529181b06a3b6\", \"80f49c76d1b711bdbf42e1001e4934cb27fc6480d0fb22a5f41ee283c108f0e3\", \"afd72ea73a3c083f46f28b8c3798650e8aa447990ce543ee733a8841917c8c76\", \"baca8910e635913b4f6e457c9bf8105fc021ed8316c2d5956e3dc1f616240588\"]}, \"state_id\": \"0efd3d816777f4a66fa85580\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 5, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 20, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 30, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 0}}]}}}", "extra_info": {"index": 18, "max_global_steps": 0, "min_global_steps": 0}, "index": 18, "prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=0\n intervention: none\n observed: Z1=0, Z2=1, Y=0\n\nExperiment 2:\n inputs: X1=0, X2=0, X3=1\n intervention: none\n observed: Z1=1, Z2=1, Y=1\n\nExperiment 3:\n inputs: X1=1, X2=0, X3=0\n intervention: none\n observed: Z1=0, Z2=1, Y=0\n\nExperiment 4:\n inputs: X1=1, X2=1, X3=0\n intervention: none\n observed: Z1=0, Z2=0, Y=1\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "raw_prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=0\n intervention: none\n observed: Z1=0, Z2=1, Y=0\n\nExperiment 2:\n inputs: X1=0, X2=0, X3=1\n intervention: none\n observed: Z1=1, Z2=1, Y=1\n\nExperiment 3:\n inputs: X1=1, X2=0, X3=0\n intervention: none\n observed: Z1=0, Z2=1, Y=0\n\nExperiment 4:\n inputs: X1=1, X2=1, X3=0\n intervention: none\n observed: Z1=0, Z2=0, Y=1\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "reward_model": {"style": "rule"}, "state_json": "{\"available_experiment_ids\": [1, 2, 3, 4, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 21, 22, 23, 24, 25, 26, 27, 28, 29, 31, 32, 33, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 4, \"family_bucket\": \"cross_family\", \"maximum_separation\": 0.3888888888888889, \"mean_separation\": 0.27777777777777773, \"minimum_separation\": 0.1111111111111111, \"normalized_mean_separation\": 0.27777777777777773, \"oracle_budget_representation_error\": 0.0, \"oracle_singleton_representation_error\": 0.19444444444444448, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.19444444444444448, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 4}, \"private\": {\"hidden_mode_id\": \"afd72ea73a3c083f46f28b8c3798650e8aa447990ce543ee733a8841917c8c76\", \"valid_mode_ids\": [\"32a4e0f61ce33542164881d30b4158b5e75af1b57767d90d355529181b06a3b6\", \"80f49c76d1b711bdbf42e1001e4934cb27fc6480d0fb22a5f41ee283c108f0e3\", \"afd72ea73a3c083f46f28b8c3798650e8aa447990ce543ee733a8841917c8c76\", \"baca8910e635913b4f6e457c9bf8105fc021ed8316c2d5956e3dc1f616240588\"]}, \"state_id\": \"0efd3d816777f4a66fa85580\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 5, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 20, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 30, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 0}}]}"} {"agent_name": "causal_micro_lab_agent_loop", "data_source": "causal_micro_lab_final_eval_v3", "env_spec_json": "{\"agent\": {}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 19, \"task\": {\"state\": {\"available_experiment_ids\": [0, 1, 2, 3, 4, 6, 7, 8, 9, 11, 12, 13, 14, 15, 16, 17, 18, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 37, 38, 39], \"metadata\": {\"evidence_size\": 4, \"family_bucket\": \"within_family\", \"maximum_separation\": 0.3611111111111111, \"mean_separation\": 0.26851851851851855, \"minimum_separation\": 0.1111111111111111, \"normalized_mean_separation\": 0.26851851851851855, \"oracle_budget_representation_error\": 0.0, \"oracle_singleton_representation_error\": 0.2013888888888889, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.2013888888888889, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 4}, \"private\": {\"hidden_mode_id\": \"90917e8bd34b527340b590ae1d2ccc9a1e5c2baf1554c3a7175e0804aac64e14\", \"valid_mode_ids\": [\"210777cf9a1f338c3d2eab3e331d47864d38cda6ebdfb26b81e4d2652f2690ca\", \"4b32be4e4ce2729a60e193bdcb5ca421dd8c48a8fca3acbf55db3adabf1614a5\", \"90917e8bd34b527340b590ae1d2ccc9a1e5c2baf1554c3a7175e0804aac64e14\", \"d74727104172d9a4b80703cd462dc66c5e1648553c014b1c592a62a41f8eab8b\"]}, \"state_id\": \"f2a59543fffb5d5adeb859b9\", \"visible_experiments\": [{\"experiment_id\": 5, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 0}}, {\"experiment_id\": 10, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 0}}, {\"experiment_id\": 19, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 1}, \"intervention\": \"DO_Z2_1\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 36, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 1}, \"intervention\": \"DO_Z1_0\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 0}}]}}}", "extra_info": {"index": 19, "max_global_steps": 0, "min_global_steps": 0}, "index": 19, "prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=1\n intervention: none\n observed: Z1=1, Z2=0, Y=0\n\nExperiment 2:\n inputs: X1=0, X2=1, X3=0\n intervention: none\n observed: Z1=1, Z2=0, Y=0\n\nExperiment 3:\n inputs: X1=0, X2=1, X3=1\n intervention: set Z2=1\n observed: Z1=1, Z2=1, Y=0\n\nExperiment 4:\n inputs: X1=1, X2=1, X3=1\n intervention: set Z1=0\n observed: Z1=0, Z2=0, Y=0\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "raw_prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=1\n intervention: none\n observed: Z1=1, Z2=0, Y=0\n\nExperiment 2:\n inputs: X1=0, X2=1, X3=0\n intervention: none\n observed: Z1=1, Z2=0, Y=0\n\nExperiment 3:\n inputs: X1=0, X2=1, X3=1\n intervention: set Z2=1\n observed: Z1=1, Z2=1, Y=0\n\nExperiment 4:\n inputs: X1=1, X2=1, X3=1\n intervention: set Z1=0\n observed: Z1=0, Z2=0, Y=0\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "reward_model": {"style": "rule"}, "state_json": "{\"available_experiment_ids\": [0, 1, 2, 3, 4, 6, 7, 8, 9, 11, 12, 13, 14, 15, 16, 17, 18, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 37, 38, 39], \"metadata\": {\"evidence_size\": 4, \"family_bucket\": \"within_family\", \"maximum_separation\": 0.3611111111111111, \"mean_separation\": 0.26851851851851855, \"minimum_separation\": 0.1111111111111111, \"normalized_mean_separation\": 0.26851851851851855, \"oracle_budget_representation_error\": 0.0, \"oracle_singleton_representation_error\": 0.2013888888888889, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.2013888888888889, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 4}, \"private\": {\"hidden_mode_id\": \"90917e8bd34b527340b590ae1d2ccc9a1e5c2baf1554c3a7175e0804aac64e14\", \"valid_mode_ids\": [\"210777cf9a1f338c3d2eab3e331d47864d38cda6ebdfb26b81e4d2652f2690ca\", \"4b32be4e4ce2729a60e193bdcb5ca421dd8c48a8fca3acbf55db3adabf1614a5\", \"90917e8bd34b527340b590ae1d2ccc9a1e5c2baf1554c3a7175e0804aac64e14\", \"d74727104172d9a4b80703cd462dc66c5e1648553c014b1c592a62a41f8eab8b\"]}, \"state_id\": \"f2a59543fffb5d5adeb859b9\", \"visible_experiments\": [{\"experiment_id\": 5, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 0}}, {\"experiment_id\": 10, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 0}}, {\"experiment_id\": 19, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 1}, \"intervention\": \"DO_Z2_1\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 36, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 1}, \"intervention\": \"DO_Z1_0\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 0}}]}"} {"agent_name": "causal_micro_lab_agent_loop", "data_source": "causal_micro_lab_final_eval_v3", "env_spec_json": "{\"agent\": {}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 20, \"task\": {\"state\": {\"available_experiment_ids\": [0, 2, 4, 6, 7, 8, 9, 10, 11, 12, 13, 14, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 4, \"family_bucket\": \"cross_family\", \"maximum_separation\": 0.5555555555555556, \"mean_separation\": 0.3703703703703703, \"minimum_separation\": 0.2777777777777778, \"normalized_mean_separation\": 0.3703703703703703, \"oracle_budget_representation_error\": 0.0, \"oracle_singleton_representation_error\": 0.20833333333333334, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.20833333333333334, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 4}, \"private\": {\"hidden_mode_id\": \"dc1231cce18a2275cfb2431efac635939a7134f2fe4f387161d009c6b0045009\", \"valid_mode_ids\": [\"19c530d6a261d96a8e933d3f4c86b15bd94f6a647bfd58c5f1d4173ab28c6a71\", \"c526afffc271a0a57afa56c05cea54e39bb1ac3d88a7b815e7c75062367d6a4a\", \"d1c036d3a51d419c81852e82b9aca4742776a1d18598ac51e7a82dc06911e11c\", \"dc1231cce18a2275cfb2431efac635939a7134f2fe4f387161d009c6b0045009\"]}, \"state_id\": \"b7c3bdaafa0cec0ebb6de0b5\", \"visible_experiments\": [{\"experiment_id\": 1, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z1_0\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 3, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z2_0\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 0}}, {\"experiment_id\": 5, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 15, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 0}}]}}}", "extra_info": {"index": 20, "max_global_steps": 0, "min_global_steps": 0}, "index": 20, "prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=0\n intervention: set Z1=0\n observed: Z1=0, Z2=1, Y=0\n\nExperiment 2:\n inputs: X1=0, X2=0, X3=0\n intervention: set Z2=0\n observed: Z1=1, Z2=0, Y=0\n\nExperiment 3:\n inputs: X1=0, X2=0, X3=1\n intervention: none\n observed: Z1=0, Z2=0, Y=1\n\nExperiment 4:\n inputs: X1=0, X2=1, X3=1\n intervention: none\n observed: Z1=0, Z2=0, Y=1\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "raw_prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=0\n intervention: set Z1=0\n observed: Z1=0, Z2=1, Y=0\n\nExperiment 2:\n inputs: X1=0, X2=0, X3=0\n intervention: set Z2=0\n observed: Z1=1, Z2=0, Y=0\n\nExperiment 3:\n inputs: X1=0, X2=0, X3=1\n intervention: none\n observed: Z1=0, Z2=0, Y=1\n\nExperiment 4:\n inputs: X1=0, X2=1, X3=1\n intervention: none\n observed: Z1=0, Z2=0, Y=1\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "reward_model": {"style": "rule"}, "state_json": "{\"available_experiment_ids\": [0, 2, 4, 6, 7, 8, 9, 10, 11, 12, 13, 14, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 4, \"family_bucket\": \"cross_family\", \"maximum_separation\": 0.5555555555555556, \"mean_separation\": 0.3703703703703703, \"minimum_separation\": 0.2777777777777778, \"normalized_mean_separation\": 0.3703703703703703, \"oracle_budget_representation_error\": 0.0, \"oracle_singleton_representation_error\": 0.20833333333333334, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.20833333333333334, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 4}, \"private\": {\"hidden_mode_id\": \"dc1231cce18a2275cfb2431efac635939a7134f2fe4f387161d009c6b0045009\", \"valid_mode_ids\": [\"19c530d6a261d96a8e933d3f4c86b15bd94f6a647bfd58c5f1d4173ab28c6a71\", \"c526afffc271a0a57afa56c05cea54e39bb1ac3d88a7b815e7c75062367d6a4a\", \"d1c036d3a51d419c81852e82b9aca4742776a1d18598ac51e7a82dc06911e11c\", \"dc1231cce18a2275cfb2431efac635939a7134f2fe4f387161d009c6b0045009\"]}, \"state_id\": \"b7c3bdaafa0cec0ebb6de0b5\", \"visible_experiments\": [{\"experiment_id\": 1, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z1_0\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 3, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z2_0\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 0}}, {\"experiment_id\": 5, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 15, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 0}}]}"} {"agent_name": "causal_micro_lab_agent_loop", "data_source": "causal_micro_lab_final_eval_v3", "env_spec_json": "{\"agent\": {}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 21, \"task\": {\"state\": {\"available_experiment_ids\": [0, 1, 2, 3, 4, 5, 6, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 26, 27, 28, 29, 31, 32, 33, 34, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 4, \"family_bucket\": \"cross_family\", \"maximum_separation\": 0.5555555555555556, \"mean_separation\": 0.34722222222222227, \"minimum_separation\": 0.1111111111111111, \"normalized_mean_separation\": 0.34722222222222227, \"oracle_budget_representation_error\": 0.0, \"oracle_singleton_representation_error\": 0.2152777777777778, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.2152777777777778, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 4}, \"private\": {\"hidden_mode_id\": \"3a12ab7d06b76d8280932f5e65086fccb68aa68d20dc99bd358869b2a373e902\", \"valid_mode_ids\": [\"3a12ab7d06b76d8280932f5e65086fccb68aa68d20dc99bd358869b2a373e902\", \"43623d43ef73afb6d62f4ea2fe1848c32328748f67bcbf9cc76da261b6071291\", \"59976fc8a81df3cb242f2de31fc4afa54ee02e8639c71be76de758e533f008d6\", \"89b18b8882ab80ec22f66e435dd023c3c98d73fc2dfab1db1852c8616e88967b\"]}, \"state_id\": \"4ff78a6e9b4c6c8062c98934\", \"visible_experiments\": [{\"experiment_id\": 7, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 1}, \"intervention\": \"DO_Z1_1\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 25, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 30, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 35, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 0}}]}}}", "extra_info": {"index": 21, "max_global_steps": 0, "min_global_steps": 0}, "index": 21, "prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=1\n intervention: set Z1=1\n observed: Z1=1, Z2=1, Y=0\n\nExperiment 2:\n inputs: X1=1, X2=0, X3=1\n intervention: none\n observed: Z1=0, Z2=0, Y=0\n\nExperiment 3:\n inputs: X1=1, X2=1, X3=0\n intervention: none\n observed: Z1=0, Z2=0, Y=0\n\nExperiment 4:\n inputs: X1=1, X2=1, X3=1\n intervention: none\n observed: Z1=1, Z2=0, Y=1\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "raw_prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=1\n intervention: set Z1=1\n observed: Z1=1, Z2=1, Y=0\n\nExperiment 2:\n inputs: X1=1, X2=0, X3=1\n intervention: none\n observed: Z1=0, Z2=0, Y=0\n\nExperiment 3:\n inputs: X1=1, X2=1, X3=0\n intervention: none\n observed: Z1=0, Z2=0, Y=0\n\nExperiment 4:\n inputs: X1=1, X2=1, X3=1\n intervention: none\n observed: Z1=1, Z2=0, Y=1\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "reward_model": {"style": "rule"}, "state_json": "{\"available_experiment_ids\": [0, 1, 2, 3, 4, 5, 6, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 26, 27, 28, 29, 31, 32, 33, 34, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 4, \"family_bucket\": \"cross_family\", \"maximum_separation\": 0.5555555555555556, \"mean_separation\": 0.34722222222222227, \"minimum_separation\": 0.1111111111111111, \"normalized_mean_separation\": 0.34722222222222227, \"oracle_budget_representation_error\": 0.0, \"oracle_singleton_representation_error\": 0.2152777777777778, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.2152777777777778, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 4}, \"private\": {\"hidden_mode_id\": \"3a12ab7d06b76d8280932f5e65086fccb68aa68d20dc99bd358869b2a373e902\", \"valid_mode_ids\": [\"3a12ab7d06b76d8280932f5e65086fccb68aa68d20dc99bd358869b2a373e902\", \"43623d43ef73afb6d62f4ea2fe1848c32328748f67bcbf9cc76da261b6071291\", \"59976fc8a81df3cb242f2de31fc4afa54ee02e8639c71be76de758e533f008d6\", \"89b18b8882ab80ec22f66e435dd023c3c98d73fc2dfab1db1852c8616e88967b\"]}, \"state_id\": \"4ff78a6e9b4c6c8062c98934\", \"visible_experiments\": [{\"experiment_id\": 7, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 1}, \"intervention\": \"DO_Z1_1\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 25, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 30, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 35, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 0}}]}"} {"agent_name": "causal_micro_lab_agent_loop", "data_source": "causal_micro_lab_final_eval_v3", "env_spec_json": "{\"agent\": {}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 22, \"task\": {\"state\": {\"available_experiment_ids\": [0, 1, 2, 3, 4, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 17, 18, 19, 21, 22, 23, 24, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 4, \"family_bucket\": \"cross_family\", \"maximum_separation\": 0.5555555555555556, \"mean_separation\": 0.3888888888888889, \"minimum_separation\": 0.16666666666666666, \"normalized_mean_separation\": 0.3888888888888889, \"oracle_budget_representation_error\": 0.0, \"oracle_singleton_representation_error\": 0.2222222222222222, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.2222222222222222, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 4}, \"private\": {\"hidden_mode_id\": \"738093b263662c8090f878b497da54bc300c9d1ea3772520b402da656fe42681\", \"valid_mode_ids\": [\"5596b98e65cd59b3e84d447f4f4e6fdb0f47a5563c58f8f732e3f16ee6b61a39\", \"738093b263662c8090f878b497da54bc300c9d1ea3772520b402da656fe42681\", \"d140b4e6b7f039ad0f44811cd46c94c98ab2e6de8aede531356dc9db7142f512\", \"f02f19d5c5029fc233ea27e9b957292894814a3487f980b9f940972e88ea3286\"]}, \"state_id\": \"e2151bed19c9146c9204f2e2\", \"visible_experiments\": [{\"experiment_id\": 5, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 16, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 1}, \"intervention\": \"DO_Z1_0\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 20, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 0}}, {\"experiment_id\": 25, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 0}}]}}}", "extra_info": {"index": 22, "max_global_steps": 0, "min_global_steps": 0}, "index": 22, "prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=1\n intervention: none\n observed: Z1=1, Z2=1, Y=1\n\nExperiment 2:\n inputs: X1=0, X2=1, X3=1\n intervention: set Z1=0\n observed: Z1=0, Z2=0, Y=0\n\nExperiment 3:\n inputs: X1=1, X2=0, X3=0\n intervention: none\n observed: Z1=1, Z2=0, Y=1\n\nExperiment 4:\n inputs: X1=1, X2=0, X3=1\n intervention: none\n observed: Z1=0, Z2=0, Y=1\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "raw_prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=1\n intervention: none\n observed: Z1=1, Z2=1, Y=1\n\nExperiment 2:\n inputs: X1=0, X2=1, X3=1\n intervention: set Z1=0\n observed: Z1=0, Z2=0, Y=0\n\nExperiment 3:\n inputs: X1=1, X2=0, X3=0\n intervention: none\n observed: Z1=1, Z2=0, Y=1\n\nExperiment 4:\n inputs: X1=1, X2=0, X3=1\n intervention: none\n observed: Z1=0, Z2=0, Y=1\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "reward_model": {"style": "rule"}, "state_json": "{\"available_experiment_ids\": [0, 1, 2, 3, 4, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 17, 18, 19, 21, 22, 23, 24, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 4, \"family_bucket\": \"cross_family\", \"maximum_separation\": 0.5555555555555556, \"mean_separation\": 0.3888888888888889, \"minimum_separation\": 0.16666666666666666, \"normalized_mean_separation\": 0.3888888888888889, \"oracle_budget_representation_error\": 0.0, \"oracle_singleton_representation_error\": 0.2222222222222222, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.2222222222222222, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 4}, \"private\": {\"hidden_mode_id\": \"738093b263662c8090f878b497da54bc300c9d1ea3772520b402da656fe42681\", \"valid_mode_ids\": [\"5596b98e65cd59b3e84d447f4f4e6fdb0f47a5563c58f8f732e3f16ee6b61a39\", \"738093b263662c8090f878b497da54bc300c9d1ea3772520b402da656fe42681\", \"d140b4e6b7f039ad0f44811cd46c94c98ab2e6de8aede531356dc9db7142f512\", \"f02f19d5c5029fc233ea27e9b957292894814a3487f980b9f940972e88ea3286\"]}, \"state_id\": \"e2151bed19c9146c9204f2e2\", \"visible_experiments\": [{\"experiment_id\": 5, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 16, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 1}, \"intervention\": \"DO_Z1_0\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 20, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 0}}, {\"experiment_id\": 25, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 0}}]}"} {"agent_name": "causal_micro_lab_agent_loop", "data_source": "causal_micro_lab_final_eval_v3", "env_spec_json": "{\"agent\": {}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 23, \"task\": {\"state\": {\"available_experiment_ids\": [1, 2, 3, 4, 5, 6, 7, 8, 9, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 31, 32, 33, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 3, \"family_bucket\": \"cross_family\", \"maximum_separation\": 0.5405405405405406, \"mean_separation\": 0.3963963963963965, \"minimum_separation\": 0.10810810810810811, \"normalized_mean_separation\": 0.3963963963963965, \"oracle_budget_representation_error\": 0.0, \"oracle_singleton_representation_error\": 0.22972972972972974, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.22972972972972974, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 4}, \"private\": {\"hidden_mode_id\": \"687000cb2bd94027bfa2fdff5c270fabbe8c010dc7c92a71a62b4cbaa61a4c08\", \"valid_mode_ids\": [\"502cba069b7f42fb980c2d116995da1d9ea4ad2dfaae28c3953e729cff5e98ac\", \"687000cb2bd94027bfa2fdff5c270fabbe8c010dc7c92a71a62b4cbaa61a4c08\", \"d1576b826d6eeb4ecd0420b50dfa54a4790c17250dabba7ac2c7d8eee4327754\", \"dfa11729d98cd1b355a4bdbc00a349e1bcf8845d352fc92189686ccb574cd4c9\"]}, \"state_id\": \"0481c074a983f1ed0c7c250b\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 10, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 0}}, {\"experiment_id\": 30, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 1}}]}}}", "extra_info": {"index": 23, "max_global_steps": 0, "min_global_steps": 0}, "index": 23, "prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=0\n intervention: none\n observed: Z1=0, Z2=1, Y=1\n\nExperiment 2:\n inputs: X1=0, X2=1, X3=0\n intervention: none\n observed: Z1=1, Z2=0, Y=1\n\nExperiment 3:\n inputs: X1=1, X2=1, X3=0\n intervention: none\n observed: Z1=0, Z2=1, Y=1\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "raw_prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=0\n intervention: none\n observed: Z1=0, Z2=1, Y=1\n\nExperiment 2:\n inputs: X1=0, X2=1, X3=0\n intervention: none\n observed: Z1=1, Z2=0, Y=1\n\nExperiment 3:\n inputs: X1=1, X2=1, X3=0\n intervention: none\n observed: Z1=0, Z2=1, Y=1\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "reward_model": {"style": "rule"}, "state_json": "{\"available_experiment_ids\": [1, 2, 3, 4, 5, 6, 7, 8, 9, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 31, 32, 33, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 3, \"family_bucket\": \"cross_family\", \"maximum_separation\": 0.5405405405405406, \"mean_separation\": 0.3963963963963965, \"minimum_separation\": 0.10810810810810811, \"normalized_mean_separation\": 0.3963963963963965, \"oracle_budget_representation_error\": 0.0, \"oracle_singleton_representation_error\": 0.22972972972972974, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.22972972972972974, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 4}, \"private\": {\"hidden_mode_id\": \"687000cb2bd94027bfa2fdff5c270fabbe8c010dc7c92a71a62b4cbaa61a4c08\", \"valid_mode_ids\": [\"502cba069b7f42fb980c2d116995da1d9ea4ad2dfaae28c3953e729cff5e98ac\", \"687000cb2bd94027bfa2fdff5c270fabbe8c010dc7c92a71a62b4cbaa61a4c08\", \"d1576b826d6eeb4ecd0420b50dfa54a4790c17250dabba7ac2c7d8eee4327754\", \"dfa11729d98cd1b355a4bdbc00a349e1bcf8845d352fc92189686ccb574cd4c9\"]}, \"state_id\": \"0481c074a983f1ed0c7c250b\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 10, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 0}}, {\"experiment_id\": 30, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 1}}]}"} {"agent_name": "causal_micro_lab_agent_loop", "data_source": "causal_micro_lab_final_eval_v3", "env_spec_json": "{\"agent\": {}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 24, \"task\": {\"state\": {\"available_experiment_ids\": [0, 1, 3, 4, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 21, 22, 23, 24, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 4, \"family_bucket\": \"cross_family\", \"maximum_separation\": 0.5555555555555556, \"mean_separation\": 0.36111111111111116, \"minimum_separation\": 0.1111111111111111, \"normalized_mean_separation\": 0.36111111111111116, \"oracle_budget_representation_error\": 0.0, \"oracle_singleton_representation_error\": 0.23611111111111113, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.23611111111111113, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 4}, \"private\": {\"hidden_mode_id\": \"a2087f61515f6aa7886171923a0324a1fe76d8b08ba59984f1cb5ea9791f5a84\", \"valid_mode_ids\": [\"332ed0e776ab6a548bc49adbc9d88fc0b331a60d4821a96d9b22d2229abd2ebd\", \"a2087f61515f6aa7886171923a0324a1fe76d8b08ba59984f1cb5ea9791f5a84\", \"b2379cb4416aea4a2671da895b0317ec975cdc73e5b552d3137f5fee95f9c09a\", \"be4568c648cd68f2a76d7403f7dc9e7e55a061e9e48d2a311f17eec120d2225b\"]}, \"state_id\": \"85f19a8461393508b2eeabe9\", \"visible_experiments\": [{\"experiment_id\": 2, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z1_1\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 0}}, {\"experiment_id\": 5, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 20, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 25, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 1}}]}}}", "extra_info": {"index": 24, "max_global_steps": 0, "min_global_steps": 0}, "index": 24, "prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=0\n intervention: set Z1=1\n observed: Z1=1, Z2=0, Y=0\n\nExperiment 2:\n inputs: X1=0, X2=0, X3=1\n intervention: none\n observed: Z1=0, Z2=1, Y=1\n\nExperiment 3:\n inputs: X1=1, X2=0, X3=0\n intervention: none\n observed: Z1=0, Z2=1, Y=0\n\nExperiment 4:\n inputs: X1=1, X2=0, X3=1\n intervention: none\n observed: Z1=1, Z2=1, Y=1\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "raw_prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=0\n intervention: set Z1=1\n observed: Z1=1, Z2=0, Y=0\n\nExperiment 2:\n inputs: X1=0, X2=0, X3=1\n intervention: none\n observed: Z1=0, Z2=1, Y=1\n\nExperiment 3:\n inputs: X1=1, X2=0, X3=0\n intervention: none\n observed: Z1=0, Z2=1, Y=0\n\nExperiment 4:\n inputs: X1=1, X2=0, X3=1\n intervention: none\n observed: Z1=1, Z2=1, Y=1\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "reward_model": {"style": "rule"}, "state_json": "{\"available_experiment_ids\": [0, 1, 3, 4, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 21, 22, 23, 24, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 4, \"family_bucket\": \"cross_family\", \"maximum_separation\": 0.5555555555555556, \"mean_separation\": 0.36111111111111116, \"minimum_separation\": 0.1111111111111111, \"normalized_mean_separation\": 0.36111111111111116, \"oracle_budget_representation_error\": 0.0, \"oracle_singleton_representation_error\": 0.23611111111111113, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.23611111111111113, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 4}, \"private\": {\"hidden_mode_id\": \"a2087f61515f6aa7886171923a0324a1fe76d8b08ba59984f1cb5ea9791f5a84\", \"valid_mode_ids\": [\"332ed0e776ab6a548bc49adbc9d88fc0b331a60d4821a96d9b22d2229abd2ebd\", \"a2087f61515f6aa7886171923a0324a1fe76d8b08ba59984f1cb5ea9791f5a84\", \"b2379cb4416aea4a2671da895b0317ec975cdc73e5b552d3137f5fee95f9c09a\", \"be4568c648cd68f2a76d7403f7dc9e7e55a061e9e48d2a311f17eec120d2225b\"]}, \"state_id\": \"85f19a8461393508b2eeabe9\", \"visible_experiments\": [{\"experiment_id\": 2, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z1_1\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 0}}, {\"experiment_id\": 5, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 20, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 25, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 1}}]}"} {"agent_name": "causal_micro_lab_agent_loop", "data_source": "causal_micro_lab_final_eval_v3", "env_spec_json": "{\"agent\": {}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 25, \"task\": {\"state\": {\"available_experiment_ids\": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 3, \"family_bucket\": \"within_family\", \"maximum_separation\": 0.43243243243243246, \"mean_separation\": 0.32432432432432434, \"minimum_separation\": 0.21621621621621623, \"normalized_mean_separation\": 0.32432432432432434, \"oracle_budget_representation_error\": 0.0, \"oracle_singleton_representation_error\": 0.24324324324324326, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.24324324324324326, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 4}, \"private\": {\"hidden_mode_id\": \"cf917258436297c3ce54f64cf0663bdbc37d3dd25f5e7b14c5d1fb05b3bd1b40\", \"valid_mode_ids\": [\"35f4fed2dd5af37191c8716dbcd68498e45b22434d34bf67cc183e202052f542\", \"5be72daa4e04e276c8fc947b08e7fd5dbf1f798f98606d520e6baa34f29137ae\", \"6fa185cc38be31b9a2fd4f35ac7e721d43b63ecd4c7cbef0ee38c11ba4358429\", \"cf917258436297c3ce54f64cf0663bdbc37d3dd25f5e7b14c5d1fb05b3bd1b40\"]}, \"state_id\": \"2b3a623f38eaa484fa9a5333\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 20, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 0}}, {\"experiment_id\": 35, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 0}}]}}}", "extra_info": {"index": 25, "max_global_steps": 0, "min_global_steps": 0}, "index": 25, "prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=0\n intervention: none\n observed: Z1=0, Z2=1, Y=1\n\nExperiment 2:\n inputs: X1=1, X2=0, X3=0\n intervention: none\n observed: Z1=1, Z2=0, Y=0\n\nExperiment 3:\n inputs: X1=1, X2=1, X3=1\n intervention: none\n observed: Z1=0, Z2=0, Y=0\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "raw_prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=0\n intervention: none\n observed: Z1=0, Z2=1, Y=1\n\nExperiment 2:\n inputs: X1=1, X2=0, X3=0\n intervention: none\n observed: Z1=1, Z2=0, Y=0\n\nExperiment 3:\n inputs: X1=1, X2=1, X3=1\n intervention: none\n observed: Z1=0, Z2=0, Y=0\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "reward_model": {"style": "rule"}, "state_json": "{\"available_experiment_ids\": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 3, \"family_bucket\": \"within_family\", \"maximum_separation\": 0.43243243243243246, \"mean_separation\": 0.32432432432432434, \"minimum_separation\": 0.21621621621621623, \"normalized_mean_separation\": 0.32432432432432434, \"oracle_budget_representation_error\": 0.0, \"oracle_singleton_representation_error\": 0.24324324324324326, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.24324324324324326, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 4}, \"private\": {\"hidden_mode_id\": \"cf917258436297c3ce54f64cf0663bdbc37d3dd25f5e7b14c5d1fb05b3bd1b40\", \"valid_mode_ids\": [\"35f4fed2dd5af37191c8716dbcd68498e45b22434d34bf67cc183e202052f542\", \"5be72daa4e04e276c8fc947b08e7fd5dbf1f798f98606d520e6baa34f29137ae\", \"6fa185cc38be31b9a2fd4f35ac7e721d43b63ecd4c7cbef0ee38c11ba4358429\", \"cf917258436297c3ce54f64cf0663bdbc37d3dd25f5e7b14c5d1fb05b3bd1b40\"]}, \"state_id\": \"2b3a623f38eaa484fa9a5333\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 20, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 0}}, {\"experiment_id\": 35, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 0}}]}"} {"agent_name": "causal_micro_lab_agent_loop", "data_source": "causal_micro_lab_final_eval_v3", "env_spec_json": "{\"agent\": {}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 26, \"task\": {\"state\": {\"available_experiment_ids\": [0, 1, 2, 3, 4, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 21, 22, 23, 24, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 3, \"family_bucket\": \"mixed\", \"maximum_separation\": 0.3783783783783784, \"mean_separation\": 0.3423423423423424, \"minimum_separation\": 0.2702702702702703, \"normalized_mean_separation\": 0.3423423423423424, \"oracle_budget_representation_error\": 0.0, \"oracle_singleton_representation_error\": 0.2567567567567568, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.2567567567567568, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 4}, \"private\": {\"hidden_mode_id\": \"8570922b541a84b264e37ef34b148cb4547f6f807d56f98e547958c2bd6a0a80\", \"valid_mode_ids\": [\"496d2c9b9865e8c7a41435977b1383187fabd7437e5c4b52eef00c9ab2579d7d\", \"4f7790c7d7973a48829951a08202f6212ba716160242163fe4c92e0575f66bb1\", \"8570922b541a84b264e37ef34b148cb4547f6f807d56f98e547958c2bd6a0a80\", \"a22b5a3d3417b1fa6c5c7815f4917e56824bfd55401c5cf4de196a27fa6404d9\"]}, \"state_id\": \"c6f68fc2807b455e6ec93ddc\", \"visible_experiments\": [{\"experiment_id\": 5, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 20, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 25, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 0}}]}}}", "extra_info": {"index": 26, "max_global_steps": 0, "min_global_steps": 0}, "index": 26, "prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=1\n intervention: none\n observed: Z1=0, Z2=0, Y=1\n\nExperiment 2:\n inputs: X1=1, X2=0, X3=0\n intervention: none\n observed: Z1=0, Z2=1, Y=1\n\nExperiment 3:\n inputs: X1=1, X2=0, X3=1\n intervention: none\n observed: Z1=1, Z2=0, Y=0\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "raw_prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=1\n intervention: none\n observed: Z1=0, Z2=0, Y=1\n\nExperiment 2:\n inputs: X1=1, X2=0, X3=0\n intervention: none\n observed: Z1=0, Z2=1, Y=1\n\nExperiment 3:\n inputs: X1=1, X2=0, X3=1\n intervention: none\n observed: Z1=1, Z2=0, Y=0\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "reward_model": {"style": "rule"}, "state_json": "{\"available_experiment_ids\": [0, 1, 2, 3, 4, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 21, 22, 23, 24, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 3, \"family_bucket\": \"mixed\", \"maximum_separation\": 0.3783783783783784, \"mean_separation\": 0.3423423423423424, \"minimum_separation\": 0.2702702702702703, \"normalized_mean_separation\": 0.3423423423423424, \"oracle_budget_representation_error\": 0.0, \"oracle_singleton_representation_error\": 0.2567567567567568, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.2567567567567568, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 4}, \"private\": {\"hidden_mode_id\": \"8570922b541a84b264e37ef34b148cb4547f6f807d56f98e547958c2bd6a0a80\", \"valid_mode_ids\": [\"496d2c9b9865e8c7a41435977b1383187fabd7437e5c4b52eef00c9ab2579d7d\", \"4f7790c7d7973a48829951a08202f6212ba716160242163fe4c92e0575f66bb1\", \"8570922b541a84b264e37ef34b148cb4547f6f807d56f98e547958c2bd6a0a80\", \"a22b5a3d3417b1fa6c5c7815f4917e56824bfd55401c5cf4de196a27fa6404d9\"]}, \"state_id\": \"c6f68fc2807b455e6ec93ddc\", \"visible_experiments\": [{\"experiment_id\": 5, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 20, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 25, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 0}}]}"} {"agent_name": "causal_micro_lab_agent_loop", "data_source": "causal_micro_lab_final_eval_v3", "env_spec_json": "{\"agent\": {}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 27, \"task\": {\"state\": {\"available_experiment_ids\": [0, 1, 2, 3, 4, 6, 7, 8, 9, 10, 11, 12, 14, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 31, 32, 33, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 4, \"family_bucket\": \"mixed\", \"maximum_separation\": 0.4444444444444444, \"mean_separation\": 0.3518518518518518, \"minimum_separation\": 0.2777777777777778, \"normalized_mean_separation\": 0.3518518518518518, \"oracle_budget_representation_error\": 0.0, \"oracle_singleton_representation_error\": 0.26388888888888884, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.26388888888888884, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 4}, \"private\": {\"hidden_mode_id\": \"9b8c9550c357b6be6bf08f6c6280b3b080511434534e37ca91049585884f4211\", \"valid_mode_ids\": [\"4b2bfa997c0ae15285ea1cb27c41ac2a48d5c755f8ca07999d493ba34ec0aa41\", \"6ceeb2baf5450360872dcf4ed73f03480a35d69987894f3e209b304616c75188\", \"7aa84d28fdc995bb214b37cc7b898978b041831c5313a188d48a7cd72b6c67da\", \"9b8c9550c357b6be6bf08f6c6280b3b080511434534e37ca91049585884f4211\"]}, \"state_id\": \"fecd793a39c4b198ff067198\", \"visible_experiments\": [{\"experiment_id\": 5, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 13, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 0}, \"intervention\": \"DO_Z2_0\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 15, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 30, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 0}}]}}}", "extra_info": {"index": 27, "max_global_steps": 0, "min_global_steps": 0}, "index": 27, "prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=1\n intervention: none\n observed: Z1=0, Z2=0, Y=1\n\nExperiment 2:\n inputs: X1=0, X2=1, X3=0\n intervention: set Z2=0\n observed: Z1=0, Z2=0, Y=0\n\nExperiment 3:\n inputs: X1=0, X2=1, X3=1\n intervention: none\n observed: Z1=0, Z2=1, Y=1\n\nExperiment 4:\n inputs: X1=1, X2=1, X3=0\n intervention: none\n observed: Z1=0, Z2=0, Y=1\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "raw_prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=1\n intervention: none\n observed: Z1=0, Z2=0, Y=1\n\nExperiment 2:\n inputs: X1=0, X2=1, X3=0\n intervention: set Z2=0\n observed: Z1=0, Z2=0, Y=0\n\nExperiment 3:\n inputs: X1=0, X2=1, X3=1\n intervention: none\n observed: Z1=0, Z2=1, Y=1\n\nExperiment 4:\n inputs: X1=1, X2=1, X3=0\n intervention: none\n observed: Z1=0, Z2=0, Y=1\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "reward_model": {"style": "rule"}, "state_json": "{\"available_experiment_ids\": [0, 1, 2, 3, 4, 6, 7, 8, 9, 10, 11, 12, 14, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 31, 32, 33, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 4, \"family_bucket\": \"mixed\", \"maximum_separation\": 0.4444444444444444, \"mean_separation\": 0.3518518518518518, \"minimum_separation\": 0.2777777777777778, \"normalized_mean_separation\": 0.3518518518518518, \"oracle_budget_representation_error\": 0.0, \"oracle_singleton_representation_error\": 0.26388888888888884, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.26388888888888884, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 4}, \"private\": {\"hidden_mode_id\": \"9b8c9550c357b6be6bf08f6c6280b3b080511434534e37ca91049585884f4211\", \"valid_mode_ids\": [\"4b2bfa997c0ae15285ea1cb27c41ac2a48d5c755f8ca07999d493ba34ec0aa41\", \"6ceeb2baf5450360872dcf4ed73f03480a35d69987894f3e209b304616c75188\", \"7aa84d28fdc995bb214b37cc7b898978b041831c5313a188d48a7cd72b6c67da\", \"9b8c9550c357b6be6bf08f6c6280b3b080511434534e37ca91049585884f4211\"]}, \"state_id\": \"fecd793a39c4b198ff067198\", \"visible_experiments\": [{\"experiment_id\": 5, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 13, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 0}, \"intervention\": \"DO_Z2_0\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 15, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 30, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 0}}]}"} {"agent_name": "causal_micro_lab_agent_loop", "data_source": "causal_micro_lab_final_eval_v3", "env_spec_json": "{\"agent\": {}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 28, \"task\": {\"state\": {\"available_experiment_ids\": [1, 2, 3, 4, 6, 7, 8, 9, 10, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 3, \"family_bucket\": \"cross_family\", \"maximum_separation\": 0.5405405405405406, \"mean_separation\": 0.37837837837837834, \"minimum_separation\": 0.2702702702702703, \"normalized_mean_separation\": 0.37837837837837834, \"oracle_budget_representation_error\": 0.0, \"oracle_singleton_representation_error\": 0.2702702702702703, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.2702702702702703, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 4}, \"private\": {\"hidden_mode_id\": \"5f489a3324e3cfeec6cfb0851c2f825aa29417b604a14530adc5ff328f1e3a1a\", \"valid_mode_ids\": [\"225f80182f7260417110dc338ca8281f4d9016e21dd6ffcd7043ebf4713dc4d1\", \"5f489a3324e3cfeec6cfb0851c2f825aa29417b604a14530adc5ff328f1e3a1a\", \"cabe639bb6da4423ac543b9a061b36446e894c609485955dd1bececc0468dedc\", \"d70aa5eff321900fd5cb0bb219b2943671b7c19aefa847101bb45097de99d8f6\"]}, \"state_id\": \"b22172633824d932171ae4e4\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 5, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 11, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 0}, \"intervention\": \"DO_Z1_0\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}]}}}", "extra_info": {"index": 28, "max_global_steps": 0, "min_global_steps": 0}, "index": 28, "prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=0\n intervention: none\n observed: Z1=1, Z2=1, Y=0\n\nExperiment 2:\n inputs: X1=0, X2=0, X3=1\n intervention: none\n observed: Z1=0, Z2=1, Y=1\n\nExperiment 3:\n inputs: X1=0, X2=1, X3=0\n intervention: set Z1=0\n observed: Z1=0, Z2=1, Y=0\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "raw_prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=0\n intervention: none\n observed: Z1=1, Z2=1, Y=0\n\nExperiment 2:\n inputs: X1=0, X2=0, X3=1\n intervention: none\n observed: Z1=0, Z2=1, Y=1\n\nExperiment 3:\n inputs: X1=0, X2=1, X3=0\n intervention: set Z1=0\n observed: Z1=0, Z2=1, Y=0\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "reward_model": {"style": "rule"}, "state_json": "{\"available_experiment_ids\": [1, 2, 3, 4, 6, 7, 8, 9, 10, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 3, \"family_bucket\": \"cross_family\", \"maximum_separation\": 0.5405405405405406, \"mean_separation\": 0.37837837837837834, \"minimum_separation\": 0.2702702702702703, \"normalized_mean_separation\": 0.37837837837837834, \"oracle_budget_representation_error\": 0.0, \"oracle_singleton_representation_error\": 0.2702702702702703, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.2702702702702703, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 4}, \"private\": {\"hidden_mode_id\": \"5f489a3324e3cfeec6cfb0851c2f825aa29417b604a14530adc5ff328f1e3a1a\", \"valid_mode_ids\": [\"225f80182f7260417110dc338ca8281f4d9016e21dd6ffcd7043ebf4713dc4d1\", \"5f489a3324e3cfeec6cfb0851c2f825aa29417b604a14530adc5ff328f1e3a1a\", \"cabe639bb6da4423ac543b9a061b36446e894c609485955dd1bececc0468dedc\", \"d70aa5eff321900fd5cb0bb219b2943671b7c19aefa847101bb45097de99d8f6\"]}, \"state_id\": \"b22172633824d932171ae4e4\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 5, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 11, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 0}, \"intervention\": \"DO_Z1_0\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}]}"} {"agent_name": "causal_micro_lab_agent_loop", "data_source": "causal_micro_lab_final_eval_v3", "env_spec_json": "{\"agent\": {}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 29, \"task\": {\"state\": {\"available_experiment_ids\": [1, 2, 3, 4, 5, 6, 7, 8, 9, 11, 12, 13, 14, 15, 16, 17, 18, 19, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 3, \"family_bucket\": \"mixed\", \"maximum_separation\": 0.5135135135135135, \"mean_separation\": 0.36936936936936937, \"minimum_separation\": 0.2702702702702703, \"normalized_mean_separation\": 0.36936936936936937, \"oracle_budget_representation_error\": 0.0, \"oracle_singleton_representation_error\": 0.27702702702702703, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.27702702702702703, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 4}, \"private\": {\"hidden_mode_id\": \"43e51a9572e0787218e2e3d9a52f98f4fb22a8b446809a37749d0fa5f8fde6ac\", \"valid_mode_ids\": [\"24ec158fcf60880ca76fb1b497e04436533c0ef85bda0754800a4065c394aa4e\", \"43e51a9572e0787218e2e3d9a52f98f4fb22a8b446809a37749d0fa5f8fde6ac\", \"56e62d317318994903af717acd31f8df9aa4c8d4970212aaa587968f5fe2bdfb\", \"6c97d4b3277f3e87895af08dc9f1093043c274a49e1161c7276c9e3f7e85e0b7\"]}, \"state_id\": \"c1de02ff1b84f3616ebe61b7\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 10, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 20, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 0}}]}}}", "extra_info": {"index": 29, "max_global_steps": 0, "min_global_steps": 0}, "index": 29, "prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=0\n intervention: none\n observed: Z1=1, Z2=1, Y=0\n\nExperiment 2:\n inputs: X1=0, X2=1, X3=0\n intervention: none\n observed: Z1=1, Z2=1, Y=1\n\nExperiment 3:\n inputs: X1=1, X2=0, X3=0\n intervention: none\n observed: Z1=1, Z2=0, Y=1\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "raw_prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=0\n intervention: none\n observed: Z1=1, Z2=1, Y=0\n\nExperiment 2:\n inputs: X1=0, X2=1, X3=0\n intervention: none\n observed: Z1=1, Z2=1, Y=1\n\nExperiment 3:\n inputs: X1=1, X2=0, X3=0\n intervention: none\n observed: Z1=1, Z2=0, Y=1\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "reward_model": {"style": "rule"}, "state_json": "{\"available_experiment_ids\": [1, 2, 3, 4, 5, 6, 7, 8, 9, 11, 12, 13, 14, 15, 16, 17, 18, 19, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 3, \"family_bucket\": \"mixed\", \"maximum_separation\": 0.5135135135135135, \"mean_separation\": 0.36936936936936937, \"minimum_separation\": 0.2702702702702703, \"normalized_mean_separation\": 0.36936936936936937, \"oracle_budget_representation_error\": 0.0, \"oracle_singleton_representation_error\": 0.27702702702702703, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.27702702702702703, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 4}, \"private\": {\"hidden_mode_id\": \"43e51a9572e0787218e2e3d9a52f98f4fb22a8b446809a37749d0fa5f8fde6ac\", \"valid_mode_ids\": [\"24ec158fcf60880ca76fb1b497e04436533c0ef85bda0754800a4065c394aa4e\", \"43e51a9572e0787218e2e3d9a52f98f4fb22a8b446809a37749d0fa5f8fde6ac\", \"56e62d317318994903af717acd31f8df9aa4c8d4970212aaa587968f5fe2bdfb\", \"6c97d4b3277f3e87895af08dc9f1093043c274a49e1161c7276c9e3f7e85e0b7\"]}, \"state_id\": \"c1de02ff1b84f3616ebe61b7\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 10, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 20, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 0}}]}"} {"agent_name": "causal_micro_lab_agent_loop", "data_source": "causal_micro_lab_final_eval_v3", "env_spec_json": "{\"agent\": {}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 30, \"task\": {\"state\": {\"available_experiment_ids\": [1, 2, 3, 4, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 3, \"family_bucket\": \"mixed\", \"maximum_separation\": 0.5405405405405406, \"mean_separation\": 0.3873873873873874, \"minimum_separation\": 0.16216216216216217, \"normalized_mean_separation\": 0.3873873873873874, \"oracle_budget_representation_error\": 0.0, \"oracle_singleton_representation_error\": 0.28378378378378377, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.28378378378378377, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 4}, \"private\": {\"hidden_mode_id\": \"91022b45c93f91dd857a2af50fc3b39c817b5385c27870670d6cee083705718c\", \"valid_mode_ids\": [\"21d1032c72c47a78eaac4ca30dad91e0127190381802ad4fcc3a2d531b800d7e\", \"7d63fbc409261e98443354169540f153671c5c4e95a6fa3327660ceb144b28dc\", \"91022b45c93f91dd857a2af50fc3b39c817b5385c27870670d6cee083705718c\", \"9ea3eb2bb323fec1b1cfe0c9fd6063436b41ba430cf68acecdd8662654a9b8eb\"]}, \"state_id\": \"4d4a48117f8dd116e8d37e79\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 5, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 0}}, {\"experiment_id\": 20, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}]}}}", "extra_info": {"index": 30, "max_global_steps": 0, "min_global_steps": 0}, "index": 30, "prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=0\n intervention: none\n observed: Z1=0, Z2=1, Y=1\n\nExperiment 2:\n inputs: X1=0, X2=0, X3=1\n intervention: none\n observed: Z1=1, Z2=0, Y=1\n\nExperiment 3:\n inputs: X1=1, X2=0, X3=0\n intervention: none\n observed: Z1=1, Z2=1, Y=0\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "raw_prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=0\n intervention: none\n observed: Z1=0, Z2=1, Y=1\n\nExperiment 2:\n inputs: X1=0, X2=0, X3=1\n intervention: none\n observed: Z1=1, Z2=0, Y=1\n\nExperiment 3:\n inputs: X1=1, X2=0, X3=0\n intervention: none\n observed: Z1=1, Z2=1, Y=0\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "reward_model": {"style": "rule"}, "state_json": "{\"available_experiment_ids\": [1, 2, 3, 4, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 3, \"family_bucket\": \"mixed\", \"maximum_separation\": 0.5405405405405406, \"mean_separation\": 0.3873873873873874, \"minimum_separation\": 0.16216216216216217, \"normalized_mean_separation\": 0.3873873873873874, \"oracle_budget_representation_error\": 0.0, \"oracle_singleton_representation_error\": 0.28378378378378377, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.28378378378378377, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 4}, \"private\": {\"hidden_mode_id\": \"91022b45c93f91dd857a2af50fc3b39c817b5385c27870670d6cee083705718c\", \"valid_mode_ids\": [\"21d1032c72c47a78eaac4ca30dad91e0127190381802ad4fcc3a2d531b800d7e\", \"7d63fbc409261e98443354169540f153671c5c4e95a6fa3327660ceb144b28dc\", \"91022b45c93f91dd857a2af50fc3b39c817b5385c27870670d6cee083705718c\", \"9ea3eb2bb323fec1b1cfe0c9fd6063436b41ba430cf68acecdd8662654a9b8eb\"]}, \"state_id\": \"4d4a48117f8dd116e8d37e79\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 5, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 0}}, {\"experiment_id\": 20, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}]}"} {"agent_name": "causal_micro_lab_agent_loop", "data_source": "causal_micro_lab_final_eval_v3", "env_spec_json": "{\"agent\": {}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 31, \"task\": {\"state\": {\"available_experiment_ids\": [1, 2, 3, 4, 5, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 4, \"family_bucket\": \"mixed\", \"maximum_separation\": 0.5277777777777778, \"mean_separation\": 0.3796296296296296, \"minimum_separation\": 0.2777777777777778, \"normalized_mean_separation\": 0.3796296296296296, \"oracle_budget_representation_error\": 0.0, \"oracle_singleton_representation_error\": 0.2847222222222222, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.2847222222222222, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 4}, \"private\": {\"hidden_mode_id\": \"c5ccc08de435382e40c74113bbcc2a3adfb0a6713b582c67a94802929a7956ff\", \"valid_mode_ids\": [\"159623df4e51b29ec6a791cc71f81df7a476d0587541278ede8abc5aa5251e7d\", \"c5ccc08de435382e40c74113bbcc2a3adfb0a6713b582c67a94802929a7956ff\", \"e6070d40dcf1282ba19e6befedf75bed8d89de9549e3f1126f8a0859faeb29ef\", \"ea13719d2f0e74b892e2ba62d4d2702ddaeef8b57d04ae07ae6ec3cf01141076\"]}, \"state_id\": \"0d8ac2729adc5715b8491fb9\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 0}}, {\"experiment_id\": 6, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 1}, \"intervention\": \"DO_Z1_0\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 20, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 21, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z1_0\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 0}}]}}}", "extra_info": {"index": 31, "max_global_steps": 0, "min_global_steps": 0}, "index": 31, "prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=0\n intervention: none\n observed: Z1=1, Z2=0, Y=0\n\nExperiment 2:\n inputs: X1=0, X2=0, X3=1\n intervention: set Z1=0\n observed: Z1=0, Z2=0, Y=1\n\nExperiment 3:\n inputs: X1=1, X2=0, X3=0\n intervention: none\n observed: Z1=1, Z2=1, Y=1\n\nExperiment 4:\n inputs: X1=1, X2=0, X3=0\n intervention: set Z1=0\n observed: Z1=0, Z2=0, Y=1\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "raw_prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=0\n intervention: none\n observed: Z1=1, Z2=0, Y=0\n\nExperiment 2:\n inputs: X1=0, X2=0, X3=1\n intervention: set Z1=0\n observed: Z1=0, Z2=0, Y=1\n\nExperiment 3:\n inputs: X1=1, X2=0, X3=0\n intervention: none\n observed: Z1=1, Z2=1, Y=1\n\nExperiment 4:\n inputs: X1=1, X2=0, X3=0\n intervention: set Z1=0\n observed: Z1=0, Z2=0, Y=1\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "reward_model": {"style": "rule"}, "state_json": "{\"available_experiment_ids\": [1, 2, 3, 4, 5, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 4, \"family_bucket\": \"mixed\", \"maximum_separation\": 0.5277777777777778, \"mean_separation\": 0.3796296296296296, \"minimum_separation\": 0.2777777777777778, \"normalized_mean_separation\": 0.3796296296296296, \"oracle_budget_representation_error\": 0.0, \"oracle_singleton_representation_error\": 0.2847222222222222, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.2847222222222222, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 4}, \"private\": {\"hidden_mode_id\": \"c5ccc08de435382e40c74113bbcc2a3adfb0a6713b582c67a94802929a7956ff\", \"valid_mode_ids\": [\"159623df4e51b29ec6a791cc71f81df7a476d0587541278ede8abc5aa5251e7d\", \"c5ccc08de435382e40c74113bbcc2a3adfb0a6713b582c67a94802929a7956ff\", \"e6070d40dcf1282ba19e6befedf75bed8d89de9549e3f1126f8a0859faeb29ef\", \"ea13719d2f0e74b892e2ba62d4d2702ddaeef8b57d04ae07ae6ec3cf01141076\"]}, \"state_id\": \"0d8ac2729adc5715b8491fb9\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 0}}, {\"experiment_id\": 6, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 1}, \"intervention\": \"DO_Z1_0\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 20, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 21, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z1_0\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 0}}]}"} {"agent_name": "causal_micro_lab_agent_loop", "data_source": "causal_micro_lab_final_eval_v3", "env_spec_json": "{\"agent\": {}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 32, \"task\": {\"state\": {\"available_experiment_ids\": [2, 3, 4, 5, 6, 7, 8, 9, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 3, \"family_bucket\": \"cross_family\", \"maximum_separation\": 0.5405405405405406, \"mean_separation\": 0.4684684684684684, \"minimum_separation\": 0.10810810810810811, \"normalized_mean_separation\": 0.4684684684684684, \"oracle_budget_representation_error\": 0.0, \"oracle_singleton_representation_error\": 0.2972972972972973, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.2972972972972973, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 4}, \"private\": {\"hidden_mode_id\": \"1a307d87e83265f91382a3cc6efbce723b66c3c317789fa374467b91dd07848e\", \"valid_mode_ids\": [\"1a307d87e83265f91382a3cc6efbce723b66c3c317789fa374467b91dd07848e\", \"265a5b06733ac80e6879b512771306cd28db15f8941f94e116280fb5f6a80ae5\", \"390a681ce1fb112c5e03ba6d95632c25a1f5ee0cc1a523093f8c9c04e939e6ca\", \"7480346d0629f559c6e8dc7b7d316e0a8a3397948894e6637bf99159e8473b64\"]}, \"state_id\": \"0c9b19fbc12b15988f0cc496\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 1, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z1_0\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 10, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 0}}]}}}", "extra_info": {"index": 32, "max_global_steps": 0, "min_global_steps": 0}, "index": 32, "prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=0\n intervention: none\n observed: Z1=1, Z2=1, Y=1\n\nExperiment 2:\n inputs: X1=0, X2=0, X3=0\n intervention: set Z1=0\n observed: Z1=0, Z2=1, Y=1\n\nExperiment 3:\n inputs: X1=0, X2=1, X3=0\n intervention: none\n observed: Z1=0, Z2=0, Y=1\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "raw_prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=0\n intervention: none\n observed: Z1=1, Z2=1, Y=1\n\nExperiment 2:\n inputs: X1=0, X2=0, X3=0\n intervention: set Z1=0\n observed: Z1=0, Z2=1, Y=1\n\nExperiment 3:\n inputs: X1=0, X2=1, X3=0\n intervention: none\n observed: Z1=0, Z2=0, Y=1\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "reward_model": {"style": "rule"}, "state_json": "{\"available_experiment_ids\": [2, 3, 4, 5, 6, 7, 8, 9, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 3, \"family_bucket\": \"cross_family\", \"maximum_separation\": 0.5405405405405406, \"mean_separation\": 0.4684684684684684, \"minimum_separation\": 0.10810810810810811, \"normalized_mean_separation\": 0.4684684684684684, \"oracle_budget_representation_error\": 0.0, \"oracle_singleton_representation_error\": 0.2972972972972973, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.2972972972972973, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 4}, \"private\": {\"hidden_mode_id\": \"1a307d87e83265f91382a3cc6efbce723b66c3c317789fa374467b91dd07848e\", \"valid_mode_ids\": [\"1a307d87e83265f91382a3cc6efbce723b66c3c317789fa374467b91dd07848e\", \"265a5b06733ac80e6879b512771306cd28db15f8941f94e116280fb5f6a80ae5\", \"390a681ce1fb112c5e03ba6d95632c25a1f5ee0cc1a523093f8c9c04e939e6ca\", \"7480346d0629f559c6e8dc7b7d316e0a8a3397948894e6637bf99159e8473b64\"]}, \"state_id\": \"0c9b19fbc12b15988f0cc496\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 1, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z1_0\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 10, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 0}}]}"} {"agent_name": "causal_micro_lab_agent_loop", "data_source": "causal_micro_lab_final_eval_v3", "env_spec_json": "{\"agent\": {}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 33, \"task\": {\"state\": {\"available_experiment_ids\": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 31, 32, 33, 34, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 3, \"family_bucket\": \"within_family\", \"maximum_separation\": 0.5675675675675675, \"mean_separation\": 0.40540540540540543, \"minimum_separation\": 0.32432432432432434, \"normalized_mean_separation\": 0.40540540540540543, \"oracle_budget_representation_error\": 0.0, \"oracle_singleton_representation_error\": 0.30405405405405406, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.30405405405405406, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 4}, \"private\": {\"hidden_mode_id\": \"65d535d7587e2865a86a7c613bb3316224a01c833ed3161d52004b6454a44268\", \"valid_mode_ids\": [\"5a84117c91aff9598e259da82d33184a2e9cb371d1f8ecae6b16c411f14af727\", \"65d535d7587e2865a86a7c613bb3316224a01c833ed3161d52004b6454a44268\", \"6666aec68e780d69f66628f88502b39fe730d79c46a59f19d8638e5d0976d64d\", \"c9a0ef9960845ed6ea90d07d124afc294122a7a141b66fba07578b5d4b32b2bb\"]}, \"state_id\": \"d3faf6e2ce7a8e05ffb6b0d2\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 30, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 0}}, {\"experiment_id\": 35, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 0}}]}}}", "extra_info": {"index": 33, "max_global_steps": 0, "min_global_steps": 0}, "index": 33, "prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=0\n intervention: none\n observed: Z1=0, Z2=0, Y=1\n\nExperiment 2:\n inputs: X1=1, X2=1, X3=0\n intervention: none\n observed: Z1=1, Z2=0, Y=1\n\nExperiment 3:\n inputs: X1=1, X2=1, X3=1\n intervention: none\n observed: Z1=0, Z2=0, Y=1\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "raw_prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=0\n intervention: none\n observed: Z1=0, Z2=0, Y=1\n\nExperiment 2:\n inputs: X1=1, X2=1, X3=0\n intervention: none\n observed: Z1=1, Z2=0, Y=1\n\nExperiment 3:\n inputs: X1=1, X2=1, X3=1\n intervention: none\n observed: Z1=0, Z2=0, Y=1\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "reward_model": {"style": "rule"}, "state_json": "{\"available_experiment_ids\": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 31, 32, 33, 34, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 3, \"family_bucket\": \"within_family\", \"maximum_separation\": 0.5675675675675675, \"mean_separation\": 0.40540540540540543, \"minimum_separation\": 0.32432432432432434, \"normalized_mean_separation\": 0.40540540540540543, \"oracle_budget_representation_error\": 0.0, \"oracle_singleton_representation_error\": 0.30405405405405406, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.30405405405405406, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 4}, \"private\": {\"hidden_mode_id\": \"65d535d7587e2865a86a7c613bb3316224a01c833ed3161d52004b6454a44268\", \"valid_mode_ids\": [\"5a84117c91aff9598e259da82d33184a2e9cb371d1f8ecae6b16c411f14af727\", \"65d535d7587e2865a86a7c613bb3316224a01c833ed3161d52004b6454a44268\", \"6666aec68e780d69f66628f88502b39fe730d79c46a59f19d8638e5d0976d64d\", \"c9a0ef9960845ed6ea90d07d124afc294122a7a141b66fba07578b5d4b32b2bb\"]}, \"state_id\": \"d3faf6e2ce7a8e05ffb6b0d2\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 30, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 0}}, {\"experiment_id\": 35, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 0}}]}"} {"agent_name": "causal_micro_lab_agent_loop", "data_source": "causal_micro_lab_final_eval_v3", "env_spec_json": "{\"agent\": {}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 34, \"task\": {\"state\": {\"available_experiment_ids\": [0, 2, 3, 4, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 3, \"family_bucket\": \"within_family\", \"maximum_separation\": 0.5945945945945946, \"mean_separation\": 0.41441441441441446, \"minimum_separation\": 0.32432432432432434, \"normalized_mean_separation\": 0.41441441441441446, \"oracle_budget_representation_error\": 0.0, \"oracle_singleton_representation_error\": 0.31081081081081086, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.31081081081081086, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 4}, \"private\": {\"hidden_mode_id\": \"b91909fd84555abcf427041f77ec0c4d71f9210170a2c76ae0fc29317867606d\", \"valid_mode_ids\": [\"2837ba51c6dc437a21b1f4bcbe8a1b476281b5a7e0d3db127d8d2e2733ddf9c1\", \"b91909fd84555abcf427041f77ec0c4d71f9210170a2c76ae0fc29317867606d\", \"cc2cfc8a1c424fecd91dda0af35865224e20f1536864c1ec9f1f9ad93fe02f40\", \"d15c20ed77fe7340aafce055fd67cd179bdfd7bbde26f59b6627631a42aece28\"]}, \"state_id\": \"8a10b66ae92c8413ab52517d\", \"visible_experiments\": [{\"experiment_id\": 1, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z1_0\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 5, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 35, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 0}}]}}}", "extra_info": {"index": 34, "max_global_steps": 0, "min_global_steps": 0}, "index": 34, "prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=0\n intervention: set Z1=0\n observed: Z1=0, Z2=0, Y=1\n\nExperiment 2:\n inputs: X1=0, X2=0, X3=1\n intervention: none\n observed: Z1=0, Z2=1, Y=0\n\nExperiment 3:\n inputs: X1=1, X2=1, X3=1\n intervention: none\n observed: Z1=0, Z2=0, Y=0\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "raw_prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=0\n intervention: set Z1=0\n observed: Z1=0, Z2=0, Y=1\n\nExperiment 2:\n inputs: X1=0, X2=0, X3=1\n intervention: none\n observed: Z1=0, Z2=1, Y=0\n\nExperiment 3:\n inputs: X1=1, X2=1, X3=1\n intervention: none\n observed: Z1=0, Z2=0, Y=0\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "reward_model": {"style": "rule"}, "state_json": "{\"available_experiment_ids\": [0, 2, 3, 4, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 3, \"family_bucket\": \"within_family\", \"maximum_separation\": 0.5945945945945946, \"mean_separation\": 0.41441441441441446, \"minimum_separation\": 0.32432432432432434, \"normalized_mean_separation\": 0.41441441441441446, \"oracle_budget_representation_error\": 0.0, \"oracle_singleton_representation_error\": 0.31081081081081086, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.31081081081081086, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 4}, \"private\": {\"hidden_mode_id\": \"b91909fd84555abcf427041f77ec0c4d71f9210170a2c76ae0fc29317867606d\", \"valid_mode_ids\": [\"2837ba51c6dc437a21b1f4bcbe8a1b476281b5a7e0d3db127d8d2e2733ddf9c1\", \"b91909fd84555abcf427041f77ec0c4d71f9210170a2c76ae0fc29317867606d\", \"cc2cfc8a1c424fecd91dda0af35865224e20f1536864c1ec9f1f9ad93fe02f40\", \"d15c20ed77fe7340aafce055fd67cd179bdfd7bbde26f59b6627631a42aece28\"]}, \"state_id\": \"8a10b66ae92c8413ab52517d\", \"visible_experiments\": [{\"experiment_id\": 1, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z1_0\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 5, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 35, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 0}}]}"} {"agent_name": "causal_micro_lab_agent_loop", "data_source": "causal_micro_lab_final_eval_v3", "env_spec_json": "{\"agent\": {}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 35, \"task\": {\"state\": {\"available_experiment_ids\": [0, 1, 2, 3, 4, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 19, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 4, \"family_bucket\": \"within_family\", \"maximum_separation\": 0.5555555555555556, \"mean_separation\": 0.4259259259259259, \"minimum_separation\": 0.16666666666666666, \"normalized_mean_separation\": 0.4259259259259259, \"oracle_budget_representation_error\": 0.0, \"oracle_singleton_representation_error\": 0.3194444444444444, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.3194444444444444, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 4}, \"private\": {\"hidden_mode_id\": \"75f4b7add668d7933a99b36e1d436a1e0e1110e3c05088c9a306b112d3988dfb\", \"valid_mode_ids\": [\"69515479dd9de0e8f042b044fa299ccc18f7a83a72416b925c2c27c571db2e1a\", \"6981eef84a1f248db16842a287e04ac526e942644cb332b20d0d1f4d32181bd1\", \"75f4b7add668d7933a99b36e1d436a1e0e1110e3c05088c9a306b112d3988dfb\", \"92ed51ac15898021279492b29f03d704c758d7609bd842a5c4ada75157c3856f\"]}, \"state_id\": \"70a6ccf6ec976c50699f11f0\", \"visible_experiments\": [{\"experiment_id\": 5, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 18, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 1}, \"intervention\": \"DO_Z2_0\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 0}}, {\"experiment_id\": 20, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 0}}, {\"experiment_id\": 21, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z1_0\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 1}}]}}}", "extra_info": {"index": 35, "max_global_steps": 0, "min_global_steps": 0}, "index": 35, "prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=1\n intervention: none\n observed: Z1=1, Z2=1, Y=0\n\nExperiment 2:\n inputs: X1=0, X2=1, X3=1\n intervention: set Z2=0\n observed: Z1=1, Z2=0, Y=0\n\nExperiment 3:\n inputs: X1=1, X2=0, X3=0\n intervention: none\n observed: Z1=1, Z2=0, Y=1\n\nExperiment 4:\n inputs: X1=1, X2=0, X3=0\n intervention: set Z1=0\n observed: Z1=0, Z2=1, Y=1\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "raw_prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=1\n intervention: none\n observed: Z1=1, Z2=1, Y=0\n\nExperiment 2:\n inputs: X1=0, X2=1, X3=1\n intervention: set Z2=0\n observed: Z1=1, Z2=0, Y=0\n\nExperiment 3:\n inputs: X1=1, X2=0, X3=0\n intervention: none\n observed: Z1=1, Z2=0, Y=1\n\nExperiment 4:\n inputs: X1=1, X2=0, X3=0\n intervention: set Z1=0\n observed: Z1=0, Z2=1, Y=1\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "reward_model": {"style": "rule"}, "state_json": "{\"available_experiment_ids\": [0, 1, 2, 3, 4, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 19, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 4, \"family_bucket\": \"within_family\", \"maximum_separation\": 0.5555555555555556, \"mean_separation\": 0.4259259259259259, \"minimum_separation\": 0.16666666666666666, \"normalized_mean_separation\": 0.4259259259259259, \"oracle_budget_representation_error\": 0.0, \"oracle_singleton_representation_error\": 0.3194444444444444, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.3194444444444444, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 4}, \"private\": {\"hidden_mode_id\": \"75f4b7add668d7933a99b36e1d436a1e0e1110e3c05088c9a306b112d3988dfb\", \"valid_mode_ids\": [\"69515479dd9de0e8f042b044fa299ccc18f7a83a72416b925c2c27c571db2e1a\", \"6981eef84a1f248db16842a287e04ac526e942644cb332b20d0d1f4d32181bd1\", \"75f4b7add668d7933a99b36e1d436a1e0e1110e3c05088c9a306b112d3988dfb\", \"92ed51ac15898021279492b29f03d704c758d7609bd842a5c4ada75157c3856f\"]}, \"state_id\": \"70a6ccf6ec976c50699f11f0\", \"visible_experiments\": [{\"experiment_id\": 5, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 18, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 1}, \"intervention\": \"DO_Z2_0\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 0}}, {\"experiment_id\": 20, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 0}}, {\"experiment_id\": 21, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z1_0\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 1}}]}"} {"agent_name": "causal_micro_lab_agent_loop", "data_source": "causal_micro_lab_final_eval_v3", "env_spec_json": "{\"agent\": {}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 36, \"task\": {\"state\": {\"available_experiment_ids\": [1, 3, 4, 5, 6, 7, 8, 9, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 4, \"family_bucket\": \"within_family\", \"maximum_separation\": 0.6111111111111112, \"mean_separation\": 0.425925925925926, \"minimum_separation\": 0.1111111111111111, \"normalized_mean_separation\": 0.425925925925926, \"oracle_budget_representation_error\": 0.0, \"oracle_singleton_representation_error\": 0.3194444444444445, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.3194444444444445, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 4}, \"private\": {\"hidden_mode_id\": \"ca90628bf4e4f8b4f2cd5038b15dff14c36f4b5f426f9e9048a1ed04a351f9a9\", \"valid_mode_ids\": [\"0608e55ea9226531ad95b618e0696d6bdaaba6ab20bf7fc2aa2d8da486bb7e14\", \"7789603744d895b3132fe0dcf6822cfeb795b5cfab0acd3086f3c50c087b9c09\", \"ca90628bf4e4f8b4f2cd5038b15dff14c36f4b5f426f9e9048a1ed04a351f9a9\", \"e136d5542ed02969ec1a751722d2111fc451d4388b66b0abc6a08d723aae3db1\"]}, \"state_id\": \"cf79afcbfa787d17f39ee5ef\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 2, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z1_1\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 0}}, {\"experiment_id\": 10, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 25, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 0}}]}}}", "extra_info": {"index": 36, "max_global_steps": 0, "min_global_steps": 0}, "index": 36, "prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=0\n intervention: none\n observed: Z1=0, Z2=0, Y=1\n\nExperiment 2:\n inputs: X1=0, X2=0, X3=0\n intervention: set Z1=1\n observed: Z1=1, Z2=0, Y=1\n\nExperiment 3:\n inputs: X1=0, X2=1, X3=0\n intervention: none\n observed: Z1=1, Z2=1, Y=1\n\nExperiment 4:\n inputs: X1=1, X2=0, X3=1\n intervention: none\n observed: Z1=0, Z2=0, Y=0\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "raw_prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=0\n intervention: none\n observed: Z1=0, Z2=0, Y=1\n\nExperiment 2:\n inputs: X1=0, X2=0, X3=0\n intervention: set Z1=1\n observed: Z1=1, Z2=0, Y=1\n\nExperiment 3:\n inputs: X1=0, X2=1, X3=0\n intervention: none\n observed: Z1=1, Z2=1, Y=1\n\nExperiment 4:\n inputs: X1=1, X2=0, X3=1\n intervention: none\n observed: Z1=0, Z2=0, Y=0\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "reward_model": {"style": "rule"}, "state_json": "{\"available_experiment_ids\": [1, 3, 4, 5, 6, 7, 8, 9, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 4, \"family_bucket\": \"within_family\", \"maximum_separation\": 0.6111111111111112, \"mean_separation\": 0.425925925925926, \"minimum_separation\": 0.1111111111111111, \"normalized_mean_separation\": 0.425925925925926, \"oracle_budget_representation_error\": 0.0, \"oracle_singleton_representation_error\": 0.3194444444444445, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.3194444444444445, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 4}, \"private\": {\"hidden_mode_id\": \"ca90628bf4e4f8b4f2cd5038b15dff14c36f4b5f426f9e9048a1ed04a351f9a9\", \"valid_mode_ids\": [\"0608e55ea9226531ad95b618e0696d6bdaaba6ab20bf7fc2aa2d8da486bb7e14\", \"7789603744d895b3132fe0dcf6822cfeb795b5cfab0acd3086f3c50c087b9c09\", \"ca90628bf4e4f8b4f2cd5038b15dff14c36f4b5f426f9e9048a1ed04a351f9a9\", \"e136d5542ed02969ec1a751722d2111fc451d4388b66b0abc6a08d723aae3db1\"]}, \"state_id\": \"cf79afcbfa787d17f39ee5ef\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 2, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z1_1\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 0}}, {\"experiment_id\": 10, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 25, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 0}}]}"} {"agent_name": "causal_micro_lab_agent_loop", "data_source": "causal_micro_lab_final_eval_v3", "env_spec_json": "{\"agent\": {}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 37, \"task\": {\"state\": {\"available_experiment_ids\": [1, 2, 3, 4, 5, 6, 7, 8, 9, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 31, 32, 33, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 3, \"family_bucket\": \"mixed\", \"maximum_separation\": 0.6216216216216216, \"mean_separation\": 0.4684684684684684, \"minimum_separation\": 0.32432432432432434, \"normalized_mean_separation\": 0.4684684684684684, \"oracle_budget_representation_error\": 0.0, \"oracle_singleton_representation_error\": 0.3310810810810811, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.3310810810810811, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 4}, \"private\": {\"hidden_mode_id\": \"6056cd039538245b2a6d60ee9f80b39b1df78632d735ff60ebf561d498d35b48\", \"valid_mode_ids\": [\"24ec158fcf60880ca76fb1b497e04436533c0ef85bda0754800a4065c394aa4e\", \"56e62d317318994903af717acd31f8df9aa4c8d4970212aaa587968f5fe2bdfb\", \"5d0475244746b98ca2a38cf631192cbd01ea20e4962a374ff596631bb8614806\", \"6056cd039538245b2a6d60ee9f80b39b1df78632d735ff60ebf561d498d35b48\"]}, \"state_id\": \"1da8791616cfdfdbfc5d7488\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 10, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 30, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 0}}]}}}", "extra_info": {"index": 37, "max_global_steps": 0, "min_global_steps": 0}, "index": 37, "prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=0\n intervention: none\n observed: Z1=1, Z2=1, Y=0\n\nExperiment 2:\n inputs: X1=0, X2=1, X3=0\n intervention: none\n observed: Z1=1, Z2=1, Y=1\n\nExperiment 3:\n inputs: X1=1, X2=1, X3=0\n intervention: none\n observed: Z1=1, Z2=0, Y=0\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "raw_prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=0\n intervention: none\n observed: Z1=1, Z2=1, Y=0\n\nExperiment 2:\n inputs: X1=0, X2=1, X3=0\n intervention: none\n observed: Z1=1, Z2=1, Y=1\n\nExperiment 3:\n inputs: X1=1, X2=1, X3=0\n intervention: none\n observed: Z1=1, Z2=0, Y=0\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "reward_model": {"style": "rule"}, "state_json": "{\"available_experiment_ids\": [1, 2, 3, 4, 5, 6, 7, 8, 9, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 31, 32, 33, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 3, \"family_bucket\": \"mixed\", \"maximum_separation\": 0.6216216216216216, \"mean_separation\": 0.4684684684684684, \"minimum_separation\": 0.32432432432432434, \"normalized_mean_separation\": 0.4684684684684684, \"oracle_budget_representation_error\": 0.0, \"oracle_singleton_representation_error\": 0.3310810810810811, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.3310810810810811, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 4}, \"private\": {\"hidden_mode_id\": \"6056cd039538245b2a6d60ee9f80b39b1df78632d735ff60ebf561d498d35b48\", \"valid_mode_ids\": [\"24ec158fcf60880ca76fb1b497e04436533c0ef85bda0754800a4065c394aa4e\", \"56e62d317318994903af717acd31f8df9aa4c8d4970212aaa587968f5fe2bdfb\", \"5d0475244746b98ca2a38cf631192cbd01ea20e4962a374ff596631bb8614806\", \"6056cd039538245b2a6d60ee9f80b39b1df78632d735ff60ebf561d498d35b48\"]}, \"state_id\": \"1da8791616cfdfdbfc5d7488\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 10, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 30, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 0}}]}"} {"agent_name": "causal_micro_lab_agent_loop", "data_source": "causal_micro_lab_final_eval_v3", "env_spec_json": "{\"agent\": {}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 38, \"task\": {\"state\": {\"available_experiment_ids\": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 37, 38, 39], \"metadata\": {\"evidence_size\": 4, \"family_bucket\": \"within_family\", \"maximum_separation\": 0.6388888888888888, \"mean_separation\": 0.45370370370370366, \"minimum_separation\": 0.16666666666666666, \"normalized_mean_separation\": 0.45370370370370366, \"oracle_budget_representation_error\": 0.0, \"oracle_singleton_representation_error\": 0.3402777777777778, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.3402777777777778, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 4}, \"private\": {\"hidden_mode_id\": \"0b653e36b463396a2593af5090a10004f2d1bde89bd330647a3b93bb80a36cd9\", \"valid_mode_ids\": [\"0b653e36b463396a2593af5090a10004f2d1bde89bd330647a3b93bb80a36cd9\", \"22726387e78ba14597c9af50b5676d0442117cfb701fe95f9052de9c38f71a55\", \"6cb73cf6fabcd0ecde4bdf4397676b0f8f9b0365180b26f11776279800e8c8aa\", \"b6c56cd2d9bed927a4e3f545e68d36be66ff9077f14efa121fc62cd4fbb0d31f\"]}, \"state_id\": \"21b375c107525a9a235b506b\", \"visible_experiments\": [{\"experiment_id\": 10, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 23, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z2_0\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 0}}, {\"experiment_id\": 35, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 36, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 1}, \"intervention\": \"DO_Z1_0\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 0}}]}}}", "extra_info": {"index": 38, "max_global_steps": 0, "min_global_steps": 0}, "index": 38, "prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=1, X3=0\n intervention: none\n observed: Z1=1, Z2=1, Y=1\n\nExperiment 2:\n inputs: X1=1, X2=0, X3=0\n intervention: set Z2=0\n observed: Z1=1, Z2=0, Y=1\n\nExperiment 3:\n inputs: X1=1, X2=1, X3=1\n intervention: none\n observed: Z1=1, Z2=1, Y=0\n\nExperiment 4:\n inputs: X1=1, X2=1, X3=1\n intervention: set Z1=0\n observed: Z1=0, Z2=0, Y=1\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "raw_prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=1, X3=0\n intervention: none\n observed: Z1=1, Z2=1, Y=1\n\nExperiment 2:\n inputs: X1=1, X2=0, X3=0\n intervention: set Z2=0\n observed: Z1=1, Z2=0, Y=1\n\nExperiment 3:\n inputs: X1=1, X2=1, X3=1\n intervention: none\n observed: Z1=1, Z2=1, Y=0\n\nExperiment 4:\n inputs: X1=1, X2=1, X3=1\n intervention: set Z1=0\n observed: Z1=0, Z2=0, Y=1\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "reward_model": {"style": "rule"}, "state_json": "{\"available_experiment_ids\": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 37, 38, 39], \"metadata\": {\"evidence_size\": 4, \"family_bucket\": \"within_family\", \"maximum_separation\": 0.6388888888888888, \"mean_separation\": 0.45370370370370366, \"minimum_separation\": 0.16666666666666666, \"normalized_mean_separation\": 0.45370370370370366, \"oracle_budget_representation_error\": 0.0, \"oracle_singleton_representation_error\": 0.3402777777777778, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.3402777777777778, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 4}, \"private\": {\"hidden_mode_id\": \"0b653e36b463396a2593af5090a10004f2d1bde89bd330647a3b93bb80a36cd9\", \"valid_mode_ids\": [\"0b653e36b463396a2593af5090a10004f2d1bde89bd330647a3b93bb80a36cd9\", \"22726387e78ba14597c9af50b5676d0442117cfb701fe95f9052de9c38f71a55\", \"6cb73cf6fabcd0ecde4bdf4397676b0f8f9b0365180b26f11776279800e8c8aa\", \"b6c56cd2d9bed927a4e3f545e68d36be66ff9077f14efa121fc62cd4fbb0d31f\"]}, \"state_id\": \"21b375c107525a9a235b506b\", \"visible_experiments\": [{\"experiment_id\": 10, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 23, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z2_0\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 0}}, {\"experiment_id\": 35, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 36, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 1}, \"intervention\": \"DO_Z1_0\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 0}}]}"} {"agent_name": "causal_micro_lab_agent_loop", "data_source": "causal_micro_lab_final_eval_v3", "env_spec_json": "{\"agent\": {}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 39, \"task\": {\"state\": {\"available_experiment_ids\": [1, 2, 3, 4, 6, 7, 8, 9, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 4, \"family_bucket\": \"mixed\", \"maximum_separation\": 0.6388888888888888, \"mean_separation\": 0.4537037037037037, \"minimum_separation\": 0.16666666666666666, \"normalized_mean_separation\": 0.4537037037037037, \"oracle_budget_representation_error\": 0.0, \"oracle_singleton_representation_error\": 0.3402777777777778, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.3402777777777778, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 4}, \"private\": {\"hidden_mode_id\": \"64b7d6554875ee615f5d5194f9d48b6129fc323f849d8b9f5ce5f6d94dacef2a\", \"valid_mode_ids\": [\"28b1c4d8ca4342d743406084699d0a512d2629048460520a1030edf09b96b118\", \"64b7d6554875ee615f5d5194f9d48b6129fc323f849d8b9f5ce5f6d94dacef2a\", \"a8a17a604fb76c917f89486a9e890d1bef4c1b3c08430883f909efe562c9029e\", \"c92706f42f6e4a45b572b8633c0885c99f035fde5d4b292be41b2b845bd5dea3\"]}, \"state_id\": \"243e4ca77cb371fb52ed54f3\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 5, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 10, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 34, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 0}, \"intervention\": \"DO_Z2_1\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}]}}}", "extra_info": {"index": 39, "max_global_steps": 0, "min_global_steps": 0}, "index": 39, "prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=0\n intervention: none\n observed: Z1=0, Z2=1, Y=0\n\nExperiment 2:\n inputs: X1=0, X2=0, X3=1\n intervention: none\n observed: Z1=0, Z2=1, Y=0\n\nExperiment 3:\n inputs: X1=0, X2=1, X3=0\n intervention: none\n observed: Z1=0, Z2=0, Y=1\n\nExperiment 4:\n inputs: X1=1, X2=1, X3=0\n intervention: set Z2=1\n observed: Z1=0, Z2=1, Y=0\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "raw_prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=0\n intervention: none\n observed: Z1=0, Z2=1, Y=0\n\nExperiment 2:\n inputs: X1=0, X2=0, X3=1\n intervention: none\n observed: Z1=0, Z2=1, Y=0\n\nExperiment 3:\n inputs: X1=0, X2=1, X3=0\n intervention: none\n observed: Z1=0, Z2=0, Y=1\n\nExperiment 4:\n inputs: X1=1, X2=1, X3=0\n intervention: set Z2=1\n observed: Z1=0, Z2=1, Y=0\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "reward_model": {"style": "rule"}, "state_json": "{\"available_experiment_ids\": [1, 2, 3, 4, 6, 7, 8, 9, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 4, \"family_bucket\": \"mixed\", \"maximum_separation\": 0.6388888888888888, \"mean_separation\": 0.4537037037037037, \"minimum_separation\": 0.16666666666666666, \"normalized_mean_separation\": 0.4537037037037037, \"oracle_budget_representation_error\": 0.0, \"oracle_singleton_representation_error\": 0.3402777777777778, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.3402777777777778, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 4}, \"private\": {\"hidden_mode_id\": \"64b7d6554875ee615f5d5194f9d48b6129fc323f849d8b9f5ce5f6d94dacef2a\", \"valid_mode_ids\": [\"28b1c4d8ca4342d743406084699d0a512d2629048460520a1030edf09b96b118\", \"64b7d6554875ee615f5d5194f9d48b6129fc323f849d8b9f5ce5f6d94dacef2a\", \"a8a17a604fb76c917f89486a9e890d1bef4c1b3c08430883f909efe562c9029e\", \"c92706f42f6e4a45b572b8633c0885c99f035fde5d4b292be41b2b845bd5dea3\"]}, \"state_id\": \"243e4ca77cb371fb52ed54f3\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 5, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 10, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 34, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 0}, \"intervention\": \"DO_Z2_1\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}]}"} {"agent_name": "causal_micro_lab_agent_loop", "data_source": "causal_micro_lab_final_eval_v3", "env_spec_json": "{\"agent\": {}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 40, \"task\": {\"state\": {\"available_experiment_ids\": [0, 1, 2, 3, 4, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 27, 28, 29, 30, 31, 32, 33, 34, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 4, \"family_bucket\": \"mixed\", \"maximum_separation\": 0.6388888888888888, \"mean_separation\": 0.45370370370370366, \"minimum_separation\": 0.16666666666666666, \"normalized_mean_separation\": 0.45370370370370366, \"oracle_budget_representation_error\": 0.0, \"oracle_singleton_representation_error\": 0.3402777777777778, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.3402777777777778, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 4}, \"private\": {\"hidden_mode_id\": \"c5921be4e971452f352cf4a19d9a8d77b01a82d39b6c6cb5793aea78b539c032\", \"valid_mode_ids\": [\"7329535b8d7a2b8ea28861ef24d1f84994020b8d07e403884fb80d21fed9c320\", \"9ab4db99d7f4734481be8a9ebaf54b685b54edc80a54d2b239bbbb97182b5cdc\", \"c5921be4e971452f352cf4a19d9a8d77b01a82d39b6c6cb5793aea78b539c032\", \"fe9e8365ef1b3251a868be625c813965b3e97341fdae1a47c96041b9d85c4d8a\"]}, \"state_id\": \"a9b386a2b7aa92a125765f0a\", \"visible_experiments\": [{\"experiment_id\": 5, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 25, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 26, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 1}, \"intervention\": \"DO_Z1_0\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 35, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}]}}}", "extra_info": {"index": 40, "max_global_steps": 0, "min_global_steps": 0}, "index": 40, "prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=1\n intervention: none\n observed: Z1=0, Z2=0, Y=1\n\nExperiment 2:\n inputs: X1=1, X2=0, X3=1\n intervention: none\n observed: Z1=1, Z2=1, Y=1\n\nExperiment 3:\n inputs: X1=1, X2=0, X3=1\n intervention: set Z1=0\n observed: Z1=0, Z2=0, Y=1\n\nExperiment 4:\n inputs: X1=1, X2=1, X3=1\n intervention: none\n observed: Z1=0, Z2=1, Y=0\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "raw_prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=1\n intervention: none\n observed: Z1=0, Z2=0, Y=1\n\nExperiment 2:\n inputs: X1=1, X2=0, X3=1\n intervention: none\n observed: Z1=1, Z2=1, Y=1\n\nExperiment 3:\n inputs: X1=1, X2=0, X3=1\n intervention: set Z1=0\n observed: Z1=0, Z2=0, Y=1\n\nExperiment 4:\n inputs: X1=1, X2=1, X3=1\n intervention: none\n observed: Z1=0, Z2=1, Y=0\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "reward_model": {"style": "rule"}, "state_json": "{\"available_experiment_ids\": [0, 1, 2, 3, 4, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 27, 28, 29, 30, 31, 32, 33, 34, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 4, \"family_bucket\": \"mixed\", \"maximum_separation\": 0.6388888888888888, \"mean_separation\": 0.45370370370370366, \"minimum_separation\": 0.16666666666666666, \"normalized_mean_separation\": 0.45370370370370366, \"oracle_budget_representation_error\": 0.0, \"oracle_singleton_representation_error\": 0.3402777777777778, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.3402777777777778, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 4}, \"private\": {\"hidden_mode_id\": \"c5921be4e971452f352cf4a19d9a8d77b01a82d39b6c6cb5793aea78b539c032\", \"valid_mode_ids\": [\"7329535b8d7a2b8ea28861ef24d1f84994020b8d07e403884fb80d21fed9c320\", \"9ab4db99d7f4734481be8a9ebaf54b685b54edc80a54d2b239bbbb97182b5cdc\", \"c5921be4e971452f352cf4a19d9a8d77b01a82d39b6c6cb5793aea78b539c032\", \"fe9e8365ef1b3251a868be625c813965b3e97341fdae1a47c96041b9d85c4d8a\"]}, \"state_id\": \"a9b386a2b7aa92a125765f0a\", \"visible_experiments\": [{\"experiment_id\": 5, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 25, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 26, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 1}, \"intervention\": \"DO_Z1_0\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 35, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}]}"} {"agent_name": "causal_micro_lab_agent_loop", "data_source": "causal_micro_lab_final_eval_v3", "env_spec_json": "{\"agent\": {}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 41, \"task\": {\"state\": {\"available_experiment_ids\": [0, 1, 3, 4, 5, 6, 7, 8, 9, 11, 12, 13, 14, 15, 16, 17, 18, 19, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 4, \"family_bucket\": \"mixed\", \"maximum_separation\": 0.6388888888888888, \"mean_separation\": 0.45370370370370366, \"minimum_separation\": 0.16666666666666666, \"normalized_mean_separation\": 0.45370370370370366, \"oracle_budget_representation_error\": 0.0, \"oracle_singleton_representation_error\": 0.3402777777777778, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.3402777777777778, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 4}, \"private\": {\"hidden_mode_id\": \"5f6faeff46c2c5d52f01236904239b097270956b66ed0406aa6d00224e109545\", \"valid_mode_ids\": [\"2caea8b0aa8ffcb5e6506d7069569ec4847202804153eac8074aa4881de4362d\", \"5f6faeff46c2c5d52f01236904239b097270956b66ed0406aa6d00224e109545\", \"6ec0c5a64d210cf7815ac9fe6de14b2b8495f443d70d33488cb7e01c51e7364f\", \"a48e1c6fdd67fe32fe046c2c66cd6b86dfd0f27d674130653413fcba0721c619\"]}, \"state_id\": \"c23008c390d995f7e76aaa6a\", \"visible_experiments\": [{\"experiment_id\": 2, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z1_1\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 10, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 20, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 0}}, {\"experiment_id\": 35, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 0}}]}}}", "extra_info": {"index": 41, "max_global_steps": 0, "min_global_steps": 0}, "index": 41, "prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=0\n intervention: set Z1=1\n observed: Z1=1, Z2=1, Y=0\n\nExperiment 2:\n inputs: X1=0, X2=1, X3=0\n intervention: none\n observed: Z1=0, Z2=0, Y=1\n\nExperiment 3:\n inputs: X1=1, X2=0, X3=0\n intervention: none\n observed: Z1=1, Z2=0, Y=0\n\nExperiment 4:\n inputs: X1=1, X2=1, X3=1\n intervention: none\n observed: Z1=1, Z2=0, Y=0\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "raw_prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=0\n intervention: set Z1=1\n observed: Z1=1, Z2=1, Y=0\n\nExperiment 2:\n inputs: X1=0, X2=1, X3=0\n intervention: none\n observed: Z1=0, Z2=0, Y=1\n\nExperiment 3:\n inputs: X1=1, X2=0, X3=0\n intervention: none\n observed: Z1=1, Z2=0, Y=0\n\nExperiment 4:\n inputs: X1=1, X2=1, X3=1\n intervention: none\n observed: Z1=1, Z2=0, Y=0\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "reward_model": {"style": "rule"}, "state_json": "{\"available_experiment_ids\": [0, 1, 3, 4, 5, 6, 7, 8, 9, 11, 12, 13, 14, 15, 16, 17, 18, 19, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 4, \"family_bucket\": \"mixed\", \"maximum_separation\": 0.6388888888888888, \"mean_separation\": 0.45370370370370366, \"minimum_separation\": 0.16666666666666666, \"normalized_mean_separation\": 0.45370370370370366, \"oracle_budget_representation_error\": 0.0, \"oracle_singleton_representation_error\": 0.3402777777777778, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.3402777777777778, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 4}, \"private\": {\"hidden_mode_id\": \"5f6faeff46c2c5d52f01236904239b097270956b66ed0406aa6d00224e109545\", \"valid_mode_ids\": [\"2caea8b0aa8ffcb5e6506d7069569ec4847202804153eac8074aa4881de4362d\", \"5f6faeff46c2c5d52f01236904239b097270956b66ed0406aa6d00224e109545\", \"6ec0c5a64d210cf7815ac9fe6de14b2b8495f443d70d33488cb7e01c51e7364f\", \"a48e1c6fdd67fe32fe046c2c66cd6b86dfd0f27d674130653413fcba0721c619\"]}, \"state_id\": \"c23008c390d995f7e76aaa6a\", \"visible_experiments\": [{\"experiment_id\": 2, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z1_1\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 10, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 20, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 0}}, {\"experiment_id\": 35, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 0}}]}"} {"agent_name": "causal_micro_lab_agent_loop", "data_source": "causal_micro_lab_final_eval_v3", "env_spec_json": "{\"agent\": {}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 42, \"task\": {\"state\": {\"available_experiment_ids\": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 33, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 4, \"family_bucket\": \"cross_family\", \"maximum_separation\": 0.6944444444444444, \"mean_separation\": 0.486111111111111, \"minimum_separation\": 0.2777777777777778, \"normalized_mean_separation\": 0.486111111111111, \"oracle_budget_representation_error\": 0.0, \"oracle_singleton_representation_error\": 0.3402777777777778, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.3402777777777778, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 4}, \"private\": {\"hidden_mode_id\": \"c45fc665fd02b9bb03fca3e495c8ade396f17295ff0cf7e7d62105c0fe24caa7\", \"valid_mode_ids\": [\"0b00d06d3f56285e6bb734ff911ba7f5f500e0045b44ebc07e82d5f11d9762a7\", \"1d3a00e3c7363b03b6d6231a52b9b627b828c03e8eaa62eb0e69b9ab1c299714\", \"7b43f4970b7dcce46d1f0e59ecb11ae172c600a69fbb271c8ec57dcf25d7aa3b\", \"c45fc665fd02b9bb03fca3e495c8ade396f17295ff0cf7e7d62105c0fe24caa7\"]}, \"state_id\": \"e0b98153a694eba3f5109192\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 22, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z1_1\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 0}}, {\"experiment_id\": 25, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 30, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 1}}]}}}", "extra_info": {"index": 42, "max_global_steps": 0, "min_global_steps": 0}, "index": 42, "prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=0\n intervention: none\n observed: Z1=0, Z2=1, Y=0\n\nExperiment 2:\n inputs: X1=1, X2=0, X3=0\n intervention: set Z1=1\n observed: Z1=1, Z2=0, Y=0\n\nExperiment 3:\n inputs: X1=1, X2=0, X3=1\n intervention: none\n observed: Z1=0, Z2=1, Y=1\n\nExperiment 4:\n inputs: X1=1, X2=1, X3=0\n intervention: none\n observed: Z1=0, Z2=1, Y=1\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "raw_prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=0\n intervention: none\n observed: Z1=0, Z2=1, Y=0\n\nExperiment 2:\n inputs: X1=1, X2=0, X3=0\n intervention: set Z1=1\n observed: Z1=1, Z2=0, Y=0\n\nExperiment 3:\n inputs: X1=1, X2=0, X3=1\n intervention: none\n observed: Z1=0, Z2=1, Y=1\n\nExperiment 4:\n inputs: X1=1, X2=1, X3=0\n intervention: none\n observed: Z1=0, Z2=1, Y=1\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "reward_model": {"style": "rule"}, "state_json": "{\"available_experiment_ids\": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 33, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 4, \"family_bucket\": \"cross_family\", \"maximum_separation\": 0.6944444444444444, \"mean_separation\": 0.486111111111111, \"minimum_separation\": 0.2777777777777778, \"normalized_mean_separation\": 0.486111111111111, \"oracle_budget_representation_error\": 0.0, \"oracle_singleton_representation_error\": 0.3402777777777778, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.3402777777777778, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 4}, \"private\": {\"hidden_mode_id\": \"c45fc665fd02b9bb03fca3e495c8ade396f17295ff0cf7e7d62105c0fe24caa7\", \"valid_mode_ids\": [\"0b00d06d3f56285e6bb734ff911ba7f5f500e0045b44ebc07e82d5f11d9762a7\", \"1d3a00e3c7363b03b6d6231a52b9b627b828c03e8eaa62eb0e69b9ab1c299714\", \"7b43f4970b7dcce46d1f0e59ecb11ae172c600a69fbb271c8ec57dcf25d7aa3b\", \"c45fc665fd02b9bb03fca3e495c8ade396f17295ff0cf7e7d62105c0fe24caa7\"]}, \"state_id\": \"e0b98153a694eba3f5109192\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 22, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z1_1\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 0}}, {\"experiment_id\": 25, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 30, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 1}}]}"} {"agent_name": "causal_micro_lab_agent_loop", "data_source": "causal_micro_lab_final_eval_v3", "env_spec_json": "{\"agent\": {}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 43, \"task\": {\"state\": {\"available_experiment_ids\": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 16, 17, 18, 19, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 3, \"family_bucket\": \"mixed\", \"maximum_separation\": 0.5945945945945946, \"mean_separation\": 0.48648648648648657, \"minimum_separation\": 0.32432432432432434, \"normalized_mean_separation\": 0.48648648648648657, \"oracle_budget_representation_error\": 0.0, \"oracle_singleton_representation_error\": 0.36486486486486486, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.36486486486486486, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 4}, \"private\": {\"hidden_mode_id\": \"90a24e77e629ad2d8f442a7f17670db2f38760fd3540bbb759a74bb4b14fd520\", \"valid_mode_ids\": [\"1a491f42b44c83448c87ca28d9b1fff5a0343fbe2ce4da356fc061b916d2c94b\", \"2447e7c00f617056c303fe171f79f73e4d8893d4125c7b9fec98ff6322ba93ee\", \"8c9246bca6f0bb412a6a6151f3b2bf8e4fb74f70cd738d6d17499a9d72f86af7\", \"90a24e77e629ad2d8f442a7f17670db2f38760fd3540bbb759a74bb4b14fd520\"]}, \"state_id\": \"ba14282ef189381fcf6c34ba\", \"visible_experiments\": [{\"experiment_id\": 15, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 20, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 35, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 0}}]}}}", "extra_info": {"index": 43, "max_global_steps": 0, "min_global_steps": 0}, "index": 43, "prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=1, X3=1\n intervention: none\n observed: Z1=1, Z2=1, Y=0\n\nExperiment 2:\n inputs: X1=1, X2=0, X3=0\n intervention: none\n observed: Z1=0, Z2=1, Y=0\n\nExperiment 3:\n inputs: X1=1, X2=1, X3=1\n intervention: none\n observed: Z1=0, Z2=0, Y=0\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "raw_prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=1, X3=1\n intervention: none\n observed: Z1=1, Z2=1, Y=0\n\nExperiment 2:\n inputs: X1=1, X2=0, X3=0\n intervention: none\n observed: Z1=0, Z2=1, Y=0\n\nExperiment 3:\n inputs: X1=1, X2=1, X3=1\n intervention: none\n observed: Z1=0, Z2=0, Y=0\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "reward_model": {"style": "rule"}, "state_json": "{\"available_experiment_ids\": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 16, 17, 18, 19, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 3, \"family_bucket\": \"mixed\", \"maximum_separation\": 0.5945945945945946, \"mean_separation\": 0.48648648648648657, \"minimum_separation\": 0.32432432432432434, \"normalized_mean_separation\": 0.48648648648648657, \"oracle_budget_representation_error\": 0.0, \"oracle_singleton_representation_error\": 0.36486486486486486, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.36486486486486486, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 4}, \"private\": {\"hidden_mode_id\": \"90a24e77e629ad2d8f442a7f17670db2f38760fd3540bbb759a74bb4b14fd520\", \"valid_mode_ids\": [\"1a491f42b44c83448c87ca28d9b1fff5a0343fbe2ce4da356fc061b916d2c94b\", \"2447e7c00f617056c303fe171f79f73e4d8893d4125c7b9fec98ff6322ba93ee\", \"8c9246bca6f0bb412a6a6151f3b2bf8e4fb74f70cd738d6d17499a9d72f86af7\", \"90a24e77e629ad2d8f442a7f17670db2f38760fd3540bbb759a74bb4b14fd520\"]}, \"state_id\": \"ba14282ef189381fcf6c34ba\", \"visible_experiments\": [{\"experiment_id\": 15, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 20, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 35, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 0}}]}"} {"agent_name": "causal_micro_lab_agent_loop", "data_source": "causal_micro_lab_final_eval_v3", "env_spec_json": "{\"agent\": {}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 44, \"task\": {\"state\": {\"available_experiment_ids\": [0, 2, 3, 4, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 26, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 4, \"family_bucket\": \"within_family\", \"maximum_separation\": 0.6666666666666666, \"mean_separation\": 0.5092592592592592, \"minimum_separation\": 0.3333333333333333, \"normalized_mean_separation\": 0.5092592592592592, \"oracle_budget_representation_error\": 0.0, \"oracle_singleton_representation_error\": 0.375, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.375, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 4}, \"private\": {\"hidden_mode_id\": \"c9cfc51c17d0e672055a42e3b8c3ac7f8f3b83e18a5e47aad6a700f90e8b6579\", \"valid_mode_ids\": [\"2b81f1ce29dd6274ea3bfdcfcc9e13aeedf725ee492f601b85ddbf599be4bb50\", \"a40126323172b5213d29de657c193f8a1aa0b06c0f8c4d5e851e9abf9cc896f9\", \"a6432fbc3fd136cc540aa7b2a5e192a5e8e4de16b5cfc6646d66023ff4013446\", \"c9cfc51c17d0e672055a42e3b8c3ac7f8f3b83e18a5e47aad6a700f90e8b6579\"]}, \"state_id\": \"6d681851b32ab0efcd22a0d9\", \"visible_experiments\": [{\"experiment_id\": 1, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z1_0\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 5, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 0}}, {\"experiment_id\": 25, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 27, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 1}, \"intervention\": \"DO_Z1_1\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}]}}}", "extra_info": {"index": 44, "max_global_steps": 0, "min_global_steps": 0}, "index": 44, "prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=0\n intervention: set Z1=0\n observed: Z1=0, Z2=0, Y=0\n\nExperiment 2:\n inputs: X1=0, X2=0, X3=1\n intervention: none\n observed: Z1=1, Z2=0, Y=1\n\nExperiment 3:\n inputs: X1=1, X2=0, X3=1\n intervention: none\n observed: Z1=0, Z2=0, Y=1\n\nExperiment 4:\n inputs: X1=1, X2=0, X3=1\n intervention: set Z1=1\n observed: Z1=1, Z2=1, Y=0\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "raw_prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=0\n intervention: set Z1=0\n observed: Z1=0, Z2=0, Y=0\n\nExperiment 2:\n inputs: X1=0, X2=0, X3=1\n intervention: none\n observed: Z1=1, Z2=0, Y=1\n\nExperiment 3:\n inputs: X1=1, X2=0, X3=1\n intervention: none\n observed: Z1=0, Z2=0, Y=1\n\nExperiment 4:\n inputs: X1=1, X2=0, X3=1\n intervention: set Z1=1\n observed: Z1=1, Z2=1, Y=0\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "reward_model": {"style": "rule"}, "state_json": "{\"available_experiment_ids\": [0, 2, 3, 4, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 26, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 4, \"family_bucket\": \"within_family\", \"maximum_separation\": 0.6666666666666666, \"mean_separation\": 0.5092592592592592, \"minimum_separation\": 0.3333333333333333, \"normalized_mean_separation\": 0.5092592592592592, \"oracle_budget_representation_error\": 0.0, \"oracle_singleton_representation_error\": 0.375, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.375, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 4}, \"private\": {\"hidden_mode_id\": \"c9cfc51c17d0e672055a42e3b8c3ac7f8f3b83e18a5e47aad6a700f90e8b6579\", \"valid_mode_ids\": [\"2b81f1ce29dd6274ea3bfdcfcc9e13aeedf725ee492f601b85ddbf599be4bb50\", \"a40126323172b5213d29de657c193f8a1aa0b06c0f8c4d5e851e9abf9cc896f9\", \"a6432fbc3fd136cc540aa7b2a5e192a5e8e4de16b5cfc6646d66023ff4013446\", \"c9cfc51c17d0e672055a42e3b8c3ac7f8f3b83e18a5e47aad6a700f90e8b6579\"]}, \"state_id\": \"6d681851b32ab0efcd22a0d9\", \"visible_experiments\": [{\"experiment_id\": 1, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z1_0\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 5, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 0}}, {\"experiment_id\": 25, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 27, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 1}, \"intervention\": \"DO_Z1_1\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}]}"} {"agent_name": "causal_micro_lab_agent_loop", "data_source": "causal_micro_lab_final_eval_v3", "env_spec_json": "{\"agent\": {}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 45, \"task\": {\"state\": {\"available_experiment_ids\": [0, 1, 2, 3, 4, 6, 7, 8, 9, 10, 11, 12, 13, 15, 16, 17, 18, 19, 21, 22, 23, 24, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 4, \"family_bucket\": \"mixed\", \"maximum_separation\": 0.6944444444444444, \"mean_separation\": 0.5092592592592592, \"minimum_separation\": 0.2777777777777778, \"normalized_mean_separation\": 0.5092592592592592, \"oracle_budget_representation_error\": 0.0, \"oracle_singleton_representation_error\": 0.3819444444444444, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.3819444444444444, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 4}, \"private\": {\"hidden_mode_id\": \"b44c8ee1eb84c142dd56e13d8eea44d594ef43c794663136006013b76825b733\", \"valid_mode_ids\": [\"1b1b750c6440f3f420a60158b5060c8d70cbdcfc109ca71797bbc072822529e6\", \"53e5ffbd97b8e60135f1c20bfd26518a0e62c2ed6b1bf35b7477b4deed43b74a\", \"b44c8ee1eb84c142dd56e13d8eea44d594ef43c794663136006013b76825b733\", \"ef03fe49c7a47f29a6353cac6ccfaff8b0420b87f889066c59dfc25e5d575591\"]}, \"state_id\": \"801cf13de386c97aa3f43c4d\", \"visible_experiments\": [{\"experiment_id\": 5, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 14, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 0}, \"intervention\": \"DO_Z2_1\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 20, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 25, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 0}}]}}}", "extra_info": {"index": 45, "max_global_steps": 0, "min_global_steps": 0}, "index": 45, "prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=1\n intervention: none\n observed: Z1=0, Z2=1, Y=1\n\nExperiment 2:\n inputs: X1=0, X2=1, X3=0\n intervention: set Z2=1\n observed: Z1=0, Z2=1, Y=0\n\nExperiment 3:\n inputs: X1=1, X2=0, X3=0\n intervention: none\n observed: Z1=0, Z2=1, Y=1\n\nExperiment 4:\n inputs: X1=1, X2=0, X3=1\n intervention: none\n observed: Z1=1, Z2=0, Y=1\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "raw_prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=1\n intervention: none\n observed: Z1=0, Z2=1, Y=1\n\nExperiment 2:\n inputs: X1=0, X2=1, X3=0\n intervention: set Z2=1\n observed: Z1=0, Z2=1, Y=0\n\nExperiment 3:\n inputs: X1=1, X2=0, X3=0\n intervention: none\n observed: Z1=0, Z2=1, Y=1\n\nExperiment 4:\n inputs: X1=1, X2=0, X3=1\n intervention: none\n observed: Z1=1, Z2=0, Y=1\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "reward_model": {"style": "rule"}, "state_json": "{\"available_experiment_ids\": [0, 1, 2, 3, 4, 6, 7, 8, 9, 10, 11, 12, 13, 15, 16, 17, 18, 19, 21, 22, 23, 24, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 4, \"family_bucket\": \"mixed\", \"maximum_separation\": 0.6944444444444444, \"mean_separation\": 0.5092592592592592, \"minimum_separation\": 0.2777777777777778, \"normalized_mean_separation\": 0.5092592592592592, \"oracle_budget_representation_error\": 0.0, \"oracle_singleton_representation_error\": 0.3819444444444444, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.3819444444444444, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 4}, \"private\": {\"hidden_mode_id\": \"b44c8ee1eb84c142dd56e13d8eea44d594ef43c794663136006013b76825b733\", \"valid_mode_ids\": [\"1b1b750c6440f3f420a60158b5060c8d70cbdcfc109ca71797bbc072822529e6\", \"53e5ffbd97b8e60135f1c20bfd26518a0e62c2ed6b1bf35b7477b4deed43b74a\", \"b44c8ee1eb84c142dd56e13d8eea44d594ef43c794663136006013b76825b733\", \"ef03fe49c7a47f29a6353cac6ccfaff8b0420b87f889066c59dfc25e5d575591\"]}, \"state_id\": \"801cf13de386c97aa3f43c4d\", \"visible_experiments\": [{\"experiment_id\": 5, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 14, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 0}, \"intervention\": \"DO_Z2_1\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 20, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 25, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 0}}]}"} {"agent_name": "causal_micro_lab_agent_loop", "data_source": "causal_micro_lab_final_eval_v3", "env_spec_json": "{\"agent\": {}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 46, \"task\": {\"state\": {\"available_experiment_ids\": [0, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 3, \"family_bucket\": \"within_family\", \"maximum_separation\": 0.7027027027027027, \"mean_separation\": 0.5225225225225225, \"minimum_separation\": 0.32432432432432434, \"normalized_mean_separation\": 0.5225225225225225, \"oracle_budget_representation_error\": 0.0, \"oracle_singleton_representation_error\": 0.39189189189189194, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.39189189189189194, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 4}, \"private\": {\"hidden_mode_id\": \"84adfd50416733f0573aee86ee00ad359768326e64facc3e73ee119d07a07550\", \"valid_mode_ids\": [\"10ce8df7bade524c2ece40302a7d4bd3a19e18b6125d3c7fca63559facb935d5\", \"2e2be84cbc515dfb3a2b3208eec8d928105f007d4c345de6254f32c1bea90c65\", \"794cc948a46d3ffca49bc1cd034e0c9035192a7cef28143cdf0f6f20131170d4\", \"84adfd50416733f0573aee86ee00ad359768326e64facc3e73ee119d07a07550\"]}, \"state_id\": \"532df63f8026610389c801de\", \"visible_experiments\": [{\"experiment_id\": 1, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z1_0\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 20, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 35, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 0}}]}}}", "extra_info": {"index": 46, "max_global_steps": 0, "min_global_steps": 0}, "index": 46, "prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=0\n intervention: set Z1=0\n observed: Z1=0, Z2=1, Y=0\n\nExperiment 2:\n inputs: X1=1, X2=0, X3=0\n intervention: none\n observed: Z1=0, Z2=0, Y=1\n\nExperiment 3:\n inputs: X1=1, X2=1, X3=1\n intervention: none\n observed: Z1=0, Z2=0, Y=0\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "raw_prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=0\n intervention: set Z1=0\n observed: Z1=0, Z2=1, Y=0\n\nExperiment 2:\n inputs: X1=1, X2=0, X3=0\n intervention: none\n observed: Z1=0, Z2=0, Y=1\n\nExperiment 3:\n inputs: X1=1, X2=1, X3=1\n intervention: none\n observed: Z1=0, Z2=0, Y=0\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "reward_model": {"style": "rule"}, "state_json": "{\"available_experiment_ids\": [0, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 3, \"family_bucket\": \"within_family\", \"maximum_separation\": 0.7027027027027027, \"mean_separation\": 0.5225225225225225, \"minimum_separation\": 0.32432432432432434, \"normalized_mean_separation\": 0.5225225225225225, \"oracle_budget_representation_error\": 0.0, \"oracle_singleton_representation_error\": 0.39189189189189194, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.39189189189189194, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 4}, \"private\": {\"hidden_mode_id\": \"84adfd50416733f0573aee86ee00ad359768326e64facc3e73ee119d07a07550\", \"valid_mode_ids\": [\"10ce8df7bade524c2ece40302a7d4bd3a19e18b6125d3c7fca63559facb935d5\", \"2e2be84cbc515dfb3a2b3208eec8d928105f007d4c345de6254f32c1bea90c65\", \"794cc948a46d3ffca49bc1cd034e0c9035192a7cef28143cdf0f6f20131170d4\", \"84adfd50416733f0573aee86ee00ad359768326e64facc3e73ee119d07a07550\"]}, \"state_id\": \"532df63f8026610389c801de\", \"visible_experiments\": [{\"experiment_id\": 1, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z1_0\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 20, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 35, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 0}}]}"} {"agent_name": "causal_micro_lab_agent_loop", "data_source": "causal_micro_lab_final_eval_v3", "env_spec_json": "{\"agent\": {}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 47, \"task\": {\"state\": {\"available_experiment_ids\": [0, 2, 3, 4, 5, 6, 7, 8, 9, 10, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 26, 27, 28, 29, 31, 32, 33, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 4, \"family_bucket\": \"mixed\", \"maximum_separation\": 0.7222222222222222, \"mean_separation\": 0.5370370370370371, \"minimum_separation\": 0.3333333333333333, \"normalized_mean_separation\": 0.5370370370370371, \"oracle_budget_representation_error\": 0.0, \"oracle_singleton_representation_error\": 0.40277777777777773, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.40277777777777773, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 4}, \"private\": {\"hidden_mode_id\": \"036e2cd219b847d920f4b5c2db9af769ac5492ff188c1fa7f73c067ec83c3cfe\", \"valid_mode_ids\": [\"036e2cd219b847d920f4b5c2db9af769ac5492ff188c1fa7f73c067ec83c3cfe\", \"3b1ce5f91a752213b18aaaa3f79e42c1e1302685d693ec31cb64f42e5cbd2415\", \"95310675029890c1e48372c23f7e78b2ae9c577016aaa2d9ee1be731349ee56b\", \"e43e6c0d65b2d4cb77d32fa6b3e3fff74c8fa660a4ff85d383eb4ab4fa32d06e\"]}, \"state_id\": \"467ca3afc25bcc1b364d7cc0\", \"visible_experiments\": [{\"experiment_id\": 1, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z1_0\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 11, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 0}, \"intervention\": \"DO_Z1_0\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 25, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 30, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 0}}]}}}", "extra_info": {"index": 47, "max_global_steps": 0, "min_global_steps": 0}, "index": 47, "prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=0\n intervention: set Z1=0\n observed: Z1=0, Z2=0, Y=0\n\nExperiment 2:\n inputs: X1=0, X2=1, X3=0\n intervention: set Z1=0\n observed: Z1=0, Z2=1, Y=1\n\nExperiment 3:\n inputs: X1=1, X2=0, X3=1\n intervention: none\n observed: Z1=0, Z2=1, Y=0\n\nExperiment 4:\n inputs: X1=1, X2=1, X3=0\n intervention: none\n observed: Z1=0, Z2=0, Y=0\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "raw_prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=0\n intervention: set Z1=0\n observed: Z1=0, Z2=0, Y=0\n\nExperiment 2:\n inputs: X1=0, X2=1, X3=0\n intervention: set Z1=0\n observed: Z1=0, Z2=1, Y=1\n\nExperiment 3:\n inputs: X1=1, X2=0, X3=1\n intervention: none\n observed: Z1=0, Z2=1, Y=0\n\nExperiment 4:\n inputs: X1=1, X2=1, X3=0\n intervention: none\n observed: Z1=0, Z2=0, Y=0\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "reward_model": {"style": "rule"}, "state_json": "{\"available_experiment_ids\": [0, 2, 3, 4, 5, 6, 7, 8, 9, 10, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 26, 27, 28, 29, 31, 32, 33, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 4, \"family_bucket\": \"mixed\", \"maximum_separation\": 0.7222222222222222, \"mean_separation\": 0.5370370370370371, \"minimum_separation\": 0.3333333333333333, \"normalized_mean_separation\": 0.5370370370370371, \"oracle_budget_representation_error\": 0.0, \"oracle_singleton_representation_error\": 0.40277777777777773, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.40277777777777773, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 4}, \"private\": {\"hidden_mode_id\": \"036e2cd219b847d920f4b5c2db9af769ac5492ff188c1fa7f73c067ec83c3cfe\", \"valid_mode_ids\": [\"036e2cd219b847d920f4b5c2db9af769ac5492ff188c1fa7f73c067ec83c3cfe\", \"3b1ce5f91a752213b18aaaa3f79e42c1e1302685d693ec31cb64f42e5cbd2415\", \"95310675029890c1e48372c23f7e78b2ae9c577016aaa2d9ee1be731349ee56b\", \"e43e6c0d65b2d4cb77d32fa6b3e3fff74c8fa660a4ff85d383eb4ab4fa32d06e\"]}, \"state_id\": \"467ca3afc25bcc1b364d7cc0\", \"visible_experiments\": [{\"experiment_id\": 1, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z1_0\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 11, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 0}, \"intervention\": \"DO_Z1_0\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 25, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 30, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 0}}]}"} {"agent_name": "causal_micro_lab_agent_loop", "data_source": "causal_micro_lab_final_eval_v3", "env_spec_json": "{\"agent\": {}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 48, \"task\": {\"state\": {\"available_experiment_ids\": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 26, 27, 28, 29, 30, 31, 32, 34, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 4, \"family_bucket\": \"within_family\", \"maximum_separation\": 0.3055555555555556, \"mean_separation\": 0.19345238095238093, \"minimum_separation\": 0.1111111111111111, \"normalized_mean_separation\": 0.19345238095238093, \"oracle_budget_representation_error\": 0.05555555555555555, \"oracle_singleton_representation_error\": 0.13541666666666666, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.0798611111111111, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 8}, \"private\": {\"hidden_mode_id\": \"88a3a3fa2c9ae845cf31651eca5af2b28688770bcc5c82a694f7eae3b93cfa4a\", \"valid_mode_ids\": [\"092bcf1cdc6592295e055cc95d81d61c9c3db8384b2b5ed144414d9fb898873b\", \"0c166f1603ec1de061cd6e25a66f5a17dd326788af6ba8b0984f9d8520fb790c\", \"0cd09c374f02cff12c81589ebe1ef0c81644f020540ee039dc0ac239cef9f94c\", \"579677cb2f1759d9e22a844796e975f3508c8de19e63fe2653f2c9e359115fe3\", \"88a3a3fa2c9ae845cf31651eca5af2b28688770bcc5c82a694f7eae3b93cfa4a\", \"9da9fc0c86d4d451353f5552eb985a057265d859c4780bff2550510ba5f5a133\", \"ba5b240bb4c122fe6a1bba7663aff8ea6b90e3e00dcec55f0a620709e7e2da4b\", \"bc3606021c01a7122a60bb2f6bbc97d966d40be86021a77c0421283311b52c01\"]}, \"state_id\": \"25e78a955bdaaacfe1e8790d\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 25, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 33, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 0}, \"intervention\": \"DO_Z2_0\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 35, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}]}}}", "extra_info": {"index": 48, "max_global_steps": 0, "min_global_steps": 0}, "index": 48, "prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=0\n intervention: none\n observed: Z1=0, Z2=0, Y=1\n\nExperiment 2:\n inputs: X1=1, X2=0, X3=1\n intervention: none\n observed: Z1=0, Z2=0, Y=0\n\nExperiment 3:\n inputs: X1=1, X2=1, X3=0\n intervention: set Z2=0\n observed: Z1=0, Z2=0, Y=0\n\nExperiment 4:\n inputs: X1=1, X2=1, X3=1\n intervention: none\n observed: Z1=1, Z2=1, Y=0\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "raw_prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=0\n intervention: none\n observed: Z1=0, Z2=0, Y=1\n\nExperiment 2:\n inputs: X1=1, X2=0, X3=1\n intervention: none\n observed: Z1=0, Z2=0, Y=0\n\nExperiment 3:\n inputs: X1=1, X2=1, X3=0\n intervention: set Z2=0\n observed: Z1=0, Z2=0, Y=0\n\nExperiment 4:\n inputs: X1=1, X2=1, X3=1\n intervention: none\n observed: Z1=1, Z2=1, Y=0\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "reward_model": {"style": "rule"}, "state_json": "{\"available_experiment_ids\": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 26, 27, 28, 29, 30, 31, 32, 34, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 4, \"family_bucket\": \"within_family\", \"maximum_separation\": 0.3055555555555556, \"mean_separation\": 0.19345238095238093, \"minimum_separation\": 0.1111111111111111, \"normalized_mean_separation\": 0.19345238095238093, \"oracle_budget_representation_error\": 0.05555555555555555, \"oracle_singleton_representation_error\": 0.13541666666666666, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.0798611111111111, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 8}, \"private\": {\"hidden_mode_id\": \"88a3a3fa2c9ae845cf31651eca5af2b28688770bcc5c82a694f7eae3b93cfa4a\", \"valid_mode_ids\": [\"092bcf1cdc6592295e055cc95d81d61c9c3db8384b2b5ed144414d9fb898873b\", \"0c166f1603ec1de061cd6e25a66f5a17dd326788af6ba8b0984f9d8520fb790c\", \"0cd09c374f02cff12c81589ebe1ef0c81644f020540ee039dc0ac239cef9f94c\", \"579677cb2f1759d9e22a844796e975f3508c8de19e63fe2653f2c9e359115fe3\", \"88a3a3fa2c9ae845cf31651eca5af2b28688770bcc5c82a694f7eae3b93cfa4a\", \"9da9fc0c86d4d451353f5552eb985a057265d859c4780bff2550510ba5f5a133\", \"ba5b240bb4c122fe6a1bba7663aff8ea6b90e3e00dcec55f0a620709e7e2da4b\", \"bc3606021c01a7122a60bb2f6bbc97d966d40be86021a77c0421283311b52c01\"]}, \"state_id\": \"25e78a955bdaaacfe1e8790d\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 25, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 33, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 0}, \"intervention\": \"DO_Z2_0\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 35, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}]}"} {"agent_name": "causal_micro_lab_agent_loop", "data_source": "causal_micro_lab_final_eval_v3", "env_spec_json": "{\"agent\": {}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 49, \"task\": {\"state\": {\"available_experiment_ids\": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 29, 30, 31, 32, 33, 34, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 4, \"family_bucket\": \"within_family\", \"maximum_separation\": 0.3055555555555556, \"mean_separation\": 0.19345238095238096, \"minimum_separation\": 0.1111111111111111, \"normalized_mean_separation\": 0.19345238095238096, \"oracle_budget_representation_error\": 0.05555555555555555, \"oracle_singleton_representation_error\": 0.13541666666666666, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.0798611111111111, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 8}, \"private\": {\"hidden_mode_id\": \"6772bddba12298f49b444d15ad57e203fd611a3ac6bd94e15bcc5561b0a342a3\", \"valid_mode_ids\": [\"4bb8abb2bbcd0a078ea66628b5be979aa5c6a440462e7b1bb7f9d2518427e7de\", \"5deba5f15e80867dc7cb17de0c139aad32fff8c0194331bf5bc732f1d7b4feaf\", \"6772bddba12298f49b444d15ad57e203fd611a3ac6bd94e15bcc5561b0a342a3\", \"a9cc83b490da116e3150acafcff292396f2a3f42b9546f8fbec2cf2730ff5221\", \"cd4ae5af8015a05e44d5e0143985990a9f040f625b994ff808d12e8ebc39998a\", \"daac5e5d1e0ea84731fd67dda33b570330f9b6daad25a6f2ef26a16a197d22ff\", \"ddbc87d58c377ed34071ab0b4aa39f16d71be31c3a1516d35f2990e608126979\", \"f225194ae50c9b62d78456156ce5b358b55473d7cacc361509c551d6e0285a95\"]}, \"state_id\": \"c837a7aa87232143502caecd\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 15, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 28, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 1}, \"intervention\": \"DO_Z2_0\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 35, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}]}}}", "extra_info": {"index": 49, "max_global_steps": 0, "min_global_steps": 0}, "index": 49, "prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=0\n intervention: none\n observed: Z1=0, Z2=0, Y=1\n\nExperiment 2:\n inputs: X1=0, X2=1, X3=1\n intervention: none\n observed: Z1=0, Z2=0, Y=0\n\nExperiment 3:\n inputs: X1=1, X2=0, X3=1\n intervention: set Z2=0\n observed: Z1=0, Z2=0, Y=1\n\nExperiment 4:\n inputs: X1=1, X2=1, X3=1\n intervention: none\n observed: Z1=1, Z2=1, Y=0\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "raw_prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=0\n intervention: none\n observed: Z1=0, Z2=0, Y=1\n\nExperiment 2:\n inputs: X1=0, X2=1, X3=1\n intervention: none\n observed: Z1=0, Z2=0, Y=0\n\nExperiment 3:\n inputs: X1=1, X2=0, X3=1\n intervention: set Z2=0\n observed: Z1=0, Z2=0, Y=1\n\nExperiment 4:\n inputs: X1=1, X2=1, X3=1\n intervention: none\n observed: Z1=1, Z2=1, Y=0\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "reward_model": {"style": "rule"}, "state_json": "{\"available_experiment_ids\": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 29, 30, 31, 32, 33, 34, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 4, \"family_bucket\": \"within_family\", \"maximum_separation\": 0.3055555555555556, \"mean_separation\": 0.19345238095238096, \"minimum_separation\": 0.1111111111111111, \"normalized_mean_separation\": 0.19345238095238096, \"oracle_budget_representation_error\": 0.05555555555555555, \"oracle_singleton_representation_error\": 0.13541666666666666, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.0798611111111111, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 8}, \"private\": {\"hidden_mode_id\": \"6772bddba12298f49b444d15ad57e203fd611a3ac6bd94e15bcc5561b0a342a3\", \"valid_mode_ids\": [\"4bb8abb2bbcd0a078ea66628b5be979aa5c6a440462e7b1bb7f9d2518427e7de\", \"5deba5f15e80867dc7cb17de0c139aad32fff8c0194331bf5bc732f1d7b4feaf\", \"6772bddba12298f49b444d15ad57e203fd611a3ac6bd94e15bcc5561b0a342a3\", \"a9cc83b490da116e3150acafcff292396f2a3f42b9546f8fbec2cf2730ff5221\", \"cd4ae5af8015a05e44d5e0143985990a9f040f625b994ff808d12e8ebc39998a\", \"daac5e5d1e0ea84731fd67dda33b570330f9b6daad25a6f2ef26a16a197d22ff\", \"ddbc87d58c377ed34071ab0b4aa39f16d71be31c3a1516d35f2990e608126979\", \"f225194ae50c9b62d78456156ce5b358b55473d7cacc361509c551d6e0285a95\"]}, \"state_id\": \"c837a7aa87232143502caecd\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 15, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 28, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 1}, \"intervention\": \"DO_Z2_0\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 35, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}]}"} {"agent_name": "causal_micro_lab_agent_loop", "data_source": "causal_micro_lab_final_eval_v3", "env_spec_json": "{\"agent\": {}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 50, \"task\": {\"state\": {\"available_experiment_ids\": [0, 1, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 31, 32, 33, 34, 35, 36, 37, 39], \"metadata\": {\"evidence_size\": 4, \"family_bucket\": \"mixed\", \"maximum_separation\": 0.2777777777777778, \"mean_separation\": 0.20436507936507933, \"minimum_separation\": 0.1111111111111111, \"normalized_mean_separation\": 0.20436507936507933, \"oracle_budget_representation_error\": 0.059027777777777776, \"oracle_singleton_representation_error\": 0.16666666666666669, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.10763888888888891, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 8}, \"private\": {\"hidden_mode_id\": \"134516fd1c617667691395c5d3e7a2a257f313ba69a5f4e16e17cfa8d3eb04f4\", \"valid_mode_ids\": [\"134516fd1c617667691395c5d3e7a2a257f313ba69a5f4e16e17cfa8d3eb04f4\", \"1635d71433a5d18c6f2fea9126902a00bc42658081adbe5f94ff608f57861842\", \"22778409da13c52ee52044ac552aa828f6d220e678c05f654867d75b1669ea6e\", \"5f0f886114a1515348e59fb4ec45793faee636d76671e418e412774656f52c61\", \"7de0e3c91980af93755919e879a9e1c9f4b001a9b5019be540fc16946115739c\", \"89e0c0c860c3c9606a7b0e0bcc01988b089269ce2d4c0f7c38d5c77d1496fb69\", \"9be4c6d81d80c0f7d6eb046914491e8eb4a7675fa83da04b6bae9b9e545daed6\", \"e562d53dbe1400172acd35d6a64fe00a8f77f6a810ed15d19295fcafe5a76db8\"]}, \"state_id\": \"ac8fa9cd36b85c6272531921\", \"visible_experiments\": [{\"experiment_id\": 2, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z1_1\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 0}}, {\"experiment_id\": 15, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 30, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 0}}, {\"experiment_id\": 38, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 1}, \"intervention\": \"DO_Z2_0\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 0}}]}}}", "extra_info": {"index": 50, "max_global_steps": 0, "min_global_steps": 0}, "index": 50, "prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=0\n intervention: set Z1=1\n observed: Z1=1, Z2=0, Y=0\n\nExperiment 2:\n inputs: X1=0, X2=1, X3=1\n intervention: none\n observed: Z1=0, Z2=0, Y=0\n\nExperiment 3:\n inputs: X1=1, X2=1, X3=0\n intervention: none\n observed: Z1=1, Z2=0, Y=0\n\nExperiment 4:\n inputs: X1=1, X2=1, X3=1\n intervention: set Z2=0\n observed: Z1=1, Z2=0, Y=1\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "raw_prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=0\n intervention: set Z1=1\n observed: Z1=1, Z2=0, Y=0\n\nExperiment 2:\n inputs: X1=0, X2=1, X3=1\n intervention: none\n observed: Z1=0, Z2=0, Y=0\n\nExperiment 3:\n inputs: X1=1, X2=1, X3=0\n intervention: none\n observed: Z1=1, Z2=0, Y=0\n\nExperiment 4:\n inputs: X1=1, X2=1, X3=1\n intervention: set Z2=0\n observed: Z1=1, Z2=0, Y=1\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "reward_model": {"style": "rule"}, "state_json": "{\"available_experiment_ids\": [0, 1, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 31, 32, 33, 34, 35, 36, 37, 39], \"metadata\": {\"evidence_size\": 4, \"family_bucket\": \"mixed\", \"maximum_separation\": 0.2777777777777778, \"mean_separation\": 0.20436507936507933, \"minimum_separation\": 0.1111111111111111, \"normalized_mean_separation\": 0.20436507936507933, \"oracle_budget_representation_error\": 0.059027777777777776, \"oracle_singleton_representation_error\": 0.16666666666666669, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.10763888888888891, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 8}, \"private\": {\"hidden_mode_id\": \"134516fd1c617667691395c5d3e7a2a257f313ba69a5f4e16e17cfa8d3eb04f4\", \"valid_mode_ids\": [\"134516fd1c617667691395c5d3e7a2a257f313ba69a5f4e16e17cfa8d3eb04f4\", \"1635d71433a5d18c6f2fea9126902a00bc42658081adbe5f94ff608f57861842\", \"22778409da13c52ee52044ac552aa828f6d220e678c05f654867d75b1669ea6e\", \"5f0f886114a1515348e59fb4ec45793faee636d76671e418e412774656f52c61\", \"7de0e3c91980af93755919e879a9e1c9f4b001a9b5019be540fc16946115739c\", \"89e0c0c860c3c9606a7b0e0bcc01988b089269ce2d4c0f7c38d5c77d1496fb69\", \"9be4c6d81d80c0f7d6eb046914491e8eb4a7675fa83da04b6bae9b9e545daed6\", \"e562d53dbe1400172acd35d6a64fe00a8f77f6a810ed15d19295fcafe5a76db8\"]}, \"state_id\": \"ac8fa9cd36b85c6272531921\", \"visible_experiments\": [{\"experiment_id\": 2, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z1_1\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 0}}, {\"experiment_id\": 15, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 30, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 0}}, {\"experiment_id\": 38, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 1}, \"intervention\": \"DO_Z2_0\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 0}}]}"} {"agent_name": "causal_micro_lab_agent_loop", "data_source": "causal_micro_lab_final_eval_v3", "env_spec_json": "{\"agent\": {}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 51, \"task\": {\"state\": {\"available_experiment_ids\": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 19, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 3, \"family_bucket\": \"cross_family\", \"maximum_separation\": 0.21621621621621623, \"mean_separation\": 0.18532818532818535, \"minimum_separation\": 0.10810810810810811, \"normalized_mean_separation\": 0.18532818532818535, \"oracle_budget_representation_error\": 0.05405405405405406, \"oracle_singleton_representation_error\": 0.16216216216216217, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.10810810810810811, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 8}, \"private\": {\"hidden_mode_id\": \"783a305aa61c72e1eb43cde9c8fc6ce94fd2688918087a0ad85562b751f7b0f4\", \"valid_mode_ids\": [\"0dfee86f2fc725e68690aa58cded5299a7bb881d29633a375d88054643849f52\", \"127b411608c21d5f69f8e2b43194560db4317e8eb341a46e51e3256aba81259e\", \"168760e2c2555a4968f9218c9232501604c6482723071303ddae9f33a2cd04df\", \"783a305aa61c72e1eb43cde9c8fc6ce94fd2688918087a0ad85562b751f7b0f4\", \"7b03c065aace3311b27b6d7eb13863fd89e046e9ba0b4ef70cbb92abc93d764b\", \"dee6063d59e7a1ac45e14636125204846c249976de8aeb9e18049dfba6e91598\", \"e2f529a77c49ac4e82deaad0b35d61578aaa0018e2d847a241f76cd40ddc665f\", \"ee5eb1d13a21c2aa89ffe986275bf0c00d850b374e37af407687ea7fd3719ae6\"]}, \"state_id\": \"a84d63b75a3a0cbcd5422221\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 18, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 1}, \"intervention\": \"DO_Z2_0\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 20, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 0}}]}}}", "extra_info": {"index": 51, "max_global_steps": 0, "min_global_steps": 0}, "index": 51, "prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=0\n intervention: none\n observed: Z1=0, Z2=1, Y=0\n\nExperiment 2:\n inputs: X1=0, X2=1, X3=1\n intervention: set Z2=0\n observed: Z1=0, Z2=0, Y=0\n\nExperiment 3:\n inputs: X1=1, X2=0, X3=0\n intervention: none\n observed: Z1=1, Z2=0, Y=1\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "raw_prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=0\n intervention: none\n observed: Z1=0, Z2=1, Y=0\n\nExperiment 2:\n inputs: X1=0, X2=1, X3=1\n intervention: set Z2=0\n observed: Z1=0, Z2=0, Y=0\n\nExperiment 3:\n inputs: X1=1, X2=0, X3=0\n intervention: none\n observed: Z1=1, Z2=0, Y=1\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "reward_model": {"style": "rule"}, "state_json": "{\"available_experiment_ids\": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 19, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 3, \"family_bucket\": \"cross_family\", \"maximum_separation\": 0.21621621621621623, \"mean_separation\": 0.18532818532818535, \"minimum_separation\": 0.10810810810810811, \"normalized_mean_separation\": 0.18532818532818535, \"oracle_budget_representation_error\": 0.05405405405405406, \"oracle_singleton_representation_error\": 0.16216216216216217, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.10810810810810811, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 8}, \"private\": {\"hidden_mode_id\": \"783a305aa61c72e1eb43cde9c8fc6ce94fd2688918087a0ad85562b751f7b0f4\", \"valid_mode_ids\": [\"0dfee86f2fc725e68690aa58cded5299a7bb881d29633a375d88054643849f52\", \"127b411608c21d5f69f8e2b43194560db4317e8eb341a46e51e3256aba81259e\", \"168760e2c2555a4968f9218c9232501604c6482723071303ddae9f33a2cd04df\", \"783a305aa61c72e1eb43cde9c8fc6ce94fd2688918087a0ad85562b751f7b0f4\", \"7b03c065aace3311b27b6d7eb13863fd89e046e9ba0b4ef70cbb92abc93d764b\", \"dee6063d59e7a1ac45e14636125204846c249976de8aeb9e18049dfba6e91598\", \"e2f529a77c49ac4e82deaad0b35d61578aaa0018e2d847a241f76cd40ddc665f\", \"ee5eb1d13a21c2aa89ffe986275bf0c00d850b374e37af407687ea7fd3719ae6\"]}, \"state_id\": \"a84d63b75a3a0cbcd5422221\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 18, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 1}, \"intervention\": \"DO_Z2_0\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 20, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 0}}]}"} {"agent_name": "causal_micro_lab_agent_loop", "data_source": "causal_micro_lab_final_eval_v3", "env_spec_json": "{\"agent\": {}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 52, \"task\": {\"state\": {\"available_experiment_ids\": [1, 2, 3, 4, 5, 6, 8, 9, 11, 12, 13, 14, 15, 16, 17, 18, 19, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 4, \"family_bucket\": \"mixed\", \"maximum_separation\": 0.3333333333333333, \"mean_separation\": 0.2341269841269841, \"minimum_separation\": 0.1111111111111111, \"normalized_mean_separation\": 0.2341269841269841, \"oracle_budget_representation_error\": 0.06944444444444445, \"oracle_singleton_representation_error\": 0.18055555555555558, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.11111111111111113, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 8}, \"private\": {\"hidden_mode_id\": \"da5a65053b1d1fcb1efdbea444ae0ddad17cb55683aad2d27e9a8fbedf0fa4cc\", \"valid_mode_ids\": [\"051df43319ef5de7b577cdefa056be91d6aac148e52d14c14e66fe1e4629ee4f\", \"0930614667180e82e4472f707fbad762c2adb586cb0a2cce68306240bd79b870\", \"1109391bc6a55cd5686fed2420e8445ec2a9ea179edc2b7f95a1045670b733af\", \"563fe8d7961321cd9612a942f03b17509b0ae76f846a28fc7a5596a651bb2db8\", \"cf266215f427de123b7037f4d51a9565012eaca915289608f7251de24935cbaa\", \"d59ee5d925d102c3f0b03bb7e489a84ccb239617c8058006fe25dee3a0992988\", \"da5a65053b1d1fcb1efdbea444ae0ddad17cb55683aad2d27e9a8fbedf0fa4cc\", \"ec46d079545dfac775e3d19ee62644f1016315a01a994b679f334f9af139d113\"]}, \"state_id\": \"9893605a9e3231227bf46b5d\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 7, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 1}, \"intervention\": \"DO_Z1_1\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 0}}, {\"experiment_id\": 10, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 0}}, {\"experiment_id\": 20, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}]}}}", "extra_info": {"index": 52, "max_global_steps": 0, "min_global_steps": 0}, "index": 52, "prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=0\n intervention: none\n observed: Z1=0, Z2=0, Y=0\n\nExperiment 2:\n inputs: X1=0, X2=0, X3=1\n intervention: set Z1=1\n observed: Z1=1, Z2=0, Y=0\n\nExperiment 3:\n inputs: X1=0, X2=1, X3=0\n intervention: none\n observed: Z1=1, Z2=0, Y=1\n\nExperiment 4:\n inputs: X1=1, X2=0, X3=0\n intervention: none\n observed: Z1=1, Z2=1, Y=0\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "raw_prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=0\n intervention: none\n observed: Z1=0, Z2=0, Y=0\n\nExperiment 2:\n inputs: X1=0, X2=0, X3=1\n intervention: set Z1=1\n observed: Z1=1, Z2=0, Y=0\n\nExperiment 3:\n inputs: X1=0, X2=1, X3=0\n intervention: none\n observed: Z1=1, Z2=0, Y=1\n\nExperiment 4:\n inputs: X1=1, X2=0, X3=0\n intervention: none\n observed: Z1=1, Z2=1, Y=0\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "reward_model": {"style": "rule"}, "state_json": "{\"available_experiment_ids\": [1, 2, 3, 4, 5, 6, 8, 9, 11, 12, 13, 14, 15, 16, 17, 18, 19, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 4, \"family_bucket\": \"mixed\", \"maximum_separation\": 0.3333333333333333, \"mean_separation\": 0.2341269841269841, \"minimum_separation\": 0.1111111111111111, \"normalized_mean_separation\": 0.2341269841269841, \"oracle_budget_representation_error\": 0.06944444444444445, \"oracle_singleton_representation_error\": 0.18055555555555558, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.11111111111111113, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 8}, \"private\": {\"hidden_mode_id\": \"da5a65053b1d1fcb1efdbea444ae0ddad17cb55683aad2d27e9a8fbedf0fa4cc\", \"valid_mode_ids\": [\"051df43319ef5de7b577cdefa056be91d6aac148e52d14c14e66fe1e4629ee4f\", \"0930614667180e82e4472f707fbad762c2adb586cb0a2cce68306240bd79b870\", \"1109391bc6a55cd5686fed2420e8445ec2a9ea179edc2b7f95a1045670b733af\", \"563fe8d7961321cd9612a942f03b17509b0ae76f846a28fc7a5596a651bb2db8\", \"cf266215f427de123b7037f4d51a9565012eaca915289608f7251de24935cbaa\", \"d59ee5d925d102c3f0b03bb7e489a84ccb239617c8058006fe25dee3a0992988\", \"da5a65053b1d1fcb1efdbea444ae0ddad17cb55683aad2d27e9a8fbedf0fa4cc\", \"ec46d079545dfac775e3d19ee62644f1016315a01a994b679f334f9af139d113\"]}, \"state_id\": \"9893605a9e3231227bf46b5d\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 7, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 1}, \"intervention\": \"DO_Z1_1\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 0}}, {\"experiment_id\": 10, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 0}}, {\"experiment_id\": 20, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}]}"} {"agent_name": "causal_micro_lab_agent_loop", "data_source": "causal_micro_lab_final_eval_v3", "env_spec_json": "{\"agent\": {}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 53, \"task\": {\"state\": {\"available_experiment_ids\": [1, 2, 3, 5, 6, 7, 8, 9, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 4, \"family_bucket\": \"within_family\", \"maximum_separation\": 0.2222222222222222, \"mean_separation\": 0.1904761904761905, \"minimum_separation\": 0.1111111111111111, \"normalized_mean_separation\": 0.1904761904761905, \"oracle_budget_representation_error\": 0.05555555555555555, \"oracle_singleton_representation_error\": 0.16666666666666669, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.11111111111111113, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 8}, \"private\": {\"hidden_mode_id\": \"79096ab1b36aa1c51a1dfa81ae1f3364f6f35f01dbbce4b48b6187cd65b0daa0\", \"valid_mode_ids\": [\"552533dd45bc46588f7ff816f57fbaa33ae8d4126141b5506fda281168aca562\", \"6eb68e5f8d006248571071215637ca23c1e39b00862e4b52dad6ce4dc067327c\", \"79096ab1b36aa1c51a1dfa81ae1f3364f6f35f01dbbce4b48b6187cd65b0daa0\", \"98d536b1dea987fd9527579f3c20180432843d4156bfe6ca1e64e05a3892a679\", \"9f61efce69e0fa50c563d66913b84285967f930f7b225b8b02610f6a8a6fd78e\", \"b260d061e6bd1e759ec5aeb4b554314dcca88548ab63cb26db12de978ed908f0\", \"e0f2fc5ca21130144c042bdef9f0430fb2ddcdbf2b81dc94a688114b9fcb9f71\", \"fd766301b9a7845a6241ff38405c75d9d2b58bc55d13e2d78dbde8a6d8990b6e\"]}, \"state_id\": \"9d011c0724f2c5358a10ea97\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 4, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z2_1\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 10, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 25, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 0}}]}}}", "extra_info": {"index": 53, "max_global_steps": 0, "min_global_steps": 0}, "index": 53, "prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=0\n intervention: none\n observed: Z1=0, Z2=0, Y=1\n\nExperiment 2:\n inputs: X1=0, X2=0, X3=0\n intervention: set Z2=1\n observed: Z1=0, Z2=1, Y=1\n\nExperiment 3:\n inputs: X1=0, X2=1, X3=0\n intervention: none\n observed: Z1=1, Z2=1, Y=0\n\nExperiment 4:\n inputs: X1=1, X2=0, X3=1\n intervention: none\n observed: Z1=0, Z2=0, Y=1\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "raw_prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=0\n intervention: none\n observed: Z1=0, Z2=0, Y=1\n\nExperiment 2:\n inputs: X1=0, X2=0, X3=0\n intervention: set Z2=1\n observed: Z1=0, Z2=1, Y=1\n\nExperiment 3:\n inputs: X1=0, X2=1, X3=0\n intervention: none\n observed: Z1=1, Z2=1, Y=0\n\nExperiment 4:\n inputs: X1=1, X2=0, X3=1\n intervention: none\n observed: Z1=0, Z2=0, Y=1\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "reward_model": {"style": "rule"}, "state_json": "{\"available_experiment_ids\": [1, 2, 3, 5, 6, 7, 8, 9, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 4, \"family_bucket\": \"within_family\", \"maximum_separation\": 0.2222222222222222, \"mean_separation\": 0.1904761904761905, \"minimum_separation\": 0.1111111111111111, \"normalized_mean_separation\": 0.1904761904761905, \"oracle_budget_representation_error\": 0.05555555555555555, \"oracle_singleton_representation_error\": 0.16666666666666669, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.11111111111111113, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 8}, \"private\": {\"hidden_mode_id\": \"79096ab1b36aa1c51a1dfa81ae1f3364f6f35f01dbbce4b48b6187cd65b0daa0\", \"valid_mode_ids\": [\"552533dd45bc46588f7ff816f57fbaa33ae8d4126141b5506fda281168aca562\", \"6eb68e5f8d006248571071215637ca23c1e39b00862e4b52dad6ce4dc067327c\", \"79096ab1b36aa1c51a1dfa81ae1f3364f6f35f01dbbce4b48b6187cd65b0daa0\", \"98d536b1dea987fd9527579f3c20180432843d4156bfe6ca1e64e05a3892a679\", \"9f61efce69e0fa50c563d66913b84285967f930f7b225b8b02610f6a8a6fd78e\", \"b260d061e6bd1e759ec5aeb4b554314dcca88548ab63cb26db12de978ed908f0\", \"e0f2fc5ca21130144c042bdef9f0430fb2ddcdbf2b81dc94a688114b9fcb9f71\", \"fd766301b9a7845a6241ff38405c75d9d2b58bc55d13e2d78dbde8a6d8990b6e\"]}, \"state_id\": \"9d011c0724f2c5358a10ea97\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 4, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z2_1\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 10, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 25, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 0}}]}"} {"agent_name": "causal_micro_lab_agent_loop", "data_source": "causal_micro_lab_final_eval_v3", "env_spec_json": "{\"agent\": {}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 54, \"task\": {\"state\": {\"available_experiment_ids\": [0, 1, 3, 4, 5, 6, 7, 8, 9, 11, 12, 13, 14, 15, 16, 17, 18, 19, 21, 22, 23, 24, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 4, \"family_bucket\": \"within_family\", \"maximum_separation\": 0.3055555555555556, \"mean_separation\": 0.2093253968253968, \"minimum_separation\": 0.1111111111111111, \"normalized_mean_separation\": 0.2093253968253968, \"oracle_budget_representation_error\": 0.059027777777777776, \"oracle_singleton_representation_error\": 0.1736111111111111, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.11458333333333333, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 8}, \"private\": {\"hidden_mode_id\": \"5ad5edd76ef3489f848d395b9d1bbf39dce9ecb747dbf02d375c8fecc1c6904d\", \"valid_mode_ids\": [\"2e50f28466ed7db9f46dfa6a21663c5307a44b911dfb0c2fc73584890c4825ef\", \"570d5cb4a2cd40e0aa87b751f9943303f65c7108f16c94348d8f0ee14c9287d3\", \"58bfe7857b8d96d2f998d2f4f7bf4a24062022f6537a360c1ac0f4a3ce6ab240\", \"5ad5edd76ef3489f848d395b9d1bbf39dce9ecb747dbf02d375c8fecc1c6904d\", \"5c5ce2f1c011077accb5a876d8329cc2575353836e719a0e39376b6a58e0cd27\", \"646de55fc91e7eaebd2534dbe4d56e713adb20856486c142d7ad7055b736050e\", \"880ed1f2f114af2bab4a1ad954c1f40f5f9d1621839c0af1fbc9ae4baf0033df\", \"c04b0ca7adcd06b5fc6b6f089f635d9691c2c08ebe11156f082362e5b941ac90\"]}, \"state_id\": \"52db1b916fdf7558e3fa3c15\", \"visible_experiments\": [{\"experiment_id\": 2, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z1_1\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 10, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 20, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 25, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}]}}}", "extra_info": {"index": 54, "max_global_steps": 0, "min_global_steps": 0}, "index": 54, "prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=0\n intervention: set Z1=1\n observed: Z1=1, Z2=1, Y=0\n\nExperiment 2:\n inputs: X1=0, X2=1, X3=0\n intervention: none\n observed: Z1=1, Z2=1, Y=0\n\nExperiment 3:\n inputs: X1=1, X2=0, X3=0\n intervention: none\n observed: Z1=1, Z2=1, Y=1\n\nExperiment 4:\n inputs: X1=1, X2=0, X3=1\n intervention: none\n observed: Z1=1, Z2=1, Y=0\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "raw_prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=0\n intervention: set Z1=1\n observed: Z1=1, Z2=1, Y=0\n\nExperiment 2:\n inputs: X1=0, X2=1, X3=0\n intervention: none\n observed: Z1=1, Z2=1, Y=0\n\nExperiment 3:\n inputs: X1=1, X2=0, X3=0\n intervention: none\n observed: Z1=1, Z2=1, Y=1\n\nExperiment 4:\n inputs: X1=1, X2=0, X3=1\n intervention: none\n observed: Z1=1, Z2=1, Y=0\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "reward_model": {"style": "rule"}, "state_json": "{\"available_experiment_ids\": [0, 1, 3, 4, 5, 6, 7, 8, 9, 11, 12, 13, 14, 15, 16, 17, 18, 19, 21, 22, 23, 24, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 4, \"family_bucket\": \"within_family\", \"maximum_separation\": 0.3055555555555556, \"mean_separation\": 0.2093253968253968, \"minimum_separation\": 0.1111111111111111, \"normalized_mean_separation\": 0.2093253968253968, \"oracle_budget_representation_error\": 0.059027777777777776, \"oracle_singleton_representation_error\": 0.1736111111111111, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.11458333333333333, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 8}, \"private\": {\"hidden_mode_id\": \"5ad5edd76ef3489f848d395b9d1bbf39dce9ecb747dbf02d375c8fecc1c6904d\", \"valid_mode_ids\": [\"2e50f28466ed7db9f46dfa6a21663c5307a44b911dfb0c2fc73584890c4825ef\", \"570d5cb4a2cd40e0aa87b751f9943303f65c7108f16c94348d8f0ee14c9287d3\", \"58bfe7857b8d96d2f998d2f4f7bf4a24062022f6537a360c1ac0f4a3ce6ab240\", \"5ad5edd76ef3489f848d395b9d1bbf39dce9ecb747dbf02d375c8fecc1c6904d\", \"5c5ce2f1c011077accb5a876d8329cc2575353836e719a0e39376b6a58e0cd27\", \"646de55fc91e7eaebd2534dbe4d56e713adb20856486c142d7ad7055b736050e\", \"880ed1f2f114af2bab4a1ad954c1f40f5f9d1621839c0af1fbc9ae4baf0033df\", \"c04b0ca7adcd06b5fc6b6f089f635d9691c2c08ebe11156f082362e5b941ac90\"]}, \"state_id\": \"52db1b916fdf7558e3fa3c15\", \"visible_experiments\": [{\"experiment_id\": 2, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z1_1\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 10, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 20, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 25, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}]}"} {"agent_name": "causal_micro_lab_agent_loop", "data_source": "causal_micro_lab_final_eval_v3", "env_spec_json": "{\"agent\": {}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 55, \"task\": {\"state\": {\"available_experiment_ids\": [1, 2, 3, 4, 6, 7, 8, 9, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 4, \"family_bucket\": \"within_family\", \"maximum_separation\": 0.3333333333333333, \"mean_separation\": 0.23412698412698413, \"minimum_separation\": 0.1111111111111111, \"normalized_mean_separation\": 0.23412698412698413, \"oracle_budget_representation_error\": 0.0625, \"oracle_singleton_representation_error\": 0.18055555555555558, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.11805555555555558, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 8}, \"private\": {\"hidden_mode_id\": \"c0efbc92b616f68e4ac7817573cca0d5cec1dcd80d541e5bca0b51cb9ca2d40c\", \"valid_mode_ids\": [\"4bd2a163d85e2a23047db081b9c5f021e9e43dda79cc5f41698926daea2ebd8e\", \"5815de9acda68144f4abd0c6e79136bf5ad9489d08b105f6d6ea9c6bd305f976\", \"5acfdb762751fa6669973d469b023c9ecb8a4ae34ca43dc9e8c467e35c71562d\", \"68afdc7077d7743a4be1ae6d6b8d09603ace061eeb29e661d3c308560719faf7\", \"80d83326f563ae7bfc7b9db37efd357238be55d09977edeee425e5c56b8e3b63\", \"9a9bc06b3414b5b4407f29108d2ac64643431eae3c2125eb560b2116786ec5ca\", \"c0efbc92b616f68e4ac7817573cca0d5cec1dcd80d541e5bca0b51cb9ca2d40c\", \"d030a31ca0c87e098ac4eaf224abe9c99edd881c112fe590f28f75ac57c2edea\"]}, \"state_id\": \"501a4df10f4d92b7f32f5c47\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 5, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 10, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 23, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z2_0\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 0}}]}}}", "extra_info": {"index": 55, "max_global_steps": 0, "min_global_steps": 0}, "index": 55, "prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=0\n intervention: none\n observed: Z1=0, Z2=0, Y=0\n\nExperiment 2:\n inputs: X1=0, X2=0, X3=1\n intervention: none\n observed: Z1=0, Z2=0, Y=0\n\nExperiment 3:\n inputs: X1=0, X2=1, X3=0\n intervention: none\n observed: Z1=1, Z2=1, Y=0\n\nExperiment 4:\n inputs: X1=1, X2=0, X3=0\n intervention: set Z2=0\n observed: Z1=0, Z2=0, Y=1\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "raw_prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=0\n intervention: none\n observed: Z1=0, Z2=0, Y=0\n\nExperiment 2:\n inputs: X1=0, X2=0, X3=1\n intervention: none\n observed: Z1=0, Z2=0, Y=0\n\nExperiment 3:\n inputs: X1=0, X2=1, X3=0\n intervention: none\n observed: Z1=1, Z2=1, Y=0\n\nExperiment 4:\n inputs: X1=1, X2=0, X3=0\n intervention: set Z2=0\n observed: Z1=0, Z2=0, Y=1\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "reward_model": {"style": "rule"}, "state_json": "{\"available_experiment_ids\": [1, 2, 3, 4, 6, 7, 8, 9, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 4, \"family_bucket\": \"within_family\", \"maximum_separation\": 0.3333333333333333, \"mean_separation\": 0.23412698412698413, \"minimum_separation\": 0.1111111111111111, \"normalized_mean_separation\": 0.23412698412698413, \"oracle_budget_representation_error\": 0.0625, \"oracle_singleton_representation_error\": 0.18055555555555558, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.11805555555555558, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 8}, \"private\": {\"hidden_mode_id\": \"c0efbc92b616f68e4ac7817573cca0d5cec1dcd80d541e5bca0b51cb9ca2d40c\", \"valid_mode_ids\": [\"4bd2a163d85e2a23047db081b9c5f021e9e43dda79cc5f41698926daea2ebd8e\", \"5815de9acda68144f4abd0c6e79136bf5ad9489d08b105f6d6ea9c6bd305f976\", \"5acfdb762751fa6669973d469b023c9ecb8a4ae34ca43dc9e8c467e35c71562d\", \"68afdc7077d7743a4be1ae6d6b8d09603ace061eeb29e661d3c308560719faf7\", \"80d83326f563ae7bfc7b9db37efd357238be55d09977edeee425e5c56b8e3b63\", \"9a9bc06b3414b5b4407f29108d2ac64643431eae3c2125eb560b2116786ec5ca\", \"c0efbc92b616f68e4ac7817573cca0d5cec1dcd80d541e5bca0b51cb9ca2d40c\", \"d030a31ca0c87e098ac4eaf224abe9c99edd881c112fe590f28f75ac57c2edea\"]}, \"state_id\": \"501a4df10f4d92b7f32f5c47\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 5, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 10, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 23, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z2_0\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 0}}]}"} {"agent_name": "causal_micro_lab_agent_loop", "data_source": "causal_micro_lab_final_eval_v3", "env_spec_json": "{\"agent\": {}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 56, \"task\": {\"state\": {\"available_experiment_ids\": [0, 2, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 17, 18, 19, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 4, \"family_bucket\": \"within_family\", \"maximum_separation\": 0.3333333333333333, \"mean_separation\": 0.26785714285714285, \"minimum_separation\": 0.16666666666666666, \"normalized_mean_separation\": 0.26785714285714285, \"oracle_budget_representation_error\": 0.08333333333333333, \"oracle_singleton_representation_error\": 0.20833333333333331, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.12499999999999999, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 8}, \"private\": {\"hidden_mode_id\": \"63e77b85928b5d7ee995d684336e9d65fb91136a638a4c60704ece63f82b6289\", \"valid_mode_ids\": [\"0321e1b80b7fd6bdea615add6a706d7112625343d59d78ed53b3fb50d2164cbb\", \"168d70e27f7506cd301e63503cebe1c567f71b90b5729d0a686adc35c3cc9ebb\", \"2da8ab7c4739631f1deda99dd42c863f34f0aed01aa08097f1f0b98f0efe8058\", \"5e35f09d07a0009599159c1d19b9595e32cc9be97021a87e43ee33f7e7200694\", \"63e77b85928b5d7ee995d684336e9d65fb91136a638a4c60704ece63f82b6289\", \"a0c79d9824aba5c919fdf7252d6fdb990d587a5c3c5845afb413e1641e44cd92\", \"d83a9bb3800b39ae5a9a2c2c1479fca5c0fcfc7b5c6ec05b8f532f4f6b8120f0\", \"e9db96420e1f5d98211122bb72247e5934aca04bb212b17046e869379c221aa5\"]}, \"state_id\": \"8514c7452005e9876ac7efe0\", \"visible_experiments\": [{\"experiment_id\": 1, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z1_0\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 3, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z2_0\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 0}}, {\"experiment_id\": 16, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 1}, \"intervention\": \"DO_Z1_0\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 20, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 0}}]}}}", "extra_info": {"index": 56, "max_global_steps": 0, "min_global_steps": 0}, "index": 56, "prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=0\n intervention: set Z1=0\n observed: Z1=0, Z2=0, Y=0\n\nExperiment 2:\n inputs: X1=0, X2=0, X3=0\n intervention: set Z2=0\n observed: Z1=1, Z2=0, Y=1\n\nExperiment 3:\n inputs: X1=0, X2=1, X3=1\n intervention: set Z1=0\n observed: Z1=0, Z2=1, Y=0\n\nExperiment 4:\n inputs: X1=1, X2=0, X3=0\n intervention: none\n observed: Z1=0, Z2=0, Y=0\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "raw_prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=0\n intervention: set Z1=0\n observed: Z1=0, Z2=0, Y=0\n\nExperiment 2:\n inputs: X1=0, X2=0, X3=0\n intervention: set Z2=0\n observed: Z1=1, Z2=0, Y=1\n\nExperiment 3:\n inputs: X1=0, X2=1, X3=1\n intervention: set Z1=0\n observed: Z1=0, Z2=1, Y=0\n\nExperiment 4:\n inputs: X1=1, X2=0, X3=0\n intervention: none\n observed: Z1=0, Z2=0, Y=0\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "reward_model": {"style": "rule"}, "state_json": "{\"available_experiment_ids\": [0, 2, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 17, 18, 19, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 4, \"family_bucket\": \"within_family\", \"maximum_separation\": 0.3333333333333333, \"mean_separation\": 0.26785714285714285, \"minimum_separation\": 0.16666666666666666, \"normalized_mean_separation\": 0.26785714285714285, \"oracle_budget_representation_error\": 0.08333333333333333, \"oracle_singleton_representation_error\": 0.20833333333333331, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.12499999999999999, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 8}, \"private\": {\"hidden_mode_id\": \"63e77b85928b5d7ee995d684336e9d65fb91136a638a4c60704ece63f82b6289\", \"valid_mode_ids\": [\"0321e1b80b7fd6bdea615add6a706d7112625343d59d78ed53b3fb50d2164cbb\", \"168d70e27f7506cd301e63503cebe1c567f71b90b5729d0a686adc35c3cc9ebb\", \"2da8ab7c4739631f1deda99dd42c863f34f0aed01aa08097f1f0b98f0efe8058\", \"5e35f09d07a0009599159c1d19b9595e32cc9be97021a87e43ee33f7e7200694\", \"63e77b85928b5d7ee995d684336e9d65fb91136a638a4c60704ece63f82b6289\", \"a0c79d9824aba5c919fdf7252d6fdb990d587a5c3c5845afb413e1641e44cd92\", \"d83a9bb3800b39ae5a9a2c2c1479fca5c0fcfc7b5c6ec05b8f532f4f6b8120f0\", \"e9db96420e1f5d98211122bb72247e5934aca04bb212b17046e869379c221aa5\"]}, \"state_id\": \"8514c7452005e9876ac7efe0\", \"visible_experiments\": [{\"experiment_id\": 1, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z1_0\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 3, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z2_0\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 0}}, {\"experiment_id\": 16, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 1}, \"intervention\": \"DO_Z1_0\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 20, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 0}}]}"} {"agent_name": "causal_micro_lab_agent_loop", "data_source": "causal_micro_lab_final_eval_v3", "env_spec_json": "{\"agent\": {}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 57, \"task\": {\"state\": {\"available_experiment_ids\": [1, 2, 3, 4, 5, 6, 7, 8, 9, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 3, \"family_bucket\": \"within_family\", \"maximum_separation\": 0.32432432432432434, \"mean_separation\": 0.2509652509652511, \"minimum_separation\": 0.10810810810810811, \"normalized_mean_separation\": 0.2509652509652511, \"oracle_budget_representation_error\": 0.06756756756756757, \"oracle_singleton_representation_error\": 0.195945945945946, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.12837837837837843, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 8}, \"private\": {\"hidden_mode_id\": \"a7a892759a02694b4b928d53a49de162036cc22125fd51cd6623213ff7711086\", \"valid_mode_ids\": [\"169d08e729922ca4595f54512d44690bfabfcbcb4adc52865d3b4a561e1321db\", \"887197481ac7d101840c602d27938875dd1bd7cc4b94014ad8ded3c86da8e18a\", \"94521810a47f679711356862bb27690574a5f07f8c50aa07dfc7a89961680a67\", \"a7a892759a02694b4b928d53a49de162036cc22125fd51cd6623213ff7711086\", \"d4c94c258c4e3dbc90b1ef42322bd115372553ccb10f05fc3c0f83f1e35ab914\", \"dee9d86f82cfb490cce34e7473b4d3dc0114aabcd41c85c1a1140864c6aeaf5b\", \"f6630838c4be4c0a26caab455e2291e301aa0d2cf7e6b3776f926631b0cdbcf0\", \"f7d585a6c3916d0e5f62509e920abff092a8f8617406afdddf181ffdec276816\"]}, \"state_id\": \"34d352b20ac686ddac67b077\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 10, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 25, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}]}}}", "extra_info": {"index": 57, "max_global_steps": 0, "min_global_steps": 0}, "index": 57, "prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=0\n intervention: none\n observed: Z1=0, Z2=0, Y=1\n\nExperiment 2:\n inputs: X1=0, X2=1, X3=0\n intervention: none\n observed: Z1=1, Z2=1, Y=0\n\nExperiment 3:\n inputs: X1=1, X2=0, X3=1\n intervention: none\n observed: Z1=0, Z2=1, Y=0\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "raw_prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=0\n intervention: none\n observed: Z1=0, Z2=0, Y=1\n\nExperiment 2:\n inputs: X1=0, X2=1, X3=0\n intervention: none\n observed: Z1=1, Z2=1, Y=0\n\nExperiment 3:\n inputs: X1=1, X2=0, X3=1\n intervention: none\n observed: Z1=0, Z2=1, Y=0\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "reward_model": {"style": "rule"}, "state_json": "{\"available_experiment_ids\": [1, 2, 3, 4, 5, 6, 7, 8, 9, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 3, \"family_bucket\": \"within_family\", \"maximum_separation\": 0.32432432432432434, \"mean_separation\": 0.2509652509652511, \"minimum_separation\": 0.10810810810810811, \"normalized_mean_separation\": 0.2509652509652511, \"oracle_budget_representation_error\": 0.06756756756756757, \"oracle_singleton_representation_error\": 0.195945945945946, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.12837837837837843, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 8}, \"private\": {\"hidden_mode_id\": \"a7a892759a02694b4b928d53a49de162036cc22125fd51cd6623213ff7711086\", \"valid_mode_ids\": [\"169d08e729922ca4595f54512d44690bfabfcbcb4adc52865d3b4a561e1321db\", \"887197481ac7d101840c602d27938875dd1bd7cc4b94014ad8ded3c86da8e18a\", \"94521810a47f679711356862bb27690574a5f07f8c50aa07dfc7a89961680a67\", \"a7a892759a02694b4b928d53a49de162036cc22125fd51cd6623213ff7711086\", \"d4c94c258c4e3dbc90b1ef42322bd115372553ccb10f05fc3c0f83f1e35ab914\", \"dee9d86f82cfb490cce34e7473b4d3dc0114aabcd41c85c1a1140864c6aeaf5b\", \"f6630838c4be4c0a26caab455e2291e301aa0d2cf7e6b3776f926631b0cdbcf0\", \"f7d585a6c3916d0e5f62509e920abff092a8f8617406afdddf181ffdec276816\"]}, \"state_id\": \"34d352b20ac686ddac67b077\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 10, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 25, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}]}"} {"agent_name": "causal_micro_lab_agent_loop", "data_source": "causal_micro_lab_final_eval_v3", "env_spec_json": "{\"agent\": {}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 58, \"task\": {\"state\": {\"available_experiment_ids\": [1, 2, 3, 4, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 21, 22, 23, 24, 25, 26, 27, 28, 29, 31, 32, 33, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 4, \"family_bucket\": \"cross_family\", \"maximum_separation\": 0.4444444444444444, \"mean_separation\": 0.28769841269841273, \"minimum_separation\": 0.1111111111111111, \"normalized_mean_separation\": 0.28769841269841273, \"oracle_budget_representation_error\": 0.06944444444444445, \"oracle_singleton_representation_error\": 0.20833333333333331, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.13888888888888887, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 8}, \"private\": {\"hidden_mode_id\": \"8a41b49ebf3bd396cf18e91075b7e2bd2228a6cf166c7e02bb76591d7e134ce1\", \"valid_mode_ids\": [\"0922323f544a6efe0f89f249da70773b41a5f057de93e71b9d166f75f273adfc\", \"5c4bd28f2c8882f551ffdf8b2bdf60c346ff42644ee00ef5ed2a732e42c9ca70\", \"61208ab1a8a63228386b263c8f02b2b111d11922f106112d88ace3334799ceaa\", \"75ff5027c0f75c70b325b279372f747fa04319beac6ee8c0b1a113880e99527d\", \"814a369a258a7519be301b801f1da57628319a1dbb8cc021d45c442c55c7f901\", \"8a41b49ebf3bd396cf18e91075b7e2bd2228a6cf166c7e02bb76591d7e134ce1\", \"9d6e31c7f1350256cc31bbb52862ec69eccfcbca66287f54d2c7e00684f81867\", \"c39dde438539351b948bf95bfebe7cda4c947ef8f07439720e6190105ab403a1\"]}, \"state_id\": \"5f8ea36bbf945664af6b0dcb\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 0}}, {\"experiment_id\": 5, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 0}}, {\"experiment_id\": 20, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 30, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 1}}]}}}", "extra_info": {"index": 58, "max_global_steps": 0, "min_global_steps": 0}, "index": 58, "prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=0\n intervention: none\n observed: Z1=1, Z2=0, Y=0\n\nExperiment 2:\n inputs: X1=0, X2=0, X3=1\n intervention: none\n observed: Z1=1, Z2=0, Y=0\n\nExperiment 3:\n inputs: X1=1, X2=0, X3=0\n intervention: none\n observed: Z1=1, Z2=1, Y=0\n\nExperiment 4:\n inputs: X1=1, X2=1, X3=0\n intervention: none\n observed: Z1=0, Z2=1, Y=1\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "raw_prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=0\n intervention: none\n observed: Z1=1, Z2=0, Y=0\n\nExperiment 2:\n inputs: X1=0, X2=0, X3=1\n intervention: none\n observed: Z1=1, Z2=0, Y=0\n\nExperiment 3:\n inputs: X1=1, X2=0, X3=0\n intervention: none\n observed: Z1=1, Z2=1, Y=0\n\nExperiment 4:\n inputs: X1=1, X2=1, X3=0\n intervention: none\n observed: Z1=0, Z2=1, Y=1\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "reward_model": {"style": "rule"}, "state_json": "{\"available_experiment_ids\": [1, 2, 3, 4, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 21, 22, 23, 24, 25, 26, 27, 28, 29, 31, 32, 33, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 4, \"family_bucket\": \"cross_family\", \"maximum_separation\": 0.4444444444444444, \"mean_separation\": 0.28769841269841273, \"minimum_separation\": 0.1111111111111111, \"normalized_mean_separation\": 0.28769841269841273, \"oracle_budget_representation_error\": 0.06944444444444445, \"oracle_singleton_representation_error\": 0.20833333333333331, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.13888888888888887, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 8}, \"private\": {\"hidden_mode_id\": \"8a41b49ebf3bd396cf18e91075b7e2bd2228a6cf166c7e02bb76591d7e134ce1\", \"valid_mode_ids\": [\"0922323f544a6efe0f89f249da70773b41a5f057de93e71b9d166f75f273adfc\", \"5c4bd28f2c8882f551ffdf8b2bdf60c346ff42644ee00ef5ed2a732e42c9ca70\", \"61208ab1a8a63228386b263c8f02b2b111d11922f106112d88ace3334799ceaa\", \"75ff5027c0f75c70b325b279372f747fa04319beac6ee8c0b1a113880e99527d\", \"814a369a258a7519be301b801f1da57628319a1dbb8cc021d45c442c55c7f901\", \"8a41b49ebf3bd396cf18e91075b7e2bd2228a6cf166c7e02bb76591d7e134ce1\", \"9d6e31c7f1350256cc31bbb52862ec69eccfcbca66287f54d2c7e00684f81867\", \"c39dde438539351b948bf95bfebe7cda4c947ef8f07439720e6190105ab403a1\"]}, \"state_id\": \"5f8ea36bbf945664af6b0dcb\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 0}}, {\"experiment_id\": 5, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 0}}, {\"experiment_id\": 20, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 30, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 1}}]}"} {"agent_name": "causal_micro_lab_agent_loop", "data_source": "causal_micro_lab_final_eval_v3", "env_spec_json": "{\"agent\": {}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 59, \"task\": {\"state\": {\"available_experiment_ids\": [0, 1, 3, 4, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 32, 33, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 3, \"family_bucket\": \"within_family\", \"maximum_separation\": 0.4864864864864865, \"mean_separation\": 0.28378378378378394, \"minimum_separation\": 0.16216216216216217, \"normalized_mean_separation\": 0.28378378378378394, \"oracle_budget_representation_error\": 0.08108108108108109, \"oracle_singleton_representation_error\": 0.22297297297297297, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.14189189189189189, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 8}, \"private\": {\"hidden_mode_id\": \"899020c23f47fe69e437a95b17caaa0a82516751145aabf16f93e308047d70c6\", \"valid_mode_ids\": [\"02ec0a3ab7a626cdd73999072b4b482b197329b64800dc195bd329de12d582a9\", \"184b515f2a52cf1c768abb1d05ad8901664aab327d7a1f5523b3f79477b7dfec\", \"2bebbf1eaabf2e43a8adfbe128786340a16f54ceecf1ad4b389fb4a605751092\", \"34f21a36aa8c6f806d6357039fe3e7b4882830f54672ba898ba82a42db953a4b\", \"5fe2e784d485ee3b1028a5cbd356d6437b91761c5f792e8acaa212f4d7e353fc\", \"899020c23f47fe69e437a95b17caaa0a82516751145aabf16f93e308047d70c6\", \"a6dae20e7c262aa98d286d8ec36857aadb5831707e8b774ddc6aa6c98e45a7c0\", \"b9b01b66cfb20a237cc68c71bb99607aa555ce516abbf874fd5920c5249a9120\"]}, \"state_id\": \"871fb1f886d6960764ceb190\", \"visible_experiments\": [{\"experiment_id\": 2, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z1_1\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 5, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 31, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 0}, \"intervention\": \"DO_Z1_0\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 0}}]}}}", "extra_info": {"index": 59, "max_global_steps": 0, "min_global_steps": 0}, "index": 59, "prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=0\n intervention: set Z1=1\n observed: Z1=1, Z2=1, Y=0\n\nExperiment 2:\n inputs: X1=0, X2=0, X3=1\n intervention: none\n observed: Z1=0, Z2=0, Y=1\n\nExperiment 3:\n inputs: X1=1, X2=1, X3=0\n intervention: set Z1=0\n observed: Z1=0, Z2=0, Y=0\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "raw_prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=0\n intervention: set Z1=1\n observed: Z1=1, Z2=1, Y=0\n\nExperiment 2:\n inputs: X1=0, X2=0, X3=1\n intervention: none\n observed: Z1=0, Z2=0, Y=1\n\nExperiment 3:\n inputs: X1=1, X2=1, X3=0\n intervention: set Z1=0\n observed: Z1=0, Z2=0, Y=0\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "reward_model": {"style": "rule"}, "state_json": "{\"available_experiment_ids\": [0, 1, 3, 4, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 32, 33, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 3, \"family_bucket\": \"within_family\", \"maximum_separation\": 0.4864864864864865, \"mean_separation\": 0.28378378378378394, \"minimum_separation\": 0.16216216216216217, \"normalized_mean_separation\": 0.28378378378378394, \"oracle_budget_representation_error\": 0.08108108108108109, \"oracle_singleton_representation_error\": 0.22297297297297297, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.14189189189189189, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 8}, \"private\": {\"hidden_mode_id\": \"899020c23f47fe69e437a95b17caaa0a82516751145aabf16f93e308047d70c6\", \"valid_mode_ids\": [\"02ec0a3ab7a626cdd73999072b4b482b197329b64800dc195bd329de12d582a9\", \"184b515f2a52cf1c768abb1d05ad8901664aab327d7a1f5523b3f79477b7dfec\", \"2bebbf1eaabf2e43a8adfbe128786340a16f54ceecf1ad4b389fb4a605751092\", \"34f21a36aa8c6f806d6357039fe3e7b4882830f54672ba898ba82a42db953a4b\", \"5fe2e784d485ee3b1028a5cbd356d6437b91761c5f792e8acaa212f4d7e353fc\", \"899020c23f47fe69e437a95b17caaa0a82516751145aabf16f93e308047d70c6\", \"a6dae20e7c262aa98d286d8ec36857aadb5831707e8b774ddc6aa6c98e45a7c0\", \"b9b01b66cfb20a237cc68c71bb99607aa555ce516abbf874fd5920c5249a9120\"]}, \"state_id\": \"871fb1f886d6960764ceb190\", \"visible_experiments\": [{\"experiment_id\": 2, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z1_1\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 5, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 31, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 0}, \"intervention\": \"DO_Z1_0\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 0}}]}"} {"agent_name": "causal_micro_lab_agent_loop", "data_source": "causal_micro_lab_final_eval_v3", "env_spec_json": "{\"agent\": {}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 60, \"task\": {\"state\": {\"available_experiment_ids\": [0, 1, 2, 3, 4, 6, 7, 8, 9, 11, 13, 14, 15, 16, 17, 18, 19, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 4, \"family_bucket\": \"cross_family\", \"maximum_separation\": 0.3888888888888889, \"mean_separation\": 0.2837301587301587, \"minimum_separation\": 0.1111111111111111, \"normalized_mean_separation\": 0.2837301587301587, \"oracle_budget_representation_error\": 0.06944444444444445, \"oracle_singleton_representation_error\": 0.2152777777777778, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.14583333333333334, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 8}, \"private\": {\"hidden_mode_id\": \"e2d37c1642b87f7c15a4c6a5cf953ac5167c33d9e8ac88db46a8c3359f6a6b68\", \"valid_mode_ids\": [\"246d6203ca00590c6a77197e907cbf86f34ac471dd22b8fed1747664b64c33e5\", \"5f912e4c241b7b6ab111cb88257958354b69d5269e2f7d28dabc4bc62b065d21\", \"650cf85cb8d150f02d501759ec1ce9445bba947499920402dcc551a6bd0ca8fc\", \"846db74f5d40839a7ec3a48d901710ebb812d71de99556e30aa575a78f767799\", \"cf8c41ff44c540c51931109fbdb831a6ecc16370459a0148d67b55519df47ab5\", \"d4bf43ccfad10433e9901e86703ef79ec775caeb6c5977f7d316cc6d8a74b494\", \"dcc85c82afe73c79ef621ebe61fa409495d3c5a129d00d6dc3457a1abce1f9c4\", \"e2d37c1642b87f7c15a4c6a5cf953ac5167c33d9e8ac88db46a8c3359f6a6b68\"]}, \"state_id\": \"7542f3bd2537f4b5a46dc9d6\", \"visible_experiments\": [{\"experiment_id\": 5, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 0}}, {\"experiment_id\": 10, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 12, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 0}, \"intervention\": \"DO_Z1_1\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 0}}, {\"experiment_id\": 20, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}]}}}", "extra_info": {"index": 60, "max_global_steps": 0, "min_global_steps": 0}, "index": 60, "prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=1\n intervention: none\n observed: Z1=1, Z2=0, Y=0\n\nExperiment 2:\n inputs: X1=0, X2=1, X3=0\n intervention: none\n observed: Z1=0, Z2=0, Y=0\n\nExperiment 3:\n inputs: X1=0, X2=1, X3=0\n intervention: set Z1=1\n observed: Z1=1, Z2=0, Y=0\n\nExperiment 4:\n inputs: X1=1, X2=0, X3=0\n intervention: none\n observed: Z1=0, Z2=1, Y=0\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "raw_prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=1\n intervention: none\n observed: Z1=1, Z2=0, Y=0\n\nExperiment 2:\n inputs: X1=0, X2=1, X3=0\n intervention: none\n observed: Z1=0, Z2=0, Y=0\n\nExperiment 3:\n inputs: X1=0, X2=1, X3=0\n intervention: set Z1=1\n observed: Z1=1, Z2=0, Y=0\n\nExperiment 4:\n inputs: X1=1, X2=0, X3=0\n intervention: none\n observed: Z1=0, Z2=1, Y=0\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "reward_model": {"style": "rule"}, "state_json": "{\"available_experiment_ids\": [0, 1, 2, 3, 4, 6, 7, 8, 9, 11, 13, 14, 15, 16, 17, 18, 19, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 4, \"family_bucket\": \"cross_family\", \"maximum_separation\": 0.3888888888888889, \"mean_separation\": 0.2837301587301587, \"minimum_separation\": 0.1111111111111111, \"normalized_mean_separation\": 0.2837301587301587, \"oracle_budget_representation_error\": 0.06944444444444445, \"oracle_singleton_representation_error\": 0.2152777777777778, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.14583333333333334, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 8}, \"private\": {\"hidden_mode_id\": \"e2d37c1642b87f7c15a4c6a5cf953ac5167c33d9e8ac88db46a8c3359f6a6b68\", \"valid_mode_ids\": [\"246d6203ca00590c6a77197e907cbf86f34ac471dd22b8fed1747664b64c33e5\", \"5f912e4c241b7b6ab111cb88257958354b69d5269e2f7d28dabc4bc62b065d21\", \"650cf85cb8d150f02d501759ec1ce9445bba947499920402dcc551a6bd0ca8fc\", \"846db74f5d40839a7ec3a48d901710ebb812d71de99556e30aa575a78f767799\", \"cf8c41ff44c540c51931109fbdb831a6ecc16370459a0148d67b55519df47ab5\", \"d4bf43ccfad10433e9901e86703ef79ec775caeb6c5977f7d316cc6d8a74b494\", \"dcc85c82afe73c79ef621ebe61fa409495d3c5a129d00d6dc3457a1abce1f9c4\", \"e2d37c1642b87f7c15a4c6a5cf953ac5167c33d9e8ac88db46a8c3359f6a6b68\"]}, \"state_id\": \"7542f3bd2537f4b5a46dc9d6\", \"visible_experiments\": [{\"experiment_id\": 5, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 0}}, {\"experiment_id\": 10, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 12, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 0}, \"intervention\": \"DO_Z1_1\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 0}}, {\"experiment_id\": 20, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}]}"} {"agent_name": "causal_micro_lab_agent_loop", "data_source": "causal_micro_lab_final_eval_v3", "env_spec_json": "{\"agent\": {}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 61, \"task\": {\"state\": {\"available_experiment_ids\": [1, 2, 3, 4, 5, 6, 7, 8, 9, 11, 13, 14, 15, 16, 17, 18, 19, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 4, \"family_bucket\": \"mixed\", \"maximum_separation\": 0.5, \"mean_separation\": 0.2926587301587302, \"minimum_separation\": 0.16666666666666666, \"normalized_mean_separation\": 0.2926587301587302, \"oracle_budget_representation_error\": 0.08333333333333333, \"oracle_singleton_representation_error\": 0.2361111111111111, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.1527777777777778, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 8}, \"private\": {\"hidden_mode_id\": \"75cc27ae2585d2736687c43fd20382997c8ea4dfb59a1b015a9e1a57e64755f6\", \"valid_mode_ids\": [\"75cc27ae2585d2736687c43fd20382997c8ea4dfb59a1b015a9e1a57e64755f6\", \"9856269fb17dbea1e8cd85d8cd92bd57bff976ea2dec50d237ca083f94632504\", \"be4ec6b2ef883582d2af98aa7d6d35946888c13cf0b518e40f87ed4fb6020df0\", \"ccac6e7d94c89695e16a81d71b8331aadc5ec2d68a7b913105d26b3654b94707\", \"cd1546e96e3d18dbf7203620db1f108444c4c9d6de98698e0807d5e0eb158ad7\", \"d7a02734928a565421b23431cfd6aaa50dfb31f4fedbd3b09db7dfb15d1f04a7\", \"db6d56cdfc850c475e42db0c7f22c59ad6fc680f61ac43d47121d7eaf1ac462e\", \"efecf268b51776e0e8e28914dd2c29c04db47756a86eae063b66418be088dec0\"]}, \"state_id\": \"630e6167a4bb429333911210\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 10, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 12, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 0}, \"intervention\": \"DO_Z1_1\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 0}}, {\"experiment_id\": 20, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 0}}]}}}", "extra_info": {"index": 61, "max_global_steps": 0, "min_global_steps": 0}, "index": 61, "prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=0\n intervention: none\n observed: Z1=0, Z2=0, Y=0\n\nExperiment 2:\n inputs: X1=0, X2=1, X3=0\n intervention: none\n observed: Z1=0, Z2=1, Y=1\n\nExperiment 3:\n inputs: X1=0, X2=1, X3=0\n intervention: set Z1=1\n observed: Z1=1, Z2=0, Y=0\n\nExperiment 4:\n inputs: X1=1, X2=0, X3=0\n intervention: none\n observed: Z1=0, Z2=0, Y=1\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "raw_prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=0\n intervention: none\n observed: Z1=0, Z2=0, Y=0\n\nExperiment 2:\n inputs: X1=0, X2=1, X3=0\n intervention: none\n observed: Z1=0, Z2=1, Y=1\n\nExperiment 3:\n inputs: X1=0, X2=1, X3=0\n intervention: set Z1=1\n observed: Z1=1, Z2=0, Y=0\n\nExperiment 4:\n inputs: X1=1, X2=0, X3=0\n intervention: none\n observed: Z1=0, Z2=0, Y=1\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "reward_model": {"style": "rule"}, "state_json": "{\"available_experiment_ids\": [1, 2, 3, 4, 5, 6, 7, 8, 9, 11, 13, 14, 15, 16, 17, 18, 19, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 4, \"family_bucket\": \"mixed\", \"maximum_separation\": 0.5, \"mean_separation\": 0.2926587301587302, \"minimum_separation\": 0.16666666666666666, \"normalized_mean_separation\": 0.2926587301587302, \"oracle_budget_representation_error\": 0.08333333333333333, \"oracle_singleton_representation_error\": 0.2361111111111111, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.1527777777777778, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 8}, \"private\": {\"hidden_mode_id\": \"75cc27ae2585d2736687c43fd20382997c8ea4dfb59a1b015a9e1a57e64755f6\", \"valid_mode_ids\": [\"75cc27ae2585d2736687c43fd20382997c8ea4dfb59a1b015a9e1a57e64755f6\", \"9856269fb17dbea1e8cd85d8cd92bd57bff976ea2dec50d237ca083f94632504\", \"be4ec6b2ef883582d2af98aa7d6d35946888c13cf0b518e40f87ed4fb6020df0\", \"ccac6e7d94c89695e16a81d71b8331aadc5ec2d68a7b913105d26b3654b94707\", \"cd1546e96e3d18dbf7203620db1f108444c4c9d6de98698e0807d5e0eb158ad7\", \"d7a02734928a565421b23431cfd6aaa50dfb31f4fedbd3b09db7dfb15d1f04a7\", \"db6d56cdfc850c475e42db0c7f22c59ad6fc680f61ac43d47121d7eaf1ac462e\", \"efecf268b51776e0e8e28914dd2c29c04db47756a86eae063b66418be088dec0\"]}, \"state_id\": \"630e6167a4bb429333911210\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 10, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 12, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 0}, \"intervention\": \"DO_Z1_1\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 0}}, {\"experiment_id\": 20, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 0}}]}"} {"agent_name": "causal_micro_lab_agent_loop", "data_source": "causal_micro_lab_final_eval_v3", "env_spec_json": "{\"agent\": {}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 62, \"task\": {\"state\": {\"available_experiment_ids\": [0, 1, 2, 3, 4, 6, 7, 8, 10, 11, 12, 13, 14, 16, 17, 18, 19, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 4, \"family_bucket\": \"mixed\", \"maximum_separation\": 0.3611111111111111, \"mean_separation\": 0.2738095238095238, \"minimum_separation\": 0.16666666666666666, \"normalized_mean_separation\": 0.2738095238095238, \"oracle_budget_representation_error\": 0.08333333333333333, \"oracle_singleton_representation_error\": 0.23958333333333334, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.15625, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 8}, \"private\": {\"hidden_mode_id\": \"836a40f068341a902e1a50cba03f129521b64c5819830fbc979d0a9f73a0de1c\", \"valid_mode_ids\": [\"4428ef78525ac80cf3720fc53ffcc0ebfe504f986be444f24bcca926adab3fc0\", \"5fac9bf8a58a70c78a0cd8b5662e7b94926e4d93dc5a0bacda4dbc7fb803c575\", \"682dfcbdc19c8843d127be428af09fee919c6167d9f0ae59c17a4ab6a6ff3b82\", \"836a40f068341a902e1a50cba03f129521b64c5819830fbc979d0a9f73a0de1c\", \"8ee50a5cef16f0573a29405ecf994244742890d6ba1bc7f2ecca3a27693bb5d2\", \"8fe34fb48282c12dc411ae01b5dac26563192a4bf46352b39ce4563948c29ec7\", \"9628f5675a843a3d895eb5eda2493539fad9fdc14f78e983c489a23212ae813b\", \"9a2d4ab07f6bf7bf70247c5423e77bf5642cdf2b4dd9b448234b49f99d2f93b9\"]}, \"state_id\": \"c6b3ac7e0d4e6c042c503a60\", \"visible_experiments\": [{\"experiment_id\": 5, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 9, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 1}, \"intervention\": \"DO_Z2_1\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 15, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 20, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 1}}]}}}", "extra_info": {"index": 62, "max_global_steps": 0, "min_global_steps": 0}, "index": 62, "prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=1\n intervention: none\n observed: Z1=0, Z2=0, Y=0\n\nExperiment 2:\n inputs: X1=0, X2=0, X3=1\n intervention: set Z2=1\n observed: Z1=0, Z2=1, Y=0\n\nExperiment 3:\n inputs: X1=0, X2=1, X3=1\n intervention: none\n observed: Z1=0, Z2=1, Y=1\n\nExperiment 4:\n inputs: X1=1, X2=0, X3=0\n intervention: none\n observed: Z1=0, Z2=1, Y=1\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "raw_prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=1\n intervention: none\n observed: Z1=0, Z2=0, Y=0\n\nExperiment 2:\n inputs: X1=0, X2=0, X3=1\n intervention: set Z2=1\n observed: Z1=0, Z2=1, Y=0\n\nExperiment 3:\n inputs: X1=0, X2=1, X3=1\n intervention: none\n observed: Z1=0, Z2=1, Y=1\n\nExperiment 4:\n inputs: X1=1, X2=0, X3=0\n intervention: none\n observed: Z1=0, Z2=1, Y=1\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "reward_model": {"style": "rule"}, "state_json": "{\"available_experiment_ids\": [0, 1, 2, 3, 4, 6, 7, 8, 10, 11, 12, 13, 14, 16, 17, 18, 19, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 4, \"family_bucket\": \"mixed\", \"maximum_separation\": 0.3611111111111111, \"mean_separation\": 0.2738095238095238, \"minimum_separation\": 0.16666666666666666, \"normalized_mean_separation\": 0.2738095238095238, \"oracle_budget_representation_error\": 0.08333333333333333, \"oracle_singleton_representation_error\": 0.23958333333333334, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.15625, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 8}, \"private\": {\"hidden_mode_id\": \"836a40f068341a902e1a50cba03f129521b64c5819830fbc979d0a9f73a0de1c\", \"valid_mode_ids\": [\"4428ef78525ac80cf3720fc53ffcc0ebfe504f986be444f24bcca926adab3fc0\", \"5fac9bf8a58a70c78a0cd8b5662e7b94926e4d93dc5a0bacda4dbc7fb803c575\", \"682dfcbdc19c8843d127be428af09fee919c6167d9f0ae59c17a4ab6a6ff3b82\", \"836a40f068341a902e1a50cba03f129521b64c5819830fbc979d0a9f73a0de1c\", \"8ee50a5cef16f0573a29405ecf994244742890d6ba1bc7f2ecca3a27693bb5d2\", \"8fe34fb48282c12dc411ae01b5dac26563192a4bf46352b39ce4563948c29ec7\", \"9628f5675a843a3d895eb5eda2493539fad9fdc14f78e983c489a23212ae813b\", \"9a2d4ab07f6bf7bf70247c5423e77bf5642cdf2b4dd9b448234b49f99d2f93b9\"]}, \"state_id\": \"c6b3ac7e0d4e6c042c503a60\", \"visible_experiments\": [{\"experiment_id\": 5, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 9, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 1}, \"intervention\": \"DO_Z2_1\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 15, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 20, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 1}}]}"} {"agent_name": "causal_micro_lab_agent_loop", "data_source": "causal_micro_lab_final_eval_v3", "env_spec_json": "{\"agent\": {}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 63, \"task\": {\"state\": {\"available_experiment_ids\": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 11, 12, 13, 14, 15, 16, 17, 18, 19, 21, 22, 23, 24, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 3, \"family_bucket\": \"cross_family\", \"maximum_separation\": 0.4864864864864865, \"mean_separation\": 0.3156370656370656, \"minimum_separation\": 0.16216216216216217, \"normalized_mean_separation\": 0.3156370656370656, \"oracle_budget_representation_error\": 0.08108108108108109, \"oracle_singleton_representation_error\": 0.24324324324324326, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.16216216216216217, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 8}, \"private\": {\"hidden_mode_id\": \"34a83c5db7719b7b8e992fb1e43de8a388ad182266c47065be28ae626fa46be6\", \"valid_mode_ids\": [\"14cab11d4d042ade2c15becac7b84854f2f4c5ff82a4aa8812cdbb5913dadede\", \"2f48668fcd4fff3223901d443154b8106ae687ead81f2da83d33b42d7797467d\", \"34a83c5db7719b7b8e992fb1e43de8a388ad182266c47065be28ae626fa46be6\", \"449cd64d3b2e0f10df59a7744fb43cb4a186fde280c8bf8a52bcff2aa7b2adbf\", \"92c9465c154fd58603fb4e992cd82a67afcc7f96b6593a54b4905b1df49531be\", \"a3c81dadbd3074ea206074091c98f3ff5f91865f24956c3e0cdfcffa5ec15ed3\", \"a8e5d5c21cfc6781f1da23f9c418c6db6131af60be83bdae5fcdd5e78ef1d544\", \"fcc262138b4c4ae189ee20e49f34cbfa39598903312e014eb2b78af1e8870f90\"]}, \"state_id\": \"6159a851ce425ee90114a413\", \"visible_experiments\": [{\"experiment_id\": 10, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 20, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 25, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 1}}]}}}", "extra_info": {"index": 63, "max_global_steps": 0, "min_global_steps": 0}, "index": 63, "prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=1, X3=0\n intervention: none\n observed: Z1=0, Z2=1, Y=0\n\nExperiment 2:\n inputs: X1=1, X2=0, X3=0\n intervention: none\n observed: Z1=1, Z2=1, Y=0\n\nExperiment 3:\n inputs: X1=1, X2=0, X3=1\n intervention: none\n observed: Z1=0, Z2=1, Y=1\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "raw_prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=1, X3=0\n intervention: none\n observed: Z1=0, Z2=1, Y=0\n\nExperiment 2:\n inputs: X1=1, X2=0, X3=0\n intervention: none\n observed: Z1=1, Z2=1, Y=0\n\nExperiment 3:\n inputs: X1=1, X2=0, X3=1\n intervention: none\n observed: Z1=0, Z2=1, Y=1\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "reward_model": {"style": "rule"}, "state_json": "{\"available_experiment_ids\": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 11, 12, 13, 14, 15, 16, 17, 18, 19, 21, 22, 23, 24, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 3, \"family_bucket\": \"cross_family\", \"maximum_separation\": 0.4864864864864865, \"mean_separation\": 0.3156370656370656, \"minimum_separation\": 0.16216216216216217, \"normalized_mean_separation\": 0.3156370656370656, \"oracle_budget_representation_error\": 0.08108108108108109, \"oracle_singleton_representation_error\": 0.24324324324324326, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.16216216216216217, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 8}, \"private\": {\"hidden_mode_id\": \"34a83c5db7719b7b8e992fb1e43de8a388ad182266c47065be28ae626fa46be6\", \"valid_mode_ids\": [\"14cab11d4d042ade2c15becac7b84854f2f4c5ff82a4aa8812cdbb5913dadede\", \"2f48668fcd4fff3223901d443154b8106ae687ead81f2da83d33b42d7797467d\", \"34a83c5db7719b7b8e992fb1e43de8a388ad182266c47065be28ae626fa46be6\", \"449cd64d3b2e0f10df59a7744fb43cb4a186fde280c8bf8a52bcff2aa7b2adbf\", \"92c9465c154fd58603fb4e992cd82a67afcc7f96b6593a54b4905b1df49531be\", \"a3c81dadbd3074ea206074091c98f3ff5f91865f24956c3e0cdfcffa5ec15ed3\", \"a8e5d5c21cfc6781f1da23f9c418c6db6131af60be83bdae5fcdd5e78ef1d544\", \"fcc262138b4c4ae189ee20e49f34cbfa39598903312e014eb2b78af1e8870f90\"]}, \"state_id\": \"6159a851ce425ee90114a413\", \"visible_experiments\": [{\"experiment_id\": 10, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 20, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 25, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 1}}]}"} {"agent_name": "causal_micro_lab_agent_loop", "data_source": "causal_micro_lab_final_eval_v3", "env_spec_json": "{\"agent\": {}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 64, \"task\": {\"state\": {\"available_experiment_ids\": [0, 1, 2, 3, 4, 5, 6, 7, 8, 11, 12, 13, 14, 15, 16, 17, 18, 19, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 3, \"family_bucket\": \"mixed\", \"maximum_separation\": 0.5135135135135135, \"mean_separation\": 0.3378378378378379, \"minimum_separation\": 0.10810810810810811, \"normalized_mean_separation\": 0.3378378378378379, \"oracle_budget_representation_error\": 0.08783783783783784, \"oracle_singleton_representation_error\": 0.25675675675675674, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.16891891891891891, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 8}, \"private\": {\"hidden_mode_id\": \"e0d7a4fc25339666cc8020db63512c41022634b06b699d612a81dedfa8e7ace3\", \"valid_mode_ids\": [\"2db2113366872ef932b3767f709283642ebdeb9249abeee9f8addefbed4261b9\", \"5877e63c7049851520cb2843b08f2f06c323d3fce652919bac2eddc9d819867b\", \"6f81f8de31dff586db478e4981157c16857e9db038b2fef225274efb81b68d7a\", \"7b6f590372517416134900a2dcc0fae095e81cc3a89e96efaf092fd13f55d4ba\", \"aeaa979037a70dbaf3f42b3dd385a4efe9f41c47d68750f54a4a17a79522a704\", \"b6605ad7994c2969dec7b4473b46a9e3df668dc3d6c39c52010037993746325f\", \"e0d7a4fc25339666cc8020db63512c41022634b06b699d612a81dedfa8e7ace3\", \"fc13e111a280d9cf2eee868223ef9b9761a3a3cafbebd623e233e48e7ae0adc8\"]}, \"state_id\": \"b061fe7d1f2c4a46bac885d7\", \"visible_experiments\": [{\"experiment_id\": 9, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 1}, \"intervention\": \"DO_Z2_1\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 10, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 20, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 1}}]}}}", "extra_info": {"index": 64, "max_global_steps": 0, "min_global_steps": 0}, "index": 64, "prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=1\n intervention: set Z2=1\n observed: Z1=1, Z2=1, Y=0\n\nExperiment 2:\n inputs: X1=0, X2=1, X3=0\n intervention: none\n observed: Z1=0, Z2=1, Y=0\n\nExperiment 3:\n inputs: X1=1, X2=0, X3=0\n intervention: none\n observed: Z1=0, Z2=1, Y=1\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "raw_prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=1\n intervention: set Z2=1\n observed: Z1=1, Z2=1, Y=0\n\nExperiment 2:\n inputs: X1=0, X2=1, X3=0\n intervention: none\n observed: Z1=0, Z2=1, Y=0\n\nExperiment 3:\n inputs: X1=1, X2=0, X3=0\n intervention: none\n observed: Z1=0, Z2=1, Y=1\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "reward_model": {"style": "rule"}, "state_json": "{\"available_experiment_ids\": [0, 1, 2, 3, 4, 5, 6, 7, 8, 11, 12, 13, 14, 15, 16, 17, 18, 19, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 3, \"family_bucket\": \"mixed\", \"maximum_separation\": 0.5135135135135135, \"mean_separation\": 0.3378378378378379, \"minimum_separation\": 0.10810810810810811, \"normalized_mean_separation\": 0.3378378378378379, \"oracle_budget_representation_error\": 0.08783783783783784, \"oracle_singleton_representation_error\": 0.25675675675675674, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.16891891891891891, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 8}, \"private\": {\"hidden_mode_id\": \"e0d7a4fc25339666cc8020db63512c41022634b06b699d612a81dedfa8e7ace3\", \"valid_mode_ids\": [\"2db2113366872ef932b3767f709283642ebdeb9249abeee9f8addefbed4261b9\", \"5877e63c7049851520cb2843b08f2f06c323d3fce652919bac2eddc9d819867b\", \"6f81f8de31dff586db478e4981157c16857e9db038b2fef225274efb81b68d7a\", \"7b6f590372517416134900a2dcc0fae095e81cc3a89e96efaf092fd13f55d4ba\", \"aeaa979037a70dbaf3f42b3dd385a4efe9f41c47d68750f54a4a17a79522a704\", \"b6605ad7994c2969dec7b4473b46a9e3df668dc3d6c39c52010037993746325f\", \"e0d7a4fc25339666cc8020db63512c41022634b06b699d612a81dedfa8e7ace3\", \"fc13e111a280d9cf2eee868223ef9b9761a3a3cafbebd623e233e48e7ae0adc8\"]}, \"state_id\": \"b061fe7d1f2c4a46bac885d7\", \"visible_experiments\": [{\"experiment_id\": 9, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 1}, \"intervention\": \"DO_Z2_1\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 10, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 20, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 1}}]}"} {"agent_name": "causal_micro_lab_agent_loop", "data_source": "causal_micro_lab_final_eval_v3", "env_spec_json": "{\"agent\": {}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 65, \"task\": {\"state\": {\"available_experiment_ids\": [0, 1, 2, 3, 4, 6, 7, 8, 9, 10, 11, 12, 13, 14, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 29, 30, 31, 32, 33, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 4, \"family_bucket\": \"cross_family\", \"maximum_separation\": 0.4444444444444444, \"mean_separation\": 0.2738095238095238, \"minimum_separation\": 0.1111111111111111, \"normalized_mean_separation\": 0.2738095238095238, \"oracle_budget_representation_error\": 0.05555555555555555, \"oracle_singleton_representation_error\": 0.22916666666666666, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.1736111111111111, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 8}, \"private\": {\"hidden_mode_id\": \"7a3c84daf5fd3b34420791d06e33a73ccf37234372f25dcd42ca360643b56334\", \"valid_mode_ids\": [\"1fd983a0d4102d8e0d7492102fd0d414e5e8518bcffcf59a6ddb3c14252e0d22\", \"7a3c84daf5fd3b34420791d06e33a73ccf37234372f25dcd42ca360643b56334\", \"7ac2736d3aaf54f1b12223126906afd23bf76672df987c78ac97d7f4d5959f13\", \"a0d3a6039d1b9b832420de87eb69c94bfac5e4fa22babdc18b48b7e24026950b\", \"a7c8d14405b9c54f79774a34a0245e6844aedd12addd4d9d097bef4787c1932b\", \"c85ddeba671e671961ad1c5c0b974ab5fb438fb7ca3de2a0179c949d21d02906\", \"e704377b1c8475624d9d377308b0146bc9576ba28a55f2b4cb310fe9b98c5ae9\", \"f5135a5a333fe684288d0ae8b8723a29e2008fe8b07ae0fa0baaa74605a0f22e\"]}, \"state_id\": \"e2c42ded323fb4acc8cfeeee\", \"visible_experiments\": [{\"experiment_id\": 5, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 15, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 28, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 1}, \"intervention\": \"DO_Z2_0\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 0}}, {\"experiment_id\": 34, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 0}, \"intervention\": \"DO_Z2_1\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}]}}}", "extra_info": {"index": 65, "max_global_steps": 0, "min_global_steps": 0}, "index": 65, "prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=1\n intervention: none\n observed: Z1=0, Z2=1, Y=0\n\nExperiment 2:\n inputs: X1=0, X2=1, X3=1\n intervention: none\n observed: Z1=0, Z2=0, Y=0\n\nExperiment 3:\n inputs: X1=1, X2=0, X3=1\n intervention: set Z2=0\n observed: Z1=1, Z2=0, Y=1\n\nExperiment 4:\n inputs: X1=1, X2=1, X3=0\n intervention: set Z2=1\n observed: Z1=0, Z2=1, Y=0\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "raw_prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=1\n intervention: none\n observed: Z1=0, Z2=1, Y=0\n\nExperiment 2:\n inputs: X1=0, X2=1, X3=1\n intervention: none\n observed: Z1=0, Z2=0, Y=0\n\nExperiment 3:\n inputs: X1=1, X2=0, X3=1\n intervention: set Z2=0\n observed: Z1=1, Z2=0, Y=1\n\nExperiment 4:\n inputs: X1=1, X2=1, X3=0\n intervention: set Z2=1\n observed: Z1=0, Z2=1, Y=0\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "reward_model": {"style": "rule"}, "state_json": "{\"available_experiment_ids\": [0, 1, 2, 3, 4, 6, 7, 8, 9, 10, 11, 12, 13, 14, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 29, 30, 31, 32, 33, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 4, \"family_bucket\": \"cross_family\", \"maximum_separation\": 0.4444444444444444, \"mean_separation\": 0.2738095238095238, \"minimum_separation\": 0.1111111111111111, \"normalized_mean_separation\": 0.2738095238095238, \"oracle_budget_representation_error\": 0.05555555555555555, \"oracle_singleton_representation_error\": 0.22916666666666666, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.1736111111111111, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 8}, \"private\": {\"hidden_mode_id\": \"7a3c84daf5fd3b34420791d06e33a73ccf37234372f25dcd42ca360643b56334\", \"valid_mode_ids\": [\"1fd983a0d4102d8e0d7492102fd0d414e5e8518bcffcf59a6ddb3c14252e0d22\", \"7a3c84daf5fd3b34420791d06e33a73ccf37234372f25dcd42ca360643b56334\", \"7ac2736d3aaf54f1b12223126906afd23bf76672df987c78ac97d7f4d5959f13\", \"a0d3a6039d1b9b832420de87eb69c94bfac5e4fa22babdc18b48b7e24026950b\", \"a7c8d14405b9c54f79774a34a0245e6844aedd12addd4d9d097bef4787c1932b\", \"c85ddeba671e671961ad1c5c0b974ab5fb438fb7ca3de2a0179c949d21d02906\", \"e704377b1c8475624d9d377308b0146bc9576ba28a55f2b4cb310fe9b98c5ae9\", \"f5135a5a333fe684288d0ae8b8723a29e2008fe8b07ae0fa0baaa74605a0f22e\"]}, \"state_id\": \"e2c42ded323fb4acc8cfeeee\", \"visible_experiments\": [{\"experiment_id\": 5, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 15, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 28, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 1}, \"intervention\": \"DO_Z2_0\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 0}}, {\"experiment_id\": 34, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 0}, \"intervention\": \"DO_Z2_1\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}]}"} {"agent_name": "causal_micro_lab_agent_loop", "data_source": "causal_micro_lab_final_eval_v3", "env_spec_json": "{\"agent\": {}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 66, \"task\": {\"state\": {\"available_experiment_ids\": [0, 1, 2, 3, 4, 6, 7, 8, 9, 11, 12, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 4, \"family_bucket\": \"mixed\", \"maximum_separation\": 0.4166666666666667, \"mean_separation\": 0.2797619047619047, \"minimum_separation\": 0.1111111111111111, \"normalized_mean_separation\": 0.2797619047619047, \"oracle_budget_representation_error\": 0.0625, \"oracle_singleton_representation_error\": 0.24305555555555555, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.18055555555555555, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 8}, \"private\": {\"hidden_mode_id\": \"b95a237280ae2e4e8f65b9d6054274acb290395c09b0e503fd388317a4c1cf3f\", \"valid_mode_ids\": [\"1fe9dbe3c8b5d755e241ff700339091f6b8c6cd7240e54de8c5e855ea162c70f\", \"709bb94bd9aee6c561262ea70c30cff643e48d2fe17ba2072d67a903a1497ed8\", \"75600ac3d3cf48c2947a81c2d0519533953a4dfcf172f90d9ac7501e680e4b53\", \"90c34af2cff7b253255d3719fe9ff3f51061235edc642e2a7425721b26ed8dce\", \"9f093dab9002ba7aeab644b801c80765c2e085fcfbdddbad8eaa5c2cd4e3f147\", \"b95a237280ae2e4e8f65b9d6054274acb290395c09b0e503fd388317a4c1cf3f\", \"f698a3ea0712407fc7dbf1db4f2d88c88f49b22b0b646f462f10aec8d61e1181\", \"fd54fa9eeb12df93d571fa8ec7ddb1ad1f339953f7cc866bace0567e12616d77\"]}, \"state_id\": \"aae9ad8a470a819cb826bdeb\", \"visible_experiments\": [{\"experiment_id\": 5, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 10, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 13, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 0}, \"intervention\": \"DO_Z2_0\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 35, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 1}}]}}}", "extra_info": {"index": 66, "max_global_steps": 0, "min_global_steps": 0}, "index": 66, "prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=1\n intervention: none\n observed: Z1=1, Z2=1, Y=0\n\nExperiment 2:\n inputs: X1=0, X2=1, X3=0\n intervention: none\n observed: Z1=0, Z2=1, Y=1\n\nExperiment 3:\n inputs: X1=0, X2=1, X3=0\n intervention: set Z2=0\n observed: Z1=0, Z2=0, Y=1\n\nExperiment 4:\n inputs: X1=1, X2=1, X3=1\n intervention: none\n observed: Z1=1, Z2=1, Y=1\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "raw_prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=1\n intervention: none\n observed: Z1=1, Z2=1, Y=0\n\nExperiment 2:\n inputs: X1=0, X2=1, X3=0\n intervention: none\n observed: Z1=0, Z2=1, Y=1\n\nExperiment 3:\n inputs: X1=0, X2=1, X3=0\n intervention: set Z2=0\n observed: Z1=0, Z2=0, Y=1\n\nExperiment 4:\n inputs: X1=1, X2=1, X3=1\n intervention: none\n observed: Z1=1, Z2=1, Y=1\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "reward_model": {"style": "rule"}, "state_json": "{\"available_experiment_ids\": [0, 1, 2, 3, 4, 6, 7, 8, 9, 11, 12, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 4, \"family_bucket\": \"mixed\", \"maximum_separation\": 0.4166666666666667, \"mean_separation\": 0.2797619047619047, \"minimum_separation\": 0.1111111111111111, \"normalized_mean_separation\": 0.2797619047619047, \"oracle_budget_representation_error\": 0.0625, \"oracle_singleton_representation_error\": 0.24305555555555555, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.18055555555555555, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 8}, \"private\": {\"hidden_mode_id\": \"b95a237280ae2e4e8f65b9d6054274acb290395c09b0e503fd388317a4c1cf3f\", \"valid_mode_ids\": [\"1fe9dbe3c8b5d755e241ff700339091f6b8c6cd7240e54de8c5e855ea162c70f\", \"709bb94bd9aee6c561262ea70c30cff643e48d2fe17ba2072d67a903a1497ed8\", \"75600ac3d3cf48c2947a81c2d0519533953a4dfcf172f90d9ac7501e680e4b53\", \"90c34af2cff7b253255d3719fe9ff3f51061235edc642e2a7425721b26ed8dce\", \"9f093dab9002ba7aeab644b801c80765c2e085fcfbdddbad8eaa5c2cd4e3f147\", \"b95a237280ae2e4e8f65b9d6054274acb290395c09b0e503fd388317a4c1cf3f\", \"f698a3ea0712407fc7dbf1db4f2d88c88f49b22b0b646f462f10aec8d61e1181\", \"fd54fa9eeb12df93d571fa8ec7ddb1ad1f339953f7cc866bace0567e12616d77\"]}, \"state_id\": \"aae9ad8a470a819cb826bdeb\", \"visible_experiments\": [{\"experiment_id\": 5, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 10, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 13, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 0}, \"intervention\": \"DO_Z2_0\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 35, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 1}}]}"} {"agent_name": "causal_micro_lab_agent_loop", "data_source": "causal_micro_lab_final_eval_v3", "env_spec_json": "{\"agent\": {}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 67, \"task\": {\"state\": {\"available_experiment_ids\": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 16, 18, 19, 20, 21, 22, 23, 24, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 3, \"family_bucket\": \"cross_family\", \"maximum_separation\": 0.5675675675675675, \"mean_separation\": 0.38127413127413134, \"minimum_separation\": 0.10810810810810811, \"normalized_mean_separation\": 0.38127413127413134, \"oracle_budget_representation_error\": 0.09121621621621623, \"oracle_singleton_representation_error\": 0.27702702702702703, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.1858108108108108, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 8}, \"private\": {\"hidden_mode_id\": \"5bab352a1a50ebb51df73be3583dde359d289781790e89ef1833b14e4465e276\", \"valid_mode_ids\": [\"36e2ed8c75bc9cbdea2e21e143331f63e5c40b93ca72bd8eb81f0d3f990fbd9f\", \"4d1890940d098cdcffaab764fcffaae4226dfb6347855035f20f2096dc44ef5a\", \"5bab352a1a50ebb51df73be3583dde359d289781790e89ef1833b14e4465e276\", \"6f8135fa7001c4647e99f98bbfba2743d474bb0941edf2ca5cb33028e402b140\", \"775902656fc706fbd9bfe1625eb0a14b1322e087154ea14c872881d46574b161\", \"86ce8869358a6a9625eb654ddc08ac490c07ff47b3eb1687181eb6bbcf1397de\", \"b377feb8696d9f4300c62df8a363cb6c1f41ad71f02bb4d881734799e1caf838\", \"ebaada51f9ff5f363a94d38074ce9c205d67bf44438a0d18ec2e113d0021e4a1\"]}, \"state_id\": \"09f4ded2ba50808e5e1a878f\", \"visible_experiments\": [{\"experiment_id\": 15, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 17, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 1}, \"intervention\": \"DO_Z1_1\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 0}}, {\"experiment_id\": 25, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 0}}]}}}", "extra_info": {"index": 67, "max_global_steps": 0, "min_global_steps": 0}, "index": 67, "prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=1, X3=1\n intervention: none\n observed: Z1=0, Z2=1, Y=0\n\nExperiment 2:\n inputs: X1=0, X2=1, X3=1\n intervention: set Z1=1\n observed: Z1=1, Z2=0, Y=1\n\nExperiment 3:\n inputs: X1=1, X2=0, X3=1\n intervention: none\n observed: Z1=0, Z2=0, Y=0\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "raw_prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=1, X3=1\n intervention: none\n observed: Z1=0, Z2=1, Y=0\n\nExperiment 2:\n inputs: X1=0, X2=1, X3=1\n intervention: set Z1=1\n observed: Z1=1, Z2=0, Y=1\n\nExperiment 3:\n inputs: X1=1, X2=0, X3=1\n intervention: none\n observed: Z1=0, Z2=0, Y=0\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "reward_model": {"style": "rule"}, "state_json": "{\"available_experiment_ids\": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 16, 18, 19, 20, 21, 22, 23, 24, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 3, \"family_bucket\": \"cross_family\", \"maximum_separation\": 0.5675675675675675, \"mean_separation\": 0.38127413127413134, \"minimum_separation\": 0.10810810810810811, \"normalized_mean_separation\": 0.38127413127413134, \"oracle_budget_representation_error\": 0.09121621621621623, \"oracle_singleton_representation_error\": 0.27702702702702703, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.1858108108108108, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 8}, \"private\": {\"hidden_mode_id\": \"5bab352a1a50ebb51df73be3583dde359d289781790e89ef1833b14e4465e276\", \"valid_mode_ids\": [\"36e2ed8c75bc9cbdea2e21e143331f63e5c40b93ca72bd8eb81f0d3f990fbd9f\", \"4d1890940d098cdcffaab764fcffaae4226dfb6347855035f20f2096dc44ef5a\", \"5bab352a1a50ebb51df73be3583dde359d289781790e89ef1833b14e4465e276\", \"6f8135fa7001c4647e99f98bbfba2743d474bb0941edf2ca5cb33028e402b140\", \"775902656fc706fbd9bfe1625eb0a14b1322e087154ea14c872881d46574b161\", \"86ce8869358a6a9625eb654ddc08ac490c07ff47b3eb1687181eb6bbcf1397de\", \"b377feb8696d9f4300c62df8a363cb6c1f41ad71f02bb4d881734799e1caf838\", \"ebaada51f9ff5f363a94d38074ce9c205d67bf44438a0d18ec2e113d0021e4a1\"]}, \"state_id\": \"09f4ded2ba50808e5e1a878f\", \"visible_experiments\": [{\"experiment_id\": 15, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 17, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 1}, \"intervention\": \"DO_Z1_1\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 0}}, {\"experiment_id\": 25, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 0}}]}"} {"agent_name": "causal_micro_lab_agent_loop", "data_source": "causal_micro_lab_final_eval_v3", "env_spec_json": "{\"agent\": {}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 68, \"task\": {\"state\": {\"available_experiment_ids\": [0, 1, 3, 4, 5, 6, 7, 8, 9, 11, 12, 13, 14, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 4, \"family_bucket\": \"cross_family\", \"maximum_separation\": 0.6111111111111112, \"mean_separation\": 0.3869047619047619, \"minimum_separation\": 0.19444444444444445, \"normalized_mean_separation\": 0.3869047619047619, \"oracle_budget_representation_error\": 0.09722222222222222, \"oracle_singleton_representation_error\": 0.2881944444444445, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.19097222222222227, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 8}, \"private\": {\"hidden_mode_id\": \"fd0f1b76c3d2674bedec9aa50d2515c0de42dd4116087d9e312552730c281e1e\", \"valid_mode_ids\": [\"47ea1763a86201285d326d2eee0389542cb4465013666d38437e3121e33695ea\", \"77c686f8887b25e60ac5593571edf4937268642554e585ddc4a916b65537b864\", \"80493911a6888647b574b915411548c39cb1f860fd244bde561795b7077a1a28\", \"83dd5ed3704665ed66146f2a801efde5cd2cf96b9435f69a15432665d61ba7f3\", \"8a6888d5e27a3dd9a0975e6548e7fd0f21eb11c6c435755b6d00823a3ca91121\", \"9ec870c4e62c58e2dc7631705ed1c581febd201e58ce6c1152c016eb5d5d342f\", \"c7c77da108920b09aa8bc749b5f814f69efce72b7faa4a219e9af2bbbdee87c3\", \"fd0f1b76c3d2674bedec9aa50d2515c0de42dd4116087d9e312552730c281e1e\"]}, \"state_id\": \"243363792f9291ec361a190e\", \"visible_experiments\": [{\"experiment_id\": 2, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z1_1\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 0}}, {\"experiment_id\": 10, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 0}}, {\"experiment_id\": 15, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 27, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 1}, \"intervention\": \"DO_Z1_1\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 0}}]}}}", "extra_info": {"index": 68, "max_global_steps": 0, "min_global_steps": 0}, "index": 68, "prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=0\n intervention: set Z1=1\n observed: Z1=1, Z2=0, Y=1\n\nExperiment 2:\n inputs: X1=0, X2=1, X3=0\n intervention: none\n observed: Z1=1, Z2=0, Y=1\n\nExperiment 3:\n inputs: X1=0, X2=1, X3=1\n intervention: none\n observed: Z1=0, Z2=0, Y=0\n\nExperiment 4:\n inputs: X1=1, X2=0, X3=1\n intervention: set Z1=1\n observed: Z1=1, Z2=0, Y=1\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "raw_prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=0\n intervention: set Z1=1\n observed: Z1=1, Z2=0, Y=1\n\nExperiment 2:\n inputs: X1=0, X2=1, X3=0\n intervention: none\n observed: Z1=1, Z2=0, Y=1\n\nExperiment 3:\n inputs: X1=0, X2=1, X3=1\n intervention: none\n observed: Z1=0, Z2=0, Y=0\n\nExperiment 4:\n inputs: X1=1, X2=0, X3=1\n intervention: set Z1=1\n observed: Z1=1, Z2=0, Y=1\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "reward_model": {"style": "rule"}, "state_json": "{\"available_experiment_ids\": [0, 1, 3, 4, 5, 6, 7, 8, 9, 11, 12, 13, 14, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 4, \"family_bucket\": \"cross_family\", \"maximum_separation\": 0.6111111111111112, \"mean_separation\": 0.3869047619047619, \"minimum_separation\": 0.19444444444444445, \"normalized_mean_separation\": 0.3869047619047619, \"oracle_budget_representation_error\": 0.09722222222222222, \"oracle_singleton_representation_error\": 0.2881944444444445, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.19097222222222227, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 8}, \"private\": {\"hidden_mode_id\": \"fd0f1b76c3d2674bedec9aa50d2515c0de42dd4116087d9e312552730c281e1e\", \"valid_mode_ids\": [\"47ea1763a86201285d326d2eee0389542cb4465013666d38437e3121e33695ea\", \"77c686f8887b25e60ac5593571edf4937268642554e585ddc4a916b65537b864\", \"80493911a6888647b574b915411548c39cb1f860fd244bde561795b7077a1a28\", \"83dd5ed3704665ed66146f2a801efde5cd2cf96b9435f69a15432665d61ba7f3\", \"8a6888d5e27a3dd9a0975e6548e7fd0f21eb11c6c435755b6d00823a3ca91121\", \"9ec870c4e62c58e2dc7631705ed1c581febd201e58ce6c1152c016eb5d5d342f\", \"c7c77da108920b09aa8bc749b5f814f69efce72b7faa4a219e9af2bbbdee87c3\", \"fd0f1b76c3d2674bedec9aa50d2515c0de42dd4116087d9e312552730c281e1e\"]}, \"state_id\": \"243363792f9291ec361a190e\", \"visible_experiments\": [{\"experiment_id\": 2, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z1_1\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 0}}, {\"experiment_id\": 10, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 0}}, {\"experiment_id\": 15, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 27, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 1}, \"intervention\": \"DO_Z1_1\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 0}}]}"} {"agent_name": "causal_micro_lab_agent_loop", "data_source": "causal_micro_lab_final_eval_v3", "env_spec_json": "{\"agent\": {}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 69, \"task\": {\"state\": {\"available_experiment_ids\": [0, 1, 2, 3, 4, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 21, 22, 23, 24, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 3, \"family_bucket\": \"mixed\", \"maximum_separation\": 0.5135135135135135, \"mean_separation\": 0.32818532818532825, \"minimum_separation\": 0.16216216216216217, \"normalized_mean_separation\": 0.32818532818532825, \"oracle_budget_representation_error\": 0.08108108108108109, \"oracle_singleton_representation_error\": 0.2770270270270271, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.195945945945946, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 8}, \"private\": {\"hidden_mode_id\": \"db87114cfcc253931f6f2f9873d8f980589bbb94a80241e0a4b985d5f60824e3\", \"valid_mode_ids\": [\"260674f75f5239fa208e333652decdc4221a7d9498a6329032e0486eec7c4cd5\", \"276a0f764c1788eb11c8a0891efd1a8332510b6f1c3888a3364e9cc7ea33f21d\", \"52ee087c5152fd32a4ae987ac1eba3f23e965c123a8c834a76027e44d7682eb5\", \"6dbdcd4c448ab0a0d9e057f1a4e7a557015f690bee215f36ae99132ac4c373e2\", \"6e416035797c446fe14a81e0542174c004a9175c7a61c3eb9bddf72fc32a060f\", \"ce4b3fc790893017204aa1a9568c3dee0dc222620dc87f6b675def468dd7cf84\", \"db87114cfcc253931f6f2f9873d8f980589bbb94a80241e0a4b985d5f60824e3\", \"f9b26f6759e42eee6f293a556d3999197111e928b5cef60b49844ce4ac602124\"]}, \"state_id\": \"7f3e04e4370c45bf3b3e7658\", \"visible_experiments\": [{\"experiment_id\": 5, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 0}}, {\"experiment_id\": 20, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 0}}, {\"experiment_id\": 25, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 0}}]}}}", "extra_info": {"index": 69, "max_global_steps": 0, "min_global_steps": 0}, "index": 69, "prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=1\n intervention: none\n observed: Z1=1, Z2=0, Y=0\n\nExperiment 2:\n inputs: X1=1, X2=0, X3=0\n intervention: none\n observed: Z1=1, Z2=0, Y=1\n\nExperiment 3:\n inputs: X1=1, X2=0, X3=1\n intervention: none\n observed: Z1=0, Z2=0, Y=0\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "raw_prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=1\n intervention: none\n observed: Z1=1, Z2=0, Y=0\n\nExperiment 2:\n inputs: X1=1, X2=0, X3=0\n intervention: none\n observed: Z1=1, Z2=0, Y=1\n\nExperiment 3:\n inputs: X1=1, X2=0, X3=1\n intervention: none\n observed: Z1=0, Z2=0, Y=0\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "reward_model": {"style": "rule"}, "state_json": "{\"available_experiment_ids\": [0, 1, 2, 3, 4, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 21, 22, 23, 24, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 3, \"family_bucket\": \"mixed\", \"maximum_separation\": 0.5135135135135135, \"mean_separation\": 0.32818532818532825, \"minimum_separation\": 0.16216216216216217, \"normalized_mean_separation\": 0.32818532818532825, \"oracle_budget_representation_error\": 0.08108108108108109, \"oracle_singleton_representation_error\": 0.2770270270270271, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.195945945945946, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 8}, \"private\": {\"hidden_mode_id\": \"db87114cfcc253931f6f2f9873d8f980589bbb94a80241e0a4b985d5f60824e3\", \"valid_mode_ids\": [\"260674f75f5239fa208e333652decdc4221a7d9498a6329032e0486eec7c4cd5\", \"276a0f764c1788eb11c8a0891efd1a8332510b6f1c3888a3364e9cc7ea33f21d\", \"52ee087c5152fd32a4ae987ac1eba3f23e965c123a8c834a76027e44d7682eb5\", \"6dbdcd4c448ab0a0d9e057f1a4e7a557015f690bee215f36ae99132ac4c373e2\", \"6e416035797c446fe14a81e0542174c004a9175c7a61c3eb9bddf72fc32a060f\", \"ce4b3fc790893017204aa1a9568c3dee0dc222620dc87f6b675def468dd7cf84\", \"db87114cfcc253931f6f2f9873d8f980589bbb94a80241e0a4b985d5f60824e3\", \"f9b26f6759e42eee6f293a556d3999197111e928b5cef60b49844ce4ac602124\"]}, \"state_id\": \"7f3e04e4370c45bf3b3e7658\", \"visible_experiments\": [{\"experiment_id\": 5, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 0}}, {\"experiment_id\": 20, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 0}}, {\"experiment_id\": 25, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 0}}]}"} {"agent_name": "causal_micro_lab_agent_loop", "data_source": "causal_micro_lab_final_eval_v3", "env_spec_json": "{\"agent\": {}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 70, \"task\": {\"state\": {\"available_experiment_ids\": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 16, 17, 18, 19, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 3, \"family_bucket\": \"cross_family\", \"maximum_separation\": 0.5945945945945946, \"mean_separation\": 0.3996138996138997, \"minimum_separation\": 0.10810810810810811, \"normalized_mean_separation\": 0.3996138996138997, \"oracle_budget_representation_error\": 0.10135135135135136, \"oracle_singleton_representation_error\": 0.30405405405405406, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.2027027027027027, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 8}, \"private\": {\"hidden_mode_id\": \"c9e393943d2e2f33514a0201712a4dc3744f69e27458e90015d307e8ba675b6a\", \"valid_mode_ids\": [\"05084d1f28fab0856a52cbf233d8c1628d1bfba35663e825572e9bebee5080f8\", \"3026ae83e00c482d14f62056809e7db2e0c5bc08c2d3fcb28854b9f077ae7ae9\", \"68f34c28d36a3387e0185a8c0050b039dcdc3550104152aa5c823d8c9f6e2693\", \"c2f4f41637350623651251c6d97ce744c7bd78ac7a7f240b28d65add1d436a02\", \"c62532959de0b6655cd85ecdbe9c09681cbf811500188d9186b5adfe9b6e5744\", \"c7e1b2ae3bb27e97664d43f6af7e0a0159ad7e43ebd5644fcacad4d01a5ed095\", \"c9e393943d2e2f33514a0201712a4dc3744f69e27458e90015d307e8ba675b6a\", \"d3029a14caf3b06095e5dd34eead8d7a3078eaaa455b25fe4b67b101d5cd07fe\"]}, \"state_id\": \"a1adb752dd8f310e394fdeb3\", \"visible_experiments\": [{\"experiment_id\": 15, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 0}}, {\"experiment_id\": 20, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 35, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 0}}]}}}", "extra_info": {"index": 70, "max_global_steps": 0, "min_global_steps": 0}, "index": 70, "prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=1, X3=1\n intervention: none\n observed: Z1=1, Z2=0, Y=0\n\nExperiment 2:\n inputs: X1=1, X2=0, X3=0\n intervention: none\n observed: Z1=0, Z2=0, Y=1\n\nExperiment 3:\n inputs: X1=1, X2=1, X3=1\n intervention: none\n observed: Z1=0, Z2=0, Y=1\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "raw_prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=1, X3=1\n intervention: none\n observed: Z1=1, Z2=0, Y=0\n\nExperiment 2:\n inputs: X1=1, X2=0, X3=0\n intervention: none\n observed: Z1=0, Z2=0, Y=1\n\nExperiment 3:\n inputs: X1=1, X2=1, X3=1\n intervention: none\n observed: Z1=0, Z2=0, Y=1\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "reward_model": {"style": "rule"}, "state_json": "{\"available_experiment_ids\": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 16, 17, 18, 19, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 3, \"family_bucket\": \"cross_family\", \"maximum_separation\": 0.5945945945945946, \"mean_separation\": 0.3996138996138997, \"minimum_separation\": 0.10810810810810811, \"normalized_mean_separation\": 0.3996138996138997, \"oracle_budget_representation_error\": 0.10135135135135136, \"oracle_singleton_representation_error\": 0.30405405405405406, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.2027027027027027, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 8}, \"private\": {\"hidden_mode_id\": \"c9e393943d2e2f33514a0201712a4dc3744f69e27458e90015d307e8ba675b6a\", \"valid_mode_ids\": [\"05084d1f28fab0856a52cbf233d8c1628d1bfba35663e825572e9bebee5080f8\", \"3026ae83e00c482d14f62056809e7db2e0c5bc08c2d3fcb28854b9f077ae7ae9\", \"68f34c28d36a3387e0185a8c0050b039dcdc3550104152aa5c823d8c9f6e2693\", \"c2f4f41637350623651251c6d97ce744c7bd78ac7a7f240b28d65add1d436a02\", \"c62532959de0b6655cd85ecdbe9c09681cbf811500188d9186b5adfe9b6e5744\", \"c7e1b2ae3bb27e97664d43f6af7e0a0159ad7e43ebd5644fcacad4d01a5ed095\", \"c9e393943d2e2f33514a0201712a4dc3744f69e27458e90015d307e8ba675b6a\", \"d3029a14caf3b06095e5dd34eead8d7a3078eaaa455b25fe4b67b101d5cd07fe\"]}, \"state_id\": \"a1adb752dd8f310e394fdeb3\", \"visible_experiments\": [{\"experiment_id\": 15, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 0}}, {\"experiment_id\": 20, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 35, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 0}}]}"} {"agent_name": "causal_micro_lab_agent_loop", "data_source": "causal_micro_lab_final_eval_v3", "env_spec_json": "{\"agent\": {}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 71, \"task\": {\"state\": {\"available_experiment_ids\": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 11, 12, 13, 14, 16, 17, 18, 19, 20, 21, 22, 23, 24, 26, 27, 28, 29, 30, 31, 33, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 4, \"family_bucket\": \"mixed\", \"maximum_separation\": 0.5555555555555556, \"mean_separation\": 0.3849206349206349, \"minimum_separation\": 0.1111111111111111, \"normalized_mean_separation\": 0.3849206349206349, \"oracle_budget_representation_error\": 0.09722222222222224, \"oracle_singleton_representation_error\": 0.3055555555555556, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.20833333333333334, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 8}, \"private\": {\"hidden_mode_id\": \"b49f44ccf144f6bee892dfadce0f0d621879800835a9277b78999e6320b9de69\", \"valid_mode_ids\": [\"08c08419fd637f6318a0881d9871e3706bc8de07c31dbb59fa2b29fc9d1cfe02\", \"196b59e1541f2b74fa39254394dbe7976f8d0cf50bf581cf9b7877cbc6a68b0a\", \"2a2999625d05970b6b499de0ac7300824dc0f1b3fd6b2ab15f926994eb410000\", \"2b80369d2924a463ac11e9f31ce69672517fa45b042d16cfdd87c93a93677e4c\", \"3340e37a98ca24ba7a7cd4ffed228eb8e2d32d0143b449f4c5ceef7f49c78a97\", \"73d343aca7c7cf4d0f1e5527c90e30ee05a5fee54a92ee9bc7d0861ee359f411\", \"8a270fb3d23475c4e0663324db3e76d8d2e37e9ae835c3fe9f96d3c80f9efc2c\", \"b49f44ccf144f6bee892dfadce0f0d621879800835a9277b78999e6320b9de69\"]}, \"state_id\": \"1cf138d74f37923b97c86190\", \"visible_experiments\": [{\"experiment_id\": 10, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 15, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 0}}, {\"experiment_id\": 25, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 32, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 0}, \"intervention\": \"DO_Z1_1\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 1}}]}}}", "extra_info": {"index": 71, "max_global_steps": 0, "min_global_steps": 0}, "index": 71, "prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=1, X3=0\n intervention: none\n observed: Z1=1, Z2=1, Y=0\n\nExperiment 2:\n inputs: X1=0, X2=1, X3=1\n intervention: none\n observed: Z1=1, Z2=0, Y=0\n\nExperiment 3:\n inputs: X1=1, X2=0, X3=1\n intervention: none\n observed: Z1=0, Z2=1, Y=0\n\nExperiment 4:\n inputs: X1=1, X2=1, X3=0\n intervention: set Z1=1\n observed: Z1=1, Z2=1, Y=1\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "raw_prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=1, X3=0\n intervention: none\n observed: Z1=1, Z2=1, Y=0\n\nExperiment 2:\n inputs: X1=0, X2=1, X3=1\n intervention: none\n observed: Z1=1, Z2=0, Y=0\n\nExperiment 3:\n inputs: X1=1, X2=0, X3=1\n intervention: none\n observed: Z1=0, Z2=1, Y=0\n\nExperiment 4:\n inputs: X1=1, X2=1, X3=0\n intervention: set Z1=1\n observed: Z1=1, Z2=1, Y=1\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "reward_model": {"style": "rule"}, "state_json": "{\"available_experiment_ids\": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 11, 12, 13, 14, 16, 17, 18, 19, 20, 21, 22, 23, 24, 26, 27, 28, 29, 30, 31, 33, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 4, \"family_bucket\": \"mixed\", \"maximum_separation\": 0.5555555555555556, \"mean_separation\": 0.3849206349206349, \"minimum_separation\": 0.1111111111111111, \"normalized_mean_separation\": 0.3849206349206349, \"oracle_budget_representation_error\": 0.09722222222222224, \"oracle_singleton_representation_error\": 0.3055555555555556, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.20833333333333334, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 8}, \"private\": {\"hidden_mode_id\": \"b49f44ccf144f6bee892dfadce0f0d621879800835a9277b78999e6320b9de69\", \"valid_mode_ids\": [\"08c08419fd637f6318a0881d9871e3706bc8de07c31dbb59fa2b29fc9d1cfe02\", \"196b59e1541f2b74fa39254394dbe7976f8d0cf50bf581cf9b7877cbc6a68b0a\", \"2a2999625d05970b6b499de0ac7300824dc0f1b3fd6b2ab15f926994eb410000\", \"2b80369d2924a463ac11e9f31ce69672517fa45b042d16cfdd87c93a93677e4c\", \"3340e37a98ca24ba7a7cd4ffed228eb8e2d32d0143b449f4c5ceef7f49c78a97\", \"73d343aca7c7cf4d0f1e5527c90e30ee05a5fee54a92ee9bc7d0861ee359f411\", \"8a270fb3d23475c4e0663324db3e76d8d2e37e9ae835c3fe9f96d3c80f9efc2c\", \"b49f44ccf144f6bee892dfadce0f0d621879800835a9277b78999e6320b9de69\"]}, \"state_id\": \"1cf138d74f37923b97c86190\", \"visible_experiments\": [{\"experiment_id\": 10, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 15, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 0}}, {\"experiment_id\": 25, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 32, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 0}, \"intervention\": \"DO_Z1_1\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 1}}]}"} {"agent_name": "causal_micro_lab_agent_loop", "data_source": "causal_micro_lab_final_eval_v3", "env_spec_json": "{\"agent\": {}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 72, \"task\": {\"state\": {\"available_experiment_ids\": [0, 1, 3, 4, 6, 7, 8, 9, 11, 12, 13, 14, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 4, \"family_bucket\": \"cross_family\", \"maximum_separation\": 0.6944444444444444, \"mean_separation\": 0.4583333333333334, \"minimum_separation\": 0.2222222222222222, \"normalized_mean_separation\": 0.4583333333333334, \"oracle_budget_representation_error\": 0.13194444444444445, \"oracle_singleton_representation_error\": 0.34375, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.21180555555555555, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 8}, \"private\": {\"hidden_mode_id\": \"5a4b706517b4e795066eaf9d478970fa040b6600246b516841389b424845b12c\", \"valid_mode_ids\": [\"5a4b706517b4e795066eaf9d478970fa040b6600246b516841389b424845b12c\", \"617ccf8ff59b88111c77a824af1c2d9848ebc06a2d355fe0607d3d8684c0a8c3\", \"61ee2c99effc2e23816523f131330757aebadac608fe17a79f539af8561b5d88\", \"6bb335242813e6d7a379563c753c9aba4ceb85bff2d005abc20a4ca853b244ef\", \"6c18c3e74408e37f5e04280aa60153c988d5d5f69a9ca4ec4ee0db3da1e21754\", \"c1dcc175daf1ec8aad9c4829d00d0d86989e84a81cc63243914bfa9742b7d7b3\", \"c6e1c3b6ccdeae7ad544c348ce693faef80ffb867be37c9d0335b684def8322f\", \"ff32747f2fc57a6b52b6e3d6ee86b69cc80d546afb1b3c2b867f0ef8444ee728\"]}, \"state_id\": \"a7d979c872f4d86765d06aa6\", \"visible_experiments\": [{\"experiment_id\": 2, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z1_1\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 0}}, {\"experiment_id\": 5, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 0}}, {\"experiment_id\": 10, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 0}}, {\"experiment_id\": 15, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}]}}}", "extra_info": {"index": 72, "max_global_steps": 0, "min_global_steps": 0}, "index": 72, "prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=0\n intervention: set Z1=1\n observed: Z1=1, Z2=0, Y=1\n\nExperiment 2:\n inputs: X1=0, X2=0, X3=1\n intervention: none\n observed: Z1=1, Z2=0, Y=1\n\nExperiment 3:\n inputs: X1=0, X2=1, X3=0\n intervention: none\n observed: Z1=1, Z2=0, Y=1\n\nExperiment 4:\n inputs: X1=0, X2=1, X3=1\n intervention: none\n observed: Z1=0, Z2=1, Y=0\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "raw_prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=0\n intervention: set Z1=1\n observed: Z1=1, Z2=0, Y=1\n\nExperiment 2:\n inputs: X1=0, X2=0, X3=1\n intervention: none\n observed: Z1=1, Z2=0, Y=1\n\nExperiment 3:\n inputs: X1=0, X2=1, X3=0\n intervention: none\n observed: Z1=1, Z2=0, Y=1\n\nExperiment 4:\n inputs: X1=0, X2=1, X3=1\n intervention: none\n observed: Z1=0, Z2=1, Y=0\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "reward_model": {"style": "rule"}, "state_json": "{\"available_experiment_ids\": [0, 1, 3, 4, 6, 7, 8, 9, 11, 12, 13, 14, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 4, \"family_bucket\": \"cross_family\", \"maximum_separation\": 0.6944444444444444, \"mean_separation\": 0.4583333333333334, \"minimum_separation\": 0.2222222222222222, \"normalized_mean_separation\": 0.4583333333333334, \"oracle_budget_representation_error\": 0.13194444444444445, \"oracle_singleton_representation_error\": 0.34375, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.21180555555555555, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 8}, \"private\": {\"hidden_mode_id\": \"5a4b706517b4e795066eaf9d478970fa040b6600246b516841389b424845b12c\", \"valid_mode_ids\": [\"5a4b706517b4e795066eaf9d478970fa040b6600246b516841389b424845b12c\", \"617ccf8ff59b88111c77a824af1c2d9848ebc06a2d355fe0607d3d8684c0a8c3\", \"61ee2c99effc2e23816523f131330757aebadac608fe17a79f539af8561b5d88\", \"6bb335242813e6d7a379563c753c9aba4ceb85bff2d005abc20a4ca853b244ef\", \"6c18c3e74408e37f5e04280aa60153c988d5d5f69a9ca4ec4ee0db3da1e21754\", \"c1dcc175daf1ec8aad9c4829d00d0d86989e84a81cc63243914bfa9742b7d7b3\", \"c6e1c3b6ccdeae7ad544c348ce693faef80ffb867be37c9d0335b684def8322f\", \"ff32747f2fc57a6b52b6e3d6ee86b69cc80d546afb1b3c2b867f0ef8444ee728\"]}, \"state_id\": \"a7d979c872f4d86765d06aa6\", \"visible_experiments\": [{\"experiment_id\": 2, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z1_1\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 0}}, {\"experiment_id\": 5, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 0}}, {\"experiment_id\": 10, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 0}}, {\"experiment_id\": 15, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}]}"} {"agent_name": "causal_micro_lab_agent_loop", "data_source": "causal_micro_lab_final_eval_v3", "env_spec_json": "{\"agent\": {}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 73, \"task\": {\"state\": {\"available_experiment_ids\": [0, 1, 2, 3, 4, 5, 7, 8, 9, 11, 12, 13, 14, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 3, \"family_bucket\": \"within_family\", \"maximum_separation\": 0.5945945945945946, \"mean_separation\": 0.3861003861003861, \"minimum_separation\": 0.16216216216216217, \"normalized_mean_separation\": 0.3861003861003861, \"oracle_budget_representation_error\": 0.08108108108108109, \"oracle_singleton_representation_error\": 0.3006756756756757, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.21959459459459463, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 8}, \"private\": {\"hidden_mode_id\": \"0859f12541e5e950a57dbe8cfb1e004b08a6f1b54254e5c982489823ab2e3ef6\", \"valid_mode_ids\": [\"0859f12541e5e950a57dbe8cfb1e004b08a6f1b54254e5c982489823ab2e3ef6\", \"3e328e6ee94231e23cfc27d4f94e11784c08aa9408ffe4d43dcf8e559b415334\", \"92227d6b41179946cba5f53dd0cb9e4bf92093666c3de1fa4624ce3e020d2744\", \"a84d3e8e5b0f8db5cca9404e1d0dc9a746340748ffa0a6c16dd569104359aa88\", \"d3529e0e84fb78a5fd6f11597fca3594d31c7636bc1a1f5ae79a74eebcda0f7c\", \"decae397f33889fea1b8ae9a0353f854da81f117d05f39484f7bd4b99f80c0ce\", \"e220f2309275363636be1d9498c763a0b898255e4e0d3b70f74170e6442f7c1c\", \"ec8ca80a8898af689719c20d35504abb3484f9cbae46a46b60b77ff226405d86\"]}, \"state_id\": \"6ca6c3fbc9fa0f1d2ca51c05\", \"visible_experiments\": [{\"experiment_id\": 6, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 1}, \"intervention\": \"DO_Z1_0\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 10, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 0}}, {\"experiment_id\": 15, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}]}}}", "extra_info": {"index": 73, "max_global_steps": 0, "min_global_steps": 0}, "index": 73, "prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=1\n intervention: set Z1=0\n observed: Z1=0, Z2=1, Y=1\n\nExperiment 2:\n inputs: X1=0, X2=1, X3=0\n intervention: none\n observed: Z1=1, Z2=0, Y=0\n\nExperiment 3:\n inputs: X1=0, X2=1, X3=1\n intervention: none\n observed: Z1=0, Z2=1, Y=0\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "raw_prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=1\n intervention: set Z1=0\n observed: Z1=0, Z2=1, Y=1\n\nExperiment 2:\n inputs: X1=0, X2=1, X3=0\n intervention: none\n observed: Z1=1, Z2=0, Y=0\n\nExperiment 3:\n inputs: X1=0, X2=1, X3=1\n intervention: none\n observed: Z1=0, Z2=1, Y=0\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "reward_model": {"style": "rule"}, "state_json": "{\"available_experiment_ids\": [0, 1, 2, 3, 4, 5, 7, 8, 9, 11, 12, 13, 14, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 3, \"family_bucket\": \"within_family\", \"maximum_separation\": 0.5945945945945946, \"mean_separation\": 0.3861003861003861, \"minimum_separation\": 0.16216216216216217, \"normalized_mean_separation\": 0.3861003861003861, \"oracle_budget_representation_error\": 0.08108108108108109, \"oracle_singleton_representation_error\": 0.3006756756756757, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.21959459459459463, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 8}, \"private\": {\"hidden_mode_id\": \"0859f12541e5e950a57dbe8cfb1e004b08a6f1b54254e5c982489823ab2e3ef6\", \"valid_mode_ids\": [\"0859f12541e5e950a57dbe8cfb1e004b08a6f1b54254e5c982489823ab2e3ef6\", \"3e328e6ee94231e23cfc27d4f94e11784c08aa9408ffe4d43dcf8e559b415334\", \"92227d6b41179946cba5f53dd0cb9e4bf92093666c3de1fa4624ce3e020d2744\", \"a84d3e8e5b0f8db5cca9404e1d0dc9a746340748ffa0a6c16dd569104359aa88\", \"d3529e0e84fb78a5fd6f11597fca3594d31c7636bc1a1f5ae79a74eebcda0f7c\", \"decae397f33889fea1b8ae9a0353f854da81f117d05f39484f7bd4b99f80c0ce\", \"e220f2309275363636be1d9498c763a0b898255e4e0d3b70f74170e6442f7c1c\", \"ec8ca80a8898af689719c20d35504abb3484f9cbae46a46b60b77ff226405d86\"]}, \"state_id\": \"6ca6c3fbc9fa0f1d2ca51c05\", \"visible_experiments\": [{\"experiment_id\": 6, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 1}, \"intervention\": \"DO_Z1_0\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 10, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 0}}, {\"experiment_id\": 15, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}]}"} {"agent_name": "causal_micro_lab_agent_loop", "data_source": "causal_micro_lab_final_eval_v3", "env_spec_json": "{\"agent\": {}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 74, \"task\": {\"state\": {\"available_experiment_ids\": [1, 2, 3, 4, 5, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 3, \"family_bucket\": \"cross_family\", \"maximum_separation\": 0.7027027027027027, \"mean_separation\": 0.46718146718146725, \"minimum_separation\": 0.21621621621621623, \"normalized_mean_separation\": 0.46718146718146725, \"oracle_budget_representation_error\": 0.1283783783783784, \"oracle_singleton_representation_error\": 0.3513513513513514, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.22297297297297303, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 8}, \"private\": {\"hidden_mode_id\": \"d439886819d0143e264902eaf27a3a57c836553caa942615438fb0bfd5e7ef27\", \"valid_mode_ids\": [\"0a915d9b8ff6b990bae354557ee8123677b03cee76f97a83c048b886f35ae0c0\", \"3e6389a3b4a056b173f00fcf88cd183cda17fb8300f971884bd9170231ee3ffa\", \"417a1c075d7118ddf5955d7b524385cadb766f2cc62f9d941bc4a09800ac3c5d\", \"92ffb5ff6181d7d2ddeb626b7773dadd88e9e7b959d2d54e86f90f0d645f120b\", \"bbf6fa662ccc916381d7f74433c0dac2411da24c2d828820638a3520d0603a18\", \"cbfa44d40188452a8d87ab979c860a172217c0203e3ab34455147caf953ebde5\", \"d439886819d0143e264902eaf27a3a57c836553caa942615438fb0bfd5e7ef27\", \"d9968cda7d5918bdd80fd84a6d7eb4f1dace1db5133229c3a55a28f43ebc3213\"]}, \"state_id\": \"d1d4412bcd4dea443fd33fd5\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 6, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 1}, \"intervention\": \"DO_Z1_0\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 20, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 0}}]}}}", "extra_info": {"index": 74, "max_global_steps": 0, "min_global_steps": 0}, "index": 74, "prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=0\n intervention: none\n observed: Z1=1, Z2=1, Y=0\n\nExperiment 2:\n inputs: X1=0, X2=0, X3=1\n intervention: set Z1=0\n observed: Z1=0, Z2=0, Y=1\n\nExperiment 3:\n inputs: X1=1, X2=0, X3=0\n intervention: none\n observed: Z1=1, Z2=0, Y=0\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "raw_prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=0\n intervention: none\n observed: Z1=1, Z2=1, Y=0\n\nExperiment 2:\n inputs: X1=0, X2=0, X3=1\n intervention: set Z1=0\n observed: Z1=0, Z2=0, Y=1\n\nExperiment 3:\n inputs: X1=1, X2=0, X3=0\n intervention: none\n observed: Z1=1, Z2=0, Y=0\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "reward_model": {"style": "rule"}, "state_json": "{\"available_experiment_ids\": [1, 2, 3, 4, 5, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 3, \"family_bucket\": \"cross_family\", \"maximum_separation\": 0.7027027027027027, \"mean_separation\": 0.46718146718146725, \"minimum_separation\": 0.21621621621621623, \"normalized_mean_separation\": 0.46718146718146725, \"oracle_budget_representation_error\": 0.1283783783783784, \"oracle_singleton_representation_error\": 0.3513513513513514, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.22297297297297303, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 8}, \"private\": {\"hidden_mode_id\": \"d439886819d0143e264902eaf27a3a57c836553caa942615438fb0bfd5e7ef27\", \"valid_mode_ids\": [\"0a915d9b8ff6b990bae354557ee8123677b03cee76f97a83c048b886f35ae0c0\", \"3e6389a3b4a056b173f00fcf88cd183cda17fb8300f971884bd9170231ee3ffa\", \"417a1c075d7118ddf5955d7b524385cadb766f2cc62f9d941bc4a09800ac3c5d\", \"92ffb5ff6181d7d2ddeb626b7773dadd88e9e7b959d2d54e86f90f0d645f120b\", \"bbf6fa662ccc916381d7f74433c0dac2411da24c2d828820638a3520d0603a18\", \"cbfa44d40188452a8d87ab979c860a172217c0203e3ab34455147caf953ebde5\", \"d439886819d0143e264902eaf27a3a57c836553caa942615438fb0bfd5e7ef27\", \"d9968cda7d5918bdd80fd84a6d7eb4f1dace1db5133229c3a55a28f43ebc3213\"]}, \"state_id\": \"d1d4412bcd4dea443fd33fd5\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 6, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 1}, \"intervention\": \"DO_Z1_0\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 20, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 0}}]}"} {"agent_name": "causal_micro_lab_agent_loop", "data_source": "causal_micro_lab_final_eval_v3", "env_spec_json": "{\"agent\": {}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 75, \"task\": {\"state\": {\"available_experiment_ids\": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 38, 39], \"metadata\": {\"evidence_size\": 3, \"family_bucket\": \"within_family\", \"maximum_separation\": 0.5945945945945946, \"mean_separation\": 0.37644787644787653, \"minimum_separation\": 0.16216216216216217, \"normalized_mean_separation\": 0.37644787644787653, \"oracle_budget_representation_error\": 0.08108108108108109, \"oracle_singleton_representation_error\": 0.31081081081081086, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.22972972972972977, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 8}, \"private\": {\"hidden_mode_id\": \"98abd6e322587f3a3aa44acc21ff1849ef79950ade0c8e77c7e46f4ea5e02703\", \"valid_mode_ids\": [\"0663fe10cbe11b01a099d5fdd8e11912c2bb268985cffbf8129953cdb0337e67\", \"1ed7f388df4c3c3454efee3994cce951070140ab2b2b6eb4c3aaddba25746451\", \"591ac9610a9f39507a019ba043674cd7d52833569e57cd02a152f2b5245005db\", \"6316b64d597a997ce2adbd38e32e75a8395c7525cf03ce9478d4c4994530b048\", \"671f8446fcee3747379810f3ee5ac643cccd337a51faa0646e3358e44b66cb4e\", \"8e2241b29c49ec2b3ed4b3373da9f980e2fce07a578803ee75db7182d30802a3\", \"98abd6e322587f3a3aa44acc21ff1849ef79950ade0c8e77c7e46f4ea5e02703\", \"9fb20d3659cfddee1ceeb541f5903b3b54dbcc2d29db62b1debb563c6664fbb2\"]}, \"state_id\": \"cce4e0303f308db70b24475b\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 15, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 37, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 1}, \"intervention\": \"DO_Z1_1\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 0}}]}}}", "extra_info": {"index": 75, "max_global_steps": 0, "min_global_steps": 0}, "index": 75, "prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=0\n intervention: none\n observed: Z1=0, Z2=0, Y=1\n\nExperiment 2:\n inputs: X1=0, X2=1, X3=1\n intervention: none\n observed: Z1=0, Z2=0, Y=1\n\nExperiment 3:\n inputs: X1=1, X2=1, X3=1\n intervention: set Z1=1\n observed: Z1=1, Z2=0, Y=1\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "raw_prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=0\n intervention: none\n observed: Z1=0, Z2=0, Y=1\n\nExperiment 2:\n inputs: X1=0, X2=1, X3=1\n intervention: none\n observed: Z1=0, Z2=0, Y=1\n\nExperiment 3:\n inputs: X1=1, X2=1, X3=1\n intervention: set Z1=1\n observed: Z1=1, Z2=0, Y=1\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "reward_model": {"style": "rule"}, "state_json": "{\"available_experiment_ids\": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 38, 39], \"metadata\": {\"evidence_size\": 3, \"family_bucket\": \"within_family\", \"maximum_separation\": 0.5945945945945946, \"mean_separation\": 0.37644787644787653, \"minimum_separation\": 0.16216216216216217, \"normalized_mean_separation\": 0.37644787644787653, \"oracle_budget_representation_error\": 0.08108108108108109, \"oracle_singleton_representation_error\": 0.31081081081081086, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.22972972972972977, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 8}, \"private\": {\"hidden_mode_id\": \"98abd6e322587f3a3aa44acc21ff1849ef79950ade0c8e77c7e46f4ea5e02703\", \"valid_mode_ids\": [\"0663fe10cbe11b01a099d5fdd8e11912c2bb268985cffbf8129953cdb0337e67\", \"1ed7f388df4c3c3454efee3994cce951070140ab2b2b6eb4c3aaddba25746451\", \"591ac9610a9f39507a019ba043674cd7d52833569e57cd02a152f2b5245005db\", \"6316b64d597a997ce2adbd38e32e75a8395c7525cf03ce9478d4c4994530b048\", \"671f8446fcee3747379810f3ee5ac643cccd337a51faa0646e3358e44b66cb4e\", \"8e2241b29c49ec2b3ed4b3373da9f980e2fce07a578803ee75db7182d30802a3\", \"98abd6e322587f3a3aa44acc21ff1849ef79950ade0c8e77c7e46f4ea5e02703\", \"9fb20d3659cfddee1ceeb541f5903b3b54dbcc2d29db62b1debb563c6664fbb2\"]}, \"state_id\": \"cce4e0303f308db70b24475b\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 15, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 37, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 1}, \"intervention\": \"DO_Z1_1\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 0}}]}"} {"agent_name": "causal_micro_lab_agent_loop", "data_source": "causal_micro_lab_final_eval_v3", "env_spec_json": "{\"agent\": {}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 76, \"task\": {\"state\": {\"available_experiment_ids\": [0, 1, 2, 3, 4, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 27, 28, 29, 30, 31, 32, 33, 34, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 4, \"family_bucket\": \"cross_family\", \"maximum_separation\": 0.6388888888888888, \"mean_separation\": 0.4206349206349208, \"minimum_separation\": 0.1111111111111111, \"normalized_mean_separation\": 0.4206349206349208, \"oracle_budget_representation_error\": 0.09722222222222222, \"oracle_singleton_representation_error\": 0.3333333333333333, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.2361111111111111, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 8}, \"private\": {\"hidden_mode_id\": \"2e7a62e8189260fb634be01a9ff9ef9eef0a16cc76373565065ac5ca05b1650f\", \"valid_mode_ids\": [\"16d318ce7192c9c64469f973478005413265f5df3d826367d779a02e5a609290\", \"25bca665262dcfab507cc83837928c1dad8f1f787eecaa7901b962917a36eb0c\", \"2e7a62e8189260fb634be01a9ff9ef9eef0a16cc76373565065ac5ca05b1650f\", \"3120974d5e8e3745ec6512725fba54db94fa4f0e93a3d44bf8f071d08a9d70ae\", \"33cd858814e906381cb25ffd0bc30bb43dbc6e1a846b76bda9dbadb739eb1127\", \"4ae8618338373c9d612e271b9158989e47db9019f220b758d71570dc2d15639d\", \"9160e1192756c95f6bde9199b7b2c67608647b621d63c7933395d2550f842ac7\", \"9a7c9d76702d0fd6541904a8e92ae4487ec49bc68e3f03797f5762d6fc7254f6\"]}, \"state_id\": \"8f30e97a21bd933df6fe8f65\", \"visible_experiments\": [{\"experiment_id\": 5, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 25, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 26, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 1}, \"intervention\": \"DO_Z1_0\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 35, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 0}}]}}}", "extra_info": {"index": 76, "max_global_steps": 0, "min_global_steps": 0}, "index": 76, "prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=1\n intervention: none\n observed: Z1=0, Z2=1, Y=0\n\nExperiment 2:\n inputs: X1=1, X2=0, X3=1\n intervention: none\n observed: Z1=1, Z2=1, Y=1\n\nExperiment 3:\n inputs: X1=1, X2=0, X3=1\n intervention: set Z1=0\n observed: Z1=0, Z2=1, Y=1\n\nExperiment 4:\n inputs: X1=1, X2=1, X3=1\n intervention: none\n observed: Z1=0, Z2=0, Y=1\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "raw_prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=1\n intervention: none\n observed: Z1=0, Z2=1, Y=0\n\nExperiment 2:\n inputs: X1=1, X2=0, X3=1\n intervention: none\n observed: Z1=1, Z2=1, Y=1\n\nExperiment 3:\n inputs: X1=1, X2=0, X3=1\n intervention: set Z1=0\n observed: Z1=0, Z2=1, Y=1\n\nExperiment 4:\n inputs: X1=1, X2=1, X3=1\n intervention: none\n observed: Z1=0, Z2=0, Y=1\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "reward_model": {"style": "rule"}, "state_json": "{\"available_experiment_ids\": [0, 1, 2, 3, 4, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 27, 28, 29, 30, 31, 32, 33, 34, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 4, \"family_bucket\": \"cross_family\", \"maximum_separation\": 0.6388888888888888, \"mean_separation\": 0.4206349206349208, \"minimum_separation\": 0.1111111111111111, \"normalized_mean_separation\": 0.4206349206349208, \"oracle_budget_representation_error\": 0.09722222222222222, \"oracle_singleton_representation_error\": 0.3333333333333333, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.2361111111111111, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 8}, \"private\": {\"hidden_mode_id\": \"2e7a62e8189260fb634be01a9ff9ef9eef0a16cc76373565065ac5ca05b1650f\", \"valid_mode_ids\": [\"16d318ce7192c9c64469f973478005413265f5df3d826367d779a02e5a609290\", \"25bca665262dcfab507cc83837928c1dad8f1f787eecaa7901b962917a36eb0c\", \"2e7a62e8189260fb634be01a9ff9ef9eef0a16cc76373565065ac5ca05b1650f\", \"3120974d5e8e3745ec6512725fba54db94fa4f0e93a3d44bf8f071d08a9d70ae\", \"33cd858814e906381cb25ffd0bc30bb43dbc6e1a846b76bda9dbadb739eb1127\", \"4ae8618338373c9d612e271b9158989e47db9019f220b758d71570dc2d15639d\", \"9160e1192756c95f6bde9199b7b2c67608647b621d63c7933395d2550f842ac7\", \"9a7c9d76702d0fd6541904a8e92ae4487ec49bc68e3f03797f5762d6fc7254f6\"]}, \"state_id\": \"8f30e97a21bd933df6fe8f65\", \"visible_experiments\": [{\"experiment_id\": 5, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 25, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 26, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 1}, \"intervention\": \"DO_Z1_0\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 35, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 0}}]}"} {"agent_name": "causal_micro_lab_agent_loop", "data_source": "causal_micro_lab_final_eval_v3", "env_spec_json": "{\"agent\": {}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 77, \"task\": {\"state\": {\"available_experiment_ids\": [0, 1, 2, 3, 4, 6, 7, 8, 9, 10, 11, 12, 13, 14, 16, 17, 18, 19, 20, 22, 23, 24, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 4, \"family_bucket\": \"cross_family\", \"maximum_separation\": 0.6944444444444444, \"mean_separation\": 0.40079365079365076, \"minimum_separation\": 0.1111111111111111, \"normalized_mean_separation\": 0.40079365079365076, \"oracle_budget_representation_error\": 0.05555555555555555, \"oracle_singleton_representation_error\": 0.2951388888888889, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.23958333333333334, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 8}, \"private\": {\"hidden_mode_id\": \"d3d308ea679f85b74122da54e63cdf2b1c8dd377c905698a1b3b0ece6d02b50a\", \"valid_mode_ids\": [\"1550c4579b0a0e96ba08e3f70cc362764da2f78a839970610c11a8c122ef2fd6\", \"2d354f2c5626ef2a818f8821f24f85d8c5227cf3bdecf37ee96f22cff9bb12cd\", \"411cb616f3b3b71728cdbb648fba7c59e8ebccdca052573f130d1f64779acd4e\", \"41308432b1caeb741b8a16875b3e0efa3eefaa2a6e18d2761e054fe768390cf0\", \"4e82411b0a2b5744b212d0d408f07c72cbf750d8a7b9d3b321ba29e02ed34b28\", \"d35644d7bf9e467e95d51753f5077e417bdfdfc3dc68041474dd505a6eea0a6f\", \"d3d308ea679f85b74122da54e63cdf2b1c8dd377c905698a1b3b0ece6d02b50a\", \"edc5e82f79e02be8c616436fc51ffa656e320a7f17cd86fabe68a864c03ce577\"]}, \"state_id\": \"a5adc77848157c58c445ebda\", \"visible_experiments\": [{\"experiment_id\": 5, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 15, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 21, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z1_0\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 25, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 0}}]}}}", "extra_info": {"index": 77, "max_global_steps": 0, "min_global_steps": 0}, "index": 77, "prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=1\n intervention: none\n observed: Z1=1, Z2=1, Y=1\n\nExperiment 2:\n inputs: X1=0, X2=1, X3=1\n intervention: none\n observed: Z1=0, Z2=0, Y=0\n\nExperiment 3:\n inputs: X1=1, X2=0, X3=0\n intervention: set Z1=0\n observed: Z1=0, Z2=1, Y=1\n\nExperiment 4:\n inputs: X1=1, X2=0, X3=1\n intervention: none\n observed: Z1=1, Z2=0, Y=1\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "raw_prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=1\n intervention: none\n observed: Z1=1, Z2=1, Y=1\n\nExperiment 2:\n inputs: X1=0, X2=1, X3=1\n intervention: none\n observed: Z1=0, Z2=0, Y=0\n\nExperiment 3:\n inputs: X1=1, X2=0, X3=0\n intervention: set Z1=0\n observed: Z1=0, Z2=1, Y=1\n\nExperiment 4:\n inputs: X1=1, X2=0, X3=1\n intervention: none\n observed: Z1=1, Z2=0, Y=1\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "reward_model": {"style": "rule"}, "state_json": "{\"available_experiment_ids\": [0, 1, 2, 3, 4, 6, 7, 8, 9, 10, 11, 12, 13, 14, 16, 17, 18, 19, 20, 22, 23, 24, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 4, \"family_bucket\": \"cross_family\", \"maximum_separation\": 0.6944444444444444, \"mean_separation\": 0.40079365079365076, \"minimum_separation\": 0.1111111111111111, \"normalized_mean_separation\": 0.40079365079365076, \"oracle_budget_representation_error\": 0.05555555555555555, \"oracle_singleton_representation_error\": 0.2951388888888889, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.23958333333333334, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 8}, \"private\": {\"hidden_mode_id\": \"d3d308ea679f85b74122da54e63cdf2b1c8dd377c905698a1b3b0ece6d02b50a\", \"valid_mode_ids\": [\"1550c4579b0a0e96ba08e3f70cc362764da2f78a839970610c11a8c122ef2fd6\", \"2d354f2c5626ef2a818f8821f24f85d8c5227cf3bdecf37ee96f22cff9bb12cd\", \"411cb616f3b3b71728cdbb648fba7c59e8ebccdca052573f130d1f64779acd4e\", \"41308432b1caeb741b8a16875b3e0efa3eefaa2a6e18d2761e054fe768390cf0\", \"4e82411b0a2b5744b212d0d408f07c72cbf750d8a7b9d3b321ba29e02ed34b28\", \"d35644d7bf9e467e95d51753f5077e417bdfdfc3dc68041474dd505a6eea0a6f\", \"d3d308ea679f85b74122da54e63cdf2b1c8dd377c905698a1b3b0ece6d02b50a\", \"edc5e82f79e02be8c616436fc51ffa656e320a7f17cd86fabe68a864c03ce577\"]}, \"state_id\": \"a5adc77848157c58c445ebda\", \"visible_experiments\": [{\"experiment_id\": 5, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 15, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 21, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z1_0\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 25, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 0}}]}"} {"agent_name": "causal_micro_lab_agent_loop", "data_source": "causal_micro_lab_final_eval_v3", "env_spec_json": "{\"agent\": {}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 78, \"task\": {\"state\": {\"available_experiment_ids\": [0, 1, 3, 4, 5, 6, 7, 8, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 4, \"family_bucket\": \"mixed\", \"maximum_separation\": 0.6111111111111112, \"mean_separation\": 0.4186507936507938, \"minimum_separation\": 0.16666666666666666, \"normalized_mean_separation\": 0.4186507936507938, \"oracle_budget_representation_error\": 0.08333333333333333, \"oracle_singleton_representation_error\": 0.3298611111111111, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.2465277777777778, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 8}, \"private\": {\"hidden_mode_id\": \"8d5f22a944431d0613d2ae5cddbcdef80447f23a1d65b8659708484bbc1f67c2\", \"valid_mode_ids\": [\"237dd4fb63c52af6a591f9e1102e865b6eef126e9700d52b5da5eaa0b395fac0\", \"3c9576f9cabbbd4cee1458e5dbfd3d5f1b0ad45ebd7c45d00ad1b410a0fdb464\", \"7d533064aff791fa89a854f05b00a16791d135639a6f92fd9bd784174d73c29e\", \"8d5f22a944431d0613d2ae5cddbcdef80447f23a1d65b8659708484bbc1f67c2\", \"b727353d6ee51a88eea3816bf809c9ab582826da93579c08c4d24f93806faeb3\", \"cfde31aa058c96e988e11b16e5a5b96f7c70b06f61c832ff8aa15b2c9932170a\", \"d6e8126d72a903f50985b3795f2982de9b5da0d75c8a6088c139094c672d1054\", \"f38867f3fb5029f981f308d134eef313c4dfd017627e04d25582efc7946973a0\"]}, \"state_id\": \"6cdede9ae44376db50f065b6\", \"visible_experiments\": [{\"experiment_id\": 2, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z1_1\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 0}}, {\"experiment_id\": 9, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 1}, \"intervention\": \"DO_Z2_1\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 20, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 21, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z1_0\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 0}}]}}}", "extra_info": {"index": 78, "max_global_steps": 0, "min_global_steps": 0}, "index": 78, "prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=0\n intervention: set Z1=1\n observed: Z1=1, Z2=0, Y=1\n\nExperiment 2:\n inputs: X1=0, X2=0, X3=1\n intervention: set Z2=1\n observed: Z1=0, Z2=1, Y=1\n\nExperiment 3:\n inputs: X1=1, X2=0, X3=0\n intervention: none\n observed: Z1=1, Z2=1, Y=0\n\nExperiment 4:\n inputs: X1=1, X2=0, X3=0\n intervention: set Z1=0\n observed: Z1=0, Z2=0, Y=0\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "raw_prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=0\n intervention: set Z1=1\n observed: Z1=1, Z2=0, Y=1\n\nExperiment 2:\n inputs: X1=0, X2=0, X3=1\n intervention: set Z2=1\n observed: Z1=0, Z2=1, Y=1\n\nExperiment 3:\n inputs: X1=1, X2=0, X3=0\n intervention: none\n observed: Z1=1, Z2=1, Y=0\n\nExperiment 4:\n inputs: X1=1, X2=0, X3=0\n intervention: set Z1=0\n observed: Z1=0, Z2=0, Y=0\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "reward_model": {"style": "rule"}, "state_json": "{\"available_experiment_ids\": [0, 1, 3, 4, 5, 6, 7, 8, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 4, \"family_bucket\": \"mixed\", \"maximum_separation\": 0.6111111111111112, \"mean_separation\": 0.4186507936507938, \"minimum_separation\": 0.16666666666666666, \"normalized_mean_separation\": 0.4186507936507938, \"oracle_budget_representation_error\": 0.08333333333333333, \"oracle_singleton_representation_error\": 0.3298611111111111, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.2465277777777778, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 8}, \"private\": {\"hidden_mode_id\": \"8d5f22a944431d0613d2ae5cddbcdef80447f23a1d65b8659708484bbc1f67c2\", \"valid_mode_ids\": [\"237dd4fb63c52af6a591f9e1102e865b6eef126e9700d52b5da5eaa0b395fac0\", \"3c9576f9cabbbd4cee1458e5dbfd3d5f1b0ad45ebd7c45d00ad1b410a0fdb464\", \"7d533064aff791fa89a854f05b00a16791d135639a6f92fd9bd784174d73c29e\", \"8d5f22a944431d0613d2ae5cddbcdef80447f23a1d65b8659708484bbc1f67c2\", \"b727353d6ee51a88eea3816bf809c9ab582826da93579c08c4d24f93806faeb3\", \"cfde31aa058c96e988e11b16e5a5b96f7c70b06f61c832ff8aa15b2c9932170a\", \"d6e8126d72a903f50985b3795f2982de9b5da0d75c8a6088c139094c672d1054\", \"f38867f3fb5029f981f308d134eef313c4dfd017627e04d25582efc7946973a0\"]}, \"state_id\": \"6cdede9ae44376db50f065b6\", \"visible_experiments\": [{\"experiment_id\": 2, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z1_1\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 0}}, {\"experiment_id\": 9, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 1}, \"intervention\": \"DO_Z2_1\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 20, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 21, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z1_0\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 0}}]}"} {"agent_name": "causal_micro_lab_agent_loop", "data_source": "causal_micro_lab_final_eval_v3", "env_spec_json": "{\"agent\": {}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 79, \"task\": {\"state\": {\"available_experiment_ids\": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 33, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 3, \"family_bucket\": \"mixed\", \"maximum_separation\": 0.5675675675675675, \"mean_separation\": 0.37837837837837845, \"minimum_separation\": 0.16216216216216217, \"normalized_mean_separation\": 0.37837837837837845, \"oracle_budget_representation_error\": 0.08108108108108109, \"oracle_singleton_representation_error\": 0.33108108108108114, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.25000000000000006, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 8}, \"private\": {\"hidden_mode_id\": \"b55bcf6a0200c9c897b372b017747a7c431758efadd5ffe0b5b80a9db1d30547\", \"valid_mode_ids\": [\"1432ebe007607114a9302c1e7dcefdd96754a8d3e395fdc99423497b39bf8e2d\", \"35db948ed9111145df10f077698ec9cc5f8c3e9c7cad8fc8d75b871f5e57ba9d\", \"3e9bc30bcb4e75631058fe3b0ba67d29b5fc26a6958d5fccd6d7a9b2bfe9dbbc\", \"a7f7a3450a55a7b1264309a6b9bf31a1a100ba1780139900705ac60899d7426e\", \"b55bcf6a0200c9c897b372b017747a7c431758efadd5ffe0b5b80a9db1d30547\", \"c3b002d62c243693c58d821dd1ba7b120b30334a46f6dcd8b8372b76ba16028b\", \"e0eefa0579465075e67a63efe96aa69a6930c155cb0a5ad0a9f1dbb93a57e5fb\", \"e629aa2c5acb8381f5c8d47f38174c4d27db4cc3a02eebf867859c52cbab7055\"]}, \"state_id\": \"0dcc79e946d437fd0e204d50\", \"visible_experiments\": [{\"experiment_id\": 22, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z1_1\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 0}}, {\"experiment_id\": 25, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 30, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 0}}]}}}", "extra_info": {"index": 79, "max_global_steps": 0, "min_global_steps": 0}, "index": 79, "prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=1, X2=0, X3=0\n intervention: set Z1=1\n observed: Z1=1, Z2=0, Y=0\n\nExperiment 2:\n inputs: X1=1, X2=0, X3=1\n intervention: none\n observed: Z1=0, Z2=1, Y=0\n\nExperiment 3:\n inputs: X1=1, X2=1, X3=0\n intervention: none\n observed: Z1=0, Z2=0, Y=1\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "raw_prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=1, X2=0, X3=0\n intervention: set Z1=1\n observed: Z1=1, Z2=0, Y=0\n\nExperiment 2:\n inputs: X1=1, X2=0, X3=1\n intervention: none\n observed: Z1=0, Z2=1, Y=0\n\nExperiment 3:\n inputs: X1=1, X2=1, X3=0\n intervention: none\n observed: Z1=0, Z2=0, Y=1\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "reward_model": {"style": "rule"}, "state_json": "{\"available_experiment_ids\": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 23, 24, 26, 27, 28, 29, 31, 32, 33, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 3, \"family_bucket\": \"mixed\", \"maximum_separation\": 0.5675675675675675, \"mean_separation\": 0.37837837837837845, \"minimum_separation\": 0.16216216216216217, \"normalized_mean_separation\": 0.37837837837837845, \"oracle_budget_representation_error\": 0.08108108108108109, \"oracle_singleton_representation_error\": 0.33108108108108114, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.25000000000000006, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 8}, \"private\": {\"hidden_mode_id\": \"b55bcf6a0200c9c897b372b017747a7c431758efadd5ffe0b5b80a9db1d30547\", \"valid_mode_ids\": [\"1432ebe007607114a9302c1e7dcefdd96754a8d3e395fdc99423497b39bf8e2d\", \"35db948ed9111145df10f077698ec9cc5f8c3e9c7cad8fc8d75b871f5e57ba9d\", \"3e9bc30bcb4e75631058fe3b0ba67d29b5fc26a6958d5fccd6d7a9b2bfe9dbbc\", \"a7f7a3450a55a7b1264309a6b9bf31a1a100ba1780139900705ac60899d7426e\", \"b55bcf6a0200c9c897b372b017747a7c431758efadd5ffe0b5b80a9db1d30547\", \"c3b002d62c243693c58d821dd1ba7b120b30334a46f6dcd8b8372b76ba16028b\", \"e0eefa0579465075e67a63efe96aa69a6930c155cb0a5ad0a9f1dbb93a57e5fb\", \"e629aa2c5acb8381f5c8d47f38174c4d27db4cc3a02eebf867859c52cbab7055\"]}, \"state_id\": \"0dcc79e946d437fd0e204d50\", \"visible_experiments\": [{\"experiment_id\": 22, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z1_1\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 0}}, {\"experiment_id\": 25, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 30, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 0}}]}"} {"agent_name": "causal_micro_lab_agent_loop", "data_source": "causal_micro_lab_final_eval_v3", "env_spec_json": "{\"agent\": {}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 80, \"task\": {\"state\": {\"available_experiment_ids\": [0, 1, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 16, 17, 18, 19, 20, 21, 22, 23, 24, 26, 27, 28, 29, 31, 32, 33, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 4, \"family_bucket\": \"cross_family\", \"maximum_separation\": 0.6944444444444444, \"mean_separation\": 0.4593253968253969, \"minimum_separation\": 0.16666666666666666, \"normalized_mean_separation\": 0.4593253968253969, \"oracle_budget_representation_error\": 0.08680555555555555, \"oracle_singleton_representation_error\": 0.34375, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.2569444444444444, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 8}, \"private\": {\"hidden_mode_id\": \"231e96557430a9cb0725f3419572b9f9c20f2b67018fad23106a81598e2c433a\", \"valid_mode_ids\": [\"231e96557430a9cb0725f3419572b9f9c20f2b67018fad23106a81598e2c433a\", \"790cd7ffa2c061438ff003d4e8f55881d896971ac27ec778012d90f91f5740c8\", \"836b5db499a2809f9b08140bd2b31b45aaa5c4ce34f280db47a3d5bdd827e78d\", \"a176618116c5b68d8854c59bd8ca5ed45887f5ebe99c11011e37efba68690428\", \"a40126323172b5213d29de657c193f8a1aa0b06c0f8c4d5e851e9abf9cc896f9\", \"ae951687e09089bad95cc0536d17d0f0ad0b9e9219fbd7f1a483b33c1d16b064\", \"ca24951dc016cce58176c52370e9254e652f62be35500513c67a25bf8bae8166\", \"e077ab9d051b4084db7848540f9e9925878b51bbff083e0e01ac65c8b12f63e8\"]}, \"state_id\": \"408e556c222b818a063872d1\", \"visible_experiments\": [{\"experiment_id\": 2, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z1_1\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 0}}, {\"experiment_id\": 15, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 0}}, {\"experiment_id\": 25, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 30, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 0}}]}}}", "extra_info": {"index": 80, "max_global_steps": 0, "min_global_steps": 0}, "index": 80, "prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=0\n intervention: set Z1=1\n observed: Z1=1, Z2=0, Y=0\n\nExperiment 2:\n inputs: X1=0, X2=1, X3=1\n intervention: none\n observed: Z1=1, Z2=0, Y=1\n\nExperiment 3:\n inputs: X1=1, X2=0, X3=1\n intervention: none\n observed: Z1=0, Z2=0, Y=1\n\nExperiment 4:\n inputs: X1=1, X2=1, X3=0\n intervention: none\n observed: Z1=0, Z2=0, Y=0\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "raw_prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=0\n intervention: set Z1=1\n observed: Z1=1, Z2=0, Y=0\n\nExperiment 2:\n inputs: X1=0, X2=1, X3=1\n intervention: none\n observed: Z1=1, Z2=0, Y=1\n\nExperiment 3:\n inputs: X1=1, X2=0, X3=1\n intervention: none\n observed: Z1=0, Z2=0, Y=1\n\nExperiment 4:\n inputs: X1=1, X2=1, X3=0\n intervention: none\n observed: Z1=0, Z2=0, Y=0\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "reward_model": {"style": "rule"}, "state_json": "{\"available_experiment_ids\": [0, 1, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 16, 17, 18, 19, 20, 21, 22, 23, 24, 26, 27, 28, 29, 31, 32, 33, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 4, \"family_bucket\": \"cross_family\", \"maximum_separation\": 0.6944444444444444, \"mean_separation\": 0.4593253968253969, \"minimum_separation\": 0.16666666666666666, \"normalized_mean_separation\": 0.4593253968253969, \"oracle_budget_representation_error\": 0.08680555555555555, \"oracle_singleton_representation_error\": 0.34375, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.2569444444444444, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 8}, \"private\": {\"hidden_mode_id\": \"231e96557430a9cb0725f3419572b9f9c20f2b67018fad23106a81598e2c433a\", \"valid_mode_ids\": [\"231e96557430a9cb0725f3419572b9f9c20f2b67018fad23106a81598e2c433a\", \"790cd7ffa2c061438ff003d4e8f55881d896971ac27ec778012d90f91f5740c8\", \"836b5db499a2809f9b08140bd2b31b45aaa5c4ce34f280db47a3d5bdd827e78d\", \"a176618116c5b68d8854c59bd8ca5ed45887f5ebe99c11011e37efba68690428\", \"a40126323172b5213d29de657c193f8a1aa0b06c0f8c4d5e851e9abf9cc896f9\", \"ae951687e09089bad95cc0536d17d0f0ad0b9e9219fbd7f1a483b33c1d16b064\", \"ca24951dc016cce58176c52370e9254e652f62be35500513c67a25bf8bae8166\", \"e077ab9d051b4084db7848540f9e9925878b51bbff083e0e01ac65c8b12f63e8\"]}, \"state_id\": \"408e556c222b818a063872d1\", \"visible_experiments\": [{\"experiment_id\": 2, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z1_1\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 0}}, {\"experiment_id\": 15, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 0}}, {\"experiment_id\": 25, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 30, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 0}}]}"} {"agent_name": "causal_micro_lab_agent_loop", "data_source": "causal_micro_lab_final_eval_v3", "env_spec_json": "{\"agent\": {}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 81, \"task\": {\"state\": {\"available_experiment_ids\": [0, 1, 3, 4, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 4, \"family_bucket\": \"cross_family\", \"maximum_separation\": 0.7222222222222222, \"mean_separation\": 0.42460317460317454, \"minimum_separation\": 0.1111111111111111, \"normalized_mean_separation\": 0.42460317460317454, \"oracle_budget_representation_error\": 0.05555555555555555, \"oracle_singleton_representation_error\": 0.3194444444444444, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.26388888888888884, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 8}, \"private\": {\"hidden_mode_id\": \"2bf79a70b86073856ffa26338bb32698f983e95604e3789cc61bcf3b9d0b8e9c\", \"valid_mode_ids\": [\"12ac1ad4113731422b68dc3086d0e4b68a8a7c442880f3edc381aaabb58058cd\", \"2bf79a70b86073856ffa26338bb32698f983e95604e3789cc61bcf3b9d0b8e9c\", \"3c6386ba7596b9d1b61c9a44458140140115946abb76ddd6ac160220cfdbd4d3\", \"932a8b657eb1c5f3e93258cc087e4f7ee5425095d734c8a4379d16e093f77150\", \"c901730982dd7b860e57c68576c37302ff547ddebdc63fbce77d164cbee56f56\", \"cdfe4be7e057a36a7624c4d54900b48c7660b97372f409dccc9aca61a02243d7\", \"dc49beea9ee4975f65c649c2bdc2813cdddc8790bc58197a3f6cd79cdfd58ea5\", \"efe92018547a56ecd69ac639af8020c2ee306caf27756d88cd37da68caba81df\"]}, \"state_id\": \"af68f3b6e966ec0efdc101a6\", \"visible_experiments\": [{\"experiment_id\": 2, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z1_1\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 5, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 0}}, {\"experiment_id\": 6, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 1}, \"intervention\": \"DO_Z1_0\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 25, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 1}}]}}}", "extra_info": {"index": 81, "max_global_steps": 0, "min_global_steps": 0}, "index": 81, "prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=0\n intervention: set Z1=1\n observed: Z1=1, Z2=1, Y=1\n\nExperiment 2:\n inputs: X1=0, X2=0, X3=1\n intervention: none\n observed: Z1=1, Z2=0, Y=1\n\nExperiment 3:\n inputs: X1=0, X2=0, X3=1\n intervention: set Z1=0\n observed: Z1=0, Z2=1, Y=1\n\nExperiment 4:\n inputs: X1=1, X2=0, X3=1\n intervention: none\n observed: Z1=0, Z2=1, Y=1\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "raw_prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=0\n intervention: set Z1=1\n observed: Z1=1, Z2=1, Y=1\n\nExperiment 2:\n inputs: X1=0, X2=0, X3=1\n intervention: none\n observed: Z1=1, Z2=0, Y=1\n\nExperiment 3:\n inputs: X1=0, X2=0, X3=1\n intervention: set Z1=0\n observed: Z1=0, Z2=1, Y=1\n\nExperiment 4:\n inputs: X1=1, X2=0, X3=1\n intervention: none\n observed: Z1=0, Z2=1, Y=1\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "reward_model": {"style": "rule"}, "state_json": "{\"available_experiment_ids\": [0, 1, 3, 4, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 4, \"family_bucket\": \"cross_family\", \"maximum_separation\": 0.7222222222222222, \"mean_separation\": 0.42460317460317454, \"minimum_separation\": 0.1111111111111111, \"normalized_mean_separation\": 0.42460317460317454, \"oracle_budget_representation_error\": 0.05555555555555555, \"oracle_singleton_representation_error\": 0.3194444444444444, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.26388888888888884, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 8}, \"private\": {\"hidden_mode_id\": \"2bf79a70b86073856ffa26338bb32698f983e95604e3789cc61bcf3b9d0b8e9c\", \"valid_mode_ids\": [\"12ac1ad4113731422b68dc3086d0e4b68a8a7c442880f3edc381aaabb58058cd\", \"2bf79a70b86073856ffa26338bb32698f983e95604e3789cc61bcf3b9d0b8e9c\", \"3c6386ba7596b9d1b61c9a44458140140115946abb76ddd6ac160220cfdbd4d3\", \"932a8b657eb1c5f3e93258cc087e4f7ee5425095d734c8a4379d16e093f77150\", \"c901730982dd7b860e57c68576c37302ff547ddebdc63fbce77d164cbee56f56\", \"cdfe4be7e057a36a7624c4d54900b48c7660b97372f409dccc9aca61a02243d7\", \"dc49beea9ee4975f65c649c2bdc2813cdddc8790bc58197a3f6cd79cdfd58ea5\", \"efe92018547a56ecd69ac639af8020c2ee306caf27756d88cd37da68caba81df\"]}, \"state_id\": \"af68f3b6e966ec0efdc101a6\", \"visible_experiments\": [{\"experiment_id\": 2, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z1_1\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 5, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 0}}, {\"experiment_id\": 6, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 1}, \"intervention\": \"DO_Z1_0\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 25, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 1}}]}"} {"agent_name": "causal_micro_lab_agent_loop", "data_source": "causal_micro_lab_final_eval_v3", "env_spec_json": "{\"agent\": {}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 82, \"task\": {\"state\": {\"available_experiment_ids\": [1, 2, 3, 4, 5, 6, 7, 8, 9, 11, 12, 13, 14, 15, 16, 17, 18, 19, 21, 22, 23, 24, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 4, \"family_bucket\": \"mixed\", \"maximum_separation\": 0.6388888888888888, \"mean_separation\": 0.4146825396825396, \"minimum_separation\": 0.16666666666666666, \"normalized_mean_separation\": 0.4146825396825396, \"oracle_budget_representation_error\": 0.08333333333333333, \"oracle_singleton_representation_error\": 0.35416666666666663, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.2708333333333333, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 8}, \"private\": {\"hidden_mode_id\": \"aed5b7de43e68d10355e7050bf0f20de183094904a9af5f51747383ac87b0b76\", \"valid_mode_ids\": [\"127847a6aa1246cee4850cc0273b759c3e988976f349f6d6b691ef43ee7a0d9c\", \"158ebd24ad7223ab046d25a7573333af1274dbc96709e1bae43c5ba06b1bc270\", \"3930d52ae85fcf3a5e694dfc29c414ed27cc982d0fe8e93ee66a748d04035580\", \"3b559cf39139be71f552d6ab766b402880e7027786a68177a708a1691b0774b8\", \"6288589d0e049ff98befa75260c207dfe31f833612c182a57aee2d55cf6d7c9a\", \"9e8588d1628ba3c3c5e81cfe2f44275e9a929a7381009f6722085372e0339f03\", \"aed5b7de43e68d10355e7050bf0f20de183094904a9af5f51747383ac87b0b76\", \"e1987cb4f5c574aee02cdae87f814941e273ec718a7a3d6d29efafd5589e51f5\"]}, \"state_id\": \"dbd455cc675fdd7e4df6f445\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 10, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 20, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 0}}, {\"experiment_id\": 25, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}]}}}", "extra_info": {"index": 82, "max_global_steps": 0, "min_global_steps": 0}, "index": 82, "prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=0\n intervention: none\n observed: Z1=0, Z2=0, Y=0\n\nExperiment 2:\n inputs: X1=0, X2=1, X3=0\n intervention: none\n observed: Z1=1, Z2=1, Y=0\n\nExperiment 3:\n inputs: X1=1, X2=0, X3=0\n intervention: none\n observed: Z1=1, Z2=0, Y=1\n\nExperiment 4:\n inputs: X1=1, X2=0, X3=1\n intervention: none\n observed: Z1=1, Z2=1, Y=0\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "raw_prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=0\n intervention: none\n observed: Z1=0, Z2=0, Y=0\n\nExperiment 2:\n inputs: X1=0, X2=1, X3=0\n intervention: none\n observed: Z1=1, Z2=1, Y=0\n\nExperiment 3:\n inputs: X1=1, X2=0, X3=0\n intervention: none\n observed: Z1=1, Z2=0, Y=1\n\nExperiment 4:\n inputs: X1=1, X2=0, X3=1\n intervention: none\n observed: Z1=1, Z2=1, Y=0\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "reward_model": {"style": "rule"}, "state_json": "{\"available_experiment_ids\": [1, 2, 3, 4, 5, 6, 7, 8, 9, 11, 12, 13, 14, 15, 16, 17, 18, 19, 21, 22, 23, 24, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 4, \"family_bucket\": \"mixed\", \"maximum_separation\": 0.6388888888888888, \"mean_separation\": 0.4146825396825396, \"minimum_separation\": 0.16666666666666666, \"normalized_mean_separation\": 0.4146825396825396, \"oracle_budget_representation_error\": 0.08333333333333333, \"oracle_singleton_representation_error\": 0.35416666666666663, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.2708333333333333, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 8}, \"private\": {\"hidden_mode_id\": \"aed5b7de43e68d10355e7050bf0f20de183094904a9af5f51747383ac87b0b76\", \"valid_mode_ids\": [\"127847a6aa1246cee4850cc0273b759c3e988976f349f6d6b691ef43ee7a0d9c\", \"158ebd24ad7223ab046d25a7573333af1274dbc96709e1bae43c5ba06b1bc270\", \"3930d52ae85fcf3a5e694dfc29c414ed27cc982d0fe8e93ee66a748d04035580\", \"3b559cf39139be71f552d6ab766b402880e7027786a68177a708a1691b0774b8\", \"6288589d0e049ff98befa75260c207dfe31f833612c182a57aee2d55cf6d7c9a\", \"9e8588d1628ba3c3c5e81cfe2f44275e9a929a7381009f6722085372e0339f03\", \"aed5b7de43e68d10355e7050bf0f20de183094904a9af5f51747383ac87b0b76\", \"e1987cb4f5c574aee02cdae87f814941e273ec718a7a3d6d29efafd5589e51f5\"]}, \"state_id\": \"dbd455cc675fdd7e4df6f445\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 10, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 20, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 0}}, {\"experiment_id\": 25, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}]}"} {"agent_name": "causal_micro_lab_agent_loop", "data_source": "causal_micro_lab_final_eval_v3", "env_spec_json": "{\"agent\": {}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 83, \"task\": {\"state\": {\"available_experiment_ids\": [0, 1, 3, 4, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 4, \"family_bucket\": \"mixed\", \"maximum_separation\": 0.6944444444444444, \"mean_separation\": 0.43154761904761896, \"minimum_separation\": 0.16666666666666666, \"normalized_mean_separation\": 0.43154761904761896, \"oracle_budget_representation_error\": 0.08333333333333333, \"oracle_singleton_representation_error\": 0.3576388888888889, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.2743055555555556, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 8}, \"private\": {\"hidden_mode_id\": \"bce97b22150304e96668f2c318da5e0e11376b773270f262488175d67c4192f2\", \"valid_mode_ids\": [\"7942fbf91b9eee958acc27313b30d9ff566c925e570a6ef3c78777a7c2060261\", \"a1e4e17bc2d1f7adf1ed346df3f1246349a9439b4d7257d12bc08dff6c1d86f2\", \"a2f4a343d4d258886982fb8bdbacefdf23c17808bba29516248cd3629d43b881\", \"a6ebf019008431a983d077ab8184f52295bddc62d863d52caf22b867b4e602b9\", \"a7a892759a02694b4b928d53a49de162036cc22125fd51cd6623213ff7711086\", \"b0e4453e0dde6d8a7d7345cfe7786f9049a8eb52ea51e8288fd00c6f73f68599\", \"bce97b22150304e96668f2c318da5e0e11376b773270f262488175d67c4192f2\", \"c8d4994469cfd2536d4f3898bba9ca524055d020084a3465a5a3ba0a2e6557c6\"]}, \"state_id\": \"b8f746e8efadf39c759d40e5\", \"visible_experiments\": [{\"experiment_id\": 2, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z1_1\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 5, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 20, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 35, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}]}}}", "extra_info": {"index": 83, "max_global_steps": 0, "min_global_steps": 0}, "index": 83, "prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=0\n intervention: set Z1=1\n observed: Z1=1, Z2=1, Y=0\n\nExperiment 2:\n inputs: X1=0, X2=0, X3=1\n intervention: none\n observed: Z1=0, Z2=0, Y=1\n\nExperiment 3:\n inputs: X1=1, X2=0, X3=0\n intervention: none\n observed: Z1=0, Z2=1, Y=0\n\nExperiment 4:\n inputs: X1=1, X2=1, X3=1\n intervention: none\n observed: Z1=1, Z2=1, Y=0\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "raw_prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=0\n intervention: set Z1=1\n observed: Z1=1, Z2=1, Y=0\n\nExperiment 2:\n inputs: X1=0, X2=0, X3=1\n intervention: none\n observed: Z1=0, Z2=0, Y=1\n\nExperiment 3:\n inputs: X1=1, X2=0, X3=0\n intervention: none\n observed: Z1=0, Z2=1, Y=0\n\nExperiment 4:\n inputs: X1=1, X2=1, X3=1\n intervention: none\n observed: Z1=1, Z2=1, Y=0\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "reward_model": {"style": "rule"}, "state_json": "{\"available_experiment_ids\": [0, 1, 3, 4, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 4, \"family_bucket\": \"mixed\", \"maximum_separation\": 0.6944444444444444, \"mean_separation\": 0.43154761904761896, \"minimum_separation\": 0.16666666666666666, \"normalized_mean_separation\": 0.43154761904761896, \"oracle_budget_representation_error\": 0.08333333333333333, \"oracle_singleton_representation_error\": 0.3576388888888889, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.2743055555555556, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 8}, \"private\": {\"hidden_mode_id\": \"bce97b22150304e96668f2c318da5e0e11376b773270f262488175d67c4192f2\", \"valid_mode_ids\": [\"7942fbf91b9eee958acc27313b30d9ff566c925e570a6ef3c78777a7c2060261\", \"a1e4e17bc2d1f7adf1ed346df3f1246349a9439b4d7257d12bc08dff6c1d86f2\", \"a2f4a343d4d258886982fb8bdbacefdf23c17808bba29516248cd3629d43b881\", \"a6ebf019008431a983d077ab8184f52295bddc62d863d52caf22b867b4e602b9\", \"a7a892759a02694b4b928d53a49de162036cc22125fd51cd6623213ff7711086\", \"b0e4453e0dde6d8a7d7345cfe7786f9049a8eb52ea51e8288fd00c6f73f68599\", \"bce97b22150304e96668f2c318da5e0e11376b773270f262488175d67c4192f2\", \"c8d4994469cfd2536d4f3898bba9ca524055d020084a3465a5a3ba0a2e6557c6\"]}, \"state_id\": \"b8f746e8efadf39c759d40e5\", \"visible_experiments\": [{\"experiment_id\": 2, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z1_1\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 5, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 20, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 35, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}]}"} {"agent_name": "causal_micro_lab_agent_loop", "data_source": "causal_micro_lab_final_eval_v3", "env_spec_json": "{\"agent\": {}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 84, \"task\": {\"state\": {\"available_experiment_ids\": [0, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 13, 14, 16, 17, 18, 19, 20, 21, 22, 23, 24, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 4, \"family_bucket\": \"mixed\", \"maximum_separation\": 0.7222222222222222, \"mean_separation\": 0.4841269841269842, \"minimum_separation\": 0.16666666666666666, \"normalized_mean_separation\": 0.4841269841269842, \"oracle_budget_representation_error\": 0.12499999999999999, \"oracle_singleton_representation_error\": 0.4027777777777778, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.2777777777777778, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 8}, \"private\": {\"hidden_mode_id\": \"8d70f0c112c103f511632a5c1aafce119e13547219be995f8584db97db28118a\", \"valid_mode_ids\": [\"2cf68c03c722876d251f0a26e9f03b34279e9726edea9b3cf8c2980f6940a68b\", \"3255a5deb513f7eca1d338f60b910905c75c93fd130515e401857bff3eb2c695\", \"3fc3251115863a12d04c20e7087d3e524938f7d6c843e5e9cdd209d63a091630\", \"5f709a286ba46be171f50f64d5ec244d1995d8553756eca9db54f74db86447ff\", \"6873accc4693ce94fcce6c3b78bc94f9dcca9f5f982d77832862f0f1666e3ee9\", \"8d70f0c112c103f511632a5c1aafce119e13547219be995f8584db97db28118a\", \"a04d0873f274469a5b10d2df3bcfb6333403daaeebcbfd526e18ede281fa9225\", \"d84b48d5b33c069aab579dfc329c05f18f870358b176b337f395e6eb43a9a064\"]}, \"state_id\": \"0272c4f55911c53af4b284f5\", \"visible_experiments\": [{\"experiment_id\": 1, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z1_0\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 12, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 0}, \"intervention\": \"DO_Z1_1\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 0}}, {\"experiment_id\": 15, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 25, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 0}}]}}}", "extra_info": {"index": 84, "max_global_steps": 0, "min_global_steps": 0}, "index": 84, "prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=0\n intervention: set Z1=0\n observed: Z1=0, Z2=0, Y=0\n\nExperiment 2:\n inputs: X1=0, X2=1, X3=0\n intervention: set Z1=1\n observed: Z1=1, Z2=0, Y=0\n\nExperiment 3:\n inputs: X1=0, X2=1, X3=1\n intervention: none\n observed: Z1=0, Z2=0, Y=1\n\nExperiment 4:\n inputs: X1=1, X2=0, X3=1\n intervention: none\n observed: Z1=1, Z2=0, Y=0\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "raw_prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=0\n intervention: set Z1=0\n observed: Z1=0, Z2=0, Y=0\n\nExperiment 2:\n inputs: X1=0, X2=1, X3=0\n intervention: set Z1=1\n observed: Z1=1, Z2=0, Y=0\n\nExperiment 3:\n inputs: X1=0, X2=1, X3=1\n intervention: none\n observed: Z1=0, Z2=0, Y=1\n\nExperiment 4:\n inputs: X1=1, X2=0, X3=1\n intervention: none\n observed: Z1=1, Z2=0, Y=0\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "reward_model": {"style": "rule"}, "state_json": "{\"available_experiment_ids\": [0, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 13, 14, 16, 17, 18, 19, 20, 21, 22, 23, 24, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 4, \"family_bucket\": \"mixed\", \"maximum_separation\": 0.7222222222222222, \"mean_separation\": 0.4841269841269842, \"minimum_separation\": 0.16666666666666666, \"normalized_mean_separation\": 0.4841269841269842, \"oracle_budget_representation_error\": 0.12499999999999999, \"oracle_singleton_representation_error\": 0.4027777777777778, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.2777777777777778, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 8}, \"private\": {\"hidden_mode_id\": \"8d70f0c112c103f511632a5c1aafce119e13547219be995f8584db97db28118a\", \"valid_mode_ids\": [\"2cf68c03c722876d251f0a26e9f03b34279e9726edea9b3cf8c2980f6940a68b\", \"3255a5deb513f7eca1d338f60b910905c75c93fd130515e401857bff3eb2c695\", \"3fc3251115863a12d04c20e7087d3e524938f7d6c843e5e9cdd209d63a091630\", \"5f709a286ba46be171f50f64d5ec244d1995d8553756eca9db54f74db86447ff\", \"6873accc4693ce94fcce6c3b78bc94f9dcca9f5f982d77832862f0f1666e3ee9\", \"8d70f0c112c103f511632a5c1aafce119e13547219be995f8584db97db28118a\", \"a04d0873f274469a5b10d2df3bcfb6333403daaeebcbfd526e18ede281fa9225\", \"d84b48d5b33c069aab579dfc329c05f18f870358b176b337f395e6eb43a9a064\"]}, \"state_id\": \"0272c4f55911c53af4b284f5\", \"visible_experiments\": [{\"experiment_id\": 1, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z1_0\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 12, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 0}, \"intervention\": \"DO_Z1_1\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 0}}, {\"experiment_id\": 15, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 25, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 0}}]}"} {"agent_name": "causal_micro_lab_agent_loop", "data_source": "causal_micro_lab_final_eval_v3", "env_spec_json": "{\"agent\": {}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 85, \"task\": {\"state\": {\"available_experiment_ids\": [0, 1, 3, 4, 6, 7, 8, 9, 10, 11, 12, 13, 14, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 31, 32, 33, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 4, \"family_bucket\": \"mixed\", \"maximum_separation\": 0.6388888888888888, \"mean_separation\": 0.39682539682539675, \"minimum_separation\": 0.1111111111111111, \"normalized_mean_separation\": 0.39682539682539675, \"oracle_budget_representation_error\": 0.05555555555555555, \"oracle_singleton_representation_error\": 0.34027777777777773, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.2847222222222222, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 8}, \"private\": {\"hidden_mode_id\": \"b70a2a5deed17616046eb0eb88c87f20dc48549be3270cd56578f927f1de2432\", \"valid_mode_ids\": [\"04bc145fb9aa09fb3f6c9ba8c8e591e349cdb29b927f4aaced2dcf336ecc27b6\", \"1ec4b3cf9701a227b835b97cfdc9dc1b59a32e35261493fb49a6ee0d458fda90\", \"27ff6ead3d3b62d54000f4222054c322933163ddfe2c751a120c6231e7498b7d\", \"46b2422cd074116199947ebc650106c67d03f5fe4c43437d2b8e77d4c4dfc32d\", \"471693d105d40d67aa86503e917cdec60153dcb40f2b4df86884503e8919509e\", \"b70a2a5deed17616046eb0eb88c87f20dc48549be3270cd56578f927f1de2432\", \"cbd2ae34e91d72977274a9dbfbb51a37dc3d3bd221bde365ea585bf8daa7ed03\", \"d555579a400013777f4a535279fde63112dfe7ebcdc42a4254df90a1100d4796\"]}, \"state_id\": \"6637b9b3353b9fbeaaeaa8b0\", \"visible_experiments\": [{\"experiment_id\": 2, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z1_1\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 0}}, {\"experiment_id\": 5, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 15, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 30, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 0}}]}}}", "extra_info": {"index": 85, "max_global_steps": 0, "min_global_steps": 0}, "index": 85, "prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=0\n intervention: set Z1=1\n observed: Z1=1, Z2=0, Y=0\n\nExperiment 2:\n inputs: X1=0, X2=0, X3=1\n intervention: none\n observed: Z1=0, Z2=0, Y=0\n\nExperiment 3:\n inputs: X1=0, X2=1, X3=1\n intervention: none\n observed: Z1=0, Z2=0, Y=1\n\nExperiment 4:\n inputs: X1=1, X2=1, X3=0\n intervention: none\n observed: Z1=0, Z2=0, Y=0\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "raw_prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=0\n intervention: set Z1=1\n observed: Z1=1, Z2=0, Y=0\n\nExperiment 2:\n inputs: X1=0, X2=0, X3=1\n intervention: none\n observed: Z1=0, Z2=0, Y=0\n\nExperiment 3:\n inputs: X1=0, X2=1, X3=1\n intervention: none\n observed: Z1=0, Z2=0, Y=1\n\nExperiment 4:\n inputs: X1=1, X2=1, X3=0\n intervention: none\n observed: Z1=0, Z2=0, Y=0\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "reward_model": {"style": "rule"}, "state_json": "{\"available_experiment_ids\": [0, 1, 3, 4, 6, 7, 8, 9, 10, 11, 12, 13, 14, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 31, 32, 33, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 4, \"family_bucket\": \"mixed\", \"maximum_separation\": 0.6388888888888888, \"mean_separation\": 0.39682539682539675, \"minimum_separation\": 0.1111111111111111, \"normalized_mean_separation\": 0.39682539682539675, \"oracle_budget_representation_error\": 0.05555555555555555, \"oracle_singleton_representation_error\": 0.34027777777777773, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.2847222222222222, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 8}, \"private\": {\"hidden_mode_id\": \"b70a2a5deed17616046eb0eb88c87f20dc48549be3270cd56578f927f1de2432\", \"valid_mode_ids\": [\"04bc145fb9aa09fb3f6c9ba8c8e591e349cdb29b927f4aaced2dcf336ecc27b6\", \"1ec4b3cf9701a227b835b97cfdc9dc1b59a32e35261493fb49a6ee0d458fda90\", \"27ff6ead3d3b62d54000f4222054c322933163ddfe2c751a120c6231e7498b7d\", \"46b2422cd074116199947ebc650106c67d03f5fe4c43437d2b8e77d4c4dfc32d\", \"471693d105d40d67aa86503e917cdec60153dcb40f2b4df86884503e8919509e\", \"b70a2a5deed17616046eb0eb88c87f20dc48549be3270cd56578f927f1de2432\", \"cbd2ae34e91d72977274a9dbfbb51a37dc3d3bd221bde365ea585bf8daa7ed03\", \"d555579a400013777f4a535279fde63112dfe7ebcdc42a4254df90a1100d4796\"]}, \"state_id\": \"6637b9b3353b9fbeaaeaa8b0\", \"visible_experiments\": [{\"experiment_id\": 2, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z1_1\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 0}}, {\"experiment_id\": 5, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 15, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 30, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 0}}]}"} {"agent_name": "causal_micro_lab_agent_loop", "data_source": "causal_micro_lab_final_eval_v3", "env_spec_json": "{\"agent\": {}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 86, \"task\": {\"state\": {\"available_experiment_ids\": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 16, 17, 18, 19, 20, 21, 22, 23, 24, 26, 27, 28, 29, 31, 32, 33, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 4, \"family_bucket\": \"mixed\", \"maximum_separation\": 0.6388888888888888, \"mean_separation\": 0.44345238095238093, \"minimum_separation\": 0.1111111111111111, \"normalized_mean_separation\": 0.44345238095238093, \"oracle_budget_representation_error\": 0.06944444444444445, \"oracle_singleton_representation_error\": 0.3611111111111111, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.29166666666666663, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 8}, \"private\": {\"hidden_mode_id\": \"4b7a258a7593ac5f36e7f9fbaa18c60a8eb36c47b96def287698d6746c6b83b2\", \"valid_mode_ids\": [\"4b7a258a7593ac5f36e7f9fbaa18c60a8eb36c47b96def287698d6746c6b83b2\", \"6814fad80b91df1b5c3718b6858d17deecbb1f5f0612898afc4459807c88e022\", \"7efa4b295cad5efa31efb1e8b9d391d74929f9c069b993fd6d76fe4cfd1d79d0\", \"8083a1426e4916166c87ff8dc134f6c419be1169609984314661ae66d3872131\", \"ae43a8cadaf851b05d6155982aafbfc2c9bee2fc43704c1d41fe371319729c0d\", \"bae41f8f248112eff1dc191e81585ecbc2f590cd7a42beb3aaf478daba7fc8b2\", \"c14256143a81eaf641d43ceb72a433ef0e136fe9c95b11ef05f33329b5dfe462\", \"ca6cb2d97061a6854205046a0d089997b316303a7291cc461efca716c34fa337\"]}, \"state_id\": \"34d1b447b0bfd0af420e429d\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 15, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 0}}, {\"experiment_id\": 25, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 30, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}]}}}", "extra_info": {"index": 86, "max_global_steps": 0, "min_global_steps": 0}, "index": 86, "prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=0\n intervention: none\n observed: Z1=0, Z2=0, Y=0\n\nExperiment 2:\n inputs: X1=0, X2=1, X3=1\n intervention: none\n observed: Z1=1, Z2=0, Y=0\n\nExperiment 3:\n inputs: X1=1, X2=0, X3=1\n intervention: none\n observed: Z1=0, Z2=0, Y=0\n\nExperiment 4:\n inputs: X1=1, X2=1, X3=0\n intervention: none\n observed: Z1=1, Z2=1, Y=0\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "raw_prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=0\n intervention: none\n observed: Z1=0, Z2=0, Y=0\n\nExperiment 2:\n inputs: X1=0, X2=1, X3=1\n intervention: none\n observed: Z1=1, Z2=0, Y=0\n\nExperiment 3:\n inputs: X1=1, X2=0, X3=1\n intervention: none\n observed: Z1=0, Z2=0, Y=0\n\nExperiment 4:\n inputs: X1=1, X2=1, X3=0\n intervention: none\n observed: Z1=1, Z2=1, Y=0\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "reward_model": {"style": "rule"}, "state_json": "{\"available_experiment_ids\": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 16, 17, 18, 19, 20, 21, 22, 23, 24, 26, 27, 28, 29, 31, 32, 33, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 4, \"family_bucket\": \"mixed\", \"maximum_separation\": 0.6388888888888888, \"mean_separation\": 0.44345238095238093, \"minimum_separation\": 0.1111111111111111, \"normalized_mean_separation\": 0.44345238095238093, \"oracle_budget_representation_error\": 0.06944444444444445, \"oracle_singleton_representation_error\": 0.3611111111111111, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.29166666666666663, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 8}, \"private\": {\"hidden_mode_id\": \"4b7a258a7593ac5f36e7f9fbaa18c60a8eb36c47b96def287698d6746c6b83b2\", \"valid_mode_ids\": [\"4b7a258a7593ac5f36e7f9fbaa18c60a8eb36c47b96def287698d6746c6b83b2\", \"6814fad80b91df1b5c3718b6858d17deecbb1f5f0612898afc4459807c88e022\", \"7efa4b295cad5efa31efb1e8b9d391d74929f9c069b993fd6d76fe4cfd1d79d0\", \"8083a1426e4916166c87ff8dc134f6c419be1169609984314661ae66d3872131\", \"ae43a8cadaf851b05d6155982aafbfc2c9bee2fc43704c1d41fe371319729c0d\", \"bae41f8f248112eff1dc191e81585ecbc2f590cd7a42beb3aaf478daba7fc8b2\", \"c14256143a81eaf641d43ceb72a433ef0e136fe9c95b11ef05f33329b5dfe462\", \"ca6cb2d97061a6854205046a0d089997b316303a7291cc461efca716c34fa337\"]}, \"state_id\": \"34d1b447b0bfd0af420e429d\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 15, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 0}}, {\"experiment_id\": 25, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 30, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}]}"} {"agent_name": "causal_micro_lab_agent_loop", "data_source": "causal_micro_lab_final_eval_v3", "env_spec_json": "{\"agent\": {}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 87, \"task\": {\"state\": {\"available_experiment_ids\": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 16, 17, 18, 19, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 3, \"family_bucket\": \"within_family\", \"maximum_separation\": 0.6486486486486487, \"mean_separation\": 0.40154440154440163, \"minimum_separation\": 0.10810810810810811, \"normalized_mean_separation\": 0.40154440154440163, \"oracle_budget_representation_error\": 0.05405405405405406, \"oracle_singleton_representation_error\": 0.3513513513513513, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.29729729729729726, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 8}, \"private\": {\"hidden_mode_id\": \"14d74a2854ba3df4d55886edd02c8edff384915162dbee72e670a53127ff9770\", \"valid_mode_ids\": [\"0069c60ab1b29bee968b1a49ef761cf9b99e4349cecee6bdff8a3c449f7a2345\", \"0209effbdbec43d6a1dc2488f141e5af571526fc24837fa8e607680012a059da\", \"14d74a2854ba3df4d55886edd02c8edff384915162dbee72e670a53127ff9770\", \"1ffde91a44c2b2083674f0f0268926a7e217182e7cc7ece222b443e2517cebe4\", \"396ff05ed8a675d8868c3103c0808230d6099759a781d395d9cd1540d2b4d478\", \"5ef41c25b72ce1ff9e50bac63bed89faf9c977faa119157dd391fa5d6329501b\", \"6e17c38c23a6f57d8940a23680fe57d3cc990bdaaa8e1d9826e2c25751a7b4a5\", \"71792ee4a47092f131d9dc7dface86d53fe063d5573c1d42b94968d46d665bbf\"]}, \"state_id\": \"42bc1c653cb890c15b99b66a\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 15, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 20, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 1}}]}}}", "extra_info": {"index": 87, "max_global_steps": 0, "min_global_steps": 0}, "index": 87, "prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=0\n intervention: none\n observed: Z1=0, Z2=0, Y=1\n\nExperiment 2:\n inputs: X1=0, X2=1, X3=1\n intervention: none\n observed: Z1=0, Z2=0, Y=0\n\nExperiment 3:\n inputs: X1=1, X2=0, X3=0\n intervention: none\n observed: Z1=1, Z2=1, Y=1\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "raw_prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=0\n intervention: none\n observed: Z1=0, Z2=0, Y=1\n\nExperiment 2:\n inputs: X1=0, X2=1, X3=1\n intervention: none\n observed: Z1=0, Z2=0, Y=0\n\nExperiment 3:\n inputs: X1=1, X2=0, X3=0\n intervention: none\n observed: Z1=1, Z2=1, Y=1\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "reward_model": {"style": "rule"}, "state_json": "{\"available_experiment_ids\": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 16, 17, 18, 19, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 3, \"family_bucket\": \"within_family\", \"maximum_separation\": 0.6486486486486487, \"mean_separation\": 0.40154440154440163, \"minimum_separation\": 0.10810810810810811, \"normalized_mean_separation\": 0.40154440154440163, \"oracle_budget_representation_error\": 0.05405405405405406, \"oracle_singleton_representation_error\": 0.3513513513513513, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.29729729729729726, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 8}, \"private\": {\"hidden_mode_id\": \"14d74a2854ba3df4d55886edd02c8edff384915162dbee72e670a53127ff9770\", \"valid_mode_ids\": [\"0069c60ab1b29bee968b1a49ef761cf9b99e4349cecee6bdff8a3c449f7a2345\", \"0209effbdbec43d6a1dc2488f141e5af571526fc24837fa8e607680012a059da\", \"14d74a2854ba3df4d55886edd02c8edff384915162dbee72e670a53127ff9770\", \"1ffde91a44c2b2083674f0f0268926a7e217182e7cc7ece222b443e2517cebe4\", \"396ff05ed8a675d8868c3103c0808230d6099759a781d395d9cd1540d2b4d478\", \"5ef41c25b72ce1ff9e50bac63bed89faf9c977faa119157dd391fa5d6329501b\", \"6e17c38c23a6f57d8940a23680fe57d3cc990bdaaa8e1d9826e2c25751a7b4a5\", \"71792ee4a47092f131d9dc7dface86d53fe063d5573c1d42b94968d46d665bbf\"]}, \"state_id\": \"42bc1c653cb890c15b99b66a\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 15, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 20, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 1}}]}"} {"agent_name": "causal_micro_lab_agent_loop", "data_source": "causal_micro_lab_final_eval_v3", "env_spec_json": "{\"agent\": {}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 88, \"task\": {\"state\": {\"available_experiment_ids\": [0, 1, 2, 3, 4, 6, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 3, \"family_bucket\": \"mixed\", \"maximum_separation\": 0.7027027027027027, \"mean_separation\": 0.4536679536679537, \"minimum_separation\": 0.16216216216216217, \"normalized_mean_separation\": 0.4536679536679537, \"oracle_budget_representation_error\": 0.08108108108108109, \"oracle_singleton_representation_error\": 0.38175675675675674, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.30067567567567566, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 8}, \"private\": {\"hidden_mode_id\": \"e4a5684932ff64d561f7d0978fb3fa053de6574cf7124f699e6f6bba3eddea1a\", \"valid_mode_ids\": [\"13191ca9f7a730889feaee4b244130e2d6563a7775ca6f98c63ac1493bdd8e8b\", \"3ec5cf445652e69765a48311e4b1328b7921cb0493ffff9a1999dd7b4f739afb\", \"4f7790c7d7973a48829951a08202f6212ba716160242163fe4c92e0575f66bb1\", \"8794ebf7816c67d563702c53ecc4d3e6ca5e23b48b2ac811791bde4332f1a4ba\", \"8fba7f12d9435a27cf38dac02e90d70dcd6ea0627e8ea43cd8fc028472d162a7\", \"a48e1c6fdd67fe32fe046c2c66cd6b86dfd0f27d674130653413fcba0721c619\", \"dbda48371e0c0645a66255633c2644faa56ddaa7de61fff9a46c26dc06818109\", \"e4a5684932ff64d561f7d0978fb3fa053de6574cf7124f699e6f6bba3eddea1a\"]}, \"state_id\": \"629654f3f8d5f9a438d34193\", \"visible_experiments\": [{\"experiment_id\": 5, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 7, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 1}, \"intervention\": \"DO_Z1_1\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 25, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 0}}]}}}", "extra_info": {"index": 88, "max_global_steps": 0, "min_global_steps": 0}, "index": 88, "prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=1\n intervention: none\n observed: Z1=0, Z2=0, Y=1\n\nExperiment 2:\n inputs: X1=0, X2=0, X3=1\n intervention: set Z1=1\n observed: Z1=1, Z2=1, Y=0\n\nExperiment 3:\n inputs: X1=1, X2=0, X3=1\n intervention: none\n observed: Z1=1, Z2=0, Y=0\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "raw_prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=1\n intervention: none\n observed: Z1=0, Z2=0, Y=1\n\nExperiment 2:\n inputs: X1=0, X2=0, X3=1\n intervention: set Z1=1\n observed: Z1=1, Z2=1, Y=0\n\nExperiment 3:\n inputs: X1=1, X2=0, X3=1\n intervention: none\n observed: Z1=1, Z2=0, Y=0\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "reward_model": {"style": "rule"}, "state_json": "{\"available_experiment_ids\": [0, 1, 2, 3, 4, 6, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 3, \"family_bucket\": \"mixed\", \"maximum_separation\": 0.7027027027027027, \"mean_separation\": 0.4536679536679537, \"minimum_separation\": 0.16216216216216217, \"normalized_mean_separation\": 0.4536679536679537, \"oracle_budget_representation_error\": 0.08108108108108109, \"oracle_singleton_representation_error\": 0.38175675675675674, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.30067567567567566, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 8}, \"private\": {\"hidden_mode_id\": \"e4a5684932ff64d561f7d0978fb3fa053de6574cf7124f699e6f6bba3eddea1a\", \"valid_mode_ids\": [\"13191ca9f7a730889feaee4b244130e2d6563a7775ca6f98c63ac1493bdd8e8b\", \"3ec5cf445652e69765a48311e4b1328b7921cb0493ffff9a1999dd7b4f739afb\", \"4f7790c7d7973a48829951a08202f6212ba716160242163fe4c92e0575f66bb1\", \"8794ebf7816c67d563702c53ecc4d3e6ca5e23b48b2ac811791bde4332f1a4ba\", \"8fba7f12d9435a27cf38dac02e90d70dcd6ea0627e8ea43cd8fc028472d162a7\", \"a48e1c6fdd67fe32fe046c2c66cd6b86dfd0f27d674130653413fcba0721c619\", \"dbda48371e0c0645a66255633c2644faa56ddaa7de61fff9a46c26dc06818109\", \"e4a5684932ff64d561f7d0978fb3fa053de6574cf7124f699e6f6bba3eddea1a\"]}, \"state_id\": \"629654f3f8d5f9a438d34193\", \"visible_experiments\": [{\"experiment_id\": 5, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 7, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 1}, \"intervention\": \"DO_Z1_1\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 25, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 0}}]}"} {"agent_name": "causal_micro_lab_agent_loop", "data_source": "causal_micro_lab_final_eval_v3", "env_spec_json": "{\"agent\": {}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 89, \"task\": {\"state\": {\"available_experiment_ids\": [1, 2, 3, 4, 5, 6, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 3, \"family_bucket\": \"mixed\", \"maximum_separation\": 0.7027027027027027, \"mean_separation\": 0.45945945945945954, \"minimum_separation\": 0.16216216216216217, \"normalized_mean_separation\": 0.45945945945945954, \"oracle_budget_representation_error\": 0.08108108108108109, \"oracle_singleton_representation_error\": 0.3851351351351352, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.3040540540540541, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 8}, \"private\": {\"hidden_mode_id\": \"e6b8ebf19fa1b930c76c7a5fc86862af407b299c6e540748d17f30bb369f05ab\", \"valid_mode_ids\": [\"08ad19fe185150babe3510a4eeb1924af58fe30ea158c9455390ad2e676b49af\", \"44ab2803e5fdde45b33e8ca221557f14dee6d8755581cfc276b52d5060bb3dcb\", \"5f1b9b9171e8ff25ec49487b3bfc776838d2ada082e3d4ce422424f07cbaab01\", \"7081efb63254fbf76eab962076dfc1022af9e62981086a98e9c361825f68dbec\", \"85982ed2aa01178f60573c223dae65d278437466654e43ee5b88f25f155b7506\", \"a8635d96e7a4a08439d7656980380596aeadfe19869f3e0730dc7b14a8d09945\", \"cb4e65785d9ba8d2b0c666d61ecfa06286daba8a12188801369cf49cf9c3b839\", \"e6b8ebf19fa1b930c76c7a5fc86862af407b299c6e540748d17f30bb369f05ab\"]}, \"state_id\": \"21574f158482c328ad7c3cb4\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 7, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 1}, \"intervention\": \"DO_Z1_1\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 25, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}]}}}", "extra_info": {"index": 89, "max_global_steps": 0, "min_global_steps": 0}, "index": 89, "prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=0\n intervention: none\n observed: Z1=0, Z2=1, Y=1\n\nExperiment 2:\n inputs: X1=0, X2=0, X3=1\n intervention: set Z1=1\n observed: Z1=1, Z2=1, Y=0\n\nExperiment 3:\n inputs: X1=1, X2=0, X3=1\n intervention: none\n observed: Z1=0, Z2=1, Y=0\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "raw_prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=0\n intervention: none\n observed: Z1=0, Z2=1, Y=1\n\nExperiment 2:\n inputs: X1=0, X2=0, X3=1\n intervention: set Z1=1\n observed: Z1=1, Z2=1, Y=0\n\nExperiment 3:\n inputs: X1=1, X2=0, X3=1\n intervention: none\n observed: Z1=0, Z2=1, Y=0\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "reward_model": {"style": "rule"}, "state_json": "{\"available_experiment_ids\": [1, 2, 3, 4, 5, 6, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 3, \"family_bucket\": \"mixed\", \"maximum_separation\": 0.7027027027027027, \"mean_separation\": 0.45945945945945954, \"minimum_separation\": 0.16216216216216217, \"normalized_mean_separation\": 0.45945945945945954, \"oracle_budget_representation_error\": 0.08108108108108109, \"oracle_singleton_representation_error\": 0.3851351351351352, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.3040540540540541, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 8}, \"private\": {\"hidden_mode_id\": \"e6b8ebf19fa1b930c76c7a5fc86862af407b299c6e540748d17f30bb369f05ab\", \"valid_mode_ids\": [\"08ad19fe185150babe3510a4eeb1924af58fe30ea158c9455390ad2e676b49af\", \"44ab2803e5fdde45b33e8ca221557f14dee6d8755581cfc276b52d5060bb3dcb\", \"5f1b9b9171e8ff25ec49487b3bfc776838d2ada082e3d4ce422424f07cbaab01\", \"7081efb63254fbf76eab962076dfc1022af9e62981086a98e9c361825f68dbec\", \"85982ed2aa01178f60573c223dae65d278437466654e43ee5b88f25f155b7506\", \"a8635d96e7a4a08439d7656980380596aeadfe19869f3e0730dc7b14a8d09945\", \"cb4e65785d9ba8d2b0c666d61ecfa06286daba8a12188801369cf49cf9c3b839\", \"e6b8ebf19fa1b930c76c7a5fc86862af407b299c6e540748d17f30bb369f05ab\"]}, \"state_id\": \"21574f158482c328ad7c3cb4\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 7, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 1}, \"intervention\": \"DO_Z1_1\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 25, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}]}"} {"agent_name": "causal_micro_lab_agent_loop", "data_source": "causal_micro_lab_final_eval_v3", "env_spec_json": "{\"agent\": {}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 90, \"task\": {\"state\": {\"available_experiment_ids\": [0, 1, 2, 3, 5, 6, 7, 8, 9, 10, 11, 13, 14, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 31, 32, 33, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 4, \"family_bucket\": \"within_family\", \"maximum_separation\": 0.6111111111111112, \"mean_separation\": 0.4126984126984126, \"minimum_separation\": 0.1111111111111111, \"normalized_mean_separation\": 0.4126984126984126, \"oracle_budget_representation_error\": 0.05555555555555555, \"oracle_singleton_representation_error\": 0.36111111111111116, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.3055555555555556, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 8}, \"private\": {\"hidden_mode_id\": \"517822c8a1342f7f6563cb828267350041458f8adaee1e20cc8e7ef473f369ac\", \"valid_mode_ids\": [\"0b91c69da119e0db89702835a60c877bedcb7cf59b99bbb68f3dc760b1051359\", \"1e1f3d3dd10ef238b0950799be56e17f898f34fd2ff6ed34accaf51bc33276d3\", \"2233b42072e99eaacb6ac074a9d2eaee31e585d86cd6973ec80c6556ea371ff1\", \"343dbecd3553fe41590f8433c12a61511caf3c962861e9aedd93b672f863121b\", \"517822c8a1342f7f6563cb828267350041458f8adaee1e20cc8e7ef473f369ac\", \"9ca0eab6fdf9615c1d60b0e80d307937c563af1b5cc85837c555cbbf28c6532f\", \"b7b3a23f2b17be532d1bcaab82344e42be377a7374339f4d9a8f5fc38e611ffa\", \"f411a1a1e2b6b996bf389d7365046c692b7188cccb3a0e3556afefb72ad3bc76\"]}, \"state_id\": \"f716783c8b0a92080f4490ed\", \"visible_experiments\": [{\"experiment_id\": 4, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z2_1\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 12, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 0}, \"intervention\": \"DO_Z1_1\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 0}}, {\"experiment_id\": 15, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 30, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 0}}]}}}", "extra_info": {"index": 90, "max_global_steps": 0, "min_global_steps": 0}, "index": 90, "prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=0\n intervention: set Z2=1\n observed: Z1=0, Z2=1, Y=1\n\nExperiment 2:\n inputs: X1=0, X2=1, X3=0\n intervention: set Z1=1\n observed: Z1=1, Z2=0, Y=0\n\nExperiment 3:\n inputs: X1=0, X2=1, X3=1\n intervention: none\n observed: Z1=0, Z2=0, Y=0\n\nExperiment 4:\n inputs: X1=1, X2=1, X3=0\n intervention: none\n observed: Z1=0, Z2=0, Y=0\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "raw_prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=0\n intervention: set Z2=1\n observed: Z1=0, Z2=1, Y=1\n\nExperiment 2:\n inputs: X1=0, X2=1, X3=0\n intervention: set Z1=1\n observed: Z1=1, Z2=0, Y=0\n\nExperiment 3:\n inputs: X1=0, X2=1, X3=1\n intervention: none\n observed: Z1=0, Z2=0, Y=0\n\nExperiment 4:\n inputs: X1=1, X2=1, X3=0\n intervention: none\n observed: Z1=0, Z2=0, Y=0\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "reward_model": {"style": "rule"}, "state_json": "{\"available_experiment_ids\": [0, 1, 2, 3, 5, 6, 7, 8, 9, 10, 11, 13, 14, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 31, 32, 33, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 4, \"family_bucket\": \"within_family\", \"maximum_separation\": 0.6111111111111112, \"mean_separation\": 0.4126984126984126, \"minimum_separation\": 0.1111111111111111, \"normalized_mean_separation\": 0.4126984126984126, \"oracle_budget_representation_error\": 0.05555555555555555, \"oracle_singleton_representation_error\": 0.36111111111111116, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.3055555555555556, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 8}, \"private\": {\"hidden_mode_id\": \"517822c8a1342f7f6563cb828267350041458f8adaee1e20cc8e7ef473f369ac\", \"valid_mode_ids\": [\"0b91c69da119e0db89702835a60c877bedcb7cf59b99bbb68f3dc760b1051359\", \"1e1f3d3dd10ef238b0950799be56e17f898f34fd2ff6ed34accaf51bc33276d3\", \"2233b42072e99eaacb6ac074a9d2eaee31e585d86cd6973ec80c6556ea371ff1\", \"343dbecd3553fe41590f8433c12a61511caf3c962861e9aedd93b672f863121b\", \"517822c8a1342f7f6563cb828267350041458f8adaee1e20cc8e7ef473f369ac\", \"9ca0eab6fdf9615c1d60b0e80d307937c563af1b5cc85837c555cbbf28c6532f\", \"b7b3a23f2b17be532d1bcaab82344e42be377a7374339f4d9a8f5fc38e611ffa\", \"f411a1a1e2b6b996bf389d7365046c692b7188cccb3a0e3556afefb72ad3bc76\"]}, \"state_id\": \"f716783c8b0a92080f4490ed\", \"visible_experiments\": [{\"experiment_id\": 4, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z2_1\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 12, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 0}, \"intervention\": \"DO_Z1_1\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 0}}, {\"experiment_id\": 15, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 30, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 0}}]}"} {"agent_name": "causal_micro_lab_agent_loop", "data_source": "causal_micro_lab_final_eval_v3", "env_spec_json": "{\"agent\": {}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 91, \"task\": {\"state\": {\"available_experiment_ids\": [1, 2, 3, 4, 5, 6, 7, 8, 9, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 3, \"family_bucket\": \"mixed\", \"maximum_separation\": 0.7027027027027027, \"mean_separation\": 0.47297297297297286, \"minimum_separation\": 0.16216216216216217, \"normalized_mean_separation\": 0.47297297297297286, \"oracle_budget_representation_error\": 0.08108108108108109, \"oracle_singleton_representation_error\": 0.40540540540540543, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.32432432432432434, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 8}, \"private\": {\"hidden_mode_id\": \"3c2a8e1409f40080771e145e2c64c28e1824e12afa5bd546809fe73fa6948930\", \"valid_mode_ids\": [\"3c2a8e1409f40080771e145e2c64c28e1824e12afa5bd546809fe73fa6948930\", \"502cba069b7f42fb980c2d116995da1d9ea4ad2dfaae28c3953e729cff5e98ac\", \"53ad9b8c6a47bec0c007e5ef774634dcd24863f9e57a9764cb007fc15a4310ba\", \"580b429dbe205e5a04110281b3fdb18567f7b9b620b65f563708e63a7d898c61\", \"5a84962209e3c662253c0b04cfa7dc26176e99bc5e171cef31ed2fccc0b8d93b\", \"c89eb32dda1919c276f854fe04925b3196c2fb540247f3d2f13dc0d5f9af5b59\", \"e1fd754b85339bf07543b841513f73d617d25aa020fae3829d9e65c29e350931\", \"e81af24a304eb5b6d5fbab5c339e06d11e66fc334b9509b970ae6afbde502a84\"]}, \"state_id\": \"81cb8f308ed071183f8d8e44\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 10, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 0}}, {\"experiment_id\": 24, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z2_1\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}]}}}", "extra_info": {"index": 91, "max_global_steps": 0, "min_global_steps": 0}, "index": 91, "prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=0\n intervention: none\n observed: Z1=0, Z2=1, Y=1\n\nExperiment 2:\n inputs: X1=0, X2=1, X3=0\n intervention: none\n observed: Z1=1, Z2=0, Y=1\n\nExperiment 3:\n inputs: X1=1, X2=0, X3=0\n intervention: set Z2=1\n observed: Z1=1, Z2=1, Y=0\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "raw_prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=0\n intervention: none\n observed: Z1=0, Z2=1, Y=1\n\nExperiment 2:\n inputs: X1=0, X2=1, X3=0\n intervention: none\n observed: Z1=1, Z2=0, Y=1\n\nExperiment 3:\n inputs: X1=1, X2=0, X3=0\n intervention: set Z2=1\n observed: Z1=1, Z2=1, Y=0\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "reward_model": {"style": "rule"}, "state_json": "{\"available_experiment_ids\": [1, 2, 3, 4, 5, 6, 7, 8, 9, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 3, \"family_bucket\": \"mixed\", \"maximum_separation\": 0.7027027027027027, \"mean_separation\": 0.47297297297297286, \"minimum_separation\": 0.16216216216216217, \"normalized_mean_separation\": 0.47297297297297286, \"oracle_budget_representation_error\": 0.08108108108108109, \"oracle_singleton_representation_error\": 0.40540540540540543, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.32432432432432434, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 8}, \"private\": {\"hidden_mode_id\": \"3c2a8e1409f40080771e145e2c64c28e1824e12afa5bd546809fe73fa6948930\", \"valid_mode_ids\": [\"3c2a8e1409f40080771e145e2c64c28e1824e12afa5bd546809fe73fa6948930\", \"502cba069b7f42fb980c2d116995da1d9ea4ad2dfaae28c3953e729cff5e98ac\", \"53ad9b8c6a47bec0c007e5ef774634dcd24863f9e57a9764cb007fc15a4310ba\", \"580b429dbe205e5a04110281b3fdb18567f7b9b620b65f563708e63a7d898c61\", \"5a84962209e3c662253c0b04cfa7dc26176e99bc5e171cef31ed2fccc0b8d93b\", \"c89eb32dda1919c276f854fe04925b3196c2fb540247f3d2f13dc0d5f9af5b59\", \"e1fd754b85339bf07543b841513f73d617d25aa020fae3829d9e65c29e350931\", \"e81af24a304eb5b6d5fbab5c339e06d11e66fc334b9509b970ae6afbde502a84\"]}, \"state_id\": \"81cb8f308ed071183f8d8e44\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 10, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 0}}, {\"experiment_id\": 24, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z2_1\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}]}"} {"agent_name": "causal_micro_lab_agent_loop", "data_source": "causal_micro_lab_final_eval_v3", "env_spec_json": "{\"agent\": {}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 92, \"task\": {\"state\": {\"available_experiment_ids\": [0, 1, 2, 3, 4, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 31, 32, 33, 34, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 3, \"family_bucket\": \"within_family\", \"maximum_separation\": 0.6756756756756757, \"mean_separation\": 0.467181467181467, \"minimum_separation\": 0.16216216216216217, \"normalized_mean_separation\": 0.467181467181467, \"oracle_budget_representation_error\": 0.08108108108108109, \"oracle_singleton_representation_error\": 0.40878378378378377, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.3277027027027027, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 8}, \"private\": {\"hidden_mode_id\": \"3be92f5af7da7733d13daa273bc5cabd4db17035654aa6267cc2652e01fa979c\", \"valid_mode_ids\": [\"22b6a370c03a493ab2b484f2e47d75548821e91753d729105e47ac26632fa057\", \"3be92f5af7da7733d13daa273bc5cabd4db17035654aa6267cc2652e01fa979c\", \"5d17ddc8f64033a28f6a4203a13a6b7ce458cffae76caa96051e95768f5d3831\", \"5e514cae0111a3dba4d637ffc6a4bef4a12951c487a82b5737357952eec6abc9\", \"882e7d220c7986c27cd8660c15d78f9dcf0ecb39f55617094fccedb662dd9d3c\", \"c71e5bcf1b0242fe82d99bea8f1981dd3579687befc5fe5539df1978270e4dd6\", \"d96aec6d1a620367e187d06967754f80882898853a2e7c8e8acf2d111da04982\", \"f4ec2d4afb49e5a961b57463c49805bff8975ecc601c6d582372e818a4e3fab0\"]}, \"state_id\": \"ab8f005d74872b4fd9426a57\", \"visible_experiments\": [{\"experiment_id\": 5, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 0}}, {\"experiment_id\": 30, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 35, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 0}}]}}}", "extra_info": {"index": 92, "max_global_steps": 0, "min_global_steps": 0}, "index": 92, "prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=1\n intervention: none\n observed: Z1=1, Z2=0, Y=1\n\nExperiment 2:\n inputs: X1=1, X2=1, X3=0\n intervention: none\n observed: Z1=1, Z2=1, Y=1\n\nExperiment 3:\n inputs: X1=1, X2=1, X3=1\n intervention: none\n observed: Z1=1, Z2=0, Y=0\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "raw_prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=1\n intervention: none\n observed: Z1=1, Z2=0, Y=1\n\nExperiment 2:\n inputs: X1=1, X2=1, X3=0\n intervention: none\n observed: Z1=1, Z2=1, Y=1\n\nExperiment 3:\n inputs: X1=1, X2=1, X3=1\n intervention: none\n observed: Z1=1, Z2=0, Y=0\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "reward_model": {"style": "rule"}, "state_json": "{\"available_experiment_ids\": [0, 1, 2, 3, 4, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 31, 32, 33, 34, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 3, \"family_bucket\": \"within_family\", \"maximum_separation\": 0.6756756756756757, \"mean_separation\": 0.467181467181467, \"minimum_separation\": 0.16216216216216217, \"normalized_mean_separation\": 0.467181467181467, \"oracle_budget_representation_error\": 0.08108108108108109, \"oracle_singleton_representation_error\": 0.40878378378378377, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.3277027027027027, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 8}, \"private\": {\"hidden_mode_id\": \"3be92f5af7da7733d13daa273bc5cabd4db17035654aa6267cc2652e01fa979c\", \"valid_mode_ids\": [\"22b6a370c03a493ab2b484f2e47d75548821e91753d729105e47ac26632fa057\", \"3be92f5af7da7733d13daa273bc5cabd4db17035654aa6267cc2652e01fa979c\", \"5d17ddc8f64033a28f6a4203a13a6b7ce458cffae76caa96051e95768f5d3831\", \"5e514cae0111a3dba4d637ffc6a4bef4a12951c487a82b5737357952eec6abc9\", \"882e7d220c7986c27cd8660c15d78f9dcf0ecb39f55617094fccedb662dd9d3c\", \"c71e5bcf1b0242fe82d99bea8f1981dd3579687befc5fe5539df1978270e4dd6\", \"d96aec6d1a620367e187d06967754f80882898853a2e7c8e8acf2d111da04982\", \"f4ec2d4afb49e5a961b57463c49805bff8975ecc601c6d582372e818a4e3fab0\"]}, \"state_id\": \"ab8f005d74872b4fd9426a57\", \"visible_experiments\": [{\"experiment_id\": 5, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 0}}, {\"experiment_id\": 30, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 35, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 0}}]}"} {"agent_name": "causal_micro_lab_agent_loop", "data_source": "causal_micro_lab_final_eval_v3", "env_spec_json": "{\"agent\": {}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 93, \"task\": {\"state\": {\"available_experiment_ids\": [0, 1, 2, 3, 4, 6, 7, 8, 9, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 3, \"family_bucket\": \"within_family\", \"maximum_separation\": 0.7297297297297297, \"mean_separation\": 0.47683397683397677, \"minimum_separation\": 0.16216216216216217, \"normalized_mean_separation\": 0.47683397683397677, \"oracle_budget_representation_error\": 0.08108108108108109, \"oracle_singleton_representation_error\": 0.41216216216216217, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.3310810810810811, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 8}, \"private\": {\"hidden_mode_id\": \"5cff455473b9b2ae18cdd708ced2850675f87648d22d46f07ee0435946bc527c\", \"valid_mode_ids\": [\"3a19c814d55419d4fb30a9c3738add496084a543491522580c6319d4d945c876\", \"3e5353b6eaacc5aab74c33aa0ec5afdaa8b61aab71ab1d03a8c873cf92472b91\", \"406425084338cf51f091ca9d8e8e6ed279d37325b3437129d37258b08c0d0b97\", \"5cff455473b9b2ae18cdd708ced2850675f87648d22d46f07ee0435946bc527c\", \"8b1e19e17db726980dbec2264f58bdf003f87fe912717bf94219ac1630935188\", \"af7110f1e5d82686fe67310b9db70cff4d3262656f2671b31c3c24e77e77e051\", \"b632abca43c7f5e0cb66985cab8ca7fec569f0610ed3d0908d05ed141e41157d\", \"c45adc0034fd9e2aff7eaf3d727f47c5e84a97ee398fe4694e9bcab20407dd4b\"]}, \"state_id\": \"e26b63a265ca1fedb190bc16\", \"visible_experiments\": [{\"experiment_id\": 5, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 10, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 25, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 0}}]}}}", "extra_info": {"index": 93, "max_global_steps": 0, "min_global_steps": 0}, "index": 93, "prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=1\n intervention: none\n observed: Z1=0, Z2=1, Y=0\n\nExperiment 2:\n inputs: X1=0, X2=1, X3=0\n intervention: none\n observed: Z1=0, Z2=0, Y=1\n\nExperiment 3:\n inputs: X1=1, X2=0, X3=1\n intervention: none\n observed: Z1=1, Z2=0, Y=0\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "raw_prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=1\n intervention: none\n observed: Z1=0, Z2=1, Y=0\n\nExperiment 2:\n inputs: X1=0, X2=1, X3=0\n intervention: none\n observed: Z1=0, Z2=0, Y=1\n\nExperiment 3:\n inputs: X1=1, X2=0, X3=1\n intervention: none\n observed: Z1=1, Z2=0, Y=0\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "reward_model": {"style": "rule"}, "state_json": "{\"available_experiment_ids\": [0, 1, 2, 3, 4, 6, 7, 8, 9, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 3, \"family_bucket\": \"within_family\", \"maximum_separation\": 0.7297297297297297, \"mean_separation\": 0.47683397683397677, \"minimum_separation\": 0.16216216216216217, \"normalized_mean_separation\": 0.47683397683397677, \"oracle_budget_representation_error\": 0.08108108108108109, \"oracle_singleton_representation_error\": 0.41216216216216217, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.3310810810810811, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 8}, \"private\": {\"hidden_mode_id\": \"5cff455473b9b2ae18cdd708ced2850675f87648d22d46f07ee0435946bc527c\", \"valid_mode_ids\": [\"3a19c814d55419d4fb30a9c3738add496084a543491522580c6319d4d945c876\", \"3e5353b6eaacc5aab74c33aa0ec5afdaa8b61aab71ab1d03a8c873cf92472b91\", \"406425084338cf51f091ca9d8e8e6ed279d37325b3437129d37258b08c0d0b97\", \"5cff455473b9b2ae18cdd708ced2850675f87648d22d46f07ee0435946bc527c\", \"8b1e19e17db726980dbec2264f58bdf003f87fe912717bf94219ac1630935188\", \"af7110f1e5d82686fe67310b9db70cff4d3262656f2671b31c3c24e77e77e051\", \"b632abca43c7f5e0cb66985cab8ca7fec569f0610ed3d0908d05ed141e41157d\", \"c45adc0034fd9e2aff7eaf3d727f47c5e84a97ee398fe4694e9bcab20407dd4b\"]}, \"state_id\": \"e26b63a265ca1fedb190bc16\", \"visible_experiments\": [{\"experiment_id\": 5, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 10, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 25, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 0}}]}"} {"agent_name": "causal_micro_lab_agent_loop", "data_source": "causal_micro_lab_final_eval_v3", "env_spec_json": "{\"agent\": {}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 94, \"task\": {\"state\": {\"available_experiment_ids\": [0, 1, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 3, \"family_bucket\": \"within_family\", \"maximum_separation\": 0.8108108108108109, \"mean_separation\": 0.5521235521235522, \"minimum_separation\": 0.21621621621621623, \"normalized_mean_separation\": 0.5521235521235522, \"oracle_budget_representation_error\": 0.13513513513513514, \"oracle_singleton_representation_error\": 0.47297297297297297, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.33783783783783783, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 8}, \"private\": {\"hidden_mode_id\": \"be40e1671e1e56631c72f55ec301bab4d0f733d141755ba4d16eac64be6ab5d8\", \"valid_mode_ids\": [\"09ce4e0e6df8df85d7d98a95f9da61e770462e8445411fa588ac9116483a1fe8\", \"0cfbc0f2d7028f54518b23a0c25d8db5044e916c6b07511a669b3331be21f2f5\", \"10ce8df7bade524c2ece40302a7d4bd3a19e18b6125d3c7fca63559facb935d5\", \"2e2be84cbc515dfb3a2b3208eec8d928105f007d4c345de6254f32c1bea90c65\", \"3fc92e4bd68abb6d676762752a681eb6911b2a2ce701b682d27115631fbcc677\", \"794cc948a46d3ffca49bc1cd034e0c9035192a7cef28143cdf0f6f20131170d4\", \"84adfd50416733f0573aee86ee00ad359768326e64facc3e73ee119d07a07550\", \"be40e1671e1e56631c72f55ec301bab4d0f733d141755ba4d16eac64be6ab5d8\"]}, \"state_id\": \"54a9e4a4372d427e0b338434\", \"visible_experiments\": [{\"experiment_id\": 2, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z1_1\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 20, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 35, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 0}}]}}}", "extra_info": {"index": 94, "max_global_steps": 0, "min_global_steps": 0}, "index": 94, "prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=0\n intervention: set Z1=1\n observed: Z1=1, Z2=1, Y=0\n\nExperiment 2:\n inputs: X1=1, X2=0, X3=0\n intervention: none\n observed: Z1=0, Z2=0, Y=1\n\nExperiment 3:\n inputs: X1=1, X2=1, X3=1\n intervention: none\n observed: Z1=0, Z2=0, Y=0\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "raw_prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=0\n intervention: set Z1=1\n observed: Z1=1, Z2=1, Y=0\n\nExperiment 2:\n inputs: X1=1, X2=0, X3=0\n intervention: none\n observed: Z1=0, Z2=0, Y=1\n\nExperiment 3:\n inputs: X1=1, X2=1, X3=1\n intervention: none\n observed: Z1=0, Z2=0, Y=0\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "reward_model": {"style": "rule"}, "state_json": "{\"available_experiment_ids\": [0, 1, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 3, \"family_bucket\": \"within_family\", \"maximum_separation\": 0.8108108108108109, \"mean_separation\": 0.5521235521235522, \"minimum_separation\": 0.21621621621621623, \"normalized_mean_separation\": 0.5521235521235522, \"oracle_budget_representation_error\": 0.13513513513513514, \"oracle_singleton_representation_error\": 0.47297297297297297, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.33783783783783783, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 8}, \"private\": {\"hidden_mode_id\": \"be40e1671e1e56631c72f55ec301bab4d0f733d141755ba4d16eac64be6ab5d8\", \"valid_mode_ids\": [\"09ce4e0e6df8df85d7d98a95f9da61e770462e8445411fa588ac9116483a1fe8\", \"0cfbc0f2d7028f54518b23a0c25d8db5044e916c6b07511a669b3331be21f2f5\", \"10ce8df7bade524c2ece40302a7d4bd3a19e18b6125d3c7fca63559facb935d5\", \"2e2be84cbc515dfb3a2b3208eec8d928105f007d4c345de6254f32c1bea90c65\", \"3fc92e4bd68abb6d676762752a681eb6911b2a2ce701b682d27115631fbcc677\", \"794cc948a46d3ffca49bc1cd034e0c9035192a7cef28143cdf0f6f20131170d4\", \"84adfd50416733f0573aee86ee00ad359768326e64facc3e73ee119d07a07550\", \"be40e1671e1e56631c72f55ec301bab4d0f733d141755ba4d16eac64be6ab5d8\"]}, \"state_id\": \"54a9e4a4372d427e0b338434\", \"visible_experiments\": [{\"experiment_id\": 2, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z1_1\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 20, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 35, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 0}}]}"} {"agent_name": "causal_micro_lab_agent_loop", "data_source": "causal_micro_lab_final_eval_v3", "env_spec_json": "{\"agent\": {}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 95, \"task\": {\"state\": {\"available_experiment_ids\": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 26, 27, 28, 29, 30, 31, 32, 33, 34, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 3, \"family_bucket\": \"within_family\", \"maximum_separation\": 0.6756756756756757, \"mean_separation\": 0.4826254826254826, \"minimum_separation\": 0.16216216216216217, \"normalized_mean_separation\": 0.4826254826254826, \"oracle_budget_representation_error\": 0.08108108108108109, \"oracle_singleton_representation_error\": 0.42229729729729737, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.3412162162162163, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 8}, \"private\": {\"hidden_mode_id\": \"6e3cab91a32d68fa773cd19b944cd1198bd6162d76f127226ff6274389023b92\", \"valid_mode_ids\": [\"5917d866171c82fee642166fedf8f887b4e13dbbccfa525b651886616ba221c6\", \"6e3cab91a32d68fa773cd19b944cd1198bd6162d76f127226ff6274389023b92\", \"745a9bca26c451cbf6db5001647387bb254befba91bc4bdb79141a43a34914fe\", \"803fd1319e047591f6346de240f903977610c9deaeba21b3af9a024db3e83864\", \"84630c31faa96d89ca4370bd045444e383ef30630caba47b2b4cff653919ac27\", \"93c862ccc905ea6ee1bd70b68ad1ce8b06094af250a742b14d7531123d0b0cba\", \"97558dd1667b2bc797f0e6095d9445e6e43e0580387d0e62b93ed43c82c04d0f\", \"acbeb01d4213632162329ce184e3c8254d50500130e0dc6874589064302ff645\"]}, \"state_id\": \"a4991bb7a1706b6aad3de517\", \"visible_experiments\": [{\"experiment_id\": 10, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 0}}, {\"experiment_id\": 25, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 0}}, {\"experiment_id\": 35, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 0}}]}}}", "extra_info": {"index": 95, "max_global_steps": 0, "min_global_steps": 0}, "index": 95, "prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=1, X3=0\n intervention: none\n observed: Z1=1, Z2=0, Y=1\n\nExperiment 2:\n inputs: X1=1, X2=0, X3=1\n intervention: none\n observed: Z1=1, Z2=0, Y=1\n\nExperiment 3:\n inputs: X1=1, X2=1, X3=1\n intervention: none\n observed: Z1=1, Z2=0, Y=0\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "raw_prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=1, X3=0\n intervention: none\n observed: Z1=1, Z2=0, Y=1\n\nExperiment 2:\n inputs: X1=1, X2=0, X3=1\n intervention: none\n observed: Z1=1, Z2=0, Y=1\n\nExperiment 3:\n inputs: X1=1, X2=1, X3=1\n intervention: none\n observed: Z1=1, Z2=0, Y=0\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "reward_model": {"style": "rule"}, "state_json": "{\"available_experiment_ids\": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 26, 27, 28, 29, 30, 31, 32, 33, 34, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 3, \"family_bucket\": \"within_family\", \"maximum_separation\": 0.6756756756756757, \"mean_separation\": 0.4826254826254826, \"minimum_separation\": 0.16216216216216217, \"normalized_mean_separation\": 0.4826254826254826, \"oracle_budget_representation_error\": 0.08108108108108109, \"oracle_singleton_representation_error\": 0.42229729729729737, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.3412162162162163, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 8}, \"private\": {\"hidden_mode_id\": \"6e3cab91a32d68fa773cd19b944cd1198bd6162d76f127226ff6274389023b92\", \"valid_mode_ids\": [\"5917d866171c82fee642166fedf8f887b4e13dbbccfa525b651886616ba221c6\", \"6e3cab91a32d68fa773cd19b944cd1198bd6162d76f127226ff6274389023b92\", \"745a9bca26c451cbf6db5001647387bb254befba91bc4bdb79141a43a34914fe\", \"803fd1319e047591f6346de240f903977610c9deaeba21b3af9a024db3e83864\", \"84630c31faa96d89ca4370bd045444e383ef30630caba47b2b4cff653919ac27\", \"93c862ccc905ea6ee1bd70b68ad1ce8b06094af250a742b14d7531123d0b0cba\", \"97558dd1667b2bc797f0e6095d9445e6e43e0580387d0e62b93ed43c82c04d0f\", \"acbeb01d4213632162329ce184e3c8254d50500130e0dc6874589064302ff645\"]}, \"state_id\": \"a4991bb7a1706b6aad3de517\", \"visible_experiments\": [{\"experiment_id\": 10, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 0}}, {\"experiment_id\": 25, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 0}}, {\"experiment_id\": 35, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 0}}]}"} {"agent_name": "causal_micro_lab_agent_loop", "data_source": "causal_micro_lab_final_eval_v3", "env_spec_json": "{\"agent\": {}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 96, \"task\": {\"state\": {\"available_experiment_ids\": [0, 1, 2, 3, 4, 6, 7, 8, 9, 11, 12, 13, 14, 15, 16, 17, 18, 19, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 4, \"family_bucket\": \"mixed\", \"maximum_separation\": 0.3333333333333333, \"mean_separation\": 0.1902356902356901, \"minimum_separation\": 0.1111111111111111, \"normalized_mean_separation\": 0.20250896057347653, \"oracle_budget_representation_error\": 0.07407407407407408, \"oracle_singleton_representation_error\": 0.14351851851851852, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.06944444444444443, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 12}, \"private\": {\"hidden_mode_id\": \"49eb42f38f288638ed82d864e067f0f5b71f15c0accace4b4dfc8e54bd9946b0\", \"valid_mode_ids\": [\"0220e0c52d14f72bb174b2f853e19c8154d57ffa44202fb6f0fa14a348acd211\", \"27b544fefe6d98adad95de1fa71c4d58fae5aedfa6ad4a7d6e928e2ac3dcf7f3\", \"39033529d272b1535a7d5fb1a5dd092c7a9349b0bf69d6eb2f0248b56e27b44d\", \"3bcc23eeea61a84290fe05af801ab68c0ff55036d683ae1db9289f3e56977d8c\", \"4830bb66a067c0cf60752e445a419d83901b090eac2de2b2b1adbc3bf320ef6d\", \"49eb42f38f288638ed82d864e067f0f5b71f15c0accace4b4dfc8e54bd9946b0\", \"9b314815deb64325af4b6f203ba5b3ae4adcb34baa6d4d870b249ef9f6913ea7\", \"cdf42959c6ebdbe85f01f141c4e59ce3c68d771b3d5b56e5412a3b77408af51d\", \"d47e2c73a07831f27c3851cbe91d2f709938a24a0f6fbbc5103e9d30648797ea\", \"dea4b3977f75aadc5488d2ff65acd55cee7960191c145ba31cdc17501d458746\", \"defbda67c51f1c75cf5478821598bb6115ca7449af791fb8affa97e7be323316\", \"e13af923245715525ecd88c8756a1689d7b01646d2feb8a7c776fc448e14585f\"]}, \"state_id\": \"ac15233c2ea02fa871c7777d\", \"visible_experiments\": [{\"experiment_id\": 5, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 10, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 20, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 35, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 1}}]}}}", "extra_info": {"index": 96, "max_global_steps": 0, "min_global_steps": 0}, "index": 96, "prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=1\n intervention: none\n observed: Z1=0, Z2=0, Y=0\n\nExperiment 2:\n inputs: X1=0, X2=1, X3=0\n intervention: none\n observed: Z1=1, Z2=1, Y=0\n\nExperiment 3:\n inputs: X1=1, X2=0, X3=0\n intervention: none\n observed: Z1=1, Z2=1, Y=1\n\nExperiment 4:\n inputs: X1=1, X2=1, X3=1\n intervention: none\n observed: Z1=1, Z2=1, Y=1\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "raw_prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=1\n intervention: none\n observed: Z1=0, Z2=0, Y=0\n\nExperiment 2:\n inputs: X1=0, X2=1, X3=0\n intervention: none\n observed: Z1=1, Z2=1, Y=0\n\nExperiment 3:\n inputs: X1=1, X2=0, X3=0\n intervention: none\n observed: Z1=1, Z2=1, Y=1\n\nExperiment 4:\n inputs: X1=1, X2=1, X3=1\n intervention: none\n observed: Z1=1, Z2=1, Y=1\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "reward_model": {"style": "rule"}, "state_json": "{\"available_experiment_ids\": [0, 1, 2, 3, 4, 6, 7, 8, 9, 11, 12, 13, 14, 15, 16, 17, 18, 19, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 4, \"family_bucket\": \"mixed\", \"maximum_separation\": 0.3333333333333333, \"mean_separation\": 0.1902356902356901, \"minimum_separation\": 0.1111111111111111, \"normalized_mean_separation\": 0.20250896057347653, \"oracle_budget_representation_error\": 0.07407407407407408, \"oracle_singleton_representation_error\": 0.14351851851851852, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.06944444444444443, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 12}, \"private\": {\"hidden_mode_id\": \"49eb42f38f288638ed82d864e067f0f5b71f15c0accace4b4dfc8e54bd9946b0\", \"valid_mode_ids\": [\"0220e0c52d14f72bb174b2f853e19c8154d57ffa44202fb6f0fa14a348acd211\", \"27b544fefe6d98adad95de1fa71c4d58fae5aedfa6ad4a7d6e928e2ac3dcf7f3\", \"39033529d272b1535a7d5fb1a5dd092c7a9349b0bf69d6eb2f0248b56e27b44d\", \"3bcc23eeea61a84290fe05af801ab68c0ff55036d683ae1db9289f3e56977d8c\", \"4830bb66a067c0cf60752e445a419d83901b090eac2de2b2b1adbc3bf320ef6d\", \"49eb42f38f288638ed82d864e067f0f5b71f15c0accace4b4dfc8e54bd9946b0\", \"9b314815deb64325af4b6f203ba5b3ae4adcb34baa6d4d870b249ef9f6913ea7\", \"cdf42959c6ebdbe85f01f141c4e59ce3c68d771b3d5b56e5412a3b77408af51d\", \"d47e2c73a07831f27c3851cbe91d2f709938a24a0f6fbbc5103e9d30648797ea\", \"dea4b3977f75aadc5488d2ff65acd55cee7960191c145ba31cdc17501d458746\", \"defbda67c51f1c75cf5478821598bb6115ca7449af791fb8affa97e7be323316\", \"e13af923245715525ecd88c8756a1689d7b01646d2feb8a7c776fc448e14585f\"]}, \"state_id\": \"ac15233c2ea02fa871c7777d\", \"visible_experiments\": [{\"experiment_id\": 5, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 10, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 20, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 35, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 1}}]}"} {"agent_name": "causal_micro_lab_agent_loop", "data_source": "causal_micro_lab_final_eval_v3", "env_spec_json": "{\"agent\": {}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 97, \"task\": {\"state\": {\"available_experiment_ids\": [0, 1, 2, 3, 4, 6, 7, 8, 9, 11, 12, 13, 14, 15, 16, 17, 18, 19, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 4, \"family_bucket\": \"cross_family\", \"maximum_separation\": 0.3333333333333333, \"mean_separation\": 0.186026936026936, \"minimum_separation\": 0.05555555555555555, \"normalized_mean_separation\": 0.19802867383512543, \"oracle_budget_representation_error\": 0.06481481481481483, \"oracle_singleton_representation_error\": 0.14351851851851852, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.07870370370370369, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 12}, \"private\": {\"hidden_mode_id\": \"4c25b59e18fe2042c0c307490e119562e9bce2711a4327af18e9274c716c8689\", \"valid_mode_ids\": [\"1a7511456b04d51181e99064c9172f7a2219b477ecec5728222762d0344f5d8a\", \"2796ab5c97f63b07bb6eb0b96865993bfe7df67dcc3c7941fc2d0bd4d794f709\", \"28a23c2e375b7469da43c0f99eeb38cb6edc7bdf3a0764c0941c1bbc1ae7c9da\", \"3ec9d02e79d8c813ec749dec456867f8bb83a7fd4bd5ea1cbd2931fb29f05fba\", \"4c22a47ffb3cfac2a4214ffba3f7f5775ce1af69cbfbb8d46633b4736bc1ac3d\", \"4c25b59e18fe2042c0c307490e119562e9bce2711a4327af18e9274c716c8689\", \"536ab61b8f5cdc0b4d94bff87d4390b95cc6e55dfc52112c21e2928c65ccec7e\", \"5ec77adb632655bc91d4324311c7697181eab147fa3a7d909559cbe9c664f01e\", \"69ae98da11d6b69aafb2237c05cb76a05bdcb3dcff87afa688c641e873b86bbe\", \"8985f7dd61c1294465de1d7c21e2fd6e91e99156e0bab2315cbc644f13fb5fda\", \"c5a7220466ed1fa0dff84c1da738b46065450dc92ccaa419156862615bc838fd\", \"e43af06b7b5d1a917ea17f085562e5a3d1ccae3255c5c848e32281f35bb04a73\"]}, \"state_id\": \"7a29628c5da420ac0cf069ae\", \"visible_experiments\": [{\"experiment_id\": 5, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 10, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 20, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 0}}, {\"experiment_id\": 35, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 1}}]}}}", "extra_info": {"index": 97, "max_global_steps": 0, "min_global_steps": 0}, "index": 97, "prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=1\n intervention: none\n observed: Z1=1, Z2=1, Y=1\n\nExperiment 2:\n inputs: X1=0, X2=1, X3=0\n intervention: none\n observed: Z1=0, Z2=0, Y=0\n\nExperiment 3:\n inputs: X1=1, X2=0, X3=0\n intervention: none\n observed: Z1=1, Z2=0, Y=1\n\nExperiment 4:\n inputs: X1=1, X2=1, X3=1\n intervention: none\n observed: Z1=1, Z2=1, Y=1\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "raw_prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=1\n intervention: none\n observed: Z1=1, Z2=1, Y=1\n\nExperiment 2:\n inputs: X1=0, X2=1, X3=0\n intervention: none\n observed: Z1=0, Z2=0, Y=0\n\nExperiment 3:\n inputs: X1=1, X2=0, X3=0\n intervention: none\n observed: Z1=1, Z2=0, Y=1\n\nExperiment 4:\n inputs: X1=1, X2=1, X3=1\n intervention: none\n observed: Z1=1, Z2=1, Y=1\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "reward_model": {"style": "rule"}, "state_json": "{\"available_experiment_ids\": [0, 1, 2, 3, 4, 6, 7, 8, 9, 11, 12, 13, 14, 15, 16, 17, 18, 19, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 4, \"family_bucket\": \"cross_family\", \"maximum_separation\": 0.3333333333333333, \"mean_separation\": 0.186026936026936, \"minimum_separation\": 0.05555555555555555, \"normalized_mean_separation\": 0.19802867383512543, \"oracle_budget_representation_error\": 0.06481481481481483, \"oracle_singleton_representation_error\": 0.14351851851851852, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.07870370370370369, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 12}, \"private\": {\"hidden_mode_id\": \"4c25b59e18fe2042c0c307490e119562e9bce2711a4327af18e9274c716c8689\", \"valid_mode_ids\": [\"1a7511456b04d51181e99064c9172f7a2219b477ecec5728222762d0344f5d8a\", \"2796ab5c97f63b07bb6eb0b96865993bfe7df67dcc3c7941fc2d0bd4d794f709\", \"28a23c2e375b7469da43c0f99eeb38cb6edc7bdf3a0764c0941c1bbc1ae7c9da\", \"3ec9d02e79d8c813ec749dec456867f8bb83a7fd4bd5ea1cbd2931fb29f05fba\", \"4c22a47ffb3cfac2a4214ffba3f7f5775ce1af69cbfbb8d46633b4736bc1ac3d\", \"4c25b59e18fe2042c0c307490e119562e9bce2711a4327af18e9274c716c8689\", \"536ab61b8f5cdc0b4d94bff87d4390b95cc6e55dfc52112c21e2928c65ccec7e\", \"5ec77adb632655bc91d4324311c7697181eab147fa3a7d909559cbe9c664f01e\", \"69ae98da11d6b69aafb2237c05cb76a05bdcb3dcff87afa688c641e873b86bbe\", \"8985f7dd61c1294465de1d7c21e2fd6e91e99156e0bab2315cbc644f13fb5fda\", \"c5a7220466ed1fa0dff84c1da738b46065450dc92ccaa419156862615bc838fd\", \"e43af06b7b5d1a917ea17f085562e5a3d1ccae3255c5c848e32281f35bb04a73\"]}, \"state_id\": \"7a29628c5da420ac0cf069ae\", \"visible_experiments\": [{\"experiment_id\": 5, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 10, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 20, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 0}}, {\"experiment_id\": 35, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 1}}]}"} {"agent_name": "causal_micro_lab_agent_loop", "data_source": "causal_micro_lab_final_eval_v3", "env_spec_json": "{\"agent\": {}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 98, \"task\": {\"state\": {\"available_experiment_ids\": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 16, 17, 18, 19, 20, 21, 22, 23, 24, 26, 27, 28, 29, 31, 32, 33, 34, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 4, \"family_bucket\": \"cross_family\", \"maximum_separation\": 0.3333333333333333, \"mean_separation\": 0.18602693602693593, \"minimum_separation\": 0.05555555555555555, \"normalized_mean_separation\": 0.19802867383512535, \"oracle_budget_representation_error\": 0.06481481481481483, \"oracle_singleton_representation_error\": 0.14351851851851855, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.07870370370370372, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 12}, \"private\": {\"hidden_mode_id\": \"7c61ad5d3dd197237145334c08f1d65c1a701f3f07fc6dd0775b5f15da3b3430\", \"valid_mode_ids\": [\"33b785b87a7566c6f8dec2d063e62e73ce2008b79b36b2395e0d6e02cf2506cd\", \"3a1fb55bd84ac04dc087be0b8c237cd992f52d59f0a9c0feb90d4fd143a9d980\", \"408a9f502f27f2339ccf481c79f6196d3f8a6db6a9088a81514dec36bf0657ac\", \"7c61ad5d3dd197237145334c08f1d65c1a701f3f07fc6dd0775b5f15da3b3430\", \"9d0193bea7b7ed9a249b4a34b87b07c158636dce387ac72cec472756a5058550\", \"a11379a3ebac0a3eab7bd29870e62edc0be167c9f754fb51a72c70676dd53cc7\", \"a4d07fa8fb1a0966b97a771937de847415f6ab2611688b7a1d1b05f0a4b82f95\", \"bbe3f58b609d8a2e902a02ba0e6a4eaa851645db25c21512acebe86be05da539\", \"c43fda05524fbb9e6bcb1f2e11cb63262e0c00640aa6990aa535262b1280a5ea\", \"d2db513f265fab924b24b61ca56f96ea9c7d3ce3ded51003663ca920c1045155\", \"d82fa7034aa213d2dba7101f0e5bc2cb323c9ab15978fcd28204e02ca915e0ed\", \"e3830e80be587ce66c6241c8c8bc1eba7e6239e036922f1b8dd9002edc6e29ef\"]}, \"state_id\": \"bd9112eba818ec6f971f1df4\", \"visible_experiments\": [{\"experiment_id\": 15, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 25, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 30, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 35, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 1}}]}}}", "extra_info": {"index": 98, "max_global_steps": 0, "min_global_steps": 0}, "index": 98, "prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=1, X3=1\n intervention: none\n observed: Z1=0, Z2=1, Y=0\n\nExperiment 2:\n inputs: X1=1, X2=0, X3=1\n intervention: none\n observed: Z1=1, Z2=1, Y=1\n\nExperiment 3:\n inputs: X1=1, X2=1, X3=0\n intervention: none\n observed: Z1=0, Z2=0, Y=0\n\nExperiment 4:\n inputs: X1=1, X2=1, X3=1\n intervention: none\n observed: Z1=1, Z2=1, Y=1\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "raw_prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=1, X3=1\n intervention: none\n observed: Z1=0, Z2=1, Y=0\n\nExperiment 2:\n inputs: X1=1, X2=0, X3=1\n intervention: none\n observed: Z1=1, Z2=1, Y=1\n\nExperiment 3:\n inputs: X1=1, X2=1, X3=0\n intervention: none\n observed: Z1=0, Z2=0, Y=0\n\nExperiment 4:\n inputs: X1=1, X2=1, X3=1\n intervention: none\n observed: Z1=1, Z2=1, Y=1\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "reward_model": {"style": "rule"}, "state_json": "{\"available_experiment_ids\": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 16, 17, 18, 19, 20, 21, 22, 23, 24, 26, 27, 28, 29, 31, 32, 33, 34, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 4, \"family_bucket\": \"cross_family\", \"maximum_separation\": 0.3333333333333333, \"mean_separation\": 0.18602693602693593, \"minimum_separation\": 0.05555555555555555, \"normalized_mean_separation\": 0.19802867383512535, \"oracle_budget_representation_error\": 0.06481481481481483, \"oracle_singleton_representation_error\": 0.14351851851851855, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.07870370370370372, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 12}, \"private\": {\"hidden_mode_id\": \"7c61ad5d3dd197237145334c08f1d65c1a701f3f07fc6dd0775b5f15da3b3430\", \"valid_mode_ids\": [\"33b785b87a7566c6f8dec2d063e62e73ce2008b79b36b2395e0d6e02cf2506cd\", \"3a1fb55bd84ac04dc087be0b8c237cd992f52d59f0a9c0feb90d4fd143a9d980\", \"408a9f502f27f2339ccf481c79f6196d3f8a6db6a9088a81514dec36bf0657ac\", \"7c61ad5d3dd197237145334c08f1d65c1a701f3f07fc6dd0775b5f15da3b3430\", \"9d0193bea7b7ed9a249b4a34b87b07c158636dce387ac72cec472756a5058550\", \"a11379a3ebac0a3eab7bd29870e62edc0be167c9f754fb51a72c70676dd53cc7\", \"a4d07fa8fb1a0966b97a771937de847415f6ab2611688b7a1d1b05f0a4b82f95\", \"bbe3f58b609d8a2e902a02ba0e6a4eaa851645db25c21512acebe86be05da539\", \"c43fda05524fbb9e6bcb1f2e11cb63262e0c00640aa6990aa535262b1280a5ea\", \"d2db513f265fab924b24b61ca56f96ea9c7d3ce3ded51003663ca920c1045155\", \"d82fa7034aa213d2dba7101f0e5bc2cb323c9ab15978fcd28204e02ca915e0ed\", \"e3830e80be587ce66c6241c8c8bc1eba7e6239e036922f1b8dd9002edc6e29ef\"]}, \"state_id\": \"bd9112eba818ec6f971f1df4\", \"visible_experiments\": [{\"experiment_id\": 15, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 25, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 30, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 35, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 1}}]}"} {"agent_name": "causal_micro_lab_agent_loop", "data_source": "causal_micro_lab_final_eval_v3", "env_spec_json": "{\"agent\": {}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 99, \"task\": {\"state\": {\"available_experiment_ids\": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 3, \"family_bucket\": \"within_family\", \"maximum_separation\": 0.32432432432432434, \"mean_separation\": 0.25798525798525795, \"minimum_separation\": 0.10810810810810811, \"normalized_mean_separation\": 0.2746294681778552, \"oracle_budget_representation_error\": 0.10360360360360361, \"oracle_singleton_representation_error\": 0.19819819819819817, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.09459459459459456, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 12}, \"private\": {\"hidden_mode_id\": \"9ab4db99d7f4734481be8a9ebaf54b685b54edc80a54d2b239bbbb97182b5cdc\", \"valid_mode_ids\": [\"03ba12d06bf425ea089e1b2c6f46250208473c22003e08538cd2c3cc5911b384\", \"142e73e961b05e8f63943c6859fe80753415943f33d425e97d4d97ae3fff8fc0\", \"2564533ecb9186ba76a0afa8c04b68032d88341a7a51db2cc9778ce117a715a0\", \"38b1e57ad731d08f8997bf689ce87fa511cece78a725c975cc5571592d969718\", \"4342848b35b04c87eb426cddf54215c566e57b11d0259add009ae653fd4aedd9\", \"5adcb93b8a870ca22d1242a25174df62a37b82f676d8e74f8f50452a251db495\", \"7329535b8d7a2b8ea28861ef24d1f84994020b8d07e403884fb80d21fed9c320\", \"9ab4db99d7f4734481be8a9ebaf54b685b54edc80a54d2b239bbbb97182b5cdc\", \"eb9ce571722da7d882d20973f060992cb76c0272b202cc86169380ed96ba277c\", \"ec1f05b3f8be9f247fd99e4ce98b89a53331b3b089d0d0d188b84162d71bdb39\", \"f4244e4c2bff81cfa82538f84b2ba198b13bda813be6d6d89c6f861513986fbb\", \"fe09c9e5bd40462ffa473fbfb75d9fecc6da2c3de6601ea960f98f81956610a4\"]}, \"state_id\": \"bdf33d767a2c625a7c1f1078\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 20, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 33, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 0}, \"intervention\": \"DO_Z2_0\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 0}}]}}}", "extra_info": {"index": 99, "max_global_steps": 0, "min_global_steps": 0}, "index": 99, "prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=0\n intervention: none\n observed: Z1=0, Z2=0, Y=1\n\nExperiment 2:\n inputs: X1=1, X2=0, X3=0\n intervention: none\n observed: Z1=1, Z2=1, Y=1\n\nExperiment 3:\n inputs: X1=1, X2=1, X3=0\n intervention: set Z2=0\n observed: Z1=0, Z2=0, Y=0\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "raw_prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=0\n intervention: none\n observed: Z1=0, Z2=0, Y=1\n\nExperiment 2:\n inputs: X1=1, X2=0, X3=0\n intervention: none\n observed: Z1=1, Z2=1, Y=1\n\nExperiment 3:\n inputs: X1=1, X2=1, X3=0\n intervention: set Z2=0\n observed: Z1=0, Z2=0, Y=0\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "reward_model": {"style": "rule"}, "state_json": "{\"available_experiment_ids\": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 3, \"family_bucket\": \"within_family\", \"maximum_separation\": 0.32432432432432434, \"mean_separation\": 0.25798525798525795, \"minimum_separation\": 0.10810810810810811, \"normalized_mean_separation\": 0.2746294681778552, \"oracle_budget_representation_error\": 0.10360360360360361, \"oracle_singleton_representation_error\": 0.19819819819819817, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.09459459459459456, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 12}, \"private\": {\"hidden_mode_id\": \"9ab4db99d7f4734481be8a9ebaf54b685b54edc80a54d2b239bbbb97182b5cdc\", \"valid_mode_ids\": [\"03ba12d06bf425ea089e1b2c6f46250208473c22003e08538cd2c3cc5911b384\", \"142e73e961b05e8f63943c6859fe80753415943f33d425e97d4d97ae3fff8fc0\", \"2564533ecb9186ba76a0afa8c04b68032d88341a7a51db2cc9778ce117a715a0\", \"38b1e57ad731d08f8997bf689ce87fa511cece78a725c975cc5571592d969718\", \"4342848b35b04c87eb426cddf54215c566e57b11d0259add009ae653fd4aedd9\", \"5adcb93b8a870ca22d1242a25174df62a37b82f676d8e74f8f50452a251db495\", \"7329535b8d7a2b8ea28861ef24d1f84994020b8d07e403884fb80d21fed9c320\", \"9ab4db99d7f4734481be8a9ebaf54b685b54edc80a54d2b239bbbb97182b5cdc\", \"eb9ce571722da7d882d20973f060992cb76c0272b202cc86169380ed96ba277c\", \"ec1f05b3f8be9f247fd99e4ce98b89a53331b3b089d0d0d188b84162d71bdb39\", \"f4244e4c2bff81cfa82538f84b2ba198b13bda813be6d6d89c6f861513986fbb\", \"fe09c9e5bd40462ffa473fbfb75d9fecc6da2c3de6601ea960f98f81956610a4\"]}, \"state_id\": \"bdf33d767a2c625a7c1f1078\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 20, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 33, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 0}, \"intervention\": \"DO_Z2_0\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 0}}]}"} {"agent_name": "causal_micro_lab_agent_loop", "data_source": "causal_micro_lab_final_eval_v3", "env_spec_json": "{\"agent\": {}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 100, \"task\": {\"state\": {\"available_experiment_ids\": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 31, 32, 33, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 3, \"family_bucket\": \"within_family\", \"maximum_separation\": 0.32432432432432434, \"mean_separation\": 0.2579852579852579, \"minimum_separation\": 0.10810810810810811, \"normalized_mean_separation\": 0.27462946817785516, \"oracle_budget_representation_error\": 0.1036036036036036, \"oracle_singleton_representation_error\": 0.19819819819819817, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.09459459459459457, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 12}, \"private\": {\"hidden_mode_id\": \"270ace18d60414d91a763cea1efa564cecff77e56d830cb0d689056947160ace\", \"valid_mode_ids\": [\"270ace18d60414d91a763cea1efa564cecff77e56d830cb0d689056947160ace\", \"2837ba51c6dc437a21b1f4bcbe8a1b476281b5a7e0d3db127d8d2e2733ddf9c1\", \"4f1922413fc9d694b19fa63a5604a77f420eb0c598decc9051fcea768fe37668\", \"50c7de00b758e2b68b6dba49773498c716a00639fb5e7c1d2d2452f1168f0806\", \"6505271d28809af9c5f42f3b9395405861df963d0ff44518b4d05410cf9a0967\", \"693b51c015dc251411105a29b0e1c4777aafc82ea61be537eb3dcbcf72415425\", \"99753ba8b4dd8e5643bd167ebce9c6362fb4876fddcc252fce85a27cc0dfdb59\", \"99edab4b0bb751dedb9f93423109282780066e9a5fa5f4669aa2d18d0ba02a0a\", \"a54887bff6870156610dc16a9a1c6ea6bf4a83aced3681a2f6e9532f18a2fc19\", \"b91909fd84555abcf427041f77ec0c4d71f9210170a2c76ae0fc29317867606d\", \"bce02214503a8f8d347e98bbeb6e50ac0fd18805b52f7950a0968fd721068d90\", \"fd7673be7d6b8d2cb90240755973939e892851618c6db3ce74b4fbbcdc9144e4\"]}, \"state_id\": \"6e0d13adfa4d5444b0f48379\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 13, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 0}, \"intervention\": \"DO_Z2_0\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 0}}, {\"experiment_id\": 30, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 1}}]}}}", "extra_info": {"index": 100, "max_global_steps": 0, "min_global_steps": 0}, "index": 100, "prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=0\n intervention: none\n observed: Z1=0, Z2=0, Y=1\n\nExperiment 2:\n inputs: X1=0, X2=1, X3=0\n intervention: set Z2=0\n observed: Z1=1, Z2=0, Y=1\n\nExperiment 3:\n inputs: X1=1, X2=1, X3=0\n intervention: none\n observed: Z1=0, Z2=1, Y=1\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "raw_prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=0\n intervention: none\n observed: Z1=0, Z2=0, Y=1\n\nExperiment 2:\n inputs: X1=0, X2=1, X3=0\n intervention: set Z2=0\n observed: Z1=1, Z2=0, Y=1\n\nExperiment 3:\n inputs: X1=1, X2=1, X3=0\n intervention: none\n observed: Z1=0, Z2=1, Y=1\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "reward_model": {"style": "rule"}, "state_json": "{\"available_experiment_ids\": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 31, 32, 33, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 3, \"family_bucket\": \"within_family\", \"maximum_separation\": 0.32432432432432434, \"mean_separation\": 0.2579852579852579, \"minimum_separation\": 0.10810810810810811, \"normalized_mean_separation\": 0.27462946817785516, \"oracle_budget_representation_error\": 0.1036036036036036, \"oracle_singleton_representation_error\": 0.19819819819819817, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.09459459459459457, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 12}, \"private\": {\"hidden_mode_id\": \"270ace18d60414d91a763cea1efa564cecff77e56d830cb0d689056947160ace\", \"valid_mode_ids\": [\"270ace18d60414d91a763cea1efa564cecff77e56d830cb0d689056947160ace\", \"2837ba51c6dc437a21b1f4bcbe8a1b476281b5a7e0d3db127d8d2e2733ddf9c1\", \"4f1922413fc9d694b19fa63a5604a77f420eb0c598decc9051fcea768fe37668\", \"50c7de00b758e2b68b6dba49773498c716a00639fb5e7c1d2d2452f1168f0806\", \"6505271d28809af9c5f42f3b9395405861df963d0ff44518b4d05410cf9a0967\", \"693b51c015dc251411105a29b0e1c4777aafc82ea61be537eb3dcbcf72415425\", \"99753ba8b4dd8e5643bd167ebce9c6362fb4876fddcc252fce85a27cc0dfdb59\", \"99edab4b0bb751dedb9f93423109282780066e9a5fa5f4669aa2d18d0ba02a0a\", \"a54887bff6870156610dc16a9a1c6ea6bf4a83aced3681a2f6e9532f18a2fc19\", \"b91909fd84555abcf427041f77ec0c4d71f9210170a2c76ae0fc29317867606d\", \"bce02214503a8f8d347e98bbeb6e50ac0fd18805b52f7950a0968fd721068d90\", \"fd7673be7d6b8d2cb90240755973939e892851618c6db3ce74b4fbbcdc9144e4\"]}, \"state_id\": \"6e0d13adfa4d5444b0f48379\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 13, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 0}, \"intervention\": \"DO_Z2_0\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 0}}, {\"experiment_id\": 30, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 1}}]}"} {"agent_name": "causal_micro_lab_agent_loop", "data_source": "causal_micro_lab_final_eval_v3", "env_spec_json": "{\"agent\": {}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 101, \"task\": {\"state\": {\"available_experiment_ids\": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38], \"metadata\": {\"evidence_size\": 3, \"family_bucket\": \"within_family\", \"maximum_separation\": 0.32432432432432434, \"mean_separation\": 0.25798525798525795, \"minimum_separation\": 0.10810810810810811, \"normalized_mean_separation\": 0.2746294681778552, \"oracle_budget_representation_error\": 0.1036036036036036, \"oracle_singleton_representation_error\": 0.19819819819819826, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.09459459459459466, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 12}, \"private\": {\"hidden_mode_id\": \"6afab583408e70e29d6681bde90f5072b14aa5ba1decdebb7b588ec64d2956b1\", \"valid_mode_ids\": [\"08e7ecbb42930b5392239c59a36091df8da48f71e9dced02e9e4d2cf683f0b30\", \"0a3d21674f035e7f35d6dc8ad4e022cea87fac9ad159f749da766a667a75c41d\", \"29dbfd41c330b78cde3a448cd0407bc2da2210b67d77b4d1fa82f4c685f87245\", \"2ae0df23e964fb017f3948aba1af30f081ade18295150d7b07dc5c2a4d58a719\", \"2d22f82ae158a401c9b99308b7f7fe88699627c06da43a91ea482983f224c722\", \"5fc1179f614c39b212ec80bbc5d2123677db5fccaed2bb2c9edc5ee92b7d39e4\", \"6afab583408e70e29d6681bde90f5072b14aa5ba1decdebb7b588ec64d2956b1\", \"82823f6fceaaf3d79cdec1167b8ecb476aec98cdc92ca9a8d9fce26c23448023\", \"8436a782fe1117666f06f0c982877e29192b4c4c3587791f5a1518878f7a3434\", \"98f735e6ead22f50b046f6bc592c7a3ad658595092bb7b33ebbb4b6dee26f56e\", \"a123eb11a9d90f55045a04d81d2aceaecd7cfb81f81b5c69a1cfc11b1ee9bbd4\", \"e2ae0f431ed97b14143c79407c77dd28b160dbbbb44897c768c9fe64fe2977bf\"]}, \"state_id\": \"194894474cf2a65e72e69f13\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 15, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 39, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 1}, \"intervention\": \"DO_Z2_1\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 1}}]}}}", "extra_info": {"index": 101, "max_global_steps": 0, "min_global_steps": 0}, "index": 101, "prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=0\n intervention: none\n observed: Z1=0, Z2=0, Y=1\n\nExperiment 2:\n inputs: X1=0, X2=1, X3=1\n intervention: none\n observed: Z1=0, Z2=1, Y=1\n\nExperiment 3:\n inputs: X1=1, X2=1, X3=1\n intervention: set Z2=1\n observed: Z1=0, Z2=1, Y=1\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "raw_prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=0\n intervention: none\n observed: Z1=0, Z2=0, Y=1\n\nExperiment 2:\n inputs: X1=0, X2=1, X3=1\n intervention: none\n observed: Z1=0, Z2=1, Y=1\n\nExperiment 3:\n inputs: X1=1, X2=1, X3=1\n intervention: set Z2=1\n observed: Z1=0, Z2=1, Y=1\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "reward_model": {"style": "rule"}, "state_json": "{\"available_experiment_ids\": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38], \"metadata\": {\"evidence_size\": 3, \"family_bucket\": \"within_family\", \"maximum_separation\": 0.32432432432432434, \"mean_separation\": 0.25798525798525795, \"minimum_separation\": 0.10810810810810811, \"normalized_mean_separation\": 0.2746294681778552, \"oracle_budget_representation_error\": 0.1036036036036036, \"oracle_singleton_representation_error\": 0.19819819819819826, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.09459459459459466, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 12}, \"private\": {\"hidden_mode_id\": \"6afab583408e70e29d6681bde90f5072b14aa5ba1decdebb7b588ec64d2956b1\", \"valid_mode_ids\": [\"08e7ecbb42930b5392239c59a36091df8da48f71e9dced02e9e4d2cf683f0b30\", \"0a3d21674f035e7f35d6dc8ad4e022cea87fac9ad159f749da766a667a75c41d\", \"29dbfd41c330b78cde3a448cd0407bc2da2210b67d77b4d1fa82f4c685f87245\", \"2ae0df23e964fb017f3948aba1af30f081ade18295150d7b07dc5c2a4d58a719\", \"2d22f82ae158a401c9b99308b7f7fe88699627c06da43a91ea482983f224c722\", \"5fc1179f614c39b212ec80bbc5d2123677db5fccaed2bb2c9edc5ee92b7d39e4\", \"6afab583408e70e29d6681bde90f5072b14aa5ba1decdebb7b588ec64d2956b1\", \"82823f6fceaaf3d79cdec1167b8ecb476aec98cdc92ca9a8d9fce26c23448023\", \"8436a782fe1117666f06f0c982877e29192b4c4c3587791f5a1518878f7a3434\", \"98f735e6ead22f50b046f6bc592c7a3ad658595092bb7b33ebbb4b6dee26f56e\", \"a123eb11a9d90f55045a04d81d2aceaecd7cfb81f81b5c69a1cfc11b1ee9bbd4\", \"e2ae0f431ed97b14143c79407c77dd28b160dbbbb44897c768c9fe64fe2977bf\"]}, \"state_id\": \"194894474cf2a65e72e69f13\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 15, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 39, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 1}, \"intervention\": \"DO_Z2_1\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 1}}]}"} {"agent_name": "causal_micro_lab_agent_loop", "data_source": "causal_micro_lab_final_eval_v3", "env_spec_json": "{\"agent\": {}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 102, \"task\": {\"state\": {\"available_experiment_ids\": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 26, 27, 28, 29, 30, 31, 32, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 3, \"family_bucket\": \"within_family\", \"maximum_separation\": 0.3783783783783784, \"mean_separation\": 0.26494676494676483, \"minimum_separation\": 0.10810810810810811, \"normalized_mean_separation\": 0.28204010462074963, \"oracle_budget_representation_error\": 0.10360360360360361, \"oracle_singleton_representation_error\": 0.20945945945945946, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.10585585585585584, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 12}, \"private\": {\"hidden_mode_id\": \"9cb3052055fd56fbc9a2540046b14a8c297f7b9b26339deaea8d94a13d8374ee\", \"valid_mode_ids\": [\"06460229e40e5d49bb3c19a000025a90c419e6a17a6097700263dd52ecdb3415\", \"21a255dd60e090c915f6c98b47186da5e4cfe6cdc23bb1f062201a5a81cb5542\", \"55ec925ec51b153da5bd3df80ac8cad33e4b5154a3dca6209a22f320b240e953\", \"6b6eec7f4272ded850e0d0022558b76365cef47fa059c822791ccd900584b39d\", \"8645a87de25b39d941b364f96aba90637b5dc6329e871c791b4a2d5ddf2ab147\", \"8b145e1125d7382c777bf85f1cd9f8f0ce02ab9c030de0de8db7161d5f7a5d0d\", \"90c977a69a789df2e9a5cfc4e2764a7118eda2634c7c932dcadf6dd0fe09d539\", \"9cb3052055fd56fbc9a2540046b14a8c297f7b9b26339deaea8d94a13d8374ee\", \"a7ea0f872dc0941bd8d15045509033308cf6c7359935821204b31759d3f860ee\", \"c697f9fdb3eaa987fbc95c407bf422f80975cb524a256d15b4fbc37aa9bd5856\", \"dc16f84aa87a6567c54663cbdc1e805b855c2f602decebe2becc910115ad4306\", \"dfa257ce54d82e90621a758484e50fdc3b233f36fb4df049a56620a101043c9e\"]}, \"state_id\": \"0534bf74b91f8958a9ce19fc\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 25, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 33, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 0}, \"intervention\": \"DO_Z2_0\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 0}}]}}}", "extra_info": {"index": 102, "max_global_steps": 0, "min_global_steps": 0}, "index": 102, "prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=0\n intervention: none\n observed: Z1=0, Z2=0, Y=1\n\nExperiment 2:\n inputs: X1=1, X2=0, X3=1\n intervention: none\n observed: Z1=0, Z2=1, Y=1\n\nExperiment 3:\n inputs: X1=1, X2=1, X3=0\n intervention: set Z2=0\n observed: Z1=0, Z2=0, Y=1\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "raw_prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=0\n intervention: none\n observed: Z1=0, Z2=0, Y=1\n\nExperiment 2:\n inputs: X1=1, X2=0, X3=1\n intervention: none\n observed: Z1=0, Z2=1, Y=1\n\nExperiment 3:\n inputs: X1=1, X2=1, X3=0\n intervention: set Z2=0\n observed: Z1=0, Z2=0, Y=1\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "reward_model": {"style": "rule"}, "state_json": "{\"available_experiment_ids\": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 26, 27, 28, 29, 30, 31, 32, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 3, \"family_bucket\": \"within_family\", \"maximum_separation\": 0.3783783783783784, \"mean_separation\": 0.26494676494676483, \"minimum_separation\": 0.10810810810810811, \"normalized_mean_separation\": 0.28204010462074963, \"oracle_budget_representation_error\": 0.10360360360360361, \"oracle_singleton_representation_error\": 0.20945945945945946, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.10585585585585584, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 12}, \"private\": {\"hidden_mode_id\": \"9cb3052055fd56fbc9a2540046b14a8c297f7b9b26339deaea8d94a13d8374ee\", \"valid_mode_ids\": [\"06460229e40e5d49bb3c19a000025a90c419e6a17a6097700263dd52ecdb3415\", \"21a255dd60e090c915f6c98b47186da5e4cfe6cdc23bb1f062201a5a81cb5542\", \"55ec925ec51b153da5bd3df80ac8cad33e4b5154a3dca6209a22f320b240e953\", \"6b6eec7f4272ded850e0d0022558b76365cef47fa059c822791ccd900584b39d\", \"8645a87de25b39d941b364f96aba90637b5dc6329e871c791b4a2d5ddf2ab147\", \"8b145e1125d7382c777bf85f1cd9f8f0ce02ab9c030de0de8db7161d5f7a5d0d\", \"90c977a69a789df2e9a5cfc4e2764a7118eda2634c7c932dcadf6dd0fe09d539\", \"9cb3052055fd56fbc9a2540046b14a8c297f7b9b26339deaea8d94a13d8374ee\", \"a7ea0f872dc0941bd8d15045509033308cf6c7359935821204b31759d3f860ee\", \"c697f9fdb3eaa987fbc95c407bf422f80975cb524a256d15b4fbc37aa9bd5856\", \"dc16f84aa87a6567c54663cbdc1e805b855c2f602decebe2becc910115ad4306\", \"dfa257ce54d82e90621a758484e50fdc3b233f36fb4df049a56620a101043c9e\"]}, \"state_id\": \"0534bf74b91f8958a9ce19fc\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 25, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 33, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 0}, \"intervention\": \"DO_Z2_0\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 0}}]}"} {"agent_name": "causal_micro_lab_agent_loop", "data_source": "causal_micro_lab_final_eval_v3", "env_spec_json": "{\"agent\": {}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 103, \"task\": {\"state\": {\"available_experiment_ids\": [0, 1, 2, 3, 4, 6, 7, 8, 9, 10, 11, 12, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 3, \"family_bucket\": \"within_family\", \"maximum_separation\": 0.4864864864864865, \"mean_separation\": 0.2882882882882883, \"minimum_separation\": 0.10810810810810811, \"normalized_mean_separation\": 0.3068875326939843, \"oracle_budget_representation_error\": 0.11261261261261264, \"oracle_singleton_representation_error\": 0.22522522522522528, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.11261261261261264, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 12}, \"private\": {\"hidden_mode_id\": \"b66aa75f683f7ac23879278af9dbdcc580c86598818091d13a3f7f5e2e139122\", \"valid_mode_ids\": [\"0d43bc23c8c701f84f78ce5ac03fef668e5935eb18ae8bd1c943a243dbdcaeb0\", \"11dcabd69d94180833119839d45d88ff416edbb1bd2217d2087272c5f64385a0\", \"4cf48e04ca6db409ec069ee88eba5831309a0bec72bbf2057c243cfc0f85861a\", \"4e42b2bda8a45b583623c7a3fc8f31c622680ebc4d93f5c602ea8c789379fdd4\", \"65833c7795b876457814bb657a55ccfd456a4bf43ca5ecdd5a0d9a8922a6aa6d\", \"7115536064e4ccc16960c928ac34c752038787b1b7bb28efa915665a134d835d\", \"a51656b720f0f8e9d694bc7ae1ab527c95126b70b6c2478c7698d46ce5126776\", \"a631fef95a5179db1cd7c69b19a23b0e90a8bd8e1a4f0e497146e948ef1f712f\", \"adadef750ccd32cbb5b6119a17a61c3515024c6a4bbdc36a1b166bff9dc3abab\", \"b66aa75f683f7ac23879278af9dbdcc580c86598818091d13a3f7f5e2e139122\", \"c4234906ab5d5fc259beac2f28fc5606597827b3e03cf80c931ae8227f793a00\", \"c86e5e028b45ef69bd571aa84fbb05a7d5de6dbaa13c319a4a26994f1db91134\"]}, \"state_id\": \"78d4ffe195017d1f771ee9f2\", \"visible_experiments\": [{\"experiment_id\": 5, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 0}}, {\"experiment_id\": 13, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 0}, \"intervention\": \"DO_Z2_0\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 24, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z2_1\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}]}}}", "extra_info": {"index": 103, "max_global_steps": 0, "min_global_steps": 0}, "index": 103, "prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=1\n intervention: none\n observed: Z1=1, Z2=0, Y=0\n\nExperiment 2:\n inputs: X1=0, X2=1, X3=0\n intervention: set Z2=0\n observed: Z1=0, Z2=0, Y=1\n\nExperiment 3:\n inputs: X1=1, X2=0, X3=0\n intervention: set Z2=1\n observed: Z1=0, Z2=1, Y=0\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "raw_prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=1\n intervention: none\n observed: Z1=1, Z2=0, Y=0\n\nExperiment 2:\n inputs: X1=0, X2=1, X3=0\n intervention: set Z2=0\n observed: Z1=0, Z2=0, Y=1\n\nExperiment 3:\n inputs: X1=1, X2=0, X3=0\n intervention: set Z2=1\n observed: Z1=0, Z2=1, Y=0\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "reward_model": {"style": "rule"}, "state_json": "{\"available_experiment_ids\": [0, 1, 2, 3, 4, 6, 7, 8, 9, 10, 11, 12, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 3, \"family_bucket\": \"within_family\", \"maximum_separation\": 0.4864864864864865, \"mean_separation\": 0.2882882882882883, \"minimum_separation\": 0.10810810810810811, \"normalized_mean_separation\": 0.3068875326939843, \"oracle_budget_representation_error\": 0.11261261261261264, \"oracle_singleton_representation_error\": 0.22522522522522528, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.11261261261261264, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 12}, \"private\": {\"hidden_mode_id\": \"b66aa75f683f7ac23879278af9dbdcc580c86598818091d13a3f7f5e2e139122\", \"valid_mode_ids\": [\"0d43bc23c8c701f84f78ce5ac03fef668e5935eb18ae8bd1c943a243dbdcaeb0\", \"11dcabd69d94180833119839d45d88ff416edbb1bd2217d2087272c5f64385a0\", \"4cf48e04ca6db409ec069ee88eba5831309a0bec72bbf2057c243cfc0f85861a\", \"4e42b2bda8a45b583623c7a3fc8f31c622680ebc4d93f5c602ea8c789379fdd4\", \"65833c7795b876457814bb657a55ccfd456a4bf43ca5ecdd5a0d9a8922a6aa6d\", \"7115536064e4ccc16960c928ac34c752038787b1b7bb28efa915665a134d835d\", \"a51656b720f0f8e9d694bc7ae1ab527c95126b70b6c2478c7698d46ce5126776\", \"a631fef95a5179db1cd7c69b19a23b0e90a8bd8e1a4f0e497146e948ef1f712f\", \"adadef750ccd32cbb5b6119a17a61c3515024c6a4bbdc36a1b166bff9dc3abab\", \"b66aa75f683f7ac23879278af9dbdcc580c86598818091d13a3f7f5e2e139122\", \"c4234906ab5d5fc259beac2f28fc5606597827b3e03cf80c931ae8227f793a00\", \"c86e5e028b45ef69bd571aa84fbb05a7d5de6dbaa13c319a4a26994f1db91134\"]}, \"state_id\": \"78d4ffe195017d1f771ee9f2\", \"visible_experiments\": [{\"experiment_id\": 5, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 0}}, {\"experiment_id\": 13, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 0}, \"intervention\": \"DO_Z2_0\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 24, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z2_1\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}]}"} {"agent_name": "causal_micro_lab_agent_loop", "data_source": "causal_micro_lab_final_eval_v3", "env_spec_json": "{\"agent\": {}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 104, \"task\": {\"state\": {\"available_experiment_ids\": [0, 1, 2, 3, 4, 6, 7, 8, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 4, \"family_bucket\": \"cross_family\", \"maximum_separation\": 0.5, \"mean_separation\": 0.2904040404040405, \"minimum_separation\": 0.1111111111111111, \"normalized_mean_separation\": 0.3091397849462366, \"oracle_budget_representation_error\": 0.07870370370370372, \"oracle_singleton_representation_error\": 0.19444444444444445, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.11574074074074073, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 12}, \"private\": {\"hidden_mode_id\": \"d7ee17d7ef6a517726e2494042360e02607cf57ae154b6dd11120a74c417ccae\", \"valid_mode_ids\": [\"1ba1c574e30398425eafb4296e86ee6eed724c136a4f207360f7b8db5b7607ef\", \"3a52dd367c7a75cbf705eaa731e5c05dc353039c4a54dca6b744b6063e924b04\", \"3f790c0e45d53b159775f775c36f81524e80a5ac34e73773a425466951db8fca\", \"554fe25fed25a8545fec008462ee116dc44b95dd34bafd26c7a1213bdc6f625b\", \"5670b259b3e279e79e4e6d5daf2fc37828a248b92b1b5bebabceebfc63d3cd1d\", \"74540d39e11f1f27ce7481c6cbe9ec89cbea28b48be30ca3b4a1a5ba734b3951\", \"92e812b97b55ef3c26592b9dd4f7317406ee3de1d1b00ddcb907ef4169427d4d\", \"a313920e022f366886e12d3607cbe5936b863297d7054e6cea6fbdebec6080e9\", \"aabab255790b55326ea6472edb6a179931c757efd1dafc47a826e28d03eec409\", \"b20023429b177b3de40b6d0bdff23229fb6cf8215035b9eea6ed234c8d8bcab7\", \"cf221c7a9f69fe141635d153eea13e0b4b5f4e59fb5c1805fb1b7217addadac8\", \"d7ee17d7ef6a517726e2494042360e02607cf57ae154b6dd11120a74c417ccae\"]}, \"state_id\": \"a0580f912142bba075a892a0\", \"visible_experiments\": [{\"experiment_id\": 5, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 0}}, {\"experiment_id\": 9, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 1}, \"intervention\": \"DO_Z2_1\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 20, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 35, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 1}}]}}}", "extra_info": {"index": 104, "max_global_steps": 0, "min_global_steps": 0}, "index": 104, "prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=1\n intervention: none\n observed: Z1=1, Z2=0, Y=0\n\nExperiment 2:\n inputs: X1=0, X2=0, X3=1\n intervention: set Z2=1\n observed: Z1=1, Z2=1, Y=0\n\nExperiment 3:\n inputs: X1=1, X2=0, X3=0\n intervention: none\n observed: Z1=1, Z2=1, Y=1\n\nExperiment 4:\n inputs: X1=1, X2=1, X3=1\n intervention: none\n observed: Z1=1, Z2=1, Y=1\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "raw_prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=1\n intervention: none\n observed: Z1=1, Z2=0, Y=0\n\nExperiment 2:\n inputs: X1=0, X2=0, X3=1\n intervention: set Z2=1\n observed: Z1=1, Z2=1, Y=0\n\nExperiment 3:\n inputs: X1=1, X2=0, X3=0\n intervention: none\n observed: Z1=1, Z2=1, Y=1\n\nExperiment 4:\n inputs: X1=1, X2=1, X3=1\n intervention: none\n observed: Z1=1, Z2=1, Y=1\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "reward_model": {"style": "rule"}, "state_json": "{\"available_experiment_ids\": [0, 1, 2, 3, 4, 6, 7, 8, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 4, \"family_bucket\": \"cross_family\", \"maximum_separation\": 0.5, \"mean_separation\": 0.2904040404040405, \"minimum_separation\": 0.1111111111111111, \"normalized_mean_separation\": 0.3091397849462366, \"oracle_budget_representation_error\": 0.07870370370370372, \"oracle_singleton_representation_error\": 0.19444444444444445, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.11574074074074073, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 12}, \"private\": {\"hidden_mode_id\": \"d7ee17d7ef6a517726e2494042360e02607cf57ae154b6dd11120a74c417ccae\", \"valid_mode_ids\": [\"1ba1c574e30398425eafb4296e86ee6eed724c136a4f207360f7b8db5b7607ef\", \"3a52dd367c7a75cbf705eaa731e5c05dc353039c4a54dca6b744b6063e924b04\", \"3f790c0e45d53b159775f775c36f81524e80a5ac34e73773a425466951db8fca\", \"554fe25fed25a8545fec008462ee116dc44b95dd34bafd26c7a1213bdc6f625b\", \"5670b259b3e279e79e4e6d5daf2fc37828a248b92b1b5bebabceebfc63d3cd1d\", \"74540d39e11f1f27ce7481c6cbe9ec89cbea28b48be30ca3b4a1a5ba734b3951\", \"92e812b97b55ef3c26592b9dd4f7317406ee3de1d1b00ddcb907ef4169427d4d\", \"a313920e022f366886e12d3607cbe5936b863297d7054e6cea6fbdebec6080e9\", \"aabab255790b55326ea6472edb6a179931c757efd1dafc47a826e28d03eec409\", \"b20023429b177b3de40b6d0bdff23229fb6cf8215035b9eea6ed234c8d8bcab7\", \"cf221c7a9f69fe141635d153eea13e0b4b5f4e59fb5c1805fb1b7217addadac8\", \"d7ee17d7ef6a517726e2494042360e02607cf57ae154b6dd11120a74c417ccae\"]}, \"state_id\": \"a0580f912142bba075a892a0\", \"visible_experiments\": [{\"experiment_id\": 5, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 0}}, {\"experiment_id\": 9, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 1}, \"intervention\": \"DO_Z2_1\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 20, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 35, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 1}}]}"} {"agent_name": "causal_micro_lab_agent_loop", "data_source": "causal_micro_lab_final_eval_v3", "env_spec_json": "{\"agent\": {}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 105, \"task\": {\"state\": {\"available_experiment_ids\": [1, 2, 4, 5, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 3, \"family_bucket\": \"within_family\", \"maximum_separation\": 0.4864864864864865, \"mean_separation\": 0.28009828009828003, \"minimum_separation\": 0.16216216216216217, \"normalized_mean_separation\": 0.2981691368788142, \"oracle_budget_representation_error\": 0.10810810810810811, \"oracle_singleton_representation_error\": 0.22972972972972974, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.12162162162162163, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 12}, \"private\": {\"hidden_mode_id\": \"b693a56b580da1d1d3eb30ef9e54cb1da05062602be1c4c8ffb69b4229f37d39\", \"valid_mode_ids\": [\"06fe009e3fd1a73f5cfeb992961c9b77037fd7aa4c8880f62b253913971d37e8\", \"23bd0b2c299498d31c0d0e44e2600e87c61829148fc513b6887f7d01ae535426\", \"286f2e60a231e9b52441fb3642736797a90cc3523b2b10da152bb24243cb2ca4\", \"40b1fbf0535e23eaf9915a938b19a2f14609fa85e75d4d582ca84aba4db588c6\", \"726ce8e31f38429efca06fa70cc050604517eab08dd0a1d1aced1bf2b6a34b6c\", \"861f0217308f090084a5166ae6a3dd294363bd09215bd9275fed96cff118c2bb\", \"9708e82112476e234316d71a4bc1349b8a6ec22f57a688fd79332e8a27676f8c\", \"b693a56b580da1d1d3eb30ef9e54cb1da05062602be1c4c8ffb69b4229f37d39\", \"dc328ce9eb6b3b60d2c2a71deeca15efba3259772fa225017120146450da4277\", \"eaf2eadcccaf30f40c099bee9575eefb68119568a841050c1b3c6ed792f6c9b3\", \"f62278b61cf4143e4e4652b6b8d2df32ba725699f015f763a1dc6f67b3529d07\", \"fdaab22ef99cc64510f7d16a6cce238f8af959956954b322e49dd07158fe756c\"]}, \"state_id\": \"dbd2990d37235407ec2992a5\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 3, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z2_0\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 6, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 1}, \"intervention\": \"DO_Z1_0\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 0}}]}}}", "extra_info": {"index": 105, "max_global_steps": 0, "min_global_steps": 0}, "index": 105, "prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=0\n intervention: none\n observed: Z1=0, Z2=1, Y=0\n\nExperiment 2:\n inputs: X1=0, X2=0, X3=0\n intervention: set Z2=0\n observed: Z1=0, Z2=0, Y=1\n\nExperiment 3:\n inputs: X1=0, X2=0, X3=1\n intervention: set Z1=0\n observed: Z1=0, Z2=0, Y=1\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "raw_prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=0\n intervention: none\n observed: Z1=0, Z2=1, Y=0\n\nExperiment 2:\n inputs: X1=0, X2=0, X3=0\n intervention: set Z2=0\n observed: Z1=0, Z2=0, Y=1\n\nExperiment 3:\n inputs: X1=0, X2=0, X3=1\n intervention: set Z1=0\n observed: Z1=0, Z2=0, Y=1\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "reward_model": {"style": "rule"}, "state_json": "{\"available_experiment_ids\": [1, 2, 4, 5, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 3, \"family_bucket\": \"within_family\", \"maximum_separation\": 0.4864864864864865, \"mean_separation\": 0.28009828009828003, \"minimum_separation\": 0.16216216216216217, \"normalized_mean_separation\": 0.2981691368788142, \"oracle_budget_representation_error\": 0.10810810810810811, \"oracle_singleton_representation_error\": 0.22972972972972974, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.12162162162162163, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 12}, \"private\": {\"hidden_mode_id\": \"b693a56b580da1d1d3eb30ef9e54cb1da05062602be1c4c8ffb69b4229f37d39\", \"valid_mode_ids\": [\"06fe009e3fd1a73f5cfeb992961c9b77037fd7aa4c8880f62b253913971d37e8\", \"23bd0b2c299498d31c0d0e44e2600e87c61829148fc513b6887f7d01ae535426\", \"286f2e60a231e9b52441fb3642736797a90cc3523b2b10da152bb24243cb2ca4\", \"40b1fbf0535e23eaf9915a938b19a2f14609fa85e75d4d582ca84aba4db588c6\", \"726ce8e31f38429efca06fa70cc050604517eab08dd0a1d1aced1bf2b6a34b6c\", \"861f0217308f090084a5166ae6a3dd294363bd09215bd9275fed96cff118c2bb\", \"9708e82112476e234316d71a4bc1349b8a6ec22f57a688fd79332e8a27676f8c\", \"b693a56b580da1d1d3eb30ef9e54cb1da05062602be1c4c8ffb69b4229f37d39\", \"dc328ce9eb6b3b60d2c2a71deeca15efba3259772fa225017120146450da4277\", \"eaf2eadcccaf30f40c099bee9575eefb68119568a841050c1b3c6ed792f6c9b3\", \"f62278b61cf4143e4e4652b6b8d2df32ba725699f015f763a1dc6f67b3529d07\", \"fdaab22ef99cc64510f7d16a6cce238f8af959956954b322e49dd07158fe756c\"]}, \"state_id\": \"dbd2990d37235407ec2992a5\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 3, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z2_0\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 6, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 1}, \"intervention\": \"DO_Z1_0\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 0}}]}"} {"agent_name": "causal_micro_lab_agent_loop", "data_source": "causal_micro_lab_final_eval_v3", "env_spec_json": "{\"agent\": {}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 106, \"task\": {\"state\": {\"available_experiment_ids\": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 16, 17, 18, 19, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 3, \"family_bucket\": \"within_family\", \"maximum_separation\": 0.43243243243243246, \"mean_separation\": 0.2882882882882882, \"minimum_separation\": 0.10810810810810811, \"normalized_mean_separation\": 0.30688753269398417, \"oracle_budget_representation_error\": 0.11711711711711713, \"oracle_singleton_representation_error\": 0.24324324324324323, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.12612612612612611, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 12}, \"private\": {\"hidden_mode_id\": \"04f334b7589196ec51ae5b66faf845ab69bb5734361a8d201c415d0b6464e3fd\", \"valid_mode_ids\": [\"04f334b7589196ec51ae5b66faf845ab69bb5734361a8d201c415d0b6464e3fd\", \"1429d0db55d787a30a3e922af4b0d4514a7b738279786f145cbaf31422cf0a0d\", \"1cc83d77a5fce22b371569643a1091978fb1c05dba50bfb488dec9c71153a878\", \"3d2db5d7f898472ddef68872c64c890b0c950c1388d3d09379494fdd899ea0f6\", \"60d159c8f942ed1bb7fbdc62e1e976bd0a3c13d24293d7a89c94b5edc165b550\", \"82d6733f698f078534eb9165e2f2c711526e780a26a19e56f75e34414dad9824\", \"a48e1c6fdd67fe32fe046c2c66cd6b86dfd0f27d674130653413fcba0721c619\", \"b7dc35b3e226d58e7a5f58f4f13fc30996c9e7d2d8474b79642a16788c34746c\", \"e2bdf0e0ce297cc60b47cb4ba23bbeba1770d2e0be56eaedf86b1865fb0d437a\", \"ee177a696c8d8b2aedfffe9294363e5ab8a21e29f26c57c802df88c07e6136fe\", \"f8ba66d49756a48585d36c9e0bf2bbdab17b766c142a902130dfe5c61384fe77\", \"febababb589a5c86df2638df6382c6f97c2cca155dc661b82f1722f240b1b363\"]}, \"state_id\": \"1b5b9d86eeb03abafb359981\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 15, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 20, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 0}}]}}}", "extra_info": {"index": 106, "max_global_steps": 0, "min_global_steps": 0}, "index": 106, "prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=0\n intervention: none\n observed: Z1=0, Z2=0, Y=1\n\nExperiment 2:\n inputs: X1=0, X2=1, X3=1\n intervention: none\n observed: Z1=0, Z2=0, Y=1\n\nExperiment 3:\n inputs: X1=1, X2=0, X3=0\n intervention: none\n observed: Z1=1, Z2=0, Y=0\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "raw_prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=0\n intervention: none\n observed: Z1=0, Z2=0, Y=1\n\nExperiment 2:\n inputs: X1=0, X2=1, X3=1\n intervention: none\n observed: Z1=0, Z2=0, Y=1\n\nExperiment 3:\n inputs: X1=1, X2=0, X3=0\n intervention: none\n observed: Z1=1, Z2=0, Y=0\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "reward_model": {"style": "rule"}, "state_json": "{\"available_experiment_ids\": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 16, 17, 18, 19, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 3, \"family_bucket\": \"within_family\", \"maximum_separation\": 0.43243243243243246, \"mean_separation\": 0.2882882882882882, \"minimum_separation\": 0.10810810810810811, \"normalized_mean_separation\": 0.30688753269398417, \"oracle_budget_representation_error\": 0.11711711711711713, \"oracle_singleton_representation_error\": 0.24324324324324323, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.12612612612612611, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 12}, \"private\": {\"hidden_mode_id\": \"04f334b7589196ec51ae5b66faf845ab69bb5734361a8d201c415d0b6464e3fd\", \"valid_mode_ids\": [\"04f334b7589196ec51ae5b66faf845ab69bb5734361a8d201c415d0b6464e3fd\", \"1429d0db55d787a30a3e922af4b0d4514a7b738279786f145cbaf31422cf0a0d\", \"1cc83d77a5fce22b371569643a1091978fb1c05dba50bfb488dec9c71153a878\", \"3d2db5d7f898472ddef68872c64c890b0c950c1388d3d09379494fdd899ea0f6\", \"60d159c8f942ed1bb7fbdc62e1e976bd0a3c13d24293d7a89c94b5edc165b550\", \"82d6733f698f078534eb9165e2f2c711526e780a26a19e56f75e34414dad9824\", \"a48e1c6fdd67fe32fe046c2c66cd6b86dfd0f27d674130653413fcba0721c619\", \"b7dc35b3e226d58e7a5f58f4f13fc30996c9e7d2d8474b79642a16788c34746c\", \"e2bdf0e0ce297cc60b47cb4ba23bbeba1770d2e0be56eaedf86b1865fb0d437a\", \"ee177a696c8d8b2aedfffe9294363e5ab8a21e29f26c57c802df88c07e6136fe\", \"f8ba66d49756a48585d36c9e0bf2bbdab17b766c142a902130dfe5c61384fe77\", \"febababb589a5c86df2638df6382c6f97c2cca155dc661b82f1722f240b1b363\"]}, \"state_id\": \"1b5b9d86eeb03abafb359981\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 15, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 20, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 0}}]}"} {"agent_name": "causal_micro_lab_agent_loop", "data_source": "causal_micro_lab_final_eval_v3", "env_spec_json": "{\"agent\": {}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 107, \"task\": {\"state\": {\"available_experiment_ids\": [0, 1, 2, 3, 4, 6, 7, 8, 9, 11, 12, 13, 14, 16, 17, 18, 19, 20, 21, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 4, \"family_bucket\": \"within_family\", \"maximum_separation\": 0.4166666666666667, \"mean_separation\": 0.2525252525252526, \"minimum_separation\": 0.1111111111111111, \"normalized_mean_separation\": 0.2688172043010753, \"oracle_budget_representation_error\": 0.08796296296296297, \"oracle_singleton_representation_error\": 0.2222222222222222, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.13425925925925924, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 12}, \"private\": {\"hidden_mode_id\": \"58b07740c5b7f7a47f00b293ae36204de94f31dc8eb6ba23b17b8ffb2e8086d4\", \"valid_mode_ids\": [\"06b3ad40f1d4c97e0708cb8baed2cb22d0dd14a00177645f58816c75ce5ec5e6\", \"1954bb47d68d8d3e52740a99d04bd0cdb6718ca98464a94451e6aecf32c2bc0b\", \"1abaa1643895e4dba2f0c3046564783aa1cc301d3c7232e26b486027dfabad4c\", \"402877b176541a8a8483294ea0bcdbe1cf142f9b6094261b7eaf697c62afe410\", \"42abf7613adb82abca30aead7db5f9c5f9ede3cd5e2fe7388c4638ec66d1f8bd\", \"48e2469740a71a1334e2eba66c88bcbf48ed4c77dfcac76f7241e5b8f5521459\", \"58b07740c5b7f7a47f00b293ae36204de94f31dc8eb6ba23b17b8ffb2e8086d4\", \"5fe2e784d485ee3b1028a5cbd356d6437b91761c5f792e8acaa212f4d7e353fc\", \"7eebeafbb5a55b0348d36006ecf53258a3dce8a5c00d2bcf3d21a46025be0fa0\", \"899020c23f47fe69e437a95b17caaa0a82516751145aabf16f93e308047d70c6\", \"93f3e34bd9e3434a1ed28f56c6de2f5f45cc86da232a964c081e69a124ea7837\", \"b9b01b66cfb20a237cc68c71bb99607aa555ce516abbf874fd5920c5249a9120\"]}, \"state_id\": \"7e46646100d963f2849580b3\", \"visible_experiments\": [{\"experiment_id\": 5, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 10, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 15, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 22, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z1_1\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}]}}}", "extra_info": {"index": 107, "max_global_steps": 0, "min_global_steps": 0}, "index": 107, "prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=1\n intervention: none\n observed: Z1=0, Z2=0, Y=1\n\nExperiment 2:\n inputs: X1=0, X2=1, X3=0\n intervention: none\n observed: Z1=0, Z2=0, Y=0\n\nExperiment 3:\n inputs: X1=0, X2=1, X3=1\n intervention: none\n observed: Z1=0, Z2=0, Y=1\n\nExperiment 4:\n inputs: X1=1, X2=0, X3=0\n intervention: set Z1=1\n observed: Z1=1, Z2=1, Y=0\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "raw_prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=1\n intervention: none\n observed: Z1=0, Z2=0, Y=1\n\nExperiment 2:\n inputs: X1=0, X2=1, X3=0\n intervention: none\n observed: Z1=0, Z2=0, Y=0\n\nExperiment 3:\n inputs: X1=0, X2=1, X3=1\n intervention: none\n observed: Z1=0, Z2=0, Y=1\n\nExperiment 4:\n inputs: X1=1, X2=0, X3=0\n intervention: set Z1=1\n observed: Z1=1, Z2=1, Y=0\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "reward_model": {"style": "rule"}, "state_json": "{\"available_experiment_ids\": [0, 1, 2, 3, 4, 6, 7, 8, 9, 11, 12, 13, 14, 16, 17, 18, 19, 20, 21, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 4, \"family_bucket\": \"within_family\", \"maximum_separation\": 0.4166666666666667, \"mean_separation\": 0.2525252525252526, \"minimum_separation\": 0.1111111111111111, \"normalized_mean_separation\": 0.2688172043010753, \"oracle_budget_representation_error\": 0.08796296296296297, \"oracle_singleton_representation_error\": 0.2222222222222222, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.13425925925925924, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 12}, \"private\": {\"hidden_mode_id\": \"58b07740c5b7f7a47f00b293ae36204de94f31dc8eb6ba23b17b8ffb2e8086d4\", \"valid_mode_ids\": [\"06b3ad40f1d4c97e0708cb8baed2cb22d0dd14a00177645f58816c75ce5ec5e6\", \"1954bb47d68d8d3e52740a99d04bd0cdb6718ca98464a94451e6aecf32c2bc0b\", \"1abaa1643895e4dba2f0c3046564783aa1cc301d3c7232e26b486027dfabad4c\", \"402877b176541a8a8483294ea0bcdbe1cf142f9b6094261b7eaf697c62afe410\", \"42abf7613adb82abca30aead7db5f9c5f9ede3cd5e2fe7388c4638ec66d1f8bd\", \"48e2469740a71a1334e2eba66c88bcbf48ed4c77dfcac76f7241e5b8f5521459\", \"58b07740c5b7f7a47f00b293ae36204de94f31dc8eb6ba23b17b8ffb2e8086d4\", \"5fe2e784d485ee3b1028a5cbd356d6437b91761c5f792e8acaa212f4d7e353fc\", \"7eebeafbb5a55b0348d36006ecf53258a3dce8a5c00d2bcf3d21a46025be0fa0\", \"899020c23f47fe69e437a95b17caaa0a82516751145aabf16f93e308047d70c6\", \"93f3e34bd9e3434a1ed28f56c6de2f5f45cc86da232a964c081e69a124ea7837\", \"b9b01b66cfb20a237cc68c71bb99607aa555ce516abbf874fd5920c5249a9120\"]}, \"state_id\": \"7e46646100d963f2849580b3\", \"visible_experiments\": [{\"experiment_id\": 5, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 10, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 15, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 22, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z1_1\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}]}"} {"agent_name": "causal_micro_lab_agent_loop", "data_source": "causal_micro_lab_final_eval_v3", "env_spec_json": "{\"agent\": {}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 108, \"task\": {\"state\": {\"available_experiment_ids\": [0, 1, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 16, 17, 18, 19, 20, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 4, \"family_bucket\": \"mixed\", \"maximum_separation\": 0.3888888888888889, \"mean_separation\": 0.25252525252525254, \"minimum_separation\": 0.1111111111111111, \"normalized_mean_separation\": 0.26881720430107525, \"oracle_budget_representation_error\": 0.0925925925925926, \"oracle_singleton_representation_error\": 0.23148148148148148, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.1388888888888889, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 12}, \"private\": {\"hidden_mode_id\": \"ed5bb2acfea71916b711eeaeb336927e040af0536829ef4df9799e15b9ce049c\", \"valid_mode_ids\": [\"34d549bab6d9b507cbbebe0fbc586694564c583bd0bf96e5867703b9f6acbabc\", \"4cd84ce8ea85e04a115510300e95d0ed1b70a59c054f088c2a1faf66dd3b76f0\", \"545e4b7056d9c9469fc77e3084db60db47e2e4970637a42f424ba61f8a3647b8\", \"5f3fa171afcb2f85214aba66d5a29fa8bf0db011e0e6d1b77dc1392b88ee9b69\", \"78e51354c83ef01f381d393d5b0c3f3d273dc85e3fa1d776c1dd55837efa7b95\", \"804afa1e4d28ffec891f8960d8680ac9e69bc49e6d633e11ed754710b70dd78a\", \"80a689f27591d33d85d5100fe71a0cb47db8829e46576d5dc12ed2f3015044b0\", \"a954f012a798a4db538a3db5a8c88965fc019b29884774afabe329fafb97e0b4\", \"b0ed27572da430cf76a793687a2ea48db475e5a7147e4300ad189785e1b6ca18\", \"b9d8e378f0578f39379c4c23c584a9f3bbbf167a54ead4e458c4bb91d2446d13\", \"ea2c828d2678d19afac78d917219fc5d90f4fb86039b13b083f88d8223faec74\", \"ed5bb2acfea71916b711eeaeb336927e040af0536829ef4df9799e15b9ce049c\"]}, \"state_id\": \"a13a662154fd0c1b0bb7e6f2\", \"visible_experiments\": [{\"experiment_id\": 2, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z1_1\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 0}}, {\"experiment_id\": 15, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 21, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z1_0\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 35, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 1}}]}}}", "extra_info": {"index": 108, "max_global_steps": 0, "min_global_steps": 0}, "index": 108, "prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=0\n intervention: set Z1=1\n observed: Z1=1, Z2=0, Y=0\n\nExperiment 2:\n inputs: X1=0, X2=1, X3=1\n intervention: none\n observed: Z1=0, Z2=1, Y=0\n\nExperiment 3:\n inputs: X1=1, X2=0, X3=0\n intervention: set Z1=0\n observed: Z1=0, Z2=1, Y=1\n\nExperiment 4:\n inputs: X1=1, X2=1, X3=1\n intervention: none\n observed: Z1=1, Z2=1, Y=1\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "raw_prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=0\n intervention: set Z1=1\n observed: Z1=1, Z2=0, Y=0\n\nExperiment 2:\n inputs: X1=0, X2=1, X3=1\n intervention: none\n observed: Z1=0, Z2=1, Y=0\n\nExperiment 3:\n inputs: X1=1, X2=0, X3=0\n intervention: set Z1=0\n observed: Z1=0, Z2=1, Y=1\n\nExperiment 4:\n inputs: X1=1, X2=1, X3=1\n intervention: none\n observed: Z1=1, Z2=1, Y=1\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "reward_model": {"style": "rule"}, "state_json": "{\"available_experiment_ids\": [0, 1, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 16, 17, 18, 19, 20, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 4, \"family_bucket\": \"mixed\", \"maximum_separation\": 0.3888888888888889, \"mean_separation\": 0.25252525252525254, \"minimum_separation\": 0.1111111111111111, \"normalized_mean_separation\": 0.26881720430107525, \"oracle_budget_representation_error\": 0.0925925925925926, \"oracle_singleton_representation_error\": 0.23148148148148148, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.1388888888888889, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 12}, \"private\": {\"hidden_mode_id\": \"ed5bb2acfea71916b711eeaeb336927e040af0536829ef4df9799e15b9ce049c\", \"valid_mode_ids\": [\"34d549bab6d9b507cbbebe0fbc586694564c583bd0bf96e5867703b9f6acbabc\", \"4cd84ce8ea85e04a115510300e95d0ed1b70a59c054f088c2a1faf66dd3b76f0\", \"545e4b7056d9c9469fc77e3084db60db47e2e4970637a42f424ba61f8a3647b8\", \"5f3fa171afcb2f85214aba66d5a29fa8bf0db011e0e6d1b77dc1392b88ee9b69\", \"78e51354c83ef01f381d393d5b0c3f3d273dc85e3fa1d776c1dd55837efa7b95\", \"804afa1e4d28ffec891f8960d8680ac9e69bc49e6d633e11ed754710b70dd78a\", \"80a689f27591d33d85d5100fe71a0cb47db8829e46576d5dc12ed2f3015044b0\", \"a954f012a798a4db538a3db5a8c88965fc019b29884774afabe329fafb97e0b4\", \"b0ed27572da430cf76a793687a2ea48db475e5a7147e4300ad189785e1b6ca18\", \"b9d8e378f0578f39379c4c23c584a9f3bbbf167a54ead4e458c4bb91d2446d13\", \"ea2c828d2678d19afac78d917219fc5d90f4fb86039b13b083f88d8223faec74\", \"ed5bb2acfea71916b711eeaeb336927e040af0536829ef4df9799e15b9ce049c\"]}, \"state_id\": \"a13a662154fd0c1b0bb7e6f2\", \"visible_experiments\": [{\"experiment_id\": 2, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z1_1\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 0}}, {\"experiment_id\": 15, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 21, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z1_0\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 35, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 1}}]}"} {"agent_name": "causal_micro_lab_agent_loop", "data_source": "causal_micro_lab_final_eval_v3", "env_spec_json": "{\"agent\": {}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 109, \"task\": {\"state\": {\"available_experiment_ids\": [0, 1, 2, 3, 4, 6, 8, 9, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 4, \"family_bucket\": \"mixed\", \"maximum_separation\": 0.4444444444444444, \"mean_separation\": 0.287037037037037, \"minimum_separation\": 0.05555555555555555, \"normalized_mean_separation\": 0.30555555555555547, \"oracle_budget_representation_error\": 0.06944444444444446, \"oracle_singleton_representation_error\": 0.21296296296296294, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.1435185185185185, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 12}, \"private\": {\"hidden_mode_id\": \"cb7cb546cf856786099abab0d13cc3c5487fcd676b6f13c084eed85bfb864ca2\", \"valid_mode_ids\": [\"1228f7978fbb069d5ff54fd3f703abccf87e83a83ec979050a7df3e5857adf80\", \"17cadb51da9da53cf64e08d1074241d60cba46ccb457969a33054da18bc135f6\", \"4f4c4bb4aee1d2bf24d0d981cc4a87764e183a220229e42670eeabb97e84482b\", \"677011cafb149de02c58b8ae36a2409d2464ddf92e9df620544a7fb56a9e92af\", \"68e08f4e742ae12fab9ea6ef62dd65b4c07cdcef44759a64b3acdc949a7894d7\", \"9dd53dd88ff797f0cbd9ec6cb653ba878304d25c512cd451a3a712fd0fc441fb\", \"b0e746930ed83e245cb95f951d5fb624eac9b71d4cce95ff994106410ac214d6\", \"b2f97275cb3677b99364013b340f94ce8100998bc00b53e6f2e3eba3a040ff18\", \"c01ebcd2c6bdbe4b4cedb50296918886f8d9cf14b822efa8828de50b4aba66e9\", \"c89bc25eee7c6ad091fd34a0fd006ff3b9864705dbba402d36367faa58ae3c1c\", \"cb7cb546cf856786099abab0d13cc3c5487fcd676b6f13c084eed85bfb864ca2\", \"ced6f001b8a2ff8fa6708ba03c4aec36bbdbeb129d26c38c8aeaae2e81842f02\"]}, \"state_id\": \"2ca93ff312a93c38f8d99162\", \"visible_experiments\": [{\"experiment_id\": 5, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 7, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 1}, \"intervention\": \"DO_Z1_1\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 0}}, {\"experiment_id\": 10, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 11, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 0}, \"intervention\": \"DO_Z1_0\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 0}}]}}}", "extra_info": {"index": 109, "max_global_steps": 0, "min_global_steps": 0}, "index": 109, "prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=1\n intervention: none\n observed: Z1=0, Z2=1, Y=1\n\nExperiment 2:\n inputs: X1=0, X2=0, X3=1\n intervention: set Z1=1\n observed: Z1=1, Z2=0, Y=1\n\nExperiment 3:\n inputs: X1=0, X2=1, X3=0\n intervention: none\n observed: Z1=1, Z2=1, Y=1\n\nExperiment 4:\n inputs: X1=0, X2=1, X3=0\n intervention: set Z1=0\n observed: Z1=0, Z2=0, Y=0\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "raw_prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=1\n intervention: none\n observed: Z1=0, Z2=1, Y=1\n\nExperiment 2:\n inputs: X1=0, X2=0, X3=1\n intervention: set Z1=1\n observed: Z1=1, Z2=0, Y=1\n\nExperiment 3:\n inputs: X1=0, X2=1, X3=0\n intervention: none\n observed: Z1=1, Z2=1, Y=1\n\nExperiment 4:\n inputs: X1=0, X2=1, X3=0\n intervention: set Z1=0\n observed: Z1=0, Z2=0, Y=0\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "reward_model": {"style": "rule"}, "state_json": "{\"available_experiment_ids\": [0, 1, 2, 3, 4, 6, 8, 9, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 4, \"family_bucket\": \"mixed\", \"maximum_separation\": 0.4444444444444444, \"mean_separation\": 0.287037037037037, \"minimum_separation\": 0.05555555555555555, \"normalized_mean_separation\": 0.30555555555555547, \"oracle_budget_representation_error\": 0.06944444444444446, \"oracle_singleton_representation_error\": 0.21296296296296294, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.1435185185185185, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 12}, \"private\": {\"hidden_mode_id\": \"cb7cb546cf856786099abab0d13cc3c5487fcd676b6f13c084eed85bfb864ca2\", \"valid_mode_ids\": [\"1228f7978fbb069d5ff54fd3f703abccf87e83a83ec979050a7df3e5857adf80\", \"17cadb51da9da53cf64e08d1074241d60cba46ccb457969a33054da18bc135f6\", \"4f4c4bb4aee1d2bf24d0d981cc4a87764e183a220229e42670eeabb97e84482b\", \"677011cafb149de02c58b8ae36a2409d2464ddf92e9df620544a7fb56a9e92af\", \"68e08f4e742ae12fab9ea6ef62dd65b4c07cdcef44759a64b3acdc949a7894d7\", \"9dd53dd88ff797f0cbd9ec6cb653ba878304d25c512cd451a3a712fd0fc441fb\", \"b0e746930ed83e245cb95f951d5fb624eac9b71d4cce95ff994106410ac214d6\", \"b2f97275cb3677b99364013b340f94ce8100998bc00b53e6f2e3eba3a040ff18\", \"c01ebcd2c6bdbe4b4cedb50296918886f8d9cf14b822efa8828de50b4aba66e9\", \"c89bc25eee7c6ad091fd34a0fd006ff3b9864705dbba402d36367faa58ae3c1c\", \"cb7cb546cf856786099abab0d13cc3c5487fcd676b6f13c084eed85bfb864ca2\", \"ced6f001b8a2ff8fa6708ba03c4aec36bbdbeb129d26c38c8aeaae2e81842f02\"]}, \"state_id\": \"2ca93ff312a93c38f8d99162\", \"visible_experiments\": [{\"experiment_id\": 5, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 7, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 1}, \"intervention\": \"DO_Z1_1\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 0}}, {\"experiment_id\": 10, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 11, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 0}, \"intervention\": \"DO_Z1_0\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 0}}]}"} {"agent_name": "causal_micro_lab_agent_loop", "data_source": "causal_micro_lab_final_eval_v3", "env_spec_json": "{\"agent\": {}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 110, \"task\": {\"state\": {\"available_experiment_ids\": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 26, 27, 28, 29, 31, 32, 33, 34, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 3, \"family_bucket\": \"cross_family\", \"maximum_separation\": 0.4864864864864865, \"mean_separation\": 0.29361179361179357, \"minimum_separation\": 0.10810810810810811, \"normalized_mean_separation\": 0.3125544899738448, \"oracle_budget_representation_error\": 0.08558558558558559, \"oracle_singleton_representation_error\": 0.2342342342342342, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.14864864864864863, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 12}, \"private\": {\"hidden_mode_id\": \"2162a666469922116af424c2f2a1f47e08c70ddfd4aeaa883687faef5577747b\", \"valid_mode_ids\": [\"1bb20f762cea1cc1f8bc12665e6312b65b6e1350593c631a4c75535bbcd8e36a\", \"1ce2d62151ef768781355e3121d869cd2a60fff7808f7dd6d31a94e781a44dfc\", \"2162a666469922116af424c2f2a1f47e08c70ddfd4aeaa883687faef5577747b\", \"2f2f4f83e52ee7f638db03aaff95183826fec810cfd251c1357bd21895d58d78\", \"46976ea1e09bd0c4fc64b7ef040eb86426a5a343f28f39029b196a4a125464d3\", \"57b6b198c71bd9f1c1fd0dbd9dd11423ad740a52c00018746269d83d152bbf40\", \"69da89644662503aa46fc7d1a4f42ea589b100f206ec987245606a5ef4d716b1\", \"6c3ea3912ca118bffa2b92afb94e4d5a7b050880a0322cef346204f1f6c68a8f\", \"895eac14afbed68a083e3d0f88e09f2a8514acc5563099bc04086fbccf160cd2\", \"c4234906ab5d5fc259beac2f28fc5606597827b3e03cf80c931ae8227f793a00\", \"c86e5e028b45ef69bd571aa84fbb05a7d5de6dbaa13c319a4a26994f1db91134\", \"e9e8dae1174a4845c536aeb9cc0ed773c2837f24199fae862c101cf7ff6921f4\"]}, \"state_id\": \"4ef4f4853007d9c7470435fd\", \"visible_experiments\": [{\"experiment_id\": 25, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 0}}, {\"experiment_id\": 30, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 35, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 1}}]}}}", "extra_info": {"index": 110, "max_global_steps": 0, "min_global_steps": 0}, "index": 110, "prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=1, X2=0, X3=1\n intervention: none\n observed: Z1=1, Z2=0, Y=0\n\nExperiment 2:\n inputs: X1=1, X2=1, X3=0\n intervention: none\n observed: Z1=0, Z2=0, Y=1\n\nExperiment 3:\n inputs: X1=1, X2=1, X3=1\n intervention: none\n observed: Z1=1, Z2=1, Y=1\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "raw_prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=1, X2=0, X3=1\n intervention: none\n observed: Z1=1, Z2=0, Y=0\n\nExperiment 2:\n inputs: X1=1, X2=1, X3=0\n intervention: none\n observed: Z1=0, Z2=0, Y=1\n\nExperiment 3:\n inputs: X1=1, X2=1, X3=1\n intervention: none\n observed: Z1=1, Z2=1, Y=1\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "reward_model": {"style": "rule"}, "state_json": "{\"available_experiment_ids\": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 26, 27, 28, 29, 31, 32, 33, 34, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 3, \"family_bucket\": \"cross_family\", \"maximum_separation\": 0.4864864864864865, \"mean_separation\": 0.29361179361179357, \"minimum_separation\": 0.10810810810810811, \"normalized_mean_separation\": 0.3125544899738448, \"oracle_budget_representation_error\": 0.08558558558558559, \"oracle_singleton_representation_error\": 0.2342342342342342, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.14864864864864863, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 12}, \"private\": {\"hidden_mode_id\": \"2162a666469922116af424c2f2a1f47e08c70ddfd4aeaa883687faef5577747b\", \"valid_mode_ids\": [\"1bb20f762cea1cc1f8bc12665e6312b65b6e1350593c631a4c75535bbcd8e36a\", \"1ce2d62151ef768781355e3121d869cd2a60fff7808f7dd6d31a94e781a44dfc\", \"2162a666469922116af424c2f2a1f47e08c70ddfd4aeaa883687faef5577747b\", \"2f2f4f83e52ee7f638db03aaff95183826fec810cfd251c1357bd21895d58d78\", \"46976ea1e09bd0c4fc64b7ef040eb86426a5a343f28f39029b196a4a125464d3\", \"57b6b198c71bd9f1c1fd0dbd9dd11423ad740a52c00018746269d83d152bbf40\", \"69da89644662503aa46fc7d1a4f42ea589b100f206ec987245606a5ef4d716b1\", \"6c3ea3912ca118bffa2b92afb94e4d5a7b050880a0322cef346204f1f6c68a8f\", \"895eac14afbed68a083e3d0f88e09f2a8514acc5563099bc04086fbccf160cd2\", \"c4234906ab5d5fc259beac2f28fc5606597827b3e03cf80c931ae8227f793a00\", \"c86e5e028b45ef69bd571aa84fbb05a7d5de6dbaa13c319a4a26994f1db91134\", \"e9e8dae1174a4845c536aeb9cc0ed773c2837f24199fae862c101cf7ff6921f4\"]}, \"state_id\": \"4ef4f4853007d9c7470435fd\", \"visible_experiments\": [{\"experiment_id\": 25, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 0}}, {\"experiment_id\": 30, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 35, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 1}}]}"} {"agent_name": "causal_micro_lab_agent_loop", "data_source": "causal_micro_lab_final_eval_v3", "env_spec_json": "{\"agent\": {}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 111, \"task\": {\"state\": {\"available_experiment_ids\": [1, 2, 3, 4, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 3, \"family_bucket\": \"mixed\", \"maximum_separation\": 0.5135135135135135, \"mean_separation\": 0.2997542997542995, \"minimum_separation\": 0.16216216216216217, \"normalized_mean_separation\": 0.31909328683522203, \"oracle_budget_representation_error\": 0.10810810810810811, \"oracle_singleton_representation_error\": 0.2612612612612613, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.1531531531531532, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 12}, \"private\": {\"hidden_mode_id\": \"47be9e02ba77ae49bbfed2c51a5c0fbbd0c81ef9f726653f3870e0e5cc9025a8\", \"valid_mode_ids\": [\"049a55d5d512b7deabcdd65c31151bbceb4f56bf3a47856dd7f5cef771ceec72\", \"19cefdd776f10b29ad3a8a742a22d9384e8a5859f80affab33f7b04cba6bfa4b\", \"1acadbf1cebcd65c59d8aad85a7ddd118355b2b13e09772e8f3cdedd45404574\", \"20dd688378813daef96fc20a2a594ceecc41f99f4d0b452a69cef45e37963bd7\", \"47be9e02ba77ae49bbfed2c51a5c0fbbd0c81ef9f726653f3870e0e5cc9025a8\", \"6205de37be50fedb1d771e9be005a216850469b2ca5d6c5937909781174df951\", \"6ff71329c90f0666af76d3fbd577ddc17b0e201a38e251de82a81ef074870e29\", \"7548c9d6088e6c316f46615b71652613924c5712c86766f0b2620ebfa53177dd\", \"99dd6600f6d4817cd7de7e587b421074b0f109be224065cc6fa43d4811eb53ad\", \"dcccf9abe3f0b961efa9fcf957cd175a66fe00c6aefd271755dc3da7b4d8ddb1\", \"ddcfdff9e7802777575108842092e1a73989c962475ad150767ea0020d868ded\", \"f5b1175f6510e9223bc5ded77f1222a2f9090cfe882434e57277c93cbf7ec8ac\"]}, \"state_id\": \"e5274c5cdf2b6642d39d2b10\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 0}}, {\"experiment_id\": 5, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 0}}, {\"experiment_id\": 20, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 0}}]}}}", "extra_info": {"index": 111, "max_global_steps": 0, "min_global_steps": 0}, "index": 111, "prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=0\n intervention: none\n observed: Z1=1, Z2=0, Y=0\n\nExperiment 2:\n inputs: X1=0, X2=0, X3=1\n intervention: none\n observed: Z1=1, Z2=0, Y=1\n\nExperiment 3:\n inputs: X1=1, X2=0, X3=0\n intervention: none\n observed: Z1=0, Z2=0, Y=1\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "raw_prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=0\n intervention: none\n observed: Z1=1, Z2=0, Y=0\n\nExperiment 2:\n inputs: X1=0, X2=0, X3=1\n intervention: none\n observed: Z1=1, Z2=0, Y=1\n\nExperiment 3:\n inputs: X1=1, X2=0, X3=0\n intervention: none\n observed: Z1=0, Z2=0, Y=1\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "reward_model": {"style": "rule"}, "state_json": "{\"available_experiment_ids\": [1, 2, 3, 4, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 3, \"family_bucket\": \"mixed\", \"maximum_separation\": 0.5135135135135135, \"mean_separation\": 0.2997542997542995, \"minimum_separation\": 0.16216216216216217, \"normalized_mean_separation\": 0.31909328683522203, \"oracle_budget_representation_error\": 0.10810810810810811, \"oracle_singleton_representation_error\": 0.2612612612612613, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.1531531531531532, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 12}, \"private\": {\"hidden_mode_id\": \"47be9e02ba77ae49bbfed2c51a5c0fbbd0c81ef9f726653f3870e0e5cc9025a8\", \"valid_mode_ids\": [\"049a55d5d512b7deabcdd65c31151bbceb4f56bf3a47856dd7f5cef771ceec72\", \"19cefdd776f10b29ad3a8a742a22d9384e8a5859f80affab33f7b04cba6bfa4b\", \"1acadbf1cebcd65c59d8aad85a7ddd118355b2b13e09772e8f3cdedd45404574\", \"20dd688378813daef96fc20a2a594ceecc41f99f4d0b452a69cef45e37963bd7\", \"47be9e02ba77ae49bbfed2c51a5c0fbbd0c81ef9f726653f3870e0e5cc9025a8\", \"6205de37be50fedb1d771e9be005a216850469b2ca5d6c5937909781174df951\", \"6ff71329c90f0666af76d3fbd577ddc17b0e201a38e251de82a81ef074870e29\", \"7548c9d6088e6c316f46615b71652613924c5712c86766f0b2620ebfa53177dd\", \"99dd6600f6d4817cd7de7e587b421074b0f109be224065cc6fa43d4811eb53ad\", \"dcccf9abe3f0b961efa9fcf957cd175a66fe00c6aefd271755dc3da7b4d8ddb1\", \"ddcfdff9e7802777575108842092e1a73989c962475ad150767ea0020d868ded\", \"f5b1175f6510e9223bc5ded77f1222a2f9090cfe882434e57277c93cbf7ec8ac\"]}, \"state_id\": \"e5274c5cdf2b6642d39d2b10\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 0}}, {\"experiment_id\": 5, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 0}}, {\"experiment_id\": 20, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 0}}]}"} {"agent_name": "causal_micro_lab_agent_loop", "data_source": "causal_micro_lab_final_eval_v3", "env_spec_json": "{\"agent\": {}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 112, \"task\": {\"state\": {\"available_experiment_ids\": [0, 1, 2, 3, 4, 6, 7, 8, 9, 10, 11, 12, 13, 14, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 31, 32, 33, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 3, \"family_bucket\": \"cross_family\", \"maximum_separation\": 0.6756756756756757, \"mean_separation\": 0.4275184275184274, \"minimum_separation\": 0.21621621621621623, \"normalized_mean_separation\": 0.4551002615518743, \"oracle_budget_representation_error\": 0.15765765765765766, \"oracle_singleton_representation_error\": 0.3175675675675676, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.15990990990990994, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 12}, \"private\": {\"hidden_mode_id\": \"c285a98e3b64e713208924f6e644ec35b6c62f7e82262687d1b5d5b86857dbaf\", \"valid_mode_ids\": [\"179eef57c80f22b79f4d7c7464658f59af70cb14630c0c32c05010b576db7da0\", \"48831b5d75b21195ff5add08d0c618bc7819d22108c779127c253fe1a4f8ba5d\", \"4f232f2ad82a7c226b4dc1e3526bd3a5d7cf005479e98052411c5c7e12cf5202\", \"6012923ffb0243dd7c24fd696051457a7cb4bdfcb248dd0ccf05d031b96b35e8\", \"653726d0462a013b3b5c32d4623d26e67a807adbdae80bab16cef744ab3762d8\", \"8d49effb0bc40d5513fa28c36126d88200d22867b878f5a1e9e914e7fef668c7\", \"c285a98e3b64e713208924f6e644ec35b6c62f7e82262687d1b5d5b86857dbaf\", \"c4a41584a686bf756328b9a240547ffd675bc1763c95bd0f2fb516732db9e0b1\", \"d3248d39970009129206cea1b9d8f57f5ef1f07197f4beb591792ec877ae0e65\", \"d4bd14a7d9563704f8c4938485964286196053531600e93e1bb4a5203a49266b\", \"d58ac2374f86068570f13d81e6a2dd53f8432183b5638c279d929d3fce9be308\", \"e72fce616f1f3d6459f766c210b93b1e289697e47a9fcb80126ddffb92621ee7\"]}, \"state_id\": \"8a7b1a6a129fb871906d0024\", \"visible_experiments\": [{\"experiment_id\": 5, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 15, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 30, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 0}}]}}}", "extra_info": {"index": 112, "max_global_steps": 0, "min_global_steps": 0}, "index": 112, "prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=1\n intervention: none\n observed: Z1=1, Z2=1, Y=0\n\nExperiment 2:\n inputs: X1=0, X2=1, X3=1\n intervention: none\n observed: Z1=0, Z2=1, Y=1\n\nExperiment 3:\n inputs: X1=1, X2=1, X3=0\n intervention: none\n observed: Z1=1, Z2=0, Y=1\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "raw_prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=1\n intervention: none\n observed: Z1=1, Z2=1, Y=0\n\nExperiment 2:\n inputs: X1=0, X2=1, X3=1\n intervention: none\n observed: Z1=0, Z2=1, Y=1\n\nExperiment 3:\n inputs: X1=1, X2=1, X3=0\n intervention: none\n observed: Z1=1, Z2=0, Y=1\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "reward_model": {"style": "rule"}, "state_json": "{\"available_experiment_ids\": [0, 1, 2, 3, 4, 6, 7, 8, 9, 10, 11, 12, 13, 14, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 31, 32, 33, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 3, \"family_bucket\": \"cross_family\", \"maximum_separation\": 0.6756756756756757, \"mean_separation\": 0.4275184275184274, \"minimum_separation\": 0.21621621621621623, \"normalized_mean_separation\": 0.4551002615518743, \"oracle_budget_representation_error\": 0.15765765765765766, \"oracle_singleton_representation_error\": 0.3175675675675676, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.15990990990990994, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 12}, \"private\": {\"hidden_mode_id\": \"c285a98e3b64e713208924f6e644ec35b6c62f7e82262687d1b5d5b86857dbaf\", \"valid_mode_ids\": [\"179eef57c80f22b79f4d7c7464658f59af70cb14630c0c32c05010b576db7da0\", \"48831b5d75b21195ff5add08d0c618bc7819d22108c779127c253fe1a4f8ba5d\", \"4f232f2ad82a7c226b4dc1e3526bd3a5d7cf005479e98052411c5c7e12cf5202\", \"6012923ffb0243dd7c24fd696051457a7cb4bdfcb248dd0ccf05d031b96b35e8\", \"653726d0462a013b3b5c32d4623d26e67a807adbdae80bab16cef744ab3762d8\", \"8d49effb0bc40d5513fa28c36126d88200d22867b878f5a1e9e914e7fef668c7\", \"c285a98e3b64e713208924f6e644ec35b6c62f7e82262687d1b5d5b86857dbaf\", \"c4a41584a686bf756328b9a240547ffd675bc1763c95bd0f2fb516732db9e0b1\", \"d3248d39970009129206cea1b9d8f57f5ef1f07197f4beb591792ec877ae0e65\", \"d4bd14a7d9563704f8c4938485964286196053531600e93e1bb4a5203a49266b\", \"d58ac2374f86068570f13d81e6a2dd53f8432183b5638c279d929d3fce9be308\", \"e72fce616f1f3d6459f766c210b93b1e289697e47a9fcb80126ddffb92621ee7\"]}, \"state_id\": \"8a7b1a6a129fb871906d0024\", \"visible_experiments\": [{\"experiment_id\": 5, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 15, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 30, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 0}}]}"} {"agent_name": "causal_micro_lab_agent_loop", "data_source": "causal_micro_lab_final_eval_v3", "env_spec_json": "{\"agent\": {}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 113, \"task\": {\"state\": {\"available_experiment_ids\": [0, 1, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 16, 17, 18, 19, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 4, \"family_bucket\": \"mixed\", \"maximum_separation\": 0.4444444444444444, \"mean_separation\": 0.2803030303030303, \"minimum_separation\": 0.1111111111111111, \"normalized_mean_separation\": 0.2983870967741935, \"oracle_budget_representation_error\": 0.07407407407407408, \"oracle_singleton_representation_error\": 0.24074074074074073, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.16666666666666663, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 12}, \"private\": {\"hidden_mode_id\": \"b946faff04d49277d94ce60580393b59bdb6a38fff67f84f8ed853daad46a8da\", \"valid_mode_ids\": [\"26d583690c1894e6418f72dc79db8d9203c52b1719fbf279aaa5c182a8926634\", \"2b1c6ddb9760e66eb2ff7ca0b86e9b46a69873975bc6a17f55c39d7570458fd4\", \"2b48d668d7de3f20b134cbbe6af1397bb308b00e1b10fbfbee229bf7628e6a3a\", \"550e14cf33aea1e681db23c355e88598d2c2db5f97564072b977618aa333b550\", \"6ca3041b98031b55cb7955659262d3f2570e51d1a524c2ebea3c53686b0c6e4e\", \"6e585b96fe39fc84cc51de3c8d89bdaeb369d208ac464d580021966b068f9b9d\", \"7dfe99573d1c93957291d75b03251ca4bfe7f20ff40ce7712bf8f2323da39ed2\", \"7ee7904406754f033861ef08218a2cac2d2a4a52368a11d6150a80f97d789873\", \"a4fdb88e37da23263c1667c4a50b94ec182038e89c6019e193c95300baf2863d\", \"b946faff04d49277d94ce60580393b59bdb6a38fff67f84f8ed853daad46a8da\", \"d454f72ea193c3cbb804a6a439779f0ab8c8e7b4092ec3e48fc6de11c1e2b1ab\", \"d97d5621e095826619531bc8131ca745be6d70c2459f3dafebc7dc8aa5547047\"]}, \"state_id\": \"42e28b25748ea27c8140455c\", \"visible_experiments\": [{\"experiment_id\": 2, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z1_1\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 15, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 20, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 35, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 1}}]}}}", "extra_info": {"index": 113, "max_global_steps": 0, "min_global_steps": 0}, "index": 113, "prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=0\n intervention: set Z1=1\n observed: Z1=1, Z2=1, Y=0\n\nExperiment 2:\n inputs: X1=0, X2=1, X3=1\n intervention: none\n observed: Z1=0, Z2=0, Y=0\n\nExperiment 3:\n inputs: X1=1, X2=0, X3=0\n intervention: none\n observed: Z1=1, Z2=1, Y=0\n\nExperiment 4:\n inputs: X1=1, X2=1, X3=1\n intervention: none\n observed: Z1=1, Z2=1, Y=1\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "raw_prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=0\n intervention: set Z1=1\n observed: Z1=1, Z2=1, Y=0\n\nExperiment 2:\n inputs: X1=0, X2=1, X3=1\n intervention: none\n observed: Z1=0, Z2=0, Y=0\n\nExperiment 3:\n inputs: X1=1, X2=0, X3=0\n intervention: none\n observed: Z1=1, Z2=1, Y=0\n\nExperiment 4:\n inputs: X1=1, X2=1, X3=1\n intervention: none\n observed: Z1=1, Z2=1, Y=1\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "reward_model": {"style": "rule"}, "state_json": "{\"available_experiment_ids\": [0, 1, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 16, 17, 18, 19, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 4, \"family_bucket\": \"mixed\", \"maximum_separation\": 0.4444444444444444, \"mean_separation\": 0.2803030303030303, \"minimum_separation\": 0.1111111111111111, \"normalized_mean_separation\": 0.2983870967741935, \"oracle_budget_representation_error\": 0.07407407407407408, \"oracle_singleton_representation_error\": 0.24074074074074073, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.16666666666666663, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 12}, \"private\": {\"hidden_mode_id\": \"b946faff04d49277d94ce60580393b59bdb6a38fff67f84f8ed853daad46a8da\", \"valid_mode_ids\": [\"26d583690c1894e6418f72dc79db8d9203c52b1719fbf279aaa5c182a8926634\", \"2b1c6ddb9760e66eb2ff7ca0b86e9b46a69873975bc6a17f55c39d7570458fd4\", \"2b48d668d7de3f20b134cbbe6af1397bb308b00e1b10fbfbee229bf7628e6a3a\", \"550e14cf33aea1e681db23c355e88598d2c2db5f97564072b977618aa333b550\", \"6ca3041b98031b55cb7955659262d3f2570e51d1a524c2ebea3c53686b0c6e4e\", \"6e585b96fe39fc84cc51de3c8d89bdaeb369d208ac464d580021966b068f9b9d\", \"7dfe99573d1c93957291d75b03251ca4bfe7f20ff40ce7712bf8f2323da39ed2\", \"7ee7904406754f033861ef08218a2cac2d2a4a52368a11d6150a80f97d789873\", \"a4fdb88e37da23263c1667c4a50b94ec182038e89c6019e193c95300baf2863d\", \"b946faff04d49277d94ce60580393b59bdb6a38fff67f84f8ed853daad46a8da\", \"d454f72ea193c3cbb804a6a439779f0ab8c8e7b4092ec3e48fc6de11c1e2b1ab\", \"d97d5621e095826619531bc8131ca745be6d70c2459f3dafebc7dc8aa5547047\"]}, \"state_id\": \"42e28b25748ea27c8140455c\", \"visible_experiments\": [{\"experiment_id\": 2, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z1_1\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 15, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 20, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 35, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 1}}]}"} {"agent_name": "causal_micro_lab_agent_loop", "data_source": "causal_micro_lab_final_eval_v3", "env_spec_json": "{\"agent\": {}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 114, \"task\": {\"state\": {\"available_experiment_ids\": [0, 1, 2, 3, 4, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 26, 27, 28, 29, 30, 31, 32, 33, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 3, \"family_bucket\": \"cross_family\", \"maximum_separation\": 0.5405405405405406, \"mean_separation\": 0.3112203112203113, \"minimum_separation\": 0.10810810810810811, \"normalized_mean_separation\": 0.3312990409764604, \"oracle_budget_representation_error\": 0.0855855855855856, \"oracle_singleton_representation_error\": 0.2567567567567568, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.1711711711711712, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 12}, \"private\": {\"hidden_mode_id\": \"43623d43ef73afb6d62f4ea2fe1848c32328748f67bcbf9cc76da261b6071291\", \"valid_mode_ids\": [\"0484be52783b0448fe89f3076822101587ceee0189b2f582d8f6bf51bc92fe7c\", \"21f2be4a5f667cef82718567552bde4e7285b36b7894e2cad37aa8de9e914381\", \"3a12ab7d06b76d8280932f5e65086fccb68aa68d20dc99bd358869b2a373e902\", \"415a89b6319c01f6d3b05be88b199970b47c6e6a2a6b134738301387d828fdb7\", \"43623d43ef73afb6d62f4ea2fe1848c32328748f67bcbf9cc76da261b6071291\", \"46549da9a07a99319effc90447ef50cf2f1c73eee2e9fadf4e81229a4bdc32c0\", \"56a42039bbef56c76af93904384e7a742919dfa25ebf31d9a3d4823083e0a21b\", \"89b18b8882ab80ec22f66e435dd023c3c98d73fc2dfab1db1852c8616e88967b\", \"8c55456d8ecd729f03e287ed76102f48daa02cd1412485d94a8a34b275bac924\", \"c307624668c6388db3eda123b805c54f5ceaf9116b971dd93b654564f0ac125d\", \"cc12b1d7343edfc129b047f272546733ff55c3759011fb94144c3bdc25b4a4c5\", \"e6617d54f73006fe2927a374cb742669d2cd9237266d0470a2919a3131806009\"]}, \"state_id\": \"533794b12853200a6f65df27\", \"visible_experiments\": [{\"experiment_id\": 5, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 25, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 34, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 0}, \"intervention\": \"DO_Z2_1\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}]}}}", "extra_info": {"index": 114, "max_global_steps": 0, "min_global_steps": 0}, "index": 114, "prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=1\n intervention: none\n observed: Z1=0, Z2=1, Y=0\n\nExperiment 2:\n inputs: X1=1, X2=0, X3=1\n intervention: none\n observed: Z1=0, Z2=0, Y=0\n\nExperiment 3:\n inputs: X1=1, X2=1, X3=0\n intervention: set Z2=1\n observed: Z1=0, Z2=1, Y=0\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "raw_prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=1\n intervention: none\n observed: Z1=0, Z2=1, Y=0\n\nExperiment 2:\n inputs: X1=1, X2=0, X3=1\n intervention: none\n observed: Z1=0, Z2=0, Y=0\n\nExperiment 3:\n inputs: X1=1, X2=1, X3=0\n intervention: set Z2=1\n observed: Z1=0, Z2=1, Y=0\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "reward_model": {"style": "rule"}, "state_json": "{\"available_experiment_ids\": [0, 1, 2, 3, 4, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 26, 27, 28, 29, 30, 31, 32, 33, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 3, \"family_bucket\": \"cross_family\", \"maximum_separation\": 0.5405405405405406, \"mean_separation\": 0.3112203112203113, \"minimum_separation\": 0.10810810810810811, \"normalized_mean_separation\": 0.3312990409764604, \"oracle_budget_representation_error\": 0.0855855855855856, \"oracle_singleton_representation_error\": 0.2567567567567568, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.1711711711711712, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 12}, \"private\": {\"hidden_mode_id\": \"43623d43ef73afb6d62f4ea2fe1848c32328748f67bcbf9cc76da261b6071291\", \"valid_mode_ids\": [\"0484be52783b0448fe89f3076822101587ceee0189b2f582d8f6bf51bc92fe7c\", \"21f2be4a5f667cef82718567552bde4e7285b36b7894e2cad37aa8de9e914381\", \"3a12ab7d06b76d8280932f5e65086fccb68aa68d20dc99bd358869b2a373e902\", \"415a89b6319c01f6d3b05be88b199970b47c6e6a2a6b134738301387d828fdb7\", \"43623d43ef73afb6d62f4ea2fe1848c32328748f67bcbf9cc76da261b6071291\", \"46549da9a07a99319effc90447ef50cf2f1c73eee2e9fadf4e81229a4bdc32c0\", \"56a42039bbef56c76af93904384e7a742919dfa25ebf31d9a3d4823083e0a21b\", \"89b18b8882ab80ec22f66e435dd023c3c98d73fc2dfab1db1852c8616e88967b\", \"8c55456d8ecd729f03e287ed76102f48daa02cd1412485d94a8a34b275bac924\", \"c307624668c6388db3eda123b805c54f5ceaf9116b971dd93b654564f0ac125d\", \"cc12b1d7343edfc129b047f272546733ff55c3759011fb94144c3bdc25b4a4c5\", \"e6617d54f73006fe2927a374cb742669d2cd9237266d0470a2919a3131806009\"]}, \"state_id\": \"533794b12853200a6f65df27\", \"visible_experiments\": [{\"experiment_id\": 5, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 25, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 34, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 0}, \"intervention\": \"DO_Z2_1\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}]}"} {"agent_name": "causal_micro_lab_agent_loop", "data_source": "causal_micro_lab_final_eval_v3", "env_spec_json": "{\"agent\": {}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 115, \"task\": {\"state\": {\"available_experiment_ids\": [0, 1, 2, 3, 4, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 3, \"family_bucket\": \"within_family\", \"maximum_separation\": 0.5675675675675675, \"mean_separation\": 0.3775593775593774, \"minimum_separation\": 0.10810810810810811, \"normalized_mean_separation\": 0.4019180470793372, \"oracle_budget_representation_error\": 0.11261261261261264, \"oracle_singleton_representation_error\": 0.29279279279279286, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.18018018018018023, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 12}, \"private\": {\"hidden_mode_id\": \"9d3db603304beb1d0b793fac0d0c16ddb01176215f5cd342d09d2e16a25b05d2\", \"valid_mode_ids\": [\"0854df4633b33f4ec700da9010aa986d895e1bcc2981fd1fa5daee2092e751a0\", \"1e2c581d967dabaef0318c1d35265e72b4056d68ed782b50dc2e27eac6140abf\", \"45b2caa48971ebc4b10df741c57c4ec900eefde6dae8a9a83907a9edf28e1cf7\", \"5a9afdbaaa947262c56ccc28acf6cee3e7f24d1c488c84ad351983b311e4c980\", \"5c52f1f8589f3b17d41c6b932a05f76d0fc7aa618a0f1edd47ca454a14d3d033\", \"60e2b59ab611b9d2325e813c169becac01eaf28aaaa97a23a090d184c1199d86\", \"635c7282a3557bd37e7bd92a52657e49e3ac9098534013b85628ee96035432c0\", \"756e2f2500b3ddd2fec396adcdb1af0d068a786742774e148db7f36e2c187e54\", \"9d3db603304beb1d0b793fac0d0c16ddb01176215f5cd342d09d2e16a25b05d2\", \"c7aa98a6444f3d036ce0b4ff0851e5979a18f423cbfca3a8d5cf450a4e4208e4\", \"ccff980c38ba99362806bd51c9f03d13030e76513bf6b1d5b16ba2532cdea3fc\", \"fe8170a99f553602bf06e7698af80e10015e60dcdb28fa04c14e681a79a6cfa6\"]}, \"state_id\": \"04ba6e3ef3a00f048e9e60b9\", \"visible_experiments\": [{\"experiment_id\": 5, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 6, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 1}, \"intervention\": \"DO_Z1_0\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 20, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 1}}]}}}", "extra_info": {"index": 115, "max_global_steps": 0, "min_global_steps": 0}, "index": 115, "prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=1\n intervention: none\n observed: Z1=1, Z2=1, Y=0\n\nExperiment 2:\n inputs: X1=0, X2=0, X3=1\n intervention: set Z1=0\n observed: Z1=0, Z2=1, Y=1\n\nExperiment 3:\n inputs: X1=1, X2=0, X3=0\n intervention: none\n observed: Z1=1, Z2=1, Y=1\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "raw_prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=1\n intervention: none\n observed: Z1=1, Z2=1, Y=0\n\nExperiment 2:\n inputs: X1=0, X2=0, X3=1\n intervention: set Z1=0\n observed: Z1=0, Z2=1, Y=1\n\nExperiment 3:\n inputs: X1=1, X2=0, X3=0\n intervention: none\n observed: Z1=1, Z2=1, Y=1\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "reward_model": {"style": "rule"}, "state_json": "{\"available_experiment_ids\": [0, 1, 2, 3, 4, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 3, \"family_bucket\": \"within_family\", \"maximum_separation\": 0.5675675675675675, \"mean_separation\": 0.3775593775593774, \"minimum_separation\": 0.10810810810810811, \"normalized_mean_separation\": 0.4019180470793372, \"oracle_budget_representation_error\": 0.11261261261261264, \"oracle_singleton_representation_error\": 0.29279279279279286, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.18018018018018023, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 12}, \"private\": {\"hidden_mode_id\": \"9d3db603304beb1d0b793fac0d0c16ddb01176215f5cd342d09d2e16a25b05d2\", \"valid_mode_ids\": [\"0854df4633b33f4ec700da9010aa986d895e1bcc2981fd1fa5daee2092e751a0\", \"1e2c581d967dabaef0318c1d35265e72b4056d68ed782b50dc2e27eac6140abf\", \"45b2caa48971ebc4b10df741c57c4ec900eefde6dae8a9a83907a9edf28e1cf7\", \"5a9afdbaaa947262c56ccc28acf6cee3e7f24d1c488c84ad351983b311e4c980\", \"5c52f1f8589f3b17d41c6b932a05f76d0fc7aa618a0f1edd47ca454a14d3d033\", \"60e2b59ab611b9d2325e813c169becac01eaf28aaaa97a23a090d184c1199d86\", \"635c7282a3557bd37e7bd92a52657e49e3ac9098534013b85628ee96035432c0\", \"756e2f2500b3ddd2fec396adcdb1af0d068a786742774e148db7f36e2c187e54\", \"9d3db603304beb1d0b793fac0d0c16ddb01176215f5cd342d09d2e16a25b05d2\", \"c7aa98a6444f3d036ce0b4ff0851e5979a18f423cbfca3a8d5cf450a4e4208e4\", \"ccff980c38ba99362806bd51c9f03d13030e76513bf6b1d5b16ba2532cdea3fc\", \"fe8170a99f553602bf06e7698af80e10015e60dcdb28fa04c14e681a79a6cfa6\"]}, \"state_id\": \"04ba6e3ef3a00f048e9e60b9\", \"visible_experiments\": [{\"experiment_id\": 5, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 6, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 1}, \"intervention\": \"DO_Z1_0\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 20, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 1}}]}"} {"agent_name": "causal_micro_lab_agent_loop", "data_source": "causal_micro_lab_final_eval_v3", "env_spec_json": "{\"agent\": {}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 116, \"task\": {\"state\": {\"available_experiment_ids\": [1, 3, 4, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 4, \"family_bucket\": \"cross_family\", \"maximum_separation\": 0.6388888888888888, \"mean_separation\": 0.3914141414141413, \"minimum_separation\": 0.1111111111111111, \"normalized_mean_separation\": 0.4166666666666666, \"oracle_budget_representation_error\": 0.10879629629629632, \"oracle_singleton_representation_error\": 0.2916666666666667, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.18287037037037035, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 12}, \"private\": {\"hidden_mode_id\": \"c028882932fcc7aab4194270df7bc2e972fc3c583ae9fedb748bd186e7e41a94\", \"valid_mode_ids\": [\"7cdf763dc65e523227dcec04d777614b76e56c2de3cbc77969cf7f73bee690f3\", \"80a84547e7f2696261de0addf77155eb9e783dc59395149ee59bf3deec826652\", \"8cafe5a6153a4abb4d178ba7dfa048a502447e5f9a7e7ae7fbe57dc56802d072\", \"9210c6be48de8af6d81a7539b37f467f1318bc47c170b19bb59ea3823fbd8b94\", \"a2c7d0dc249b54f3de59ba0167ce8c02480bd9a7fd717e8f7fbc9bb5983deca7\", \"ab5a598f8d8c3cbf72ebc7cbcafaf3622c00746749c743aa8bf9fb2c2539dd21\", \"bb89ad93566c47f28163d784e34dcc6888bf4627ce39238bf87d8c9b9c86825a\", \"c028882932fcc7aab4194270df7bc2e972fc3c583ae9fedb748bd186e7e41a94\", \"cdb9dba8d46e4da8c13f93cdefb2e435d89c9fab2f4ec7b4db2b3402de1ce52d\", \"d099180d52479c33036caaa83f7319ffa9e72673c63dc190f37a7e8efae79414\", \"d0f97d2f454c5b2ec43596837b80a6e75d368cee12a1c28bc24ce74f95e2e957\", \"ddb6126be1e16bb9e2e70d6ea1032dc38ed56e2ed95377de0ede3f47750b7d90\"]}, \"state_id\": \"11ef88e09670f13b1617b5e1\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 2, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z1_1\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 5, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 20, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 1}}]}}}", "extra_info": {"index": 116, "max_global_steps": 0, "min_global_steps": 0}, "index": 116, "prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=0\n intervention: none\n observed: Z1=0, Z2=1, Y=0\n\nExperiment 2:\n inputs: X1=0, X2=0, X3=0\n intervention: set Z1=1\n observed: Z1=1, Z2=1, Y=1\n\nExperiment 3:\n inputs: X1=0, X2=0, X3=1\n intervention: none\n observed: Z1=1, Z2=1, Y=1\n\nExperiment 4:\n inputs: X1=1, X2=0, X3=0\n intervention: none\n observed: Z1=1, Z2=1, Y=1\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "raw_prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=0\n intervention: none\n observed: Z1=0, Z2=1, Y=0\n\nExperiment 2:\n inputs: X1=0, X2=0, X3=0\n intervention: set Z1=1\n observed: Z1=1, Z2=1, Y=1\n\nExperiment 3:\n inputs: X1=0, X2=0, X3=1\n intervention: none\n observed: Z1=1, Z2=1, Y=1\n\nExperiment 4:\n inputs: X1=1, X2=0, X3=0\n intervention: none\n observed: Z1=1, Z2=1, Y=1\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "reward_model": {"style": "rule"}, "state_json": "{\"available_experiment_ids\": [1, 3, 4, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 4, \"family_bucket\": \"cross_family\", \"maximum_separation\": 0.6388888888888888, \"mean_separation\": 0.3914141414141413, \"minimum_separation\": 0.1111111111111111, \"normalized_mean_separation\": 0.4166666666666666, \"oracle_budget_representation_error\": 0.10879629629629632, \"oracle_singleton_representation_error\": 0.2916666666666667, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.18287037037037035, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 12}, \"private\": {\"hidden_mode_id\": \"c028882932fcc7aab4194270df7bc2e972fc3c583ae9fedb748bd186e7e41a94\", \"valid_mode_ids\": [\"7cdf763dc65e523227dcec04d777614b76e56c2de3cbc77969cf7f73bee690f3\", \"80a84547e7f2696261de0addf77155eb9e783dc59395149ee59bf3deec826652\", \"8cafe5a6153a4abb4d178ba7dfa048a502447e5f9a7e7ae7fbe57dc56802d072\", \"9210c6be48de8af6d81a7539b37f467f1318bc47c170b19bb59ea3823fbd8b94\", \"a2c7d0dc249b54f3de59ba0167ce8c02480bd9a7fd717e8f7fbc9bb5983deca7\", \"ab5a598f8d8c3cbf72ebc7cbcafaf3622c00746749c743aa8bf9fb2c2539dd21\", \"bb89ad93566c47f28163d784e34dcc6888bf4627ce39238bf87d8c9b9c86825a\", \"c028882932fcc7aab4194270df7bc2e972fc3c583ae9fedb748bd186e7e41a94\", \"cdb9dba8d46e4da8c13f93cdefb2e435d89c9fab2f4ec7b4db2b3402de1ce52d\", \"d099180d52479c33036caaa83f7319ffa9e72673c63dc190f37a7e8efae79414\", \"d0f97d2f454c5b2ec43596837b80a6e75d368cee12a1c28bc24ce74f95e2e957\", \"ddb6126be1e16bb9e2e70d6ea1032dc38ed56e2ed95377de0ede3f47750b7d90\"]}, \"state_id\": \"11ef88e09670f13b1617b5e1\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 2, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z1_1\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 5, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 20, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 1}}]}"} {"agent_name": "causal_micro_lab_agent_loop", "data_source": "causal_micro_lab_final_eval_v3", "env_spec_json": "{\"agent\": {}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 117, \"task\": {\"state\": {\"available_experiment_ids\": [0, 1, 2, 3, 4, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 3, \"family_bucket\": \"mixed\", \"maximum_separation\": 0.6486486486486487, \"mean_separation\": 0.44307944307944275, \"minimum_separation\": 0.2702702702702703, \"normalized_mean_separation\": 0.4716652136006971, \"oracle_budget_representation_error\": 0.18018018018018014, \"oracle_singleton_representation_error\": 0.36936936936936937, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.18918918918918923, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 12}, \"private\": {\"hidden_mode_id\": \"1b6a32a14ed69273178124fa5b320d3fded9b2ea575dd7ed3ba48e810dda83fe\", \"valid_mode_ids\": [\"05061e1ea342f206e2e3dec239e6084a06a6faea685cbb2ace22a5ab2ee30d96\", \"07cf0e74fdf39fba7d9a37662386973dd939ae70e9a6c87b5bc1554a4bba0626\", \"1b6a32a14ed69273178124fa5b320d3fded9b2ea575dd7ed3ba48e810dda83fe\", \"2cca5cd347791d0d5ed76efc951d97234ecce98b2d4f1a51b95917df989bccc0\", \"575eb8704d75832d2b4a6d4157a26de0ae1b415af198f5cf2bc8cd90bad6bf0b\", \"6ec0c5a64d210cf7815ac9fe6de14b2b8495f443d70d33488cb7e01c51e7364f\", \"733c02f6685b3c1e697593133c53ccb36ec60593fb5b147615104630da34c861\", \"81f1ef2fb4ed62633b5a6ec770e25b32bcac52ee7c4d76dc039bd637d4d6c429\", \"b3c4ac4f9bfda4a50b80cfcfefe1d1c9373b13ff3a8e2184a935f38534e82178\", \"b98f049a1a2e268e5014289bef211ca22a0b25d89cd30609a65566d5867a7e77\", \"c90e6e137d9b5f5a06f793e235d233cf248152f70e1786574346dd71e4fa1421\", \"fdd2992d8d744a58db36dae811625b0fdc81cae78b641f482c30f78d3a4002c3\"]}, \"state_id\": \"993267d45473c48e638ec006\", \"visible_experiments\": [{\"experiment_id\": 5, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 24, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z2_1\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 35, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 0}}]}}}", "extra_info": {"index": 117, "max_global_steps": 0, "min_global_steps": 0}, "index": 117, "prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=1\n intervention: none\n observed: Z1=1, Z2=1, Y=0\n\nExperiment 2:\n inputs: X1=1, X2=0, X3=0\n intervention: set Z2=1\n observed: Z1=1, Z2=1, Y=0\n\nExperiment 3:\n inputs: X1=1, X2=1, X3=1\n intervention: none\n observed: Z1=1, Z2=0, Y=0\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "raw_prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=1\n intervention: none\n observed: Z1=1, Z2=1, Y=0\n\nExperiment 2:\n inputs: X1=1, X2=0, X3=0\n intervention: set Z2=1\n observed: Z1=1, Z2=1, Y=0\n\nExperiment 3:\n inputs: X1=1, X2=1, X3=1\n intervention: none\n observed: Z1=1, Z2=0, Y=0\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "reward_model": {"style": "rule"}, "state_json": "{\"available_experiment_ids\": [0, 1, 2, 3, 4, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 3, \"family_bucket\": \"mixed\", \"maximum_separation\": 0.6486486486486487, \"mean_separation\": 0.44307944307944275, \"minimum_separation\": 0.2702702702702703, \"normalized_mean_separation\": 0.4716652136006971, \"oracle_budget_representation_error\": 0.18018018018018014, \"oracle_singleton_representation_error\": 0.36936936936936937, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.18918918918918923, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 12}, \"private\": {\"hidden_mode_id\": \"1b6a32a14ed69273178124fa5b320d3fded9b2ea575dd7ed3ba48e810dda83fe\", \"valid_mode_ids\": [\"05061e1ea342f206e2e3dec239e6084a06a6faea685cbb2ace22a5ab2ee30d96\", \"07cf0e74fdf39fba7d9a37662386973dd939ae70e9a6c87b5bc1554a4bba0626\", \"1b6a32a14ed69273178124fa5b320d3fded9b2ea575dd7ed3ba48e810dda83fe\", \"2cca5cd347791d0d5ed76efc951d97234ecce98b2d4f1a51b95917df989bccc0\", \"575eb8704d75832d2b4a6d4157a26de0ae1b415af198f5cf2bc8cd90bad6bf0b\", \"6ec0c5a64d210cf7815ac9fe6de14b2b8495f443d70d33488cb7e01c51e7364f\", \"733c02f6685b3c1e697593133c53ccb36ec60593fb5b147615104630da34c861\", \"81f1ef2fb4ed62633b5a6ec770e25b32bcac52ee7c4d76dc039bd637d4d6c429\", \"b3c4ac4f9bfda4a50b80cfcfefe1d1c9373b13ff3a8e2184a935f38534e82178\", \"b98f049a1a2e268e5014289bef211ca22a0b25d89cd30609a65566d5867a7e77\", \"c90e6e137d9b5f5a06f793e235d233cf248152f70e1786574346dd71e4fa1421\", \"fdd2992d8d744a58db36dae811625b0fdc81cae78b641f482c30f78d3a4002c3\"]}, \"state_id\": \"993267d45473c48e638ec006\", \"visible_experiments\": [{\"experiment_id\": 5, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 24, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z2_1\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 35, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 0}}]}"} {"agent_name": "causal_micro_lab_agent_loop", "data_source": "causal_micro_lab_final_eval_v3", "env_spec_json": "{\"agent\": {}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 118, \"task\": {\"state\": {\"available_experiment_ids\": [0, 1, 2, 3, 4, 7, 8, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 4, \"family_bucket\": \"cross_family\", \"maximum_separation\": 0.7222222222222222, \"mean_separation\": 0.4305555555555556, \"minimum_separation\": 0.1111111111111111, \"normalized_mean_separation\": 0.4583333333333333, \"oracle_budget_representation_error\": 0.13194444444444445, \"oracle_singleton_representation_error\": 0.3263888888888889, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.19444444444444445, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 12}, \"private\": {\"hidden_mode_id\": \"f8ccf51d4f3904a254710e00b0631d56122dbed0e51f9af5cc685869361e85b8\", \"valid_mode_ids\": [\"1c6e98e70b3d4bc9528b91d637f00e2ca4058901c67ed087e0417f717fdb1e50\", \"1dfc5295705211b1d6b32529c0d2ed4ae759eecf681f0a219933dde4f4eb2464\", \"3a76ab6c52670f1e46c92c666ebad423c3df5291eea0671e9b5a529d67d49eef\", \"402cc99d1e99d0c811b63cd866841bf7bd92e95d31621621886f9a91472faaae\", \"51726957d9d1f21356343fcfa281b5c8e56d3fab7afcc7ff4558266b1e71cb48\", \"548fa8da25012355c7b7ac9b647b4e074ddc188219484e043e52dc8f4a32d3b8\", \"90deca10d250669d22d2204929ed17db604003e2289e64244410b70cf87f5d47\", \"914befe3adef476b9518a3a749e5b40af5a6cf6e28a3b13b00eda69f1c0ba574\", \"b19bdad3ac02fe07d3c2d10be040ac6518fbf806c785aa2eaae41940b743c2fc\", \"badd6ae61e9526bb748e01af0098aa97519a6e083dedab09e4b5d17e42450f01\", \"d693c76987d44824199ca352a5ab291eb12ce201b3010dc52216c95d40a908ce\", \"f8ccf51d4f3904a254710e00b0631d56122dbed0e51f9af5cc685869361e85b8\"]}, \"state_id\": \"6d2cf5b2e3a1b466db2ef199\", \"visible_experiments\": [{\"experiment_id\": 5, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 0}}, {\"experiment_id\": 6, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 1}, \"intervention\": \"DO_Z1_0\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 9, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 1}, \"intervention\": \"DO_Z2_1\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 20, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 1}}]}}}", "extra_info": {"index": 118, "max_global_steps": 0, "min_global_steps": 0}, "index": 118, "prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=1\n intervention: none\n observed: Z1=1, Z2=0, Y=1\n\nExperiment 2:\n inputs: X1=0, X2=0, X3=1\n intervention: set Z1=0\n observed: Z1=0, Z2=1, Y=0\n\nExperiment 3:\n inputs: X1=0, X2=0, X3=1\n intervention: set Z2=1\n observed: Z1=1, Z2=1, Y=1\n\nExperiment 4:\n inputs: X1=1, X2=0, X3=0\n intervention: none\n observed: Z1=1, Z2=1, Y=1\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "raw_prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=1\n intervention: none\n observed: Z1=1, Z2=0, Y=1\n\nExperiment 2:\n inputs: X1=0, X2=0, X3=1\n intervention: set Z1=0\n observed: Z1=0, Z2=1, Y=0\n\nExperiment 3:\n inputs: X1=0, X2=0, X3=1\n intervention: set Z2=1\n observed: Z1=1, Z2=1, Y=1\n\nExperiment 4:\n inputs: X1=1, X2=0, X3=0\n intervention: none\n observed: Z1=1, Z2=1, Y=1\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "reward_model": {"style": "rule"}, "state_json": "{\"available_experiment_ids\": [0, 1, 2, 3, 4, 7, 8, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 4, \"family_bucket\": \"cross_family\", \"maximum_separation\": 0.7222222222222222, \"mean_separation\": 0.4305555555555556, \"minimum_separation\": 0.1111111111111111, \"normalized_mean_separation\": 0.4583333333333333, \"oracle_budget_representation_error\": 0.13194444444444445, \"oracle_singleton_representation_error\": 0.3263888888888889, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.19444444444444445, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 12}, \"private\": {\"hidden_mode_id\": \"f8ccf51d4f3904a254710e00b0631d56122dbed0e51f9af5cc685869361e85b8\", \"valid_mode_ids\": [\"1c6e98e70b3d4bc9528b91d637f00e2ca4058901c67ed087e0417f717fdb1e50\", \"1dfc5295705211b1d6b32529c0d2ed4ae759eecf681f0a219933dde4f4eb2464\", \"3a76ab6c52670f1e46c92c666ebad423c3df5291eea0671e9b5a529d67d49eef\", \"402cc99d1e99d0c811b63cd866841bf7bd92e95d31621621886f9a91472faaae\", \"51726957d9d1f21356343fcfa281b5c8e56d3fab7afcc7ff4558266b1e71cb48\", \"548fa8da25012355c7b7ac9b647b4e074ddc188219484e043e52dc8f4a32d3b8\", \"90deca10d250669d22d2204929ed17db604003e2289e64244410b70cf87f5d47\", \"914befe3adef476b9518a3a749e5b40af5a6cf6e28a3b13b00eda69f1c0ba574\", \"b19bdad3ac02fe07d3c2d10be040ac6518fbf806c785aa2eaae41940b743c2fc\", \"badd6ae61e9526bb748e01af0098aa97519a6e083dedab09e4b5d17e42450f01\", \"d693c76987d44824199ca352a5ab291eb12ce201b3010dc52216c95d40a908ce\", \"f8ccf51d4f3904a254710e00b0631d56122dbed0e51f9af5cc685869361e85b8\"]}, \"state_id\": \"6d2cf5b2e3a1b466db2ef199\", \"visible_experiments\": [{\"experiment_id\": 5, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 0}}, {\"experiment_id\": 6, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 1}, \"intervention\": \"DO_Z1_0\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 9, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 1}, \"intervention\": \"DO_Z2_1\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 20, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 1}}]}"} {"agent_name": "causal_micro_lab_agent_loop", "data_source": "causal_micro_lab_final_eval_v3", "env_spec_json": "{\"agent\": {}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 119, \"task\": {\"state\": {\"available_experiment_ids\": [1, 2, 3, 4, 5, 6, 7, 8, 9, 11, 12, 13, 14, 15, 16, 17, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 3, \"family_bucket\": \"within_family\", \"maximum_separation\": 0.5945945945945946, \"mean_separation\": 0.3947583947583945, \"minimum_separation\": 0.16216216216216217, \"normalized_mean_separation\": 0.42022667829119414, \"oracle_budget_representation_error\": 0.13063063063063066, \"oracle_singleton_representation_error\": 0.3310810810810811, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.20045045045045043, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 12}, \"private\": {\"hidden_mode_id\": \"11ceef732dc61bad19e07ce7e767adb741d43a916735d7b1a6c823e11cf51bc2\", \"valid_mode_ids\": [\"083fe2a4bc2be55d5b46e80f5ad5612462fa82e318db6026e81fd96d338e1ea3\", \"11ceef732dc61bad19e07ce7e767adb741d43a916735d7b1a6c823e11cf51bc2\", \"423725ebbf92ddf9f68aea48c992d67ec649811b11d8f021d2dcb1afd2184697\", \"5c95c467817d76e45009acad7d85bc152d75c1e30c4d3b844d10128089937e3d\", \"6214aa046761eae59024bde1f6b84ce9b3180fe4e4037bbfb98926cb5f6a39c9\", \"903a390fdf0ffdbb814404d1434082c71f0136bafaa0f9118fc2fbf19f201a4b\", \"93ad148f94dc378adfe802df2a202e627a37ddf73dca0db86cb48e99cb6dbd3f\", \"b7f5508c901d558a5612ef4d6dba8098856cf47efefc0555b2a5d41ad606e7e6\", \"ba6c938c23a16cf90d22be8559740a3e3599409abce1edb743842df29006cebb\", \"c1506a6b818e87a19171aaa690b091c0ea76337e81d7bc654032adb09be8b97d\", \"ccbfb36c504644ceacc575767f6af83c21d2e285f8027d2b2c1c48510a6d1300\", \"e3be4aab386be6e3901e45df8a7315cbce089ad6cae4f17fdb19f9d80e637b3c\"]}, \"state_id\": \"82df7ba97147619f2937df23\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 10, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 18, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 1}, \"intervention\": \"DO_Z2_0\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 0}}]}}}", "extra_info": {"index": 119, "max_global_steps": 0, "min_global_steps": 0}, "index": 119, "prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=0\n intervention: none\n observed: Z1=0, Z2=1, Y=1\n\nExperiment 2:\n inputs: X1=0, X2=1, X3=0\n intervention: none\n observed: Z1=0, Z2=1, Y=0\n\nExperiment 3:\n inputs: X1=0, X2=1, X3=1\n intervention: set Z2=0\n observed: Z1=1, Z2=0, Y=1\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "raw_prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=0\n intervention: none\n observed: Z1=0, Z2=1, Y=1\n\nExperiment 2:\n inputs: X1=0, X2=1, X3=0\n intervention: none\n observed: Z1=0, Z2=1, Y=0\n\nExperiment 3:\n inputs: X1=0, X2=1, X3=1\n intervention: set Z2=0\n observed: Z1=1, Z2=0, Y=1\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "reward_model": {"style": "rule"}, "state_json": "{\"available_experiment_ids\": [1, 2, 3, 4, 5, 6, 7, 8, 9, 11, 12, 13, 14, 15, 16, 17, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 3, \"family_bucket\": \"within_family\", \"maximum_separation\": 0.5945945945945946, \"mean_separation\": 0.3947583947583945, \"minimum_separation\": 0.16216216216216217, \"normalized_mean_separation\": 0.42022667829119414, \"oracle_budget_representation_error\": 0.13063063063063066, \"oracle_singleton_representation_error\": 0.3310810810810811, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.20045045045045043, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 12}, \"private\": {\"hidden_mode_id\": \"11ceef732dc61bad19e07ce7e767adb741d43a916735d7b1a6c823e11cf51bc2\", \"valid_mode_ids\": [\"083fe2a4bc2be55d5b46e80f5ad5612462fa82e318db6026e81fd96d338e1ea3\", \"11ceef732dc61bad19e07ce7e767adb741d43a916735d7b1a6c823e11cf51bc2\", \"423725ebbf92ddf9f68aea48c992d67ec649811b11d8f021d2dcb1afd2184697\", \"5c95c467817d76e45009acad7d85bc152d75c1e30c4d3b844d10128089937e3d\", \"6214aa046761eae59024bde1f6b84ce9b3180fe4e4037bbfb98926cb5f6a39c9\", \"903a390fdf0ffdbb814404d1434082c71f0136bafaa0f9118fc2fbf19f201a4b\", \"93ad148f94dc378adfe802df2a202e627a37ddf73dca0db86cb48e99cb6dbd3f\", \"b7f5508c901d558a5612ef4d6dba8098856cf47efefc0555b2a5d41ad606e7e6\", \"ba6c938c23a16cf90d22be8559740a3e3599409abce1edb743842df29006cebb\", \"c1506a6b818e87a19171aaa690b091c0ea76337e81d7bc654032adb09be8b97d\", \"ccbfb36c504644ceacc575767f6af83c21d2e285f8027d2b2c1c48510a6d1300\", \"e3be4aab386be6e3901e45df8a7315cbce089ad6cae4f17fdb19f9d80e637b3c\"]}, \"state_id\": \"82df7ba97147619f2937df23\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 10, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 18, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 1}, \"intervention\": \"DO_Z2_0\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 0}}]}"} {"agent_name": "causal_micro_lab_agent_loop", "data_source": "causal_micro_lab_final_eval_v3", "env_spec_json": "{\"agent\": {}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 120, \"task\": {\"state\": {\"available_experiment_ids\": [0, 1, 2, 3, 5, 6, 7, 8, 9, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 26, 27, 28, 29, 30, 32, 33, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 4, \"family_bucket\": \"cross_family\", \"maximum_separation\": 0.6388888888888888, \"mean_separation\": 0.4149831649831652, \"minimum_separation\": 0.1111111111111111, \"normalized_mean_separation\": 0.4417562724014339, \"oracle_budget_representation_error\": 0.12037037037037035, \"oracle_singleton_representation_error\": 0.3263888888888889, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.20601851851851855, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 12}, \"private\": {\"hidden_mode_id\": \"6bf97601e111d238de37b5654786a78dd87359a97ed3fe12f5b9088d794e808a\", \"valid_mode_ids\": [\"02b2c1caf37caf2a6d2cba8528396ab856ee2b9f3b37771b47f18a246fced2d7\", \"22f7be6b030b024f1185fe03ca68bb9c80f5b99810b1a684f2b18065c4d9eacc\", \"594fcd2a6f4df36c558b6ed86dcedddce61a0b2a839f0ddb49e3eb4f5134abb6\", \"617ccf8ff59b88111c77a824af1c2d9848ebc06a2d355fe0607d3d8684c0a8c3\", \"6bf97601e111d238de37b5654786a78dd87359a97ed3fe12f5b9088d794e808a\", \"9664afcb80e4d40bbdae039932a86193c80b37ab52f51d6776b107500384bf15\", \"b8afdd3097e70d5babb83285fd5cc6f407defc11bd309cc3f792599c0c22dee2\", \"ba761f0ff48757483487b26b6854677ba3997aec40abe6b3f297448b39b305e4\", \"bfb68e83fd16aa9f53fce605cf94524d4964e5e4822c065326d687402b8c80e9\", \"dad4ee5b4e9e687801a0d6c935766dc796ad8d7c08f7932c8ee848e2320c02bb\", \"e028f9bf095712c318919ca692ca50e3d8854f829a509de042ff7fa50d8e32cb\", \"f754da5f71a3588dc4e9a72a91e8fc5ff5e7e1178f0568ec7078ff8a481ca24f\"]}, \"state_id\": \"a30e02cf1b93f48caf718057\", \"visible_experiments\": [{\"experiment_id\": 4, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z2_1\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 10, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 0}}, {\"experiment_id\": 25, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 0}}, {\"experiment_id\": 31, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 0}, \"intervention\": \"DO_Z1_0\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 0}}]}}}", "extra_info": {"index": 120, "max_global_steps": 0, "min_global_steps": 0}, "index": 120, "prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=0\n intervention: set Z2=1\n observed: Z1=0, Z2=1, Y=0\n\nExperiment 2:\n inputs: X1=0, X2=1, X3=0\n intervention: none\n observed: Z1=1, Z2=0, Y=1\n\nExperiment 3:\n inputs: X1=1, X2=0, X3=1\n intervention: none\n observed: Z1=1, Z2=0, Y=1\n\nExperiment 4:\n inputs: X1=1, X2=1, X3=0\n intervention: set Z1=0\n observed: Z1=0, Z2=0, Y=0\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "raw_prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=0\n intervention: set Z2=1\n observed: Z1=0, Z2=1, Y=0\n\nExperiment 2:\n inputs: X1=0, X2=1, X3=0\n intervention: none\n observed: Z1=1, Z2=0, Y=1\n\nExperiment 3:\n inputs: X1=1, X2=0, X3=1\n intervention: none\n observed: Z1=1, Z2=0, Y=1\n\nExperiment 4:\n inputs: X1=1, X2=1, X3=0\n intervention: set Z1=0\n observed: Z1=0, Z2=0, Y=0\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "reward_model": {"style": "rule"}, "state_json": "{\"available_experiment_ids\": [0, 1, 2, 3, 5, 6, 7, 8, 9, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 26, 27, 28, 29, 30, 32, 33, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 4, \"family_bucket\": \"cross_family\", \"maximum_separation\": 0.6388888888888888, \"mean_separation\": 0.4149831649831652, \"minimum_separation\": 0.1111111111111111, \"normalized_mean_separation\": 0.4417562724014339, \"oracle_budget_representation_error\": 0.12037037037037035, \"oracle_singleton_representation_error\": 0.3263888888888889, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.20601851851851855, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 12}, \"private\": {\"hidden_mode_id\": \"6bf97601e111d238de37b5654786a78dd87359a97ed3fe12f5b9088d794e808a\", \"valid_mode_ids\": [\"02b2c1caf37caf2a6d2cba8528396ab856ee2b9f3b37771b47f18a246fced2d7\", \"22f7be6b030b024f1185fe03ca68bb9c80f5b99810b1a684f2b18065c4d9eacc\", \"594fcd2a6f4df36c558b6ed86dcedddce61a0b2a839f0ddb49e3eb4f5134abb6\", \"617ccf8ff59b88111c77a824af1c2d9848ebc06a2d355fe0607d3d8684c0a8c3\", \"6bf97601e111d238de37b5654786a78dd87359a97ed3fe12f5b9088d794e808a\", \"9664afcb80e4d40bbdae039932a86193c80b37ab52f51d6776b107500384bf15\", \"b8afdd3097e70d5babb83285fd5cc6f407defc11bd309cc3f792599c0c22dee2\", \"ba761f0ff48757483487b26b6854677ba3997aec40abe6b3f297448b39b305e4\", \"bfb68e83fd16aa9f53fce605cf94524d4964e5e4822c065326d687402b8c80e9\", \"dad4ee5b4e9e687801a0d6c935766dc796ad8d7c08f7932c8ee848e2320c02bb\", \"e028f9bf095712c318919ca692ca50e3d8854f829a509de042ff7fa50d8e32cb\", \"f754da5f71a3588dc4e9a72a91e8fc5ff5e7e1178f0568ec7078ff8a481ca24f\"]}, \"state_id\": \"a30e02cf1b93f48caf718057\", \"visible_experiments\": [{\"experiment_id\": 4, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z2_1\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 10, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 0}}, {\"experiment_id\": 25, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 0}}, {\"experiment_id\": 31, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 0}, \"intervention\": \"DO_Z1_0\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 0}}]}"} {"agent_name": "causal_micro_lab_agent_loop", "data_source": "causal_micro_lab_final_eval_v3", "env_spec_json": "{\"agent\": {}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 121, \"task\": {\"state\": {\"available_experiment_ids\": [1, 2, 3, 4, 5, 6, 7, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 3, \"family_bucket\": \"cross_family\", \"maximum_separation\": 0.7027027027027027, \"mean_separation\": 0.47092547092547077, \"minimum_separation\": 0.10810810810810811, \"normalized_mean_separation\": 0.5013077593722753, \"oracle_budget_representation_error\": 0.15765765765765766, \"oracle_singleton_representation_error\": 0.36936936936936937, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.21171171171171171, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 12}, \"private\": {\"hidden_mode_id\": \"02b6a764582959408fa064f0cfe0a6c173f2abd1f1dbcff693bb131976bddcb7\", \"valid_mode_ids\": [\"02b6a764582959408fa064f0cfe0a6c173f2abd1f1dbcff693bb131976bddcb7\", \"12930e0a91c87e5633baf3e1fe5696d46488cc3830b104b6588f020c8b94d105\", \"1597b8384548dd68bd223e95478aab8de0778fa76245fb5e3b85deee4ee64da0\", \"1e0a8a3e11265bdcb96268a926fe2586ca3711731a5917edcb2ef8a30edad38c\", \"39e2ee57d0c44b5291328528de4c1d02eaa8d1183dd9c85ae183ec1175245dcb\", \"668686f49462c5f97379ab1a5d622af6bdd7efdc46993947b9e3e887ee525f30\", \"6eb010ae9e6728f7cf9ffe97c318750d49823446d867b5f3f0841618b0f0349a\", \"9381d498c843263bc2d033adb57c42d35ee8fb1e0731b812d4697736b70f8c49\", \"b5dd5f39d6cfa2068a41fb80a227afc2336345947964cff825ae32cc64d1bc44\", \"ea306abc9c74c90c1b04156ee009205ba64019ab4223ea29eb30979559126282\", \"eebcc30f002bbf837cdd728b73460b4537b34f26e048e4556aff7f52f9b38540\", \"f197990e9a3b9667eaecaccb164ea3cc4f9771993de3453972b8b52ee52b3a8a\"]}, \"state_id\": \"3e787bbe54c9200a89c43e60\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 8, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 1}, \"intervention\": \"DO_Z2_0\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 0}}, {\"experiment_id\": 20, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 1}}]}}}", "extra_info": {"index": 121, "max_global_steps": 0, "min_global_steps": 0}, "index": 121, "prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=0\n intervention: none\n observed: Z1=1, Z2=1, Y=0\n\nExperiment 2:\n inputs: X1=0, X2=0, X3=1\n intervention: set Z2=0\n observed: Z1=1, Z2=0, Y=1\n\nExperiment 3:\n inputs: X1=1, X2=0, X3=0\n intervention: none\n observed: Z1=0, Z2=1, Y=1\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "raw_prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=0\n intervention: none\n observed: Z1=1, Z2=1, Y=0\n\nExperiment 2:\n inputs: X1=0, X2=0, X3=1\n intervention: set Z2=0\n observed: Z1=1, Z2=0, Y=1\n\nExperiment 3:\n inputs: X1=1, X2=0, X3=0\n intervention: none\n observed: Z1=0, Z2=1, Y=1\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "reward_model": {"style": "rule"}, "state_json": "{\"available_experiment_ids\": [1, 2, 3, 4, 5, 6, 7, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 3, \"family_bucket\": \"cross_family\", \"maximum_separation\": 0.7027027027027027, \"mean_separation\": 0.47092547092547077, \"minimum_separation\": 0.10810810810810811, \"normalized_mean_separation\": 0.5013077593722753, \"oracle_budget_representation_error\": 0.15765765765765766, \"oracle_singleton_representation_error\": 0.36936936936936937, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.21171171171171171, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 12}, \"private\": {\"hidden_mode_id\": \"02b6a764582959408fa064f0cfe0a6c173f2abd1f1dbcff693bb131976bddcb7\", \"valid_mode_ids\": [\"02b6a764582959408fa064f0cfe0a6c173f2abd1f1dbcff693bb131976bddcb7\", \"12930e0a91c87e5633baf3e1fe5696d46488cc3830b104b6588f020c8b94d105\", \"1597b8384548dd68bd223e95478aab8de0778fa76245fb5e3b85deee4ee64da0\", \"1e0a8a3e11265bdcb96268a926fe2586ca3711731a5917edcb2ef8a30edad38c\", \"39e2ee57d0c44b5291328528de4c1d02eaa8d1183dd9c85ae183ec1175245dcb\", \"668686f49462c5f97379ab1a5d622af6bdd7efdc46993947b9e3e887ee525f30\", \"6eb010ae9e6728f7cf9ffe97c318750d49823446d867b5f3f0841618b0f0349a\", \"9381d498c843263bc2d033adb57c42d35ee8fb1e0731b812d4697736b70f8c49\", \"b5dd5f39d6cfa2068a41fb80a227afc2336345947964cff825ae32cc64d1bc44\", \"ea306abc9c74c90c1b04156ee009205ba64019ab4223ea29eb30979559126282\", \"eebcc30f002bbf837cdd728b73460b4537b34f26e048e4556aff7f52f9b38540\", \"f197990e9a3b9667eaecaccb164ea3cc4f9771993de3453972b8b52ee52b3a8a\"]}, \"state_id\": \"3e787bbe54c9200a89c43e60\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 8, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 1}, \"intervention\": \"DO_Z2_0\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 0}}, {\"experiment_id\": 20, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 1}}]}"} {"agent_name": "causal_micro_lab_agent_loop", "data_source": "causal_micro_lab_final_eval_v3", "env_spec_json": "{\"agent\": {}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 122, \"task\": {\"state\": {\"available_experiment_ids\": [0, 1, 2, 4, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 21, 22, 23, 24, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 4, \"family_bucket\": \"cross_family\", \"maximum_separation\": 0.6944444444444444, \"mean_separation\": 0.44402356902356904, \"minimum_separation\": 0.1111111111111111, \"normalized_mean_separation\": 0.47267025089605735, \"oracle_budget_representation_error\": 0.1273148148148148, \"oracle_singleton_representation_error\": 0.34490740740740744, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.21759259259259264, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 12}, \"private\": {\"hidden_mode_id\": \"765aad93448c4d21b4be9a424b8db627e5e10dbf06863acec366296e884f7ae3\", \"valid_mode_ids\": [\"0804d4083712f9ca9a23682ae7b79ceecc6bf73bb8cc1c846a9666bf5dd94410\", \"17762877d5eac65b00a2f4ba6caecd2a0c046bceec12f2cfff6a47b0560fe436\", \"33dd79a28ad77e0307c436bed058edd701f11c7303556c3abec4516ad1205826\", \"38e867de9871a3c946200f236e2d5f8bc02cba15e59d20516dba8ece447e6a1e\", \"4640df55cc8ba9ca5c41fd143132a07095862244259f25bcac53801bd158984c\", \"6d16f7fe8a2d277a896b81bdd10f8723ba9b6a2863ce93e407c76b439e4302ec\", \"765aad93448c4d21b4be9a424b8db627e5e10dbf06863acec366296e884f7ae3\", \"83b63d1abff70d3ba6a05fb3a562f2132fc4127465bbd3948fec2381f47d9067\", \"8d13c73d68ccb97e25b3a9589d39cfdb4664f855b675de01224dba147901e7f1\", \"a8900e2f75dd7634796c23a513a2d2ef85b0abbac8e7bb8a9df287edc03ef00c\", \"abdbfe2eb8971237d4426b4317389d15ac9670935de65156805e4689cbedefcb\", \"c1506a6b818e87a19171aaa690b091c0ea76337e81d7bc654032adb09be8b97d\"]}, \"state_id\": \"3e214ab97ecb671c26abb26a\", \"visible_experiments\": [{\"experiment_id\": 3, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z2_0\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 5, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 0}}, {\"experiment_id\": 20, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 0}}, {\"experiment_id\": 25, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 1}}]}}}", "extra_info": {"index": 122, "max_global_steps": 0, "min_global_steps": 0}, "index": 122, "prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=0\n intervention: set Z2=0\n observed: Z1=0, Z2=0, Y=0\n\nExperiment 2:\n inputs: X1=0, X2=0, X3=1\n intervention: none\n observed: Z1=1, Z2=0, Y=0\n\nExperiment 3:\n inputs: X1=1, X2=0, X3=0\n intervention: none\n observed: Z1=1, Z2=0, Y=0\n\nExperiment 4:\n inputs: X1=1, X2=0, X3=1\n intervention: none\n observed: Z1=0, Z2=1, Y=1\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "raw_prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=0\n intervention: set Z2=0\n observed: Z1=0, Z2=0, Y=0\n\nExperiment 2:\n inputs: X1=0, X2=0, X3=1\n intervention: none\n observed: Z1=1, Z2=0, Y=0\n\nExperiment 3:\n inputs: X1=1, X2=0, X3=0\n intervention: none\n observed: Z1=1, Z2=0, Y=0\n\nExperiment 4:\n inputs: X1=1, X2=0, X3=1\n intervention: none\n observed: Z1=0, Z2=1, Y=1\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "reward_model": {"style": "rule"}, "state_json": "{\"available_experiment_ids\": [0, 1, 2, 4, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 21, 22, 23, 24, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 4, \"family_bucket\": \"cross_family\", \"maximum_separation\": 0.6944444444444444, \"mean_separation\": 0.44402356902356904, \"minimum_separation\": 0.1111111111111111, \"normalized_mean_separation\": 0.47267025089605735, \"oracle_budget_representation_error\": 0.1273148148148148, \"oracle_singleton_representation_error\": 0.34490740740740744, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.21759259259259264, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 12}, \"private\": {\"hidden_mode_id\": \"765aad93448c4d21b4be9a424b8db627e5e10dbf06863acec366296e884f7ae3\", \"valid_mode_ids\": [\"0804d4083712f9ca9a23682ae7b79ceecc6bf73bb8cc1c846a9666bf5dd94410\", \"17762877d5eac65b00a2f4ba6caecd2a0c046bceec12f2cfff6a47b0560fe436\", \"33dd79a28ad77e0307c436bed058edd701f11c7303556c3abec4516ad1205826\", \"38e867de9871a3c946200f236e2d5f8bc02cba15e59d20516dba8ece447e6a1e\", \"4640df55cc8ba9ca5c41fd143132a07095862244259f25bcac53801bd158984c\", \"6d16f7fe8a2d277a896b81bdd10f8723ba9b6a2863ce93e407c76b439e4302ec\", \"765aad93448c4d21b4be9a424b8db627e5e10dbf06863acec366296e884f7ae3\", \"83b63d1abff70d3ba6a05fb3a562f2132fc4127465bbd3948fec2381f47d9067\", \"8d13c73d68ccb97e25b3a9589d39cfdb4664f855b675de01224dba147901e7f1\", \"a8900e2f75dd7634796c23a513a2d2ef85b0abbac8e7bb8a9df287edc03ef00c\", \"abdbfe2eb8971237d4426b4317389d15ac9670935de65156805e4689cbedefcb\", \"c1506a6b818e87a19171aaa690b091c0ea76337e81d7bc654032adb09be8b97d\"]}, \"state_id\": \"3e214ab97ecb671c26abb26a\", \"visible_experiments\": [{\"experiment_id\": 3, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z2_0\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 5, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 0}}, {\"experiment_id\": 20, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 0}}, {\"experiment_id\": 25, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 1}}]}"} {"agent_name": "causal_micro_lab_agent_loop", "data_source": "causal_micro_lab_final_eval_v3", "env_spec_json": "{\"agent\": {}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 123, \"task\": {\"state\": {\"available_experiment_ids\": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 31, 32, 33, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 3, \"family_bucket\": \"cross_family\", \"maximum_separation\": 0.6756756756756757, \"mean_separation\": 0.47993447993447996, \"minimum_separation\": 0.21621621621621623, \"normalized_mean_separation\": 0.5108979947689625, \"oracle_budget_representation_error\": 0.1801801801801802, \"oracle_singleton_representation_error\": 0.40315315315315314, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.22297297297297294, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 12}, \"private\": {\"hidden_mode_id\": \"f3bc0194f063aa09bccd26f031803cd8649f2a87f8b3b66a9b90e125318ab89d\", \"valid_mode_ids\": [\"43e51a9572e0787218e2e3d9a52f98f4fb22a8b446809a37749d0fa5f8fde6ac\", \"559fc5f75e1a41642ebc26fe60366dbf72742ced7d9cf84b1158265a5ec867ad\", \"6eb0d51f53f06367437fc1790cd7eb8fe6909fff6ece6193169e89b1362edda2\", \"73d070d570ab2ba57b967089b6fbe1ae710b27ca7ce35641dcdbd17f04514094\", \"74efddc00dfc3d36d4e1c1a2334117a54f28a6b07c487629c5486985650a6051\", \"790394d4ef2f3e0cf39dd546d712054e16a2e91954f9e75da96fa82bc6dc2f7a\", \"a9d99be6f44bbb1b11c7feb1cdba5fd51e7cd93a2b4aa293be08b74b8b3315f2\", \"cabe639bb6da4423ac543b9a061b36446e894c609485955dd1bececc0468dedc\", \"d70aa5eff321900fd5cb0bb219b2943671b7c19aefa847101bb45097de99d8f6\", \"ef6e7b1ce4885b6ad18858a7dd07c077735c44b17ccdcc6724e08ddac4bd44dc\", \"f3bc0194f063aa09bccd26f031803cd8649f2a87f8b3b66a9b90e125318ab89d\", \"ff1531be0c1925171aff6146737f14f4ec2e053b294255260c8a43ce67277c2d\"]}, \"state_id\": \"6550e8aa6f7448d3bf86c344\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 15, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 30, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 0}}]}}}", "extra_info": {"index": 123, "max_global_steps": 0, "min_global_steps": 0}, "index": 123, "prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=0\n intervention: none\n observed: Z1=1, Z2=1, Y=0\n\nExperiment 2:\n inputs: X1=0, X2=1, X3=1\n intervention: none\n observed: Z1=0, Z2=1, Y=1\n\nExperiment 3:\n inputs: X1=1, X2=1, X3=0\n intervention: none\n observed: Z1=1, Z2=0, Y=1\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "raw_prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=0\n intervention: none\n observed: Z1=1, Z2=1, Y=0\n\nExperiment 2:\n inputs: X1=0, X2=1, X3=1\n intervention: none\n observed: Z1=0, Z2=1, Y=1\n\nExperiment 3:\n inputs: X1=1, X2=1, X3=0\n intervention: none\n observed: Z1=1, Z2=0, Y=1\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "reward_model": {"style": "rule"}, "state_json": "{\"available_experiment_ids\": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 31, 32, 33, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 3, \"family_bucket\": \"cross_family\", \"maximum_separation\": 0.6756756756756757, \"mean_separation\": 0.47993447993447996, \"minimum_separation\": 0.21621621621621623, \"normalized_mean_separation\": 0.5108979947689625, \"oracle_budget_representation_error\": 0.1801801801801802, \"oracle_singleton_representation_error\": 0.40315315315315314, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.22297297297297294, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 12}, \"private\": {\"hidden_mode_id\": \"f3bc0194f063aa09bccd26f031803cd8649f2a87f8b3b66a9b90e125318ab89d\", \"valid_mode_ids\": [\"43e51a9572e0787218e2e3d9a52f98f4fb22a8b446809a37749d0fa5f8fde6ac\", \"559fc5f75e1a41642ebc26fe60366dbf72742ced7d9cf84b1158265a5ec867ad\", \"6eb0d51f53f06367437fc1790cd7eb8fe6909fff6ece6193169e89b1362edda2\", \"73d070d570ab2ba57b967089b6fbe1ae710b27ca7ce35641dcdbd17f04514094\", \"74efddc00dfc3d36d4e1c1a2334117a54f28a6b07c487629c5486985650a6051\", \"790394d4ef2f3e0cf39dd546d712054e16a2e91954f9e75da96fa82bc6dc2f7a\", \"a9d99be6f44bbb1b11c7feb1cdba5fd51e7cd93a2b4aa293be08b74b8b3315f2\", \"cabe639bb6da4423ac543b9a061b36446e894c609485955dd1bececc0468dedc\", \"d70aa5eff321900fd5cb0bb219b2943671b7c19aefa847101bb45097de99d8f6\", \"ef6e7b1ce4885b6ad18858a7dd07c077735c44b17ccdcc6724e08ddac4bd44dc\", \"f3bc0194f063aa09bccd26f031803cd8649f2a87f8b3b66a9b90e125318ab89d\", \"ff1531be0c1925171aff6146737f14f4ec2e053b294255260c8a43ce67277c2d\"]}, \"state_id\": \"6550e8aa6f7448d3bf86c344\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 15, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 30, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 0}}]}"} {"agent_name": "causal_micro_lab_agent_loop", "data_source": "causal_micro_lab_final_eval_v3", "env_spec_json": "{\"agent\": {}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 124, \"task\": {\"state\": {\"available_experiment_ids\": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 31, 33, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 3, \"family_bucket\": \"within_family\", \"maximum_separation\": 0.6216216216216216, \"mean_separation\": 0.39189189189189194, \"minimum_separation\": 0.10810810810810811, \"normalized_mean_separation\": 0.41717523975588494, \"oracle_budget_representation_error\": 0.09459459459459461, \"oracle_singleton_representation_error\": 0.3243243243243243, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.22972972972972966, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 12}, \"private\": {\"hidden_mode_id\": \"c4c5cf37dc58f5d880937299fcb2c98e46e00e93764bb6c610718639480d170f\", \"valid_mode_ids\": [\"0f71db75e75f447821aa2e0d867b86c98174180bfe7188c6e65ff5ff3e3a68d8\", \"349071895f291714c73f456250d9bb9e832b3ac7bb433eef8d25a0e2dc70365b\", \"3ef232d32b0fe16168d7bcc2bd447bd22b826f5964e39e8649e3a1dcc53e6420\", \"5aafac8c4bbcfd9445af8fee8615ba4e185cb0cfffef79002c05cf8e267577cf\", \"9973428349e7a8468726b4b3bd78e806fedfc06417533e87f67d4a944d1276ff\", \"b0a0943526367e0b94a7aef4e6fbd150f16a7ec2ce7c3792fc21c33def4f2f49\", \"c1629eed6cfb2bb4184ed31f50bdb35c0c42361e2627364e3fbd44a2ddabe9a2\", \"c4c5cf37dc58f5d880937299fcb2c98e46e00e93764bb6c610718639480d170f\", \"cacbe3bc5d7a5558148a05178b657f467b90a719573e045d51585b7eb6a42e28\", \"dadc7512454806ec78c00b5a91f1c7f345f5e39776fa2838b6d8a393373488f2\", \"e38ee28c6da5515e19dc2d09aaaaf666ed7894aca29fdff3b56a1fe1937112da\", \"f48bc6a56423f04845e162b7b75d75ba84637570ebabc5d63e75cdc2bafddd4a\"]}, \"state_id\": \"3f039a2acda09888f97c997d\", \"visible_experiments\": [{\"experiment_id\": 15, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 30, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 32, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 0}, \"intervention\": \"DO_Z1_1\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}]}}}", "extra_info": {"index": 124, "max_global_steps": 0, "min_global_steps": 0}, "index": 124, "prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=1, X3=1\n intervention: none\n observed: Z1=0, Z2=0, Y=0\n\nExperiment 2:\n inputs: X1=1, X2=1, X3=0\n intervention: none\n observed: Z1=0, Z2=0, Y=1\n\nExperiment 3:\n inputs: X1=1, X2=1, X3=0\n intervention: set Z1=1\n observed: Z1=1, Z2=1, Y=0\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "raw_prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=1, X3=1\n intervention: none\n observed: Z1=0, Z2=0, Y=0\n\nExperiment 2:\n inputs: X1=1, X2=1, X3=0\n intervention: none\n observed: Z1=0, Z2=0, Y=1\n\nExperiment 3:\n inputs: X1=1, X2=1, X3=0\n intervention: set Z1=1\n observed: Z1=1, Z2=1, Y=0\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "reward_model": {"style": "rule"}, "state_json": "{\"available_experiment_ids\": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 31, 33, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 3, \"family_bucket\": \"within_family\", \"maximum_separation\": 0.6216216216216216, \"mean_separation\": 0.39189189189189194, \"minimum_separation\": 0.10810810810810811, \"normalized_mean_separation\": 0.41717523975588494, \"oracle_budget_representation_error\": 0.09459459459459461, \"oracle_singleton_representation_error\": 0.3243243243243243, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.22972972972972966, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 12}, \"private\": {\"hidden_mode_id\": \"c4c5cf37dc58f5d880937299fcb2c98e46e00e93764bb6c610718639480d170f\", \"valid_mode_ids\": [\"0f71db75e75f447821aa2e0d867b86c98174180bfe7188c6e65ff5ff3e3a68d8\", \"349071895f291714c73f456250d9bb9e832b3ac7bb433eef8d25a0e2dc70365b\", \"3ef232d32b0fe16168d7bcc2bd447bd22b826f5964e39e8649e3a1dcc53e6420\", \"5aafac8c4bbcfd9445af8fee8615ba4e185cb0cfffef79002c05cf8e267577cf\", \"9973428349e7a8468726b4b3bd78e806fedfc06417533e87f67d4a944d1276ff\", \"b0a0943526367e0b94a7aef4e6fbd150f16a7ec2ce7c3792fc21c33def4f2f49\", \"c1629eed6cfb2bb4184ed31f50bdb35c0c42361e2627364e3fbd44a2ddabe9a2\", \"c4c5cf37dc58f5d880937299fcb2c98e46e00e93764bb6c610718639480d170f\", \"cacbe3bc5d7a5558148a05178b657f467b90a719573e045d51585b7eb6a42e28\", \"dadc7512454806ec78c00b5a91f1c7f345f5e39776fa2838b6d8a393373488f2\", \"e38ee28c6da5515e19dc2d09aaaaf666ed7894aca29fdff3b56a1fe1937112da\", \"f48bc6a56423f04845e162b7b75d75ba84637570ebabc5d63e75cdc2bafddd4a\"]}, \"state_id\": \"3f039a2acda09888f97c997d\", \"visible_experiments\": [{\"experiment_id\": 15, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 30, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 32, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 0}, \"intervention\": \"DO_Z1_1\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}]}"} {"agent_name": "causal_micro_lab_agent_loop", "data_source": "causal_micro_lab_final_eval_v3", "env_spec_json": "{\"agent\": {}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 125, \"task\": {\"state\": {\"available_experiment_ids\": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 3, \"family_bucket\": \"within_family\", \"maximum_separation\": 0.6216216216216216, \"mean_separation\": 0.3996723996723997, \"minimum_separation\": 0.16216216216216217, \"normalized_mean_separation\": 0.42545771578029645, \"oracle_budget_representation_error\": 0.10810810810810811, \"oracle_singleton_representation_error\": 0.3423423423423424, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.23423423423423428, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 12}, \"private\": {\"hidden_mode_id\": \"88c39d8a0e2b77d9b8c2b0c5fd1c80635aaecdab923879a2c9b2d3e5db186924\", \"valid_mode_ids\": [\"1429d0db55d787a30a3e922af4b0d4514a7b738279786f145cbaf31422cf0a0d\", \"32d3435bdc6c3fc95c9c3f332d11a726012542af3d5a11faa876bb2bd35aa16e\", \"3d2db5d7f898472ddef68872c64c890b0c950c1388d3d09379494fdd899ea0f6\", \"4f7790c7d7973a48829951a08202f6212ba716160242163fe4c92e0575f66bb1\", \"88c39d8a0e2b77d9b8c2b0c5fd1c80635aaecdab923879a2c9b2d3e5db186924\", \"89d50c230fc2a0db7ee0c84ea41dc6d700ddfc49a13e9752a9ecdca6f2cc4cc4\", \"a0106bff2fd46bc11fce1524a528cda90dece11ac254b9016cba5de77a0360bd\", \"a48e1c6fdd67fe32fe046c2c66cd6b86dfd0f27d674130653413fcba0721c619\", \"d74ff9a82124119c18b11556f43727070afd00ecb499289b11cf2fa7901a80f0\", \"ed7ca5fe16113d32fd61be02ff05e0c9c67545bfcf07c87985103e92c498b993\", \"f58ffea5c0bba87222ac996babb3ffb674a589a4846c62c1b80eb55a7af0710e\", \"f8ba66d49756a48585d36c9e0bf2bbdab17b766c142a902130dfe5c61384fe77\"]}, \"state_id\": \"751994db2c4b28fdfcec9194\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 15, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 35, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 0}}]}}}", "extra_info": {"index": 125, "max_global_steps": 0, "min_global_steps": 0}, "index": 125, "prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=0\n intervention: none\n observed: Z1=0, Z2=0, Y=1\n\nExperiment 2:\n inputs: X1=0, X2=1, X3=1\n intervention: none\n observed: Z1=0, Z2=0, Y=1\n\nExperiment 3:\n inputs: X1=1, X2=1, X3=1\n intervention: none\n observed: Z1=1, Z2=0, Y=0\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "raw_prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=0\n intervention: none\n observed: Z1=0, Z2=0, Y=1\n\nExperiment 2:\n inputs: X1=0, X2=1, X3=1\n intervention: none\n observed: Z1=0, Z2=0, Y=1\n\nExperiment 3:\n inputs: X1=1, X2=1, X3=1\n intervention: none\n observed: Z1=1, Z2=0, Y=0\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "reward_model": {"style": "rule"}, "state_json": "{\"available_experiment_ids\": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 3, \"family_bucket\": \"within_family\", \"maximum_separation\": 0.6216216216216216, \"mean_separation\": 0.3996723996723997, \"minimum_separation\": 0.16216216216216217, \"normalized_mean_separation\": 0.42545771578029645, \"oracle_budget_representation_error\": 0.10810810810810811, \"oracle_singleton_representation_error\": 0.3423423423423424, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.23423423423423428, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 12}, \"private\": {\"hidden_mode_id\": \"88c39d8a0e2b77d9b8c2b0c5fd1c80635aaecdab923879a2c9b2d3e5db186924\", \"valid_mode_ids\": [\"1429d0db55d787a30a3e922af4b0d4514a7b738279786f145cbaf31422cf0a0d\", \"32d3435bdc6c3fc95c9c3f332d11a726012542af3d5a11faa876bb2bd35aa16e\", \"3d2db5d7f898472ddef68872c64c890b0c950c1388d3d09379494fdd899ea0f6\", \"4f7790c7d7973a48829951a08202f6212ba716160242163fe4c92e0575f66bb1\", \"88c39d8a0e2b77d9b8c2b0c5fd1c80635aaecdab923879a2c9b2d3e5db186924\", \"89d50c230fc2a0db7ee0c84ea41dc6d700ddfc49a13e9752a9ecdca6f2cc4cc4\", \"a0106bff2fd46bc11fce1524a528cda90dece11ac254b9016cba5de77a0360bd\", \"a48e1c6fdd67fe32fe046c2c66cd6b86dfd0f27d674130653413fcba0721c619\", \"d74ff9a82124119c18b11556f43727070afd00ecb499289b11cf2fa7901a80f0\", \"ed7ca5fe16113d32fd61be02ff05e0c9c67545bfcf07c87985103e92c498b993\", \"f58ffea5c0bba87222ac996babb3ffb674a589a4846c62c1b80eb55a7af0710e\", \"f8ba66d49756a48585d36c9e0bf2bbdab17b766c142a902130dfe5c61384fe77\"]}, \"state_id\": \"751994db2c4b28fdfcec9194\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 15, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 35, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 0}}]}"} {"agent_name": "causal_micro_lab_agent_loop", "data_source": "causal_micro_lab_final_eval_v3", "env_spec_json": "{\"agent\": {}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 126, \"task\": {\"state\": {\"available_experiment_ids\": [0, 1, 2, 3, 4, 6, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 26, 27, 28, 29, 31, 32, 33, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 4, \"family_bucket\": \"mixed\", \"maximum_separation\": 0.6388888888888888, \"mean_separation\": 0.4166666666666666, \"minimum_separation\": 0.1111111111111111, \"normalized_mean_separation\": 0.4435483870967741, \"oracle_budget_representation_error\": 0.12037037037037039, \"oracle_singleton_representation_error\": 0.3611111111111111, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.2407407407407407, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 12}, \"private\": {\"hidden_mode_id\": \"a70e527578834901db5c53b9a1ef68e624e1eb5686573956b87bb8404d4f28f3\", \"valid_mode_ids\": [\"03e937a2d8bae7e0023b05e9af1c0b4489c8a46dd2a1ad2a84eb559c24fc79b9\", \"2c5e3ccfd1fbe99f7473e51ba908b57bf342e2df46e78407e9b4b316aa35c8ff\", \"6484f77be771973cc43218e6155e3c2b9af2e411c68b33adcf45428d6ab6293b\", \"64a7c1c7559c46df10e222549a7377707f07b432ef05351fb15d761778ebe308\", \"66c7e3ce13059989e3e472ae787b1b29414636384ffe85da8ecc65ac1f79ae25\", \"a70e527578834901db5c53b9a1ef68e624e1eb5686573956b87bb8404d4f28f3\", \"b154334e1a5908deabba9659ff13665667327d6f52b517be889ba89831142166\", \"bc28c0e58cfb1504ae6db0e82643c23eeffa69d1e6b90935132a3fa9afe70e94\", \"d7bd5f8c15182e77b8372e32d5b5118478d2253cd7683211271763d680acff23\", \"e768dce766cb038da126adcbe9066c3b64d933d578c10047199c1dcc0c5fab82\", \"f2930c65b7a74e44685a780513ea0296340b8f88608debb3cb5a4f5785eb3d02\", \"fa72dc7b4535e7f518e7acac0e89600345ddd5e083a437cabc9eaa8082e3da4f\"]}, \"state_id\": \"607d8bbf02638a1716572eaf\", \"visible_experiments\": [{\"experiment_id\": 5, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 7, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 1}, \"intervention\": \"DO_Z1_1\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 25, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 30, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}]}}}", "extra_info": {"index": 126, "max_global_steps": 0, "min_global_steps": 0}, "index": 126, "prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=1\n intervention: none\n observed: Z1=0, Z2=0, Y=0\n\nExperiment 2:\n inputs: X1=0, X2=0, X3=1\n intervention: set Z1=1\n observed: Z1=1, Z2=1, Y=0\n\nExperiment 3:\n inputs: X1=1, X2=0, X3=1\n intervention: none\n observed: Z1=0, Z2=0, Y=0\n\nExperiment 4:\n inputs: X1=1, X2=1, X3=0\n intervention: none\n observed: Z1=1, Z2=1, Y=0\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "raw_prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=1\n intervention: none\n observed: Z1=0, Z2=0, Y=0\n\nExperiment 2:\n inputs: X1=0, X2=0, X3=1\n intervention: set Z1=1\n observed: Z1=1, Z2=1, Y=0\n\nExperiment 3:\n inputs: X1=1, X2=0, X3=1\n intervention: none\n observed: Z1=0, Z2=0, Y=0\n\nExperiment 4:\n inputs: X1=1, X2=1, X3=0\n intervention: none\n observed: Z1=1, Z2=1, Y=0\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "reward_model": {"style": "rule"}, "state_json": "{\"available_experiment_ids\": [0, 1, 2, 3, 4, 6, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 26, 27, 28, 29, 31, 32, 33, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 4, \"family_bucket\": \"mixed\", \"maximum_separation\": 0.6388888888888888, \"mean_separation\": 0.4166666666666666, \"minimum_separation\": 0.1111111111111111, \"normalized_mean_separation\": 0.4435483870967741, \"oracle_budget_representation_error\": 0.12037037037037039, \"oracle_singleton_representation_error\": 0.3611111111111111, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.2407407407407407, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 12}, \"private\": {\"hidden_mode_id\": \"a70e527578834901db5c53b9a1ef68e624e1eb5686573956b87bb8404d4f28f3\", \"valid_mode_ids\": [\"03e937a2d8bae7e0023b05e9af1c0b4489c8a46dd2a1ad2a84eb559c24fc79b9\", \"2c5e3ccfd1fbe99f7473e51ba908b57bf342e2df46e78407e9b4b316aa35c8ff\", \"6484f77be771973cc43218e6155e3c2b9af2e411c68b33adcf45428d6ab6293b\", \"64a7c1c7559c46df10e222549a7377707f07b432ef05351fb15d761778ebe308\", \"66c7e3ce13059989e3e472ae787b1b29414636384ffe85da8ecc65ac1f79ae25\", \"a70e527578834901db5c53b9a1ef68e624e1eb5686573956b87bb8404d4f28f3\", \"b154334e1a5908deabba9659ff13665667327d6f52b517be889ba89831142166\", \"bc28c0e58cfb1504ae6db0e82643c23eeffa69d1e6b90935132a3fa9afe70e94\", \"d7bd5f8c15182e77b8372e32d5b5118478d2253cd7683211271763d680acff23\", \"e768dce766cb038da126adcbe9066c3b64d933d578c10047199c1dcc0c5fab82\", \"f2930c65b7a74e44685a780513ea0296340b8f88608debb3cb5a4f5785eb3d02\", \"fa72dc7b4535e7f518e7acac0e89600345ddd5e083a437cabc9eaa8082e3da4f\"]}, \"state_id\": \"607d8bbf02638a1716572eaf\", \"visible_experiments\": [{\"experiment_id\": 5, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 7, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 1}, \"intervention\": \"DO_Z1_1\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 25, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 30, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}]}"} {"agent_name": "causal_micro_lab_agent_loop", "data_source": "causal_micro_lab_final_eval_v3", "env_spec_json": "{\"agent\": {}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 127, \"task\": {\"state\": {\"available_experiment_ids\": [0, 1, 2, 3, 4, 6, 7, 8, 9, 10, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 3, \"family_bucket\": \"cross_family\", \"maximum_separation\": 0.7297297297297297, \"mean_separation\": 0.5339885339885337, \"minimum_separation\": 0.32432432432432434, \"normalized_mean_separation\": 0.5684394071490843, \"oracle_budget_representation_error\": 0.22522522522522526, \"oracle_singleton_representation_error\": 0.47072072072072074, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.24549549549549549, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 12}, \"private\": {\"hidden_mode_id\": \"a2946263c702aed897ca5f94b80f1bcc2a01c200717310cb8fe7f5aae2160a2d\", \"valid_mode_ids\": [\"0e6bacec0ba9f1f98718d4fe2ee16d464bbe59313848beb12858ce9251bceac9\", \"15dbb814b2e90a55a69e3d23c42ac916dc9e33ccc2786c3590957d17c3b1c0d6\", \"23277f227defe5bf1ca1e83770495ad7e8eab8c4b797778335a8385993f60957\", \"56e62d317318994903af717acd31f8df9aa4c8d4970212aaa587968f5fe2bdfb\", \"5d0475244746b98ca2a38cf631192cbd01ea20e4962a374ff596631bb8614806\", \"7c7ee78123a4c89d0ae8d0741e339d936962f10d27e16508b0dd6f392e378a20\", \"a2946263c702aed897ca5f94b80f1bcc2a01c200717310cb8fe7f5aae2160a2d\", \"b91909fd84555abcf427041f77ec0c4d71f9210170a2c76ae0fc29317867606d\", \"cc2cfc8a1c424fecd91dda0af35865224e20f1536864c1ec9f1f9ad93fe02f40\", \"dea47f3def9291b4db64c7358c360714de5884d34724def6a528a44b7051f2fc\", \"df32d4e54f74f5e69e9789601a3fd4e0120e05161379e83031189fb357b48f9e\", \"ec56a36c7b91513839d5ae4fb4da45ac452980fa0ad61dc6025b9bd21b5098be\"]}, \"state_id\": \"ec19b307e322d96931ff3db3\", \"visible_experiments\": [{\"experiment_id\": 5, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 11, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 0}, \"intervention\": \"DO_Z1_0\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 35, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 0}}]}}}", "extra_info": {"index": 127, "max_global_steps": 0, "min_global_steps": 0}, "index": 127, "prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=1\n intervention: none\n observed: Z1=0, Z2=1, Y=0\n\nExperiment 2:\n inputs: X1=0, X2=1, X3=0\n intervention: set Z1=0\n observed: Z1=0, Z2=1, Y=1\n\nExperiment 3:\n inputs: X1=1, X2=1, X3=1\n intervention: none\n observed: Z1=0, Z2=0, Y=0\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "raw_prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=1\n intervention: none\n observed: Z1=0, Z2=1, Y=0\n\nExperiment 2:\n inputs: X1=0, X2=1, X3=0\n intervention: set Z1=0\n observed: Z1=0, Z2=1, Y=1\n\nExperiment 3:\n inputs: X1=1, X2=1, X3=1\n intervention: none\n observed: Z1=0, Z2=0, Y=0\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "reward_model": {"style": "rule"}, "state_json": "{\"available_experiment_ids\": [0, 1, 2, 3, 4, 6, 7, 8, 9, 10, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 3, \"family_bucket\": \"cross_family\", \"maximum_separation\": 0.7297297297297297, \"mean_separation\": 0.5339885339885337, \"minimum_separation\": 0.32432432432432434, \"normalized_mean_separation\": 0.5684394071490843, \"oracle_budget_representation_error\": 0.22522522522522526, \"oracle_singleton_representation_error\": 0.47072072072072074, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.24549549549549549, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 12}, \"private\": {\"hidden_mode_id\": \"a2946263c702aed897ca5f94b80f1bcc2a01c200717310cb8fe7f5aae2160a2d\", \"valid_mode_ids\": [\"0e6bacec0ba9f1f98718d4fe2ee16d464bbe59313848beb12858ce9251bceac9\", \"15dbb814b2e90a55a69e3d23c42ac916dc9e33ccc2786c3590957d17c3b1c0d6\", \"23277f227defe5bf1ca1e83770495ad7e8eab8c4b797778335a8385993f60957\", \"56e62d317318994903af717acd31f8df9aa4c8d4970212aaa587968f5fe2bdfb\", \"5d0475244746b98ca2a38cf631192cbd01ea20e4962a374ff596631bb8614806\", \"7c7ee78123a4c89d0ae8d0741e339d936962f10d27e16508b0dd6f392e378a20\", \"a2946263c702aed897ca5f94b80f1bcc2a01c200717310cb8fe7f5aae2160a2d\", \"b91909fd84555abcf427041f77ec0c4d71f9210170a2c76ae0fc29317867606d\", \"cc2cfc8a1c424fecd91dda0af35865224e20f1536864c1ec9f1f9ad93fe02f40\", \"dea47f3def9291b4db64c7358c360714de5884d34724def6a528a44b7051f2fc\", \"df32d4e54f74f5e69e9789601a3fd4e0120e05161379e83031189fb357b48f9e\", \"ec56a36c7b91513839d5ae4fb4da45ac452980fa0ad61dc6025b9bd21b5098be\"]}, \"state_id\": \"ec19b307e322d96931ff3db3\", \"visible_experiments\": [{\"experiment_id\": 5, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 11, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 0}, \"intervention\": \"DO_Z1_0\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 35, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 0}}]}"} {"agent_name": "causal_micro_lab_agent_loop", "data_source": "causal_micro_lab_final_eval_v3", "env_spec_json": "{\"agent\": {}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 128, \"task\": {\"state\": {\"available_experiment_ids\": [0, 1, 3, 4, 6, 7, 8, 9, 10, 11, 12, 13, 14, 16, 17, 18, 19, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 4, \"family_bucket\": \"mixed\", \"maximum_separation\": 0.7222222222222222, \"mean_separation\": 0.45033670033670037, \"minimum_separation\": 0.1111111111111111, \"normalized_mean_separation\": 0.47939068100358423, \"oracle_budget_representation_error\": 0.13888888888888887, \"oracle_singleton_representation_error\": 0.39120370370370366, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.25231481481481477, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 12}, \"private\": {\"hidden_mode_id\": \"2fdf179c141fe7966b7674162d3b7472013927fe7c76478c14954d1a464d0831\", \"valid_mode_ids\": [\"1a53f613c48f19b1d14e16293f900a57fc39d6975f9a5a26e97886e60e6cbcaf\", \"2f7016b2f7831a83a18b88509139a933c0229cf67c8ecdd06cce1c3c4a8b4016\", \"2fdf179c141fe7966b7674162d3b7472013927fe7c76478c14954d1a464d0831\", \"4139c01b98a1896936244bd9d32bebb917a19b9c1d8df25e87171a300f881eca\", \"5b1142e95a57446da24708f1eb8da9460dfa44c4b71b260353cc0bf521ae645a\", \"73553834bd63e18fae06df279e068c298bed6ef9b465507d95ae175c75c27010\", \"a1ae3f6357b4a1e8eb52ae1ea33c174bfcd01179ae6d3bfc176a60fb6c36e807\", \"bc28c0e58cfb1504ae6db0e82643c23eeffa69d1e6b90935132a3fa9afe70e94\", \"cb9db68f166faeb6139e5a97031d7ef452af2a97b5b6f3ca7b22bec10d1f08d2\", \"cf6742483e6e757565dfffefdd8dbdc362cc3dc89d2aca4a32b79853599ba1e1\", \"e5bece65e66d5ae8a18a7e27ceac0be60a571039acf9b7f40bcf2dbe5208db9c\", \"ed7f926b83f0233eb31a39cf95a32a269cf145d6500e4c5fcc6536f1199e4b05\"]}, \"state_id\": \"b23fc6f920da5b7dcf055336\", \"visible_experiments\": [{\"experiment_id\": 2, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z1_1\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 5, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 15, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 20, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}]}}}", "extra_info": {"index": 128, "max_global_steps": 0, "min_global_steps": 0}, "index": 128, "prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=0\n intervention: set Z1=1\n observed: Z1=1, Z2=1, Y=0\n\nExperiment 2:\n inputs: X1=0, X2=0, X3=1\n intervention: none\n observed: Z1=0, Z2=0, Y=0\n\nExperiment 3:\n inputs: X1=0, X2=1, X3=1\n intervention: none\n observed: Z1=0, Z2=0, Y=1\n\nExperiment 4:\n inputs: X1=1, X2=0, X3=0\n intervention: none\n observed: Z1=1, Z2=1, Y=0\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "raw_prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=0\n intervention: set Z1=1\n observed: Z1=1, Z2=1, Y=0\n\nExperiment 2:\n inputs: X1=0, X2=0, X3=1\n intervention: none\n observed: Z1=0, Z2=0, Y=0\n\nExperiment 3:\n inputs: X1=0, X2=1, X3=1\n intervention: none\n observed: Z1=0, Z2=0, Y=1\n\nExperiment 4:\n inputs: X1=1, X2=0, X3=0\n intervention: none\n observed: Z1=1, Z2=1, Y=0\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "reward_model": {"style": "rule"}, "state_json": "{\"available_experiment_ids\": [0, 1, 3, 4, 6, 7, 8, 9, 10, 11, 12, 13, 14, 16, 17, 18, 19, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 4, \"family_bucket\": \"mixed\", \"maximum_separation\": 0.7222222222222222, \"mean_separation\": 0.45033670033670037, \"minimum_separation\": 0.1111111111111111, \"normalized_mean_separation\": 0.47939068100358423, \"oracle_budget_representation_error\": 0.13888888888888887, \"oracle_singleton_representation_error\": 0.39120370370370366, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.25231481481481477, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 12}, \"private\": {\"hidden_mode_id\": \"2fdf179c141fe7966b7674162d3b7472013927fe7c76478c14954d1a464d0831\", \"valid_mode_ids\": [\"1a53f613c48f19b1d14e16293f900a57fc39d6975f9a5a26e97886e60e6cbcaf\", \"2f7016b2f7831a83a18b88509139a933c0229cf67c8ecdd06cce1c3c4a8b4016\", \"2fdf179c141fe7966b7674162d3b7472013927fe7c76478c14954d1a464d0831\", \"4139c01b98a1896936244bd9d32bebb917a19b9c1d8df25e87171a300f881eca\", \"5b1142e95a57446da24708f1eb8da9460dfa44c4b71b260353cc0bf521ae645a\", \"73553834bd63e18fae06df279e068c298bed6ef9b465507d95ae175c75c27010\", \"a1ae3f6357b4a1e8eb52ae1ea33c174bfcd01179ae6d3bfc176a60fb6c36e807\", \"bc28c0e58cfb1504ae6db0e82643c23eeffa69d1e6b90935132a3fa9afe70e94\", \"cb9db68f166faeb6139e5a97031d7ef452af2a97b5b6f3ca7b22bec10d1f08d2\", \"cf6742483e6e757565dfffefdd8dbdc362cc3dc89d2aca4a32b79853599ba1e1\", \"e5bece65e66d5ae8a18a7e27ceac0be60a571039acf9b7f40bcf2dbe5208db9c\", \"ed7f926b83f0233eb31a39cf95a32a269cf145d6500e4c5fcc6536f1199e4b05\"]}, \"state_id\": \"b23fc6f920da5b7dcf055336\", \"visible_experiments\": [{\"experiment_id\": 2, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z1_1\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 5, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 15, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 20, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}]}"} {"agent_name": "causal_micro_lab_agent_loop", "data_source": "causal_micro_lab_final_eval_v3", "env_spec_json": "{\"agent\": {}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 129, \"task\": {\"state\": {\"available_experiment_ids\": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 12, 13, 14, 15, 16, 17, 18, 19, 21, 22, 23, 24, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 3, \"family_bucket\": \"cross_family\", \"maximum_separation\": 0.7567567567567568, \"mean_separation\": 0.47993447993447963, \"minimum_separation\": 0.16216216216216217, \"normalized_mean_separation\": 0.5108979947689621, \"oracle_budget_representation_error\": 0.13063063063063066, \"oracle_singleton_representation_error\": 0.3896396396396397, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.25900900900900903, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 12}, \"private\": {\"hidden_mode_id\": \"9d1659a535df67069c0d9143404880df6fa6963d971b94282ebd82e14535be77\", \"valid_mode_ids\": [\"182f960360fd63f1365ff3884d953970dac472bb8c7ef0fac6641d6b269969fd\", \"4f86fc8435c73b1cb68399901705263b19b78b493fa6c0c1dc00be5bb34065b4\", \"63b403f3b88752f7dc2aeb7d305c34b625d4ea877f70ce98199806ee6fa619ee\", \"6b1a580d9ea1aed95290f18ceb572dfce3d84b2b202aa7d91701423e64860731\", \"8c9246bca6f0bb412a6a6151f3b2bf8e4fb74f70cd738d6d17499a9d72f86af7\", \"9d1659a535df67069c0d9143404880df6fa6963d971b94282ebd82e14535be77\", \"a0d4801ead990b75ba39cc47c82b0f6aebc8ded9990d7da44bf50ff5737175e7\", \"c6cedd05cc7c0a0b3ad5dc77f1c7dc1776cefdfdbb2178b4fdb7f96b33e82a20\", \"c98ba2925ab381a405b23baf5d2afe6da900a371c9630a2f956277c25c37a2a7\", \"db70c97a7fcadaa8b243399cce61f8237f95eb5fef0ce73e8c00bfa3cb211499\", \"e8d9866ba9fa0c6c232131263f358dc2690db89828ae4e0fa887f7dc7465c975\", \"ee1dbc848b56c206af2cc09fe63f9836799a4a13e5f700003d5c252ed51e8dc7\"]}, \"state_id\": \"7799a922b9da78f0a399c9a9\", \"visible_experiments\": [{\"experiment_id\": 11, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 0}, \"intervention\": \"DO_Z1_0\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 20, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 25, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 0}}]}}}", "extra_info": {"index": 129, "max_global_steps": 0, "min_global_steps": 0}, "index": 129, "prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=1, X3=0\n intervention: set Z1=0\n observed: Z1=0, Z2=0, Y=1\n\nExperiment 2:\n inputs: X1=1, X2=0, X3=0\n intervention: none\n observed: Z1=0, Z2=1, Y=0\n\nExperiment 3:\n inputs: X1=1, X2=0, X3=1\n intervention: none\n observed: Z1=0, Z2=0, Y=1\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "raw_prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=1, X3=0\n intervention: set Z1=0\n observed: Z1=0, Z2=0, Y=1\n\nExperiment 2:\n inputs: X1=1, X2=0, X3=0\n intervention: none\n observed: Z1=0, Z2=1, Y=0\n\nExperiment 3:\n inputs: X1=1, X2=0, X3=1\n intervention: none\n observed: Z1=0, Z2=0, Y=1\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "reward_model": {"style": "rule"}, "state_json": "{\"available_experiment_ids\": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 12, 13, 14, 15, 16, 17, 18, 19, 21, 22, 23, 24, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 3, \"family_bucket\": \"cross_family\", \"maximum_separation\": 0.7567567567567568, \"mean_separation\": 0.47993447993447963, \"minimum_separation\": 0.16216216216216217, \"normalized_mean_separation\": 0.5108979947689621, \"oracle_budget_representation_error\": 0.13063063063063066, \"oracle_singleton_representation_error\": 0.3896396396396397, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.25900900900900903, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 12}, \"private\": {\"hidden_mode_id\": \"9d1659a535df67069c0d9143404880df6fa6963d971b94282ebd82e14535be77\", \"valid_mode_ids\": [\"182f960360fd63f1365ff3884d953970dac472bb8c7ef0fac6641d6b269969fd\", \"4f86fc8435c73b1cb68399901705263b19b78b493fa6c0c1dc00be5bb34065b4\", \"63b403f3b88752f7dc2aeb7d305c34b625d4ea877f70ce98199806ee6fa619ee\", \"6b1a580d9ea1aed95290f18ceb572dfce3d84b2b202aa7d91701423e64860731\", \"8c9246bca6f0bb412a6a6151f3b2bf8e4fb74f70cd738d6d17499a9d72f86af7\", \"9d1659a535df67069c0d9143404880df6fa6963d971b94282ebd82e14535be77\", \"a0d4801ead990b75ba39cc47c82b0f6aebc8ded9990d7da44bf50ff5737175e7\", \"c6cedd05cc7c0a0b3ad5dc77f1c7dc1776cefdfdbb2178b4fdb7f96b33e82a20\", \"c98ba2925ab381a405b23baf5d2afe6da900a371c9630a2f956277c25c37a2a7\", \"db70c97a7fcadaa8b243399cce61f8237f95eb5fef0ce73e8c00bfa3cb211499\", \"e8d9866ba9fa0c6c232131263f358dc2690db89828ae4e0fa887f7dc7465c975\", \"ee1dbc848b56c206af2cc09fe63f9836799a4a13e5f700003d5c252ed51e8dc7\"]}, \"state_id\": \"7799a922b9da78f0a399c9a9\", \"visible_experiments\": [{\"experiment_id\": 11, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 0}, \"intervention\": \"DO_Z1_0\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 20, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 25, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 0}}]}"} {"agent_name": "causal_micro_lab_agent_loop", "data_source": "causal_micro_lab_final_eval_v3", "env_spec_json": "{\"agent\": {}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 130, \"task\": {\"state\": {\"available_experiment_ids\": [0, 1, 2, 3, 4, 5, 6, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 26, 27, 28, 29, 30, 31, 32, 33, 34, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 3, \"family_bucket\": \"mixed\", \"maximum_separation\": 0.7567567567567568, \"mean_separation\": 0.5249795249795247, \"minimum_separation\": 0.2702702702702703, \"normalized_mean_separation\": 0.5588491717523972, \"oracle_budget_representation_error\": 0.20720720720720723, \"oracle_singleton_representation_error\": 0.47072072072072074, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.2635135135135135, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 12}, \"private\": {\"hidden_mode_id\": \"355f604b65eb746e11b3e73ab86727ae01f19f33a3e380e157405391d8da260a\", \"valid_mode_ids\": [\"1bd90a160561f3efae3f2adfce4528322048c01de4d70de0103fa230235ce634\", \"355f604b65eb746e11b3e73ab86727ae01f19f33a3e380e157405391d8da260a\", \"35c23e2c4cb2921cc99a53aab5e86ae81c8ca01596c8145ecccc506aff2b810a\", \"3718b4376fa51149dc18adf6f52273c0ee432dd29cd71498b1cd34e831874247\", \"59a14c843fcc7773a9e375cad90396056a40aec5e4ce823ef3fa0bf31d42e54e\", \"8ca140dbc611987e1d130caaa8ecbe98d32a32bc5ecdd5f03bb5468ba0ad9842\", \"940cea0e6be5c8a77ca54c834108ffbc0ee48f69ca2d8ff883ef1d6f07d54ae7\", \"b3dfa2f0f45546000d7b290926857e293b5db77b415b8e01ac48b8b8664793bc\", \"be0c30f18db439521f5c882e80577faeef6286a458508a783affdd14b016c8d4\", \"d58e7fca8ae624ead43e834ee9c8a9f250541b0079e322a24f77dabe40b1794b\", \"d5e53f22b2c4e34aa716862bf66d118d78107c7903b9d8750f4201a1968c48b9\", \"f6c28df206468fc73fec7641f4ebb0230f373fdb41d69d42de0344538f22fa61\"]}, \"state_id\": \"15f43018afcbba4ec34d9d76\", \"visible_experiments\": [{\"experiment_id\": 7, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 1}, \"intervention\": \"DO_Z1_1\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 0}}, {\"experiment_id\": 25, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 0}}, {\"experiment_id\": 35, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 0}}]}}}", "extra_info": {"index": 130, "max_global_steps": 0, "min_global_steps": 0}, "index": 130, "prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=1\n intervention: set Z1=1\n observed: Z1=1, Z2=0, Y=0\n\nExperiment 2:\n inputs: X1=1, X2=0, X3=1\n intervention: none\n observed: Z1=1, Z2=0, Y=1\n\nExperiment 3:\n inputs: X1=1, X2=1, X3=1\n intervention: none\n observed: Z1=0, Z2=0, Y=0\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "raw_prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=1\n intervention: set Z1=1\n observed: Z1=1, Z2=0, Y=0\n\nExperiment 2:\n inputs: X1=1, X2=0, X3=1\n intervention: none\n observed: Z1=1, Z2=0, Y=1\n\nExperiment 3:\n inputs: X1=1, X2=1, X3=1\n intervention: none\n observed: Z1=0, Z2=0, Y=0\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "reward_model": {"style": "rule"}, "state_json": "{\"available_experiment_ids\": [0, 1, 2, 3, 4, 5, 6, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 26, 27, 28, 29, 30, 31, 32, 33, 34, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 3, \"family_bucket\": \"mixed\", \"maximum_separation\": 0.7567567567567568, \"mean_separation\": 0.5249795249795247, \"minimum_separation\": 0.2702702702702703, \"normalized_mean_separation\": 0.5588491717523972, \"oracle_budget_representation_error\": 0.20720720720720723, \"oracle_singleton_representation_error\": 0.47072072072072074, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.2635135135135135, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 12}, \"private\": {\"hidden_mode_id\": \"355f604b65eb746e11b3e73ab86727ae01f19f33a3e380e157405391d8da260a\", \"valid_mode_ids\": [\"1bd90a160561f3efae3f2adfce4528322048c01de4d70de0103fa230235ce634\", \"355f604b65eb746e11b3e73ab86727ae01f19f33a3e380e157405391d8da260a\", \"35c23e2c4cb2921cc99a53aab5e86ae81c8ca01596c8145ecccc506aff2b810a\", \"3718b4376fa51149dc18adf6f52273c0ee432dd29cd71498b1cd34e831874247\", \"59a14c843fcc7773a9e375cad90396056a40aec5e4ce823ef3fa0bf31d42e54e\", \"8ca140dbc611987e1d130caaa8ecbe98d32a32bc5ecdd5f03bb5468ba0ad9842\", \"940cea0e6be5c8a77ca54c834108ffbc0ee48f69ca2d8ff883ef1d6f07d54ae7\", \"b3dfa2f0f45546000d7b290926857e293b5db77b415b8e01ac48b8b8664793bc\", \"be0c30f18db439521f5c882e80577faeef6286a458508a783affdd14b016c8d4\", \"d58e7fca8ae624ead43e834ee9c8a9f250541b0079e322a24f77dabe40b1794b\", \"d5e53f22b2c4e34aa716862bf66d118d78107c7903b9d8750f4201a1968c48b9\", \"f6c28df206468fc73fec7641f4ebb0230f373fdb41d69d42de0344538f22fa61\"]}, \"state_id\": \"15f43018afcbba4ec34d9d76\", \"visible_experiments\": [{\"experiment_id\": 7, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 1}, \"intervention\": \"DO_Z1_1\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 0}}, {\"experiment_id\": 25, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 0}}, {\"experiment_id\": 35, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 0}}]}"} {"agent_name": "causal_micro_lab_agent_loop", "data_source": "causal_micro_lab_final_eval_v3", "env_spec_json": "{\"agent\": {}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 131, \"task\": {\"state\": {\"available_experiment_ids\": [0, 1, 3, 4, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 3, \"family_bucket\": \"mixed\", \"maximum_separation\": 0.7027027027027027, \"mean_separation\": 0.4422604422604419, \"minimum_separation\": 0.16216216216216217, \"normalized_mean_separation\": 0.4707933740191801, \"oracle_budget_representation_error\": 0.10810810810810811, \"oracle_singleton_representation_error\": 0.37837837837837845, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.27027027027027034, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 12}, \"private\": {\"hidden_mode_id\": \"394210444b32e0bcac5750bbf1eeab0ab6f4339a527008a3eef550d62abf2c80\", \"valid_mode_ids\": [\"0a6987e57358f7d4c04bb26d077804a9bbf1e562b97c12c93aaa4b551b464b01\", \"27c24940881494cf0deab9395df1a218eea8b68aaba51cc4896f5d4ab5c24f3b\", \"37523b182f023c0b108c5b5191f05962ab82d6b7e6b763df094e25b6870c0a05\", \"394210444b32e0bcac5750bbf1eeab0ab6f4339a527008a3eef550d62abf2c80\", \"5e06487ada7a9af9ad40b0023bd48ea5c0ed19b7b026b9a2a3222b961ef875e2\", \"67c4f95d4b9bd716dcccbba2b7770653c4383b5902f0d33464f26f60dff17439\", \"8b1ebc20fc4e96ae74c705e395b5ee18e764c6af802eb2f92cc688ff46d9ddd2\", \"ab9788ab8ac201d511102c83ccb70d08d6d3ebbf704d1d14fd16270e534c72b4\", \"b8fb707b9b30f1b94dbee14d2c380f07e0bb4f25398ad801e220fd2118c61bc6\", \"d6c8d662ec69c8995270de8c2439ffb28a0c20ace8f12222a158e841e31cd792\", \"e1d02f309ad1499b5a9435bf1c17af778f686b3ecea3bcf70059c19891690271\", \"f7d585a6c3916d0e5f62509e920abff092a8f8617406afdddf181ffdec276816\"]}, \"state_id\": \"80f5f70754ab11bbcd4c95b0\", \"visible_experiments\": [{\"experiment_id\": 2, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z1_1\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 0}}, {\"experiment_id\": 5, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 35, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 0}}]}}}", "extra_info": {"index": 131, "max_global_steps": 0, "min_global_steps": 0}, "index": 131, "prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=0\n intervention: set Z1=1\n observed: Z1=1, Z2=0, Y=1\n\nExperiment 2:\n inputs: X1=0, X2=0, X3=1\n intervention: none\n observed: Z1=0, Z2=0, Y=1\n\nExperiment 3:\n inputs: X1=1, X2=1, X3=1\n intervention: none\n observed: Z1=1, Z2=0, Y=1\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "raw_prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=0\n intervention: set Z1=1\n observed: Z1=1, Z2=0, Y=1\n\nExperiment 2:\n inputs: X1=0, X2=0, X3=1\n intervention: none\n observed: Z1=0, Z2=0, Y=1\n\nExperiment 3:\n inputs: X1=1, X2=1, X3=1\n intervention: none\n observed: Z1=1, Z2=0, Y=1\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "reward_model": {"style": "rule"}, "state_json": "{\"available_experiment_ids\": [0, 1, 3, 4, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 3, \"family_bucket\": \"mixed\", \"maximum_separation\": 0.7027027027027027, \"mean_separation\": 0.4422604422604419, \"minimum_separation\": 0.16216216216216217, \"normalized_mean_separation\": 0.4707933740191801, \"oracle_budget_representation_error\": 0.10810810810810811, \"oracle_singleton_representation_error\": 0.37837837837837845, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.27027027027027034, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 12}, \"private\": {\"hidden_mode_id\": \"394210444b32e0bcac5750bbf1eeab0ab6f4339a527008a3eef550d62abf2c80\", \"valid_mode_ids\": [\"0a6987e57358f7d4c04bb26d077804a9bbf1e562b97c12c93aaa4b551b464b01\", \"27c24940881494cf0deab9395df1a218eea8b68aaba51cc4896f5d4ab5c24f3b\", \"37523b182f023c0b108c5b5191f05962ab82d6b7e6b763df094e25b6870c0a05\", \"394210444b32e0bcac5750bbf1eeab0ab6f4339a527008a3eef550d62abf2c80\", \"5e06487ada7a9af9ad40b0023bd48ea5c0ed19b7b026b9a2a3222b961ef875e2\", \"67c4f95d4b9bd716dcccbba2b7770653c4383b5902f0d33464f26f60dff17439\", \"8b1ebc20fc4e96ae74c705e395b5ee18e764c6af802eb2f92cc688ff46d9ddd2\", \"ab9788ab8ac201d511102c83ccb70d08d6d3ebbf704d1d14fd16270e534c72b4\", \"b8fb707b9b30f1b94dbee14d2c380f07e0bb4f25398ad801e220fd2118c61bc6\", \"d6c8d662ec69c8995270de8c2439ffb28a0c20ace8f12222a158e841e31cd792\", \"e1d02f309ad1499b5a9435bf1c17af778f686b3ecea3bcf70059c19891690271\", \"f7d585a6c3916d0e5f62509e920abff092a8f8617406afdddf181ffdec276816\"]}, \"state_id\": \"80f5f70754ab11bbcd4c95b0\", \"visible_experiments\": [{\"experiment_id\": 2, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z1_1\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 0}}, {\"experiment_id\": 5, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 35, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 0}}]}"} {"agent_name": "causal_micro_lab_agent_loop", "data_source": "causal_micro_lab_final_eval_v3", "env_spec_json": "{\"agent\": {}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 132, \"task\": {\"state\": {\"available_experiment_ids\": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 31, 32, 33, 34, 35, 36, 37, 39], \"metadata\": {\"evidence_size\": 3, \"family_bucket\": \"mixed\", \"maximum_separation\": 0.7027027027027027, \"mean_separation\": 0.45864045864045844, \"minimum_separation\": 0.16216216216216217, \"normalized_mean_separation\": 0.48823016564952026, \"oracle_budget_representation_error\": 0.11711711711711714, \"oracle_singleton_representation_error\": 0.39189189189189194, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.2747747747747748, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 12}, \"private\": {\"hidden_mode_id\": \"8369abb141bf357e12dd99e08b323987d618d375303b05073ab20e4f31e85faa\", \"valid_mode_ids\": [\"25d9152a2dc3f86eaa35706eadcd3a2d391723bbbe5b820db9949335bd63c239\", \"4fe956b57eb873f4baab03e273a342074620d55c8863605d908d3cbd1dce4a73\", \"7dca61cd415855cb56cb753d67580b92134585cbbef43daa5394ccc99bc56ff5\", \"7e924f6664885541e04ad68e0c459903767519d7b30a632143dcca351b4e285e\", \"8369abb141bf357e12dd99e08b323987d618d375303b05073ab20e4f31e85faa\", \"9ab137ac5ce624d04b980117ad3e6b056ff69d387e979b41eb95fd1cd7555913\", \"b50ebfb85bc5f2f08f1356cd08558f1cb3443dc33510bb8c6014b55eb73e8653\", \"c9aba7db3b12c0a430bddb3d24508ec4d69bc3447862dd7f07b09cbe92306f8f\", \"d70a193a3aa6dc74f4a959781059e115eb3c1be8674a9e2111fa7bbbaa8da014\", \"ddb6126be1e16bb9e2e70d6ea1032dc38ed56e2ed95377de0ede3f47750b7d90\", \"e0f41e5bfa050324ab1667d95012870faabf3ce962563b56dd046af5fcd920c4\", \"e15a73e8642fecb5ccaa37c6995fd6099a6c3f42c4b8b0870389ab8bf707e4d6\"]}, \"state_id\": \"985acd0b1c55323f30fd9689\", \"visible_experiments\": [{\"experiment_id\": 10, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 30, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 0}}, {\"experiment_id\": 38, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 1}, \"intervention\": \"DO_Z2_0\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 0}}]}}}", "extra_info": {"index": 132, "max_global_steps": 0, "min_global_steps": 0}, "index": 132, "prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=1, X3=0\n intervention: none\n observed: Z1=0, Z2=0, Y=1\n\nExperiment 2:\n inputs: X1=1, X2=1, X3=0\n intervention: none\n observed: Z1=1, Z2=0, Y=0\n\nExperiment 3:\n inputs: X1=1, X2=1, X3=1\n intervention: set Z2=0\n observed: Z1=0, Z2=0, Y=1\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "raw_prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=1, X3=0\n intervention: none\n observed: Z1=0, Z2=0, Y=1\n\nExperiment 2:\n inputs: X1=1, X2=1, X3=0\n intervention: none\n observed: Z1=1, Z2=0, Y=0\n\nExperiment 3:\n inputs: X1=1, X2=1, X3=1\n intervention: set Z2=0\n observed: Z1=0, Z2=0, Y=1\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "reward_model": {"style": "rule"}, "state_json": "{\"available_experiment_ids\": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 31, 32, 33, 34, 35, 36, 37, 39], \"metadata\": {\"evidence_size\": 3, \"family_bucket\": \"mixed\", \"maximum_separation\": 0.7027027027027027, \"mean_separation\": 0.45864045864045844, \"minimum_separation\": 0.16216216216216217, \"normalized_mean_separation\": 0.48823016564952026, \"oracle_budget_representation_error\": 0.11711711711711714, \"oracle_singleton_representation_error\": 0.39189189189189194, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.2747747747747748, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 12}, \"private\": {\"hidden_mode_id\": \"8369abb141bf357e12dd99e08b323987d618d375303b05073ab20e4f31e85faa\", \"valid_mode_ids\": [\"25d9152a2dc3f86eaa35706eadcd3a2d391723bbbe5b820db9949335bd63c239\", \"4fe956b57eb873f4baab03e273a342074620d55c8863605d908d3cbd1dce4a73\", \"7dca61cd415855cb56cb753d67580b92134585cbbef43daa5394ccc99bc56ff5\", \"7e924f6664885541e04ad68e0c459903767519d7b30a632143dcca351b4e285e\", \"8369abb141bf357e12dd99e08b323987d618d375303b05073ab20e4f31e85faa\", \"9ab137ac5ce624d04b980117ad3e6b056ff69d387e979b41eb95fd1cd7555913\", \"b50ebfb85bc5f2f08f1356cd08558f1cb3443dc33510bb8c6014b55eb73e8653\", \"c9aba7db3b12c0a430bddb3d24508ec4d69bc3447862dd7f07b09cbe92306f8f\", \"d70a193a3aa6dc74f4a959781059e115eb3c1be8674a9e2111fa7bbbaa8da014\", \"ddb6126be1e16bb9e2e70d6ea1032dc38ed56e2ed95377de0ede3f47750b7d90\", \"e0f41e5bfa050324ab1667d95012870faabf3ce962563b56dd046af5fcd920c4\", \"e15a73e8642fecb5ccaa37c6995fd6099a6c3f42c4b8b0870389ab8bf707e4d6\"]}, \"state_id\": \"985acd0b1c55323f30fd9689\", \"visible_experiments\": [{\"experiment_id\": 10, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 30, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 0}}, {\"experiment_id\": 38, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 1}, \"intervention\": \"DO_Z2_0\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 0}}]}"} {"agent_name": "causal_micro_lab_agent_loop", "data_source": "causal_micro_lab_final_eval_v3", "env_spec_json": "{\"agent\": {}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 133, \"task\": {\"state\": {\"available_experiment_ids\": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 11, 12, 13, 14, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 3, \"family_bucket\": \"mixed\", \"maximum_separation\": 0.7567567567567568, \"mean_separation\": 0.4963144963144963, \"minimum_separation\": 0.16216216216216217, \"normalized_mean_separation\": 0.5283347863993025, \"oracle_budget_representation_error\": 0.1441441441441442, \"oracle_singleton_representation_error\": 0.4234234234234235, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.2792792792792793, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 12}, \"private\": {\"hidden_mode_id\": \"e18e7d9c5b9f023e7306451a1d913d064d4d47abbb8fdeb7ff362ec23180cd48\", \"valid_mode_ids\": [\"45108f00137cb0a947d2cf0db6022522731a66f615e786424da97ee609d9965e\", \"49a4bb54fbcc103f93c288605d3392a8366d2cab9daee9be8d67cfc9839e8a9e\", \"4d91327b26c9e6eea0cedf9a152f24f0b8ebb67d89c4f332ca104c98b3750726\", \"6169d38f5848ce1a2d62adb3dcd4eff50dcd8ff8d8715e240c9bde6d7dc735fd\", \"8186f31aff35ed4cbe13f384ba447b79d8b680f0bdfce42bb0195730950e43e9\", \"978ce33deff91fd65bf805b7d8891cac8307e75b862a9084c24e1fe0d2c15a22\", \"9f1ef0e463a36104cabef07e7f90f29d28f9e6fb668f3f0f2f4de877d53a2c0a\", \"b34ed487c194c5622c50fdd2a86e1716c79ff0a41034afd614153161f85ac493\", \"cc8be39ff3b5cdd79fc187ae0606c49bc6af53f2a1ab843f923998c7d697be73\", \"d1b826cd817e7df76a1b02c413f028a4aee17c97fc996101c7157e1c62b59630\", \"e18e7d9c5b9f023e7306451a1d913d064d4d47abbb8fdeb7ff362ec23180cd48\", \"e507f421c896827559de7bb9745120e239ac1fcd6c6963614525770cdf83344f\"]}, \"state_id\": \"27685390f4aeefb5a44f36c2\", \"visible_experiments\": [{\"experiment_id\": 10, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 0}}, {\"experiment_id\": 15, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 27, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 1}, \"intervention\": \"DO_Z1_1\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}]}}}", "extra_info": {"index": 133, "max_global_steps": 0, "min_global_steps": 0}, "index": 133, "prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=1, X3=0\n intervention: none\n observed: Z1=1, Z2=0, Y=0\n\nExperiment 2:\n inputs: X1=0, X2=1, X3=1\n intervention: none\n observed: Z1=0, Z2=1, Y=0\n\nExperiment 3:\n inputs: X1=1, X2=0, X3=1\n intervention: set Z1=1\n observed: Z1=1, Z2=1, Y=0\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "raw_prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=1, X3=0\n intervention: none\n observed: Z1=1, Z2=0, Y=0\n\nExperiment 2:\n inputs: X1=0, X2=1, X3=1\n intervention: none\n observed: Z1=0, Z2=1, Y=0\n\nExperiment 3:\n inputs: X1=1, X2=0, X3=1\n intervention: set Z1=1\n observed: Z1=1, Z2=1, Y=0\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "reward_model": {"style": "rule"}, "state_json": "{\"available_experiment_ids\": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 11, 12, 13, 14, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 3, \"family_bucket\": \"mixed\", \"maximum_separation\": 0.7567567567567568, \"mean_separation\": 0.4963144963144963, \"minimum_separation\": 0.16216216216216217, \"normalized_mean_separation\": 0.5283347863993025, \"oracle_budget_representation_error\": 0.1441441441441442, \"oracle_singleton_representation_error\": 0.4234234234234235, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.2792792792792793, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 12}, \"private\": {\"hidden_mode_id\": \"e18e7d9c5b9f023e7306451a1d913d064d4d47abbb8fdeb7ff362ec23180cd48\", \"valid_mode_ids\": [\"45108f00137cb0a947d2cf0db6022522731a66f615e786424da97ee609d9965e\", \"49a4bb54fbcc103f93c288605d3392a8366d2cab9daee9be8d67cfc9839e8a9e\", \"4d91327b26c9e6eea0cedf9a152f24f0b8ebb67d89c4f332ca104c98b3750726\", \"6169d38f5848ce1a2d62adb3dcd4eff50dcd8ff8d8715e240c9bde6d7dc735fd\", \"8186f31aff35ed4cbe13f384ba447b79d8b680f0bdfce42bb0195730950e43e9\", \"978ce33deff91fd65bf805b7d8891cac8307e75b862a9084c24e1fe0d2c15a22\", \"9f1ef0e463a36104cabef07e7f90f29d28f9e6fb668f3f0f2f4de877d53a2c0a\", \"b34ed487c194c5622c50fdd2a86e1716c79ff0a41034afd614153161f85ac493\", \"cc8be39ff3b5cdd79fc187ae0606c49bc6af53f2a1ab843f923998c7d697be73\", \"d1b826cd817e7df76a1b02c413f028a4aee17c97fc996101c7157e1c62b59630\", \"e18e7d9c5b9f023e7306451a1d913d064d4d47abbb8fdeb7ff362ec23180cd48\", \"e507f421c896827559de7bb9745120e239ac1fcd6c6963614525770cdf83344f\"]}, \"state_id\": \"27685390f4aeefb5a44f36c2\", \"visible_experiments\": [{\"experiment_id\": 10, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 0}}, {\"experiment_id\": 15, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 27, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 1}, \"intervention\": \"DO_Z1_1\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}]}"} {"agent_name": "causal_micro_lab_agent_loop", "data_source": "causal_micro_lab_final_eval_v3", "env_spec_json": "{\"agent\": {}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 134, \"task\": {\"state\": {\"available_experiment_ids\": [1, 2, 3, 4, 5, 6, 7, 8, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 31, 32, 33, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 3, \"family_bucket\": \"within_family\", \"maximum_separation\": 0.7567567567567568, \"mean_separation\": 0.45208845208845194, \"minimum_separation\": 0.10810810810810811, \"normalized_mean_separation\": 0.4812554489973843, \"oracle_budget_representation_error\": 0.10810810810810811, \"oracle_singleton_representation_error\": 0.3918918918918919, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.28378378378378377, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 12}, \"private\": {\"hidden_mode_id\": \"e71a637c825de64f07568c8504cf9523feed1d117ec4deadebac9d90fa32009d\", \"valid_mode_ids\": [\"32cf829089f1658c63b630e3ca720bcb540ad216fc03c17f857cfd978229b389\", \"3451c43fbdd03dd8776765bab7a2795d9b6436c6b982b62618c15c5825ab8fe1\", \"4c8a9f65ec8686af49a459f1c32a1f48edeb2c3d736f30063199cf7c845a9df0\", \"56915d828586457cbf45494ae4e86894bad286df4e697dfce5584501f8fc8246\", \"6ef1f96fde0647709065833b49cfd760eb7150d376b2a1ef105ec1778088225e\", \"6fb3802530e9b79a0f2b8e74d3c89b083bc08dffefcff364a7c66fadd0ed8565\", \"8be7ed376b7d2be63a0ac8d46f6b0908dd77798c0efd42ccf3d62795383d0281\", \"d97414e898861ae6182405e9b64889e7ee76f3d048d429525a77b89577ca9438\", \"e15a944bfaf3a129ba89b5eb19b4a6afe806a0e8b9ca2a3012713d441ee48353\", \"e71a637c825de64f07568c8504cf9523feed1d117ec4deadebac9d90fa32009d\", \"f2880d855270de6188d2a5460bb1b886ce876654f980ff6464714b141164421a\", \"fbaf2285a538e39ae6741bff608d36cf7196fc4918bf8e8b2eed748b13e1a950\"]}, \"state_id\": \"e42af04a2f65fa7b7408d40f\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 9, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 1}, \"intervention\": \"DO_Z2_1\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 30, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}]}}}", "extra_info": {"index": 134, "max_global_steps": 0, "min_global_steps": 0}, "index": 134, "prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=0\n intervention: none\n observed: Z1=1, Z2=1, Y=1\n\nExperiment 2:\n inputs: X1=0, X2=0, X3=1\n intervention: set Z2=1\n observed: Z1=0, Z2=1, Y=0\n\nExperiment 3:\n inputs: X1=1, X2=1, X3=0\n intervention: none\n observed: Z1=1, Z2=1, Y=0\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "raw_prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=0\n intervention: none\n observed: Z1=1, Z2=1, Y=1\n\nExperiment 2:\n inputs: X1=0, X2=0, X3=1\n intervention: set Z2=1\n observed: Z1=0, Z2=1, Y=0\n\nExperiment 3:\n inputs: X1=1, X2=1, X3=0\n intervention: none\n observed: Z1=1, Z2=1, Y=0\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "reward_model": {"style": "rule"}, "state_json": "{\"available_experiment_ids\": [1, 2, 3, 4, 5, 6, 7, 8, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 31, 32, 33, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 3, \"family_bucket\": \"within_family\", \"maximum_separation\": 0.7567567567567568, \"mean_separation\": 0.45208845208845194, \"minimum_separation\": 0.10810810810810811, \"normalized_mean_separation\": 0.4812554489973843, \"oracle_budget_representation_error\": 0.10810810810810811, \"oracle_singleton_representation_error\": 0.3918918918918919, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.28378378378378377, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 12}, \"private\": {\"hidden_mode_id\": \"e71a637c825de64f07568c8504cf9523feed1d117ec4deadebac9d90fa32009d\", \"valid_mode_ids\": [\"32cf829089f1658c63b630e3ca720bcb540ad216fc03c17f857cfd978229b389\", \"3451c43fbdd03dd8776765bab7a2795d9b6436c6b982b62618c15c5825ab8fe1\", \"4c8a9f65ec8686af49a459f1c32a1f48edeb2c3d736f30063199cf7c845a9df0\", \"56915d828586457cbf45494ae4e86894bad286df4e697dfce5584501f8fc8246\", \"6ef1f96fde0647709065833b49cfd760eb7150d376b2a1ef105ec1778088225e\", \"6fb3802530e9b79a0f2b8e74d3c89b083bc08dffefcff364a7c66fadd0ed8565\", \"8be7ed376b7d2be63a0ac8d46f6b0908dd77798c0efd42ccf3d62795383d0281\", \"d97414e898861ae6182405e9b64889e7ee76f3d048d429525a77b89577ca9438\", \"e15a944bfaf3a129ba89b5eb19b4a6afe806a0e8b9ca2a3012713d441ee48353\", \"e71a637c825de64f07568c8504cf9523feed1d117ec4deadebac9d90fa32009d\", \"f2880d855270de6188d2a5460bb1b886ce876654f980ff6464714b141164421a\", \"fbaf2285a538e39ae6741bff608d36cf7196fc4918bf8e8b2eed748b13e1a950\"]}, \"state_id\": \"e42af04a2f65fa7b7408d40f\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 9, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 1}, \"intervention\": \"DO_Z2_1\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 30, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}]}"} {"agent_name": "causal_micro_lab_agent_loop", "data_source": "causal_micro_lab_final_eval_v3", "env_spec_json": "{\"agent\": {}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 135, \"task\": {\"state\": {\"available_experiment_ids\": [1, 2, 3, 4, 5, 6, 7, 9, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 3, \"family_bucket\": \"mixed\", \"maximum_separation\": 0.7027027027027027, \"mean_separation\": 0.45454545454545436, \"minimum_separation\": 0.16216216216216217, \"normalized_mean_separation\": 0.4838709677419353, \"oracle_budget_representation_error\": 0.10810810810810811, \"oracle_singleton_representation_error\": 0.3918918918918919, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.28378378378378377, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 12}, \"private\": {\"hidden_mode_id\": \"e7595ac1c5f7ee8d6c91c84c925912656090389639ab8070fcdeeb9b8b8893d8\", \"valid_mode_ids\": [\"1e4d3d54327cf3453cafe968270982e65eb16eb74258ccee9c3a34d3c7ff6d43\", \"3a4c7f43e2303fe360bec853fb8892f8c5e569178ff8e96a6f5ef7127556954b\", \"49181ab5d45319789cead7afbc5410cab0ef95d475de5d97f9b27dad137282c4\", \"492c969be386949f6669b9d3000e3fe73c297cfb5556b2ba80463e07aafc22c5\", \"5c5d6353809eafba86b9c30a7202a643020b5a70c38e262d77a2a4392e40c4c1\", \"70769d8a804d3bbfdc43684aa77b6ae518b0b18f1763e999045c03c4607a01b6\", \"7583c4b9364a8da62a22a2299d0e856c354841941b897b4c14f811b088792188\", \"8a796860690ba16f70c6eda3c1e4c2eb2207047b15bd520934b5ef5b5f3b8988\", \"920f70a9dbcbe27b6e292b83d2c95603bf042faa1d58362d0e7bac34cda982b2\", \"93b09709cfd43eb54ba086d0b6fb0b5904ed9adabf26ec5afca479ae410b019d\", \"b7695717796389cef1aab08a863e29e37acc067971561f72d9742c462ea0ec31\", \"e7595ac1c5f7ee8d6c91c84c925912656090389639ab8070fcdeeb9b8b8893d8\"]}, \"state_id\": \"e87ea8c850c6ae122db3b28b\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 8, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 1}, \"intervention\": \"DO_Z2_0\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 10, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}]}}}", "extra_info": {"index": 135, "max_global_steps": 0, "min_global_steps": 0}, "index": 135, "prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=0\n intervention: none\n observed: Z1=0, Z2=1, Y=1\n\nExperiment 2:\n inputs: X1=0, X2=0, X3=1\n intervention: set Z2=0\n observed: Z1=0, Z2=0, Y=0\n\nExperiment 3:\n inputs: X1=0, X2=1, X3=0\n intervention: none\n observed: Z1=1, Z2=1, Y=0\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "raw_prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=0\n intervention: none\n observed: Z1=0, Z2=1, Y=1\n\nExperiment 2:\n inputs: X1=0, X2=0, X3=1\n intervention: set Z2=0\n observed: Z1=0, Z2=0, Y=0\n\nExperiment 3:\n inputs: X1=0, X2=1, X3=0\n intervention: none\n observed: Z1=1, Z2=1, Y=0\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "reward_model": {"style": "rule"}, "state_json": "{\"available_experiment_ids\": [1, 2, 3, 4, 5, 6, 7, 9, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 3, \"family_bucket\": \"mixed\", \"maximum_separation\": 0.7027027027027027, \"mean_separation\": 0.45454545454545436, \"minimum_separation\": 0.16216216216216217, \"normalized_mean_separation\": 0.4838709677419353, \"oracle_budget_representation_error\": 0.10810810810810811, \"oracle_singleton_representation_error\": 0.3918918918918919, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.28378378378378377, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 12}, \"private\": {\"hidden_mode_id\": \"e7595ac1c5f7ee8d6c91c84c925912656090389639ab8070fcdeeb9b8b8893d8\", \"valid_mode_ids\": [\"1e4d3d54327cf3453cafe968270982e65eb16eb74258ccee9c3a34d3c7ff6d43\", \"3a4c7f43e2303fe360bec853fb8892f8c5e569178ff8e96a6f5ef7127556954b\", \"49181ab5d45319789cead7afbc5410cab0ef95d475de5d97f9b27dad137282c4\", \"492c969be386949f6669b9d3000e3fe73c297cfb5556b2ba80463e07aafc22c5\", \"5c5d6353809eafba86b9c30a7202a643020b5a70c38e262d77a2a4392e40c4c1\", \"70769d8a804d3bbfdc43684aa77b6ae518b0b18f1763e999045c03c4607a01b6\", \"7583c4b9364a8da62a22a2299d0e856c354841941b897b4c14f811b088792188\", \"8a796860690ba16f70c6eda3c1e4c2eb2207047b15bd520934b5ef5b5f3b8988\", \"920f70a9dbcbe27b6e292b83d2c95603bf042faa1d58362d0e7bac34cda982b2\", \"93b09709cfd43eb54ba086d0b6fb0b5904ed9adabf26ec5afca479ae410b019d\", \"b7695717796389cef1aab08a863e29e37acc067971561f72d9742c462ea0ec31\", \"e7595ac1c5f7ee8d6c91c84c925912656090389639ab8070fcdeeb9b8b8893d8\"]}, \"state_id\": \"e87ea8c850c6ae122db3b28b\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 8, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 1}, \"intervention\": \"DO_Z2_0\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 10, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}]}"} {"agent_name": "causal_micro_lab_agent_loop", "data_source": "causal_micro_lab_final_eval_v3", "env_spec_json": "{\"agent\": {}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 136, \"task\": {\"state\": {\"available_experiment_ids\": [0, 1, 2, 3, 5, 6, 7, 8, 9, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 3, \"family_bucket\": \"within_family\", \"maximum_separation\": 0.7567567567567568, \"mean_separation\": 0.46191646191646163, \"minimum_separation\": 0.16216216216216217, \"normalized_mean_separation\": 0.49171752397558816, \"oracle_budget_representation_error\": 0.10810810810810811, \"oracle_singleton_representation_error\": 0.40090090090090086, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.29279279279279274, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 12}, \"private\": {\"hidden_mode_id\": \"db849acc6e48197901c17b7e857077a8e4a3088e7f34b5637220798a9e31762c\", \"valid_mode_ids\": [\"0507a80c1c303355570b3663bfa72055202ae77acd1d9a4aefd695ca718849ff\", \"131c40ef367df15659dbca3c90bee3db2a757e9bd3764fb31cb7600fe34f59f8\", \"4d888063c2853fab4ff8c179ffbee34569011ee4d250c5bf23ed4053134f21fe\", \"5bf1377c4ca7da16e5c8f5c6da3d5cc376bf67d6bc5660ce37f6163d370ad7ac\", \"71bbe494998a1b1a2ab5dd54a6d49d8dfaf92121c8cff228d7a8a2fa27419249\", \"8c78a11bd9935232ad759e366847bf3133f3527083cdacce8eb84c9af350df2d\", \"941721250248d9d341d048d9fd462cbba25eb269940370f5905ed3cce1b18121\", \"9d5439a51c6419501c65897dae4edecbf349d4c83d3f484ad1f54e82c9657d7e\", \"bee6097082f9fd3c9e19592fc792d46d94c933d698534ff89b4c5ab4c584c757\", \"d9b238ac4ea7013e022dec7907127e9c3b47a9ea78eab9a33dc68e9ad782d790\", \"db849acc6e48197901c17b7e857077a8e4a3088e7f34b5637220798a9e31762c\", \"f370d0b60bb048e62d295115c29378cb09a92924dd597b7210c607c15b2a2566\"]}, \"state_id\": \"44247994418126e0f5e20ea5\", \"visible_experiments\": [{\"experiment_id\": 4, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z2_1\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 10, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 35, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}]}}}", "extra_info": {"index": 136, "max_global_steps": 0, "min_global_steps": 0}, "index": 136, "prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=0\n intervention: set Z2=1\n observed: Z1=0, Z2=1, Y=0\n\nExperiment 2:\n inputs: X1=0, X2=1, X3=0\n intervention: none\n observed: Z1=0, Z2=1, Y=1\n\nExperiment 3:\n inputs: X1=1, X2=1, X3=1\n intervention: none\n observed: Z1=0, Z2=1, Y=0\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "raw_prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=0\n intervention: set Z2=1\n observed: Z1=0, Z2=1, Y=0\n\nExperiment 2:\n inputs: X1=0, X2=1, X3=0\n intervention: none\n observed: Z1=0, Z2=1, Y=1\n\nExperiment 3:\n inputs: X1=1, X2=1, X3=1\n intervention: none\n observed: Z1=0, Z2=1, Y=0\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "reward_model": {"style": "rule"}, "state_json": "{\"available_experiment_ids\": [0, 1, 2, 3, 5, 6, 7, 8, 9, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 3, \"family_bucket\": \"within_family\", \"maximum_separation\": 0.7567567567567568, \"mean_separation\": 0.46191646191646163, \"minimum_separation\": 0.16216216216216217, \"normalized_mean_separation\": 0.49171752397558816, \"oracle_budget_representation_error\": 0.10810810810810811, \"oracle_singleton_representation_error\": 0.40090090090090086, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.29279279279279274, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 12}, \"private\": {\"hidden_mode_id\": \"db849acc6e48197901c17b7e857077a8e4a3088e7f34b5637220798a9e31762c\", \"valid_mode_ids\": [\"0507a80c1c303355570b3663bfa72055202ae77acd1d9a4aefd695ca718849ff\", \"131c40ef367df15659dbca3c90bee3db2a757e9bd3764fb31cb7600fe34f59f8\", \"4d888063c2853fab4ff8c179ffbee34569011ee4d250c5bf23ed4053134f21fe\", \"5bf1377c4ca7da16e5c8f5c6da3d5cc376bf67d6bc5660ce37f6163d370ad7ac\", \"71bbe494998a1b1a2ab5dd54a6d49d8dfaf92121c8cff228d7a8a2fa27419249\", \"8c78a11bd9935232ad759e366847bf3133f3527083cdacce8eb84c9af350df2d\", \"941721250248d9d341d048d9fd462cbba25eb269940370f5905ed3cce1b18121\", \"9d5439a51c6419501c65897dae4edecbf349d4c83d3f484ad1f54e82c9657d7e\", \"bee6097082f9fd3c9e19592fc792d46d94c933d698534ff89b4c5ab4c584c757\", \"d9b238ac4ea7013e022dec7907127e9c3b47a9ea78eab9a33dc68e9ad782d790\", \"db849acc6e48197901c17b7e857077a8e4a3088e7f34b5637220798a9e31762c\", \"f370d0b60bb048e62d295115c29378cb09a92924dd597b7210c607c15b2a2566\"]}, \"state_id\": \"44247994418126e0f5e20ea5\", \"visible_experiments\": [{\"experiment_id\": 4, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z2_1\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 10, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 35, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}]}"} {"agent_name": "causal_micro_lab_agent_loop", "data_source": "causal_micro_lab_final_eval_v3", "env_spec_json": "{\"agent\": {}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 137, \"task\": {\"state\": {\"available_experiment_ids\": [0, 1, 2, 3, 4, 5, 6, 7, 9, 10, 11, 12, 13, 14, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 31, 32, 33, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 3, \"family_bucket\": \"mixed\", \"maximum_separation\": 0.7567567567567568, \"mean_separation\": 0.43407043407043405, \"minimum_separation\": 0.10810810810810811, \"normalized_mean_separation\": 0.4620749782040104, \"oracle_budget_representation_error\": 0.07657657657657659, \"oracle_singleton_representation_error\": 0.36936936936936937, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.2927927927927928, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 12}, \"private\": {\"hidden_mode_id\": \"1ec4b3cf9701a227b835b97cfdc9dc1b59a32e35261493fb49a6ee0d458fda90\", \"valid_mode_ids\": [\"04bc145fb9aa09fb3f6c9ba8c8e591e349cdb29b927f4aaced2dcf336ecc27b6\", \"1ec4b3cf9701a227b835b97cfdc9dc1b59a32e35261493fb49a6ee0d458fda90\", \"27ff6ead3d3b62d54000f4222054c322933163ddfe2c751a120c6231e7498b7d\", \"46b2422cd074116199947ebc650106c67d03f5fe4c43437d2b8e77d4c4dfc32d\", \"471693d105d40d67aa86503e917cdec60153dcb40f2b4df86884503e8919509e\", \"7d368396f0427edca40cb50b9f92f08c5c014c3939829e74a36831d914e11104\", \"8486d2bdae19b72b93a415145db31b0a0e1c1deeb6e00f29550bc5a49905eb9d\", \"b70a2a5deed17616046eb0eb88c87f20dc48549be3270cd56578f927f1de2432\", \"c92706f42f6e4a45b572b8633c0885c99f035fde5d4b292be41b2b845bd5dea3\", \"ca4c2822e1972fa86677f116f0770d9a3620270b87ddcce5b33187dbf636a6d8\", \"cbd2ae34e91d72977274a9dbfbb51a37dc3d3bd221bde365ea585bf8daa7ed03\", \"d555579a400013777f4a535279fde63112dfe7ebcdc42a4254df90a1100d4796\"]}, \"state_id\": \"2edcc4c82b1c81e9339d391a\", \"visible_experiments\": [{\"experiment_id\": 8, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 1}, \"intervention\": \"DO_Z2_0\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 15, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 30, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 0}}]}}}", "extra_info": {"index": 137, "max_global_steps": 0, "min_global_steps": 0}, "index": 137, "prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=1\n intervention: set Z2=0\n observed: Z1=0, Z2=0, Y=0\n\nExperiment 2:\n inputs: X1=0, X2=1, X3=1\n intervention: none\n observed: Z1=0, Z2=0, Y=1\n\nExperiment 3:\n inputs: X1=1, X2=1, X3=0\n intervention: none\n observed: Z1=0, Z2=0, Y=0\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "raw_prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=1\n intervention: set Z2=0\n observed: Z1=0, Z2=0, Y=0\n\nExperiment 2:\n inputs: X1=0, X2=1, X3=1\n intervention: none\n observed: Z1=0, Z2=0, Y=1\n\nExperiment 3:\n inputs: X1=1, X2=1, X3=0\n intervention: none\n observed: Z1=0, Z2=0, Y=0\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "reward_model": {"style": "rule"}, "state_json": "{\"available_experiment_ids\": [0, 1, 2, 3, 4, 5, 6, 7, 9, 10, 11, 12, 13, 14, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 31, 32, 33, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 3, \"family_bucket\": \"mixed\", \"maximum_separation\": 0.7567567567567568, \"mean_separation\": 0.43407043407043405, \"minimum_separation\": 0.10810810810810811, \"normalized_mean_separation\": 0.4620749782040104, \"oracle_budget_representation_error\": 0.07657657657657659, \"oracle_singleton_representation_error\": 0.36936936936936937, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.2927927927927928, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 12}, \"private\": {\"hidden_mode_id\": \"1ec4b3cf9701a227b835b97cfdc9dc1b59a32e35261493fb49a6ee0d458fda90\", \"valid_mode_ids\": [\"04bc145fb9aa09fb3f6c9ba8c8e591e349cdb29b927f4aaced2dcf336ecc27b6\", \"1ec4b3cf9701a227b835b97cfdc9dc1b59a32e35261493fb49a6ee0d458fda90\", \"27ff6ead3d3b62d54000f4222054c322933163ddfe2c751a120c6231e7498b7d\", \"46b2422cd074116199947ebc650106c67d03f5fe4c43437d2b8e77d4c4dfc32d\", \"471693d105d40d67aa86503e917cdec60153dcb40f2b4df86884503e8919509e\", \"7d368396f0427edca40cb50b9f92f08c5c014c3939829e74a36831d914e11104\", \"8486d2bdae19b72b93a415145db31b0a0e1c1deeb6e00f29550bc5a49905eb9d\", \"b70a2a5deed17616046eb0eb88c87f20dc48549be3270cd56578f927f1de2432\", \"c92706f42f6e4a45b572b8633c0885c99f035fde5d4b292be41b2b845bd5dea3\", \"ca4c2822e1972fa86677f116f0770d9a3620270b87ddcce5b33187dbf636a6d8\", \"cbd2ae34e91d72977274a9dbfbb51a37dc3d3bd221bde365ea585bf8daa7ed03\", \"d555579a400013777f4a535279fde63112dfe7ebcdc42a4254df90a1100d4796\"]}, \"state_id\": \"2edcc4c82b1c81e9339d391a\", \"visible_experiments\": [{\"experiment_id\": 8, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 1}, \"intervention\": \"DO_Z2_0\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 15, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 30, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 0}}]}"} {"agent_name": "causal_micro_lab_agent_loop", "data_source": "causal_micro_lab_final_eval_v3", "env_spec_json": "{\"agent\": {}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 138, \"task\": {\"state\": {\"available_experiment_ids\": [0, 1, 2, 3, 4, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 26, 27, 28, 29, 30, 31, 32, 33, 34, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 3, \"family_bucket\": \"mixed\", \"maximum_separation\": 0.6756756756756757, \"mean_separation\": 0.4348894348894348, \"minimum_separation\": 0.10810810810810811, \"normalized_mean_separation\": 0.4629468177855274, \"oracle_budget_representation_error\": 0.0900900900900901, \"oracle_singleton_representation_error\": 0.3851351351351351, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.295045045045045, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 12}, \"private\": {\"hidden_mode_id\": \"529420369321129a8b3e329d6f3860dba185d4f514879178a1ce4a9adfe1dfe4\", \"valid_mode_ids\": [\"06b358780a87d6d2df4581386858bfbde710e76f76d0d3145d4a74a99c1ceea6\", \"1a38b83edf310a3af45c0f13e97e677d50a10f20a7bad540ec863f71518bcab8\", \"2d0f58985a1d5a99e7117d11af57ff21bef9937441fa30f2360c5a8d5c09fd67\", \"529420369321129a8b3e329d6f3860dba185d4f514879178a1ce4a9adfe1dfe4\", \"5adcb93b8a870ca22d1242a25174df62a37b82f676d8e74f8f50452a251db495\", \"7329535b8d7a2b8ea28861ef24d1f84994020b8d07e403884fb80d21fed9c320\", \"9ab4db99d7f4734481be8a9ebaf54b685b54edc80a54d2b239bbbb97182b5cdc\", \"c5921be4e971452f352cf4a19d9a8d77b01a82d39b6c6cb5793aea78b539c032\", \"e920071c01479de1fea159bdca8e4f75c7a5243aace8e96402ad14eb20671b9e\", \"eb9ce571722da7d882d20973f060992cb76c0272b202cc86169380ed96ba277c\", \"ec1f05b3f8be9f247fd99e4ce98b89a53331b3b089d0d0d188b84162d71bdb39\", \"fe9e8365ef1b3251a868be625c813965b3e97341fdae1a47c96041b9d85c4d8a\"]}, \"state_id\": \"1879d47be83d372f32bd4311\", \"visible_experiments\": [{\"experiment_id\": 5, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 25, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 35, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}]}}}", "extra_info": {"index": 138, "max_global_steps": 0, "min_global_steps": 0}, "index": 138, "prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=1\n intervention: none\n observed: Z1=0, Z2=0, Y=1\n\nExperiment 2:\n inputs: X1=1, X2=0, X3=1\n intervention: none\n observed: Z1=1, Z2=1, Y=1\n\nExperiment 3:\n inputs: X1=1, X2=1, X3=1\n intervention: none\n observed: Z1=0, Z2=1, Y=0\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "raw_prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=1\n intervention: none\n observed: Z1=0, Z2=0, Y=1\n\nExperiment 2:\n inputs: X1=1, X2=0, X3=1\n intervention: none\n observed: Z1=1, Z2=1, Y=1\n\nExperiment 3:\n inputs: X1=1, X2=1, X3=1\n intervention: none\n observed: Z1=0, Z2=1, Y=0\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "reward_model": {"style": "rule"}, "state_json": "{\"available_experiment_ids\": [0, 1, 2, 3, 4, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 26, 27, 28, 29, 30, 31, 32, 33, 34, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 3, \"family_bucket\": \"mixed\", \"maximum_separation\": 0.6756756756756757, \"mean_separation\": 0.4348894348894348, \"minimum_separation\": 0.10810810810810811, \"normalized_mean_separation\": 0.4629468177855274, \"oracle_budget_representation_error\": 0.0900900900900901, \"oracle_singleton_representation_error\": 0.3851351351351351, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.295045045045045, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 12}, \"private\": {\"hidden_mode_id\": \"529420369321129a8b3e329d6f3860dba185d4f514879178a1ce4a9adfe1dfe4\", \"valid_mode_ids\": [\"06b358780a87d6d2df4581386858bfbde710e76f76d0d3145d4a74a99c1ceea6\", \"1a38b83edf310a3af45c0f13e97e677d50a10f20a7bad540ec863f71518bcab8\", \"2d0f58985a1d5a99e7117d11af57ff21bef9937441fa30f2360c5a8d5c09fd67\", \"529420369321129a8b3e329d6f3860dba185d4f514879178a1ce4a9adfe1dfe4\", \"5adcb93b8a870ca22d1242a25174df62a37b82f676d8e74f8f50452a251db495\", \"7329535b8d7a2b8ea28861ef24d1f84994020b8d07e403884fb80d21fed9c320\", \"9ab4db99d7f4734481be8a9ebaf54b685b54edc80a54d2b239bbbb97182b5cdc\", \"c5921be4e971452f352cf4a19d9a8d77b01a82d39b6c6cb5793aea78b539c032\", \"e920071c01479de1fea159bdca8e4f75c7a5243aace8e96402ad14eb20671b9e\", \"eb9ce571722da7d882d20973f060992cb76c0272b202cc86169380ed96ba277c\", \"ec1f05b3f8be9f247fd99e4ce98b89a53331b3b089d0d0d188b84162d71bdb39\", \"fe9e8365ef1b3251a868be625c813965b3e97341fdae1a47c96041b9d85c4d8a\"]}, \"state_id\": \"1879d47be83d372f32bd4311\", \"visible_experiments\": [{\"experiment_id\": 5, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 25, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 35, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}]}"} {"agent_name": "causal_micro_lab_agent_loop", "data_source": "causal_micro_lab_final_eval_v3", "env_spec_json": "{\"agent\": {}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 139, \"task\": {\"state\": {\"available_experiment_ids\": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 11, 12, 13, 14, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 3, \"family_bucket\": \"mixed\", \"maximum_separation\": 0.6756756756756757, \"mean_separation\": 0.43488943488943477, \"minimum_separation\": 0.10810810810810811, \"normalized_mean_separation\": 0.4629468177855273, \"oracle_budget_representation_error\": 0.0900900900900901, \"oracle_singleton_representation_error\": 0.3851351351351351, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.295045045045045, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 12}, \"private\": {\"hidden_mode_id\": \"5a72041c1aabeb06a378b471e090f14d0c2ce86f1b87f38f42fa70eb5c4c984e\", \"valid_mode_ids\": [\"40a3448ed99be50e9b9dce629cbe50e6a1aefdf1d53c5d3894e98fc3fc88b994\", \"5a72041c1aabeb06a378b471e090f14d0c2ce86f1b87f38f42fa70eb5c4c984e\", \"5e59a4980df75e102e902ed6c1a1c8014f0fc57c56f45b4570eee002973b57e6\", \"61118a49406a5c886dcb9a58a7f6047a7f7e1015990e15b16f79af1b96a3728b\", \"752e1e5ac7521698c24add0bed0ea36473e92643360048961a06ea170d669550\", \"b770499ebe2fb91aeb076594b0da43c235468e0fa039bb8f5c3eaf541e98c1f1\", \"c0df16c295737143c16591d360f1e08a0d3d5a0efaab691340731ac8e15504eb\", \"cb037067e3e2d21480df5107fcdfbc0a98f427ed6ad3b9c8d1aa4390a40bc8bb\", \"d1aab7074601fdbbe56adc9aa1466cda212a1a9e200f8a2d5210300b60b10d9f\", \"de6ae105037860a51a11b0cbc6ca810b758dfa764e1b2b7d2dd84134e525c08e\", \"e39a3ce250bc197d205860082ba0f8baabb12760c38132f2d687f40fb423a2d2\", \"eba97e601ddc1ca34ea66310170436c1960b64fb4f04ebbfe02e20d40b88fb74\"]}, \"state_id\": \"5e1855c44ef97a36fb1dd59e\", \"visible_experiments\": [{\"experiment_id\": 10, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 15, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 35, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}]}}}", "extra_info": {"index": 139, "max_global_steps": 0, "min_global_steps": 0}, "index": 139, "prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=1, X3=0\n intervention: none\n observed: Z1=0, Z2=0, Y=1\n\nExperiment 2:\n inputs: X1=0, X2=1, X3=1\n intervention: none\n observed: Z1=1, Z2=1, Y=1\n\nExperiment 3:\n inputs: X1=1, X2=1, X3=1\n intervention: none\n observed: Z1=0, Z2=1, Y=0\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "raw_prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=1, X3=0\n intervention: none\n observed: Z1=0, Z2=0, Y=1\n\nExperiment 2:\n inputs: X1=0, X2=1, X3=1\n intervention: none\n observed: Z1=1, Z2=1, Y=1\n\nExperiment 3:\n inputs: X1=1, X2=1, X3=1\n intervention: none\n observed: Z1=0, Z2=1, Y=0\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "reward_model": {"style": "rule"}, "state_json": "{\"available_experiment_ids\": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 11, 12, 13, 14, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 3, \"family_bucket\": \"mixed\", \"maximum_separation\": 0.6756756756756757, \"mean_separation\": 0.43488943488943477, \"minimum_separation\": 0.10810810810810811, \"normalized_mean_separation\": 0.4629468177855273, \"oracle_budget_representation_error\": 0.0900900900900901, \"oracle_singleton_representation_error\": 0.3851351351351351, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.295045045045045, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 12}, \"private\": {\"hidden_mode_id\": \"5a72041c1aabeb06a378b471e090f14d0c2ce86f1b87f38f42fa70eb5c4c984e\", \"valid_mode_ids\": [\"40a3448ed99be50e9b9dce629cbe50e6a1aefdf1d53c5d3894e98fc3fc88b994\", \"5a72041c1aabeb06a378b471e090f14d0c2ce86f1b87f38f42fa70eb5c4c984e\", \"5e59a4980df75e102e902ed6c1a1c8014f0fc57c56f45b4570eee002973b57e6\", \"61118a49406a5c886dcb9a58a7f6047a7f7e1015990e15b16f79af1b96a3728b\", \"752e1e5ac7521698c24add0bed0ea36473e92643360048961a06ea170d669550\", \"b770499ebe2fb91aeb076594b0da43c235468e0fa039bb8f5c3eaf541e98c1f1\", \"c0df16c295737143c16591d360f1e08a0d3d5a0efaab691340731ac8e15504eb\", \"cb037067e3e2d21480df5107fcdfbc0a98f427ed6ad3b9c8d1aa4390a40bc8bb\", \"d1aab7074601fdbbe56adc9aa1466cda212a1a9e200f8a2d5210300b60b10d9f\", \"de6ae105037860a51a11b0cbc6ca810b758dfa764e1b2b7d2dd84134e525c08e\", \"e39a3ce250bc197d205860082ba0f8baabb12760c38132f2d687f40fb423a2d2\", \"eba97e601ddc1ca34ea66310170436c1960b64fb4f04ebbfe02e20d40b88fb74\"]}, \"state_id\": \"5e1855c44ef97a36fb1dd59e\", \"visible_experiments\": [{\"experiment_id\": 10, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 15, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 35, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}]}"} {"agent_name": "causal_micro_lab_agent_loop", "data_source": "causal_micro_lab_final_eval_v3", "env_spec_json": "{\"agent\": {}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 140, \"task\": {\"state\": {\"available_experiment_ids\": [0, 1, 2, 3, 4, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 21, 22, 23, 24, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 3, \"family_bucket\": \"mixed\", \"maximum_separation\": 0.7027027027027027, \"mean_separation\": 0.43243243243243235, \"minimum_separation\": 0.10810810810810811, \"normalized_mean_separation\": 0.46033129904097636, \"oracle_budget_representation_error\": 0.07657657657657659, \"oracle_singleton_representation_error\": 0.37837837837837834, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.3018018018018018, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 12}, \"private\": {\"hidden_mode_id\": \"a26ee3228e27595858a104d6cf0efaf966037f6fff57dd3ec4b41a1f7b946d16\", \"valid_mode_ids\": [\"0b91c69da119e0db89702835a60c877bedcb7cf59b99bbb68f3dc760b1051359\", \"2233b42072e99eaacb6ac074a9d2eaee31e585d86cd6973ec80c6556ea371ff1\", \"297101677169de238f69e82c02ebba1e91e85a0fac8c77b5c80ea83156b01fdd\", \"2e6fb726ae76583b450368eb6f5034e1b453e8698f9ada7b0162f2ba651fe5f3\", \"6d3c2c41d03935e7e595a32574ceb01f7c02137002709f54a03890123e7375d2\", \"8c36f31791a3f88714c86c26c0384e30f384d62f775ff89470bcfb63b7ee612c\", \"9b36e687f40997d441b6aa722428411169243f334af70ba1c343a13f4e4f6fac\", \"9ca0eab6fdf9615c1d60b0e80d307937c563af1b5cc85837c555cbbf28c6532f\", \"a26ee3228e27595858a104d6cf0efaf966037f6fff57dd3ec4b41a1f7b946d16\", \"bc036a9cdeb3192d5bd57fa82c4babc8052b2a55b5989c3674c94022403fc415\", \"f40dba9ad84ff7b136b005da927e21a6b2ec97ff8e848a7e4825f64c57acf99c\", \"f72cae2da74fd7e4093661889f190b7caddc57da825ef31789245016f3e4cd65\"]}, \"state_id\": \"7a458a3387c48975c0d3a80f\", \"visible_experiments\": [{\"experiment_id\": 5, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 20, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 25, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 1}}]}}}", "extra_info": {"index": 140, "max_global_steps": 0, "min_global_steps": 0}, "index": 140, "prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=1\n intervention: none\n observed: Z1=0, Z2=0, Y=1\n\nExperiment 2:\n inputs: X1=1, X2=0, X3=0\n intervention: none\n observed: Z1=0, Z2=0, Y=1\n\nExperiment 3:\n inputs: X1=1, X2=0, X3=1\n intervention: none\n observed: Z1=1, Z2=1, Y=1\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "raw_prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=1\n intervention: none\n observed: Z1=0, Z2=0, Y=1\n\nExperiment 2:\n inputs: X1=1, X2=0, X3=0\n intervention: none\n observed: Z1=0, Z2=0, Y=1\n\nExperiment 3:\n inputs: X1=1, X2=0, X3=1\n intervention: none\n observed: Z1=1, Z2=1, Y=1\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "reward_model": {"style": "rule"}, "state_json": "{\"available_experiment_ids\": [0, 1, 2, 3, 4, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 21, 22, 23, 24, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 3, \"family_bucket\": \"mixed\", \"maximum_separation\": 0.7027027027027027, \"mean_separation\": 0.43243243243243235, \"minimum_separation\": 0.10810810810810811, \"normalized_mean_separation\": 0.46033129904097636, \"oracle_budget_representation_error\": 0.07657657657657659, \"oracle_singleton_representation_error\": 0.37837837837837834, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.3018018018018018, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 12}, \"private\": {\"hidden_mode_id\": \"a26ee3228e27595858a104d6cf0efaf966037f6fff57dd3ec4b41a1f7b946d16\", \"valid_mode_ids\": [\"0b91c69da119e0db89702835a60c877bedcb7cf59b99bbb68f3dc760b1051359\", \"2233b42072e99eaacb6ac074a9d2eaee31e585d86cd6973ec80c6556ea371ff1\", \"297101677169de238f69e82c02ebba1e91e85a0fac8c77b5c80ea83156b01fdd\", \"2e6fb726ae76583b450368eb6f5034e1b453e8698f9ada7b0162f2ba651fe5f3\", \"6d3c2c41d03935e7e595a32574ceb01f7c02137002709f54a03890123e7375d2\", \"8c36f31791a3f88714c86c26c0384e30f384d62f775ff89470bcfb63b7ee612c\", \"9b36e687f40997d441b6aa722428411169243f334af70ba1c343a13f4e4f6fac\", \"9ca0eab6fdf9615c1d60b0e80d307937c563af1b5cc85837c555cbbf28c6532f\", \"a26ee3228e27595858a104d6cf0efaf966037f6fff57dd3ec4b41a1f7b946d16\", \"bc036a9cdeb3192d5bd57fa82c4babc8052b2a55b5989c3674c94022403fc415\", \"f40dba9ad84ff7b136b005da927e21a6b2ec97ff8e848a7e4825f64c57acf99c\", \"f72cae2da74fd7e4093661889f190b7caddc57da825ef31789245016f3e4cd65\"]}, \"state_id\": \"7a458a3387c48975c0d3a80f\", \"visible_experiments\": [{\"experiment_id\": 5, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 20, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 25, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 1}}]}"} {"agent_name": "causal_micro_lab_agent_loop", "data_source": "causal_micro_lab_final_eval_v3", "env_spec_json": "{\"agent\": {}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 141, \"task\": {\"state\": {\"available_experiment_ids\": [0, 1, 3, 4, 5, 6, 7, 8, 9, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 3, \"family_bucket\": \"within_family\", \"maximum_separation\": 0.7567567567567568, \"mean_separation\": 0.484848484848485, \"minimum_separation\": 0.16216216216216217, \"normalized_mean_separation\": 0.5161290322580646, \"oracle_budget_representation_error\": 0.10810810810810811, \"oracle_singleton_representation_error\": 0.4369369369369369, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.3288288288288288, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 12}, \"private\": {\"hidden_mode_id\": \"a340c62e9936de3a97d9ae43715aa853353e0223c557aaec62f3c359fe714e67\", \"valid_mode_ids\": [\"03346aeeb78740b78cdc35d6c2ad6305214dce1e9bf869ca2f16de4697675553\", \"329ba721e7cdfea83fd692c9cc72a5a5e8487f63b7447eb7c8637203b4f6c69f\", \"589f255f86b2f8ba0898b0982c81490ef488b435c0d268ae97fb5c06c1b91319\", \"8285c56266ae4cc019a6b3e3bca3e78fbd38afc9d6e38b0c1f5e46133cd6cb12\", \"969750d69d3553d4db297a03653d8db670eb7f911d941a3557ef477747cc4c07\", \"a20d52b6028e9b884feeac6f4161e51876285c9bd39037eb97b04b5e73e341ad\", \"a340c62e9936de3a97d9ae43715aa853353e0223c557aaec62f3c359fe714e67\", \"b524b45e6d4df1263976a657dae2b12657078ed67f10c50ec7b5b016ed3f8d8d\", \"bfd3984e1043414e6515463bc2076c16b0a6a6d1916efbcecc2154eeedd9cd77\", \"c2eea4f77c13851541aeaba58a651ae4379f7bdc5496bf926c636974b101c7c5\", \"db1c0eb4e06c38dbfc8ab7c371ce4eb860f028f5052e96d4c7e634120ea7da6d\", \"e2937333ae07175bc67964cdac70dd8ffde41e3834eeda8b4459d92ee562b96d\"]}, \"state_id\": \"2bcf0a68c9710df7b9c2f508\", \"visible_experiments\": [{\"experiment_id\": 2, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z1_1\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 10, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 0}}, {\"experiment_id\": 35, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 0}}]}}}", "extra_info": {"index": 141, "max_global_steps": 0, "min_global_steps": 0}, "index": 141, "prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=0\n intervention: set Z1=1\n observed: Z1=1, Z2=1, Y=0\n\nExperiment 2:\n inputs: X1=0, X2=1, X3=0\n intervention: none\n observed: Z1=1, Z2=0, Y=1\n\nExperiment 3:\n inputs: X1=1, X2=1, X3=1\n intervention: none\n observed: Z1=1, Z2=0, Y=0\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "raw_prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=0\n intervention: set Z1=1\n observed: Z1=1, Z2=1, Y=0\n\nExperiment 2:\n inputs: X1=0, X2=1, X3=0\n intervention: none\n observed: Z1=1, Z2=0, Y=1\n\nExperiment 3:\n inputs: X1=1, X2=1, X3=1\n intervention: none\n observed: Z1=1, Z2=0, Y=0\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "reward_model": {"style": "rule"}, "state_json": "{\"available_experiment_ids\": [0, 1, 3, 4, 5, 6, 7, 8, 9, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 3, \"family_bucket\": \"within_family\", \"maximum_separation\": 0.7567567567567568, \"mean_separation\": 0.484848484848485, \"minimum_separation\": 0.16216216216216217, \"normalized_mean_separation\": 0.5161290322580646, \"oracle_budget_representation_error\": 0.10810810810810811, \"oracle_singleton_representation_error\": 0.4369369369369369, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.3288288288288288, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 12}, \"private\": {\"hidden_mode_id\": \"a340c62e9936de3a97d9ae43715aa853353e0223c557aaec62f3c359fe714e67\", \"valid_mode_ids\": [\"03346aeeb78740b78cdc35d6c2ad6305214dce1e9bf869ca2f16de4697675553\", \"329ba721e7cdfea83fd692c9cc72a5a5e8487f63b7447eb7c8637203b4f6c69f\", \"589f255f86b2f8ba0898b0982c81490ef488b435c0d268ae97fb5c06c1b91319\", \"8285c56266ae4cc019a6b3e3bca3e78fbd38afc9d6e38b0c1f5e46133cd6cb12\", \"969750d69d3553d4db297a03653d8db670eb7f911d941a3557ef477747cc4c07\", \"a20d52b6028e9b884feeac6f4161e51876285c9bd39037eb97b04b5e73e341ad\", \"a340c62e9936de3a97d9ae43715aa853353e0223c557aaec62f3c359fe714e67\", \"b524b45e6d4df1263976a657dae2b12657078ed67f10c50ec7b5b016ed3f8d8d\", \"bfd3984e1043414e6515463bc2076c16b0a6a6d1916efbcecc2154eeedd9cd77\", \"c2eea4f77c13851541aeaba58a651ae4379f7bdc5496bf926c636974b101c7c5\", \"db1c0eb4e06c38dbfc8ab7c371ce4eb860f028f5052e96d4c7e634120ea7da6d\", \"e2937333ae07175bc67964cdac70dd8ffde41e3834eeda8b4459d92ee562b96d\"]}, \"state_id\": \"2bcf0a68c9710df7b9c2f508\", \"visible_experiments\": [{\"experiment_id\": 2, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z1_1\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 10, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 0}}, {\"experiment_id\": 35, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 0}}]}"} {"agent_name": "causal_micro_lab_agent_loop", "data_source": "causal_micro_lab_final_eval_v3", "env_spec_json": "{\"agent\": {}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 142, \"task\": {\"state\": {\"available_experiment_ids\": [0, 1, 3, 4, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 3, \"family_bucket\": \"within_family\", \"maximum_separation\": 0.7567567567567568, \"mean_separation\": 0.4848484848484848, \"minimum_separation\": 0.16216216216216217, \"normalized_mean_separation\": 0.5161290322580644, \"oracle_budget_representation_error\": 0.10810810810810811, \"oracle_singleton_representation_error\": 0.4369369369369369, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.3288288288288288, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 12}, \"private\": {\"hidden_mode_id\": \"aa502f9007def1d7a76d9bf0c79a45e0c946be283817cad4c9a8d376f95c916e\", \"valid_mode_ids\": [\"1f697825fb56c97fe0a2ac3eaadcb6ffa5d246020271cf83bd2d42a066b1322f\", \"22b6a370c03a493ab2b484f2e47d75548821e91753d729105e47ac26632fa057\", \"3be92f5af7da7733d13daa273bc5cabd4db17035654aa6267cc2652e01fa979c\", \"5d17ddc8f64033a28f6a4203a13a6b7ce458cffae76caa96051e95768f5d3831\", \"5e514cae0111a3dba4d637ffc6a4bef4a12951c487a82b5737357952eec6abc9\", \"882e7d220c7986c27cd8660c15d78f9dcf0ecb39f55617094fccedb662dd9d3c\", \"aa502f9007def1d7a76d9bf0c79a45e0c946be283817cad4c9a8d376f95c916e\", \"c71e5bcf1b0242fe82d99bea8f1981dd3579687befc5fe5539df1978270e4dd6\", \"d96aec6d1a620367e187d06967754f80882898853a2e7c8e8acf2d111da04982\", \"f4ec2d4afb49e5a961b57463c49805bff8975ecc601c6d582372e818a4e3fab0\", \"f889774bf45cbd2c8194b4bcf6feb0035e54403806ead1b86eab2557b40325b1\", \"fc85f484002304195e57d2096decdc1413d5a9214c7c6bdd23c3d6ab1515220a\"]}, \"state_id\": \"6902103872dba7dfd6bbabc4\", \"visible_experiments\": [{\"experiment_id\": 2, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z1_1\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 5, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 0}}, {\"experiment_id\": 35, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 0}}]}}}", "extra_info": {"index": 142, "max_global_steps": 0, "min_global_steps": 0}, "index": 142, "prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=0\n intervention: set Z1=1\n observed: Z1=1, Z2=1, Y=0\n\nExperiment 2:\n inputs: X1=0, X2=0, X3=1\n intervention: none\n observed: Z1=1, Z2=0, Y=1\n\nExperiment 3:\n inputs: X1=1, X2=1, X3=1\n intervention: none\n observed: Z1=1, Z2=0, Y=0\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "raw_prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=0\n intervention: set Z1=1\n observed: Z1=1, Z2=1, Y=0\n\nExperiment 2:\n inputs: X1=0, X2=0, X3=1\n intervention: none\n observed: Z1=1, Z2=0, Y=1\n\nExperiment 3:\n inputs: X1=1, X2=1, X3=1\n intervention: none\n observed: Z1=1, Z2=0, Y=0\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "reward_model": {"style": "rule"}, "state_json": "{\"available_experiment_ids\": [0, 1, 3, 4, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 3, \"family_bucket\": \"within_family\", \"maximum_separation\": 0.7567567567567568, \"mean_separation\": 0.4848484848484848, \"minimum_separation\": 0.16216216216216217, \"normalized_mean_separation\": 0.5161290322580644, \"oracle_budget_representation_error\": 0.10810810810810811, \"oracle_singleton_representation_error\": 0.4369369369369369, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.3288288288288288, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 12}, \"private\": {\"hidden_mode_id\": \"aa502f9007def1d7a76d9bf0c79a45e0c946be283817cad4c9a8d376f95c916e\", \"valid_mode_ids\": [\"1f697825fb56c97fe0a2ac3eaadcb6ffa5d246020271cf83bd2d42a066b1322f\", \"22b6a370c03a493ab2b484f2e47d75548821e91753d729105e47ac26632fa057\", \"3be92f5af7da7733d13daa273bc5cabd4db17035654aa6267cc2652e01fa979c\", \"5d17ddc8f64033a28f6a4203a13a6b7ce458cffae76caa96051e95768f5d3831\", \"5e514cae0111a3dba4d637ffc6a4bef4a12951c487a82b5737357952eec6abc9\", \"882e7d220c7986c27cd8660c15d78f9dcf0ecb39f55617094fccedb662dd9d3c\", \"aa502f9007def1d7a76d9bf0c79a45e0c946be283817cad4c9a8d376f95c916e\", \"c71e5bcf1b0242fe82d99bea8f1981dd3579687befc5fe5539df1978270e4dd6\", \"d96aec6d1a620367e187d06967754f80882898853a2e7c8e8acf2d111da04982\", \"f4ec2d4afb49e5a961b57463c49805bff8975ecc601c6d582372e818a4e3fab0\", \"f889774bf45cbd2c8194b4bcf6feb0035e54403806ead1b86eab2557b40325b1\", \"fc85f484002304195e57d2096decdc1413d5a9214c7c6bdd23c3d6ab1515220a\"]}, \"state_id\": \"6902103872dba7dfd6bbabc4\", \"visible_experiments\": [{\"experiment_id\": 2, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z1_1\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 5, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 0}}, {\"experiment_id\": 35, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 0}}]}"} {"agent_name": "causal_micro_lab_agent_loop", "data_source": "causal_micro_lab_final_eval_v3", "env_spec_json": "{\"agent\": {}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 143, \"task\": {\"state\": {\"available_experiment_ids\": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 3, \"family_bucket\": \"within_family\", \"maximum_separation\": 0.7297297297297297, \"mean_separation\": 0.48976248976248965, \"minimum_separation\": 0.16216216216216217, \"normalized_mean_separation\": 0.5213600697471664, \"oracle_budget_representation_error\": 0.10810810810810811, \"oracle_singleton_representation_error\": 0.445945945945946, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.3378378378378379, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 12}, \"private\": {\"hidden_mode_id\": \"2a9750aa41c36252e3ab9118757d7dd3a763cadec18a8193b3b99a3bea650981\", \"valid_mode_ids\": [\"1b7d06b5704c0d5482a2f1fe83c5b308abf5d28eb424d04963efd262e167c34d\", \"2a9750aa41c36252e3ab9118757d7dd3a763cadec18a8193b3b99a3bea650981\", \"49b2782426b4a4c835f2900fcb6008b6d79b458a85ed91c647753301e52dff76\", \"4ced759ba07ce0b1a13f6ca6a97b670640b9531306871675e75b3a47515d8833\", \"92ed51ac15898021279492b29f03d704c758d7609bd842a5c4ada75157c3856f\", \"acbb69df25f32bed49cb78aa4d12c6f51e6f552a95a83dd5a564fd8e3ac505c4\", \"c98e9e1b3dae2c9a7e3272d603c59aaa216284923940d5b4d6b89e6ffed2e00e\", \"db1dc8b834f22b2f43d3d7c69bef06f0b7971e65a47bac590b3c3187c974cedd\", \"e72cbaafed51aa3f7fc5000bd0c3f121adc995a5cb8a276546bc1d629505f30e\", \"e8d1ca3e4b00119a43f3be0d00c91af3e6014568e267d7eda96c727b54eafbe0\", \"ef1255c87a9af5272b215ad4d932a35b5e689465274944d4383c2050124d8ddd\", \"f57596e53ad18b3fa8a3a55b5259764d3a0fb4299e058d13654b644ff3b7fd33\"]}, \"state_id\": \"5b7d3df95d070395e81ebc66\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 20, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 0}}, {\"experiment_id\": 35, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 0}}]}}}", "extra_info": {"index": 143, "max_global_steps": 0, "min_global_steps": 0}, "index": 143, "prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=0\n intervention: none\n observed: Z1=0, Z2=0, Y=1\n\nExperiment 2:\n inputs: X1=1, X2=0, X3=0\n intervention: none\n observed: Z1=1, Z2=0, Y=1\n\nExperiment 3:\n inputs: X1=1, X2=1, X3=1\n intervention: none\n observed: Z1=1, Z2=0, Y=0\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "raw_prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=0\n intervention: none\n observed: Z1=0, Z2=0, Y=1\n\nExperiment 2:\n inputs: X1=1, X2=0, X3=0\n intervention: none\n observed: Z1=1, Z2=0, Y=1\n\nExperiment 3:\n inputs: X1=1, X2=1, X3=1\n intervention: none\n observed: Z1=1, Z2=0, Y=0\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "reward_model": {"style": "rule"}, "state_json": "{\"available_experiment_ids\": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 3, \"family_bucket\": \"within_family\", \"maximum_separation\": 0.7297297297297297, \"mean_separation\": 0.48976248976248965, \"minimum_separation\": 0.16216216216216217, \"normalized_mean_separation\": 0.5213600697471664, \"oracle_budget_representation_error\": 0.10810810810810811, \"oracle_singleton_representation_error\": 0.445945945945946, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.3378378378378379, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 12}, \"private\": {\"hidden_mode_id\": \"2a9750aa41c36252e3ab9118757d7dd3a763cadec18a8193b3b99a3bea650981\", \"valid_mode_ids\": [\"1b7d06b5704c0d5482a2f1fe83c5b308abf5d28eb424d04963efd262e167c34d\", \"2a9750aa41c36252e3ab9118757d7dd3a763cadec18a8193b3b99a3bea650981\", \"49b2782426b4a4c835f2900fcb6008b6d79b458a85ed91c647753301e52dff76\", \"4ced759ba07ce0b1a13f6ca6a97b670640b9531306871675e75b3a47515d8833\", \"92ed51ac15898021279492b29f03d704c758d7609bd842a5c4ada75157c3856f\", \"acbb69df25f32bed49cb78aa4d12c6f51e6f552a95a83dd5a564fd8e3ac505c4\", \"c98e9e1b3dae2c9a7e3272d603c59aaa216284923940d5b4d6b89e6ffed2e00e\", \"db1dc8b834f22b2f43d3d7c69bef06f0b7971e65a47bac590b3c3187c974cedd\", \"e72cbaafed51aa3f7fc5000bd0c3f121adc995a5cb8a276546bc1d629505f30e\", \"e8d1ca3e4b00119a43f3be0d00c91af3e6014568e267d7eda96c727b54eafbe0\", \"ef1255c87a9af5272b215ad4d932a35b5e689465274944d4383c2050124d8ddd\", \"f57596e53ad18b3fa8a3a55b5259764d3a0fb4299e058d13654b644ff3b7fd33\"]}, \"state_id\": \"5b7d3df95d070395e81ebc66\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 20, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 0}}, {\"experiment_id\": 35, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 0}}]}"} {"agent_name": "causal_micro_lab_agent_loop", "data_source": "causal_micro_lab_final_eval_v3", "env_spec_json": "{\"agent\": {}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 144, \"task\": {\"state\": {\"available_experiment_ids\": [0, 1, 2, 3, 4, 5, 6, 7, 8, 10, 11, 12, 14, 15, 16, 17, 18, 19, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 3, \"family_bucket\": \"within_family\", \"maximum_separation\": 0.4864864864864865, \"mean_separation\": 0.27432432432432396, \"minimum_separation\": 0.10810810810810811, \"normalized_mean_separation\": 0.2939189189189185, \"oracle_budget_representation_error\": 0.12162162162162164, \"oracle_singleton_representation_error\": 0.22635135135135143, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.10472972972972978, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 16}, \"private\": {\"hidden_mode_id\": \"37f9d0175434d201a19a96b09ea6f21739af2ab02647faeea4be01eda6ad368f\", \"valid_mode_ids\": [\"22f2fa5c5fd152963ca2267e2a98736b062d5a04e592c06000edb20ac9524a30\", \"2803b6231db8d6d10e8bbc2e1f189bf8999aaaa82e89aef05f684a0a7f5a42b6\", \"2f1a4646ff53cbeaf42e3dc61c0fd969ed5e092cbea2e077e04b62348bba9094\", \"37f9d0175434d201a19a96b09ea6f21739af2ab02647faeea4be01eda6ad368f\", \"380e6fc3ac14029025a6d58efa752798a108eece345df4f03baf4ccdd51394ad\", \"49bf7a77c1716a36ddfd4e6d13bd166f8dabb13851369300cba12befed0d6445\", \"49eebeef2171c95a3ca589f3033fe6ca47ba6034b711705e439e8b582bdff59e\", \"5d0bc622679e2c2b7e65551f7f8b4b5fa675ba77f17cea8afba334d8ed272b87\", \"60942c6cbd14cf19527b509bb7c8c9d69b9cfa31d0fc794ac62b76afa393b950\", \"7301030f7199bc7b6370613c8367485379a6f875803e98f209142e3b9e4d7fdd\", \"7f1ebb26e1a65363dabf336152713ca942fe1852957aa25f1cb35fdf845a5cdb\", \"816686717b4879a15ef55f8b98d5f4d36448aa856b78c6f8c70c26472181e7a7\", \"b4ce3ea9f22a0184842afc9ebf5d8979aef213ca3c7c1d38c77c2302e8ffbe6b\", \"bf913507bfe1a29fcdb9249d7c474d8b39cae374922d9bab9030ae41666a26e8\", \"ea1cee705e69c90dd8aa09b9f6936251bf914fd1490c2e2fd86db29ed736c9ad\", \"f2649c6165a2b32203bbb8616417de149fe8a1bdb14f0b8a3ca8f60ad65c8167\"]}, \"state_id\": \"f065f46edebe2510cd1c1d8f\", \"visible_experiments\": [{\"experiment_id\": 9, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 1}, \"intervention\": \"DO_Z2_1\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 13, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 0}, \"intervention\": \"DO_Z2_0\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 20, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 0}}]}}}", "extra_info": {"index": 144, "max_global_steps": 0, "min_global_steps": 0}, "index": 144, "prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=1\n intervention: set Z2=1\n observed: Z1=1, Z2=1, Y=0\n\nExperiment 2:\n inputs: X1=0, X2=1, X3=0\n intervention: set Z2=0\n observed: Z1=0, Z2=0, Y=0\n\nExperiment 3:\n inputs: X1=1, X2=0, X3=0\n intervention: none\n observed: Z1=0, Z2=0, Y=1\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "raw_prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=1\n intervention: set Z2=1\n observed: Z1=1, Z2=1, Y=0\n\nExperiment 2:\n inputs: X1=0, X2=1, X3=0\n intervention: set Z2=0\n observed: Z1=0, Z2=0, Y=0\n\nExperiment 3:\n inputs: X1=1, X2=0, X3=0\n intervention: none\n observed: Z1=0, Z2=0, Y=1\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "reward_model": {"style": "rule"}, "state_json": "{\"available_experiment_ids\": [0, 1, 2, 3, 4, 5, 6, 7, 8, 10, 11, 12, 14, 15, 16, 17, 18, 19, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 3, \"family_bucket\": \"within_family\", \"maximum_separation\": 0.4864864864864865, \"mean_separation\": 0.27432432432432396, \"minimum_separation\": 0.10810810810810811, \"normalized_mean_separation\": 0.2939189189189185, \"oracle_budget_representation_error\": 0.12162162162162164, \"oracle_singleton_representation_error\": 0.22635135135135143, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.10472972972972978, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 16}, \"private\": {\"hidden_mode_id\": \"37f9d0175434d201a19a96b09ea6f21739af2ab02647faeea4be01eda6ad368f\", \"valid_mode_ids\": [\"22f2fa5c5fd152963ca2267e2a98736b062d5a04e592c06000edb20ac9524a30\", \"2803b6231db8d6d10e8bbc2e1f189bf8999aaaa82e89aef05f684a0a7f5a42b6\", \"2f1a4646ff53cbeaf42e3dc61c0fd969ed5e092cbea2e077e04b62348bba9094\", \"37f9d0175434d201a19a96b09ea6f21739af2ab02647faeea4be01eda6ad368f\", \"380e6fc3ac14029025a6d58efa752798a108eece345df4f03baf4ccdd51394ad\", \"49bf7a77c1716a36ddfd4e6d13bd166f8dabb13851369300cba12befed0d6445\", \"49eebeef2171c95a3ca589f3033fe6ca47ba6034b711705e439e8b582bdff59e\", \"5d0bc622679e2c2b7e65551f7f8b4b5fa675ba77f17cea8afba334d8ed272b87\", \"60942c6cbd14cf19527b509bb7c8c9d69b9cfa31d0fc794ac62b76afa393b950\", \"7301030f7199bc7b6370613c8367485379a6f875803e98f209142e3b9e4d7fdd\", \"7f1ebb26e1a65363dabf336152713ca942fe1852957aa25f1cb35fdf845a5cdb\", \"816686717b4879a15ef55f8b98d5f4d36448aa856b78c6f8c70c26472181e7a7\", \"b4ce3ea9f22a0184842afc9ebf5d8979aef213ca3c7c1d38c77c2302e8ffbe6b\", \"bf913507bfe1a29fcdb9249d7c474d8b39cae374922d9bab9030ae41666a26e8\", \"ea1cee705e69c90dd8aa09b9f6936251bf914fd1490c2e2fd86db29ed736c9ad\", \"f2649c6165a2b32203bbb8616417de149fe8a1bdb14f0b8a3ca8f60ad65c8167\"]}, \"state_id\": \"f065f46edebe2510cd1c1d8f\", \"visible_experiments\": [{\"experiment_id\": 9, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 1}, \"intervention\": \"DO_Z2_1\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 13, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 0}, \"intervention\": \"DO_Z2_0\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 20, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 0}}]}"} {"agent_name": "causal_micro_lab_agent_loop", "data_source": "causal_micro_lab_final_eval_v3", "env_spec_json": "{\"agent\": {}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 145, \"task\": {\"state\": {\"available_experiment_ids\": [0, 1, 2, 4, 5, 6, 7, 8, 9, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38], \"metadata\": {\"evidence_size\": 3, \"family_bucket\": \"within_family\", \"maximum_separation\": 0.4864864864864865, \"mean_separation\": 0.27837837837837803, \"minimum_separation\": 0.10810810810810811, \"normalized_mean_separation\": 0.2982625482625479, \"oracle_budget_representation_error\": 0.125, \"oracle_singleton_representation_error\": 0.23986486486486489, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.11486486486486489, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 16}, \"private\": {\"hidden_mode_id\": \"b210ad7a169b7376b4b4dd926375bed592c565c41e3a69a889ca283ad8b3ff61\", \"valid_mode_ids\": [\"1c46771e8615bbfcec00444c7bd9b619a60c25499db62018862002aed7f13360\", \"25d9152a2dc3f86eaa35706eadcd3a2d391723bbbe5b820db9949335bd63c239\", \"3e6db8006885b7e64b37ec6ae835f2d24b22553afb85796e75ca9aaa7c29aa90\", \"4fe956b57eb873f4baab03e273a342074620d55c8863605d908d3cbd1dce4a73\", \"60605461f45dd54668880155075056fa18030eea398aeff36951fe628042b923\", \"670128aebf4536b35853c0ec42e9f5875370270c0309719af47f683113d09cbc\", \"7dca61cd415855cb56cb753d67580b92134585cbbef43daa5394ccc99bc56ff5\", \"7e924f6664885541e04ad68e0c459903767519d7b30a632143dcca351b4e285e\", \"816b2fc9228cd698f5a4a9854fd2ff98908204e4ae0a53d0b6f565ce2ef1270e\", \"89e7d4e59e66575504b92a47aa1139c78abb1bba225c50dd7bd6ec52c72774e8\", \"8ec6ef627e99e973e436b6d4f5d6de3662290bc9c18807f9d4f7ea4ae9e5a7e9\", \"b162a98a6d76a8c69fdd803c50e4c704e536e31321ea1ad97b1a423c74a5913f\", \"b210ad7a169b7376b4b4dd926375bed592c565c41e3a69a889ca283ad8b3ff61\", \"c0913b0a6d8c4d34ffade4469e08f2c4013bf8fb0fd3f052f9df2650d6e4fe42\", \"c9aba7db3b12c0a430bddb3d24508ec4d69bc3447862dd7f07b09cbe92306f8f\", \"d70a193a3aa6dc74f4a959781059e115eb3c1be8674a9e2111fa7bbbaa8da014\"]}, \"state_id\": \"f72bfaba02e989637050f8c3\", \"visible_experiments\": [{\"experiment_id\": 3, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z2_0\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 10, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 39, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 1}, \"intervention\": \"DO_Z2_1\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 1}}]}}}", "extra_info": {"index": 145, "max_global_steps": 0, "min_global_steps": 0}, "index": 145, "prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=0\n intervention: set Z2=0\n observed: Z1=0, Z2=0, Y=1\n\nExperiment 2:\n inputs: X1=0, X2=1, X3=0\n intervention: none\n observed: Z1=0, Z2=0, Y=1\n\nExperiment 3:\n inputs: X1=1, X2=1, X3=1\n intervention: set Z2=1\n observed: Z1=0, Z2=1, Y=1\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "raw_prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=0\n intervention: set Z2=0\n observed: Z1=0, Z2=0, Y=1\n\nExperiment 2:\n inputs: X1=0, X2=1, X3=0\n intervention: none\n observed: Z1=0, Z2=0, Y=1\n\nExperiment 3:\n inputs: X1=1, X2=1, X3=1\n intervention: set Z2=1\n observed: Z1=0, Z2=1, Y=1\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "reward_model": {"style": "rule"}, "state_json": "{\"available_experiment_ids\": [0, 1, 2, 4, 5, 6, 7, 8, 9, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38], \"metadata\": {\"evidence_size\": 3, \"family_bucket\": \"within_family\", \"maximum_separation\": 0.4864864864864865, \"mean_separation\": 0.27837837837837803, \"minimum_separation\": 0.10810810810810811, \"normalized_mean_separation\": 0.2982625482625479, \"oracle_budget_representation_error\": 0.125, \"oracle_singleton_representation_error\": 0.23986486486486489, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.11486486486486489, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 16}, \"private\": {\"hidden_mode_id\": \"b210ad7a169b7376b4b4dd926375bed592c565c41e3a69a889ca283ad8b3ff61\", \"valid_mode_ids\": [\"1c46771e8615bbfcec00444c7bd9b619a60c25499db62018862002aed7f13360\", \"25d9152a2dc3f86eaa35706eadcd3a2d391723bbbe5b820db9949335bd63c239\", \"3e6db8006885b7e64b37ec6ae835f2d24b22553afb85796e75ca9aaa7c29aa90\", \"4fe956b57eb873f4baab03e273a342074620d55c8863605d908d3cbd1dce4a73\", \"60605461f45dd54668880155075056fa18030eea398aeff36951fe628042b923\", \"670128aebf4536b35853c0ec42e9f5875370270c0309719af47f683113d09cbc\", \"7dca61cd415855cb56cb753d67580b92134585cbbef43daa5394ccc99bc56ff5\", \"7e924f6664885541e04ad68e0c459903767519d7b30a632143dcca351b4e285e\", \"816b2fc9228cd698f5a4a9854fd2ff98908204e4ae0a53d0b6f565ce2ef1270e\", \"89e7d4e59e66575504b92a47aa1139c78abb1bba225c50dd7bd6ec52c72774e8\", \"8ec6ef627e99e973e436b6d4f5d6de3662290bc9c18807f9d4f7ea4ae9e5a7e9\", \"b162a98a6d76a8c69fdd803c50e4c704e536e31321ea1ad97b1a423c74a5913f\", \"b210ad7a169b7376b4b4dd926375bed592c565c41e3a69a889ca283ad8b3ff61\", \"c0913b0a6d8c4d34ffade4469e08f2c4013bf8fb0fd3f052f9df2650d6e4fe42\", \"c9aba7db3b12c0a430bddb3d24508ec4d69bc3447862dd7f07b09cbe92306f8f\", \"d70a193a3aa6dc74f4a959781059e115eb3c1be8674a9e2111fa7bbbaa8da014\"]}, \"state_id\": \"f72bfaba02e989637050f8c3\", \"visible_experiments\": [{\"experiment_id\": 3, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z2_0\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 10, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 39, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 1}, \"intervention\": \"DO_Z2_1\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 1}}]}"} {"agent_name": "causal_micro_lab_agent_loop", "data_source": "causal_micro_lab_final_eval_v3", "env_spec_json": "{\"agent\": {}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 146, \"task\": {\"state\": {\"available_experiment_ids\": [0, 1, 2, 3, 4, 6, 7, 8, 9, 10, 11, 12, 14, 16, 17, 18, 19, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 4, \"family_bucket\": \"cross_family\", \"maximum_separation\": 0.3333333333333333, \"mean_separation\": 0.22962962962962952, \"minimum_separation\": 0.1111111111111111, \"normalized_mean_separation\": 0.2460317460317459, \"oracle_budget_representation_error\": 0.09027777777777779, \"oracle_singleton_representation_error\": 0.20833333333333337, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.11805555555555558, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 16}, \"private\": {\"hidden_mode_id\": \"4995b9b57c37cd52c04023b098f4b8ef41f17608d7ef8368346922f1aecfb16e\", \"valid_mode_ids\": [\"0ba0e122d94d91596846840bc559517142a0a8ea152adf3aa0ee168559d71031\", \"12cec5b4a5310fddda5b11de936d7ed2bba7cad42690008998f2568923057699\", \"1e1d85da847fcd86cd246ace7ba25d7148e267b1b60df2bd70196e2f486388b6\", \"4995b9b57c37cd52c04023b098f4b8ef41f17608d7ef8368346922f1aecfb16e\", \"4d3111d737fc0499b0a23e3779e6f77cfd5a1e35df0c4ac03dadc959146b8261\", \"53a1f6f38d99f9fee486da9e4e3217f342b69136d28320ef1715b373b4fe57eb\", \"655806ad387e0545aa985bf12bbedd2b711bd32358629b03f05ff59fdc8adf91\", \"72f28e91fe930106faaa42c182167cac317e43c793ef7f2f4d9788e03ad9193b\", \"8f67d02067fc868fc3859aa2ccc0042bc8b3eab94fa3e6a36c498c6a03428767\", \"a7efdd2c4fd24f3d7f06e7352eb8fe1cd38008bc452305786636ee47111aebee\", \"c05a0ac559128e11969a5b5ba336946e691caac0348fc14bd95f4c11033fae1a\", \"c54870eed835be0569f4fe64772f93143711b145c0c567464af78daba5965aa7\", \"cff6a30372ba41bef390d8ab4ec02941cc7758703d76b6685c61b86032e787dd\", \"d451cde765232e0d15a49eb9dddaddfdb524a78b635bc83246da52e2252a745e\", \"f36c0f50a35460d38437229488e8011032e1c78d5861b8056e58225665a25a4f\", \"fb8910d7c08d9971b22942b92951ad2fcc2f37059ce2fc371bb7e50e1cf0abad\"]}, \"state_id\": \"3cb7445d11b70f7dfa7ded7e\", \"visible_experiments\": [{\"experiment_id\": 5, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 13, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 0}, \"intervention\": \"DO_Z2_0\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 0}}, {\"experiment_id\": 15, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 20, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}]}}}", "extra_info": {"index": 146, "max_global_steps": 0, "min_global_steps": 0}, "index": 146, "prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=1\n intervention: none\n observed: Z1=0, Z2=0, Y=0\n\nExperiment 2:\n inputs: X1=0, X2=1, X3=0\n intervention: set Z2=0\n observed: Z1=1, Z2=0, Y=1\n\nExperiment 3:\n inputs: X1=0, X2=1, X3=1\n intervention: none\n observed: Z1=1, Z2=1, Y=1\n\nExperiment 4:\n inputs: X1=1, X2=0, X3=0\n intervention: none\n observed: Z1=0, Z2=1, Y=0\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "raw_prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=1\n intervention: none\n observed: Z1=0, Z2=0, Y=0\n\nExperiment 2:\n inputs: X1=0, X2=1, X3=0\n intervention: set Z2=0\n observed: Z1=1, Z2=0, Y=1\n\nExperiment 3:\n inputs: X1=0, X2=1, X3=1\n intervention: none\n observed: Z1=1, Z2=1, Y=1\n\nExperiment 4:\n inputs: X1=1, X2=0, X3=0\n intervention: none\n observed: Z1=0, Z2=1, Y=0\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "reward_model": {"style": "rule"}, "state_json": "{\"available_experiment_ids\": [0, 1, 2, 3, 4, 6, 7, 8, 9, 10, 11, 12, 14, 16, 17, 18, 19, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 4, \"family_bucket\": \"cross_family\", \"maximum_separation\": 0.3333333333333333, \"mean_separation\": 0.22962962962962952, \"minimum_separation\": 0.1111111111111111, \"normalized_mean_separation\": 0.2460317460317459, \"oracle_budget_representation_error\": 0.09027777777777779, \"oracle_singleton_representation_error\": 0.20833333333333337, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.11805555555555558, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 16}, \"private\": {\"hidden_mode_id\": \"4995b9b57c37cd52c04023b098f4b8ef41f17608d7ef8368346922f1aecfb16e\", \"valid_mode_ids\": [\"0ba0e122d94d91596846840bc559517142a0a8ea152adf3aa0ee168559d71031\", \"12cec5b4a5310fddda5b11de936d7ed2bba7cad42690008998f2568923057699\", \"1e1d85da847fcd86cd246ace7ba25d7148e267b1b60df2bd70196e2f486388b6\", \"4995b9b57c37cd52c04023b098f4b8ef41f17608d7ef8368346922f1aecfb16e\", \"4d3111d737fc0499b0a23e3779e6f77cfd5a1e35df0c4ac03dadc959146b8261\", \"53a1f6f38d99f9fee486da9e4e3217f342b69136d28320ef1715b373b4fe57eb\", \"655806ad387e0545aa985bf12bbedd2b711bd32358629b03f05ff59fdc8adf91\", \"72f28e91fe930106faaa42c182167cac317e43c793ef7f2f4d9788e03ad9193b\", \"8f67d02067fc868fc3859aa2ccc0042bc8b3eab94fa3e6a36c498c6a03428767\", \"a7efdd2c4fd24f3d7f06e7352eb8fe1cd38008bc452305786636ee47111aebee\", \"c05a0ac559128e11969a5b5ba336946e691caac0348fc14bd95f4c11033fae1a\", \"c54870eed835be0569f4fe64772f93143711b145c0c567464af78daba5965aa7\", \"cff6a30372ba41bef390d8ab4ec02941cc7758703d76b6685c61b86032e787dd\", \"d451cde765232e0d15a49eb9dddaddfdb524a78b635bc83246da52e2252a745e\", \"f36c0f50a35460d38437229488e8011032e1c78d5861b8056e58225665a25a4f\", \"fb8910d7c08d9971b22942b92951ad2fcc2f37059ce2fc371bb7e50e1cf0abad\"]}, \"state_id\": \"3cb7445d11b70f7dfa7ded7e\", \"visible_experiments\": [{\"experiment_id\": 5, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 13, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 0}, \"intervention\": \"DO_Z2_0\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 0}}, {\"experiment_id\": 15, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 20, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}]}"} {"agent_name": "causal_micro_lab_agent_loop", "data_source": "causal_micro_lab_final_eval_v3", "env_spec_json": "{\"agent\": {}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 147, \"task\": {\"state\": {\"available_experiment_ids\": [1, 2, 3, 4, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 31, 32, 33, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 3, \"family_bucket\": \"mixed\", \"maximum_separation\": 0.5675675675675675, \"mean_separation\": 0.3261261261261264, \"minimum_separation\": 0.10810810810810811, \"normalized_mean_separation\": 0.3494208494208497, \"oracle_budget_representation_error\": 0.125, \"oracle_singleton_representation_error\": 0.2567567567567568, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.1317567567567568, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 16}, \"private\": {\"hidden_mode_id\": \"984ac7bf21a2af352faf089bfb4823402651f48cc59b8c56771f6890738c3092\", \"valid_mode_ids\": [\"0697eac21aa3bb2993f1732aa54715009db85b4e5ed908e18dd32fc0154d2f39\", \"2340f08a80989f191596613629d007ccaa6a035b21044c2eb27572047cc745e1\", \"3ba4f3c1e6e6d6f25a5c87e97cd7b7cd2a85aab2465120e65fab7e76126303d0\", \"3ee865dddcfb23c7bb7166822ef51670c746b98e38a9098704c4c71f51b4db83\", \"4586c5edf12497749434e253560b4d8d1e362a97603dde2aac42f2093371da05\", \"4f81f7fe1dd0d2a92353f410970eb05a97562a2a4ce785b1912ee041eb813102\", \"6a65b40217784159e933b5c38d1b9dfda0b71c68305dd011cf17a5330323d4f3\", \"729405dc3ba751b95b6e828a38db903a348886fd08abf0b16b06b20776b2e40d\", \"7817a275d03bb237e937552c33a41ad1914d14e3aac9936417addb9f53492c5b\", \"7cb1cc099e71d5766f41b9c20dcc56d828abd102067d7df922862caa343d69d2\", \"8125a339171369349a1e576a234c6d3331724aa97127264c4b8239a0d13143fa\", \"97c7f2c9e7a2191eba437eefa25388adac8ac0de9679dc68d8236e26bceaed96\", \"984ac7bf21a2af352faf089bfb4823402651f48cc59b8c56771f6890738c3092\", \"9a8a8b9421d0ba12731b6c926ec78b45353404a9b14027b56ad9d947bf702120\", \"9e3959d9454ba8fa937e99b82794d4d0ffc71de19bcdfc740fc04342fa9c44cb\", \"fa0a366e3741691b9ee5d2992f1237d8e81ec9187cb0281362fdf521f7746b42\"]}, \"state_id\": \"2d4d9599c518497c0a6365b4\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 5, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 30, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 1}}]}}}", "extra_info": {"index": 147, "max_global_steps": 0, "min_global_steps": 0}, "index": 147, "prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=0\n intervention: none\n observed: Z1=1, Z2=1, Y=1\n\nExperiment 2:\n inputs: X1=0, X2=0, X3=1\n intervention: none\n observed: Z1=0, Z2=0, Y=1\n\nExperiment 3:\n inputs: X1=1, X2=1, X3=0\n intervention: none\n observed: Z1=1, Z2=1, Y=1\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "raw_prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=0\n intervention: none\n observed: Z1=1, Z2=1, Y=1\n\nExperiment 2:\n inputs: X1=0, X2=0, X3=1\n intervention: none\n observed: Z1=0, Z2=0, Y=1\n\nExperiment 3:\n inputs: X1=1, X2=1, X3=0\n intervention: none\n observed: Z1=1, Z2=1, Y=1\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "reward_model": {"style": "rule"}, "state_json": "{\"available_experiment_ids\": [1, 2, 3, 4, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 31, 32, 33, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 3, \"family_bucket\": \"mixed\", \"maximum_separation\": 0.5675675675675675, \"mean_separation\": 0.3261261261261264, \"minimum_separation\": 0.10810810810810811, \"normalized_mean_separation\": 0.3494208494208497, \"oracle_budget_representation_error\": 0.125, \"oracle_singleton_representation_error\": 0.2567567567567568, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.1317567567567568, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 16}, \"private\": {\"hidden_mode_id\": \"984ac7bf21a2af352faf089bfb4823402651f48cc59b8c56771f6890738c3092\", \"valid_mode_ids\": [\"0697eac21aa3bb2993f1732aa54715009db85b4e5ed908e18dd32fc0154d2f39\", \"2340f08a80989f191596613629d007ccaa6a035b21044c2eb27572047cc745e1\", \"3ba4f3c1e6e6d6f25a5c87e97cd7b7cd2a85aab2465120e65fab7e76126303d0\", \"3ee865dddcfb23c7bb7166822ef51670c746b98e38a9098704c4c71f51b4db83\", \"4586c5edf12497749434e253560b4d8d1e362a97603dde2aac42f2093371da05\", \"4f81f7fe1dd0d2a92353f410970eb05a97562a2a4ce785b1912ee041eb813102\", \"6a65b40217784159e933b5c38d1b9dfda0b71c68305dd011cf17a5330323d4f3\", \"729405dc3ba751b95b6e828a38db903a348886fd08abf0b16b06b20776b2e40d\", \"7817a275d03bb237e937552c33a41ad1914d14e3aac9936417addb9f53492c5b\", \"7cb1cc099e71d5766f41b9c20dcc56d828abd102067d7df922862caa343d69d2\", \"8125a339171369349a1e576a234c6d3331724aa97127264c4b8239a0d13143fa\", \"97c7f2c9e7a2191eba437eefa25388adac8ac0de9679dc68d8236e26bceaed96\", \"984ac7bf21a2af352faf089bfb4823402651f48cc59b8c56771f6890738c3092\", \"9a8a8b9421d0ba12731b6c926ec78b45353404a9b14027b56ad9d947bf702120\", \"9e3959d9454ba8fa937e99b82794d4d0ffc71de19bcdfc740fc04342fa9c44cb\", \"fa0a366e3741691b9ee5d2992f1237d8e81ec9187cb0281362fdf521f7746b42\"]}, \"state_id\": \"2d4d9599c518497c0a6365b4\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 5, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 30, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 1}}]}"} {"agent_name": "causal_micro_lab_agent_loop", "data_source": "causal_micro_lab_final_eval_v3", "env_spec_json": "{\"agent\": {}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 148, \"task\": {\"state\": {\"available_experiment_ids\": [1, 2, 3, 4, 5, 6, 7, 8, 9, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 3, \"family_bucket\": \"within_family\", \"maximum_separation\": 0.43243243243243246, \"mean_separation\": 0.2972972972972971, \"minimum_separation\": 0.10810810810810811, \"normalized_mean_separation\": 0.3185328185328183, \"oracle_budget_representation_error\": 0.1283783783783784, \"oracle_singleton_representation_error\": 0.2601351351351352, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.1317567567567568, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 16}, \"private\": {\"hidden_mode_id\": \"05fcef3d05d53e442f8fa7360b6b7a1b6e7f7bc82c216cf93c22766091ff59c8\", \"valid_mode_ids\": [\"05fcef3d05d53e442f8fa7360b6b7a1b6e7f7bc82c216cf93c22766091ff59c8\", \"14e7f401c2191c701d7a4c75c03d4984877285697a3d5c1b03d50a8f2a998376\", \"227e634157e525119a95478a947b49b2d93c4312a0bc95637ea1dafea03f4846\", \"479c0774d2d401d69526e79e5f0d24073968831c9e74620b6d22a192407144c1\", \"4db17dc36e34b7bc1cddcc17e46e22a604c66386c266fb2f5684da2f191911d6\", \"65179147923fda6c1a3894f6212972192820a12f0c8c81e61ecfb3741693aa49\", \"6bb628abf86df63d602ba0dab0a801e6fb6a622b17e0129d079024a9d019c983\", \"78af40501ee7853b862aba6bedadeaf02a0d816b29e04651e6858fffc30f5c70\", \"7af8b96a5486ed1f48ee2c74a711272476d5bc35529e8a9e75dbb5b3602ba403\", \"7e920ead6ea5d95d2fea94e7e68f510d1150bb64ce99ecee4a22df3736ca59ed\", \"90c23eebf77fa89c93bd1610189cddff574ad5600d9e65df92689af8533b61cf\", \"95cfa929054d8ccb8c657dffec18869fa4032c17b7084650b5ecefd308b22f60\", \"b33a86b0946832ee5096ff71cc99b1290131b7b3551ff5fe8288db1d9bb64493\", \"d3b7129153899a20b85ae3e15862a8acfbaef582b2f896d9155c8784add6d3a8\", \"d5cc4684bf85c53d7ab0978fcdd63e6f02e9aa1ab954be9fb15ecb8cdbc5bc7c\", \"e43083ef8976aa4f910b3b4e2080710dd89874b4ee004dcb1ac353a1b0ea42ec\"]}, \"state_id\": \"9ac1f7509b0c4d35e24ee78f\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 10, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 25, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 1}}]}}}", "extra_info": {"index": 148, "max_global_steps": 0, "min_global_steps": 0}, "index": 148, "prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=0\n intervention: none\n observed: Z1=0, Z2=0, Y=1\n\nExperiment 2:\n inputs: X1=0, X2=1, X3=0\n intervention: none\n observed: Z1=1, Z2=1, Y=0\n\nExperiment 3:\n inputs: X1=1, X2=0, X3=1\n intervention: none\n observed: Z1=0, Z2=1, Y=1\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "raw_prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=0\n intervention: none\n observed: Z1=0, Z2=0, Y=1\n\nExperiment 2:\n inputs: X1=0, X2=1, X3=0\n intervention: none\n observed: Z1=1, Z2=1, Y=0\n\nExperiment 3:\n inputs: X1=1, X2=0, X3=1\n intervention: none\n observed: Z1=0, Z2=1, Y=1\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "reward_model": {"style": "rule"}, "state_json": "{\"available_experiment_ids\": [1, 2, 3, 4, 5, 6, 7, 8, 9, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 3, \"family_bucket\": \"within_family\", \"maximum_separation\": 0.43243243243243246, \"mean_separation\": 0.2972972972972971, \"minimum_separation\": 0.10810810810810811, \"normalized_mean_separation\": 0.3185328185328183, \"oracle_budget_representation_error\": 0.1283783783783784, \"oracle_singleton_representation_error\": 0.2601351351351352, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.1317567567567568, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 16}, \"private\": {\"hidden_mode_id\": \"05fcef3d05d53e442f8fa7360b6b7a1b6e7f7bc82c216cf93c22766091ff59c8\", \"valid_mode_ids\": [\"05fcef3d05d53e442f8fa7360b6b7a1b6e7f7bc82c216cf93c22766091ff59c8\", \"14e7f401c2191c701d7a4c75c03d4984877285697a3d5c1b03d50a8f2a998376\", \"227e634157e525119a95478a947b49b2d93c4312a0bc95637ea1dafea03f4846\", \"479c0774d2d401d69526e79e5f0d24073968831c9e74620b6d22a192407144c1\", \"4db17dc36e34b7bc1cddcc17e46e22a604c66386c266fb2f5684da2f191911d6\", \"65179147923fda6c1a3894f6212972192820a12f0c8c81e61ecfb3741693aa49\", \"6bb628abf86df63d602ba0dab0a801e6fb6a622b17e0129d079024a9d019c983\", \"78af40501ee7853b862aba6bedadeaf02a0d816b29e04651e6858fffc30f5c70\", \"7af8b96a5486ed1f48ee2c74a711272476d5bc35529e8a9e75dbb5b3602ba403\", \"7e920ead6ea5d95d2fea94e7e68f510d1150bb64ce99ecee4a22df3736ca59ed\", \"90c23eebf77fa89c93bd1610189cddff574ad5600d9e65df92689af8533b61cf\", \"95cfa929054d8ccb8c657dffec18869fa4032c17b7084650b5ecefd308b22f60\", \"b33a86b0946832ee5096ff71cc99b1290131b7b3551ff5fe8288db1d9bb64493\", \"d3b7129153899a20b85ae3e15862a8acfbaef582b2f896d9155c8784add6d3a8\", \"d5cc4684bf85c53d7ab0978fcdd63e6f02e9aa1ab954be9fb15ecb8cdbc5bc7c\", \"e43083ef8976aa4f910b3b4e2080710dd89874b4ee004dcb1ac353a1b0ea42ec\"]}, \"state_id\": \"9ac1f7509b0c4d35e24ee78f\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 10, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 25, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 1}}]}"} {"agent_name": "causal_micro_lab_agent_loop", "data_source": "causal_micro_lab_final_eval_v3", "env_spec_json": "{\"agent\": {}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 149, \"task\": {\"state\": {\"available_experiment_ids\": [0, 1, 2, 4, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 3, \"family_bucket\": \"within_family\", \"maximum_separation\": 0.5945945945945946, \"mean_separation\": 0.3047297297297297, \"minimum_separation\": 0.10810810810810811, \"normalized_mean_separation\": 0.326496138996139, \"oracle_budget_representation_error\": 0.11317567567567569, \"oracle_singleton_representation_error\": 0.2466216216216216, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.13344594594594592, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 16}, \"private\": {\"hidden_mode_id\": \"55a5eee43cb248cc4eb3d20e087e3d2c1f2a09ca28ec7999ac7a630c5023732f\", \"valid_mode_ids\": [\"14fa63dcf359260d281545bad38c4f5b65635d34eb04a47731935bf97f3c0852\", \"2a61d7fe35c8ee84d4d573f93771dbe7aaab780819103d9f0c5a1e71b65ee218\", \"3c164400183c09f774639efa8c02851eaab1b0e512cafaa80395ea355fa302a0\", \"44c77c7062349bce5fe9bc20bb6222dadb0c539dea33bc73c50ed22fd24db5fd\", \"4991828e92fd135826fc4b0b0d8483678de1c7a7ee28c9864527ee05499eeebc\", \"4eb1adf8d5ecba36192d2f67533ae69f1d47df3711f81d9e84cd4ac2c7f3a3d3\", \"55a5eee43cb248cc4eb3d20e087e3d2c1f2a09ca28ec7999ac7a630c5023732f\", \"583e03ee5a3fc55ea902d32924b601ab6586c843e2be6f0142c6db3690f6c8c0\", \"636878023f47b2b063ef297b6711c2d1201c5e1eff26f5572b1d5c1e9917a8b1\", \"8b02ec598e7dd0b1aad6173ee2a4d103b64a39d2d68d87c74c7ebc4cb00d6fc3\", \"a9de65d13543448320f41318311c8a809685fe462809784f545378b97218aa46\", \"ae71461a4cba0d580e9e9778b048b70c38ba059458dd0f70f2b3ec17c4cb04df\", \"af40eaac6834cc6b604050b730db9cb97ddb56e5f2c0b525c8b424e881d5ed31\", \"e6b8ebf19fa1b930c76c7a5fc86862af407b299c6e540748d17f30bb369f05ab\", \"e9cb311059c132074309110aa1ca78016d658b076275db70cb282c39cd0e291a\", \"fec3174e97b64e36c20553960e046f548ebb46d1fdc6bd698a642d62521c1a95\"]}, \"state_id\": \"1fd77c2f6729097c2518c558\", \"visible_experiments\": [{\"experiment_id\": 3, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z2_0\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 5, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 20, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 1}}]}}}", "extra_info": {"index": 149, "max_global_steps": 0, "min_global_steps": 0}, "index": 149, "prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=0\n intervention: set Z2=0\n observed: Z1=0, Z2=0, Y=1\n\nExperiment 2:\n inputs: X1=0, X2=0, X3=1\n intervention: none\n observed: Z1=1, Z2=1, Y=0\n\nExperiment 3:\n inputs: X1=1, X2=0, X3=0\n intervention: none\n observed: Z1=1, Z2=1, Y=1\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "raw_prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=0\n intervention: set Z2=0\n observed: Z1=0, Z2=0, Y=1\n\nExperiment 2:\n inputs: X1=0, X2=0, X3=1\n intervention: none\n observed: Z1=1, Z2=1, Y=0\n\nExperiment 3:\n inputs: X1=1, X2=0, X3=0\n intervention: none\n observed: Z1=1, Z2=1, Y=1\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "reward_model": {"style": "rule"}, "state_json": "{\"available_experiment_ids\": [0, 1, 2, 4, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 3, \"family_bucket\": \"within_family\", \"maximum_separation\": 0.5945945945945946, \"mean_separation\": 0.3047297297297297, \"minimum_separation\": 0.10810810810810811, \"normalized_mean_separation\": 0.326496138996139, \"oracle_budget_representation_error\": 0.11317567567567569, \"oracle_singleton_representation_error\": 0.2466216216216216, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.13344594594594592, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 16}, \"private\": {\"hidden_mode_id\": \"55a5eee43cb248cc4eb3d20e087e3d2c1f2a09ca28ec7999ac7a630c5023732f\", \"valid_mode_ids\": [\"14fa63dcf359260d281545bad38c4f5b65635d34eb04a47731935bf97f3c0852\", \"2a61d7fe35c8ee84d4d573f93771dbe7aaab780819103d9f0c5a1e71b65ee218\", \"3c164400183c09f774639efa8c02851eaab1b0e512cafaa80395ea355fa302a0\", \"44c77c7062349bce5fe9bc20bb6222dadb0c539dea33bc73c50ed22fd24db5fd\", \"4991828e92fd135826fc4b0b0d8483678de1c7a7ee28c9864527ee05499eeebc\", \"4eb1adf8d5ecba36192d2f67533ae69f1d47df3711f81d9e84cd4ac2c7f3a3d3\", \"55a5eee43cb248cc4eb3d20e087e3d2c1f2a09ca28ec7999ac7a630c5023732f\", \"583e03ee5a3fc55ea902d32924b601ab6586c843e2be6f0142c6db3690f6c8c0\", \"636878023f47b2b063ef297b6711c2d1201c5e1eff26f5572b1d5c1e9917a8b1\", \"8b02ec598e7dd0b1aad6173ee2a4d103b64a39d2d68d87c74c7ebc4cb00d6fc3\", \"a9de65d13543448320f41318311c8a809685fe462809784f545378b97218aa46\", \"ae71461a4cba0d580e9e9778b048b70c38ba059458dd0f70f2b3ec17c4cb04df\", \"af40eaac6834cc6b604050b730db9cb97ddb56e5f2c0b525c8b424e881d5ed31\", \"e6b8ebf19fa1b930c76c7a5fc86862af407b299c6e540748d17f30bb369f05ab\", \"e9cb311059c132074309110aa1ca78016d658b076275db70cb282c39cd0e291a\", \"fec3174e97b64e36c20553960e046f548ebb46d1fdc6bd698a642d62521c1a95\"]}, \"state_id\": \"1fd77c2f6729097c2518c558\", \"visible_experiments\": [{\"experiment_id\": 3, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z2_0\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 5, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 20, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 1}}]}"} {"agent_name": "causal_micro_lab_agent_loop", "data_source": "causal_micro_lab_final_eval_v3", "env_spec_json": "{\"agent\": {}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 150, \"task\": {\"state\": {\"available_experiment_ids\": [0, 1, 2, 3, 4, 6, 7, 8, 9, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 4, \"family_bucket\": \"within_family\", \"maximum_separation\": 0.4722222222222222, \"mean_separation\": 0.26944444444444465, \"minimum_separation\": 0.1111111111111111, \"normalized_mean_separation\": 0.2886904761904764, \"oracle_budget_representation_error\": 0.09722222222222224, \"oracle_singleton_representation_error\": 0.2326388888888889, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.13541666666666666, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 16}, \"private\": {\"hidden_mode_id\": \"abad041998266d14e750421c9673a0f04a53bcbc628fd91f7d2df6238d333b14\", \"valid_mode_ids\": [\"074047d716d4abe9416d4bbec2b5d29704c6d37a2f399eb14c7444d49c5c25a4\", \"128f250cd446ceace3309726b343859ba385656667c9f81224558ebd36863db1\", \"35cb1e2375e84a7b8896a8bcb8fffc58cc8da2999078e82bcf3edd7e06238dd3\", \"3905cc0909efc83c910a84cabb7e6a4212ae0dbb238dd2349fa84a02654a470e\", \"4020a6c1a573a54cc5072d3bdf4bed1d374d8741a5bbb4ee6ec5c8a7407adbf4\", \"6b3d436ed9bd6b6ef6297497118991cb60e266c78a570a930442c21db4b2dbb1\", \"77e7dfef880072b1a9347cde4fb2419135af3b0b6fe5fc240584c974a8c568d3\", \"782986b37de97b3147a02ad4572704798234a3e9a0b07ac2305121fc5d222ffe\", \"96a0b78433214bfb893bcd2c8f9f06ef3328bc683af90de118596be573f44be1\", \"9dac04f74c71b399f7a13c72f0f381e1c1481c418fd571db7ea411c2ab0d24dc\", \"a1f6f2e5ae9746eaeccb08e6014ca0491950ae016e9fb28c267c89a76e0c4f1f\", \"a943c8d951b442a456062818196a719444301af122ae16b839f37fc7a7c31c43\", \"abad041998266d14e750421c9673a0f04a53bcbc628fd91f7d2df6238d333b14\", \"c6e00c08aad6e2edb19850baba7a601d6e618dbdee3e2324f6020e05dd8dca6b\", \"d92cb0d5f2e265528b013ffbb6e5a3f0fd915ee8255d520497f4b950cb523da0\", \"dbf2401bf2f77ffc29d8b45ec18031e25fee5cf3f00ce65b6bc88cff50218700\"]}, \"state_id\": \"28110b1a2eae0b7d686eb2f7\", \"visible_experiments\": [{\"experiment_id\": 5, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 10, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 22, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z1_1\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 35, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 1}}]}}}", "extra_info": {"index": 150, "max_global_steps": 0, "min_global_steps": 0}, "index": 150, "prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=1\n intervention: none\n observed: Z1=0, Z2=0, Y=1\n\nExperiment 2:\n inputs: X1=0, X2=1, X3=0\n intervention: none\n observed: Z1=1, Z2=1, Y=1\n\nExperiment 3:\n inputs: X1=1, X2=0, X3=0\n intervention: set Z1=1\n observed: Z1=1, Z2=1, Y=1\n\nExperiment 4:\n inputs: X1=1, X2=1, X3=1\n intervention: none\n observed: Z1=1, Z2=1, Y=1\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "raw_prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=1\n intervention: none\n observed: Z1=0, Z2=0, Y=1\n\nExperiment 2:\n inputs: X1=0, X2=1, X3=0\n intervention: none\n observed: Z1=1, Z2=1, Y=1\n\nExperiment 3:\n inputs: X1=1, X2=0, X3=0\n intervention: set Z1=1\n observed: Z1=1, Z2=1, Y=1\n\nExperiment 4:\n inputs: X1=1, X2=1, X3=1\n intervention: none\n observed: Z1=1, Z2=1, Y=1\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "reward_model": {"style": "rule"}, "state_json": "{\"available_experiment_ids\": [0, 1, 2, 3, 4, 6, 7, 8, 9, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 4, \"family_bucket\": \"within_family\", \"maximum_separation\": 0.4722222222222222, \"mean_separation\": 0.26944444444444465, \"minimum_separation\": 0.1111111111111111, \"normalized_mean_separation\": 0.2886904761904764, \"oracle_budget_representation_error\": 0.09722222222222224, \"oracle_singleton_representation_error\": 0.2326388888888889, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.13541666666666666, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 16}, \"private\": {\"hidden_mode_id\": \"abad041998266d14e750421c9673a0f04a53bcbc628fd91f7d2df6238d333b14\", \"valid_mode_ids\": [\"074047d716d4abe9416d4bbec2b5d29704c6d37a2f399eb14c7444d49c5c25a4\", \"128f250cd446ceace3309726b343859ba385656667c9f81224558ebd36863db1\", \"35cb1e2375e84a7b8896a8bcb8fffc58cc8da2999078e82bcf3edd7e06238dd3\", \"3905cc0909efc83c910a84cabb7e6a4212ae0dbb238dd2349fa84a02654a470e\", \"4020a6c1a573a54cc5072d3bdf4bed1d374d8741a5bbb4ee6ec5c8a7407adbf4\", \"6b3d436ed9bd6b6ef6297497118991cb60e266c78a570a930442c21db4b2dbb1\", \"77e7dfef880072b1a9347cde4fb2419135af3b0b6fe5fc240584c974a8c568d3\", \"782986b37de97b3147a02ad4572704798234a3e9a0b07ac2305121fc5d222ffe\", \"96a0b78433214bfb893bcd2c8f9f06ef3328bc683af90de118596be573f44be1\", \"9dac04f74c71b399f7a13c72f0f381e1c1481c418fd571db7ea411c2ab0d24dc\", \"a1f6f2e5ae9746eaeccb08e6014ca0491950ae016e9fb28c267c89a76e0c4f1f\", \"a943c8d951b442a456062818196a719444301af122ae16b839f37fc7a7c31c43\", \"abad041998266d14e750421c9673a0f04a53bcbc628fd91f7d2df6238d333b14\", \"c6e00c08aad6e2edb19850baba7a601d6e618dbdee3e2324f6020e05dd8dca6b\", \"d92cb0d5f2e265528b013ffbb6e5a3f0fd915ee8255d520497f4b950cb523da0\", \"dbf2401bf2f77ffc29d8b45ec18031e25fee5cf3f00ce65b6bc88cff50218700\"]}, \"state_id\": \"28110b1a2eae0b7d686eb2f7\", \"visible_experiments\": [{\"experiment_id\": 5, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 10, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 22, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z1_1\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 35, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 1}}]}"} {"agent_name": "causal_micro_lab_agent_loop", "data_source": "causal_micro_lab_final_eval_v3", "env_spec_json": "{\"agent\": {}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 151, \"task\": {\"state\": {\"available_experiment_ids\": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 38, 39], \"metadata\": {\"evidence_size\": 3, \"family_bucket\": \"within_family\", \"maximum_separation\": 0.43243243243243246, \"mean_separation\": 0.31801801801801755, \"minimum_separation\": 0.16216216216216217, \"normalized_mean_separation\": 0.3407335907335902, \"oracle_budget_representation_error\": 0.13513513513513517, \"oracle_singleton_representation_error\": 0.2770270270270271, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.1418918918918919, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 16}, \"private\": {\"hidden_mode_id\": \"ec8d16d9931bcf8faa5aa787bdd627e9de1359fc43d992d18987bfab52aabe11\", \"valid_mode_ids\": [\"201f3e43dd7d5c633921c7d6c0f5880fcca564cbd628735483610fda68cf0f7f\", \"28825d6e60039a0b6c637e91f2af27fb79e7b8190bb48889f92ef15631ffa4f5\", \"30a6390a6ce0076d4daa2ce3a2b91de3a3ac3fdcc1803ba57be400c1fb627a0b\", \"35f4fed2dd5af37191c8716dbcd68498e45b22434d34bf67cc183e202052f542\", \"40192e4920916fc46a88e45b3c6ee8bb82fbb0662aaacb113b61eb0844166e3f\", \"541fe9acdaf0b64495c9ed49b409639c4addc678cf86a325cc53593d288bef97\", \"5be72daa4e04e276c8fc947b08e7fd5dbf1f798f98606d520e6baa34f29137ae\", \"6679bb085947c561fc115e16478d49174185f33033fad44ee07ae3efb3aadb6f\", \"6fa185cc38be31b9a2fd4f35ac7e721d43b63ecd4c7cbef0ee38c11ba4358429\", \"748af269b41ea1a8b7e0ce8925508228009ead1c5e0c9b5d62e5f0fef35e9633\", \"baaa307d01acff0fb3d77790ac524735d37cacb1fad35630d10e861d712215e1\", \"c37f6623753c900b0a5cb4fc0b3f397c29781c13f0117629efeea9f5d9c4f613\", \"cf917258436297c3ce54f64cf0663bdbc37d3dd25f5e7b14c5d1fb05b3bd1b40\", \"ec8d16d9931bcf8faa5aa787bdd627e9de1359fc43d992d18987bfab52aabe11\", \"ed157a8298dcb9087dc72d00f5c142f392c8daf64b299a6f7ad6bd81abe480d3\", \"fa0ac84eec7a9aee41d83cedd7bc97bf889985b3919f9b6122826f5b2823bd12\"]}, \"state_id\": \"df8f17bce6d1b1f3158cfd10\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 15, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 37, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 1}, \"intervention\": \"DO_Z1_1\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 0}}]}}}", "extra_info": {"index": 151, "max_global_steps": 0, "min_global_steps": 0}, "index": 151, "prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=0\n intervention: none\n observed: Z1=0, Z2=1, Y=1\n\nExperiment 2:\n inputs: X1=0, X2=1, X3=1\n intervention: none\n observed: Z1=1, Z2=1, Y=1\n\nExperiment 3:\n inputs: X1=1, X2=1, X3=1\n intervention: set Z1=1\n observed: Z1=1, Z2=0, Y=0\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "raw_prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=0\n intervention: none\n observed: Z1=0, Z2=1, Y=1\n\nExperiment 2:\n inputs: X1=0, X2=1, X3=1\n intervention: none\n observed: Z1=1, Z2=1, Y=1\n\nExperiment 3:\n inputs: X1=1, X2=1, X3=1\n intervention: set Z1=1\n observed: Z1=1, Z2=0, Y=0\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "reward_model": {"style": "rule"}, "state_json": "{\"available_experiment_ids\": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 38, 39], \"metadata\": {\"evidence_size\": 3, \"family_bucket\": \"within_family\", \"maximum_separation\": 0.43243243243243246, \"mean_separation\": 0.31801801801801755, \"minimum_separation\": 0.16216216216216217, \"normalized_mean_separation\": 0.3407335907335902, \"oracle_budget_representation_error\": 0.13513513513513517, \"oracle_singleton_representation_error\": 0.2770270270270271, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.1418918918918919, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 16}, \"private\": {\"hidden_mode_id\": \"ec8d16d9931bcf8faa5aa787bdd627e9de1359fc43d992d18987bfab52aabe11\", \"valid_mode_ids\": [\"201f3e43dd7d5c633921c7d6c0f5880fcca564cbd628735483610fda68cf0f7f\", \"28825d6e60039a0b6c637e91f2af27fb79e7b8190bb48889f92ef15631ffa4f5\", \"30a6390a6ce0076d4daa2ce3a2b91de3a3ac3fdcc1803ba57be400c1fb627a0b\", \"35f4fed2dd5af37191c8716dbcd68498e45b22434d34bf67cc183e202052f542\", \"40192e4920916fc46a88e45b3c6ee8bb82fbb0662aaacb113b61eb0844166e3f\", \"541fe9acdaf0b64495c9ed49b409639c4addc678cf86a325cc53593d288bef97\", \"5be72daa4e04e276c8fc947b08e7fd5dbf1f798f98606d520e6baa34f29137ae\", \"6679bb085947c561fc115e16478d49174185f33033fad44ee07ae3efb3aadb6f\", \"6fa185cc38be31b9a2fd4f35ac7e721d43b63ecd4c7cbef0ee38c11ba4358429\", \"748af269b41ea1a8b7e0ce8925508228009ead1c5e0c9b5d62e5f0fef35e9633\", \"baaa307d01acff0fb3d77790ac524735d37cacb1fad35630d10e861d712215e1\", \"c37f6623753c900b0a5cb4fc0b3f397c29781c13f0117629efeea9f5d9c4f613\", \"cf917258436297c3ce54f64cf0663bdbc37d3dd25f5e7b14c5d1fb05b3bd1b40\", \"ec8d16d9931bcf8faa5aa787bdd627e9de1359fc43d992d18987bfab52aabe11\", \"ed157a8298dcb9087dc72d00f5c142f392c8daf64b299a6f7ad6bd81abe480d3\", \"fa0ac84eec7a9aee41d83cedd7bc97bf889985b3919f9b6122826f5b2823bd12\"]}, \"state_id\": \"df8f17bce6d1b1f3158cfd10\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 15, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 37, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 1}, \"intervention\": \"DO_Z1_1\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 0}}]}"} {"agent_name": "causal_micro_lab_agent_loop", "data_source": "causal_micro_lab_final_eval_v3", "env_spec_json": "{\"agent\": {}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 152, \"task\": {\"state\": {\"available_experiment_ids\": [0, 1, 2, 3, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 26, 27, 28, 29, 30, 31, 32, 33, 34, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 4, \"family_bucket\": \"cross_family\", \"maximum_separation\": 0.5277777777777778, \"mean_separation\": 0.3523148148148145, \"minimum_separation\": 0.16666666666666666, \"normalized_mean_separation\": 0.3774801587301584, \"oracle_budget_representation_error\": 0.1388888888888889, \"oracle_singleton_representation_error\": 0.2864583333333333, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.14756944444444442, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 16}, \"private\": {\"hidden_mode_id\": \"640a68b09276ce609158afbdae1bf1479a1498285de22c932ac9f1a82918b2cd\", \"valid_mode_ids\": [\"1ce5f8dc552781afd9b7f0ca03f0fa37e98b1275a5e4ae45746e780b415fccab\", \"2040341f7bd2ec5af01ad4af0b5d712f2294670b34a1081257728ac918babd9f\", \"28214e85dec460affce37617661ce11ab079534a5e05e11ddf8c9e3689ba137f\", \"2b246549265db2bf1970075121f04ac32ad6befe3d7af208359534e2cdbd4e44\", \"3e0f3c253db4586c7867f8aa6ef331d06a17bea377108e03e7d4d7921985d567\", \"5b4554e9c34239a33c2dba7c7fdb991765a57b29646cf9c04b138ade17d283b7\", \"640a68b09276ce609158afbdae1bf1479a1498285de22c932ac9f1a82918b2cd\", \"69cdf0d3bef62749ccee077de54da457322fe8bd3a65661941559d7a0f3b92c1\", \"78dcac3f535958a4d37b8d7303815674bb1e11bf28a967a68dbc21effe9239d8\", \"9e1c1d9c6a1a0f60adf2c9413f44251ac8f45268609747ccd9868cf4ceacdbb7\", \"b77d7f2164708adb1658155d1349b1a12d7b9a1fdd4169c0493560dd15b71746\", \"e028f9bf095712c318919ca692ca50e3d8854f829a509de042ff7fa50d8e32cb\", \"e8bfde08c9bb77c4f57da4ac0f876c8d719472e9359e04d337ec5e8298efd7d5\", \"f754da5f71a3588dc4e9a72a91e8fc5ff5e7e1178f0568ec7078ff8a481ca24f\", \"fe39927af31eee6e6c72f660cb53697a7e70c8864851a32edd0926894f82636e\", \"ff42d2b3c5897f6d611a3926ef3dca095f786dcd68ff1a71167b650a187364c0\"]}, \"state_id\": \"ec6e06ef773dfebf9281fb04\", \"visible_experiments\": [{\"experiment_id\": 4, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z2_1\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 5, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 25, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 0}}, {\"experiment_id\": 35, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}]}}}", "extra_info": {"index": 152, "max_global_steps": 0, "min_global_steps": 0}, "index": 152, "prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=0\n intervention: set Z2=1\n observed: Z1=0, Z2=1, Y=0\n\nExperiment 2:\n inputs: X1=0, X2=0, X3=1\n intervention: none\n observed: Z1=0, Z2=0, Y=0\n\nExperiment 3:\n inputs: X1=1, X2=0, X3=1\n intervention: none\n observed: Z1=1, Z2=0, Y=1\n\nExperiment 4:\n inputs: X1=1, X2=1, X3=1\n intervention: none\n observed: Z1=0, Z2=1, Y=0\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "raw_prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=0\n intervention: set Z2=1\n observed: Z1=0, Z2=1, Y=0\n\nExperiment 2:\n inputs: X1=0, X2=0, X3=1\n intervention: none\n observed: Z1=0, Z2=0, Y=0\n\nExperiment 3:\n inputs: X1=1, X2=0, X3=1\n intervention: none\n observed: Z1=1, Z2=0, Y=1\n\nExperiment 4:\n inputs: X1=1, X2=1, X3=1\n intervention: none\n observed: Z1=0, Z2=1, Y=0\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "reward_model": {"style": "rule"}, "state_json": "{\"available_experiment_ids\": [0, 1, 2, 3, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 26, 27, 28, 29, 30, 31, 32, 33, 34, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 4, \"family_bucket\": \"cross_family\", \"maximum_separation\": 0.5277777777777778, \"mean_separation\": 0.3523148148148145, \"minimum_separation\": 0.16666666666666666, \"normalized_mean_separation\": 0.3774801587301584, \"oracle_budget_representation_error\": 0.1388888888888889, \"oracle_singleton_representation_error\": 0.2864583333333333, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.14756944444444442, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 16}, \"private\": {\"hidden_mode_id\": \"640a68b09276ce609158afbdae1bf1479a1498285de22c932ac9f1a82918b2cd\", \"valid_mode_ids\": [\"1ce5f8dc552781afd9b7f0ca03f0fa37e98b1275a5e4ae45746e780b415fccab\", \"2040341f7bd2ec5af01ad4af0b5d712f2294670b34a1081257728ac918babd9f\", \"28214e85dec460affce37617661ce11ab079534a5e05e11ddf8c9e3689ba137f\", \"2b246549265db2bf1970075121f04ac32ad6befe3d7af208359534e2cdbd4e44\", \"3e0f3c253db4586c7867f8aa6ef331d06a17bea377108e03e7d4d7921985d567\", \"5b4554e9c34239a33c2dba7c7fdb991765a57b29646cf9c04b138ade17d283b7\", \"640a68b09276ce609158afbdae1bf1479a1498285de22c932ac9f1a82918b2cd\", \"69cdf0d3bef62749ccee077de54da457322fe8bd3a65661941559d7a0f3b92c1\", \"78dcac3f535958a4d37b8d7303815674bb1e11bf28a967a68dbc21effe9239d8\", \"9e1c1d9c6a1a0f60adf2c9413f44251ac8f45268609747ccd9868cf4ceacdbb7\", \"b77d7f2164708adb1658155d1349b1a12d7b9a1fdd4169c0493560dd15b71746\", \"e028f9bf095712c318919ca692ca50e3d8854f829a509de042ff7fa50d8e32cb\", \"e8bfde08c9bb77c4f57da4ac0f876c8d719472e9359e04d337ec5e8298efd7d5\", \"f754da5f71a3588dc4e9a72a91e8fc5ff5e7e1178f0568ec7078ff8a481ca24f\", \"fe39927af31eee6e6c72f660cb53697a7e70c8864851a32edd0926894f82636e\", \"ff42d2b3c5897f6d611a3926ef3dca095f786dcd68ff1a71167b650a187364c0\"]}, \"state_id\": \"ec6e06ef773dfebf9281fb04\", \"visible_experiments\": [{\"experiment_id\": 4, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z2_1\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 5, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 25, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 0}}, {\"experiment_id\": 35, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}]}"} {"agent_name": "causal_micro_lab_agent_loop", "data_source": "causal_micro_lab_final_eval_v3", "env_spec_json": "{\"agent\": {}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 153, \"task\": {\"state\": {\"available_experiment_ids\": [1, 2, 3, 4, 5, 6, 7, 8, 9, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 3, \"family_bucket\": \"cross_family\", \"maximum_separation\": 0.6756756756756757, \"mean_separation\": 0.38378378378378375, \"minimum_separation\": 0.10810810810810811, \"normalized_mean_separation\": 0.41119691119691115, \"oracle_budget_representation_error\": 0.14864864864864868, \"oracle_singleton_representation_error\": 0.30067567567567577, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.15202702702702708, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 16}, \"private\": {\"hidden_mode_id\": \"2cae978b4f670fa161eb93ec3b02cf6ffbd0c9a89393a60d559697d8b51e2d6c\", \"valid_mode_ids\": [\"05b592aa5a709401a5465b6db1eb17f5dcc804ee6fec89d18ee6efa8c01ceec1\", \"0eeb79f2ebfcc6397a55d5ee6965332aa57e658bbb6738d9c77f058c25e77402\", \"2752ee27766f92919c3a3e9fefc29517f0e3a5f62ff70c1828c613cfe3edce77\", \"2a916815184c18ff5b2d8e37cd11098fd11bd2f7ad7496b162d939fc6a20c0cd\", \"2cae978b4f670fa161eb93ec3b02cf6ffbd0c9a89393a60d559697d8b51e2d6c\", \"573e4d9b9f2b7e9cddaa7e226bb529b97529856215762abd40614bb4c9a5c54f\", \"58cf7cc1081b0c10ff41b3fc07ef01f1de049b1938e64ea69de1c2b500ae1057\", \"8e140425c0288a0f18e0ff5be1b056389a65498612b723bed7c495b43ae2535e\", \"a27e452d77644692fdcd0c617837611b964b59671b529d47cb3e6ff51218d535\", \"a707a10d0324e23d331ce1e76da95734bfa53834392c43e630bd214afec3536c\", \"c4a36c09907aa3675b78d78c8983c395b249917290b2d316bc61516904d09a25\", \"ca05a4a9a780e55c3bb254494127c3ee409472b328d192726a4bedc2d4495d3a\", \"d424f21089741e3c53330d20dc396c2c2e3f832bc9b6de09c485a097b033e77a\", \"d4d7565e8bee0483f7f013e67cb4cfc4a9f8c8db2cb8c1cb81d5414c92721b86\", \"eee7b536193f09bd487444b7e609c2b202e7bb8ef190634617c755f11cc8608d\", \"f4750595cc6207311ea19d9f2339a26c7bee37b4e76a6839a019a20e34217e27\"]}, \"state_id\": \"3234b08a01d264ae895f5f98\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 0}}, {\"experiment_id\": 10, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 35, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 0}}]}}}", "extra_info": {"index": 153, "max_global_steps": 0, "min_global_steps": 0}, "index": 153, "prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=0\n intervention: none\n observed: Z1=1, Z2=0, Y=1\n\nExperiment 2:\n inputs: X1=0, X2=1, X3=0\n intervention: none\n observed: Z1=0, Z2=0, Y=0\n\nExperiment 3:\n inputs: X1=1, X2=1, X3=1\n intervention: none\n observed: Z1=0, Z2=0, Y=0\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "raw_prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=0\n intervention: none\n observed: Z1=1, Z2=0, Y=1\n\nExperiment 2:\n inputs: X1=0, X2=1, X3=0\n intervention: none\n observed: Z1=0, Z2=0, Y=0\n\nExperiment 3:\n inputs: X1=1, X2=1, X3=1\n intervention: none\n observed: Z1=0, Z2=0, Y=0\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "reward_model": {"style": "rule"}, "state_json": "{\"available_experiment_ids\": [1, 2, 3, 4, 5, 6, 7, 8, 9, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 3, \"family_bucket\": \"cross_family\", \"maximum_separation\": 0.6756756756756757, \"mean_separation\": 0.38378378378378375, \"minimum_separation\": 0.10810810810810811, \"normalized_mean_separation\": 0.41119691119691115, \"oracle_budget_representation_error\": 0.14864864864864868, \"oracle_singleton_representation_error\": 0.30067567567567577, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.15202702702702708, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 16}, \"private\": {\"hidden_mode_id\": \"2cae978b4f670fa161eb93ec3b02cf6ffbd0c9a89393a60d559697d8b51e2d6c\", \"valid_mode_ids\": [\"05b592aa5a709401a5465b6db1eb17f5dcc804ee6fec89d18ee6efa8c01ceec1\", \"0eeb79f2ebfcc6397a55d5ee6965332aa57e658bbb6738d9c77f058c25e77402\", \"2752ee27766f92919c3a3e9fefc29517f0e3a5f62ff70c1828c613cfe3edce77\", \"2a916815184c18ff5b2d8e37cd11098fd11bd2f7ad7496b162d939fc6a20c0cd\", \"2cae978b4f670fa161eb93ec3b02cf6ffbd0c9a89393a60d559697d8b51e2d6c\", \"573e4d9b9f2b7e9cddaa7e226bb529b97529856215762abd40614bb4c9a5c54f\", \"58cf7cc1081b0c10ff41b3fc07ef01f1de049b1938e64ea69de1c2b500ae1057\", \"8e140425c0288a0f18e0ff5be1b056389a65498612b723bed7c495b43ae2535e\", \"a27e452d77644692fdcd0c617837611b964b59671b529d47cb3e6ff51218d535\", \"a707a10d0324e23d331ce1e76da95734bfa53834392c43e630bd214afec3536c\", \"c4a36c09907aa3675b78d78c8983c395b249917290b2d316bc61516904d09a25\", \"ca05a4a9a780e55c3bb254494127c3ee409472b328d192726a4bedc2d4495d3a\", \"d424f21089741e3c53330d20dc396c2c2e3f832bc9b6de09c485a097b033e77a\", \"d4d7565e8bee0483f7f013e67cb4cfc4a9f8c8db2cb8c1cb81d5414c92721b86\", \"eee7b536193f09bd487444b7e609c2b202e7bb8ef190634617c755f11cc8608d\", \"f4750595cc6207311ea19d9f2339a26c7bee37b4e76a6839a019a20e34217e27\"]}, \"state_id\": \"3234b08a01d264ae895f5f98\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 0}}, {\"experiment_id\": 10, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 35, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 0}}]}"} {"agent_name": "causal_micro_lab_agent_loop", "data_source": "causal_micro_lab_final_eval_v3", "env_spec_json": "{\"agent\": {}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 154, \"task\": {\"state\": {\"available_experiment_ids\": [1, 2, 3, 4, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 23, 24, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 4, \"family_bucket\": \"mixed\", \"maximum_separation\": 0.5833333333333334, \"mean_separation\": 0.3407407407407408, \"minimum_separation\": 0.1111111111111111, \"normalized_mean_separation\": 0.3650793650793651, \"oracle_budget_representation_error\": 0.11979166666666666, \"oracle_singleton_representation_error\": 0.2777777777777778, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.15798611111111113, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 16}, \"private\": {\"hidden_mode_id\": \"ec625df0e3a9a334ff1169617f5551651c83c71a869b355eb46746c6e82eebbd\", \"valid_mode_ids\": [\"0207463cc9d037a76fd7134eec3b8bca58132bb035c94aa2eb17098c50b3d74f\", \"120f67ba2a2cd3105ef2746d4c63bac492b29a39747247b93aa3f633cded6e63\", \"319925e8225ff9750db0ccad1281e9469cda1ed9b317727c65498981e30b5386\", \"3204abb024d08d9ced3a9c14f6fd247963d6ecada51092592336a6e16ddf1959\", \"408a9f502f27f2339ccf481c79f6196d3f8a6db6a9088a81514dec36bf0657ac\", \"7d38fdace0fc2769de9305b0245ce145245a6da2f746ecf39a2f729e08be7165\", \"8a1deaa172ad84a7ee126acba28766395ba821ed0e133268ae2a9949e65ff6f7\", \"8d7eef787da496b9eb12665414e525a790e7492d7516ea1ca32f2710680e7bf9\", \"a262f7c28e8814a381c15f6376c295a0d3cf024f02d2acd4b91434dcfb37930b\", \"c2f1dff2946f4257f4c4187a85a59805d6f5d54dbd13c06e2a61022a01daa2ef\", \"d4725c245fb1c2ed7462529cb1c1a8306ccdc4dbc21113b9b5ce8f6f99476319\", \"d76d1fd686293fb10406d3492a1f50276006234510cee7893ab1b86970ca1ae0\", \"e3830e80be587ce66c6241c8c8bc1eba7e6239e036922f1b8dd9002edc6e29ef\", \"e4946e1895fdf2e994f95e68f2b2ff0662700ba32ce2ba6d90c85e38a22828d9\", \"ec625df0e3a9a334ff1169617f5551651c83c71a869b355eb46746c6e82eebbd\", \"f6c320873cb48da9ddb3b78f3925d831488ef6bb1e7bc881a4b4b0c17d34de70\"]}, \"state_id\": \"afb736e6021b5f6fc8e1283d\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 5, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 22, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z1_1\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 25, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 1}}]}}}", "extra_info": {"index": 154, "max_global_steps": 0, "min_global_steps": 0}, "index": 154, "prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=0\n intervention: none\n observed: Z1=0, Z2=0, Y=0\n\nExperiment 2:\n inputs: X1=0, X2=0, X3=1\n intervention: none\n observed: Z1=0, Z2=1, Y=0\n\nExperiment 3:\n inputs: X1=1, X2=0, X3=0\n intervention: set Z1=1\n observed: Z1=1, Z2=1, Y=0\n\nExperiment 4:\n inputs: X1=1, X2=0, X3=1\n intervention: none\n observed: Z1=1, Z2=1, Y=1\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "raw_prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=0\n intervention: none\n observed: Z1=0, Z2=0, Y=0\n\nExperiment 2:\n inputs: X1=0, X2=0, X3=1\n intervention: none\n observed: Z1=0, Z2=1, Y=0\n\nExperiment 3:\n inputs: X1=1, X2=0, X3=0\n intervention: set Z1=1\n observed: Z1=1, Z2=1, Y=0\n\nExperiment 4:\n inputs: X1=1, X2=0, X3=1\n intervention: none\n observed: Z1=1, Z2=1, Y=1\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "reward_model": {"style": "rule"}, "state_json": "{\"available_experiment_ids\": [1, 2, 3, 4, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 23, 24, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 4, \"family_bucket\": \"mixed\", \"maximum_separation\": 0.5833333333333334, \"mean_separation\": 0.3407407407407408, \"minimum_separation\": 0.1111111111111111, \"normalized_mean_separation\": 0.3650793650793651, \"oracle_budget_representation_error\": 0.11979166666666666, \"oracle_singleton_representation_error\": 0.2777777777777778, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.15798611111111113, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 16}, \"private\": {\"hidden_mode_id\": \"ec625df0e3a9a334ff1169617f5551651c83c71a869b355eb46746c6e82eebbd\", \"valid_mode_ids\": [\"0207463cc9d037a76fd7134eec3b8bca58132bb035c94aa2eb17098c50b3d74f\", \"120f67ba2a2cd3105ef2746d4c63bac492b29a39747247b93aa3f633cded6e63\", \"319925e8225ff9750db0ccad1281e9469cda1ed9b317727c65498981e30b5386\", \"3204abb024d08d9ced3a9c14f6fd247963d6ecada51092592336a6e16ddf1959\", \"408a9f502f27f2339ccf481c79f6196d3f8a6db6a9088a81514dec36bf0657ac\", \"7d38fdace0fc2769de9305b0245ce145245a6da2f746ecf39a2f729e08be7165\", \"8a1deaa172ad84a7ee126acba28766395ba821ed0e133268ae2a9949e65ff6f7\", \"8d7eef787da496b9eb12665414e525a790e7492d7516ea1ca32f2710680e7bf9\", \"a262f7c28e8814a381c15f6376c295a0d3cf024f02d2acd4b91434dcfb37930b\", \"c2f1dff2946f4257f4c4187a85a59805d6f5d54dbd13c06e2a61022a01daa2ef\", \"d4725c245fb1c2ed7462529cb1c1a8306ccdc4dbc21113b9b5ce8f6f99476319\", \"d76d1fd686293fb10406d3492a1f50276006234510cee7893ab1b86970ca1ae0\", \"e3830e80be587ce66c6241c8c8bc1eba7e6239e036922f1b8dd9002edc6e29ef\", \"e4946e1895fdf2e994f95e68f2b2ff0662700ba32ce2ba6d90c85e38a22828d9\", \"ec625df0e3a9a334ff1169617f5551651c83c71a869b355eb46746c6e82eebbd\", \"f6c320873cb48da9ddb3b78f3925d831488ef6bb1e7bc881a4b4b0c17d34de70\"]}, \"state_id\": \"afb736e6021b5f6fc8e1283d\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 5, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 22, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z1_1\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 25, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 1}}]}"} {"agent_name": "causal_micro_lab_agent_loop", "data_source": "causal_micro_lab_final_eval_v3", "env_spec_json": "{\"agent\": {}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 155, \"task\": {\"state\": {\"available_experiment_ids\": [0, 1, 2, 3, 4, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 26, 27, 28, 29, 31, 32, 33, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 3, \"family_bucket\": \"mixed\", \"maximum_separation\": 0.5405405405405406, \"mean_separation\": 0.34054054054054067, \"minimum_separation\": 0.10810810810810811, \"normalized_mean_separation\": 0.364864864864865, \"oracle_budget_representation_error\": 0.11824324324324328, \"oracle_singleton_representation_error\": 0.28040540540540543, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.16216216216216214, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 16}, \"private\": {\"hidden_mode_id\": \"869e1b4cdb1f8041da51a0a844b7382adc1055be1210ac3296031ed18df1399e\", \"valid_mode_ids\": [\"07963159e96e55fc4b5502f288e22bf0ff74703c2b972a3d33f39fb446b06a9c\", \"0aec32fad115ace4320074a9ed503662e7372676ae8ba343bf25fa0d947ded73\", \"3128ed101e7910013aa2bcfdc38c7407bb8f1e0752958cb2f64a986ee59d07e5\", \"58bfe7857b8d96d2f998d2f4f7bf4a24062022f6537a360c1ac0f4a3ce6ab240\", \"5f5ef21bd695c152263fa679654a8a9d1c2cd974f23c67cfec20d414dff27332\", \"84b944434cac25aeb7e17a2ed2d610ea322bbc41d8499dac8266a69a5a6fef12\", \"869e1b4cdb1f8041da51a0a844b7382adc1055be1210ac3296031ed18df1399e\", \"962c18131615806b506f42880af81202cc8479c143a8673b38f8404ab22730e8\", \"9e8588d1628ba3c3c5e81cfe2f44275e9a929a7381009f6722085372e0339f03\", \"a0353e263c7457486bee101f44a1d1e6c6a71dc3056ae12b1935f4b798ec6388\", \"aed5b7de43e68d10355e7050bf0f20de183094904a9af5f51747383ac87b0b76\", \"bd7d189ac1a54988146d7baf3461112de6ea8a50305e0b71cad69ab9be6b8b3e\", \"cb513c7c645aaaa9c52ec3a3634acba2a8f56de11dcd4cb0e4465c3a493c7713\", \"e0568f72bdbf937f20272a3e4015339d507d22d7d0c29021dc98ed5894902f7f\", \"e15e5e745031600b06cb3c3fc393784d402789890dd1cd94d0d9944713678487\", \"f2f044ccb83592621ed2d77c5f76309f47570038741837570ec8fee10fced4e0\"]}, \"state_id\": \"518bb4b64969ee7abfbcf546\", \"visible_experiments\": [{\"experiment_id\": 5, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 25, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 30, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 1}}]}}}", "extra_info": {"index": 155, "max_global_steps": 0, "min_global_steps": 0}, "index": 155, "prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=1\n intervention: none\n observed: Z1=0, Z2=1, Y=1\n\nExperiment 2:\n inputs: X1=1, X2=0, X3=1\n intervention: none\n observed: Z1=1, Z2=1, Y=0\n\nExperiment 3:\n inputs: X1=1, X2=1, X3=0\n intervention: none\n observed: Z1=1, Z2=1, Y=1\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "raw_prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=1\n intervention: none\n observed: Z1=0, Z2=1, Y=1\n\nExperiment 2:\n inputs: X1=1, X2=0, X3=1\n intervention: none\n observed: Z1=1, Z2=1, Y=0\n\nExperiment 3:\n inputs: X1=1, X2=1, X3=0\n intervention: none\n observed: Z1=1, Z2=1, Y=1\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "reward_model": {"style": "rule"}, "state_json": "{\"available_experiment_ids\": [0, 1, 2, 3, 4, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 26, 27, 28, 29, 31, 32, 33, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 3, \"family_bucket\": \"mixed\", \"maximum_separation\": 0.5405405405405406, \"mean_separation\": 0.34054054054054067, \"minimum_separation\": 0.10810810810810811, \"normalized_mean_separation\": 0.364864864864865, \"oracle_budget_representation_error\": 0.11824324324324328, \"oracle_singleton_representation_error\": 0.28040540540540543, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.16216216216216214, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 16}, \"private\": {\"hidden_mode_id\": \"869e1b4cdb1f8041da51a0a844b7382adc1055be1210ac3296031ed18df1399e\", \"valid_mode_ids\": [\"07963159e96e55fc4b5502f288e22bf0ff74703c2b972a3d33f39fb446b06a9c\", \"0aec32fad115ace4320074a9ed503662e7372676ae8ba343bf25fa0d947ded73\", \"3128ed101e7910013aa2bcfdc38c7407bb8f1e0752958cb2f64a986ee59d07e5\", \"58bfe7857b8d96d2f998d2f4f7bf4a24062022f6537a360c1ac0f4a3ce6ab240\", \"5f5ef21bd695c152263fa679654a8a9d1c2cd974f23c67cfec20d414dff27332\", \"84b944434cac25aeb7e17a2ed2d610ea322bbc41d8499dac8266a69a5a6fef12\", \"869e1b4cdb1f8041da51a0a844b7382adc1055be1210ac3296031ed18df1399e\", \"962c18131615806b506f42880af81202cc8479c143a8673b38f8404ab22730e8\", \"9e8588d1628ba3c3c5e81cfe2f44275e9a929a7381009f6722085372e0339f03\", \"a0353e263c7457486bee101f44a1d1e6c6a71dc3056ae12b1935f4b798ec6388\", \"aed5b7de43e68d10355e7050bf0f20de183094904a9af5f51747383ac87b0b76\", \"bd7d189ac1a54988146d7baf3461112de6ea8a50305e0b71cad69ab9be6b8b3e\", \"cb513c7c645aaaa9c52ec3a3634acba2a8f56de11dcd4cb0e4465c3a493c7713\", \"e0568f72bdbf937f20272a3e4015339d507d22d7d0c29021dc98ed5894902f7f\", \"e15e5e745031600b06cb3c3fc393784d402789890dd1cd94d0d9944713678487\", \"f2f044ccb83592621ed2d77c5f76309f47570038741837570ec8fee10fced4e0\"]}, \"state_id\": \"518bb4b64969ee7abfbcf546\", \"visible_experiments\": [{\"experiment_id\": 5, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 25, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 30, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 1}}]}"} {"agent_name": "causal_micro_lab_agent_loop", "data_source": "causal_micro_lab_final_eval_v3", "env_spec_json": "{\"agent\": {}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 156, \"task\": {\"state\": {\"available_experiment_ids\": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 16, 17, 18, 19, 20, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 3, \"family_bucket\": \"within_family\", \"maximum_separation\": 0.5945945945945946, \"mean_separation\": 0.36486486486486464, \"minimum_separation\": 0.16216216216216217, \"normalized_mean_separation\": 0.3909266409266407, \"oracle_budget_representation_error\": 0.14020270270270271, \"oracle_singleton_representation_error\": 0.3074324324324325, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.1672297297297298, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 16}, \"private\": {\"hidden_mode_id\": \"5be885e1b21489e691b7e6011f852d91b043c6ae15e38d69cfc722ec2b0f6d1d\", \"valid_mode_ids\": [\"0405c595d8c2c9e4ef42ac047fc44282db23f4c21819d2fbe27f59d7ce34e06f\", \"0aed9dda013cd6c776a35ed958aad3970ad7711637e6a90a7c2ef73d0ccfb33f\", \"20464c9c1ab73b2c19e41c906fb2b7491af18d699102f0240aa8d902b7a55f77\", \"278350a96b71ff6d889abc59b6dbe62416f787c07ab1e523844d85422df47c8e\", \"2ae0df23e964fb017f3948aba1af30f081ade18295150d7b07dc5c2a4d58a719\", \"2d22f82ae158a401c9b99308b7f7fe88699627c06da43a91ea482983f224c722\", \"5be885e1b21489e691b7e6011f852d91b043c6ae15e38d69cfc722ec2b0f6d1d\", \"604243de396c79ec98e600358c88ca49c1e118d39647b751f01f4062de18d68f\", \"65f55a23bb7a69186a278350897eea6398760ad72d49d0e6bc248f2c4a77eac9\", \"6afab583408e70e29d6681bde90f5072b14aa5ba1decdebb7b588ec64d2956b1\", \"734d2ea9c5558b98607515a1a493eb7894645ba5172125f6808dd78a4ef5fb68\", \"8192c4b1cf122f59186cecf4b16e793c445fc315e53bd3d8a8d40e49367f85c2\", \"98f735e6ead22f50b046f6bc592c7a3ad658595092bb7b33ebbb4b6dee26f56e\", \"b8480d284626ffabb63abefe13d0256a7bcfa19c16b0f75c20ccf3a6598ee617\", \"de315839bddf01d6bdc7b07dc4a0d5e8bda57c2dcee0b5029ac70b9a226a121f\", \"fa745052e69935aaef01859d906f769c51435931a5980b81d950de58f38764e9\"]}, \"state_id\": \"f4eb98dea68e9c67cc576a2a\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 15, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 21, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z1_0\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 1}}]}}}", "extra_info": {"index": 156, "max_global_steps": 0, "min_global_steps": 0}, "index": 156, "prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=0\n intervention: none\n observed: Z1=0, Z2=0, Y=1\n\nExperiment 2:\n inputs: X1=0, X2=1, X3=1\n intervention: none\n observed: Z1=0, Z2=1, Y=1\n\nExperiment 3:\n inputs: X1=1, X2=0, X3=0\n intervention: set Z1=0\n observed: Z1=0, Z2=1, Y=1\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "raw_prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=0\n intervention: none\n observed: Z1=0, Z2=0, Y=1\n\nExperiment 2:\n inputs: X1=0, X2=1, X3=1\n intervention: none\n observed: Z1=0, Z2=1, Y=1\n\nExperiment 3:\n inputs: X1=1, X2=0, X3=0\n intervention: set Z1=0\n observed: Z1=0, Z2=1, Y=1\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "reward_model": {"style": "rule"}, "state_json": "{\"available_experiment_ids\": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 16, 17, 18, 19, 20, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 3, \"family_bucket\": \"within_family\", \"maximum_separation\": 0.5945945945945946, \"mean_separation\": 0.36486486486486464, \"minimum_separation\": 0.16216216216216217, \"normalized_mean_separation\": 0.3909266409266407, \"oracle_budget_representation_error\": 0.14020270270270271, \"oracle_singleton_representation_error\": 0.3074324324324325, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.1672297297297298, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 16}, \"private\": {\"hidden_mode_id\": \"5be885e1b21489e691b7e6011f852d91b043c6ae15e38d69cfc722ec2b0f6d1d\", \"valid_mode_ids\": [\"0405c595d8c2c9e4ef42ac047fc44282db23f4c21819d2fbe27f59d7ce34e06f\", \"0aed9dda013cd6c776a35ed958aad3970ad7711637e6a90a7c2ef73d0ccfb33f\", \"20464c9c1ab73b2c19e41c906fb2b7491af18d699102f0240aa8d902b7a55f77\", \"278350a96b71ff6d889abc59b6dbe62416f787c07ab1e523844d85422df47c8e\", \"2ae0df23e964fb017f3948aba1af30f081ade18295150d7b07dc5c2a4d58a719\", \"2d22f82ae158a401c9b99308b7f7fe88699627c06da43a91ea482983f224c722\", \"5be885e1b21489e691b7e6011f852d91b043c6ae15e38d69cfc722ec2b0f6d1d\", \"604243de396c79ec98e600358c88ca49c1e118d39647b751f01f4062de18d68f\", \"65f55a23bb7a69186a278350897eea6398760ad72d49d0e6bc248f2c4a77eac9\", \"6afab583408e70e29d6681bde90f5072b14aa5ba1decdebb7b588ec64d2956b1\", \"734d2ea9c5558b98607515a1a493eb7894645ba5172125f6808dd78a4ef5fb68\", \"8192c4b1cf122f59186cecf4b16e793c445fc315e53bd3d8a8d40e49367f85c2\", \"98f735e6ead22f50b046f6bc592c7a3ad658595092bb7b33ebbb4b6dee26f56e\", \"b8480d284626ffabb63abefe13d0256a7bcfa19c16b0f75c20ccf3a6598ee617\", \"de315839bddf01d6bdc7b07dc4a0d5e8bda57c2dcee0b5029ac70b9a226a121f\", \"fa745052e69935aaef01859d906f769c51435931a5980b81d950de58f38764e9\"]}, \"state_id\": \"f4eb98dea68e9c67cc576a2a\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 15, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 21, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z1_0\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 1}}]}"} {"agent_name": "causal_micro_lab_agent_loop", "data_source": "causal_micro_lab_final_eval_v3", "env_spec_json": "{\"agent\": {}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 157, \"task\": {\"state\": {\"available_experiment_ids\": [0, 1, 2, 3, 4, 5, 6, 8, 9, 11, 12, 13, 14, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 3, \"family_bucket\": \"within_family\", \"maximum_separation\": 0.7027027027027027, \"mean_separation\": 0.40270270270270253, \"minimum_separation\": 0.16216216216216217, \"normalized_mean_separation\": 0.43146718146718127, \"oracle_budget_representation_error\": 0.13513513513513517, \"oracle_singleton_representation_error\": 0.30743243243243246, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.17229729729729729, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 16}, \"private\": {\"hidden_mode_id\": \"6382475dc3f58b2c69ad38b2cf70bae372959ae46535c8d17642eb4cb0a1a7d2\", \"valid_mode_ids\": [\"115d9a8a2c0861fe151250c71b647584fc6e9f4c9417ecc89681747bd253003e\", \"3d2495a9222f347ab69d8765b97d9e034bdcd2df8a2c16d432b5af286f3290d3\", \"45e2d715e18e12bc884314856c7319e1108360f28ff3dc57a38c20ea73d38db4\", \"4619438e4dae0d3a58c98e76c4d110597a33d3e2c8a7914a89259cd3e0096006\", \"549e70721dc6ae185294307f3821430fb44b1c8d4a99e0b80a6a033188c61a1f\", \"6382475dc3f58b2c69ad38b2cf70bae372959ae46535c8d17642eb4cb0a1a7d2\", \"688aa26318b7092534a3fc9564177ba603fd42095042d7037d849e9bd6466564\", \"794d968d13b754c36748c627c0186403ca413a226c447c4f18cf51d2752bff6b\", \"7cf75bad99b7ca0efaf4cf096b8ffef2c2dbb3f725568a5ad38d574203cc6e40\", \"84a08d11dd723aaa9ad5c0da99664fcd32e1746fd6940307a6fb0f0c4a5676ce\", \"ae76174742e244d35ff6bb39d7bf1d7daafc304901daeafb640ec0b3118f2640\", \"b220390b7782f7573d1a3a3da6499fe3faee169d14d695ff7585b9e65330f5e7\", \"b5edb20a89dd0bc007dd4dc1e22e7a279cf615b9a1d89320475b0e81fb041b36\", \"ccd473d4653ffc7345616826589f51e2b03fc2f728d6102471bcf15b11147f10\", \"e40d5da26c0e44737bf576883a7d4be174cd949a0c02633089cdc2fc6569587c\", \"ed2b2f64095cfade71ff37e5d6127a43815b4685f4d2c54eab152b6751ff8906\"]}, \"state_id\": \"f60ecd87b7df1a70ef72644e\", \"visible_experiments\": [{\"experiment_id\": 7, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 1}, \"intervention\": \"DO_Z1_1\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 0}}, {\"experiment_id\": 10, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 15, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}]}}}", "extra_info": {"index": 157, "max_global_steps": 0, "min_global_steps": 0}, "index": 157, "prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=1\n intervention: set Z1=1\n observed: Z1=1, Z2=0, Y=0\n\nExperiment 2:\n inputs: X1=0, X2=1, X3=0\n intervention: none\n observed: Z1=0, Z2=1, Y=1\n\nExperiment 3:\n inputs: X1=0, X2=1, X3=1\n intervention: none\n observed: Z1=0, Z2=1, Y=0\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "raw_prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=1\n intervention: set Z1=1\n observed: Z1=1, Z2=0, Y=0\n\nExperiment 2:\n inputs: X1=0, X2=1, X3=0\n intervention: none\n observed: Z1=0, Z2=1, Y=1\n\nExperiment 3:\n inputs: X1=0, X2=1, X3=1\n intervention: none\n observed: Z1=0, Z2=1, Y=0\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "reward_model": {"style": "rule"}, "state_json": "{\"available_experiment_ids\": [0, 1, 2, 3, 4, 5, 6, 8, 9, 11, 12, 13, 14, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 3, \"family_bucket\": \"within_family\", \"maximum_separation\": 0.7027027027027027, \"mean_separation\": 0.40270270270270253, \"minimum_separation\": 0.16216216216216217, \"normalized_mean_separation\": 0.43146718146718127, \"oracle_budget_representation_error\": 0.13513513513513517, \"oracle_singleton_representation_error\": 0.30743243243243246, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.17229729729729729, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 16}, \"private\": {\"hidden_mode_id\": \"6382475dc3f58b2c69ad38b2cf70bae372959ae46535c8d17642eb4cb0a1a7d2\", \"valid_mode_ids\": [\"115d9a8a2c0861fe151250c71b647584fc6e9f4c9417ecc89681747bd253003e\", \"3d2495a9222f347ab69d8765b97d9e034bdcd2df8a2c16d432b5af286f3290d3\", \"45e2d715e18e12bc884314856c7319e1108360f28ff3dc57a38c20ea73d38db4\", \"4619438e4dae0d3a58c98e76c4d110597a33d3e2c8a7914a89259cd3e0096006\", \"549e70721dc6ae185294307f3821430fb44b1c8d4a99e0b80a6a033188c61a1f\", \"6382475dc3f58b2c69ad38b2cf70bae372959ae46535c8d17642eb4cb0a1a7d2\", \"688aa26318b7092534a3fc9564177ba603fd42095042d7037d849e9bd6466564\", \"794d968d13b754c36748c627c0186403ca413a226c447c4f18cf51d2752bff6b\", \"7cf75bad99b7ca0efaf4cf096b8ffef2c2dbb3f725568a5ad38d574203cc6e40\", \"84a08d11dd723aaa9ad5c0da99664fcd32e1746fd6940307a6fb0f0c4a5676ce\", \"ae76174742e244d35ff6bb39d7bf1d7daafc304901daeafb640ec0b3118f2640\", \"b220390b7782f7573d1a3a3da6499fe3faee169d14d695ff7585b9e65330f5e7\", \"b5edb20a89dd0bc007dd4dc1e22e7a279cf615b9a1d89320475b0e81fb041b36\", \"ccd473d4653ffc7345616826589f51e2b03fc2f728d6102471bcf15b11147f10\", \"e40d5da26c0e44737bf576883a7d4be174cd949a0c02633089cdc2fc6569587c\", \"ed2b2f64095cfade71ff37e5d6127a43815b4685f4d2c54eab152b6751ff8906\"]}, \"state_id\": \"f60ecd87b7df1a70ef72644e\", \"visible_experiments\": [{\"experiment_id\": 7, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 1}, \"intervention\": \"DO_Z1_1\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 0}}, {\"experiment_id\": 10, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 15, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}]}"} {"agent_name": "causal_micro_lab_agent_loop", "data_source": "causal_micro_lab_final_eval_v3", "env_spec_json": "{\"agent\": {}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 158, \"task\": {\"state\": {\"available_experiment_ids\": [0, 1, 2, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 31, 32, 33, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 3, \"family_bucket\": \"within_family\", \"maximum_separation\": 0.5945945945945946, \"mean_separation\": 0.39662162162162157, \"minimum_separation\": 0.10810810810810811, \"normalized_mean_separation\": 0.42495173745173737, \"oracle_budget_representation_error\": 0.14864864864864866, \"oracle_singleton_representation_error\": 0.3260135135135135, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.17736486486486483, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 16}, \"private\": {\"hidden_mode_id\": \"f570e9c9008c1fdf3b2e2200716c68e27f19d94f40682a3e35561e511cd25185\", \"valid_mode_ids\": [\"00d0bc7b894c6b587d7611eae34e360f6d232367ee05db0fa0ac461c31fd36d0\", \"04344273f2ceff268087d2c4b4f145bb764c9c600613835f094b0cbac616df8d\", \"045eba4caa4f77e68b89d0fb087ff5ab54f18cf73be63a51542af5a640088ea9\", \"05d39431fe6faeb2ac9dc8e952866ee5f24ab65610019c5c546e330118c9e952\", \"22c28303669c2944a017306d8d72af3c9d5fbbecb99b81e3010a4f878c8bfddb\", \"3c9576f9cabbbd4cee1458e5dbfd3d5f1b0ad45ebd7c45d00ad1b410a0fdb464\", \"41c877fcc42be5c39c4b959e57ef84b48fdfa8a9874fdf199b54969ebe1d9b8b\", \"4fcad9d487581dcff4dd7b8b40c5e6b225fd062dd1316f0cf8949936dc0f2fb2\", \"71ca0295aa530050d4f3d13eef77e5959459558f8ddb3fe82cfbdf81238da55d\", \"7706ede8eba99d7af16aae5ccae04aea3884c7ba2c389e4ba488741c65692061\", \"ba5b240bb4c122fe6a1bba7663aff8ea6b90e3e00dcec55f0a620709e7e2da4b\", \"bbd46df925ea7f5da32ca201aa6755933906fa2f6a94f8c6632cda282784cf71\", \"e1fd754b85339bf07543b841513f73d617d25aa020fae3829d9e65c29e350931\", \"e25445cd24921d30f8b45f7c28078f6cc5108b6ad840a57d7c35d0f4e2028229\", \"e8e986affdc9df331811509b2add4c1323b7cc97fbaf079880483fa92f7f13cc\", \"f570e9c9008c1fdf3b2e2200716c68e27f19d94f40682a3e35561e511cd25185\"]}, \"state_id\": \"70d191d23ba36eae1c002bf9\", \"visible_experiments\": [{\"experiment_id\": 3, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z2_0\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 17, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 1}, \"intervention\": \"DO_Z1_1\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 0}}, {\"experiment_id\": 30, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 0}}]}}}", "extra_info": {"index": 158, "max_global_steps": 0, "min_global_steps": 0}, "index": 158, "prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=0\n intervention: set Z2=0\n observed: Z1=0, Z2=0, Y=1\n\nExperiment 2:\n inputs: X1=0, X2=1, X3=1\n intervention: set Z1=1\n observed: Z1=1, Z2=0, Y=1\n\nExperiment 3:\n inputs: X1=1, X2=1, X3=0\n intervention: none\n observed: Z1=0, Z2=0, Y=0\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "raw_prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=0\n intervention: set Z2=0\n observed: Z1=0, Z2=0, Y=1\n\nExperiment 2:\n inputs: X1=0, X2=1, X3=1\n intervention: set Z1=1\n observed: Z1=1, Z2=0, Y=1\n\nExperiment 3:\n inputs: X1=1, X2=1, X3=0\n intervention: none\n observed: Z1=0, Z2=0, Y=0\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "reward_model": {"style": "rule"}, "state_json": "{\"available_experiment_ids\": [0, 1, 2, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 31, 32, 33, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 3, \"family_bucket\": \"within_family\", \"maximum_separation\": 0.5945945945945946, \"mean_separation\": 0.39662162162162157, \"minimum_separation\": 0.10810810810810811, \"normalized_mean_separation\": 0.42495173745173737, \"oracle_budget_representation_error\": 0.14864864864864866, \"oracle_singleton_representation_error\": 0.3260135135135135, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.17736486486486483, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 16}, \"private\": {\"hidden_mode_id\": \"f570e9c9008c1fdf3b2e2200716c68e27f19d94f40682a3e35561e511cd25185\", \"valid_mode_ids\": [\"00d0bc7b894c6b587d7611eae34e360f6d232367ee05db0fa0ac461c31fd36d0\", \"04344273f2ceff268087d2c4b4f145bb764c9c600613835f094b0cbac616df8d\", \"045eba4caa4f77e68b89d0fb087ff5ab54f18cf73be63a51542af5a640088ea9\", \"05d39431fe6faeb2ac9dc8e952866ee5f24ab65610019c5c546e330118c9e952\", \"22c28303669c2944a017306d8d72af3c9d5fbbecb99b81e3010a4f878c8bfddb\", \"3c9576f9cabbbd4cee1458e5dbfd3d5f1b0ad45ebd7c45d00ad1b410a0fdb464\", \"41c877fcc42be5c39c4b959e57ef84b48fdfa8a9874fdf199b54969ebe1d9b8b\", \"4fcad9d487581dcff4dd7b8b40c5e6b225fd062dd1316f0cf8949936dc0f2fb2\", \"71ca0295aa530050d4f3d13eef77e5959459558f8ddb3fe82cfbdf81238da55d\", \"7706ede8eba99d7af16aae5ccae04aea3884c7ba2c389e4ba488741c65692061\", \"ba5b240bb4c122fe6a1bba7663aff8ea6b90e3e00dcec55f0a620709e7e2da4b\", \"bbd46df925ea7f5da32ca201aa6755933906fa2f6a94f8c6632cda282784cf71\", \"e1fd754b85339bf07543b841513f73d617d25aa020fae3829d9e65c29e350931\", \"e25445cd24921d30f8b45f7c28078f6cc5108b6ad840a57d7c35d0f4e2028229\", \"e8e986affdc9df331811509b2add4c1323b7cc97fbaf079880483fa92f7f13cc\", \"f570e9c9008c1fdf3b2e2200716c68e27f19d94f40682a3e35561e511cd25185\"]}, \"state_id\": \"70d191d23ba36eae1c002bf9\", \"visible_experiments\": [{\"experiment_id\": 3, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z2_0\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 17, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 1}, \"intervention\": \"DO_Z1_1\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 0}}, {\"experiment_id\": 30, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 0}}]}"} {"agent_name": "causal_micro_lab_agent_loop", "data_source": "causal_micro_lab_final_eval_v3", "env_spec_json": "{\"agent\": {}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 159, \"task\": {\"state\": {\"available_experiment_ids\": [1, 2, 3, 4, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 3, \"family_bucket\": \"cross_family\", \"maximum_separation\": 0.6756756756756757, \"mean_separation\": 0.415765765765766, \"minimum_separation\": 0.16216216216216217, \"normalized_mean_separation\": 0.4454633204633207, \"oracle_budget_representation_error\": 0.15540540540540543, \"oracle_singleton_representation_error\": 0.33952702702702703, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.1841216216216216, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 16}, \"private\": {\"hidden_mode_id\": \"7b41746c110c7d6978699c9ebfc909cc504083144b112bd8b076dd172f72da9d\", \"valid_mode_ids\": [\"1ac8be8fb4d97788fcedb0cc317b4855b7d20b4e016137c49095fb2584021789\", \"30c9bfa4cd3fb7476ab65d6a2cf3221706b36ef452ef25741b951d2497b36a96\", \"3482f5b4fec588742628362eb8173dac72d24b8c319a43a1a6bb84f79762f3c2\", \"3ac70135bdcf6d5621962dddae6d9937ddaf37734bf346d95fc876703ee4e18b\", \"3ccc358d5518131ee67618013e1f707c8f0d80c049457dd4860b04deb43ede2e\", \"48358a8c34228a7383eb5d411a29a1caa61415082101f73ddeb4ec5f0ef80176\", \"492c969be386949f6669b9d3000e3fe73c297cfb5556b2ba80463e07aafc22c5\", \"6911cb1994089262c9e68b70d4547e6345c91717c0d6f3aaa243f955b994c2ed\", \"71cda204b37237122112c0cbad9114ce7a0851f54a22efa033b93574c48baa40\", \"75506c2bf324caf7ed8a936227a294d64bda4f237374f723fddaf421385739fd\", \"7b41746c110c7d6978699c9ebfc909cc504083144b112bd8b076dd172f72da9d\", \"7b8d4b5b97e0134b392fe10ee002b202d373384469dac2c1f82bdf3ec5bc92e1\", \"8a796860690ba16f70c6eda3c1e4c2eb2207047b15bd520934b5ef5b5f3b8988\", \"db9349c79d921bdcca1b2de94e52e9bcdfdb3a8cc397f4f06d9e5767a7e1ce30\", \"e0c0e33de916ea0c07260161258fe4a56eeb6170f3415787494f680f90cdf96f\", \"ff53d759b0e9aa996df1906f95d9cb62dc01ca17e1d652faec093a199cd1e59a\"]}, \"state_id\": \"75601661b6f81da4d039b330\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 5, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 25, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 0}}]}}}", "extra_info": {"index": 159, "max_global_steps": 0, "min_global_steps": 0}, "index": 159, "prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=0\n intervention: none\n observed: Z1=0, Z2=1, Y=1\n\nExperiment 2:\n inputs: X1=0, X2=0, X3=1\n intervention: none\n observed: Z1=0, Z2=0, Y=0\n\nExperiment 3:\n inputs: X1=1, X2=0, X3=1\n intervention: none\n observed: Z1=1, Z2=0, Y=0\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "raw_prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=0\n intervention: none\n observed: Z1=0, Z2=1, Y=1\n\nExperiment 2:\n inputs: X1=0, X2=0, X3=1\n intervention: none\n observed: Z1=0, Z2=0, Y=0\n\nExperiment 3:\n inputs: X1=1, X2=0, X3=1\n intervention: none\n observed: Z1=1, Z2=0, Y=0\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "reward_model": {"style": "rule"}, "state_json": "{\"available_experiment_ids\": [1, 2, 3, 4, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 3, \"family_bucket\": \"cross_family\", \"maximum_separation\": 0.6756756756756757, \"mean_separation\": 0.415765765765766, \"minimum_separation\": 0.16216216216216217, \"normalized_mean_separation\": 0.4454633204633207, \"oracle_budget_representation_error\": 0.15540540540540543, \"oracle_singleton_representation_error\": 0.33952702702702703, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.1841216216216216, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 16}, \"private\": {\"hidden_mode_id\": \"7b41746c110c7d6978699c9ebfc909cc504083144b112bd8b076dd172f72da9d\", \"valid_mode_ids\": [\"1ac8be8fb4d97788fcedb0cc317b4855b7d20b4e016137c49095fb2584021789\", \"30c9bfa4cd3fb7476ab65d6a2cf3221706b36ef452ef25741b951d2497b36a96\", \"3482f5b4fec588742628362eb8173dac72d24b8c319a43a1a6bb84f79762f3c2\", \"3ac70135bdcf6d5621962dddae6d9937ddaf37734bf346d95fc876703ee4e18b\", \"3ccc358d5518131ee67618013e1f707c8f0d80c049457dd4860b04deb43ede2e\", \"48358a8c34228a7383eb5d411a29a1caa61415082101f73ddeb4ec5f0ef80176\", \"492c969be386949f6669b9d3000e3fe73c297cfb5556b2ba80463e07aafc22c5\", \"6911cb1994089262c9e68b70d4547e6345c91717c0d6f3aaa243f955b994c2ed\", \"71cda204b37237122112c0cbad9114ce7a0851f54a22efa033b93574c48baa40\", \"75506c2bf324caf7ed8a936227a294d64bda4f237374f723fddaf421385739fd\", \"7b41746c110c7d6978699c9ebfc909cc504083144b112bd8b076dd172f72da9d\", \"7b8d4b5b97e0134b392fe10ee002b202d373384469dac2c1f82bdf3ec5bc92e1\", \"8a796860690ba16f70c6eda3c1e4c2eb2207047b15bd520934b5ef5b5f3b8988\", \"db9349c79d921bdcca1b2de94e52e9bcdfdb3a8cc397f4f06d9e5767a7e1ce30\", \"e0c0e33de916ea0c07260161258fe4a56eeb6170f3415787494f680f90cdf96f\", \"ff53d759b0e9aa996df1906f95d9cb62dc01ca17e1d652faec093a199cd1e59a\"]}, \"state_id\": \"75601661b6f81da4d039b330\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 5, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 25, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 0}}]}"} {"agent_name": "causal_micro_lab_agent_loop", "data_source": "causal_micro_lab_final_eval_v3", "env_spec_json": "{\"agent\": {}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 160, \"task\": {\"state\": {\"available_experiment_ids\": [0, 2, 3, 4, 5, 6, 8, 9, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 3, \"family_bucket\": \"within_family\", \"maximum_separation\": 0.7837837837837838, \"mean_separation\": 0.4328828828828824, \"minimum_separation\": 0.16216216216216217, \"normalized_mean_separation\": 0.4638030888030883, \"oracle_budget_representation_error\": 0.14189189189189194, \"oracle_singleton_representation_error\": 0.33108108108108114, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.1891891891891892, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 16}, \"private\": {\"hidden_mode_id\": \"1c7ca00ac230fd4aab914ea24b23de1684855d5bb80f264ad0c5711f4d784b75\", \"valid_mode_ids\": [\"181636ad0bd086a2600ebfc779dd67f5cf31f7921f54d0805778751e0bfd7919\", \"1c7ca00ac230fd4aab914ea24b23de1684855d5bb80f264ad0c5711f4d784b75\", \"390a681ce1fb112c5e03ba6d95632c25a1f5ee0cc1a523093f8c9c04e939e6ca\", \"4991828e92fd135826fc4b0b0d8483678de1c7a7ee28c9864527ee05499eeebc\", \"4fe956b57eb873f4baab03e273a342074620d55c8863605d908d3cbd1dce4a73\", \"5f1b9b9171e8ff25ec49487b3bfc776838d2ada082e3d4ce422424f07cbaab01\", \"60fb38259fe254382a0e5abfd554bed5336d870c7d3a77d38b11f4f60c95103d\", \"7081efb63254fbf76eab962076dfc1022af9e62981086a98e9c361825f68dbec\", \"89e9550f321fbf49fa7f19f01292c9ae25f1af45ad453f08a63c7734f63dcca3\", \"911975c5c17153b3edc037e2a6177e0e2abcf2f3364af5947420c5ff19a2b23e\", \"91b6ee2114357b686ffc68426bdd2e4863d41f51a9931fafd6158658f02a8297\", \"a5dd107c44b9df974eeaef4bea765ade0726845d123a0ea6590d861c5cc39176\", \"b98f049a1a2e268e5014289bef211ca22a0b25d89cd30609a65566d5867a7e77\", \"c617b8fb8810e655b19b56c3680d33b0df2fa6765c4c644fac9fbdfbbde639e3\", \"e6b8ebf19fa1b930c76c7a5fc86862af407b299c6e540748d17f30bb369f05ab\", \"e778cdd6293d99a05f12b1eb383c351aeeb8e558c137ceae94c712f02f47eb9d\"]}, \"state_id\": \"122e26079a328bf43fc47871\", \"visible_experiments\": [{\"experiment_id\": 1, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z1_0\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 7, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 1}, \"intervention\": \"DO_Z1_1\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 10, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 0}}]}}}", "extra_info": {"index": 160, "max_global_steps": 0, "min_global_steps": 0}, "index": 160, "prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=0\n intervention: set Z1=0\n observed: Z1=0, Z2=1, Y=1\n\nExperiment 2:\n inputs: X1=0, X2=0, X3=1\n intervention: set Z1=1\n observed: Z1=1, Z2=1, Y=0\n\nExperiment 3:\n inputs: X1=0, X2=1, X3=0\n intervention: none\n observed: Z1=0, Z2=0, Y=1\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "raw_prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=0\n intervention: set Z1=0\n observed: Z1=0, Z2=1, Y=1\n\nExperiment 2:\n inputs: X1=0, X2=0, X3=1\n intervention: set Z1=1\n observed: Z1=1, Z2=1, Y=0\n\nExperiment 3:\n inputs: X1=0, X2=1, X3=0\n intervention: none\n observed: Z1=0, Z2=0, Y=1\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "reward_model": {"style": "rule"}, "state_json": "{\"available_experiment_ids\": [0, 2, 3, 4, 5, 6, 8, 9, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 3, \"family_bucket\": \"within_family\", \"maximum_separation\": 0.7837837837837838, \"mean_separation\": 0.4328828828828824, \"minimum_separation\": 0.16216216216216217, \"normalized_mean_separation\": 0.4638030888030883, \"oracle_budget_representation_error\": 0.14189189189189194, \"oracle_singleton_representation_error\": 0.33108108108108114, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.1891891891891892, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 16}, \"private\": {\"hidden_mode_id\": \"1c7ca00ac230fd4aab914ea24b23de1684855d5bb80f264ad0c5711f4d784b75\", \"valid_mode_ids\": [\"181636ad0bd086a2600ebfc779dd67f5cf31f7921f54d0805778751e0bfd7919\", \"1c7ca00ac230fd4aab914ea24b23de1684855d5bb80f264ad0c5711f4d784b75\", \"390a681ce1fb112c5e03ba6d95632c25a1f5ee0cc1a523093f8c9c04e939e6ca\", \"4991828e92fd135826fc4b0b0d8483678de1c7a7ee28c9864527ee05499eeebc\", \"4fe956b57eb873f4baab03e273a342074620d55c8863605d908d3cbd1dce4a73\", \"5f1b9b9171e8ff25ec49487b3bfc776838d2ada082e3d4ce422424f07cbaab01\", \"60fb38259fe254382a0e5abfd554bed5336d870c7d3a77d38b11f4f60c95103d\", \"7081efb63254fbf76eab962076dfc1022af9e62981086a98e9c361825f68dbec\", \"89e9550f321fbf49fa7f19f01292c9ae25f1af45ad453f08a63c7734f63dcca3\", \"911975c5c17153b3edc037e2a6177e0e2abcf2f3364af5947420c5ff19a2b23e\", \"91b6ee2114357b686ffc68426bdd2e4863d41f51a9931fafd6158658f02a8297\", \"a5dd107c44b9df974eeaef4bea765ade0726845d123a0ea6590d861c5cc39176\", \"b98f049a1a2e268e5014289bef211ca22a0b25d89cd30609a65566d5867a7e77\", \"c617b8fb8810e655b19b56c3680d33b0df2fa6765c4c644fac9fbdfbbde639e3\", \"e6b8ebf19fa1b930c76c7a5fc86862af407b299c6e540748d17f30bb369f05ab\", \"e778cdd6293d99a05f12b1eb383c351aeeb8e558c137ceae94c712f02f47eb9d\"]}, \"state_id\": \"122e26079a328bf43fc47871\", \"visible_experiments\": [{\"experiment_id\": 1, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z1_0\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 7, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 1}, \"intervention\": \"DO_Z1_1\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 10, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 0}}]}"} {"agent_name": "causal_micro_lab_agent_loop", "data_source": "causal_micro_lab_final_eval_v3", "env_spec_json": "{\"agent\": {}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 161, \"task\": {\"state\": {\"available_experiment_ids\": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 16, 17, 18, 19, 20, 21, 22, 23, 24, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 37, 38, 39], \"metadata\": {\"evidence_size\": 3, \"family_bucket\": \"mixed\", \"maximum_separation\": 0.6486486486486487, \"mean_separation\": 0.3939189189189189, \"minimum_separation\": 0.10810810810810811, \"normalized_mean_separation\": 0.42205598455598453, \"oracle_budget_representation_error\": 0.13682432432432434, \"oracle_singleton_representation_error\": 0.33108108108108103, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.1942567567567567, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 16}, \"private\": {\"hidden_mode_id\": \"37f3a5f7a9daf2b188b8e7559616f5c0e203a73afc7688b7174c73532064bf5d\", \"valid_mode_ids\": [\"01f141cb9940dc4ac265c6369d899f0548be8d2ee116bdb6d7bc7a6d14ea2259\", \"1059f60e8a89e3447506ef4b68fac45e69709ca3f39089573fe7c4ac1fb66cd2\", \"16eb296a58c9f106ab2cc44711d6f88ec94721a102f713f480ecbf656e5fdf55\", \"23f6cf3ff90eb2f34886f505d73bb5ac0a9fffd7dde0c5d548ce7da4753115c3\", \"378843a24599bc40e7673d4fb9cb26f9863bf2a2e6f1d483bfc007de9a78a035\", \"37f3a5f7a9daf2b188b8e7559616f5c0e203a73afc7688b7174c73532064bf5d\", \"4bc13c435efd883025153b23f7a588bbdd2dd1913591ef3d8463ff574a8ce9e4\", \"4c67bee8982d9363c4ac77cd2ac62c8c0a193b78f7b1a90922f740bcafb0ebb1\", \"5d2fad6adcd8326cfb1357152402f62cf903dc71d34a748db8f1a4f06ae732d1\", \"6056cd039538245b2a6d60ee9f80b39b1df78632d735ff60ebf561d498d35b48\", \"7c06182217266949aa445c008e97e0007e640139d70094784b17e6289bb271ec\", \"8241e07c9fada0832f298f82dcbac4381440e6ae23b460492713f8bef3d92ac7\", \"b967d3dbcbb716e971eb7d84bde8ec3a146a12201aa1e36dcaedfb6be443da7c\", \"c17094bc425469eecbd79526a6cb9bbe7278d9c635b173182475a063cf084489\", \"cad80a7d9b4b67e5b0c70bf541109fc0f57f21e3399922a15349152bc5d872d6\", \"f9fcdad2a06d6da0e593a73690ba137faf2fef7e06b780bb08a7b2ce16b19f33\"]}, \"state_id\": \"16797b6d85b13ea1bd8f665e\", \"visible_experiments\": [{\"experiment_id\": 15, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 25, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 36, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 1}, \"intervention\": \"DO_Z1_0\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 1}}]}}}", "extra_info": {"index": 161, "max_global_steps": 0, "min_global_steps": 0}, "index": 161, "prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=1, X3=1\n intervention: none\n observed: Z1=0, Z2=0, Y=0\n\nExperiment 2:\n inputs: X1=1, X2=0, X3=1\n intervention: none\n observed: Z1=0, Z2=1, Y=0\n\nExperiment 3:\n inputs: X1=1, X2=1, X3=1\n intervention: set Z1=0\n observed: Z1=0, Z2=1, Y=1\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "raw_prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=1, X3=1\n intervention: none\n observed: Z1=0, Z2=0, Y=0\n\nExperiment 2:\n inputs: X1=1, X2=0, X3=1\n intervention: none\n observed: Z1=0, Z2=1, Y=0\n\nExperiment 3:\n inputs: X1=1, X2=1, X3=1\n intervention: set Z1=0\n observed: Z1=0, Z2=1, Y=1\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "reward_model": {"style": "rule"}, "state_json": "{\"available_experiment_ids\": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 16, 17, 18, 19, 20, 21, 22, 23, 24, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 37, 38, 39], \"metadata\": {\"evidence_size\": 3, \"family_bucket\": \"mixed\", \"maximum_separation\": 0.6486486486486487, \"mean_separation\": 0.3939189189189189, \"minimum_separation\": 0.10810810810810811, \"normalized_mean_separation\": 0.42205598455598453, \"oracle_budget_representation_error\": 0.13682432432432434, \"oracle_singleton_representation_error\": 0.33108108108108103, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.1942567567567567, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 16}, \"private\": {\"hidden_mode_id\": \"37f3a5f7a9daf2b188b8e7559616f5c0e203a73afc7688b7174c73532064bf5d\", \"valid_mode_ids\": [\"01f141cb9940dc4ac265c6369d899f0548be8d2ee116bdb6d7bc7a6d14ea2259\", \"1059f60e8a89e3447506ef4b68fac45e69709ca3f39089573fe7c4ac1fb66cd2\", \"16eb296a58c9f106ab2cc44711d6f88ec94721a102f713f480ecbf656e5fdf55\", \"23f6cf3ff90eb2f34886f505d73bb5ac0a9fffd7dde0c5d548ce7da4753115c3\", \"378843a24599bc40e7673d4fb9cb26f9863bf2a2e6f1d483bfc007de9a78a035\", \"37f3a5f7a9daf2b188b8e7559616f5c0e203a73afc7688b7174c73532064bf5d\", \"4bc13c435efd883025153b23f7a588bbdd2dd1913591ef3d8463ff574a8ce9e4\", \"4c67bee8982d9363c4ac77cd2ac62c8c0a193b78f7b1a90922f740bcafb0ebb1\", \"5d2fad6adcd8326cfb1357152402f62cf903dc71d34a748db8f1a4f06ae732d1\", \"6056cd039538245b2a6d60ee9f80b39b1df78632d735ff60ebf561d498d35b48\", \"7c06182217266949aa445c008e97e0007e640139d70094784b17e6289bb271ec\", \"8241e07c9fada0832f298f82dcbac4381440e6ae23b460492713f8bef3d92ac7\", \"b967d3dbcbb716e971eb7d84bde8ec3a146a12201aa1e36dcaedfb6be443da7c\", \"c17094bc425469eecbd79526a6cb9bbe7278d9c635b173182475a063cf084489\", \"cad80a7d9b4b67e5b0c70bf541109fc0f57f21e3399922a15349152bc5d872d6\", \"f9fcdad2a06d6da0e593a73690ba137faf2fef7e06b780bb08a7b2ce16b19f33\"]}, \"state_id\": \"16797b6d85b13ea1bd8f665e\", \"visible_experiments\": [{\"experiment_id\": 15, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 25, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 36, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 1}, \"intervention\": \"DO_Z1_0\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 1}}]}"} {"agent_name": "causal_micro_lab_agent_loop", "data_source": "causal_micro_lab_final_eval_v3", "env_spec_json": "{\"agent\": {}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 162, \"task\": {\"state\": {\"available_experiment_ids\": [1, 2, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 3, \"family_bucket\": \"within_family\", \"maximum_separation\": 0.6216216216216216, \"mean_separation\": 0.4175675675675674, \"minimum_separation\": 0.16216216216216217, \"normalized_mean_separation\": 0.4473938223938222, \"oracle_budget_representation_error\": 0.1621621621621622, \"oracle_singleton_representation_error\": 0.36148648648648646, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.19932432432432426, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 16}, \"private\": {\"hidden_mode_id\": \"06fe009e3fd1a73f5cfeb992961c9b77037fd7aa4c8880f62b253913971d37e8\", \"valid_mode_ids\": [\"06fe009e3fd1a73f5cfeb992961c9b77037fd7aa4c8880f62b253913971d37e8\", \"11b0e82995e83f3f0f822e8a194b9b12a75372475f03824b161883ae4cf47239\", \"284e9fb0a5d377a430015665602d138db78ed3067cfc23908a93305d2423bbff\", \"312677479ec51db2be4b83d1f0d0cce410faa9db29f90fb6857dffa19e2475e5\", \"3f018ec43405891dba3512288b15488d70fc4b92803146f1985e44085c5ccab2\", \"40b1fbf0535e23eaf9915a938b19a2f14609fa85e75d4d582ca84aba4db588c6\", \"538736a4b082875b65c7a2ec54c899d9799183095b5fd3d25c602a7ccfd3e19f\", \"64b7d6554875ee615f5d5194f9d48b6129fc323f849d8b9f5ce5f6d94dacef2a\", \"96a15eab24cbfcc158ca22623506c221984a6654c239981906dc3ff7eba89048\", \"9708e82112476e234316d71a4bc1349b8a6ec22f57a688fd79332e8a27676f8c\", \"b55628886bf7833ef136633a92280b5890ca6add02e44755cf2739fc3f90e24d\", \"c2dbd76ca10427f5399d79692a2d48d2bdb9b1423e63c64e20a8345d20bba51e\", \"cb29334618338c9f76084a28c10679e21c445cfe1e059f92ac40e233380efa2e\", \"f62278b61cf4143e4e4652b6b8d2df32ba725699f015f763a1dc6f67b3529d07\", \"f74e3248399130f0bfa26aae609fb67609b89f9a8a0e8cb195b8410baa5404cb\", \"f81316d3e2e3727a5b24e6bb253e0524f80f0e53b00bb878542220bf99f8e0b6\"]}, \"state_id\": \"ed4a20707916c6d85e339a20\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 3, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z2_0\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 28, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 1}, \"intervention\": \"DO_Z2_0\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 0}}]}}}", "extra_info": {"index": 162, "max_global_steps": 0, "min_global_steps": 0}, "index": 162, "prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=0\n intervention: none\n observed: Z1=0, Z2=1, Y=0\n\nExperiment 2:\n inputs: X1=0, X2=0, X3=0\n intervention: set Z2=0\n observed: Z1=0, Z2=0, Y=1\n\nExperiment 3:\n inputs: X1=1, X2=0, X3=1\n intervention: set Z2=0\n observed: Z1=0, Z2=0, Y=1\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "raw_prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=0\n intervention: none\n observed: Z1=0, Z2=1, Y=0\n\nExperiment 2:\n inputs: X1=0, X2=0, X3=0\n intervention: set Z2=0\n observed: Z1=0, Z2=0, Y=1\n\nExperiment 3:\n inputs: X1=1, X2=0, X3=1\n intervention: set Z2=0\n observed: Z1=0, Z2=0, Y=1\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "reward_model": {"style": "rule"}, "state_json": "{\"available_experiment_ids\": [1, 2, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 3, \"family_bucket\": \"within_family\", \"maximum_separation\": 0.6216216216216216, \"mean_separation\": 0.4175675675675674, \"minimum_separation\": 0.16216216216216217, \"normalized_mean_separation\": 0.4473938223938222, \"oracle_budget_representation_error\": 0.1621621621621622, \"oracle_singleton_representation_error\": 0.36148648648648646, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.19932432432432426, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 16}, \"private\": {\"hidden_mode_id\": \"06fe009e3fd1a73f5cfeb992961c9b77037fd7aa4c8880f62b253913971d37e8\", \"valid_mode_ids\": [\"06fe009e3fd1a73f5cfeb992961c9b77037fd7aa4c8880f62b253913971d37e8\", \"11b0e82995e83f3f0f822e8a194b9b12a75372475f03824b161883ae4cf47239\", \"284e9fb0a5d377a430015665602d138db78ed3067cfc23908a93305d2423bbff\", \"312677479ec51db2be4b83d1f0d0cce410faa9db29f90fb6857dffa19e2475e5\", \"3f018ec43405891dba3512288b15488d70fc4b92803146f1985e44085c5ccab2\", \"40b1fbf0535e23eaf9915a938b19a2f14609fa85e75d4d582ca84aba4db588c6\", \"538736a4b082875b65c7a2ec54c899d9799183095b5fd3d25c602a7ccfd3e19f\", \"64b7d6554875ee615f5d5194f9d48b6129fc323f849d8b9f5ce5f6d94dacef2a\", \"96a15eab24cbfcc158ca22623506c221984a6654c239981906dc3ff7eba89048\", \"9708e82112476e234316d71a4bc1349b8a6ec22f57a688fd79332e8a27676f8c\", \"b55628886bf7833ef136633a92280b5890ca6add02e44755cf2739fc3f90e24d\", \"c2dbd76ca10427f5399d79692a2d48d2bdb9b1423e63c64e20a8345d20bba51e\", \"cb29334618338c9f76084a28c10679e21c445cfe1e059f92ac40e233380efa2e\", \"f62278b61cf4143e4e4652b6b8d2df32ba725699f015f763a1dc6f67b3529d07\", \"f74e3248399130f0bfa26aae609fb67609b89f9a8a0e8cb195b8410baa5404cb\", \"f81316d3e2e3727a5b24e6bb253e0524f80f0e53b00bb878542220bf99f8e0b6\"]}, \"state_id\": \"ed4a20707916c6d85e339a20\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 3, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z2_0\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 28, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 1}, \"intervention\": \"DO_Z2_0\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 0}}]}"} {"agent_name": "causal_micro_lab_agent_loop", "data_source": "causal_micro_lab_final_eval_v3", "env_spec_json": "{\"agent\": {}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 163, \"task\": {\"state\": {\"available_experiment_ids\": [0, 2, 3, 4, 5, 6, 7, 8, 9, 11, 12, 13, 14, 15, 16, 17, 18, 19, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 4, \"family_bucket\": \"mixed\", \"maximum_separation\": 0.5833333333333334, \"mean_separation\": 0.38379629629629614, \"minimum_separation\": 0.1111111111111111, \"normalized_mean_separation\": 0.4112103174603173, \"oracle_budget_representation_error\": 0.14062499999999997, \"oracle_singleton_representation_error\": 0.3454861111111111, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.20486111111111113, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 16}, \"private\": {\"hidden_mode_id\": \"652c6d471a192d4567a45a4197f0e0369e086c6dfbedd3350382a8101234e5df\", \"valid_mode_ids\": [\"03969e580dad0d97e20647f48161f8eb2d799cc5952634d86674ba8b6622d8e8\", \"040104f35482af67ad2623934e28d2b9511d8663066013f59ca60f2673afe7bc\", \"292521f3ac6180921b2ce29621a1328e8aabfbe97a1c80c38977cceac013fe28\", \"2df7d9905dcc62470f46b6c6665ad679264851644ae33cba35a21cb44ddca040\", \"2e5e46ba9b703ff7d868abc1ef3ee677c92bcf6773fa6cf450b2be2ed737a416\", \"554aa587aaa5929de31de69328a05e8da29dbaf5e4f7f7aeb48d99df0fea3038\", \"5e46d1dd7fae0a20d066b64eeace43d17157144b101b645d221e3c15062dd56a\", \"63afa529c7ce7f293b63afa81d8786ddfcfdaebf48f33b66df50a4b1178ca83a\", \"652c6d471a192d4567a45a4197f0e0369e086c6dfbedd3350382a8101234e5df\", \"65b0063270747649fe124ba55450992f5cb9a4109b1e91e47d9b82b9a62849c2\", \"866232280c374e75d556b4fd5e69b9320e5d78f0723db2d4f0278daf9d5284eb\", \"9a379663bec5285b99f8807e638643310e44d4f2f4611deb6f3d6677880f2ac9\", \"a18d0b1d3a5630fe7382dc8846c97e33d4d0b25be25d6963f1f5f690c4952305\", \"c0f94683e890b360226c8d62134ff9745bd901d6cc71577d20a5fc9cce8781b6\", \"d2d09dfd9aafc54046d8111ecd963d2368da70a73e48bd04c2c19e890c07a69c\", \"fd74bf1fe02be698bde8272462a58e706fb16b2e6b783c2ff87c74630bc44d0b\"]}, \"state_id\": \"b724b7a50aa0d1bd7b287ce3\", \"visible_experiments\": [{\"experiment_id\": 1, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z1_0\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 10, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 20, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 21, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z1_0\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}]}}}", "extra_info": {"index": 163, "max_global_steps": 0, "min_global_steps": 0}, "index": 163, "prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=0\n intervention: set Z1=0\n observed: Z1=0, Z2=0, Y=0\n\nExperiment 2:\n inputs: X1=0, X2=1, X3=0\n intervention: none\n observed: Z1=0, Z2=1, Y=1\n\nExperiment 3:\n inputs: X1=1, X2=0, X3=0\n intervention: none\n observed: Z1=1, Z2=1, Y=1\n\nExperiment 4:\n inputs: X1=1, X2=0, X3=0\n intervention: set Z1=0\n observed: Z1=0, Z2=1, Y=0\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "raw_prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=0\n intervention: set Z1=0\n observed: Z1=0, Z2=0, Y=0\n\nExperiment 2:\n inputs: X1=0, X2=1, X3=0\n intervention: none\n observed: Z1=0, Z2=1, Y=1\n\nExperiment 3:\n inputs: X1=1, X2=0, X3=0\n intervention: none\n observed: Z1=1, Z2=1, Y=1\n\nExperiment 4:\n inputs: X1=1, X2=0, X3=0\n intervention: set Z1=0\n observed: Z1=0, Z2=1, Y=0\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "reward_model": {"style": "rule"}, "state_json": "{\"available_experiment_ids\": [0, 2, 3, 4, 5, 6, 7, 8, 9, 11, 12, 13, 14, 15, 16, 17, 18, 19, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 4, \"family_bucket\": \"mixed\", \"maximum_separation\": 0.5833333333333334, \"mean_separation\": 0.38379629629629614, \"minimum_separation\": 0.1111111111111111, \"normalized_mean_separation\": 0.4112103174603173, \"oracle_budget_representation_error\": 0.14062499999999997, \"oracle_singleton_representation_error\": 0.3454861111111111, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.20486111111111113, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 16}, \"private\": {\"hidden_mode_id\": \"652c6d471a192d4567a45a4197f0e0369e086c6dfbedd3350382a8101234e5df\", \"valid_mode_ids\": [\"03969e580dad0d97e20647f48161f8eb2d799cc5952634d86674ba8b6622d8e8\", \"040104f35482af67ad2623934e28d2b9511d8663066013f59ca60f2673afe7bc\", \"292521f3ac6180921b2ce29621a1328e8aabfbe97a1c80c38977cceac013fe28\", \"2df7d9905dcc62470f46b6c6665ad679264851644ae33cba35a21cb44ddca040\", \"2e5e46ba9b703ff7d868abc1ef3ee677c92bcf6773fa6cf450b2be2ed737a416\", \"554aa587aaa5929de31de69328a05e8da29dbaf5e4f7f7aeb48d99df0fea3038\", \"5e46d1dd7fae0a20d066b64eeace43d17157144b101b645d221e3c15062dd56a\", \"63afa529c7ce7f293b63afa81d8786ddfcfdaebf48f33b66df50a4b1178ca83a\", \"652c6d471a192d4567a45a4197f0e0369e086c6dfbedd3350382a8101234e5df\", \"65b0063270747649fe124ba55450992f5cb9a4109b1e91e47d9b82b9a62849c2\", \"866232280c374e75d556b4fd5e69b9320e5d78f0723db2d4f0278daf9d5284eb\", \"9a379663bec5285b99f8807e638643310e44d4f2f4611deb6f3d6677880f2ac9\", \"a18d0b1d3a5630fe7382dc8846c97e33d4d0b25be25d6963f1f5f690c4952305\", \"c0f94683e890b360226c8d62134ff9745bd901d6cc71577d20a5fc9cce8781b6\", \"d2d09dfd9aafc54046d8111ecd963d2368da70a73e48bd04c2c19e890c07a69c\", \"fd74bf1fe02be698bde8272462a58e706fb16b2e6b783c2ff87c74630bc44d0b\"]}, \"state_id\": \"b724b7a50aa0d1bd7b287ce3\", \"visible_experiments\": [{\"experiment_id\": 1, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z1_0\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 10, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 20, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 21, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z1_0\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}]}"} {"agent_name": "causal_micro_lab_agent_loop", "data_source": "causal_micro_lab_final_eval_v3", "env_spec_json": "{\"agent\": {}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 164, \"task\": {\"state\": {\"available_experiment_ids\": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 16, 17, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 3, \"family_bucket\": \"within_family\", \"maximum_separation\": 0.6486486486486487, \"mean_separation\": 0.4189189189189187, \"minimum_separation\": 0.16216216216216217, \"normalized_mean_separation\": 0.4488416988416986, \"oracle_budget_representation_error\": 0.16216216216216217, \"oracle_singleton_representation_error\": 0.37162162162162166, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.20945945945945948, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 16}, \"private\": {\"hidden_mode_id\": \"c5cf958cc276f846ad354717b3f70cc6b90aa007b3dd6691d3c0c8aad2330937\", \"valid_mode_ids\": [\"069645ccb49aafca6217ae30aa982794760550d98138a1e53c18c2b48a43a88e\", \"0f55a125abf62f29093c3ab1ccde67254bca97286097f17333e8b0a644791119\", \"4454a513252290a2828ac621ed8466b71b31d6fe2e9b90977dddd9efd5acd712\", \"536f8d7904e6476d12d441a57433efe79bd6d34a9447fae726a9ea667abc7e70\", \"5af216ad6f6b3f62864b385bb54a1e71286589e5bbf9698c3598a08c029f0f00\", \"626b56b3c1f6c06fad61f864a660174de33d9dc6fd6018bd1b89d88a90f37e1f\", \"639ea1e6dbce6038c2635589235dc33fc85d2789ebfcc19c074a49a9aa4e3ab6\", \"73abc248ab3e1f3830fe7eab80d533125edfaa8e741794341d651c4b33020650\", \"7cd428a02c1d9bf9d11de88e8c204cfed4278da83cdacde37967ebddd8d87278\", \"8fca3f5b88ddf4a7124d849109d63062bf5acc86f194883219b796a3d22c17e4\", \"b35c79d69fd30937dd41c147aad2f74f8fcc9402e19cb74c99562c72509dd3fd\", \"c0e17f53a7e76652dbdcf8a3e676790b9dfb629b24b3ea52e01ca7d319cf5924\", \"c3876b7b4decdaadda51efa44fa6ac6ef1282f7b7d1495adadec3a8e3acc0a9a\", \"c5cf958cc276f846ad354717b3f70cc6b90aa007b3dd6691d3c0c8aad2330937\", \"d0feb182fe236e3ab891da3cb7c458937d35fec43e267c86b201e40b56b8d3c7\", \"d12980c21cc54b601472aa858708ba49bdfce6731987279698a319ed6883705d\"]}, \"state_id\": \"808a3afe5193b000518af0cb\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 15, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 18, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 1}, \"intervention\": \"DO_Z2_0\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 0}}]}}}", "extra_info": {"index": 164, "max_global_steps": 0, "min_global_steps": 0}, "index": 164, "prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=0\n intervention: none\n observed: Z1=0, Z2=1, Y=0\n\nExperiment 2:\n inputs: X1=0, X2=1, X3=1\n intervention: none\n observed: Z1=0, Z2=1, Y=1\n\nExperiment 3:\n inputs: X1=0, X2=1, X3=1\n intervention: set Z2=0\n observed: Z1=0, Z2=0, Y=0\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "raw_prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=0\n intervention: none\n observed: Z1=0, Z2=1, Y=0\n\nExperiment 2:\n inputs: X1=0, X2=1, X3=1\n intervention: none\n observed: Z1=0, Z2=1, Y=1\n\nExperiment 3:\n inputs: X1=0, X2=1, X3=1\n intervention: set Z2=0\n observed: Z1=0, Z2=0, Y=0\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "reward_model": {"style": "rule"}, "state_json": "{\"available_experiment_ids\": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 16, 17, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 3, \"family_bucket\": \"within_family\", \"maximum_separation\": 0.6486486486486487, \"mean_separation\": 0.4189189189189187, \"minimum_separation\": 0.16216216216216217, \"normalized_mean_separation\": 0.4488416988416986, \"oracle_budget_representation_error\": 0.16216216216216217, \"oracle_singleton_representation_error\": 0.37162162162162166, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.20945945945945948, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 16}, \"private\": {\"hidden_mode_id\": \"c5cf958cc276f846ad354717b3f70cc6b90aa007b3dd6691d3c0c8aad2330937\", \"valid_mode_ids\": [\"069645ccb49aafca6217ae30aa982794760550d98138a1e53c18c2b48a43a88e\", \"0f55a125abf62f29093c3ab1ccde67254bca97286097f17333e8b0a644791119\", \"4454a513252290a2828ac621ed8466b71b31d6fe2e9b90977dddd9efd5acd712\", \"536f8d7904e6476d12d441a57433efe79bd6d34a9447fae726a9ea667abc7e70\", \"5af216ad6f6b3f62864b385bb54a1e71286589e5bbf9698c3598a08c029f0f00\", \"626b56b3c1f6c06fad61f864a660174de33d9dc6fd6018bd1b89d88a90f37e1f\", \"639ea1e6dbce6038c2635589235dc33fc85d2789ebfcc19c074a49a9aa4e3ab6\", \"73abc248ab3e1f3830fe7eab80d533125edfaa8e741794341d651c4b33020650\", \"7cd428a02c1d9bf9d11de88e8c204cfed4278da83cdacde37967ebddd8d87278\", \"8fca3f5b88ddf4a7124d849109d63062bf5acc86f194883219b796a3d22c17e4\", \"b35c79d69fd30937dd41c147aad2f74f8fcc9402e19cb74c99562c72509dd3fd\", \"c0e17f53a7e76652dbdcf8a3e676790b9dfb629b24b3ea52e01ca7d319cf5924\", \"c3876b7b4decdaadda51efa44fa6ac6ef1282f7b7d1495adadec3a8e3acc0a9a\", \"c5cf958cc276f846ad354717b3f70cc6b90aa007b3dd6691d3c0c8aad2330937\", \"d0feb182fe236e3ab891da3cb7c458937d35fec43e267c86b201e40b56b8d3c7\", \"d12980c21cc54b601472aa858708ba49bdfce6731987279698a319ed6883705d\"]}, \"state_id\": \"808a3afe5193b000518af0cb\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 15, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 18, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 1}, \"intervention\": \"DO_Z2_0\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 0}}]}"} {"agent_name": "causal_micro_lab_agent_loop", "data_source": "causal_micro_lab_final_eval_v3", "env_spec_json": "{\"agent\": {}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 165, \"task\": {\"state\": {\"available_experiment_ids\": [0, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 27, 28, 29, 31, 32, 33, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 4, \"family_bucket\": \"cross_family\", \"maximum_separation\": 0.7222222222222222, \"mean_separation\": 0.482638888888889, \"minimum_separation\": 0.1111111111111111, \"normalized_mean_separation\": 0.5171130952380953, \"oracle_budget_representation_error\": 0.17013888888888887, \"oracle_singleton_representation_error\": 0.3854166666666667, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.21527777777777782, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 16}, \"private\": {\"hidden_mode_id\": \"d81caa1e7ea878e9a9743982d6427dbafd1b473035d93e296463c4e5c68db1c7\", \"valid_mode_ids\": [\"02b0f9e8719e6a5719306caead3aafd9496d5e88d4331f1674cb2f2f9493166f\", \"09b3ca5cfc82d0dfbbd340ab1a189a5d14d5d0c404e9ec331b3b8b3ede588bbf\", \"24dd9496cff91ea7709195e518d7b3d0bdbf7cb99ed4a118493a538e80f7869f\", \"39dd9445c9ce17d961bc1cb97b903c07a432527bf0b6f8913bb7deed78c5509d\", \"4734081eb61eabd9fcb9077321d70c9b30bd04cb039fa304ea872cba70fe71fa\", \"60942c6cbd14cf19527b509bb7c8c9d69b9cfa31d0fc794ac62b76afa393b950\", \"766cecca4833f71d36d874e3c923f0529ac775eba50e5e9bfb5759753cdf7268\", \"90939e3d5c3d5f7c868307595a6ac933d0aff571efe36c5c5b878e0286f85e4f\", \"941e5e66aa4a382cf0b34ea17722c0470f0c7ebbe57a73bb3bc1dd98e392cedd\", \"be172d33b77a8b811d5b8bc5f13a274f1b2cdf9698e7ea35b8287a5b4cae7b52\", \"c62532959de0b6655cd85ecdbe9c09681cbf811500188d9186b5adfe9b6e5744\", \"c7e1b2ae3bb27e97664d43f6af7e0a0159ad7e43ebd5644fcacad4d01a5ed095\", \"c9e393943d2e2f33514a0201712a4dc3744f69e27458e90015d307e8ba675b6a\", \"d81caa1e7ea878e9a9743982d6427dbafd1b473035d93e296463c4e5c68db1c7\", \"d92c9e566e32bf2cff2a9052f1424a15f569e15b4901c84f7d7f0da064cc16f4\", \"ed0735980bd649b50b18fad3189d032f4ecbe7f4d0b0229bedfa7930d3375414\"]}, \"state_id\": \"04f2be2c5e000258d4d8e689\", \"visible_experiments\": [{\"experiment_id\": 1, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z1_0\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 15, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 0}}, {\"experiment_id\": 26, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 1}, \"intervention\": \"DO_Z1_0\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 30, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 0}}]}}}", "extra_info": {"index": 165, "max_global_steps": 0, "min_global_steps": 0}, "index": 165, "prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=0\n intervention: set Z1=0\n observed: Z1=0, Z2=0, Y=0\n\nExperiment 2:\n inputs: X1=0, X2=1, X3=1\n intervention: none\n observed: Z1=1, Z2=0, Y=0\n\nExperiment 3:\n inputs: X1=1, X2=0, X3=1\n intervention: set Z1=0\n observed: Z1=0, Z2=0, Y=1\n\nExperiment 4:\n inputs: X1=1, X2=1, X3=0\n intervention: none\n observed: Z1=0, Z2=0, Y=1\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "raw_prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=0\n intervention: set Z1=0\n observed: Z1=0, Z2=0, Y=0\n\nExperiment 2:\n inputs: X1=0, X2=1, X3=1\n intervention: none\n observed: Z1=1, Z2=0, Y=0\n\nExperiment 3:\n inputs: X1=1, X2=0, X3=1\n intervention: set Z1=0\n observed: Z1=0, Z2=0, Y=1\n\nExperiment 4:\n inputs: X1=1, X2=1, X3=0\n intervention: none\n observed: Z1=0, Z2=0, Y=1\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "reward_model": {"style": "rule"}, "state_json": "{\"available_experiment_ids\": [0, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 27, 28, 29, 31, 32, 33, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 4, \"family_bucket\": \"cross_family\", \"maximum_separation\": 0.7222222222222222, \"mean_separation\": 0.482638888888889, \"minimum_separation\": 0.1111111111111111, \"normalized_mean_separation\": 0.5171130952380953, \"oracle_budget_representation_error\": 0.17013888888888887, \"oracle_singleton_representation_error\": 0.3854166666666667, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.21527777777777782, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 16}, \"private\": {\"hidden_mode_id\": \"d81caa1e7ea878e9a9743982d6427dbafd1b473035d93e296463c4e5c68db1c7\", \"valid_mode_ids\": [\"02b0f9e8719e6a5719306caead3aafd9496d5e88d4331f1674cb2f2f9493166f\", \"09b3ca5cfc82d0dfbbd340ab1a189a5d14d5d0c404e9ec331b3b8b3ede588bbf\", \"24dd9496cff91ea7709195e518d7b3d0bdbf7cb99ed4a118493a538e80f7869f\", \"39dd9445c9ce17d961bc1cb97b903c07a432527bf0b6f8913bb7deed78c5509d\", \"4734081eb61eabd9fcb9077321d70c9b30bd04cb039fa304ea872cba70fe71fa\", \"60942c6cbd14cf19527b509bb7c8c9d69b9cfa31d0fc794ac62b76afa393b950\", \"766cecca4833f71d36d874e3c923f0529ac775eba50e5e9bfb5759753cdf7268\", \"90939e3d5c3d5f7c868307595a6ac933d0aff571efe36c5c5b878e0286f85e4f\", \"941e5e66aa4a382cf0b34ea17722c0470f0c7ebbe57a73bb3bc1dd98e392cedd\", \"be172d33b77a8b811d5b8bc5f13a274f1b2cdf9698e7ea35b8287a5b4cae7b52\", \"c62532959de0b6655cd85ecdbe9c09681cbf811500188d9186b5adfe9b6e5744\", \"c7e1b2ae3bb27e97664d43f6af7e0a0159ad7e43ebd5644fcacad4d01a5ed095\", \"c9e393943d2e2f33514a0201712a4dc3744f69e27458e90015d307e8ba675b6a\", \"d81caa1e7ea878e9a9743982d6427dbafd1b473035d93e296463c4e5c68db1c7\", \"d92c9e566e32bf2cff2a9052f1424a15f569e15b4901c84f7d7f0da064cc16f4\", \"ed0735980bd649b50b18fad3189d032f4ecbe7f4d0b0229bedfa7930d3375414\"]}, \"state_id\": \"04f2be2c5e000258d4d8e689\", \"visible_experiments\": [{\"experiment_id\": 1, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z1_0\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 15, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 0}}, {\"experiment_id\": 26, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 1}, \"intervention\": \"DO_Z1_0\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 30, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 0}}]}"} {"agent_name": "causal_micro_lab_agent_loop", "data_source": "causal_micro_lab_final_eval_v3", "env_spec_json": "{\"agent\": {}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 166, \"task\": {\"state\": {\"available_experiment_ids\": [0, 1, 2, 3, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 3, \"family_bucket\": \"cross_family\", \"maximum_separation\": 0.7297297297297297, \"mean_separation\": 0.5000000000000003, \"minimum_separation\": 0.2702702702702703, \"normalized_mean_separation\": 0.535714285714286, \"oracle_budget_representation_error\": 0.21621621621621617, \"oracle_singleton_representation_error\": 0.43581081081081074, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.21959459459459457, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 16}, \"private\": {\"hidden_mode_id\": \"25ffe561c17e0a69f3bb2da912e83352e2ad97f84d5f06174011db05fdf3fc39\", \"valid_mode_ids\": [\"12930e0a91c87e5633baf3e1fe5696d46488cc3830b104b6588f020c8b94d105\", \"143a177b74b2a4ddee1236f8f71e92950373f45ab2400052878376e5568a80b7\", \"18cdd342ee80925b0309661dc255647e5f99cd0014f285f79a9c8e37da5cdbac\", \"19cefdd776f10b29ad3a8a742a22d9384e8a5859f80affab33f7b04cba6bfa4b\", \"1d296b6bdff84566c9d899aa480018f8befcb2ffebb76fcebb37d0d9d8bf065a\", \"25ffe561c17e0a69f3bb2da912e83352e2ad97f84d5f06174011db05fdf3fc39\", \"615562f15302d6a4d3e8eb33715c1bca4f454ee10764a4634aa4c11514ed6a4e\", \"63967d950ac8fe0086528c2f750ac8df19af05a61cac898d080640e2c0e256a7\", \"6b1ae15415aeaf4ea3f532889758400558b8a4907759fad671e2dbcf9db0c443\", \"78a4731d27aedacbb1af85652288055833aadcadbd27ddb137c855abee8e16db\", \"884329cb4cc3b9ef5ab499ea7ee76cd03a9ae8ddac3c0e739a328522323d3432\", \"9d4adb48722242ea1c69017233a1ca9dceded9ee24940e934e544c4c6ce5f782\", \"ba139fa5dada456409b624b620ed847a475e58c95c39bda8aa66b819cfa095d1\", \"d23ec04a5ea90d35e57ac7078d7c3736e310f579975a8c4dbeea784fcd2ffc66\", \"d6983e3771f7f0c3210ef3f671486f9d89b81953c763f1614e9d40e6c99878f3\", \"e13f3e823b3d9a71fb8eb1271218d98f8e23384a915bf9834c051d79b3649b25\"]}, \"state_id\": \"802fd12858b6307f1683cafe\", \"visible_experiments\": [{\"experiment_id\": 4, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z2_1\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 15, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 0}}, {\"experiment_id\": 27, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 1}, \"intervention\": \"DO_Z1_1\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 0}}]}}}", "extra_info": {"index": 166, "max_global_steps": 0, "min_global_steps": 0}, "index": 166, "prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=0\n intervention: set Z2=1\n observed: Z1=1, Z2=1, Y=0\n\nExperiment 2:\n inputs: X1=0, X2=1, X3=1\n intervention: none\n observed: Z1=1, Z2=0, Y=1\n\nExperiment 3:\n inputs: X1=1, X2=0, X3=1\n intervention: set Z1=1\n observed: Z1=1, Z2=0, Y=0\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "raw_prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=0\n intervention: set Z2=1\n observed: Z1=1, Z2=1, Y=0\n\nExperiment 2:\n inputs: X1=0, X2=1, X3=1\n intervention: none\n observed: Z1=1, Z2=0, Y=1\n\nExperiment 3:\n inputs: X1=1, X2=0, X3=1\n intervention: set Z1=1\n observed: Z1=1, Z2=0, Y=0\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "reward_model": {"style": "rule"}, "state_json": "{\"available_experiment_ids\": [0, 1, 2, 3, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 3, \"family_bucket\": \"cross_family\", \"maximum_separation\": 0.7297297297297297, \"mean_separation\": 0.5000000000000003, \"minimum_separation\": 0.2702702702702703, \"normalized_mean_separation\": 0.535714285714286, \"oracle_budget_representation_error\": 0.21621621621621617, \"oracle_singleton_representation_error\": 0.43581081081081074, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.21959459459459457, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 16}, \"private\": {\"hidden_mode_id\": \"25ffe561c17e0a69f3bb2da912e83352e2ad97f84d5f06174011db05fdf3fc39\", \"valid_mode_ids\": [\"12930e0a91c87e5633baf3e1fe5696d46488cc3830b104b6588f020c8b94d105\", \"143a177b74b2a4ddee1236f8f71e92950373f45ab2400052878376e5568a80b7\", \"18cdd342ee80925b0309661dc255647e5f99cd0014f285f79a9c8e37da5cdbac\", \"19cefdd776f10b29ad3a8a742a22d9384e8a5859f80affab33f7b04cba6bfa4b\", \"1d296b6bdff84566c9d899aa480018f8befcb2ffebb76fcebb37d0d9d8bf065a\", \"25ffe561c17e0a69f3bb2da912e83352e2ad97f84d5f06174011db05fdf3fc39\", \"615562f15302d6a4d3e8eb33715c1bca4f454ee10764a4634aa4c11514ed6a4e\", \"63967d950ac8fe0086528c2f750ac8df19af05a61cac898d080640e2c0e256a7\", \"6b1ae15415aeaf4ea3f532889758400558b8a4907759fad671e2dbcf9db0c443\", \"78a4731d27aedacbb1af85652288055833aadcadbd27ddb137c855abee8e16db\", \"884329cb4cc3b9ef5ab499ea7ee76cd03a9ae8ddac3c0e739a328522323d3432\", \"9d4adb48722242ea1c69017233a1ca9dceded9ee24940e934e544c4c6ce5f782\", \"ba139fa5dada456409b624b620ed847a475e58c95c39bda8aa66b819cfa095d1\", \"d23ec04a5ea90d35e57ac7078d7c3736e310f579975a8c4dbeea784fcd2ffc66\", \"d6983e3771f7f0c3210ef3f671486f9d89b81953c763f1614e9d40e6c99878f3\", \"e13f3e823b3d9a71fb8eb1271218d98f8e23384a915bf9834c051d79b3649b25\"]}, \"state_id\": \"802fd12858b6307f1683cafe\", \"visible_experiments\": [{\"experiment_id\": 4, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z2_1\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 15, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 0}}, {\"experiment_id\": 27, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 1}, \"intervention\": \"DO_Z1_1\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 0}}]}"} {"agent_name": "causal_micro_lab_agent_loop", "data_source": "causal_micro_lab_final_eval_v3", "env_spec_json": "{\"agent\": {}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 167, \"task\": {\"state\": {\"available_experiment_ids\": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 11, 12, 13, 14, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 3, \"family_bucket\": \"cross_family\", \"maximum_separation\": 0.7027027027027027, \"mean_separation\": 0.45157657657657657, \"minimum_separation\": 0.10810810810810811, \"normalized_mean_separation\": 0.4838320463320463, \"oracle_budget_representation_error\": 0.13513513513513517, \"oracle_singleton_representation_error\": 0.35979729729729737, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.2246621621621622, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 16}, \"private\": {\"hidden_mode_id\": \"c4e393c6005bd2fcc382728d5fa55bc775e55316a6a41ce1ddf8770b7419ec89\", \"valid_mode_ids\": [\"083dfc7dcde9e92970a7893943b40b6509d93b3147edab79c1c6b0366d269121\", \"2eaacccc198480a1139a31a744872863a5cb9e758de24e19b0c2c581118d0674\", \"376f1ac487ad0fd340993b1c26300417e0413a76027cae6996850b43dcb7fee0\", \"61ce1d629b45a2214f4425a633de94ba50108ee99ccebfabdf70c12af7d08946\", \"668575336081a73bee7d822ed34258b80a3630f583575a107eecf9029d6af390\", \"686c06ed65ddc9be1c020a25b75c5a07834618bf2cbee4937a5f19226948448f\", \"928490c437f8b25ec5709343b9209c2b93fb14fa223aba17fc5eba16e127fcfb\", \"a187c7114a14f94f43d9fe9a749c912488c0693c20cd94e2bd9b82890830ea7f\", \"c4e393c6005bd2fcc382728d5fa55bc775e55316a6a41ce1ddf8770b7419ec89\", \"d96eb64e3c88f703384c4d2bf402e51809109facd993e883c91ebdb181a874b4\", \"e5d09fa8912fd17e53605f5280992bde86f1ff9af83a7224a34b36955954c772\", \"e93ad6f9fa273947978488e89eb5818415a0cd78f72eec1e43bcd5c64262d7c3\", \"f02f19d5c5029fc233ea27e9b957292894814a3487f980b9f940972e88ea3286\", \"f11e5a29063dd8a0f6b91aebe8b4d7f0cb3f8c006e158eab678b78d9965f713e\", \"f9b26f6759e42eee6f293a556d3999197111e928b5cef60b49844ce4ac602124\", \"fe5e62a3e54f64e841845091c8d64dd3d8d509b98c79aa8f80137bac6e9d97bb\"]}, \"state_id\": \"64570792f180a43c98d9a296\", \"visible_experiments\": [{\"experiment_id\": 10, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 15, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 35, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 0}}]}}}", "extra_info": {"index": 167, "max_global_steps": 0, "min_global_steps": 0}, "index": 167, "prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=1, X3=0\n intervention: none\n observed: Z1=0, Z2=0, Y=0\n\nExperiment 2:\n inputs: X1=0, X2=1, X3=1\n intervention: none\n observed: Z1=1, Z2=1, Y=0\n\nExperiment 3:\n inputs: X1=1, X2=1, X3=1\n intervention: none\n observed: Z1=0, Z2=0, Y=0\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "raw_prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=1, X3=0\n intervention: none\n observed: Z1=0, Z2=0, Y=0\n\nExperiment 2:\n inputs: X1=0, X2=1, X3=1\n intervention: none\n observed: Z1=1, Z2=1, Y=0\n\nExperiment 3:\n inputs: X1=1, X2=1, X3=1\n intervention: none\n observed: Z1=0, Z2=0, Y=0\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "reward_model": {"style": "rule"}, "state_json": "{\"available_experiment_ids\": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 11, 12, 13, 14, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 3, \"family_bucket\": \"cross_family\", \"maximum_separation\": 0.7027027027027027, \"mean_separation\": 0.45157657657657657, \"minimum_separation\": 0.10810810810810811, \"normalized_mean_separation\": 0.4838320463320463, \"oracle_budget_representation_error\": 0.13513513513513517, \"oracle_singleton_representation_error\": 0.35979729729729737, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.2246621621621622, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 16}, \"private\": {\"hidden_mode_id\": \"c4e393c6005bd2fcc382728d5fa55bc775e55316a6a41ce1ddf8770b7419ec89\", \"valid_mode_ids\": [\"083dfc7dcde9e92970a7893943b40b6509d93b3147edab79c1c6b0366d269121\", \"2eaacccc198480a1139a31a744872863a5cb9e758de24e19b0c2c581118d0674\", \"376f1ac487ad0fd340993b1c26300417e0413a76027cae6996850b43dcb7fee0\", \"61ce1d629b45a2214f4425a633de94ba50108ee99ccebfabdf70c12af7d08946\", \"668575336081a73bee7d822ed34258b80a3630f583575a107eecf9029d6af390\", \"686c06ed65ddc9be1c020a25b75c5a07834618bf2cbee4937a5f19226948448f\", \"928490c437f8b25ec5709343b9209c2b93fb14fa223aba17fc5eba16e127fcfb\", \"a187c7114a14f94f43d9fe9a749c912488c0693c20cd94e2bd9b82890830ea7f\", \"c4e393c6005bd2fcc382728d5fa55bc775e55316a6a41ce1ddf8770b7419ec89\", \"d96eb64e3c88f703384c4d2bf402e51809109facd993e883c91ebdb181a874b4\", \"e5d09fa8912fd17e53605f5280992bde86f1ff9af83a7224a34b36955954c772\", \"e93ad6f9fa273947978488e89eb5818415a0cd78f72eec1e43bcd5c64262d7c3\", \"f02f19d5c5029fc233ea27e9b957292894814a3487f980b9f940972e88ea3286\", \"f11e5a29063dd8a0f6b91aebe8b4d7f0cb3f8c006e158eab678b78d9965f713e\", \"f9b26f6759e42eee6f293a556d3999197111e928b5cef60b49844ce4ac602124\", \"fe5e62a3e54f64e841845091c8d64dd3d8d509b98c79aa8f80137bac6e9d97bb\"]}, \"state_id\": \"64570792f180a43c98d9a296\", \"visible_experiments\": [{\"experiment_id\": 10, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 15, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 35, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 0}}]}"} {"agent_name": "causal_micro_lab_agent_loop", "data_source": "causal_micro_lab_final_eval_v3", "env_spec_json": "{\"agent\": {}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 168, \"task\": {\"state\": {\"available_experiment_ids\": [0, 1, 2, 3, 4, 6, 7, 8, 9, 10, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 3, \"family_bucket\": \"within_family\", \"maximum_separation\": 0.7567567567567568, \"mean_separation\": 0.4711711711711709, \"minimum_separation\": 0.16216216216216217, \"normalized_mean_separation\": 0.5048262548262545, \"oracle_budget_representation_error\": 0.1621621621621622, \"oracle_singleton_representation_error\": 0.391891891891892, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.2297297297297298, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 16}, \"private\": {\"hidden_mode_id\": \"4fe8c6bfba4d674f1025d91c79a4875566cb6c5615b74daac30b1454fcf497c1\", \"valid_mode_ids\": [\"032793c62b3fc0370649bbd67b3e81b2b378d2f17cf532186f81600d7e80df0a\", \"0f8502192b7a2353cf01c192f57ae908833fee23f4c3718d84339401d50c7954\", \"13d41b43d8aa93163dd3330cee76229412c8d48d73fe8673788b5fb8cd46c08d\", \"314caeb2a853c3302a1e3de41790a55606db56c4ab80ea50c3242b7f8cccad94\", \"3fb3d59d0af26934a0ec1665f2e49a623ff52bcefca095e98df9a8d7991fa4ee\", \"496d2c9b9865e8c7a41435977b1383187fabd7437e5c4b52eef00c9ab2579d7d\", \"4fe8c6bfba4d674f1025d91c79a4875566cb6c5615b74daac30b1454fcf497c1\", \"522d56a6231268f8e07f13c114694806d3b4e58316ba27724954adad122e8e55\", \"5d6bc6bf236cf7e86578e70e28f1288b5ce7b5f5f6a1a070c63b82deacaf1db6\", \"65452de27a4322099f23c9818b623d57e66dcef9cc54e196b1976389a38914e8\", \"676ddaa4b8741e0eab818620db49df71301ce49ca96dc842dc93701c5b8e7008\", \"6b3dd4d90f3aa74c62c16f3cf52d2d3ba11f277ccd5f70d0d4f533f39bdc0a52\", \"6be4352a368e75017f5a7e3411690d5a18c8ba2d0d28b07224d8ba1831fbd668\", \"da01bc6e21338300d828794bffdfac38ff748978cbb635b06ffd25f53ae8ca2f\", \"dc25aa460be7a223d25fbfe76f5f41e2fad76bc55aa56a197a5ce3c2372aab5b\", \"eaad4bb018ab948083a971890f824ab67049740b1a6218572e6117ae7636f710\"]}, \"state_id\": \"997530d6a241399853f50c5f\", \"visible_experiments\": [{\"experiment_id\": 5, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 11, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 0}, \"intervention\": \"DO_Z1_0\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 25, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 0}}]}}}", "extra_info": {"index": 168, "max_global_steps": 0, "min_global_steps": 0}, "index": 168, "prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=1\n intervention: none\n observed: Z1=0, Z2=0, Y=1\n\nExperiment 2:\n inputs: X1=0, X2=1, X3=0\n intervention: set Z1=0\n observed: Z1=0, Z2=1, Y=1\n\nExperiment 3:\n inputs: X1=1, X2=0, X3=1\n intervention: none\n observed: Z1=1, Z2=0, Y=0\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "raw_prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=1\n intervention: none\n observed: Z1=0, Z2=0, Y=1\n\nExperiment 2:\n inputs: X1=0, X2=1, X3=0\n intervention: set Z1=0\n observed: Z1=0, Z2=1, Y=1\n\nExperiment 3:\n inputs: X1=1, X2=0, X3=1\n intervention: none\n observed: Z1=1, Z2=0, Y=0\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "reward_model": {"style": "rule"}, "state_json": "{\"available_experiment_ids\": [0, 1, 2, 3, 4, 6, 7, 8, 9, 10, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 3, \"family_bucket\": \"within_family\", \"maximum_separation\": 0.7567567567567568, \"mean_separation\": 0.4711711711711709, \"minimum_separation\": 0.16216216216216217, \"normalized_mean_separation\": 0.5048262548262545, \"oracle_budget_representation_error\": 0.1621621621621622, \"oracle_singleton_representation_error\": 0.391891891891892, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.2297297297297298, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 16}, \"private\": {\"hidden_mode_id\": \"4fe8c6bfba4d674f1025d91c79a4875566cb6c5615b74daac30b1454fcf497c1\", \"valid_mode_ids\": [\"032793c62b3fc0370649bbd67b3e81b2b378d2f17cf532186f81600d7e80df0a\", \"0f8502192b7a2353cf01c192f57ae908833fee23f4c3718d84339401d50c7954\", \"13d41b43d8aa93163dd3330cee76229412c8d48d73fe8673788b5fb8cd46c08d\", \"314caeb2a853c3302a1e3de41790a55606db56c4ab80ea50c3242b7f8cccad94\", \"3fb3d59d0af26934a0ec1665f2e49a623ff52bcefca095e98df9a8d7991fa4ee\", \"496d2c9b9865e8c7a41435977b1383187fabd7437e5c4b52eef00c9ab2579d7d\", \"4fe8c6bfba4d674f1025d91c79a4875566cb6c5615b74daac30b1454fcf497c1\", \"522d56a6231268f8e07f13c114694806d3b4e58316ba27724954adad122e8e55\", \"5d6bc6bf236cf7e86578e70e28f1288b5ce7b5f5f6a1a070c63b82deacaf1db6\", \"65452de27a4322099f23c9818b623d57e66dcef9cc54e196b1976389a38914e8\", \"676ddaa4b8741e0eab818620db49df71301ce49ca96dc842dc93701c5b8e7008\", \"6b3dd4d90f3aa74c62c16f3cf52d2d3ba11f277ccd5f70d0d4f533f39bdc0a52\", \"6be4352a368e75017f5a7e3411690d5a18c8ba2d0d28b07224d8ba1831fbd668\", \"da01bc6e21338300d828794bffdfac38ff748978cbb635b06ffd25f53ae8ca2f\", \"dc25aa460be7a223d25fbfe76f5f41e2fad76bc55aa56a197a5ce3c2372aab5b\", \"eaad4bb018ab948083a971890f824ab67049740b1a6218572e6117ae7636f710\"]}, \"state_id\": \"997530d6a241399853f50c5f\", \"visible_experiments\": [{\"experiment_id\": 5, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 11, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 0}, \"intervention\": \"DO_Z1_0\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 25, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 0}}]}"} {"agent_name": "causal_micro_lab_agent_loop", "data_source": "causal_micro_lab_final_eval_v3", "env_spec_json": "{\"agent\": {}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 169, \"task\": {\"state\": {\"available_experiment_ids\": [0, 3, 4, 5, 6, 7, 8, 9, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 4, \"family_bucket\": \"mixed\", \"maximum_separation\": 0.7777777777777778, \"mean_separation\": 0.48611111111111127, \"minimum_separation\": 0.1111111111111111, \"normalized_mean_separation\": 0.5208333333333335, \"oracle_budget_representation_error\": 0.18923611111111113, \"oracle_singleton_representation_error\": 0.4270833333333333, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.23784722222222218, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 16}, \"private\": {\"hidden_mode_id\": \"4179a1bd9d25b07c2b5e3f74593afdbcde96e7a553af8e8b486f1ce96bd174d7\", \"valid_mode_ids\": [\"0734051144a3ed146bd8622fa4cdd85c14fec18c9c68918972491973bf905566\", \"0b342b704733ca6998706396c7e41d3aa7f0b75d73ca2132b503bafd7e2ca22b\", \"3a3f541b5f2b7812d2dcd54c8d770ae4113d1ba32cfe51cc603117d3355f91e1\", \"3da9fc0065dc7038d1b6688eb93eae969041cfa5f419a14a49807eecca0f292d\", \"4179a1bd9d25b07c2b5e3f74593afdbcde96e7a553af8e8b486f1ce96bd174d7\", \"45bf5671e7fd75350f34f237efda613d906763f7725de07d2b9435532d80bc4f\", \"5fd30c9cad081b3c6931be6cdfdab8366295c22331c386f279f31a092aceb69e\", \"70a449cf56a30a7dba04eeb6fb7d9d7caf81724b308d1a2a3eccade551a3bc66\", \"83795863979a8ad8b51d6c1febd0547ed47b45dd172c8b787e817064e91538ea\", \"8dd33b60cda89710666ecd8e720c4f92a777ee09df5d43561414f96fe052cfa3\", \"9194504546a6367c8280e4a111e5bcd9c6eb37e3dd3d458d0c680537feca1c11\", \"b53a6ec03a3adbb6bb87fec509fb9190a5a6e494bd4d8ab78c5c7ebb34599ce2\", \"b5e4df5a2db648cfa9e97fb0003eb3d1b063a092507ef0c0a284db756507e4c0\", \"c1185f0619f01f11a327056ae5ab2faf09f4d489654a99ca109a0cab202534a2\", \"c642724671751818f5c799ad8cd98d463ec316fd3db50c11828b19a66169c951\", \"d5f1ef064afff06366f1b1d790ec3cad8e06f1a3b7048f0af3de378fcfafec9a\"]}, \"state_id\": \"71453a379cd48d0784c9eebd\", \"visible_experiments\": [{\"experiment_id\": 1, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z1_0\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 2, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z1_1\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 10, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 35, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}]}}}", "extra_info": {"index": 169, "max_global_steps": 0, "min_global_steps": 0}, "index": 169, "prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=0\n intervention: set Z1=0\n observed: Z1=0, Z2=0, Y=0\n\nExperiment 2:\n inputs: X1=0, X2=0, X3=0\n intervention: set Z1=1\n observed: Z1=1, Z2=1, Y=0\n\nExperiment 3:\n inputs: X1=0, X2=1, X3=0\n intervention: none\n observed: Z1=0, Z2=1, Y=0\n\nExperiment 4:\n inputs: X1=1, X2=1, X3=1\n intervention: none\n observed: Z1=0, Z2=1, Y=0\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "raw_prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=0\n intervention: set Z1=0\n observed: Z1=0, Z2=0, Y=0\n\nExperiment 2:\n inputs: X1=0, X2=0, X3=0\n intervention: set Z1=1\n observed: Z1=1, Z2=1, Y=0\n\nExperiment 3:\n inputs: X1=0, X2=1, X3=0\n intervention: none\n observed: Z1=0, Z2=1, Y=0\n\nExperiment 4:\n inputs: X1=1, X2=1, X3=1\n intervention: none\n observed: Z1=0, Z2=1, Y=0\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "reward_model": {"style": "rule"}, "state_json": "{\"available_experiment_ids\": [0, 3, 4, 5, 6, 7, 8, 9, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 4, \"family_bucket\": \"mixed\", \"maximum_separation\": 0.7777777777777778, \"mean_separation\": 0.48611111111111127, \"minimum_separation\": 0.1111111111111111, \"normalized_mean_separation\": 0.5208333333333335, \"oracle_budget_representation_error\": 0.18923611111111113, \"oracle_singleton_representation_error\": 0.4270833333333333, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.23784722222222218, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 16}, \"private\": {\"hidden_mode_id\": \"4179a1bd9d25b07c2b5e3f74593afdbcde96e7a553af8e8b486f1ce96bd174d7\", \"valid_mode_ids\": [\"0734051144a3ed146bd8622fa4cdd85c14fec18c9c68918972491973bf905566\", \"0b342b704733ca6998706396c7e41d3aa7f0b75d73ca2132b503bafd7e2ca22b\", \"3a3f541b5f2b7812d2dcd54c8d770ae4113d1ba32cfe51cc603117d3355f91e1\", \"3da9fc0065dc7038d1b6688eb93eae969041cfa5f419a14a49807eecca0f292d\", \"4179a1bd9d25b07c2b5e3f74593afdbcde96e7a553af8e8b486f1ce96bd174d7\", \"45bf5671e7fd75350f34f237efda613d906763f7725de07d2b9435532d80bc4f\", \"5fd30c9cad081b3c6931be6cdfdab8366295c22331c386f279f31a092aceb69e\", \"70a449cf56a30a7dba04eeb6fb7d9d7caf81724b308d1a2a3eccade551a3bc66\", \"83795863979a8ad8b51d6c1febd0547ed47b45dd172c8b787e817064e91538ea\", \"8dd33b60cda89710666ecd8e720c4f92a777ee09df5d43561414f96fe052cfa3\", \"9194504546a6367c8280e4a111e5bcd9c6eb37e3dd3d458d0c680537feca1c11\", \"b53a6ec03a3adbb6bb87fec509fb9190a5a6e494bd4d8ab78c5c7ebb34599ce2\", \"b5e4df5a2db648cfa9e97fb0003eb3d1b063a092507ef0c0a284db756507e4c0\", \"c1185f0619f01f11a327056ae5ab2faf09f4d489654a99ca109a0cab202534a2\", \"c642724671751818f5c799ad8cd98d463ec316fd3db50c11828b19a66169c951\", \"d5f1ef064afff06366f1b1d790ec3cad8e06f1a3b7048f0af3de378fcfafec9a\"]}, \"state_id\": \"71453a379cd48d0784c9eebd\", \"visible_experiments\": [{\"experiment_id\": 1, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z1_0\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 2, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z1_1\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 10, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 35, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}]}"} {"agent_name": "causal_micro_lab_agent_loop", "data_source": "causal_micro_lab_final_eval_v3", "env_spec_json": "{\"agent\": {}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 170, \"task\": {\"state\": {\"available_experiment_ids\": [0, 1, 2, 3, 4, 6, 8, 9, 10, 11, 12, 13, 14, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 3, \"family_bucket\": \"mixed\", \"maximum_separation\": 0.7567567567567568, \"mean_separation\": 0.455855855855856, \"minimum_separation\": 0.16216216216216217, \"normalized_mean_separation\": 0.48841698841698855, \"oracle_budget_representation_error\": 0.16216216216216223, \"oracle_singleton_representation_error\": 0.4037162162162162, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.24155405405405395, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 16}, \"private\": {\"hidden_mode_id\": \"96c1f4c8da5a62bcbb39a4091f72d5b4c85efbe20438cbd522d2e0b4eb5769e6\", \"valid_mode_ids\": [\"0866bf8895f5d2b45f213c33d400cf0f0e991b561243674e79ded665349980d8\", \"291555fe677838deacbad80afce40af7fc25369b3fcee2f057caf5963caf0fc9\", \"3010049a1985c7cbadf8197b7392115e2a42fa5c964e8e13383d92cd6ae67241\", \"4023b438014bc74dbc230f5ca6dbf7fd67bc16b1ccff1b03cca29fa7fe0d064f\", \"4d980982796690abe1ebdf6de0dd080f3f4078473ad7ec838a192c9c75af2064\", \"4f2f90034192971b512438a1e84ed0f43c39c40703f387df0be17dd0c195e027\", \"66ae108529f196399347cd39747108f4c55fa057af2b46f56cae8e64b7964eb2\", \"7806ccb0ed45919bec0d77706cf8facf73f6e52c8006ecc7456d098861a9d4f0\", \"781f06eb9d14a4e450a16faa50b80a620226133de9aa3510988b331ef3723f2d\", \"96c1f4c8da5a62bcbb39a4091f72d5b4c85efbe20438cbd522d2e0b4eb5769e6\", \"9f43e031bb6f34abef635d04a7f86a67089321aced1f2a72f1cf4851851ae723\", \"b1d3ff4a8be62bd2556bc75dca38d78ad8f4bfc281808c3bf8fa26993efdb72b\", \"ba32f14826cb65d8eed14b161cdbc21a198b4b5425ad9eba21cfdc5832a6eccf\", \"dafbd4c5f0a378edf21d7c44d95052f14a7894932dc0e9e98f03405d0400a194\", \"ee55821efdf2bcf5fbcf29e4d5bb4e2b67d92b542b121b6a19902474edaacdc4\", \"f95db8e6ffd0eb1555842227de0acf8347c2de017adbb9f6dc79524ba0d5a915\"]}, \"state_id\": \"5186db4e4167d5c3362e28a6\", \"visible_experiments\": [{\"experiment_id\": 5, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 7, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 1}, \"intervention\": \"DO_Z1_1\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 15, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 0}}]}}}", "extra_info": {"index": 170, "max_global_steps": 0, "min_global_steps": 0}, "index": 170, "prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=1\n intervention: none\n observed: Z1=0, Z2=1, Y=0\n\nExperiment 2:\n inputs: X1=0, X2=0, X3=1\n intervention: set Z1=1\n observed: Z1=1, Z2=1, Y=1\n\nExperiment 3:\n inputs: X1=0, X2=1, X3=1\n intervention: none\n observed: Z1=0, Z2=0, Y=1\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "raw_prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=1\n intervention: none\n observed: Z1=0, Z2=1, Y=0\n\nExperiment 2:\n inputs: X1=0, X2=0, X3=1\n intervention: set Z1=1\n observed: Z1=1, Z2=1, Y=1\n\nExperiment 3:\n inputs: X1=0, X2=1, X3=1\n intervention: none\n observed: Z1=0, Z2=0, Y=1\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "reward_model": {"style": "rule"}, "state_json": "{\"available_experiment_ids\": [0, 1, 2, 3, 4, 6, 8, 9, 10, 11, 12, 13, 14, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 3, \"family_bucket\": \"mixed\", \"maximum_separation\": 0.7567567567567568, \"mean_separation\": 0.455855855855856, \"minimum_separation\": 0.16216216216216217, \"normalized_mean_separation\": 0.48841698841698855, \"oracle_budget_representation_error\": 0.16216216216216223, \"oracle_singleton_representation_error\": 0.4037162162162162, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.24155405405405395, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 16}, \"private\": {\"hidden_mode_id\": \"96c1f4c8da5a62bcbb39a4091f72d5b4c85efbe20438cbd522d2e0b4eb5769e6\", \"valid_mode_ids\": [\"0866bf8895f5d2b45f213c33d400cf0f0e991b561243674e79ded665349980d8\", \"291555fe677838deacbad80afce40af7fc25369b3fcee2f057caf5963caf0fc9\", \"3010049a1985c7cbadf8197b7392115e2a42fa5c964e8e13383d92cd6ae67241\", \"4023b438014bc74dbc230f5ca6dbf7fd67bc16b1ccff1b03cca29fa7fe0d064f\", \"4d980982796690abe1ebdf6de0dd080f3f4078473ad7ec838a192c9c75af2064\", \"4f2f90034192971b512438a1e84ed0f43c39c40703f387df0be17dd0c195e027\", \"66ae108529f196399347cd39747108f4c55fa057af2b46f56cae8e64b7964eb2\", \"7806ccb0ed45919bec0d77706cf8facf73f6e52c8006ecc7456d098861a9d4f0\", \"781f06eb9d14a4e450a16faa50b80a620226133de9aa3510988b331ef3723f2d\", \"96c1f4c8da5a62bcbb39a4091f72d5b4c85efbe20438cbd522d2e0b4eb5769e6\", \"9f43e031bb6f34abef635d04a7f86a67089321aced1f2a72f1cf4851851ae723\", \"b1d3ff4a8be62bd2556bc75dca38d78ad8f4bfc281808c3bf8fa26993efdb72b\", \"ba32f14826cb65d8eed14b161cdbc21a198b4b5425ad9eba21cfdc5832a6eccf\", \"dafbd4c5f0a378edf21d7c44d95052f14a7894932dc0e9e98f03405d0400a194\", \"ee55821efdf2bcf5fbcf29e4d5bb4e2b67d92b542b121b6a19902474edaacdc4\", \"f95db8e6ffd0eb1555842227de0acf8347c2de017adbb9f6dc79524ba0d5a915\"]}, \"state_id\": \"5186db4e4167d5c3362e28a6\", \"visible_experiments\": [{\"experiment_id\": 5, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 7, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 1}, \"intervention\": \"DO_Z1_1\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 15, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 0}}]}"} {"agent_name": "causal_micro_lab_agent_loop", "data_source": "causal_micro_lab_final_eval_v3", "env_spec_json": "{\"agent\": {}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 171, \"task\": {\"state\": {\"available_experiment_ids\": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 16, 17, 18, 19, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 3, \"family_bucket\": \"mixed\", \"maximum_separation\": 0.6486486486486487, \"mean_separation\": 0.454054054054054, \"minimum_separation\": 0.10810810810810811, \"normalized_mean_separation\": 0.48648648648648646, \"oracle_budget_representation_error\": 0.13175675675675674, \"oracle_singleton_representation_error\": 0.3783783783783784, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.24662162162162166, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 16}, \"private\": {\"hidden_mode_id\": \"60f0b364800fa94c49478157bd5b809b486651bdd61619188521d745657643ea\", \"valid_mode_ids\": [\"028a750bd3c279681642ea7317e2c626a8a5b1294e8523b32bfa79e1331788fc\", \"0c98a41a7b355a1bc5bdb8e76e4ba34c85d811e66e3f12c7ee1dc37f53e294b7\", \"1f1503d093ac4d90c10322c84881a0c3ffe2d63c3184f55cfb986163893ab639\", \"3373d91d3ce33ee5e34ab4ec182a19336cc30be4d9f03e6ed677c4a2994e8632\", \"475cf9c9460c4e13aff79182b53c4a6ff1fc2015b80d7139ec75c8f35f893bc1\", \"60f0b364800fa94c49478157bd5b809b486651bdd61619188521d745657643ea\", \"67d46b97129c822811f8a3751dad984f98d0dc5de8ec6c1d9eca039a932c2381\", \"690d846a6e05b47e467e9f6756fb08cdcbc591144ba1859c078fbb4f2a1a9d1f\", \"a2f4a343d4d258886982fb8bdbacefdf23c17808bba29516248cd3629d43b881\", \"a9b4284e91030afcac3f6c685b973c83c02c9bd2b4bfa1d51b5689701b9cc1fc\", \"bf63d166baeb816a4434783d257448dcafc51b850e68b03b2741a59c13079b35\", \"c8d4994469cfd2536d4f3898bba9ca524055d020084a3465a5a3ba0a2e6557c6\", \"cb7f6403217140cc49f7fb491a698c77cc70fda68bb26ad7f0dc8c426ea10a59\", \"cc843f4a572049f7080f91b633d35ef722d878289080a5633f6c92c3d6d26a07\", \"d844c43090c5584afad5201e476dedd83485c473c85a01ea70e874543be5fb23\", \"f084088cd6a07b910c2392da4252c8cca13ef5f4f4e8e6de4c56fb1305638b74\"]}, \"state_id\": \"a222884bd8e7abc2ddb9ab3a\", \"visible_experiments\": [{\"experiment_id\": 15, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 20, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 35, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}]}}}", "extra_info": {"index": 171, "max_global_steps": 0, "min_global_steps": 0}, "index": 171, "prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=1, X3=1\n intervention: none\n observed: Z1=0, Z2=0, Y=1\n\nExperiment 2:\n inputs: X1=1, X2=0, X3=0\n intervention: none\n observed: Z1=0, Z2=1, Y=0\n\nExperiment 3:\n inputs: X1=1, X2=1, X3=1\n intervention: none\n observed: Z1=1, Z2=1, Y=0\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "raw_prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=1, X3=1\n intervention: none\n observed: Z1=0, Z2=0, Y=1\n\nExperiment 2:\n inputs: X1=1, X2=0, X3=0\n intervention: none\n observed: Z1=0, Z2=1, Y=0\n\nExperiment 3:\n inputs: X1=1, X2=1, X3=1\n intervention: none\n observed: Z1=1, Z2=1, Y=0\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "reward_model": {"style": "rule"}, "state_json": "{\"available_experiment_ids\": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 16, 17, 18, 19, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 3, \"family_bucket\": \"mixed\", \"maximum_separation\": 0.6486486486486487, \"mean_separation\": 0.454054054054054, \"minimum_separation\": 0.10810810810810811, \"normalized_mean_separation\": 0.48648648648648646, \"oracle_budget_representation_error\": 0.13175675675675674, \"oracle_singleton_representation_error\": 0.3783783783783784, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.24662162162162166, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 16}, \"private\": {\"hidden_mode_id\": \"60f0b364800fa94c49478157bd5b809b486651bdd61619188521d745657643ea\", \"valid_mode_ids\": [\"028a750bd3c279681642ea7317e2c626a8a5b1294e8523b32bfa79e1331788fc\", \"0c98a41a7b355a1bc5bdb8e76e4ba34c85d811e66e3f12c7ee1dc37f53e294b7\", \"1f1503d093ac4d90c10322c84881a0c3ffe2d63c3184f55cfb986163893ab639\", \"3373d91d3ce33ee5e34ab4ec182a19336cc30be4d9f03e6ed677c4a2994e8632\", \"475cf9c9460c4e13aff79182b53c4a6ff1fc2015b80d7139ec75c8f35f893bc1\", \"60f0b364800fa94c49478157bd5b809b486651bdd61619188521d745657643ea\", \"67d46b97129c822811f8a3751dad984f98d0dc5de8ec6c1d9eca039a932c2381\", \"690d846a6e05b47e467e9f6756fb08cdcbc591144ba1859c078fbb4f2a1a9d1f\", \"a2f4a343d4d258886982fb8bdbacefdf23c17808bba29516248cd3629d43b881\", \"a9b4284e91030afcac3f6c685b973c83c02c9bd2b4bfa1d51b5689701b9cc1fc\", \"bf63d166baeb816a4434783d257448dcafc51b850e68b03b2741a59c13079b35\", \"c8d4994469cfd2536d4f3898bba9ca524055d020084a3465a5a3ba0a2e6557c6\", \"cb7f6403217140cc49f7fb491a698c77cc70fda68bb26ad7f0dc8c426ea10a59\", \"cc843f4a572049f7080f91b633d35ef722d878289080a5633f6c92c3d6d26a07\", \"d844c43090c5584afad5201e476dedd83485c473c85a01ea70e874543be5fb23\", \"f084088cd6a07b910c2392da4252c8cca13ef5f4f4e8e6de4c56fb1305638b74\"]}, \"state_id\": \"a222884bd8e7abc2ddb9ab3a\", \"visible_experiments\": [{\"experiment_id\": 15, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 20, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 35, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}]}"} {"agent_name": "causal_micro_lab_agent_loop", "data_source": "causal_micro_lab_final_eval_v3", "env_spec_json": "{\"agent\": {}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 172, \"task\": {\"state\": {\"available_experiment_ids\": [0, 1, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 21, 22, 23, 24, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 3, \"family_bucket\": \"mixed\", \"maximum_separation\": 0.7027027027027027, \"mean_separation\": 0.4585585585585584, \"minimum_separation\": 0.10810810810810811, \"normalized_mean_separation\": 0.49131274131274116, \"oracle_budget_representation_error\": 0.14864864864864863, \"oracle_singleton_representation_error\": 0.40033783783783783, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.2516891891891892, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 16}, \"private\": {\"hidden_mode_id\": \"a76eee473cc8000d791547fbf98de12282d6ef9b10496ea4072abb531a97b94d\", \"valid_mode_ids\": [\"00d6c1e96127ad1026f668ac5371f5dde2b2a0abeb4ae73eddf95f7008f98749\", \"13222c18986a9bbc3a6e36cec19f2c9f29ee9370c0c98f0a24716ab4a2488da7\", \"39c9fccd7a0e13e0e2c5626c9ac46b96555e5941b63e00dfad1c93b05ef60119\", \"5841367041e08fc1e23fecbe2968c89e8e1166c35f45b5767e6efee156ce53d3\", \"6320affa03b1dea2088a84101d177ebb1f05c3e21c1bd8eddb93321611ba000b\", \"6597ecb9a91474fd08e90430b65c52d05f62d7cb910e31d54bfad714a6e26234\", \"7c912f5a94ed3d6e8760e3124e645c988641a040f3c8d167e62f1e60cdace62a\", \"8dd55f4fe0835203045d6faf571f0ef10c4d832abc7d489442b7c705ae674716\", \"919a99e3ed2320d4a88c9998597bce3f487366b912a218d5c20bf1044c8b32b6\", \"9b885f11f05faf079a70cd24fbc18018cc071487edd2c482ae21b14ba82a644a\", \"a76eee473cc8000d791547fbf98de12282d6ef9b10496ea4072abb531a97b94d\", \"ac691b10e9e4915f042835c99dd3d9c80d6e9f2b8272dd06d573452da08115a9\", \"c350213cbc0ac08d42bbc2e86292b332848f6a8b55d25d59d881ccc00660767c\", \"d1ff272d1f433a4692d18f533c0807e5b858324d3984f8006bd60b88958ca78b\", \"df349b457ae6bbeaa92cf02e396372c71526f6aed0f8d5d15d8b1d76955b558a\", \"ed0248f022df6e4f7c5485a57397e8a23f0be87c727b05ff35e6916e38cbd05e\"]}, \"state_id\": \"d2099f6e7f25af75afc2397b\", \"visible_experiments\": [{\"experiment_id\": 2, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z1_1\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 20, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 25, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}]}}}", "extra_info": {"index": 172, "max_global_steps": 0, "min_global_steps": 0}, "index": 172, "prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=0\n intervention: set Z1=1\n observed: Z1=1, Z2=1, Y=0\n\nExperiment 2:\n inputs: X1=1, X2=0, X3=0\n intervention: none\n observed: Z1=1, Z2=1, Y=1\n\nExperiment 3:\n inputs: X1=1, X2=0, X3=1\n intervention: none\n observed: Z1=0, Z2=1, Y=0\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "raw_prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=0\n intervention: set Z1=1\n observed: Z1=1, Z2=1, Y=0\n\nExperiment 2:\n inputs: X1=1, X2=0, X3=0\n intervention: none\n observed: Z1=1, Z2=1, Y=1\n\nExperiment 3:\n inputs: X1=1, X2=0, X3=1\n intervention: none\n observed: Z1=0, Z2=1, Y=0\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "reward_model": {"style": "rule"}, "state_json": "{\"available_experiment_ids\": [0, 1, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 21, 22, 23, 24, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 3, \"family_bucket\": \"mixed\", \"maximum_separation\": 0.7027027027027027, \"mean_separation\": 0.4585585585585584, \"minimum_separation\": 0.10810810810810811, \"normalized_mean_separation\": 0.49131274131274116, \"oracle_budget_representation_error\": 0.14864864864864863, \"oracle_singleton_representation_error\": 0.40033783783783783, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.2516891891891892, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 16}, \"private\": {\"hidden_mode_id\": \"a76eee473cc8000d791547fbf98de12282d6ef9b10496ea4072abb531a97b94d\", \"valid_mode_ids\": [\"00d6c1e96127ad1026f668ac5371f5dde2b2a0abeb4ae73eddf95f7008f98749\", \"13222c18986a9bbc3a6e36cec19f2c9f29ee9370c0c98f0a24716ab4a2488da7\", \"39c9fccd7a0e13e0e2c5626c9ac46b96555e5941b63e00dfad1c93b05ef60119\", \"5841367041e08fc1e23fecbe2968c89e8e1166c35f45b5767e6efee156ce53d3\", \"6320affa03b1dea2088a84101d177ebb1f05c3e21c1bd8eddb93321611ba000b\", \"6597ecb9a91474fd08e90430b65c52d05f62d7cb910e31d54bfad714a6e26234\", \"7c912f5a94ed3d6e8760e3124e645c988641a040f3c8d167e62f1e60cdace62a\", \"8dd55f4fe0835203045d6faf571f0ef10c4d832abc7d489442b7c705ae674716\", \"919a99e3ed2320d4a88c9998597bce3f487366b912a218d5c20bf1044c8b32b6\", \"9b885f11f05faf079a70cd24fbc18018cc071487edd2c482ae21b14ba82a644a\", \"a76eee473cc8000d791547fbf98de12282d6ef9b10496ea4072abb531a97b94d\", \"ac691b10e9e4915f042835c99dd3d9c80d6e9f2b8272dd06d573452da08115a9\", \"c350213cbc0ac08d42bbc2e86292b332848f6a8b55d25d59d881ccc00660767c\", \"d1ff272d1f433a4692d18f533c0807e5b858324d3984f8006bd60b88958ca78b\", \"df349b457ae6bbeaa92cf02e396372c71526f6aed0f8d5d15d8b1d76955b558a\", \"ed0248f022df6e4f7c5485a57397e8a23f0be87c727b05ff35e6916e38cbd05e\"]}, \"state_id\": \"d2099f6e7f25af75afc2397b\", \"visible_experiments\": [{\"experiment_id\": 2, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z1_1\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 20, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 25, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}]}"} {"agent_name": "causal_micro_lab_agent_loop", "data_source": "causal_micro_lab_final_eval_v3", "env_spec_json": "{\"agent\": {}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 173, \"task\": {\"state\": {\"available_experiment_ids\": [0, 1, 3, 4, 6, 7, 8, 9, 11, 12, 13, 14, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 4, \"family_bucket\": \"mixed\", \"maximum_separation\": 0.6388888888888888, \"mean_separation\": 0.43425925925925907, \"minimum_separation\": 0.1111111111111111, \"normalized_mean_separation\": 0.46527777777777757, \"oracle_budget_representation_error\": 0.11805555555555557, \"oracle_singleton_representation_error\": 0.375, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.2569444444444444, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 16}, \"private\": {\"hidden_mode_id\": \"b13ba4897977db05483fd7e3b331ab87edfe1328c1b05e2bed0365768452df08\", \"valid_mode_ids\": [\"0b06d4b6d6e5975dd0e6adccb9269f9a7934d7c3b02533afa0eebddb2bb976be\", \"10c413195ed53df99af4b2e87c95469eca1b3a14714936ce910031c3863cb519\", \"1b13c492182384d257f7973c54ff192981bcf1efef0826b62fe67f46198cd5b5\", \"1c73fc5e499f1c71bee27a6324e476af7f5e0ab2bc589b169acc3ac429441417\", \"2e06805ef9064424ec9b4afad9ad6111c2cf4807273b48f1eb19d5bed812cfe4\", \"43b6e569bb85c044f20860f3d5bae2d00a760e66f8c7a96a981df6ea202f264c\", \"5429acf6acbe4b9d50edcf1bfa41685e34cbee4f5a8eeaf6f90466b41f461fe6\", \"5f698511b45b38c4f0bb833275a23ed5e8df301d56e3f3dcbd70c01c8104ecd0\", \"627763dcdaee96b2d3c773860044b7cdedc2a328a8f44851c3267f7cf380319f\", \"a5314a27342225ff16c27c3ac7311cc252524f874c94f7af7dabe0624df7b578\", \"a70bacd17662aad58773bd07ae4b2a7ad4fee9447b99febc52f90209b38d3245\", \"b13ba4897977db05483fd7e3b331ab87edfe1328c1b05e2bed0365768452df08\", \"b8de8e98ca625212baa41cc0b6973e8c7ca65bc3a279ea7035cc55425a9e4b2d\", \"c0876107ebb192b129fc45ae6a0f56cef793ec827d3a62f619cd0ed41ce455e9\", \"cca6f525645a29052a03e2c8b496d4e7f32bd4f9be7a75bf6f4d20beda4fdf12\", \"d74035d574a773d98926c8cf89dced26eae0bda62400291b9604f561979fa082\"]}, \"state_id\": \"f2f555e97c78c8c50f2b3e81\", \"visible_experiments\": [{\"experiment_id\": 2, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z1_1\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 0}}, {\"experiment_id\": 5, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 0}}, {\"experiment_id\": 10, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 0}}, {\"experiment_id\": 15, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 0}}]}}}", "extra_info": {"index": 173, "max_global_steps": 0, "min_global_steps": 0}, "index": 173, "prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=0\n intervention: set Z1=1\n observed: Z1=1, Z2=0, Y=1\n\nExperiment 2:\n inputs: X1=0, X2=0, X3=1\n intervention: none\n observed: Z1=1, Z2=0, Y=1\n\nExperiment 3:\n inputs: X1=0, X2=1, X3=0\n intervention: none\n observed: Z1=1, Z2=0, Y=1\n\nExperiment 4:\n inputs: X1=0, X2=1, X3=1\n intervention: none\n observed: Z1=0, Z2=0, Y=1\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "raw_prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=0\n intervention: set Z1=1\n observed: Z1=1, Z2=0, Y=1\n\nExperiment 2:\n inputs: X1=0, X2=0, X3=1\n intervention: none\n observed: Z1=1, Z2=0, Y=1\n\nExperiment 3:\n inputs: X1=0, X2=1, X3=0\n intervention: none\n observed: Z1=1, Z2=0, Y=1\n\nExperiment 4:\n inputs: X1=0, X2=1, X3=1\n intervention: none\n observed: Z1=0, Z2=0, Y=1\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "reward_model": {"style": "rule"}, "state_json": "{\"available_experiment_ids\": [0, 1, 3, 4, 6, 7, 8, 9, 11, 12, 13, 14, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 4, \"family_bucket\": \"mixed\", \"maximum_separation\": 0.6388888888888888, \"mean_separation\": 0.43425925925925907, \"minimum_separation\": 0.1111111111111111, \"normalized_mean_separation\": 0.46527777777777757, \"oracle_budget_representation_error\": 0.11805555555555557, \"oracle_singleton_representation_error\": 0.375, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.2569444444444444, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 16}, \"private\": {\"hidden_mode_id\": \"b13ba4897977db05483fd7e3b331ab87edfe1328c1b05e2bed0365768452df08\", \"valid_mode_ids\": [\"0b06d4b6d6e5975dd0e6adccb9269f9a7934d7c3b02533afa0eebddb2bb976be\", \"10c413195ed53df99af4b2e87c95469eca1b3a14714936ce910031c3863cb519\", \"1b13c492182384d257f7973c54ff192981bcf1efef0826b62fe67f46198cd5b5\", \"1c73fc5e499f1c71bee27a6324e476af7f5e0ab2bc589b169acc3ac429441417\", \"2e06805ef9064424ec9b4afad9ad6111c2cf4807273b48f1eb19d5bed812cfe4\", \"43b6e569bb85c044f20860f3d5bae2d00a760e66f8c7a96a981df6ea202f264c\", \"5429acf6acbe4b9d50edcf1bfa41685e34cbee4f5a8eeaf6f90466b41f461fe6\", \"5f698511b45b38c4f0bb833275a23ed5e8df301d56e3f3dcbd70c01c8104ecd0\", \"627763dcdaee96b2d3c773860044b7cdedc2a328a8f44851c3267f7cf380319f\", \"a5314a27342225ff16c27c3ac7311cc252524f874c94f7af7dabe0624df7b578\", \"a70bacd17662aad58773bd07ae4b2a7ad4fee9447b99febc52f90209b38d3245\", \"b13ba4897977db05483fd7e3b331ab87edfe1328c1b05e2bed0365768452df08\", \"b8de8e98ca625212baa41cc0b6973e8c7ca65bc3a279ea7035cc55425a9e4b2d\", \"c0876107ebb192b129fc45ae6a0f56cef793ec827d3a62f619cd0ed41ce455e9\", \"cca6f525645a29052a03e2c8b496d4e7f32bd4f9be7a75bf6f4d20beda4fdf12\", \"d74035d574a773d98926c8cf89dced26eae0bda62400291b9604f561979fa082\"]}, \"state_id\": \"f2f555e97c78c8c50f2b3e81\", \"visible_experiments\": [{\"experiment_id\": 2, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z1_1\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 0}}, {\"experiment_id\": 5, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 0}}, {\"experiment_id\": 10, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 0}}, {\"experiment_id\": 15, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 0}}]}"} {"agent_name": "causal_micro_lab_agent_loop", "data_source": "causal_micro_lab_final_eval_v3", "env_spec_json": "{\"agent\": {}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 174, \"task\": {\"state\": {\"available_experiment_ids\": [0, 1, 2, 3, 4, 6, 7, 8, 9, 10, 11, 12, 13, 14, 16, 17, 18, 19, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 3, \"family_bucket\": \"cross_family\", \"maximum_separation\": 0.7297297297297297, \"mean_separation\": 0.4813063063063063, \"minimum_separation\": 0.16216216216216217, \"normalized_mean_separation\": 0.5156853281853282, \"oracle_budget_representation_error\": 0.17060810810810811, \"oracle_singleton_representation_error\": 0.43243243243243246, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.26182432432432434, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 16}, \"private\": {\"hidden_mode_id\": \"ae96a882304888aee360eeef78dfdb8e58d821a1458daa129a0961101340fc6c\", \"valid_mode_ids\": [\"06460229e40e5d49bb3c19a000025a90c419e6a17a6097700263dd52ecdb3415\", \"21a255dd60e090c915f6c98b47186da5e4cfe6cdc23bb1f062201a5a81cb5542\", \"227e634157e525119a95478a947b49b2d93c4312a0bc95637ea1dafea03f4846\", \"34af9366df13c6eb8ca3f711a445af5f9745b40f1bba24ba56f72e30eb88a20a\", \"399377eeebc65c143f5a5094a68fc35e151d21796d318372f058539700a71aaa\", \"5c593f5af931207fff2c6fb3892feeb69bd9a15ceb45d19ae004c8ae83d66e56\", \"65179147923fda6c1a3894f6212972192820a12f0c8c81e61ecfb3741693aa49\", \"6fa9550b7d2e0ed07cebf3b48a068d15d810c55e7d0143c327eb94600d470e76\", \"7af8b96a5486ed1f48ee2c74a711272476d5bc35529e8a9e75dbb5b3602ba403\", \"95cfa929054d8ccb8c657dffec18869fa4032c17b7084650b5ecefd308b22f60\", \"ae96a882304888aee360eeef78dfdb8e58d821a1458daa129a0961101340fc6c\", \"bacb3f825756e99c1b631d8d49ffc29f6120256a519034d4ae7a7755f67c53bd\", \"cf52bd66999fba7a7cceee480ecbaa9acbec406e2edc50a7e71e77669086c020\", \"d75901ebee28eb8122f6594d52db0567b64f4cc961aeb571f6a00090ee226b8b\", \"e063c0e595b3ee51d90a3884460ee42febb99fd5082cb66147ba4f4eb483abea\", \"e09ed0540d0a0ada23fbba2308c857ab5266bf854ac72bb04983f827f0290944\"]}, \"state_id\": \"add7d61e402ff814bf581138\", \"visible_experiments\": [{\"experiment_id\": 5, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 15, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 0}}, {\"experiment_id\": 20, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 0}}]}}}", "extra_info": {"index": 174, "max_global_steps": 0, "min_global_steps": 0}, "index": 174, "prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=1\n intervention: none\n observed: Z1=0, Z2=1, Y=1\n\nExperiment 2:\n inputs: X1=0, X2=1, X3=1\n intervention: none\n observed: Z1=1, Z2=0, Y=0\n\nExperiment 3:\n inputs: X1=1, X2=0, X3=0\n intervention: none\n observed: Z1=0, Z2=0, Y=1\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "raw_prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=1\n intervention: none\n observed: Z1=0, Z2=1, Y=1\n\nExperiment 2:\n inputs: X1=0, X2=1, X3=1\n intervention: none\n observed: Z1=1, Z2=0, Y=0\n\nExperiment 3:\n inputs: X1=1, X2=0, X3=0\n intervention: none\n observed: Z1=0, Z2=0, Y=1\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "reward_model": {"style": "rule"}, "state_json": "{\"available_experiment_ids\": [0, 1, 2, 3, 4, 6, 7, 8, 9, 10, 11, 12, 13, 14, 16, 17, 18, 19, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 3, \"family_bucket\": \"cross_family\", \"maximum_separation\": 0.7297297297297297, \"mean_separation\": 0.4813063063063063, \"minimum_separation\": 0.16216216216216217, \"normalized_mean_separation\": 0.5156853281853282, \"oracle_budget_representation_error\": 0.17060810810810811, \"oracle_singleton_representation_error\": 0.43243243243243246, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.26182432432432434, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 16}, \"private\": {\"hidden_mode_id\": \"ae96a882304888aee360eeef78dfdb8e58d821a1458daa129a0961101340fc6c\", \"valid_mode_ids\": [\"06460229e40e5d49bb3c19a000025a90c419e6a17a6097700263dd52ecdb3415\", \"21a255dd60e090c915f6c98b47186da5e4cfe6cdc23bb1f062201a5a81cb5542\", \"227e634157e525119a95478a947b49b2d93c4312a0bc95637ea1dafea03f4846\", \"34af9366df13c6eb8ca3f711a445af5f9745b40f1bba24ba56f72e30eb88a20a\", \"399377eeebc65c143f5a5094a68fc35e151d21796d318372f058539700a71aaa\", \"5c593f5af931207fff2c6fb3892feeb69bd9a15ceb45d19ae004c8ae83d66e56\", \"65179147923fda6c1a3894f6212972192820a12f0c8c81e61ecfb3741693aa49\", \"6fa9550b7d2e0ed07cebf3b48a068d15d810c55e7d0143c327eb94600d470e76\", \"7af8b96a5486ed1f48ee2c74a711272476d5bc35529e8a9e75dbb5b3602ba403\", \"95cfa929054d8ccb8c657dffec18869fa4032c17b7084650b5ecefd308b22f60\", \"ae96a882304888aee360eeef78dfdb8e58d821a1458daa129a0961101340fc6c\", \"bacb3f825756e99c1b631d8d49ffc29f6120256a519034d4ae7a7755f67c53bd\", \"cf52bd66999fba7a7cceee480ecbaa9acbec406e2edc50a7e71e77669086c020\", \"d75901ebee28eb8122f6594d52db0567b64f4cc961aeb571f6a00090ee226b8b\", \"e063c0e595b3ee51d90a3884460ee42febb99fd5082cb66147ba4f4eb483abea\", \"e09ed0540d0a0ada23fbba2308c857ab5266bf854ac72bb04983f827f0290944\"]}, \"state_id\": \"add7d61e402ff814bf581138\", \"visible_experiments\": [{\"experiment_id\": 5, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 15, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 0}}, {\"experiment_id\": 20, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 0}}]}"} {"agent_name": "causal_micro_lab_agent_loop", "data_source": "causal_micro_lab_final_eval_v3", "env_spec_json": "{\"agent\": {}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 175, \"task\": {\"state\": {\"available_experiment_ids\": [1, 2, 3, 4, 5, 6, 7, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 3, \"family_bucket\": \"cross_family\", \"maximum_separation\": 0.8108108108108109, \"mean_separation\": 0.5364864864864864, \"minimum_separation\": 0.21621621621621623, \"normalized_mean_separation\": 0.5748069498069498, \"oracle_budget_representation_error\": 0.21959459459459463, \"oracle_singleton_representation_error\": 0.4864864864864865, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.2668918918918919, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 16}, \"private\": {\"hidden_mode_id\": \"b908b5350b71c212a0e545aa1374c7952bc6bcc4f35cf250d128cb8e8f3c7dc8\", \"valid_mode_ids\": [\"254691769db1665ed202da801e245125ea8aae77cdc72cee8652ecc978c83201\", \"2cd2c6ad5535f04aad1b72132b16f0a8665e83eae07a201efe67b543ba31c317\", \"4a4e9a8f3549f3f354514478cbfe507f99da227dbfbb602ad40239e6957723ab\", \"54df5a612ff1caa19145cbbca812833ba8519493e94951be2fceed247f899d5d\", \"5bc9d872cdf0cbcc5c5552a7b129325cf8368d11b9c0d3e6feff11d7d3eb0efa\", \"72f699940e6d12e28a2e882e18be75ee9ead41888dc5586f9bed28b643c7b4bd\", \"79d734475b11cbf8cdf891bde9a67aa76423bc019a1d671c2cd2557379115d62\", \"7dc5c1c88148dbae89ec802c9de4ccf7b7463736fcf39957803e5c01186e4336\", \"88ed2ce8172c281edbf89038fe36388b985ea874187057bfa60da0825eec2e01\", \"a009d7472021e3a65a12ef99b815d6620d2e7d5f0ad40ac71d2f4b54aff799e9\", \"b908b5350b71c212a0e545aa1374c7952bc6bcc4f35cf250d128cb8e8f3c7dc8\", \"bad18a5f08dc2ca064ce25a5a33dc2c5b6deee1018b579b3b6553a62a83bf961\", \"d22247f86439feec361b0839a80c0d87f52f80eb905e349248cac13cb47a4556\", \"d622dd3dee0ce95c22fc5da97fae724bd6b7219859ae089a4070996980ccf3c1\", \"df7f4df476e1ba74f3aedcad9559be8ec7778a74ce2bf1950e49f5e309b05f36\", \"dfac41442859b1d90a5e19f517e743c00c8c2e38ca0da84c3288eab3f4fd9d97\"]}, \"state_id\": \"59089ec132e2acecb0cf6a57\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 8, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 1}, \"intervention\": \"DO_Z2_0\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 0}}, {\"experiment_id\": 22, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z1_1\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 0}}]}}}", "extra_info": {"index": 175, "max_global_steps": 0, "min_global_steps": 0}, "index": 175, "prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=0\n intervention: none\n observed: Z1=1, Z2=1, Y=1\n\nExperiment 2:\n inputs: X1=0, X2=0, X3=1\n intervention: set Z2=0\n observed: Z1=1, Z2=0, Y=0\n\nExperiment 3:\n inputs: X1=1, X2=0, X3=0\n intervention: set Z1=1\n observed: Z1=1, Z2=0, Y=0\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "raw_prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=0\n intervention: none\n observed: Z1=1, Z2=1, Y=1\n\nExperiment 2:\n inputs: X1=0, X2=0, X3=1\n intervention: set Z2=0\n observed: Z1=1, Z2=0, Y=0\n\nExperiment 3:\n inputs: X1=1, X2=0, X3=0\n intervention: set Z1=1\n observed: Z1=1, Z2=0, Y=0\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "reward_model": {"style": "rule"}, "state_json": "{\"available_experiment_ids\": [1, 2, 3, 4, 5, 6, 7, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 3, \"family_bucket\": \"cross_family\", \"maximum_separation\": 0.8108108108108109, \"mean_separation\": 0.5364864864864864, \"minimum_separation\": 0.21621621621621623, \"normalized_mean_separation\": 0.5748069498069498, \"oracle_budget_representation_error\": 0.21959459459459463, \"oracle_singleton_representation_error\": 0.4864864864864865, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.2668918918918919, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 16}, \"private\": {\"hidden_mode_id\": \"b908b5350b71c212a0e545aa1374c7952bc6bcc4f35cf250d128cb8e8f3c7dc8\", \"valid_mode_ids\": [\"254691769db1665ed202da801e245125ea8aae77cdc72cee8652ecc978c83201\", \"2cd2c6ad5535f04aad1b72132b16f0a8665e83eae07a201efe67b543ba31c317\", \"4a4e9a8f3549f3f354514478cbfe507f99da227dbfbb602ad40239e6957723ab\", \"54df5a612ff1caa19145cbbca812833ba8519493e94951be2fceed247f899d5d\", \"5bc9d872cdf0cbcc5c5552a7b129325cf8368d11b9c0d3e6feff11d7d3eb0efa\", \"72f699940e6d12e28a2e882e18be75ee9ead41888dc5586f9bed28b643c7b4bd\", \"79d734475b11cbf8cdf891bde9a67aa76423bc019a1d671c2cd2557379115d62\", \"7dc5c1c88148dbae89ec802c9de4ccf7b7463736fcf39957803e5c01186e4336\", \"88ed2ce8172c281edbf89038fe36388b985ea874187057bfa60da0825eec2e01\", \"a009d7472021e3a65a12ef99b815d6620d2e7d5f0ad40ac71d2f4b54aff799e9\", \"b908b5350b71c212a0e545aa1374c7952bc6bcc4f35cf250d128cb8e8f3c7dc8\", \"bad18a5f08dc2ca064ce25a5a33dc2c5b6deee1018b579b3b6553a62a83bf961\", \"d22247f86439feec361b0839a80c0d87f52f80eb905e349248cac13cb47a4556\", \"d622dd3dee0ce95c22fc5da97fae724bd6b7219859ae089a4070996980ccf3c1\", \"df7f4df476e1ba74f3aedcad9559be8ec7778a74ce2bf1950e49f5e309b05f36\", \"dfac41442859b1d90a5e19f517e743c00c8c2e38ca0da84c3288eab3f4fd9d97\"]}, \"state_id\": \"59089ec132e2acecb0cf6a57\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 8, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 1}, \"intervention\": \"DO_Z2_0\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 0}}, {\"experiment_id\": 22, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z1_1\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 0}}]}"} {"agent_name": "causal_micro_lab_agent_loop", "data_source": "causal_micro_lab_final_eval_v3", "env_spec_json": "{\"agent\": {}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 176, \"task\": {\"state\": {\"available_experiment_ids\": [0, 1, 3, 4, 5, 6, 7, 8, 9, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 3, \"family_bucket\": \"cross_family\", \"maximum_separation\": 0.8108108108108109, \"mean_separation\": 0.5905405405405407, \"minimum_separation\": 0.2702702702702703, \"normalized_mean_separation\": 0.6327220077220079, \"oracle_budget_representation_error\": 0.2567567567567568, \"oracle_singleton_representation_error\": 0.5304054054054054, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.2736486486486486, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 16}, \"private\": {\"hidden_mode_id\": \"80fcb7ec1514dcc75710de685450044aecbecd4ba7b5be44adbf76db56ede260\", \"valid_mode_ids\": [\"01cd6f8c0f503c1556627d61bce75a1e9787a66cebf1fa162c7f58e0e883ae9b\", \"024795a0f70a49b8cfe7ec606951b54a1dddc9b230606e4ffe0da2da291c9b19\", \"043bd087a85811d87e95a5e0a4e054069b34e113dae30b7aa42de7c86f238770\", \"09cdffbd5ea6b6cf72360ed82e8d777657eca9aad536594c60332840c159d2c4\", \"29c1e714edae93b7140e970abc7da185ac689f063e3bdd1af0cf8bf6fc3c36e6\", \"2a7080b6922091ebe2536e45a0624a17852ee0c2ad5f16bb7df4b738749aecb1\", \"30e735719761c908efd9151e0458079c683c22dc01960cd00110db70d862cec9\", \"3fa66d740ca9ecc1a21329aa77aeb3794b45d2aae77f7e9bd2a514c0b2856c75\", \"45669660b585f86a0ab7d0594b78189e400d172f702f80eda1dfc4acd06695b6\", \"5314c9f621221f7b67ea3a48833483fe32536da7e630d175f23d7a89b2f5c842\", \"663c77256d69ea08dbb6a800f5ae638995cc2944e6e728385156dbcc5152c631\", \"7514d78c0160aa7603660cfed40ca1502a1eb2e768ab45e572bd5e7c64bed87e\", \"7bd0ad0c4c959c6362734192f190e185b4027167bfb696ef9403fee37d92f6c6\", \"80fcb7ec1514dcc75710de685450044aecbecd4ba7b5be44adbf76db56ede260\", \"ae76174742e244d35ff6bb39d7bf1d7daafc304901daeafb640ec0b3118f2640\", \"f6ab6d9d917333999b2d853c38c8435dd926efa2a7517c0bcc74c13229273e4a\"]}, \"state_id\": \"603772a8992d927dcdd030d9\", \"visible_experiments\": [{\"experiment_id\": 2, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z1_1\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 0}}, {\"experiment_id\": 10, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 35, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 0}}]}}}", "extra_info": {"index": 176, "max_global_steps": 0, "min_global_steps": 0}, "index": 176, "prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=0\n intervention: set Z1=1\n observed: Z1=1, Z2=0, Y=1\n\nExperiment 2:\n inputs: X1=0, X2=1, X3=0\n intervention: none\n observed: Z1=0, Z2=1, Y=1\n\nExperiment 3:\n inputs: X1=1, X2=1, X3=1\n intervention: none\n observed: Z1=0, Z2=0, Y=0\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "raw_prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=0\n intervention: set Z1=1\n observed: Z1=1, Z2=0, Y=1\n\nExperiment 2:\n inputs: X1=0, X2=1, X3=0\n intervention: none\n observed: Z1=0, Z2=1, Y=1\n\nExperiment 3:\n inputs: X1=1, X2=1, X3=1\n intervention: none\n observed: Z1=0, Z2=0, Y=0\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "reward_model": {"style": "rule"}, "state_json": "{\"available_experiment_ids\": [0, 1, 3, 4, 5, 6, 7, 8, 9, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 3, \"family_bucket\": \"cross_family\", \"maximum_separation\": 0.8108108108108109, \"mean_separation\": 0.5905405405405407, \"minimum_separation\": 0.2702702702702703, \"normalized_mean_separation\": 0.6327220077220079, \"oracle_budget_representation_error\": 0.2567567567567568, \"oracle_singleton_representation_error\": 0.5304054054054054, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.2736486486486486, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 16}, \"private\": {\"hidden_mode_id\": \"80fcb7ec1514dcc75710de685450044aecbecd4ba7b5be44adbf76db56ede260\", \"valid_mode_ids\": [\"01cd6f8c0f503c1556627d61bce75a1e9787a66cebf1fa162c7f58e0e883ae9b\", \"024795a0f70a49b8cfe7ec606951b54a1dddc9b230606e4ffe0da2da291c9b19\", \"043bd087a85811d87e95a5e0a4e054069b34e113dae30b7aa42de7c86f238770\", \"09cdffbd5ea6b6cf72360ed82e8d777657eca9aad536594c60332840c159d2c4\", \"29c1e714edae93b7140e970abc7da185ac689f063e3bdd1af0cf8bf6fc3c36e6\", \"2a7080b6922091ebe2536e45a0624a17852ee0c2ad5f16bb7df4b738749aecb1\", \"30e735719761c908efd9151e0458079c683c22dc01960cd00110db70d862cec9\", \"3fa66d740ca9ecc1a21329aa77aeb3794b45d2aae77f7e9bd2a514c0b2856c75\", \"45669660b585f86a0ab7d0594b78189e400d172f702f80eda1dfc4acd06695b6\", \"5314c9f621221f7b67ea3a48833483fe32536da7e630d175f23d7a89b2f5c842\", \"663c77256d69ea08dbb6a800f5ae638995cc2944e6e728385156dbcc5152c631\", \"7514d78c0160aa7603660cfed40ca1502a1eb2e768ab45e572bd5e7c64bed87e\", \"7bd0ad0c4c959c6362734192f190e185b4027167bfb696ef9403fee37d92f6c6\", \"80fcb7ec1514dcc75710de685450044aecbecd4ba7b5be44adbf76db56ede260\", \"ae76174742e244d35ff6bb39d7bf1d7daafc304901daeafb640ec0b3118f2640\", \"f6ab6d9d917333999b2d853c38c8435dd926efa2a7517c0bcc74c13229273e4a\"]}, \"state_id\": \"603772a8992d927dcdd030d9\", \"visible_experiments\": [{\"experiment_id\": 2, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z1_1\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 0}}, {\"experiment_id\": 10, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 35, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 0}}]}"} {"agent_name": "causal_micro_lab_agent_loop", "data_source": "causal_micro_lab_final_eval_v3", "env_spec_json": "{\"agent\": {}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 177, \"task\": {\"state\": {\"available_experiment_ids\": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 11, 12, 13, 14, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 3, \"family_bucket\": \"mixed\", \"maximum_separation\": 0.7567567567567568, \"mean_separation\": 0.46846846846846824, \"minimum_separation\": 0.10810810810810811, \"normalized_mean_separation\": 0.5019305019305017, \"oracle_budget_representation_error\": 0.1418918918918919, \"oracle_singleton_representation_error\": 0.41891891891891897, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.2770270270270271, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 16}, \"private\": {\"hidden_mode_id\": \"69bb00c1133a1c5905cf0624b8ef6ce3744f266684b523b59878f62a7da6900a\", \"valid_mode_ids\": [\"0a9b0fa2d18ad71303c3a1591225c7bc2e2baa25bdf4466d839a32e3f07478a0\", \"16585174e884fd5b1654dfdaf07616aa2444dd314f1f2759f962670f5ba1f2ff\", \"17d5241cab1bfdc526ff354883d28a597f5f64c1cede89188259ee5c1552d7fe\", \"1a65f107758de2b08e213b213d537203b56104410027938579179fca0ef2faf7\", \"3579bb3cdf81e6d207804ad98df45d1a926fb5ce9206d45d787e97cd4e46e93f\", \"5ff028712a5b8388f175996da270f3045566f016b77f3ecde3b8b446021d844a\", \"69bb00c1133a1c5905cf0624b8ef6ce3744f266684b523b59878f62a7da6900a\", \"6b9b3b8342ddbecb88c2443f1635ec51392005cd3341d0c22cd5236691573e8e\", \"6c827b689716c0f6c912be4c3ff1c5235f0a0721ba4f8211bb81c1f63ca99d05\", \"6fe8da9ca0609b22941fc623b28a255e74267275ab8b31672efc171641883e65\", \"74ee1fa18eda50f9c83426774add3cbea7cf356e1ef570d4a0a7541e6c49bf1d\", \"a451710f058ac3957c89d008b9b8919f06fd4ebd8d26217c3985529e8835bbb6\", \"b250262c8261dea244fd8e37bbbf8e40da4ff535aac7ba03996fdbc32dea7aa7\", \"e768dce766cb038da126adcbe9066c3b64d933d578c10047199c1dcc0c5fab82\", \"eb3bc7a32a4e35bef438a02847338934814c7b7074a72f83d6c3aeccb1ae7eee\", \"f227066a8cde147900227e542588b00987177f854070c06903c4e7f634ee7484\"]}, \"state_id\": \"f4fcb40f2627e4b5f30c937d\", \"visible_experiments\": [{\"experiment_id\": 10, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 15, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 29, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 1}, \"intervention\": \"DO_Z2_1\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}]}}}", "extra_info": {"index": 177, "max_global_steps": 0, "min_global_steps": 0}, "index": 177, "prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=1, X3=0\n intervention: none\n observed: Z1=0, Z2=1, Y=0\n\nExperiment 2:\n inputs: X1=0, X2=1, X3=1\n intervention: none\n observed: Z1=0, Z2=1, Y=1\n\nExperiment 3:\n inputs: X1=1, X2=0, X3=1\n intervention: set Z2=1\n observed: Z1=0, Z2=1, Y=0\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "raw_prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=1, X3=0\n intervention: none\n observed: Z1=0, Z2=1, Y=0\n\nExperiment 2:\n inputs: X1=0, X2=1, X3=1\n intervention: none\n observed: Z1=0, Z2=1, Y=1\n\nExperiment 3:\n inputs: X1=1, X2=0, X3=1\n intervention: set Z2=1\n observed: Z1=0, Z2=1, Y=0\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "reward_model": {"style": "rule"}, "state_json": "{\"available_experiment_ids\": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 11, 12, 13, 14, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 3, \"family_bucket\": \"mixed\", \"maximum_separation\": 0.7567567567567568, \"mean_separation\": 0.46846846846846824, \"minimum_separation\": 0.10810810810810811, \"normalized_mean_separation\": 0.5019305019305017, \"oracle_budget_representation_error\": 0.1418918918918919, \"oracle_singleton_representation_error\": 0.41891891891891897, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.2770270270270271, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 16}, \"private\": {\"hidden_mode_id\": \"69bb00c1133a1c5905cf0624b8ef6ce3744f266684b523b59878f62a7da6900a\", \"valid_mode_ids\": [\"0a9b0fa2d18ad71303c3a1591225c7bc2e2baa25bdf4466d839a32e3f07478a0\", \"16585174e884fd5b1654dfdaf07616aa2444dd314f1f2759f962670f5ba1f2ff\", \"17d5241cab1bfdc526ff354883d28a597f5f64c1cede89188259ee5c1552d7fe\", \"1a65f107758de2b08e213b213d537203b56104410027938579179fca0ef2faf7\", \"3579bb3cdf81e6d207804ad98df45d1a926fb5ce9206d45d787e97cd4e46e93f\", \"5ff028712a5b8388f175996da270f3045566f016b77f3ecde3b8b446021d844a\", \"69bb00c1133a1c5905cf0624b8ef6ce3744f266684b523b59878f62a7da6900a\", \"6b9b3b8342ddbecb88c2443f1635ec51392005cd3341d0c22cd5236691573e8e\", \"6c827b689716c0f6c912be4c3ff1c5235f0a0721ba4f8211bb81c1f63ca99d05\", \"6fe8da9ca0609b22941fc623b28a255e74267275ab8b31672efc171641883e65\", \"74ee1fa18eda50f9c83426774add3cbea7cf356e1ef570d4a0a7541e6c49bf1d\", \"a451710f058ac3957c89d008b9b8919f06fd4ebd8d26217c3985529e8835bbb6\", \"b250262c8261dea244fd8e37bbbf8e40da4ff535aac7ba03996fdbc32dea7aa7\", \"e768dce766cb038da126adcbe9066c3b64d933d578c10047199c1dcc0c5fab82\", \"eb3bc7a32a4e35bef438a02847338934814c7b7074a72f83d6c3aeccb1ae7eee\", \"f227066a8cde147900227e542588b00987177f854070c06903c4e7f634ee7484\"]}, \"state_id\": \"f4fcb40f2627e4b5f30c937d\", \"visible_experiments\": [{\"experiment_id\": 10, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 15, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 29, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 1}, \"intervention\": \"DO_Z2_1\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}]}"} {"agent_name": "causal_micro_lab_agent_loop", "data_source": "causal_micro_lab_final_eval_v3", "env_spec_json": "{\"agent\": {}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 178, \"task\": {\"state\": {\"available_experiment_ids\": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 38, 39], \"metadata\": {\"evidence_size\": 3, \"family_bucket\": \"cross_family\", \"maximum_separation\": 0.7027027027027027, \"mean_separation\": 0.4675675675675673, \"minimum_separation\": 0.16216216216216217, \"normalized_mean_separation\": 0.5009652509652507, \"oracle_budget_representation_error\": 0.12162162162162166, \"oracle_singleton_representation_error\": 0.4054054054054054, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.2837837837837837, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 16}, \"private\": {\"hidden_mode_id\": \"9b1274058fe3d7c005182c045127f92d9e80e555cb0bc2d3fe493023d47e8e98\", \"valid_mode_ids\": [\"02b6a764582959408fa064f0cfe0a6c173f2abd1f1dbcff693bb131976bddcb7\", \"0bc9a7f006048752e578f8e96fe5d4299588ea04d14cb02e615f83c0cbd04a23\", \"13427c0673ada343b15597999a2c8663abc7804fc0b9fbf9732fccdfc515e583\", \"1f5b3cb190132db2cca61f0c84ce243c154d320e6b1875f4078c290b6d91f008\", \"2f604defa702f9fd8be28c359e104d0ce8712f1ee00a1fd4d6864b79d3ec9856\", \"3a6597898f334c02768e65d00557c58b5beadb0f4291192628cf18df1eaceeaa\", \"3c808e8f3166ff07355f675820c785154f2697d09fcabf426b864dfac342a5f0\", \"481bedd7b11829fb1fc52616d38a83656e54afe23cf2410e83da3d4c46c90358\", \"5623e81676b81ddc9d4b9f9185f2312dd17fc9397b68b52fb83895cafbe5a68a\", \"5de28bc58ed25986d95ae078ca438f71d80bb88d97a863fc58c32f8eb9ca038a\", \"63262e3c0d947e9b11fe723720981b848af366cce0b7f53bb4043c679edaed4a\", \"9b1274058fe3d7c005182c045127f92d9e80e555cb0bc2d3fe493023d47e8e98\", \"a3a32270e702f32c08617ff1877d9fc3ea9f735a35d3b282bbda69c93d8c7127\", \"c37445f5e31330679d0f17a4ce12301ebbca5d11421d8dd3e1e98be59dee032a\", \"e0a1eb2d2c05f8984059a1bf65814b1f3161b5bf37f2cd38b05f7ac227beb3cd\", \"ee97bef0cf0cf72c49dfbd350a9a350eaae022ccdb40fe3e8a4ed7e93d2e5ebf\"]}, \"state_id\": \"39d7201df13f31f4fc4fdb87\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 15, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 37, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 1}, \"intervention\": \"DO_Z1_1\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}]}}}", "extra_info": {"index": 178, "max_global_steps": 0, "min_global_steps": 0}, "index": 178, "prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=0\n intervention: none\n observed: Z1=1, Z2=1, Y=0\n\nExperiment 2:\n inputs: X1=0, X2=1, X3=1\n intervention: none\n observed: Z1=1, Z2=1, Y=0\n\nExperiment 3:\n inputs: X1=1, X2=1, X3=1\n intervention: set Z1=1\n observed: Z1=1, Z2=1, Y=0\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "raw_prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=0\n intervention: none\n observed: Z1=1, Z2=1, Y=0\n\nExperiment 2:\n inputs: X1=0, X2=1, X3=1\n intervention: none\n observed: Z1=1, Z2=1, Y=0\n\nExperiment 3:\n inputs: X1=1, X2=1, X3=1\n intervention: set Z1=1\n observed: Z1=1, Z2=1, Y=0\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "reward_model": {"style": "rule"}, "state_json": "{\"available_experiment_ids\": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 38, 39], \"metadata\": {\"evidence_size\": 3, \"family_bucket\": \"cross_family\", \"maximum_separation\": 0.7027027027027027, \"mean_separation\": 0.4675675675675673, \"minimum_separation\": 0.16216216216216217, \"normalized_mean_separation\": 0.5009652509652507, \"oracle_budget_representation_error\": 0.12162162162162166, \"oracle_singleton_representation_error\": 0.4054054054054054, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.2837837837837837, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 16}, \"private\": {\"hidden_mode_id\": \"9b1274058fe3d7c005182c045127f92d9e80e555cb0bc2d3fe493023d47e8e98\", \"valid_mode_ids\": [\"02b6a764582959408fa064f0cfe0a6c173f2abd1f1dbcff693bb131976bddcb7\", \"0bc9a7f006048752e578f8e96fe5d4299588ea04d14cb02e615f83c0cbd04a23\", \"13427c0673ada343b15597999a2c8663abc7804fc0b9fbf9732fccdfc515e583\", \"1f5b3cb190132db2cca61f0c84ce243c154d320e6b1875f4078c290b6d91f008\", \"2f604defa702f9fd8be28c359e104d0ce8712f1ee00a1fd4d6864b79d3ec9856\", \"3a6597898f334c02768e65d00557c58b5beadb0f4291192628cf18df1eaceeaa\", \"3c808e8f3166ff07355f675820c785154f2697d09fcabf426b864dfac342a5f0\", \"481bedd7b11829fb1fc52616d38a83656e54afe23cf2410e83da3d4c46c90358\", \"5623e81676b81ddc9d4b9f9185f2312dd17fc9397b68b52fb83895cafbe5a68a\", \"5de28bc58ed25986d95ae078ca438f71d80bb88d97a863fc58c32f8eb9ca038a\", \"63262e3c0d947e9b11fe723720981b848af366cce0b7f53bb4043c679edaed4a\", \"9b1274058fe3d7c005182c045127f92d9e80e555cb0bc2d3fe493023d47e8e98\", \"a3a32270e702f32c08617ff1877d9fc3ea9f735a35d3b282bbda69c93d8c7127\", \"c37445f5e31330679d0f17a4ce12301ebbca5d11421d8dd3e1e98be59dee032a\", \"e0a1eb2d2c05f8984059a1bf65814b1f3161b5bf37f2cd38b05f7ac227beb3cd\", \"ee97bef0cf0cf72c49dfbd350a9a350eaae022ccdb40fe3e8a4ed7e93d2e5ebf\"]}, \"state_id\": \"39d7201df13f31f4fc4fdb87\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 15, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 37, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 1}, \"intervention\": \"DO_Z1_1\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}]}"} {"agent_name": "causal_micro_lab_agent_loop", "data_source": "causal_micro_lab_final_eval_v3", "env_spec_json": "{\"agent\": {}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 179, \"task\": {\"state\": {\"available_experiment_ids\": [1, 2, 3, 4, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 31, 32, 33, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 3, \"family_bucket\": \"within_family\", \"maximum_separation\": 0.6756756756756757, \"mean_separation\": 0.48288288288288295, \"minimum_separation\": 0.16216216216216217, \"normalized_mean_separation\": 0.5173745173745175, \"oracle_budget_representation_error\": 0.13513513513513517, \"oracle_singleton_representation_error\": 0.4256756756756756, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.29054054054054046, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 16}, \"private\": {\"hidden_mode_id\": \"d97414e898861ae6182405e9b64889e7ee76f3d048d429525a77b89577ca9438\", \"valid_mode_ids\": [\"03eea9d9a73a956b433908f14223440a288055f5c58290dc112525484fbfbec4\", \"0b4ff18b46889b89d3bd7fb45d049c7b38e93095973141fea142e678a884c4a8\", \"2bf99fa8dbd637efc09a94e2f2e5607ad5edf749291112394e2458430a6c2f50\", \"2d94d06f01e7c0590abf51b18a636da969b819bdd8b05bc2df7b2ea504c63545\", \"3451c43fbdd03dd8776765bab7a2795d9b6436c6b982b62618c15c5825ab8fe1\", \"381140f8ce8f9d85ee5e4039e9c558a91a6a41488e039aac70328ed5ed4e7955\", \"4c8a9f65ec8686af49a459f1c32a1f48edeb2c3d736f30063199cf7c845a9df0\", \"56915d828586457cbf45494ae4e86894bad286df4e697dfce5584501f8fc8246\", \"6fb3802530e9b79a0f2b8e74d3c89b083bc08dffefcff364a7c66fadd0ed8565\", \"776a53ddac718cbeb21ba5a381c6378e29ef8348cc28b708dfae5704ed39ce2f\", \"8cfa72623d8a8043054ad713e29870320e0bc082bc3aafe279dbb84ac0e06683\", \"ab6a56b50740ce7dc8e3ab2f1514e530172b4456742c3758a8380d96e504f459\", \"d97414e898861ae6182405e9b64889e7ee76f3d048d429525a77b89577ca9438\", \"e71a637c825de64f07568c8504cf9523feed1d117ec4deadebac9d90fa32009d\", \"f2880d855270de6188d2a5460bb1b886ce876654f980ff6464714b141164421a\", \"fbaf2285a538e39ae6741bff608d36cf7196fc4918bf8e8b2eed748b13e1a950\"]}, \"state_id\": \"5544376b1189b17fdd1982df\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 5, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 30, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}]}}}", "extra_info": {"index": 179, "max_global_steps": 0, "min_global_steps": 0}, "index": 179, "prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=0\n intervention: none\n observed: Z1=1, Z2=1, Y=1\n\nExperiment 2:\n inputs: X1=0, X2=0, X3=1\n intervention: none\n observed: Z1=0, Z2=0, Y=0\n\nExperiment 3:\n inputs: X1=1, X2=1, X3=0\n intervention: none\n observed: Z1=1, Z2=1, Y=0\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "raw_prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=0\n intervention: none\n observed: Z1=1, Z2=1, Y=1\n\nExperiment 2:\n inputs: X1=0, X2=0, X3=1\n intervention: none\n observed: Z1=0, Z2=0, Y=0\n\nExperiment 3:\n inputs: X1=1, X2=1, X3=0\n intervention: none\n observed: Z1=1, Z2=1, Y=0\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "reward_model": {"style": "rule"}, "state_json": "{\"available_experiment_ids\": [1, 2, 3, 4, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 31, 32, 33, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 3, \"family_bucket\": \"within_family\", \"maximum_separation\": 0.6756756756756757, \"mean_separation\": 0.48288288288288295, \"minimum_separation\": 0.16216216216216217, \"normalized_mean_separation\": 0.5173745173745175, \"oracle_budget_representation_error\": 0.13513513513513517, \"oracle_singleton_representation_error\": 0.4256756756756756, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.29054054054054046, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 16}, \"private\": {\"hidden_mode_id\": \"d97414e898861ae6182405e9b64889e7ee76f3d048d429525a77b89577ca9438\", \"valid_mode_ids\": [\"03eea9d9a73a956b433908f14223440a288055f5c58290dc112525484fbfbec4\", \"0b4ff18b46889b89d3bd7fb45d049c7b38e93095973141fea142e678a884c4a8\", \"2bf99fa8dbd637efc09a94e2f2e5607ad5edf749291112394e2458430a6c2f50\", \"2d94d06f01e7c0590abf51b18a636da969b819bdd8b05bc2df7b2ea504c63545\", \"3451c43fbdd03dd8776765bab7a2795d9b6436c6b982b62618c15c5825ab8fe1\", \"381140f8ce8f9d85ee5e4039e9c558a91a6a41488e039aac70328ed5ed4e7955\", \"4c8a9f65ec8686af49a459f1c32a1f48edeb2c3d736f30063199cf7c845a9df0\", \"56915d828586457cbf45494ae4e86894bad286df4e697dfce5584501f8fc8246\", \"6fb3802530e9b79a0f2b8e74d3c89b083bc08dffefcff364a7c66fadd0ed8565\", \"776a53ddac718cbeb21ba5a381c6378e29ef8348cc28b708dfae5704ed39ce2f\", \"8cfa72623d8a8043054ad713e29870320e0bc082bc3aafe279dbb84ac0e06683\", \"ab6a56b50740ce7dc8e3ab2f1514e530172b4456742c3758a8380d96e504f459\", \"d97414e898861ae6182405e9b64889e7ee76f3d048d429525a77b89577ca9438\", \"e71a637c825de64f07568c8504cf9523feed1d117ec4deadebac9d90fa32009d\", \"f2880d855270de6188d2a5460bb1b886ce876654f980ff6464714b141164421a\", \"fbaf2285a538e39ae6741bff608d36cf7196fc4918bf8e8b2eed748b13e1a950\"]}, \"state_id\": \"5544376b1189b17fdd1982df\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 5, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 30, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}]}"} {"agent_name": "causal_micro_lab_agent_loop", "data_source": "causal_micro_lab_final_eval_v3", "env_spec_json": "{\"agent\": {}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 180, \"task\": {\"state\": {\"available_experiment_ids\": [0, 1, 3, 4, 6, 7, 8, 9, 10, 11, 12, 13, 14, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 3, \"family_bucket\": \"mixed\", \"maximum_separation\": 0.8108108108108109, \"mean_separation\": 0.49977477477477444, \"minimum_separation\": 0.13513513513513514, \"normalized_mean_separation\": 0.5354729729729726, \"oracle_budget_representation_error\": 0.15202702702702706, \"oracle_singleton_representation_error\": 0.44425675675675685, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.2922297297297298, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 16}, \"private\": {\"hidden_mode_id\": \"58f674514a393a1f34e6a4fe89d1fc5a5e789188cea2780e19f62ad00c84c66b\", \"valid_mode_ids\": [\"0a8e35932135791c344761edbfbc6b487e1b5331f71ce776724070062f4e7f91\", \"22d4580837021bec89f63b250c9fee063bc5292e65a3a82f31e6417c3d2d2402\", \"409f60927d7ea62ce00a134c0be8c58889bcf216053b6bac1aa64aef8258663d\", \"45d60f86c25aea357fb84f8d5eb3548c774a3fc5f81115b0a333a1fbe7c2bd1d\", \"58f674514a393a1f34e6a4fe89d1fc5a5e789188cea2780e19f62ad00c84c66b\", \"60c4274b6dc88d7ac5d8422de6eaac82bf572bcaf639b43da887e2adf2538a56\", \"75e52e9bebced1250f084eaaaca44bab1bb591da7fd947e795e4908907079251\", \"97b94d0fc1f7b1e310a98602fe79a8f62f3eb3a25b1b918c57f7f4467d2961a1\", \"b51fd51fbded5595ac08f56455b4ea15743a14693424ceaf462509e4abbd3ae7\", \"b8b207f34ce9fe6bbcca2188c6fa33c7bcfd0c42cbf4c75b420515303d78a986\", \"bf71ff292dae1837ced6da3e6a3f985fddd7a5cc839a2d5c4788433753b2bdf8\", \"c25c1ec97dfe44deeca5a9217a372a8fbc93cf0439682804647c00c146d67e42\", \"d675e99f5314ee334da68a37f76ac9b10248fcba0f024029ddf9e1515370c377\", \"d82eae0ab8031939e334475a3fdae57fd2c00fc5ef6b6c342af8e5c0a98752dc\", \"f0eed085de41cf5a9c5823a6cfc9ef6b0487b60e9abb1863d3fbe8cac09a9f4b\", \"fb34873b12f03a436cc0cca77960087b91be6ac8eb3c7a091650a5d6407a48e0\"]}, \"state_id\": \"2dab07a6fe2231ad83f4b974\", \"visible_experiments\": [{\"experiment_id\": 2, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z1_1\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 5, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 15, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}]}}}", "extra_info": {"index": 180, "max_global_steps": 0, "min_global_steps": 0}, "index": 180, "prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=0\n intervention: set Z1=1\n observed: Z1=1, Z2=1, Y=0\n\nExperiment 2:\n inputs: X1=0, X2=0, X3=1\n intervention: none\n observed: Z1=0, Z2=0, Y=1\n\nExperiment 3:\n inputs: X1=0, X2=1, X3=1\n intervention: none\n observed: Z1=0, Z2=1, Y=0\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "raw_prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=0\n intervention: set Z1=1\n observed: Z1=1, Z2=1, Y=0\n\nExperiment 2:\n inputs: X1=0, X2=0, X3=1\n intervention: none\n observed: Z1=0, Z2=0, Y=1\n\nExperiment 3:\n inputs: X1=0, X2=1, X3=1\n intervention: none\n observed: Z1=0, Z2=1, Y=0\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "reward_model": {"style": "rule"}, "state_json": "{\"available_experiment_ids\": [0, 1, 3, 4, 6, 7, 8, 9, 10, 11, 12, 13, 14, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 3, \"family_bucket\": \"mixed\", \"maximum_separation\": 0.8108108108108109, \"mean_separation\": 0.49977477477477444, \"minimum_separation\": 0.13513513513513514, \"normalized_mean_separation\": 0.5354729729729726, \"oracle_budget_representation_error\": 0.15202702702702706, \"oracle_singleton_representation_error\": 0.44425675675675685, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.2922297297297298, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 16}, \"private\": {\"hidden_mode_id\": \"58f674514a393a1f34e6a4fe89d1fc5a5e789188cea2780e19f62ad00c84c66b\", \"valid_mode_ids\": [\"0a8e35932135791c344761edbfbc6b487e1b5331f71ce776724070062f4e7f91\", \"22d4580837021bec89f63b250c9fee063bc5292e65a3a82f31e6417c3d2d2402\", \"409f60927d7ea62ce00a134c0be8c58889bcf216053b6bac1aa64aef8258663d\", \"45d60f86c25aea357fb84f8d5eb3548c774a3fc5f81115b0a333a1fbe7c2bd1d\", \"58f674514a393a1f34e6a4fe89d1fc5a5e789188cea2780e19f62ad00c84c66b\", \"60c4274b6dc88d7ac5d8422de6eaac82bf572bcaf639b43da887e2adf2538a56\", \"75e52e9bebced1250f084eaaaca44bab1bb591da7fd947e795e4908907079251\", \"97b94d0fc1f7b1e310a98602fe79a8f62f3eb3a25b1b918c57f7f4467d2961a1\", \"b51fd51fbded5595ac08f56455b4ea15743a14693424ceaf462509e4abbd3ae7\", \"b8b207f34ce9fe6bbcca2188c6fa33c7bcfd0c42cbf4c75b420515303d78a986\", \"bf71ff292dae1837ced6da3e6a3f985fddd7a5cc839a2d5c4788433753b2bdf8\", \"c25c1ec97dfe44deeca5a9217a372a8fbc93cf0439682804647c00c146d67e42\", \"d675e99f5314ee334da68a37f76ac9b10248fcba0f024029ddf9e1515370c377\", \"d82eae0ab8031939e334475a3fdae57fd2c00fc5ef6b6c342af8e5c0a98752dc\", \"f0eed085de41cf5a9c5823a6cfc9ef6b0487b60e9abb1863d3fbe8cac09a9f4b\", \"fb34873b12f03a436cc0cca77960087b91be6ac8eb3c7a091650a5d6407a48e0\"]}, \"state_id\": \"2dab07a6fe2231ad83f4b974\", \"visible_experiments\": [{\"experiment_id\": 2, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z1_1\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 5, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 15, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}]}"} {"agent_name": "causal_micro_lab_agent_loop", "data_source": "causal_micro_lab_final_eval_v3", "env_spec_json": "{\"agent\": {}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 181, \"task\": {\"state\": {\"available_experiment_ids\": [1, 2, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 3, \"family_bucket\": \"within_family\", \"maximum_separation\": 0.8108108108108109, \"mean_separation\": 0.515315315315315, \"minimum_separation\": 0.16216216216216217, \"normalized_mean_separation\": 0.5521235521235518, \"oracle_budget_representation_error\": 0.16216216216216217, \"oracle_singleton_representation_error\": 0.46452702702702703, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.30236486486486486, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 16}, \"private\": {\"hidden_mode_id\": \"d2ab1278b84c726e6865f11997abc5c4d8127a56bf5ccc5f3a12664271aaa69b\", \"valid_mode_ids\": [\"0ff390a83e4108e18fdaf1d503127d452372a33f58a62f2e5bbc53ad692fe65a\", \"38d00c25c591646021d5a0a640c043a59b6f199e7c52b4a77e032617f4fdba34\", \"41e64fcebdaa053324913c08696ffa851612fe7b6170454a02faa6c7b770a9c3\", \"5a52882745917b71de30b9c55c4cb589cae55f534a099348c459e3bcc5c298b4\", \"7662949bdfa73b4019816ea6970dfbc664ef6b68c38e04ecbdbe288fe8563cb4\", \"81b7f7387d2e0c9c1a2301c1c9a4f6abe07955cb232291c45b4e8e2f121200d4\", \"87e42fc61d9d496c7fac65e541099303ee065ec4399cccca1daf3b44771b8ef4\", \"a18a83644b4ed2181215ffa77cc4d81724650a34591e9dc29e5994374bdadf3d\", \"a4a041cf8b3d3bad92a4d028ad05cf0e576ef608bf8d9d7669ce1012e11a13bb\", \"aea30326fd985be636393bb462446592cdd2b9454c9e66b948b196205589b8ca\", \"b4fa6ce8fe641022f79a065c61458abf462c31316a8a26f0f25de4faa9d2e24c\", \"ce3e911dae426b9a90980e74ffe5df05f2ed40562e721c02aa1555ed4adb3df7\", \"d2ab1278b84c726e6865f11997abc5c4d8127a56bf5ccc5f3a12664271aaa69b\", \"d4b744bc91a843c30988ca9b3c296759e866e0262431cce1367861e6b35b703b\", \"eff9ade1d2e341ca9e9c437385c83347f5944547f92904fbb3dfafa95c361477\", \"fffcfe29b8cc3fff6c17b278d42244305af2e5435fbcd7b3d6c8d915693a7ee4\"]}, \"state_id\": \"76d62453de07621b58d802cb\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 3, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z2_0\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 15, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}]}}}", "extra_info": {"index": 181, "max_global_steps": 0, "min_global_steps": 0}, "index": 181, "prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=0\n intervention: none\n observed: Z1=0, Z2=1, Y=1\n\nExperiment 2:\n inputs: X1=0, X2=0, X3=0\n intervention: set Z2=0\n observed: Z1=0, Z2=0, Y=0\n\nExperiment 3:\n inputs: X1=0, X2=1, X3=1\n intervention: none\n observed: Z1=0, Z2=1, Y=0\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "raw_prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=0\n intervention: none\n observed: Z1=0, Z2=1, Y=1\n\nExperiment 2:\n inputs: X1=0, X2=0, X3=0\n intervention: set Z2=0\n observed: Z1=0, Z2=0, Y=0\n\nExperiment 3:\n inputs: X1=0, X2=1, X3=1\n intervention: none\n observed: Z1=0, Z2=1, Y=0\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "reward_model": {"style": "rule"}, "state_json": "{\"available_experiment_ids\": [1, 2, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 3, \"family_bucket\": \"within_family\", \"maximum_separation\": 0.8108108108108109, \"mean_separation\": 0.515315315315315, \"minimum_separation\": 0.16216216216216217, \"normalized_mean_separation\": 0.5521235521235518, \"oracle_budget_representation_error\": 0.16216216216216217, \"oracle_singleton_representation_error\": 0.46452702702702703, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.30236486486486486, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 16}, \"private\": {\"hidden_mode_id\": \"d2ab1278b84c726e6865f11997abc5c4d8127a56bf5ccc5f3a12664271aaa69b\", \"valid_mode_ids\": [\"0ff390a83e4108e18fdaf1d503127d452372a33f58a62f2e5bbc53ad692fe65a\", \"38d00c25c591646021d5a0a640c043a59b6f199e7c52b4a77e032617f4fdba34\", \"41e64fcebdaa053324913c08696ffa851612fe7b6170454a02faa6c7b770a9c3\", \"5a52882745917b71de30b9c55c4cb589cae55f534a099348c459e3bcc5c298b4\", \"7662949bdfa73b4019816ea6970dfbc664ef6b68c38e04ecbdbe288fe8563cb4\", \"81b7f7387d2e0c9c1a2301c1c9a4f6abe07955cb232291c45b4e8e2f121200d4\", \"87e42fc61d9d496c7fac65e541099303ee065ec4399cccca1daf3b44771b8ef4\", \"a18a83644b4ed2181215ffa77cc4d81724650a34591e9dc29e5994374bdadf3d\", \"a4a041cf8b3d3bad92a4d028ad05cf0e576ef608bf8d9d7669ce1012e11a13bb\", \"aea30326fd985be636393bb462446592cdd2b9454c9e66b948b196205589b8ca\", \"b4fa6ce8fe641022f79a065c61458abf462c31316a8a26f0f25de4faa9d2e24c\", \"ce3e911dae426b9a90980e74ffe5df05f2ed40562e721c02aa1555ed4adb3df7\", \"d2ab1278b84c726e6865f11997abc5c4d8127a56bf5ccc5f3a12664271aaa69b\", \"d4b744bc91a843c30988ca9b3c296759e866e0262431cce1367861e6b35b703b\", \"eff9ade1d2e341ca9e9c437385c83347f5944547f92904fbb3dfafa95c361477\", \"fffcfe29b8cc3fff6c17b278d42244305af2e5435fbcd7b3d6c8d915693a7ee4\"]}, \"state_id\": \"76d62453de07621b58d802cb\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 3, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z2_0\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 15, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}]}"} {"agent_name": "causal_micro_lab_agent_loop", "data_source": "causal_micro_lab_final_eval_v3", "env_spec_json": "{\"agent\": {}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 182, \"task\": {\"state\": {\"available_experiment_ids\": [1, 2, 3, 4, 6, 7, 8, 9, 10, 11, 12, 13, 14, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 3, \"family_bucket\": \"cross_family\", \"maximum_separation\": 0.6756756756756757, \"mean_separation\": 0.4819819819819818, \"minimum_separation\": 0.16216216216216217, \"normalized_mean_separation\": 0.5164092664092662, \"oracle_budget_representation_error\": 0.12162162162162166, \"oracle_singleton_representation_error\": 0.42567567567567577, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.3040540540540541, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 16}, \"private\": {\"hidden_mode_id\": \"9775433d5f9ca59b940c774bd532f826b6f31fbd5170052364e658164d79256a\", \"valid_mode_ids\": [\"062cc12efa19c2853fea6e9b21189c8f028b17a0bd90c30bb274b3ed37b67b38\", \"0ab33edca10e2b0355d17c842d5f304b0a42e03ec12865cce551efc64331c92e\", \"12b7a52a6959260d602830f28f2f596a0ff5632e557a389aba98bafd04cfd7e4\", \"14ab0dcd6c9a929250429df00f15800b9ec151c5ddb8f89d4c0690821efcb098\", \"2ca0b81f3a486c99dd9947e4dbfa300e9b58b936c3a533167cbbd168770bcdf4\", \"47ab0964af29109b1f2a5a2c80cc567d1af2a2278a4bcedd62fd162ac660a79e\", \"5182b9b3284c26206de280fa3774dc04d65b0f81c407a181ba50e04d1dc0798c\", \"52595474ac16da2ae567de4285618dee51ab0881025ae83fb7a725fd9da8bf0e\", \"663c3947649078d3b2f1090453204ca0d1381af10356602ff4c4920a4f0d9062\", \"733c02f6685b3c1e697593133c53ccb36ec60593fb5b147615104630da34c861\", \"74c2bddba5104a7975805f0b87db2d85a8cb4c0cc9892c77bfddd2353a16da21\", \"9775433d5f9ca59b940c774bd532f826b6f31fbd5170052364e658164d79256a\", \"c2847dff8686e8956aac934a12075fd29dcec366a3011c00ae54fcdc2b5843c8\", \"dd0bebc690d2cc1d36dc7069c54f73e4d45aa77acff9b1efcbc35065f32a8d09\", \"ed00187274dbce98eb44962c2fff6d0f383f299f2569029a4cb0f87a1c5b62e4\", \"f27cab7826bde31d36ebd5bb48698f7ae5fe79b858e76004b303068bd092e3a4\"]}, \"state_id\": \"d75f2460769645e4251b27d6\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 5, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 15, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}]}}}", "extra_info": {"index": 182, "max_global_steps": 0, "min_global_steps": 0}, "index": 182, "prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=0\n intervention: none\n observed: Z1=0, Z2=1, Y=1\n\nExperiment 2:\n inputs: X1=0, X2=0, X3=1\n intervention: none\n observed: Z1=1, Z2=1, Y=0\n\nExperiment 3:\n inputs: X1=0, X2=1, X3=1\n intervention: none\n observed: Z1=1, Z2=1, Y=0\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "raw_prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=0\n intervention: none\n observed: Z1=0, Z2=1, Y=1\n\nExperiment 2:\n inputs: X1=0, X2=0, X3=1\n intervention: none\n observed: Z1=1, Z2=1, Y=0\n\nExperiment 3:\n inputs: X1=0, X2=1, X3=1\n intervention: none\n observed: Z1=1, Z2=1, Y=0\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "reward_model": {"style": "rule"}, "state_json": "{\"available_experiment_ids\": [1, 2, 3, 4, 6, 7, 8, 9, 10, 11, 12, 13, 14, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 3, \"family_bucket\": \"cross_family\", \"maximum_separation\": 0.6756756756756757, \"mean_separation\": 0.4819819819819818, \"minimum_separation\": 0.16216216216216217, \"normalized_mean_separation\": 0.5164092664092662, \"oracle_budget_representation_error\": 0.12162162162162166, \"oracle_singleton_representation_error\": 0.42567567567567577, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.3040540540540541, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 16}, \"private\": {\"hidden_mode_id\": \"9775433d5f9ca59b940c774bd532f826b6f31fbd5170052364e658164d79256a\", \"valid_mode_ids\": [\"062cc12efa19c2853fea6e9b21189c8f028b17a0bd90c30bb274b3ed37b67b38\", \"0ab33edca10e2b0355d17c842d5f304b0a42e03ec12865cce551efc64331c92e\", \"12b7a52a6959260d602830f28f2f596a0ff5632e557a389aba98bafd04cfd7e4\", \"14ab0dcd6c9a929250429df00f15800b9ec151c5ddb8f89d4c0690821efcb098\", \"2ca0b81f3a486c99dd9947e4dbfa300e9b58b936c3a533167cbbd168770bcdf4\", \"47ab0964af29109b1f2a5a2c80cc567d1af2a2278a4bcedd62fd162ac660a79e\", \"5182b9b3284c26206de280fa3774dc04d65b0f81c407a181ba50e04d1dc0798c\", \"52595474ac16da2ae567de4285618dee51ab0881025ae83fb7a725fd9da8bf0e\", \"663c3947649078d3b2f1090453204ca0d1381af10356602ff4c4920a4f0d9062\", \"733c02f6685b3c1e697593133c53ccb36ec60593fb5b147615104630da34c861\", \"74c2bddba5104a7975805f0b87db2d85a8cb4c0cc9892c77bfddd2353a16da21\", \"9775433d5f9ca59b940c774bd532f826b6f31fbd5170052364e658164d79256a\", \"c2847dff8686e8956aac934a12075fd29dcec366a3011c00ae54fcdc2b5843c8\", \"dd0bebc690d2cc1d36dc7069c54f73e4d45aa77acff9b1efcbc35065f32a8d09\", \"ed00187274dbce98eb44962c2fff6d0f383f299f2569029a4cb0f87a1c5b62e4\", \"f27cab7826bde31d36ebd5bb48698f7ae5fe79b858e76004b303068bd092e3a4\"]}, \"state_id\": \"d75f2460769645e4251b27d6\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 5, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 15, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}]}"} {"agent_name": "causal_micro_lab_agent_loop", "data_source": "causal_micro_lab_final_eval_v3", "env_spec_json": "{\"agent\": {}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 183, \"task\": {\"state\": {\"available_experiment_ids\": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 11, 12, 13, 14, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 31, 32, 33, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 3, \"family_bucket\": \"mixed\", \"maximum_separation\": 0.7297297297297297, \"mean_separation\": 0.503153153153153, \"minimum_separation\": 0.10810810810810811, \"normalized_mean_separation\": 0.5390926640926639, \"oracle_budget_representation_error\": 0.14864864864864868, \"oracle_singleton_representation_error\": 0.4577702702702703, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.3091216216216216, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 16}, \"private\": {\"hidden_mode_id\": \"6aef07588f497b0e89c8b87efafa9a412730ad9c435f993d54ffab49690a3493\", \"valid_mode_ids\": [\"045eba4caa4f77e68b89d0fb087ff5ab54f18cf73be63a51542af5a640088ea9\", \"0e75ba11d1a4a4e65676858442f8b411c5121517c37eaf42c0f9e3b5ffa37aa5\", \"0fc532697137440535b202f3801e1af7c7cd10c8fcf96db5127931abf6922f25\", \"1f7988b60578183cdbb71564c7a878ebe45a1d1f326ed9d370bf6d3b823fa1be\", \"2599c6472a2b62bfe022dbbd3e883f48a38995a4dbfa77fb918ed68ef96d222d\", \"3e189aaa9e737632a55a2fccf23d40d1a0224d9da1a75c41d50fe495a6e08edc\", \"4c983a906eda3a1ba456eb46435b738ba558fbb4aa6338abf3342bc51c4cb09b\", \"53c2d684d7b16ccf4fab54d97fb0f0b321f55b293060df1b57e1284ec697312a\", \"6aef07588f497b0e89c8b87efafa9a412730ad9c435f993d54ffab49690a3493\", \"6afef27d81d4dbc5e1bd5183fcaff0701b71f12671fb2f99cb273650b7accb8b\", \"6b5b8a18ff6314ac7791ff9dac83838f75914bb685e58aefe8810113237dfd29\", \"6ffa4a7f21737edaa892abe0c57be089b31758111c86b7146e795f60b71f3e7e\", \"7905c5a0d978eac8ea4e2586f9ce02b00d4abe1f38aea4adf00d322b5946a8bb\", \"b32616ffdff6a9c1639b5ac053e6bdedeedbc29f85f7cc2e858942dea152c291\", \"b93e53aa1158f9cbe2ce132b226a4f6fd14c0aa5326e342732212390c712f2ee\", \"c9a2b198c17e65e732a6e9978ddb97add438d5c4b8ead158caa06217232cf25c\"]}, \"state_id\": \"7c010dcc38b6bb419d23240d\", \"visible_experiments\": [{\"experiment_id\": 10, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 15, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 30, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 0}}]}}}", "extra_info": {"index": 183, "max_global_steps": 0, "min_global_steps": 0}, "index": 183, "prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=1, X3=0\n intervention: none\n observed: Z1=0, Z2=0, Y=1\n\nExperiment 2:\n inputs: X1=0, X2=1, X3=1\n intervention: none\n observed: Z1=0, Z2=1, Y=1\n\nExperiment 3:\n inputs: X1=1, X2=1, X3=0\n intervention: none\n observed: Z1=0, Z2=0, Y=0\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "raw_prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=1, X3=0\n intervention: none\n observed: Z1=0, Z2=0, Y=1\n\nExperiment 2:\n inputs: X1=0, X2=1, X3=1\n intervention: none\n observed: Z1=0, Z2=1, Y=1\n\nExperiment 3:\n inputs: X1=1, X2=1, X3=0\n intervention: none\n observed: Z1=0, Z2=0, Y=0\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "reward_model": {"style": "rule"}, "state_json": "{\"available_experiment_ids\": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 11, 12, 13, 14, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 31, 32, 33, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 3, \"family_bucket\": \"mixed\", \"maximum_separation\": 0.7297297297297297, \"mean_separation\": 0.503153153153153, \"minimum_separation\": 0.10810810810810811, \"normalized_mean_separation\": 0.5390926640926639, \"oracle_budget_representation_error\": 0.14864864864864868, \"oracle_singleton_representation_error\": 0.4577702702702703, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.3091216216216216, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 16}, \"private\": {\"hidden_mode_id\": \"6aef07588f497b0e89c8b87efafa9a412730ad9c435f993d54ffab49690a3493\", \"valid_mode_ids\": [\"045eba4caa4f77e68b89d0fb087ff5ab54f18cf73be63a51542af5a640088ea9\", \"0e75ba11d1a4a4e65676858442f8b411c5121517c37eaf42c0f9e3b5ffa37aa5\", \"0fc532697137440535b202f3801e1af7c7cd10c8fcf96db5127931abf6922f25\", \"1f7988b60578183cdbb71564c7a878ebe45a1d1f326ed9d370bf6d3b823fa1be\", \"2599c6472a2b62bfe022dbbd3e883f48a38995a4dbfa77fb918ed68ef96d222d\", \"3e189aaa9e737632a55a2fccf23d40d1a0224d9da1a75c41d50fe495a6e08edc\", \"4c983a906eda3a1ba456eb46435b738ba558fbb4aa6338abf3342bc51c4cb09b\", \"53c2d684d7b16ccf4fab54d97fb0f0b321f55b293060df1b57e1284ec697312a\", \"6aef07588f497b0e89c8b87efafa9a412730ad9c435f993d54ffab49690a3493\", \"6afef27d81d4dbc5e1bd5183fcaff0701b71f12671fb2f99cb273650b7accb8b\", \"6b5b8a18ff6314ac7791ff9dac83838f75914bb685e58aefe8810113237dfd29\", \"6ffa4a7f21737edaa892abe0c57be089b31758111c86b7146e795f60b71f3e7e\", \"7905c5a0d978eac8ea4e2586f9ce02b00d4abe1f38aea4adf00d322b5946a8bb\", \"b32616ffdff6a9c1639b5ac053e6bdedeedbc29f85f7cc2e858942dea152c291\", \"b93e53aa1158f9cbe2ce132b226a4f6fd14c0aa5326e342732212390c712f2ee\", \"c9a2b198c17e65e732a6e9978ddb97add438d5c4b8ead158caa06217232cf25c\"]}, \"state_id\": \"7c010dcc38b6bb419d23240d\", \"visible_experiments\": [{\"experiment_id\": 10, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 15, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 30, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 0}}]}"} {"agent_name": "causal_micro_lab_agent_loop", "data_source": "causal_micro_lab_final_eval_v3", "env_spec_json": "{\"agent\": {}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 184, \"task\": {\"state\": {\"available_experiment_ids\": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 38, 39], \"metadata\": {\"evidence_size\": 3, \"family_bucket\": \"within_family\", \"maximum_separation\": 0.8108108108108109, \"mean_separation\": 0.5297297297297294, \"minimum_separation\": 0.16216216216216217, \"normalized_mean_separation\": 0.5675675675675672, \"oracle_budget_representation_error\": 0.16216216216216217, \"oracle_singleton_representation_error\": 0.4780405405405406, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.31587837837837845, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 16}, \"private\": {\"hidden_mode_id\": \"4293ae243ac85d753efb824389ca97f738ff2f6351d7b83fd6d700b9a8c7463e\", \"valid_mode_ids\": [\"10c45669d19ebcaf8e632f8ef85f7f61a18437ea0eb8ee84025151ca45978ff8\", \"17b9ddd80622580bbc84ad20f4cc082d6a2448f2a15ea282ee18f1888a2abd13\", \"2c981107ac1ce1646c4878f0c0ff2f691088c0b5ffb6a0af581ab9d1dd8fdbf8\", \"3179b0fb4c01d4d4de1309b28e4673c1976487dc3a00bf214defbb34cfc32741\", \"4293ae243ac85d753efb824389ca97f738ff2f6351d7b83fd6d700b9a8c7463e\", \"4ced759ba07ce0b1a13f6ca6a97b670640b9531306871675e75b3a47515d8833\", \"5eb19dfd62b7e59e0dd3e167cb3a34576c1acb5fd09f9d47fce73041acf377cd\", \"78956eaa76f3573011ffbe27e596ff27a3157055d2bda22b4c37f9b59c2e6d1b\", \"81bce5cfa9455ca664064c8edaeabc6a030cd9ed6a77a003acd070eb2d290055\", \"93256de92ff37a6aaf4331e9db184f1e159efbb1650220fcda6721d119cf23c6\", \"c98e9e1b3dae2c9a7e3272d603c59aaa216284923940d5b4d6b89e6ffed2e00e\", \"d35644d7bf9e467e95d51753f5077e417bdfdfc3dc68041474dd505a6eea0a6f\", \"d9d3424a00b74e387a7d8b55e05ad4bb552f742a3c21bf6aac52f925aa3471d1\", \"e72cbaafed51aa3f7fc5000bd0c3f121adc995a5cb8a276546bc1d629505f30e\", \"e8d1ca3e4b00119a43f3be0d00c91af3e6014568e267d7eda96c727b54eafbe0\", \"eb8d0ec05f53b1f712e6bcaa0ba1d7aabd6ac1b0146c1df6fd3c5415e6369b69\"]}, \"state_id\": \"dd66f5a7b58bdc757d568c3b\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 15, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 37, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 1}, \"intervention\": \"DO_Z1_1\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 0}}]}}}", "extra_info": {"index": 184, "max_global_steps": 0, "min_global_steps": 0}, "index": 184, "prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=0\n intervention: none\n observed: Z1=0, Z2=0, Y=1\n\nExperiment 2:\n inputs: X1=0, X2=1, X3=1\n intervention: none\n observed: Z1=0, Z2=0, Y=0\n\nExperiment 3:\n inputs: X1=1, X2=1, X3=1\n intervention: set Z1=1\n observed: Z1=1, Z2=0, Y=0\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "raw_prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=0\n intervention: none\n observed: Z1=0, Z2=0, Y=1\n\nExperiment 2:\n inputs: X1=0, X2=1, X3=1\n intervention: none\n observed: Z1=0, Z2=0, Y=0\n\nExperiment 3:\n inputs: X1=1, X2=1, X3=1\n intervention: set Z1=1\n observed: Z1=1, Z2=0, Y=0\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "reward_model": {"style": "rule"}, "state_json": "{\"available_experiment_ids\": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 38, 39], \"metadata\": {\"evidence_size\": 3, \"family_bucket\": \"within_family\", \"maximum_separation\": 0.8108108108108109, \"mean_separation\": 0.5297297297297294, \"minimum_separation\": 0.16216216216216217, \"normalized_mean_separation\": 0.5675675675675672, \"oracle_budget_representation_error\": 0.16216216216216217, \"oracle_singleton_representation_error\": 0.4780405405405406, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.31587837837837845, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 16}, \"private\": {\"hidden_mode_id\": \"4293ae243ac85d753efb824389ca97f738ff2f6351d7b83fd6d700b9a8c7463e\", \"valid_mode_ids\": [\"10c45669d19ebcaf8e632f8ef85f7f61a18437ea0eb8ee84025151ca45978ff8\", \"17b9ddd80622580bbc84ad20f4cc082d6a2448f2a15ea282ee18f1888a2abd13\", \"2c981107ac1ce1646c4878f0c0ff2f691088c0b5ffb6a0af581ab9d1dd8fdbf8\", \"3179b0fb4c01d4d4de1309b28e4673c1976487dc3a00bf214defbb34cfc32741\", \"4293ae243ac85d753efb824389ca97f738ff2f6351d7b83fd6d700b9a8c7463e\", \"4ced759ba07ce0b1a13f6ca6a97b670640b9531306871675e75b3a47515d8833\", \"5eb19dfd62b7e59e0dd3e167cb3a34576c1acb5fd09f9d47fce73041acf377cd\", \"78956eaa76f3573011ffbe27e596ff27a3157055d2bda22b4c37f9b59c2e6d1b\", \"81bce5cfa9455ca664064c8edaeabc6a030cd9ed6a77a003acd070eb2d290055\", \"93256de92ff37a6aaf4331e9db184f1e159efbb1650220fcda6721d119cf23c6\", \"c98e9e1b3dae2c9a7e3272d603c59aaa216284923940d5b4d6b89e6ffed2e00e\", \"d35644d7bf9e467e95d51753f5077e417bdfdfc3dc68041474dd505a6eea0a6f\", \"d9d3424a00b74e387a7d8b55e05ad4bb552f742a3c21bf6aac52f925aa3471d1\", \"e72cbaafed51aa3f7fc5000bd0c3f121adc995a5cb8a276546bc1d629505f30e\", \"e8d1ca3e4b00119a43f3be0d00c91af3e6014568e267d7eda96c727b54eafbe0\", \"eb8d0ec05f53b1f712e6bcaa0ba1d7aabd6ac1b0146c1df6fd3c5415e6369b69\"]}, \"state_id\": \"dd66f5a7b58bdc757d568c3b\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 15, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 37, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 1}, \"intervention\": \"DO_Z1_1\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 0}}]}"} {"agent_name": "causal_micro_lab_agent_loop", "data_source": "causal_micro_lab_final_eval_v3", "env_spec_json": "{\"agent\": {}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 185, \"task\": {\"state\": {\"available_experiment_ids\": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 18, 19, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 3, \"family_bucket\": \"mixed\", \"maximum_separation\": 0.8108108108108109, \"mean_separation\": 0.5594594594594593, \"minimum_separation\": 0.16216216216216217, \"normalized_mean_separation\": 0.5994208494208493, \"oracle_budget_representation_error\": 0.1891891891891892, \"oracle_singleton_representation_error\": 0.508445945945946, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.31925675675675685, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 16}, \"private\": {\"hidden_mode_id\": \"384cea8d603b7a26c1e8195d13e13c811f094b742458a61ad8deb731c907af2e\", \"valid_mode_ids\": [\"2c28f32140533672e197dfa72f610690eecd566820af7b4cc8e7897f3dc3f74f\", \"3130a469e98322f7a44e213f7b79561c32f37f6bd38c5452b6044662b61a26f4\", \"384cea8d603b7a26c1e8195d13e13c811f094b742458a61ad8deb731c907af2e\", \"3fa66d740ca9ecc1a21329aa77aeb3794b45d2aae77f7e9bd2a514c0b2856c75\", \"40e0d0ef3074e21d1edc4091d1ef9307acb7b3842cfde6da9f7fc746d838ef93\", \"4b12d51f066550d2844cc29143a1a3f34e9d30b879121ec4e18e64bdaefa4bf0\", \"629c58434331946caa82688c6136e0f8d8eafe2f8adde821f8b528d96f291bfd\", \"6329e37245b2db4b2942b82e42dccf9075e87fa1c5855194d5dde68d906aab2b\", \"6330077c8ce9902138c12dc884a88cf9c9ed2d87a486b033ef2bb7cf27b1516b\", \"645fabc0e4e06c66d8b0ba919841304ed6febfe9646a3127e5290aa959171c13\", \"863b44f6f8a1ff5debf3abb933090c449329b870e994f93578761f50ff7e0318\", \"95c1f066d90aa741a6fa539329eba1295e486d34dd8e2c9417d7700517eeeb46\", \"c2fbe2208c4b83eb7e18452b09401316147233f589195b72849b05dd0f77fc1b\", \"c9b9efb5e578f473419e5e1231af33ae551babf66df513eed2c3b0bce39825df\", \"dba473e2e649230da3820751ea4b8434b50db437813d2f75d4b39eba50e0cd83\", \"ea56434ab5ae704c474b5f73d9d13c1f5d885f24a46f2070cf4122d88873e2b2\"]}, \"state_id\": \"1bb9b22d165ac34bd3d4ca1f\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 0}}, {\"experiment_id\": 17, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 1}, \"intervention\": \"DO_Z1_1\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 0}}, {\"experiment_id\": 20, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 0}}]}}}", "extra_info": {"index": 185, "max_global_steps": 0, "min_global_steps": 0}, "index": 185, "prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=0\n intervention: none\n observed: Z1=1, Z2=0, Y=1\n\nExperiment 2:\n inputs: X1=0, X2=1, X3=1\n intervention: set Z1=1\n observed: Z1=1, Z2=0, Y=1\n\nExperiment 3:\n inputs: X1=1, X2=0, X3=0\n intervention: none\n observed: Z1=1, Z2=0, Y=0\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "raw_prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=0\n intervention: none\n observed: Z1=1, Z2=0, Y=1\n\nExperiment 2:\n inputs: X1=0, X2=1, X3=1\n intervention: set Z1=1\n observed: Z1=1, Z2=0, Y=1\n\nExperiment 3:\n inputs: X1=1, X2=0, X3=0\n intervention: none\n observed: Z1=1, Z2=0, Y=0\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "reward_model": {"style": "rule"}, "state_json": "{\"available_experiment_ids\": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 18, 19, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 3, \"family_bucket\": \"mixed\", \"maximum_separation\": 0.8108108108108109, \"mean_separation\": 0.5594594594594593, \"minimum_separation\": 0.16216216216216217, \"normalized_mean_separation\": 0.5994208494208493, \"oracle_budget_representation_error\": 0.1891891891891892, \"oracle_singleton_representation_error\": 0.508445945945946, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.31925675675675685, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 16}, \"private\": {\"hidden_mode_id\": \"384cea8d603b7a26c1e8195d13e13c811f094b742458a61ad8deb731c907af2e\", \"valid_mode_ids\": [\"2c28f32140533672e197dfa72f610690eecd566820af7b4cc8e7897f3dc3f74f\", \"3130a469e98322f7a44e213f7b79561c32f37f6bd38c5452b6044662b61a26f4\", \"384cea8d603b7a26c1e8195d13e13c811f094b742458a61ad8deb731c907af2e\", \"3fa66d740ca9ecc1a21329aa77aeb3794b45d2aae77f7e9bd2a514c0b2856c75\", \"40e0d0ef3074e21d1edc4091d1ef9307acb7b3842cfde6da9f7fc746d838ef93\", \"4b12d51f066550d2844cc29143a1a3f34e9d30b879121ec4e18e64bdaefa4bf0\", \"629c58434331946caa82688c6136e0f8d8eafe2f8adde821f8b528d96f291bfd\", \"6329e37245b2db4b2942b82e42dccf9075e87fa1c5855194d5dde68d906aab2b\", \"6330077c8ce9902138c12dc884a88cf9c9ed2d87a486b033ef2bb7cf27b1516b\", \"645fabc0e4e06c66d8b0ba919841304ed6febfe9646a3127e5290aa959171c13\", \"863b44f6f8a1ff5debf3abb933090c449329b870e994f93578761f50ff7e0318\", \"95c1f066d90aa741a6fa539329eba1295e486d34dd8e2c9417d7700517eeeb46\", \"c2fbe2208c4b83eb7e18452b09401316147233f589195b72849b05dd0f77fc1b\", \"c9b9efb5e578f473419e5e1231af33ae551babf66df513eed2c3b0bce39825df\", \"dba473e2e649230da3820751ea4b8434b50db437813d2f75d4b39eba50e0cd83\", \"ea56434ab5ae704c474b5f73d9d13c1f5d885f24a46f2070cf4122d88873e2b2\"]}, \"state_id\": \"1bb9b22d165ac34bd3d4ca1f\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 0}}, {\"experiment_id\": 17, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 1}, \"intervention\": \"DO_Z1_1\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 0}}, {\"experiment_id\": 20, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 0}}]}"} {"agent_name": "causal_micro_lab_agent_loop", "data_source": "causal_micro_lab_final_eval_v3", "env_spec_json": "{\"agent\": {}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 186, \"task\": {\"state\": {\"available_experiment_ids\": [0, 1, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 3, \"family_bucket\": \"within_family\", \"maximum_separation\": 0.7567567567567568, \"mean_separation\": 0.5243243243243244, \"minimum_separation\": 0.16216216216216217, \"normalized_mean_separation\": 0.5617760617760619, \"oracle_budget_representation_error\": 0.15540540540540543, \"oracle_singleton_representation_error\": 0.47972972972972977, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.32432432432432434, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 16}, \"private\": {\"hidden_mode_id\": \"b3dfa2f0f45546000d7b290926857e293b5db77b415b8e01ac48b8b8664793bc\", \"valid_mode_ids\": [\"0738b3a479ac932364b127674ef4793324034d0c7b508cbd9e051a9d8238dedb\", \"269b676a1d29fc9b16fc67aa03bbf71eb21f768ab1f3a36d56b161bda6e004c4\", \"40f1b297034fa199fe6f95253324ae273e7a0c749a989708d808227c75af2ddc\", \"4427c27b75936c500d84a37ffbbace30855c0ab5b3dcb57d38b2120f075fd910\", \"4926607297f4066972fa3ea6f472b443075696fb67f3e07a549013212aaa60ed\", \"6205de37be50fedb1d771e9be005a216850469b2ca5d6c5937909781174df951\", \"704114701fd71fb4e25d50aba18fb97f17b779b63741c6fbcb8270caed0912e5\", \"836b5db499a2809f9b08140bd2b31b45aaa5c4ce34f280db47a3d5bdd827e78d\", \"872e077a0ebc0a0176933530444ca5fcce4b92f474071e524971921289d00183\", \"92f1c5cb2b1410c8fc4c64f95743d53cbf877333855395a92f11c3fbc872ed07\", \"a631022ffc8eabc24ecda0481cb3dae292d7a6d7f3e432c7ae9e7798b1a2cf08\", \"b3dfa2f0f45546000d7b290926857e293b5db77b415b8e01ac48b8b8664793bc\", \"c111834865c13b49b7c04c6c05d22dc7c4f749e4f9de709ae90427e2d852efe2\", \"ddcfdff9e7802777575108842092e1a73989c962475ad150767ea0020d868ded\", \"e8b2540d0abe60c1a4078f7afe1fd4c1d687c508335ee6fec2de432786df8253\", \"ebd801bcaf0db3b7b3b65cd73848d3136f5904d25bdfb185774115d61c32e5aa\"]}, \"state_id\": \"2f85d8cddf63dbe2ad604aeb\", \"visible_experiments\": [{\"experiment_id\": 2, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z1_1\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 0}}, {\"experiment_id\": 20, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 35, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 0}}]}}}", "extra_info": {"index": 186, "max_global_steps": 0, "min_global_steps": 0}, "index": 186, "prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=0\n intervention: set Z1=1\n observed: Z1=1, Z2=0, Y=0\n\nExperiment 2:\n inputs: X1=1, X2=0, X3=0\n intervention: none\n observed: Z1=0, Z2=0, Y=1\n\nExperiment 3:\n inputs: X1=1, X2=1, X3=1\n intervention: none\n observed: Z1=0, Z2=0, Y=0\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "raw_prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=0\n intervention: set Z1=1\n observed: Z1=1, Z2=0, Y=0\n\nExperiment 2:\n inputs: X1=1, X2=0, X3=0\n intervention: none\n observed: Z1=0, Z2=0, Y=1\n\nExperiment 3:\n inputs: X1=1, X2=1, X3=1\n intervention: none\n observed: Z1=0, Z2=0, Y=0\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "reward_model": {"style": "rule"}, "state_json": "{\"available_experiment_ids\": [0, 1, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 3, \"family_bucket\": \"within_family\", \"maximum_separation\": 0.7567567567567568, \"mean_separation\": 0.5243243243243244, \"minimum_separation\": 0.16216216216216217, \"normalized_mean_separation\": 0.5617760617760619, \"oracle_budget_representation_error\": 0.15540540540540543, \"oracle_singleton_representation_error\": 0.47972972972972977, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.32432432432432434, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 16}, \"private\": {\"hidden_mode_id\": \"b3dfa2f0f45546000d7b290926857e293b5db77b415b8e01ac48b8b8664793bc\", \"valid_mode_ids\": [\"0738b3a479ac932364b127674ef4793324034d0c7b508cbd9e051a9d8238dedb\", \"269b676a1d29fc9b16fc67aa03bbf71eb21f768ab1f3a36d56b161bda6e004c4\", \"40f1b297034fa199fe6f95253324ae273e7a0c749a989708d808227c75af2ddc\", \"4427c27b75936c500d84a37ffbbace30855c0ab5b3dcb57d38b2120f075fd910\", \"4926607297f4066972fa3ea6f472b443075696fb67f3e07a549013212aaa60ed\", \"6205de37be50fedb1d771e9be005a216850469b2ca5d6c5937909781174df951\", \"704114701fd71fb4e25d50aba18fb97f17b779b63741c6fbcb8270caed0912e5\", \"836b5db499a2809f9b08140bd2b31b45aaa5c4ce34f280db47a3d5bdd827e78d\", \"872e077a0ebc0a0176933530444ca5fcce4b92f474071e524971921289d00183\", \"92f1c5cb2b1410c8fc4c64f95743d53cbf877333855395a92f11c3fbc872ed07\", \"a631022ffc8eabc24ecda0481cb3dae292d7a6d7f3e432c7ae9e7798b1a2cf08\", \"b3dfa2f0f45546000d7b290926857e293b5db77b415b8e01ac48b8b8664793bc\", \"c111834865c13b49b7c04c6c05d22dc7c4f749e4f9de709ae90427e2d852efe2\", \"ddcfdff9e7802777575108842092e1a73989c962475ad150767ea0020d868ded\", \"e8b2540d0abe60c1a4078f7afe1fd4c1d687c508335ee6fec2de432786df8253\", \"ebd801bcaf0db3b7b3b65cd73848d3136f5904d25bdfb185774115d61c32e5aa\"]}, \"state_id\": \"2f85d8cddf63dbe2ad604aeb\", \"visible_experiments\": [{\"experiment_id\": 2, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z1_1\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 0}}, {\"experiment_id\": 20, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 35, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 0}}]}"} {"agent_name": "causal_micro_lab_agent_loop", "data_source": "causal_micro_lab_final_eval_v3", "env_spec_json": "{\"agent\": {}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 187, \"task\": {\"state\": {\"available_experiment_ids\": [0, 1, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 3, \"family_bucket\": \"within_family\", \"maximum_separation\": 0.7567567567567568, \"mean_separation\": 0.5243243243243242, \"minimum_separation\": 0.16216216216216217, \"normalized_mean_separation\": 0.5617760617760617, \"oracle_budget_representation_error\": 0.15540540540540543, \"oracle_singleton_representation_error\": 0.47972972972972977, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.32432432432432434, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 16}, \"private\": {\"hidden_mode_id\": \"1639d58a20fd6b926f85ef2fca3229101587ad7070884d3f88052de5edb483d9\", \"valid_mode_ids\": [\"1639d58a20fd6b926f85ef2fca3229101587ad7070884d3f88052de5edb483d9\", \"168e286901105120ecf653780e75e644e80dd044ed5cc5a1f966d947dd8b1756\", \"17b9ddd80622580bbc84ad20f4cc082d6a2448f2a15ea282ee18f1888a2abd13\", \"4f2f902f060d739380c499db01b0596501385fdceeeafa9693fa8970708cb91a\", \"507885c6612c90da54a9dd8f2cce7e39eb4b13385de77baa2ce9793baa29f105\", \"52fa6ba9d3a1304174f26f4f2bd46e9274860106bb1a2f95c470994b166a29a5\", \"7372fe4cc63fbf49a7428983bec1b8d8eb652662b681d55c818fc0873b18c1f4\", \"7f849723bef5ea8ca298b452eaba4a5b7b3b62f834069395dc9079111a7d0626\", \"ad3ede416de4f1781164598d35ecb658f1562ce4f83a70bc9e8d070ee6c08ac4\", \"b5f2acae65933c09c60517cf8db9046ad1a047815aca9961128f6331c4c86a05\", \"cc77cfc5cf829c64248fa5e4f3e0a6857ec545a50e37ca875c9f31676f36e668\", \"dba60927112d9084c0a940b3a565bb179226580546df10c22a0792be636ad414\", \"dd36f750f6d599b0ba801bedcf5844193b023c01308f8efa47bee3d623da7a19\", \"ea9d748047e17b04a7689fe552b39561d77b4b4226f4a861d143debe84667844\", \"eb8d0ec05f53b1f712e6bcaa0ba1d7aabd6ac1b0146c1df6fd3c5415e6369b69\", \"f32d633d65f4df263b48fe2816da814870b8ef7d72a4b0341438876f0adf6668\"]}, \"state_id\": \"f9954bcc30465d543d165b61\", \"visible_experiments\": [{\"experiment_id\": 2, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z1_1\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 0}}, {\"experiment_id\": 20, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 35, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 0}}]}}}", "extra_info": {"index": 187, "max_global_steps": 0, "min_global_steps": 0}, "index": 187, "prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=0\n intervention: set Z1=1\n observed: Z1=1, Z2=0, Y=1\n\nExperiment 2:\n inputs: X1=1, X2=0, X3=0\n intervention: none\n observed: Z1=0, Z2=0, Y=1\n\nExperiment 3:\n inputs: X1=1, X2=1, X3=1\n intervention: none\n observed: Z1=0, Z2=0, Y=0\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "raw_prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=0\n intervention: set Z1=1\n observed: Z1=1, Z2=0, Y=1\n\nExperiment 2:\n inputs: X1=1, X2=0, X3=0\n intervention: none\n observed: Z1=0, Z2=0, Y=1\n\nExperiment 3:\n inputs: X1=1, X2=1, X3=1\n intervention: none\n observed: Z1=0, Z2=0, Y=0\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "reward_model": {"style": "rule"}, "state_json": "{\"available_experiment_ids\": [0, 1, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 3, \"family_bucket\": \"within_family\", \"maximum_separation\": 0.7567567567567568, \"mean_separation\": 0.5243243243243242, \"minimum_separation\": 0.16216216216216217, \"normalized_mean_separation\": 0.5617760617760617, \"oracle_budget_representation_error\": 0.15540540540540543, \"oracle_singleton_representation_error\": 0.47972972972972977, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.32432432432432434, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 16}, \"private\": {\"hidden_mode_id\": \"1639d58a20fd6b926f85ef2fca3229101587ad7070884d3f88052de5edb483d9\", \"valid_mode_ids\": [\"1639d58a20fd6b926f85ef2fca3229101587ad7070884d3f88052de5edb483d9\", \"168e286901105120ecf653780e75e644e80dd044ed5cc5a1f966d947dd8b1756\", \"17b9ddd80622580bbc84ad20f4cc082d6a2448f2a15ea282ee18f1888a2abd13\", \"4f2f902f060d739380c499db01b0596501385fdceeeafa9693fa8970708cb91a\", \"507885c6612c90da54a9dd8f2cce7e39eb4b13385de77baa2ce9793baa29f105\", \"52fa6ba9d3a1304174f26f4f2bd46e9274860106bb1a2f95c470994b166a29a5\", \"7372fe4cc63fbf49a7428983bec1b8d8eb652662b681d55c818fc0873b18c1f4\", \"7f849723bef5ea8ca298b452eaba4a5b7b3b62f834069395dc9079111a7d0626\", \"ad3ede416de4f1781164598d35ecb658f1562ce4f83a70bc9e8d070ee6c08ac4\", \"b5f2acae65933c09c60517cf8db9046ad1a047815aca9961128f6331c4c86a05\", \"cc77cfc5cf829c64248fa5e4f3e0a6857ec545a50e37ca875c9f31676f36e668\", \"dba60927112d9084c0a940b3a565bb179226580546df10c22a0792be636ad414\", \"dd36f750f6d599b0ba801bedcf5844193b023c01308f8efa47bee3d623da7a19\", \"ea9d748047e17b04a7689fe552b39561d77b4b4226f4a861d143debe84667844\", \"eb8d0ec05f53b1f712e6bcaa0ba1d7aabd6ac1b0146c1df6fd3c5415e6369b69\", \"f32d633d65f4df263b48fe2816da814870b8ef7d72a4b0341438876f0adf6668\"]}, \"state_id\": \"f9954bcc30465d543d165b61\", \"visible_experiments\": [{\"experiment_id\": 2, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z1_1\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 0}}, {\"experiment_id\": 20, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 35, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 0}}]}"} {"agent_name": "causal_micro_lab_agent_loop", "data_source": "causal_micro_lab_final_eval_v3", "env_spec_json": "{\"agent\": {}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 188, \"task\": {\"state\": {\"available_experiment_ids\": [0, 1, 3, 4, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 3, \"family_bucket\": \"within_family\", \"maximum_separation\": 0.7567567567567568, \"mean_separation\": 0.5243243243243244, \"minimum_separation\": 0.16216216216216217, \"normalized_mean_separation\": 0.5617760617760619, \"oracle_budget_representation_error\": 0.15540540540540543, \"oracle_singleton_representation_error\": 0.4797297297297298, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.3243243243243244, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 16}, \"private\": {\"hidden_mode_id\": \"48130a96031e9adb4ac7ad6d677f464ae40af2aec450c69db9b9fb88d8cc5103\", \"valid_mode_ids\": [\"303ec3e6cea3f8d4264813fa4c9c83c45b791a030d41db970fc7a32c548d5a07\", \"3711ed10dd46a9d22204f957d0a9799bf94b1c76fd99c73c92d0b03c037f98c4\", \"3c9576f9cabbbd4cee1458e5dbfd3d5f1b0ad45ebd7c45d00ad1b410a0fdb464\", \"4342848b35b04c87eb426cddf54215c566e57b11d0259add009ae653fd4aedd9\", \"4440b6b655f5a030ffe3e80243f56d861d6d9fbb2a31ab97bcd47452268d1f5e\", \"45ce99acfd9a0823eb0afae0c0bf530993404ed78bcd8a80511779b0295fa226\", \"48130a96031e9adb4ac7ad6d677f464ae40af2aec450c69db9b9fb88d8cc5103\", \"672b06dc1d0744c50966c869acfa393c021c2a7522501b801e0076b940a35a09\", \"6beecb6fff166574cde8a3fe4ae573aae79abe7568af62ddb45285b1b6be4f22\", \"7138654f528eeff6d2128bafb6fe28c65cd539c6148fc284be47a877ce39c624\", \"90788ed2f970314ac5e82fc66e46cea51db72d73249a54e4286924ba5402e8d6\", \"b117edadc011386022119e1f535f7aad184fe1239af4031ed8e4e0db39ff63ff\", \"ce5d81a644e01101959104659f7eed2022c86085d0181f6029928af6efaffe0f\", \"ee36fb8f45704e3605c8ea0e7a5addf3a55b4db96c863c0a6e3a69b8b161046d\", \"f38867f3fb5029f981f308d134eef313c4dfd017627e04d25582efc7946973a0\", \"fe09c9e5bd40462ffa473fbfb75d9fecc6da2c3de6601ea960f98f81956610a4\"]}, \"state_id\": \"0a257f9d6fd46e5209955443\", \"visible_experiments\": [{\"experiment_id\": 2, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z1_1\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 0}}, {\"experiment_id\": 5, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 35, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 0}}]}}}", "extra_info": {"index": 188, "max_global_steps": 0, "min_global_steps": 0}, "index": 188, "prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=0\n intervention: set Z1=1\n observed: Z1=1, Z2=0, Y=1\n\nExperiment 2:\n inputs: X1=0, X2=0, X3=1\n intervention: none\n observed: Z1=0, Z2=0, Y=1\n\nExperiment 3:\n inputs: X1=1, X2=1, X3=1\n intervention: none\n observed: Z1=0, Z2=0, Y=0\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "raw_prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=0\n intervention: set Z1=1\n observed: Z1=1, Z2=0, Y=1\n\nExperiment 2:\n inputs: X1=0, X2=0, X3=1\n intervention: none\n observed: Z1=0, Z2=0, Y=1\n\nExperiment 3:\n inputs: X1=1, X2=1, X3=1\n intervention: none\n observed: Z1=0, Z2=0, Y=0\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "reward_model": {"style": "rule"}, "state_json": "{\"available_experiment_ids\": [0, 1, 3, 4, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 3, \"family_bucket\": \"within_family\", \"maximum_separation\": 0.7567567567567568, \"mean_separation\": 0.5243243243243244, \"minimum_separation\": 0.16216216216216217, \"normalized_mean_separation\": 0.5617760617760619, \"oracle_budget_representation_error\": 0.15540540540540543, \"oracle_singleton_representation_error\": 0.4797297297297298, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.3243243243243244, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 16}, \"private\": {\"hidden_mode_id\": \"48130a96031e9adb4ac7ad6d677f464ae40af2aec450c69db9b9fb88d8cc5103\", \"valid_mode_ids\": [\"303ec3e6cea3f8d4264813fa4c9c83c45b791a030d41db970fc7a32c548d5a07\", \"3711ed10dd46a9d22204f957d0a9799bf94b1c76fd99c73c92d0b03c037f98c4\", \"3c9576f9cabbbd4cee1458e5dbfd3d5f1b0ad45ebd7c45d00ad1b410a0fdb464\", \"4342848b35b04c87eb426cddf54215c566e57b11d0259add009ae653fd4aedd9\", \"4440b6b655f5a030ffe3e80243f56d861d6d9fbb2a31ab97bcd47452268d1f5e\", \"45ce99acfd9a0823eb0afae0c0bf530993404ed78bcd8a80511779b0295fa226\", \"48130a96031e9adb4ac7ad6d677f464ae40af2aec450c69db9b9fb88d8cc5103\", \"672b06dc1d0744c50966c869acfa393c021c2a7522501b801e0076b940a35a09\", \"6beecb6fff166574cde8a3fe4ae573aae79abe7568af62ddb45285b1b6be4f22\", \"7138654f528eeff6d2128bafb6fe28c65cd539c6148fc284be47a877ce39c624\", \"90788ed2f970314ac5e82fc66e46cea51db72d73249a54e4286924ba5402e8d6\", \"b117edadc011386022119e1f535f7aad184fe1239af4031ed8e4e0db39ff63ff\", \"ce5d81a644e01101959104659f7eed2022c86085d0181f6029928af6efaffe0f\", \"ee36fb8f45704e3605c8ea0e7a5addf3a55b4db96c863c0a6e3a69b8b161046d\", \"f38867f3fb5029f981f308d134eef313c4dfd017627e04d25582efc7946973a0\", \"fe09c9e5bd40462ffa473fbfb75d9fecc6da2c3de6601ea960f98f81956610a4\"]}, \"state_id\": \"0a257f9d6fd46e5209955443\", \"visible_experiments\": [{\"experiment_id\": 2, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z1_1\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 0}}, {\"experiment_id\": 5, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 35, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 0}}]}"} {"agent_name": "causal_micro_lab_agent_loop", "data_source": "causal_micro_lab_final_eval_v3", "env_spec_json": "{\"agent\": {}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 189, \"task\": {\"state\": {\"available_experiment_ids\": [0, 1, 3, 4, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 3, \"family_bucket\": \"within_family\", \"maximum_separation\": 0.7567567567567568, \"mean_separation\": 0.5243243243243242, \"minimum_separation\": 0.16216216216216217, \"normalized_mean_separation\": 0.5617760617760617, \"oracle_budget_representation_error\": 0.15540540540540543, \"oracle_singleton_representation_error\": 0.4797297297297298, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.3243243243243244, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 16}, \"private\": {\"hidden_mode_id\": \"18cc6b0abc83258f6839ac72d25a11df75137b71fe0688a8bd9ef990249ebe88\", \"valid_mode_ids\": [\"02469e5e4f5491c53fc32b7aedd37b989a15b9b03a4f0bd5ccfe730b5803c4ff\", \"0869e87499863afc3edeb1a168d7ac922ac924cce91c98f0685a6da885a54de5\", \"13aa76758a01e76c8b730595d17182fa782d65743ac4a230cb8dfb69c5958693\", \"18cc6b0abc83258f6839ac72d25a11df75137b71fe0688a8bd9ef990249ebe88\", \"2d5061328f778272e6068720dd80931e6cec8f9d6d001ea3dfa69f03b64979a6\", \"554c08f0c44da7f3c562cb4f31c2e65ad181a789164698f789d921b885dfabe5\", \"6d6c525669d177145da2641aae8dffe3a0a84620ea32755ae6edab98e9e5fcbb\", \"80dfd3d32d9924b8e00b1816060b76b1c4863447cb3fd087fa580f79a50e0a37\", \"8f99b7983192c1a064d4d128d4e1bee5e0cdb699d1cd26fe330b4485c5da766d\", \"c9a828962bedad5644c2dac923e8073a85c906d7041ca0575867090026c4f2e8\", \"d43d09df592ccf401aa01c981dc4feb1d7f605762685ff443c04f41e0626511b\", \"daabd733fda187fa4cee99494de3265bc1b5f0c53c848f4ccc05b631539312a5\", \"dfa1f953e60b74a3964212717eec9119ce91ed1d5e578d71134ca9cc833f0be4\", \"e6070d40dcf1282ba19e6befedf75bed8d89de9549e3f1126f8a0859faeb29ef\", \"e9b68b2a7bb05bd73da2e13e72ed233c2e70a6d7ee910188abb823b931a37094\", \"ed0735980bd649b50b18fad3189d032f4ecbe7f4d0b0229bedfa7930d3375414\"]}, \"state_id\": \"8585530ed7dfa834b5ee16bc\", \"visible_experiments\": [{\"experiment_id\": 2, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z1_1\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 0}}, {\"experiment_id\": 5, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 35, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 0}}]}}}", "extra_info": {"index": 189, "max_global_steps": 0, "min_global_steps": 0}, "index": 189, "prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=0\n intervention: set Z1=1\n observed: Z1=1, Z2=0, Y=0\n\nExperiment 2:\n inputs: X1=0, X2=0, X3=1\n intervention: none\n observed: Z1=0, Z2=0, Y=1\n\nExperiment 3:\n inputs: X1=1, X2=1, X3=1\n intervention: none\n observed: Z1=0, Z2=0, Y=0\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "raw_prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=0\n intervention: set Z1=1\n observed: Z1=1, Z2=0, Y=0\n\nExperiment 2:\n inputs: X1=0, X2=0, X3=1\n intervention: none\n observed: Z1=0, Z2=0, Y=1\n\nExperiment 3:\n inputs: X1=1, X2=1, X3=1\n intervention: none\n observed: Z1=0, Z2=0, Y=0\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "reward_model": {"style": "rule"}, "state_json": "{\"available_experiment_ids\": [0, 1, 3, 4, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 3, \"family_bucket\": \"within_family\", \"maximum_separation\": 0.7567567567567568, \"mean_separation\": 0.5243243243243242, \"minimum_separation\": 0.16216216216216217, \"normalized_mean_separation\": 0.5617760617760617, \"oracle_budget_representation_error\": 0.15540540540540543, \"oracle_singleton_representation_error\": 0.4797297297297298, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.3243243243243244, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 16}, \"private\": {\"hidden_mode_id\": \"18cc6b0abc83258f6839ac72d25a11df75137b71fe0688a8bd9ef990249ebe88\", \"valid_mode_ids\": [\"02469e5e4f5491c53fc32b7aedd37b989a15b9b03a4f0bd5ccfe730b5803c4ff\", \"0869e87499863afc3edeb1a168d7ac922ac924cce91c98f0685a6da885a54de5\", \"13aa76758a01e76c8b730595d17182fa782d65743ac4a230cb8dfb69c5958693\", \"18cc6b0abc83258f6839ac72d25a11df75137b71fe0688a8bd9ef990249ebe88\", \"2d5061328f778272e6068720dd80931e6cec8f9d6d001ea3dfa69f03b64979a6\", \"554c08f0c44da7f3c562cb4f31c2e65ad181a789164698f789d921b885dfabe5\", \"6d6c525669d177145da2641aae8dffe3a0a84620ea32755ae6edab98e9e5fcbb\", \"80dfd3d32d9924b8e00b1816060b76b1c4863447cb3fd087fa580f79a50e0a37\", \"8f99b7983192c1a064d4d128d4e1bee5e0cdb699d1cd26fe330b4485c5da766d\", \"c9a828962bedad5644c2dac923e8073a85c906d7041ca0575867090026c4f2e8\", \"d43d09df592ccf401aa01c981dc4feb1d7f605762685ff443c04f41e0626511b\", \"daabd733fda187fa4cee99494de3265bc1b5f0c53c848f4ccc05b631539312a5\", \"dfa1f953e60b74a3964212717eec9119ce91ed1d5e578d71134ca9cc833f0be4\", \"e6070d40dcf1282ba19e6befedf75bed8d89de9549e3f1126f8a0859faeb29ef\", \"e9b68b2a7bb05bd73da2e13e72ed233c2e70a6d7ee910188abb823b931a37094\", \"ed0735980bd649b50b18fad3189d032f4ecbe7f4d0b0229bedfa7930d3375414\"]}, \"state_id\": \"8585530ed7dfa834b5ee16bc\", \"visible_experiments\": [{\"experiment_id\": 2, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z1_1\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 0}}, {\"experiment_id\": 5, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 35, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 0}}]}"} {"agent_name": "causal_micro_lab_agent_loop", "data_source": "causal_micro_lab_final_eval_v3", "env_spec_json": "{\"agent\": {}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 190, \"task\": {\"state\": {\"available_experiment_ids\": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 38, 39], \"metadata\": {\"evidence_size\": 3, \"family_bucket\": \"mixed\", \"maximum_separation\": 0.6486486486486487, \"mean_separation\": 0.5081081081081078, \"minimum_separation\": 0.16216216216216217, \"normalized_mean_separation\": 0.5444015444015441, \"oracle_budget_representation_error\": 0.12162162162162166, \"oracle_singleton_representation_error\": 0.47297297297297297, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.3513513513513513, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 16}, \"private\": {\"hidden_mode_id\": \"43e23d43e967350b34c141e6b9a8336c1dbfea7390d71aa0f0f1436dab4eb5a5\", \"valid_mode_ids\": [\"43e23d43e967350b34c141e6b9a8336c1dbfea7390d71aa0f0f1436dab4eb5a5\", \"4617e5290e7befc7102aca29f389d7e9c2e33618ab1e72fefe6f4a2d868dbef2\", \"629c58434331946caa82688c6136e0f8d8eafe2f8adde821f8b528d96f291bfd\", \"63e1ad18903f6342d7c658947f5cd1e95209264dea216e25aa7947ccbf6fb466\", \"6ffa4a7f21737edaa892abe0c57be089b31758111c86b7146e795f60b71f3e7e\", \"738b45de666eff64007f1f0b98f149f72b98bbd63023e7a37dfea69499814a80\", \"84a08d11dd723aaa9ad5c0da99664fcd32e1746fd6940307a6fb0f0c4a5676ce\", \"98e247bce8ab344fe18e6e6cca3eb18bf8ab63f3f96d00d803afef7eee950f32\", \"9cc0a595182e4f553e5be48f47e2af8f428b3943f4232fae4addf0bb9a7b386d\", \"ac4194e87c56387a37d5ee3d6a5b930e25e54e30be9aaeaeb889a64241459942\", \"bc36f1ffb3d73eafb60b4562d61737da22ed1e1778361e567effbfccab922693\", \"bc3f29e152aecc1724bce245671de190a9c8b98921aed88897ac689362f9e199\", \"c43fab75d6a72b5d55ab323df877fbaafbeb0ddb18c89260593ec1a93c93c4fb\", \"dadc7512454806ec78c00b5a91f1c7f345f5e39776fa2838b6d8a393373488f2\", \"ea56434ab5ae704c474b5f73d9d13c1f5d885f24a46f2070cf4122d88873e2b2\", \"f9d325a6aa5ec45f1ed0b41fd1a8e4ff0402b57d967d0da05cbe0f34555ece34\"]}, \"state_id\": \"7780833b981e013451405e5c\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 0}}, {\"experiment_id\": 25, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 0}}, {\"experiment_id\": 37, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 1}, \"intervention\": \"DO_Z1_1\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}]}}}", "extra_info": {"index": 190, "max_global_steps": 0, "min_global_steps": 0}, "index": 190, "prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=0\n intervention: none\n observed: Z1=1, Z2=0, Y=1\n\nExperiment 2:\n inputs: X1=1, X2=0, X3=1\n intervention: none\n observed: Z1=1, Z2=0, Y=0\n\nExperiment 3:\n inputs: X1=1, X2=1, X3=1\n intervention: set Z1=1\n observed: Z1=1, Z2=1, Y=0\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "raw_prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=0, X3=0\n intervention: none\n observed: Z1=1, Z2=0, Y=1\n\nExperiment 2:\n inputs: X1=1, X2=0, X3=1\n intervention: none\n observed: Z1=1, Z2=0, Y=0\n\nExperiment 3:\n inputs: X1=1, X2=1, X3=1\n intervention: set Z1=1\n observed: Z1=1, Z2=1, Y=0\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "reward_model": {"style": "rule"}, "state_json": "{\"available_experiment_ids\": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 38, 39], \"metadata\": {\"evidence_size\": 3, \"family_bucket\": \"mixed\", \"maximum_separation\": 0.6486486486486487, \"mean_separation\": 0.5081081081081078, \"minimum_separation\": 0.16216216216216217, \"normalized_mean_separation\": 0.5444015444015441, \"oracle_budget_representation_error\": 0.12162162162162166, \"oracle_singleton_representation_error\": 0.47297297297297297, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.3513513513513513, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 16}, \"private\": {\"hidden_mode_id\": \"43e23d43e967350b34c141e6b9a8336c1dbfea7390d71aa0f0f1436dab4eb5a5\", \"valid_mode_ids\": [\"43e23d43e967350b34c141e6b9a8336c1dbfea7390d71aa0f0f1436dab4eb5a5\", \"4617e5290e7befc7102aca29f389d7e9c2e33618ab1e72fefe6f4a2d868dbef2\", \"629c58434331946caa82688c6136e0f8d8eafe2f8adde821f8b528d96f291bfd\", \"63e1ad18903f6342d7c658947f5cd1e95209264dea216e25aa7947ccbf6fb466\", \"6ffa4a7f21737edaa892abe0c57be089b31758111c86b7146e795f60b71f3e7e\", \"738b45de666eff64007f1f0b98f149f72b98bbd63023e7a37dfea69499814a80\", \"84a08d11dd723aaa9ad5c0da99664fcd32e1746fd6940307a6fb0f0c4a5676ce\", \"98e247bce8ab344fe18e6e6cca3eb18bf8ab63f3f96d00d803afef7eee950f32\", \"9cc0a595182e4f553e5be48f47e2af8f428b3943f4232fae4addf0bb9a7b386d\", \"ac4194e87c56387a37d5ee3d6a5b930e25e54e30be9aaeaeb889a64241459942\", \"bc36f1ffb3d73eafb60b4562d61737da22ed1e1778361e567effbfccab922693\", \"bc3f29e152aecc1724bce245671de190a9c8b98921aed88897ac689362f9e199\", \"c43fab75d6a72b5d55ab323df877fbaafbeb0ddb18c89260593ec1a93c93c4fb\", \"dadc7512454806ec78c00b5a91f1c7f345f5e39776fa2838b6d8a393373488f2\", \"ea56434ab5ae704c474b5f73d9d13c1f5d885f24a46f2070cf4122d88873e2b2\", \"f9d325a6aa5ec45f1ed0b41fd1a8e4ff0402b57d967d0da05cbe0f34555ece34\"]}, \"state_id\": \"7780833b981e013451405e5c\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 0}}, {\"experiment_id\": 25, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 0}}, {\"experiment_id\": 37, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 1}, \"intervention\": \"DO_Z1_1\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}]}"} {"agent_name": "causal_micro_lab_agent_loop", "data_source": "causal_micro_lab_final_eval_v3", "env_spec_json": "{\"agent\": {}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 191, \"task\": {\"state\": {\"available_experiment_ids\": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 28, 29, 30, 31, 32, 33, 34, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 3, \"family_bucket\": \"within_family\", \"maximum_separation\": 0.8108108108108109, \"mean_separation\": 0.5216216216216216, \"minimum_separation\": 0.10810810810810811, \"normalized_mean_separation\": 0.5588803088803088, \"oracle_budget_representation_error\": 0.11486486486486489, \"oracle_singleton_representation_error\": 0.4662162162162163, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.3513513513513514, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 16}, \"private\": {\"hidden_mode_id\": \"9d5439a51c6419501c65897dae4edecbf349d4c83d3f484ad1f54e82c9657d7e\", \"valid_mode_ids\": [\"131c40ef367df15659dbca3c90bee3db2a757e9bd3764fb31cb7600fe34f59f8\", \"211f1afcd4ebe1f5cf9683196c79de7f92cf8444421c3a6369ca53037486c0e6\", \"2c445e33cc75dd650535c4e24efca95cd9e8bf4a03aa45fda4fd8ce89c26cc07\", \"4d2b872e5b21a3e603f9aacbec93131b593975ced8d475da9a1c9796162ada82\", \"536731104b794b6e8fd98884085ca55f43354a82ff78e168c1d0131acb1c5389\", \"5bf1377c4ca7da16e5c8f5c6da3d5cc376bf67d6bc5660ce37f6163d370ad7ac\", \"5ee008a41a3599aa9bb55ba2996f531cd0a72680558eb9664f850d14796555e9\", \"71bbe494998a1b1a2ab5dd54a6d49d8dfaf92121c8cff228d7a8a2fa27419249\", \"941721250248d9d341d048d9fd462cbba25eb269940370f5905ed3cce1b18121\", \"9d5439a51c6419501c65897dae4edecbf349d4c83d3f484ad1f54e82c9657d7e\", \"bee6097082f9fd3c9e19592fc792d46d94c933d698534ff89b4c5ab4c584c757\", \"d704b451ad897eb187a3fbb0e12356934bedbf8b98c95fd62686c5b796060a5a\", \"d8e19dbe18cdf0ed4f4ec1980279637d2311f0eb6e484f85133629529d81ba46\", \"db849acc6e48197901c17b7e857077a8e4a3088e7f34b5637220798a9e31762c\", \"f370d0b60bb048e62d295115c29378cb09a92924dd597b7210c607c15b2a2566\", \"ffba23998eac31422efcfef4a1eb8c5f58c692e88522e215c2278f62fce16181\"]}, \"state_id\": \"f0bd6c8b1a474024e6430d11\", \"visible_experiments\": [{\"experiment_id\": 10, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 27, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 1}, \"intervention\": \"DO_Z1_1\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 35, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}]}}}", "extra_info": {"index": 191, "max_global_steps": 0, "min_global_steps": 0}, "index": 191, "prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=1, X3=0\n intervention: none\n observed: Z1=0, Z2=1, Y=1\n\nExperiment 2:\n inputs: X1=1, X2=0, X3=1\n intervention: set Z1=1\n observed: Z1=1, Z2=1, Y=1\n\nExperiment 3:\n inputs: X1=1, X2=1, X3=1\n intervention: none\n observed: Z1=0, Z2=1, Y=0\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "raw_prompt": "Infer one Boolean causal program consistent with the evidence.\n\nVariables: inputs X1,X2,X3; intermediates Z1,Z2; output Y.\nRules: exactly one shallow rule for each target Z1, Z2, Y.\nOperators: COPY(A), NOT(A), AND(A,B), OR(A,B), XOR(A,B).\nAvailable inputs:\n- Z1 rule can use: X1, X2, X3\n- Z2 rule can use: X1, X2, X3, Z1\n- Y rule can use: X1, X2, X3, Z1, Z2\nFor AND/OR/XOR, use two different inputs. Do not nest operators.\n\nEvidence:\n\nExperiment 1:\n inputs: X1=0, X2=1, X3=0\n intervention: none\n observed: Z1=0, Z2=1, Y=1\n\nExperiment 2:\n inputs: X1=1, X2=0, X3=1\n intervention: set Z1=1\n observed: Z1=1, Z2=1, Y=1\n\nExperiment 3:\n inputs: X1=1, X2=1, X3=1\n intervention: none\n observed: Z1=0, Z2=1, Y=0\n\n\nReturn exactly three lines, no extra text.\nUse this flat format:\nZ1: OP input [input]\nZ2: OP input [input]\nY: OP input [input]\nExample:\nZ1: AND X1 X2\nZ2: OR X2 Z1\nY: XOR X3 Z1", "reward_model": {"style": "rule"}, "state_json": "{\"available_experiment_ids\": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 28, 29, 30, 31, 32, 33, 34, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 3, \"family_bucket\": \"within_family\", \"maximum_separation\": 0.8108108108108109, \"mean_separation\": 0.5216216216216216, \"minimum_separation\": 0.10810810810810811, \"normalized_mean_separation\": 0.5588803088803088, \"oracle_budget_representation_error\": 0.11486486486486489, \"oracle_singleton_representation_error\": 0.4662162162162163, \"representative_budget\": 4, \"representative_coverage_opportunity\": 0.3513513513513514, \"separation_bucket\": \"continuous\", \"separation_definition\": \"full_outcome_disagreement_v3\", \"separation_targets\": [\"Z1\", \"Z2\", \"Y\"], \"valid_mode_count\": 16}, \"private\": {\"hidden_mode_id\": \"9d5439a51c6419501c65897dae4edecbf349d4c83d3f484ad1f54e82c9657d7e\", \"valid_mode_ids\": [\"131c40ef367df15659dbca3c90bee3db2a757e9bd3764fb31cb7600fe34f59f8\", \"211f1afcd4ebe1f5cf9683196c79de7f92cf8444421c3a6369ca53037486c0e6\", \"2c445e33cc75dd650535c4e24efca95cd9e8bf4a03aa45fda4fd8ce89c26cc07\", \"4d2b872e5b21a3e603f9aacbec93131b593975ced8d475da9a1c9796162ada82\", \"536731104b794b6e8fd98884085ca55f43354a82ff78e168c1d0131acb1c5389\", \"5bf1377c4ca7da16e5c8f5c6da3d5cc376bf67d6bc5660ce37f6163d370ad7ac\", \"5ee008a41a3599aa9bb55ba2996f531cd0a72680558eb9664f850d14796555e9\", \"71bbe494998a1b1a2ab5dd54a6d49d8dfaf92121c8cff228d7a8a2fa27419249\", \"941721250248d9d341d048d9fd462cbba25eb269940370f5905ed3cce1b18121\", \"9d5439a51c6419501c65897dae4edecbf349d4c83d3f484ad1f54e82c9657d7e\", \"bee6097082f9fd3c9e19592fc792d46d94c933d698534ff89b4c5ab4c584c757\", \"d704b451ad897eb187a3fbb0e12356934bedbf8b98c95fd62686c5b796060a5a\", \"d8e19dbe18cdf0ed4f4ec1980279637d2311f0eb6e484f85133629529d81ba46\", \"db849acc6e48197901c17b7e857077a8e4a3088e7f34b5637220798a9e31762c\", \"f370d0b60bb048e62d295115c29378cb09a92924dd597b7210c607c15b2a2566\", \"ffba23998eac31422efcfef4a1eb8c5f58c692e88522e215c2278f62fce16181\"]}, \"state_id\": \"f0bd6c8b1a474024e6430d11\", \"visible_experiments\": [{\"experiment_id\": 10, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 27, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 1}, \"intervention\": \"DO_Z1_1\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 35, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}]}"}